update code

README_ch.md

@@ -38,7 +38,7 @@ Res2Net200_vd预训练模型Top-1精度高达85.1%。
 * 您可以扫描下面的微信群二维码， 加入PaddleClas 微信交流群。获得更高效的问题答疑，与各行各业开发者充分交流，期待您的加入。
 
 <div align="center">
-<img src="https://user-images.githubusercontent.com/12560511/160273340-831de8c3-f31d-44d1-9437-fcd4765a6851.jpg" width="200"/>
+<img src="https://user-images.githubusercontent.com/12560511/162710270-8a249aca-4fa9-46f9-95e5-66d906fe6d66.jpg" width="200"/>
 </div>
 
 ## 快速体验

README_en.md

@@ -41,7 +41,7 @@ Four sample solutions are provided, including product recognition, vehicle recog
 * You can also scan the QR code below to join the PaddleClas WeChat group to get more efficient answers to your questions and to communicate with developers from all walks of life. We look forward to hearing from you.
 
 <div align="center">
-<img src="https://user-images.githubusercontent.com/12560511/160273340-831de8c3-f31d-44d1-9437-fcd4765a6851.jpg" width="200"/>
+<img src="https://user-images.githubusercontent.com/12560511/162710270-8a249aca-4fa9-46f9-95e5-66d906fe6d66.jpg" width="200"/>
 </div>
 
 ## Quick Start

deploy/lite/Makefile

 ARM_ABI = arm8
 export ARM_ABI
 
-include ../Makefile.def
+LITE_ROOT=./inference_lite_lib.android.armv8
 
-LITE_ROOT=../../../
+include ${LITE_ROOT}/demo/cxx/Makefile.def
 
 THIRD_PARTY_DIR=${LITE_ROOT}/third_party
 
@@ -29,7 +29,7 @@ OPENCV_LIBS = ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PATH}/libs/libopencv_im
               ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PATH}/3rdparty/libs/libtbb.a \
               ${THIRD_PARTY_DIR}/${OPENCV_VERSION}/${ARM_PATH}/3rdparty/libs/libcpufeatures.a
 
-OPENCV_INCLUDE = -I../../../third_party/${OPENCV_VERSION}/${ARM_PATH}/include
+OPENCV_INCLUDE = -I${LITE_ROOT}/third_party/${OPENCV_VERSION}/${ARM_PATH}/include
 
 CXX_INCLUDES = $(INCLUDES) ${OPENCV_INCLUDE} -I$(LITE_ROOT)/cxx/include
 

deploy/lite/config.txt

-clas_model_file ./MobileNetV3_large_x1_0.nb
-label_path ./imagenet1k_label_list.txt
+clas_model_file /data/local/tmp/arm_cpu/MobileNetV3_large_x1_0.nb
+label_path /data/local/tmp/arm_cpu/imagenet1k_label_list.txt
 resize_short_size 256
 crop_size 224
 visualize 0
+num_threads 1
+batch_size 1
+precision FP32
+runtime_device arm_cpu
 enable_benchmark 0
+tipc_benchmark 0

deploy/lite/image_classfication.cpp

@@ -21,6 +21,7 @@
 #include <opencv2/opencv.hpp>
 #include <sys/time.h>
 #include <vector>
+#include "AutoLog/auto_log/lite_autolog.h"
 
 using namespace paddle::lite_api; // NOLINT
 using namespace std;
@@ -149,8 +150,10 @@ cv::Mat CenterCropImg(const cv::Mat &img, const int &crop_size) {
 std::vector<RESULT>
 RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
              const std::map<std::string, std::string> &config,
-             const std::vector<std::string> &word_labels, double &cost_time) {
+             const std::vector<std::string> &word_labels, double &cost_time,
+             std::vector<double> *time_info) {
   // Read img
+  auto preprocess_start = std::chrono::steady_clock::now();
   int resize_short_size = stoi(config.at("resize_short_size"));
   int crop_size = stoi(config.at("crop_size"));
   int visualize = stoi(config.at("visualize"));
@@ -172,8 +175,8 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
   std::vector<float> scale = {1 / 0.229f, 1 / 0.224f, 1 / 0.225f};
   const float *dimg = reinterpret_cast<const float *>(img_fp.data);
   NeonMeanScale(dimg, data0, img_fp.rows * img_fp.cols, mean, scale);
-
-  auto start = std::chrono::system_clock::now();
+  auto preprocess_end = std::chrono::steady_clock::now();
+  auto inference_start = std::chrono::system_clock::now();
   // Run predictor
   predictor->Run();
 
@@ -181,9 +184,10 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
   std::unique_ptr<const Tensor> output_tensor(
       std::move(predictor->GetOutput(0)));
   auto *output_data = output_tensor->data<float>();
-  auto end = std::chrono::system_clock::now();
+  auto inference_end = std::chrono::system_clock::now();
+  auto postprocess_start = std::chrono::system_clock::now();
   auto duration =
-      std::chrono::duration_cast<std::chrono::microseconds>(end - start);
+      std::chrono::duration_cast<std::chrono::microseconds>(inference_end - inference_start);
   cost_time = double(duration.count()) *
               std::chrono::microseconds::period::num /
               std::chrono::microseconds::period::den;
@@ -196,6 +200,13 @@ RunClasModel(std::shared_ptr<PaddlePredictor> predictor, const cv::Mat &img,
   cv::Mat output_image;
   auto results =
       PostProcess(output_data, output_size, word_labels, output_image);
+  auto postprocess_end = std::chrono::system_clock::now();
+  std::chrono::duration<float> preprocess_diff = preprocess_end - preprocess_start;
+  time_info->push_back(double(preprocess_diff.count() * 1000));
+  std::chrono::duration<float> inference_diff = inference_end - inference_start;
+  time_info->push_back(double(inference_diff.count() * 1000));
+  std::chrono::duration<float> postprocess_diff = postprocess_end - postprocess_start;
+  time_info->push_back(double(postprocess_diff.count() * 1000));
 
   if (visualize) {
     std::string output_image_path = "./clas_result.png";
@@ -309,6 +320,12 @@ int main(int argc, char **argv) {
 
   std::string clas_model_file = config.at("clas_model_file");
   std::string label_path = config.at("label_path");
+  std::string crop_size = config.at("crop_size");
+  int num_threads = stoi(config.at("num_threads"));
+  int batch_size = stoi(config.at("batch_size"));
+  std::string precision = config.at("precision");
+  std::string runtime_device = config.at("runtime_device");
+  bool tipc_benchmark = bool(stoi(config.at("tipc_benchmark")));
 
   // Load Labels
   std::vector<std::string> word_labels = LoadLabels(label_path);
@@ -319,8 +336,9 @@ int main(int argc, char **argv) {
     cv::cvtColor(srcimg, srcimg, cv::COLOR_BGR2RGB);
 
     double run_time = 0;
+    std::vector<double> time_info;
     std::vector<RESULT> results =
-        RunClasModel(clas_predictor, srcimg, config, word_labels, run_time);
+        RunClasModel(clas_predictor, srcimg, config, word_labels, run_time, &time_info);
 
     std::cout << "===clas result for image: " << img_path << "===" << std::endl;
     for (int i = 0; i < results.size(); i++) {
@@ -338,6 +356,19 @@ int main(int argc, char **argv) {
     } else {
       std::cout << "Current time cost: " << run_time << " s." << std::endl;
     }
+    if (tipc_benchmark) {
+      AutoLogger autolog(clas_model_file,
+                         runtime_device,
+                         num_threads,
+                         batch_size,
+                         crop_size,
+                         precision,
+                         time_info,
+                         1);
+    std::cout << "=======================TIPC Lite Information=======================" << std::endl;
+    autolog.report();
+    }
+
   }
 
   return 0;

deploy/lite/readme.md

@@ -25,8 +25,8 @@ Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理
 1. [建议]直接下载，预测库下载链接如下：
       |平台|预测库下载链接|
       |-|-|
-      |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)|
-      |iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz)|
+      |iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
 
       **注**：
       1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，
@@ -44,11 +44,11 @@ git checkout develop
 
 **注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2)。
 
-直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
+直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
 预测库的文件目录如下：
 
 ```
-inference_lite_lib.android.armv8/
+inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/
 |-- cxx                                        C++ 预测库和头文件
 |   |-- include                                C++ 头文件
 |   |   |-- paddle_api.h
@@ -86,7 +86,7 @@ Python下安装 `paddlelite`，目前最高支持`Python3.7`。
 **注意**：`paddlelite`whl包版本必须和预测库版本对应。
 
 ```shell
-pip install paddlelite==2.8
+pip install paddlelite==2.10
 ```
 
 之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下
@@ -146,6 +146,24 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --
 
 **注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
 
+<a name="2.1.4"></a>
+
+#### 2.1.4 执行编译，得到可执行文件clas_system
+
+```shell
+# 克隆 Autolog 代码库，以便获取自动化日志
+cd PaddleClas_root_path
+cd deploy/lite/
+git clone https://github.com/LDOUBLEV/AutoLog.git
+```
+
+```shell
+# 编译
+make -j
+```
+
+执行 `make` 命令后，会在当前目录生成 `clas_system` 可执行文件，该文件用于 Lite 预测。
+
 <a name="2.2与手机联调"></a>
 ### 2.2 与手机联调
 
@@ -167,7 +185,7 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --
 
     win上安装需要去谷歌的安卓平台下载ADB软件包进行安装：[链接](https://developer.android.com/studio)
 
-4. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入：
+3. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入：
 
 ```shell
 adb devices
@@ -178,40 +196,18 @@ List of devices attached
 744be294    device
 ```
 
-5. 准备优化后的模型、预测库文件、测试图像和类别映射文件。
-
-```shell
-cd PaddleClas_root_path
-cd deploy/lite/
 
-# 运行prepare.sh
-# prepare.sh 会将预测库文件、测试图像和使用的字典文件放置在预测库中的demo/cxx/clas文件夹下
-sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
+4. 将优化后的模型、预测库文件、测试图像和类别映射文件push到手机上。
 
-# 进入lite demo的工作目录
-cd /{lite prediction library path}/inference_lite_lib.android.armv8/
-cd demo/cxx/clas/
-
-# 将C++预测动态库so文件复制到debug文件夹中
-cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
-```
-
-`prepare.sh` 以 `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` 作为测试图像，将测试图像复制到`demo/cxx/clas/debug/` 文件夹下。
-将 `paddle_lite_opt` 工具优化后的模型文件放置到 `/{lite prediction library path}/inference_lite_lib.android.armv8/demo/cxx/clas/debug/` 文件夹下。本例中，使用[2.1.3](#2.1.3)生成的 `MobileNetV3_large_x1_0.nb` 模型文件。
-
-执行完成后，clas文件夹下将有如下文件格式：
-
-```
-demo/cxx/clas/
-|-- debug/
-|   |--MobileNetV3_large_x1_0.nb                优化后的分类器模型文件
-|   |--tabby_cat.jpg                           	待测试图像
-|   |--imagenet1k_label_list.txt                类别映射文件
-|   |--libpaddle_light_api_shared.so    C++预测库文件
-|   |--config.txt                       分类预测超参数配置
-|-- config.txt                  				分类预测超参数配置
-|-- image_classfication.cpp            	图像分类代码文件
-|-- Makefile                    				编译文件
+```shell
+adb shell mkdir -p /data/local/tmp/arm_cpu/
+adb push clas_system /data/local/tmp/arm_cpu/
+adb shell chmod +x /data/local/tmp/arm_cpu//clas_system
+adb push inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/arm_cpu/
+adb push MobileNetV3_large_x1_0.nb /data/local/tmp/arm_cpu/
+adb push config.txt /data/local/tmp/arm_cpu/
+adb push ../../ppcls/utils/imagenet1k_label_list.txt /data/local/tmp/arm_cpu/
+adb push imgs/tabby_cat.jpg /data/local/tmp/arm_cpu/
 ```
 
 #### 注意：
@@ -224,32 +220,22 @@ clas_model_file ./MobileNetV3_large_x1_0.nb # 模型文件地址
 label_path ./imagenet1k_label_list.txt 			# 类别映射文本文件
 resize_short_size 256 # resize之后的短边边长
 crop_size 224 				# 裁剪后用于预测的边长
-visualize 0 # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为clas_result.png的图像文件。
+visualize 0 # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为clas_result.png的图像文件
+num_threads 1 # 线程数，默认是1。
+precision FP32 # 精度类型，可以选择 FP32 或者 INT8，默认是 FP32。
+runtime_device arm_cpu # 设备类型，默认是 arm_cpu
+enable_benchmark 0 # 是否开启benchmark， 默认是 0
+tipc_benchmark 0 # 是否开启tipc_benchmark，默认是 0
 ```
 
-5. 启动调试，上述步骤完成后就可以使用ADB将文件夹 `debug/` push到手机上运行，步骤如下：
+5. 执行预测命令
 
-```shell
-# 执行编译，得到可执行文件clas_system
-make -j
-
-# 将编译得到的可执行文件移动到debug文件夹中
-mv clas_system ./debug/
-
-# 将上述debug文件夹push到手机上
-adb push debug /data/local/tmp/
-
-adb shell
-cd /data/local/tmp/debug
-export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
+执行以下命令，可完成在手机上的预测。
 
-# clas_system可执行文件的使用方式为:
-# ./clas_system 配置文件路径  测试图像路径
-./clas_system ./config.txt ./tabby_cat.jpg
+```shell
+adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/clas_system /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/tabby_cat.jpg'
 ```
 
-如果对代码做了修改，则需要重新编译并push到手机上。
-
 运行效果如下：
 
 <div align="center">
@@ -263,3 +249,4 @@ A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模
 
 Q2：换一个图测试怎么做？  
 A2：替换 debug 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。
+

docs/en/algorithm_introduction/ISE_ReID_en.md

+# ISE
+---
+## Catalogue
+
+- [1. Introduction](#1)
+- [2. Performance on Market1501 and MSMT17](#2)
+- [3. Test](#3)
+- [4. Reference](#4)
+
+<a name='1'></a>
+## 1. Introduction
+
+ISE (Implicit Sample Extension) is a simple, efficient, and effective learning algorithm for unsupervised person Re-ID. ISE generates what we call support samples around the cluster boundaries. The sample generation process in ISE depends on two critical mechanisms, i.e., a progressive linear interpolation strategy and a label-preserving loss function. The generated support samples from ISE provide complementary information, which can nicely handle the "sub and mixed" clustering errors. ISE achieves superior performance than other unsupervised methods on Market1501 and MSMT17 datasets.
+
+> [**Implicit Sample Extension for Unsupervised Person Re-Identification**](https://arxiv.org/abs/2204.06892v1)<br>
+> Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang<br>
+> CVPR2022
+
+![image](../../images/ISE_ReID/ISE_pipeline.png)
+
+<a name='2'></a>
+## 2. Performance on Market1501 and MSMT17
+
+The main results on Market1501 (M) and MSMT17 (MS). PIL denotes the progressive linear interpolation strategy. LP represents the label-preserving loss function.
+
+| Methods | M | Link | MS | Link |
+| --- | -- | -- | -- | - |
+| Baseline | 82.5 (92.5) | - | 30.1 (58.6) | - |
+| ISE (+PIL) | 83.9 (93.9) | - | 33.5 (63.9) | - |
+| ISE (+LP)  | 83.6 (92.7) | - | 31.4 (59.9) | - |
+| ISE (Ours) (+PIL+LP) | **84.7 (94.0)** | [ISE_M](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ISE_M_model.pdparams) | **35.0 (64.7)** | [ISE_MS](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ISE_MS_model.pdparams) |
+
+<a name="3"></a>
+## 3. Test
+The training code is coming soon. We first release the test code with the pretrained models.
+
+**Test:** You can simply run the following script for the evaluation.
+
+```
+python tools/eval.py -c ./ppcls/configs/Person/ResNet50_UReID_infer.yaml
+```
+**Steps:**
+1. Download the pretrained model first, and put the model into: ```./pd_model_trace/ISE/```.
+2. Change the dataset name in: ```./ppcls/configs/Person/ResNet50_UReID_infer.yaml```.
+3. Run the above script.
+
+
+<a name="4"></a>
+## 4. Reference
+
+If you find ISE useful in your research, please kindly consider citing our paper:
+
+```
+@inproceedings{zhang2022Implicit,
+  title={Implicit Sample Extension for Unsupervised Person Re-Identification},
+  author={Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2022}
+}
+```

docs/en/algorithm_introduction/ImageNet_models_en.md

@@ -29,7 +29,8 @@
 - [22. TNT series](#22)
 - [23. CSwinTransformer series](#23)
 - [24. PVTV2 series](#24)
-- [25. Other models](#25)
+- [25. MobileViT series](#25)
+- [26. Other models](#26)
 - [Reference](#reference)
 
 <a name="1"></a>
@@ -532,10 +533,21 @@ The accuracy and speed indicators of PVTV2 series models are shown in the follow
 | PVT_V2_B4    | 0.836 | 0.967 | - | - | - | 9.8 | 62.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B4_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B4_infer.tar) |
 | PVT_V2_B5    | 0.837 | 0.966 | - | - | - | 11.4 | 82.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B5_infer.tar) |
 
-
 <a name="25"></a>
 
-## 25. Other models
+## 25. MobileViT series <sup>[[42](#ref42)]</sup>
+
+The accuracy and speed indicators of MobileViT series models are shown in the following table. For more introduction, please refer to:[MobileViT series model documents](../models/MobileViT_en.md)
+
+| Model       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | Pretrained Model Download Address                                               | Inference Model Download Address                                      |
+| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+|  MobileViT_XXS    | 0.6867 | 0.8878 | - | - | - | 1849.35  |  5.59   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
+|  MobileViT_XS    | 0.7454 | 0.9227 | - | - | - | 930.75  |  2.33   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XS_infer.tar) |
+|  MobileViT_S    | 0.7814 | 0.9413 | - | - | - | 337.24  |   1.28   | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
+
+<a name="26"></a>
+
+## 26. Other models
 
 The accuracy and speed indicators of AlexNet <sup>[[18](#ref18)]</sup>, SqueezeNet series <sup>[[19](#ref19)]</sup>, VGG series <sup>[[20](#ref20)]</sup>, DarkNet53 <sup>[[21](#ref21)]</sup> and other models are shown in the following table. For more information, please refer to: [Other model documents](../models/Others_en.md).
 
@@ -637,3 +649,5 @@ TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE.
 <a name="ref40">[40]</a>Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, Baining Guo. CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows.
 
 <a name="ref41">[41]</a>Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. PVTv2: Improved Baselines with Pyramid Vision Transformer.
+
+<a name="ref42">[42]</a>Sachin Mehta, Mohammad Rastegari. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

docs/en/inference_deployment/paddle_lite_deploy_en.md

@@ -18,6 +18,7 @@ If you only want to test speed, please refer to [The tutorial of Paddle-Lite mob
         - [2.1.1 [RECOMMEND] Use pip to install Paddle-Lite and optimize model](#2.1.1)
         - [2.1.2 Compile Paddle-Lite to generate opt tool](#2.1.2)
         - [2.1.3 Demo of get the optimized model](#2.1.3)
+        - [2.1.4 Compile to get the executable file clas_system](#2.1.4)
     - [2.2 Run optimized model on Phone](#2.2)
 - [3. FAQ](#3)
 
@@ -40,8 +41,8 @@ For the detailed compilation directions of different development environments, p
 
 |Platform|Inference Library Download Link|
 |-|-|
-|Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)|
-|iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
+|Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz) |
+|iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
 
 **NOTE**:
 
@@ -53,7 +54,7 @@ For the detailed compilation directions of different development environments, p
 The structure of the inference library is as follows:
 
 ```
-inference_lite_lib.android.armv8/
+inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/
 |-- cxx                                                    C++ inference library and header files
 |   |-- include                                            C++ header files
 |   |   |-- paddle_api.h
@@ -148,6 +149,23 @@ paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --
 ```
 
 When the above code command is completed, there will be ``MobileNetV3_large_x1_0.nb` in the current directory, which is the converted model file.
+<a name="2.1.4"></a>
+
+#### 2.1.4 Compile to get the executable file clas_system
+
+```shell
+# Clone the Autolog repository to get automation logs
+cd PaddleClas_root_path
+cd deploy/lite/
+git clone https://github.com/LDOUBLEV/AutoLog.git
+```
+
+```shell
+# Compile
+make -j
+```
+
+After executing the `make` command, the `clas_system` executable file is generated in the current directory, which is used for Lite prediction.
 
 <a name="2.2"></a>
 ## 2.2 Run optimized model on Phone
@@ -172,7 +190,7 @@ When the above code command is completed, there will be ``MobileNetV3_large_x1_0
     * Install ADB for windows
       If install ADB fo Windows, you need to download from Google's Android platform: [Download Link](https://developer.android.com/studio).
 
-    First, make sure the phone is connected to the computer, turn on the `USB debugging` option of the phone, and select the `file transfer` mode. Verify whether ADB is installed successfully as follows:
+3. First, make sure the phone is connected to the computer, turn on the `USB debugging` option of the phone, and select the `file transfer` mode. Verify whether ADB is installed successfully as follows:
 
     ```shell
     $ adb devices
@@ -183,42 +201,22 @@ When the above code command is completed, there will be ``MobileNetV3_large_x1_0
 
     If there is `device` output like the above, it means the installation was successful.
 
-4. Prepare optimized model, inference library files, test image and dictionary file used.
+4. Push the optimized model, prediction library file, test image and class map file to the phone.
 
 ```shell
-cd PaddleClas_root_path
-cd deploy/lite/
-
-# prepare.sh will put the inference library files, the test image and the dictionary files in demo/cxx/clas
-sh prepare.sh /{lite inference library path}/inference_lite_lib.android.armv8
-
-# enter the working directory of lite demo
-cd /{lite inference library path}/inference_lite_lib.android.armv8/
-cd demo/cxx/clas/
-
-# copy the C++ inference dynamic library file （ie. .so) to the debug folder
-cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
+```shell
+adb shell mkdir -p /data/local/tmp/arm_cpu/
+adb push clas_system /data/local/tmp/arm_cpu/
+adb shell chmod +x /data/local/tmp/arm_cpu//clas_system
+adb push inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/arm_cpu/
+adb push MobileNetV3_large_x1_0.nb /data/local/tmp/arm_cpu/
+adb push config.txt /data/local/tmp/arm_cpu/
+adb push ../../ppcls/utils/imagenet1k_label_list.txt /data/local/tmp/arm_cpu/
+adb push imgs/tabby_cat.jpg /data/local/tmp/arm_cpu/
 ```
 
-The `prepare.sh` take `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` as the test image, and copy it to the `demo/cxx/clas/debug/` directory.
-
 You should put the model that optimized by `paddle_lite_opt` under the `demo/cxx/clas/debug/` directory. In this example, use `MobileNetV3_large_x1_0.nb` model file generated in [2.1.3](#2.1.3).
 
-The structure of the clas demo is as follows after the above command is completed:
-
-```
-demo/cxx/clas/
-|-- debug/
-|   |--MobileNetV3_large_x1_0.nb                    class model
-|   |--tabby_cat.jpg                              test image
-|   |--imagenet1k_label_list.txt                    dictionary file
-|   |--libpaddle_light_api_shared.so              C++ .so file
-|   |--config.txt                                 config file
-|-- config.txt                                    config file
-|-- image_classfication.cpp                       source code
-|-- Makefile                                      compile file
-```
-
 **NOTE**:
 
 * `Imagenet1k_label_list.txt` is the category mapping file of the `ImageNet1k` dataset. If use a custom category, you need to replace the category mapping file.
@@ -229,33 +227,22 @@ clas_model_file ./MobileNetV3_large_x1_0.nb # path of model file
 label_path ./imagenet1k_label_list.txt      # path of category mapping file
 resize_short_size 256                       # the short side length after resize
 crop_size 224                               # side length used for inference after cropping
-
 visualize 0                                 # whether to visualize. If you set it to 1, an image file named 'clas_result.png' will be generated in the current directory.
+num_threads 1 # The number of threads, the default is 1
+precision FP32 # Precision type, you can choose FP32 or INT8, the default is FP32
+runtime_device arm_cpu # Device type, the default is arm_cpu
+enable_benchmark 0 # Whether to enable benchmark, the default is 0
+tipc_benchmark 0 # Whether to enable tipc_benchmark, the default is 0
 ```
 
 5. Run Model on Phone
 
-```shell
-# run compile to get the executable file 'clas_system'
-make -j
-
-# move the compiled executable file to the debug folder
-mv clas_system ./debug/
-
-# push the debug folder to Phone
-adb push debug /data/local/tmp/
+Execute the following command to complete the prediction on the mobile phone.
 
-adb shell
-cd /data/local/tmp/debug
-export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
-
-# the usage of clas_system is as follows:
-# ./clas_system "path of config file" "path of test image"
-./clas_system ./config.txt ./tabby_cat.jpg
+```shell
+adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/clas_system /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/tabby_cat.jpg'
 ```
 
-**NOTE**: If you make changes to the code, you need to recompile and repush the `debug ` folder to the phone.
-
 The result is as follows:
 
 ![](../../images/inference_deployment/lite_demo_result.png)

docs/en/models/MobileViT_en.md

+# MobileviT
+---
+## Catalogue
+
+* [1. Overview](#1)
+* [2. Accuracy, FLOPs and Parameters](#2)
+
+<a name='1'></a>
+
+## 1. Overview
+
+MobileViT is a lightweight visual Transformer network that can be used as a general backbone network in the field of computer vision. MobileViT combines the advantages of CNN and Transformer, which can better deal with global features and local features, and better solve the problem of lack of inductive bias in Transformer models.
+, and finally, under the same amount of parameters, compared with other SOTA models, the tasks of image classification, object detection, and semantic segmentation have been greatly improved. [Paper](https://arxiv.org/pdf/2110.02178.pdf)
+
+<a name='2'></a>
+
+## 2. Accuracy, FLOPs and Parameters
+
+| Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(M) | Params<br>(M) |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| MobileViT_XXS    | 0.6867 | 0.8878 | 0.690 | - | 1849.35  | 5.59   |
+| MobileViT_XS    | 0.7454 | 0.9227 | 0.747 | - | 930.75  | 2.33   |
+| MobileViT_S    | 0.7814 | 0.9413 | 0.783 | - | 337.24  | 1.28   |

docs/zh_CN/algorithm_introduction/ISE_ReID.md

+# ISE
+---
+## 目录
+
+- [1. 介绍](#1)
+- [2. 在Market1501和MSMT17上的结果](#2)
+- [3. 测试](#3)
+- [4. 引用](#4)
+
+<a name='1'></a>
+## 1. 介绍
+
+ISE (Implicit Sample Extension)是一种简单、高效、有效的无监督行人再识别学习算法。ISE在聚类蔟边界周围生成样本，我们称之为支持样本。ISE的样本生成过程依赖于两个关键机制，即渐进线性插值策略（progressive linear interpolation）和标签保留的损失函数（label-preserving loss function）。ISE生成的支持样本提供了额外补充信息，可以很好地处理“子类和混合”的聚类错误。ISE在Market1501和MSMT17数据集上取得了优于其他无监督方法的性能。
+
+> [**Implicit Sample Extension for Unsupervised Person Re-Identification**](https://arxiv.org/abs/2204.06892v1)<br>
+> Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang<br>
+> CVPR2022
+
+![image](../../images/ISE_ReID/ISE_pipeline.png)
+
+
+<a name='2'></a>
+## 2. 在Market1501和MSMT17上的结果
+
+在Market1501和MSMT17上的主要结果。“PIL”表示渐进线性插值策略。“LP”表示标签保留的损失函数。
+
+| 方法 | Market1501 | 下载链接 | MSMT17 | 下载链接 |
+| --- | -- | -- | -- | - |
+| Baseline | 82.5 (92.5) | - | 30.1 (58.6) | - |
+| ISE (+PIL) | 83.9 (93.9) | - | 33.5 (63.9) | - |
+| ISE (+LP)  | 83.6 (92.7) | - | 31.4 (59.9) | - |
+| ISE (Ours) (+PIL+LP) | **84.7 (94.0)** | [ISE_M](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ISE_M_model.pdparams) | **35.0 (64.7)** | [ISE_MS](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ISE_MS_model.pdparams) |
+
+
+<a name="3"></a>
+## 3. 测试
+我们很快会提供训练代码，首先我们提供了测试代码和模型。
+
+**测试：** 可简使用如下脚本进行模型评估。
+
+```
+python tools/eval.py -c ./ppcls/configs/Person/ResNet50_UReID_infer.yaml
+```
+**步骤：**
+1. 首先下载模型，并放入：```./pd_model_trace/ISE/```。
+2. 改变```./ppcls/configs/Person/ResNet50_UReID_infer.yaml```中的数据集名称。
+3. 运行上述脚本。
+
+
+<a name="4"></a>
+## 4. 引用
+
+如果ISE在您的研究中有启发，请考虑引用我们的论文:
+
+```
+@inproceedings{zhang2022Implicit,
+  title={Implicit Sample Extension for Unsupervised Person Re-Identification},
+  author={Xinyu Zhang, Dongdong Li, Zhigang Wang, Jian Wang, Errui Ding, Javen Qinfeng Shi, Zhaoxiang Zhang, Jingdong Wang},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2022}
+}
+```

docs/zh_CN/algorithm_introduction/ImageNet_models.md

@@ -32,7 +32,8 @@
 - [22. TNT 系列](#22)
 - [23. CSwinTransformer 系列](#23)
 - [24. PVTV2 系列](#24)
-- [25. 其他模型](#25)
+- [25. MobileViT 系列](#25)
+- [26. 其他模型](#26)
 - [参考文献](#reference)
 
 <a name="1"></a>
@@ -533,10 +534,21 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
 | PVT_V2_B5    | 0.837 | 0.966 | - | - | - | 11.4 | 82.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/PVT_V2_B5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PVT_V2_B5_infer.tar) |
 
 
-
 <a name="25"></a>
 
-## 25. 其他模型
+## 25. MobileViT 系列 <sup>[[42](#ref42)]</sup>
+
+关于 MobileViT 系列模型的精度、速度指标如下表所示，更多介绍可以参考：[MobileViT 系列模型文档](../models/MobileViT.md)。
+
+| 模型       | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址                                               | inference模型下载地址                                      |
+| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
+|  MobileViT_XXS    | 0.6867 | 0.8878 | - | - | - | 1849.35  |  5.59   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
+|  MobileViT_XS    | 0.7454 | 0.9227 | - | - | - | 930.75  |  2.33   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XS_infer.tar) |
+|  MobileViT_S    | 0.7814 | 0.9413 | - | - | - | 337.24  |   1.28   | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
+
+<a name="26"></a>
+
+## 26. 其他模型
 
 关于 AlexNet <sup>[[18](#ref18)]</sup>、SqueezeNet 系列 <sup>[[19](#ref19)]</sup>、VGG 系列 <sup>[[20](#ref20)]</sup>、DarkNet53 <sup>[[21](#ref21)]</sup> 等模型的精度、速度指标如下表所示，更多介绍可以参考：[其他模型文档](../models/Others.md)。
 
@@ -637,3 +649,5 @@ TRANSFORMERS FOR IMAGE RECOGNITION AT SCALE.
 <a name="ref40">[40]</a>Xiaoyi Dong, Jianmin Bao, Dongdong Chen, Weiming Zhang, Nenghai Yu, Lu Yuan, Dong Chen, Baining Guo. CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows.
 
 <a name="ref41">[41]</a>Wenhai Wang, Enze Xie, Xiang Li, Deng-Ping Fan, Kaitao Song, Ding Liang, Tong Lu, Ping Luo, Ling Shao. PVTv2: Improved Baselines with Pyramid Vision Transformer.
+
+<a name="ref42">[42]</a>Sachin Mehta, Mohammad Rastegari. MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer.

docs/zh_CN/inference_deployment/paddle_lite_deploy.md

-# PaddleLite 推理部署
----
-
-本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite)在移动端部署 PaddleClas 分类模型的详细步骤。识别模型的部署将在近期支持，敬请期待。
+# 端侧部署
 
-Paddle Lite 是飞桨轻量化推理引擎，为手机、IOT 端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。
+本教程将介绍基于[Paddle Lite](https://github.com/PaddlePaddle/Paddle-Lite) 在移动端部署PaddleClas分类模型的详细步骤。
 
-如果希望直接测试速度，可以参考[Paddle-Lite 移动端 benchmark 测试教程](../others/paddle_mobile_inference.md)。
+Paddle Lite是飞桨轻量化推理引擎，为手机、IOT端提供高效推理能力，并广泛整合跨平台硬件，为端侧部署及应用落地问题提供轻量化的部署方案。如果希望直接测试速度，可以参考[Paddle-Lite移动端benchmark测试教程](../../docs/zh_CN/extension/paddle_mobile_inference.md)。
 
 ---
 
@@ -18,53 +15,54 @@ Paddle Lite 是飞桨轻量化推理引擎，为手机、IOT 端提供高效推
         - [2.1.1 pip 安装 paddlelite 并进行转换](#2.1.1)
         - [2.1.2 源码编译 Paddle-Lite 生成 opt 工具](#2.1.2)
         - [2.1.3 转换示例](#2.1.3)
+        - [2.1.4 执行编译，得到可执行文件clas_system](#2.1.4)
     - [2.2 与手机联调](#2.2)
 - [3. FAQ](#3)
 
-<a name="1"></a>
 ## 1. 准备环境
 
-Paddle Lite 目前支持以下平台部署：
-* 电脑（编译 Paddle Lite）
-* 安卓手机（armv7 或 armv8）
+### 运行准备
+- 电脑（编译Paddle Lite）
+- 安卓手机（armv7或armv8）
 
-<a name="1.1"></a>
 ### 1.1 准备交叉编译环境
+交叉编译环境用于编译 Paddle Lite 和 PaddleClas 的C++ demo。
+支持多种开发环境，不同开发环境的编译流程请参考对应文档。
 
-交叉编译环境用于编译 Paddle Lite 和 PaddleClas 的 C++ demo。
-支持多种开发环境，关于 Docker、Linux、macOS、Windows 等不同开发环境的编译流程请参考[文档](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html)。
+1. [Docker](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#docker)
+2. [Linux](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#linux)
+3. [MAC OS](https://paddle-lite.readthedocs.io/zh/latest/source_compile/compile_env.html#mac-os)
 
-<a name="1.2"></a>
 ### 1.2 准备预测库
 
 预测库有两种获取方式：
 1. [建议]直接下载，预测库下载链接如下：
       |平台|预测库下载链接|
       |-|-|
-      |Android|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv7.gcc.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/Android/gcc/inference_lite_lib.android.armv8.gcc.c++_static.with_extra.with_cv.tar.gz)|
-      |iOS|[arm7](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://paddlelite-data.bj.bcebos.com/Release/2.8-rc/iOS/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
+      |Android|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv7.clang.c++_static.with_extra.with_cv.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz)|
+      |iOS|[arm7](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv7.with_cv.with_extra.tiny_publish.tar.gz) / [arm8](https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.ios.armv8.with_cv.with_extra.tiny_publish.tar.gz)|
 
       **注**：
       1. 如果是从 Paddle-Lite [官方文档](https://paddle-lite.readthedocs.io/zh/latest/quick_start/release_lib.html#android-toolchain-gcc)下载的预测库，
-      注意选择 `with_extra=ON，with_cv=ON` 的下载链接。
-      2. 如果使用量化的模型部署在端侧，建议使用 Paddle-Lite develop 分支编译预测库。
-
-2. 编译 Paddle-Lite 得到预测库，Paddle-Lite 的编译方式如下：
+      注意选择`with_extra=ON，with_cv=ON`的下载链接。
+      2. 如果使用量化的模型部署在端侧，建议使用Paddle-Lite develop分支编译预测库。
 
+2. 编译Paddle-Lite得到预测库，Paddle-Lite的编译方式如下：
 ```shell
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
-# 如果使用编译方式，建议使用 develop 分支编译预测库
+# 如果使用编译方式，建议使用develop分支编译预测库
 git checkout develop
 ./lite/tools/build_android.sh  --arch=armv8  --with_cv=ON --with_extra=ON
 ```
-**注意**：编译 Paddle-Lite 获得预测库时，需要打开`--with_cv=ON --with_extra=ON` 两个选项，`--arch` 表示 `arm` 版本，这里指定为 armv8，更多编译命令介绍请参考[Linux x86 环境下编译适用于 Android 的库](https://paddle-lite.readthedocs.io/zh/latest/source_compile/linux_x86_compile_android.html)，关于其他平台的编译操作，具体请参考[PaddleLite](https://paddle-lite.readthedocs.io/zh/latest/)中`源码编译`部分。
 
-直接下载预测库并解压后，可以得到 `inference_lite_lib.android.armv8/`文件夹，通过编译 Paddle-Lite 得到的预测库位于 `Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
+**注意**：编译Paddle-Lite获得预测库时，需要打开`--with_cv=ON --with_extra=ON`两个选项，`--arch`表示`arm`版本，这里指定为armv8，更多编译命令介绍请参考[链接](https://paddle-lite.readthedocs.io/zh/latest/user_guides/Compile/Android.html#id2)。
+
+直接下载预测库并解压后，可以得到`inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/`文件夹，通过编译Paddle-Lite得到的预测库位于`Paddle-Lite/build.lite.android.armv8.gcc/inference_lite_lib.android.armv8/`文件夹下。
 预测库的文件目录如下：
 
 ```
-inference_lite_lib.android.armv8/
+inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/
 |-- cxx                                        C++ 预测库和头文件
 |   |-- include                                C++ 头文件
 |   |   |-- paddle_api.h
@@ -77,7 +75,7 @@ inference_lite_lib.android.armv8/
 |   `-- lib                                           C++预测库
 |       |-- libpaddle_api_light_bundled.a             C++静态库
 |       `-- libpaddle_light_api_shared.so             C++动态库
-|-- java                                     Java 预测库
+|-- java                                     Java预测库
 |   |-- jar
 |   |   `-- PaddlePredictor.jar
 |   |-- so
@@ -88,47 +86,43 @@ inference_lite_lib.android.armv8/
 |   `-- java                                 Java 预测库demo
 ```
 
-<a name="2"></a>
-## 2. 开始运行
+## 2 开始运行
 
-<a name="2.1"></a>
 ### 2.1 模型优化
 
-Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合精度、Kernel 优选等方法，使用 Paddle-Lite 的 `opt` 工具可以自动对 inference 模型进行优化，目前支持两种优化方式，优化后的模型更轻量，模型运行速度更快。在进行模型优化前，需要先准备 `opt` 优化工具，有以下两种方式。
+Paddle-Lite 提供了多种策略来自动优化原始的模型，其中包括量化、子图融合、混合调度、Kernel优选等方法，使用Paddle-Lite的`opt`工具可以自动对inference模型进行优化，目前支持两种优化方式，优化后的模型更轻量，模型运行速度更快。
 
 **注意**：如果已经准备好了 `.nb` 结尾的模型文件，可以跳过此步骤。
 
-<a name="2.1.1"></a>
-#### 2.1.1 [建议]pip 安装 paddlelite 并进行转换
+#### 2.1.1 [建议]pip安装paddlelite并进行转换
 
-Python 下安装 `paddlelite`，目前最高支持 `Python3.7`。
-**注意**：`paddlelite` whl 包版本必须和预测库版本对应。
+Python下安装 `paddlelite`，目前最高支持`Python3.7`。
+**注意**：`paddlelite`whl包版本必须和预测库版本对应。
 
 ```shell
-pip install paddlelite==2.8
+pip install paddlelite==2.10
 ```
 
-之后使用 `paddle_lite_opt` 工具可以进行 inference 模型的转换。`paddle_lite_opt` 的部分参数如下
+之后使用`paddle_lite_opt`工具可以进行inference模型的转换。`paddle_lite_opt`的部分参数如下
 
 |选项|说明|
 |-|-|
-|--model_dir|待优化的 PaddlePaddle 模型（非 combined 形式）的路径|
-|--model_file|待优化的 PaddlePaddle 模型（combined 形式）的网络结构文件路径|
-|--param_file|待优化的 PaddlePaddle 模型（combined 形式）的权重文件路径|
-|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf 和 naive_buffer，其中 naive_buffer 是一种更轻量级的序列化/反序列化实现。若您需要在 mobile 端执行模型预测，请将此选项设置为 naive_buffer。默认为 protobuf|
+|--model_dir|待优化的PaddlePaddle模型（非combined形式）的路径|
+|--model_file|待优化的PaddlePaddle模型（combined形式）的网络结构文件路径|
+|--param_file|待优化的PaddlePaddle模型（combined形式）的权重文件路径|
+|--optimize_out_type|输出模型类型，目前支持两种类型：protobuf和naive_buffer，其中naive_buffer是一种更轻量级的序列化/反序列化实现。若您需要在mobile端执行模型预测，请将此选项设置为naive_buffer。默认为protobuf|
 |--optimize_out|优化模型的输出路径|
-|--valid_targets|指定模型可执行的 backend，默认为 arm。目前可支持 x86、arm、opencl、npu、xpu，可以同时指定多个 backend（以空格分隔），Model Optimize Tool 将会自动选择最佳方式。如果需要支持华为 NPU（Kirin 810/990 Soc 搭载的达芬奇架构 NPU），应当设置为 npu, arm|
-|--record_tailoring_info|当使用 根据模型裁剪库文件 功能时，则设置该选项为 true，以记录优化后模型含有的 kernel 和 OP 信息，默认为 false|
+|--valid_targets|指定模型可执行的backend，默认为arm。目前可支持x86、arm、opencl、npu、xpu，可以同时指定多个backend(以空格分隔)，Model Optimize Tool将会自动选择最佳方式。如果需要支持华为NPU（Kirin 810/990 Soc搭载的达芬奇架构NPU），应当设置为npu, arm|
+|--record_tailoring_info|当使用 根据模型裁剪库文件 功能时，则设置该选项为true，以记录优化后模型含有的kernel和OP信息，默认为false|
 
-`--model_file` 表示 inference 模型的 model 文件地址，`--param_file` 表示 inference 模型的 param 文件地址；`optimize_out` 用于指定输出文件的名称（不需要添加 `.nb` 的后缀）。直接在命令行中运行 `paddle_lite_opt`，也可以查看所有参数及其说明。
+`--model_file`表示inference模型的model文件地址，`--param_file`表示inference模型的param文件地址；`optimize_out`用于指定输出文件的名称（不需要添加`.nb`的后缀）。直接在命令行中运行`paddle_lite_opt`，也可以查看所有参数及其说明。
 
-<a name="2.1.2"></a>
-#### 2.1.2 源码编译 Paddle-Lite 生成 opt 工具
 
-模型优化需要 Paddle-Lite 的 `opt` 可执行文件，可以通过编译 Paddle-Lite 源码获得，编译步骤如下：
+#### 2.1.2 源码编译Paddle-Lite生成opt工具
 
+模型优化需要Paddle-Lite的`opt`可执行文件，可以通过编译Paddle-Lite源码获得，编译步骤如下：
 ```shell
-# 如果准备环境时已经 clone 了 Paddle-Lite，则不用重新 clone Paddle-Lite
+# 如果准备环境时已经clone了Paddle-Lite，则不用重新clone Paddle-Lite
 git clone https://github.com/PaddlePaddle/Paddle-Lite.git
 cd Paddle-Lite
 git checkout develop
@@ -136,146 +130,137 @@ git checkout develop
 ./lite/tools/build.sh build_optimize_tool
 ```
 
-编译完成后，`opt` 文件位于 `build.opt/lite/api/` 下，可通过如下方式查看 `opt` 的运行选项和使用方式：
-
+编译完成后，`opt`文件位于`build.opt/lite/api/`下，可通过如下方式查看`opt`的运行选项和使用方式；
 ```shell
 cd build.opt/lite/api/
 ./opt
 ```
 
-`opt` 的使用方式与参数与上面的 `paddle_lite_opt` 完全一致。
+`opt`的使用方式与参数与上面的`paddle_lite_opt`完全一致。
 
 <a name="2.1.3"></a>
+
 #### 2.1.3 转换示例
 
-下面以 PaddleClas 的 `MobileNetV3_large_x1_0` 模型为例，介绍使用 `paddle_lite_opt` 完成预训练模型到 inference 模型，再到 Paddle-Lite 优化模型的转换。
+下面以PaddleClas的 `MobileNetV3_large_x1_0` 模型为例，介绍使用`paddle_lite_opt`完成预训练模型到inference模型，再到Paddle-Lite优化模型的转换。
 
 ```shell
-# 进入 PaddleClas 根目录
+# 进入PaddleClas根目录
 cd PaddleClas_root_path
 
-# 下载并解压 inference 模型
+# 下载并解压inference模型
 wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
 tar -xf MobileNetV3_large_x1_0_infer.tar
 
-# 将 inference 模型转化为 Paddle-Lite 优化模型
+# 将inference模型转化为Paddle-Lite优化模型
 paddle_lite_opt --model_file=./MobileNetV3_large_x1_0_infer/inference.pdmodel --param_file=./MobileNetV3_large_x1_0_infer/inference.pdiparams --optimize_out=./MobileNetV3_large_x1_0
 ```
 
-最终在当前文件夹下生成 `MobileNetV3_large_x1_0.nb` 的文件。
+最终在当前文件夹下生成`MobileNetV3_large_x1_0.nb`的文件。
+
+**注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀`.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
+
+<a name="2.1.4"></a>
+
+#### 2.1.4 执行编译，得到可执行文件clas_system
+
+```shell
+# 克隆 Autolog 代码库，以便获取自动化日志
+cd PaddleClas_root_path
+cd deploy/lite/
+git clone https://github.com/LDOUBLEV/AutoLog.git
+```
+
+```shell
+# 编译
+make -j
+```
 
-**注意**：`--optimize_out` 参数为优化后模型的保存路径，无需加后缀 `.nb`；`--model_file` 参数为模型结构信息文件的路径，`--param_file` 参数为模型权重信息文件的路径，请注意文件名。
+执行 `make` 命令后，会在当前目录生成 `clas_system` 可执行文件，该文件用于 Lite 预测。
 
-<a name="2.2"></a>
+<a name="2.2与手机联调"></a>
 ### 2.2 与手机联调
 
 首先需要进行一些准备工作。
-1. 准备一台 arm8 的安卓手机，如果编译的预测库和 opt 文件是 armv7，则需要 arm7 的手机，并修改 Makefile 中 `ARM_ABI = arm7`。
-2. 电脑上安装 ADB 工具，用于调试。 ADB 安装方式如下：
+1. 准备一台arm8的安卓手机，如果编译的预测库和opt文件是armv7，则需要arm7的手机，并修改Makefile中`ARM_ABI = arm7`。
+2. 电脑上安装ADB工具，用于调试。 ADB安装方式如下：
+
+    3.1. MAC电脑安装ADB:
 
-    * MAC 电脑安装 ADB:
     ```shell
     brew cask install android-platform-tools
     ```
-    * Linux 安装 ADB
+    3.2. Linux安装ADB
     ```shell
     sudo apt update
     sudo apt install -y wget adb
     ```
-    * Window 安装 ADB
-    win 上安装需要去谷歌的安卓平台下载 ADB 软件包进行安装：[链接](https://developer.android.com/studio)
+    3.3. Window安装ADB
+
+    win上安装需要去谷歌的安卓平台下载ADB软件包进行安装：[链接](https://developer.android.com/studio)
 
-3. 手机连接电脑后，开启手机 `USB 调试` 选项，选择 `文件传输` 模式，在电脑终端中输入：
+3. 手机连接电脑后，开启手机`USB调试`选项，选择`文件传输`模式，在电脑终端中输入：
 
 ```shell
 adb devices
 ```
-如果有 device 输出，则表示安装成功，如下所示：
+如果有device输出，则表示安装成功，如下所示：
 ```
 List of devices attached
 744be294    device
 ```
 
-4. 准备优化后的模型、预测库文件、测试图像和类别映射文件。
 
-```shell
-cd PaddleClas_root_path
-cd deploy/lite/
-
-# 运行 prepare.sh
-# prepare.sh 会将预测库文件、测试图像和使用的字典文件放置在预测库中的 demo/cxx/clas 文件夹下
-sh prepare.sh /{lite prediction library path}/inference_lite_lib.android.armv8
-
-# 进入 lite demo 的工作目录
-cd /{lite prediction library path}/inference_lite_lib.android.armv8/
-cd demo/cxx/clas/
+4. 将优化后的模型、预测库文件、测试图像和类别映射文件push到手机上。
 
-# 将 C++ 预测动态库 so 文件复制到 debug 文件夹中
-cp ../../../cxx/lib/libpaddle_light_api_shared.so ./debug/
-```
-
-`prepare.sh` 以 `PaddleClas/deploy/lite/imgs/tabby_cat.jpg` 作为测试图像，将测试图像复制到 `demo/cxx/clas/debug/` 文件夹下。
-将 `paddle_lite_opt` 工具优化后的模型文件放置到 `/{lite prediction library path}/inference_lite_lib.android.armv8/demo/cxx/clas/debug/` 文件夹下。本例中，使用 [2.1.3 转换示例](#2.1.3) 生成的 `MobileNetV3_large_x1_0.nb` 模型文件。
-
-执行完成后，clas 文件夹下将有如下文件格式：
-
-```
-demo/cxx/clas/
-|-- debug/
-|   |--MobileNetV3_large_x1_0.nb                优化后的分类器模型文件
-|   |--tabby_cat.jpg                               待测试图像
-|   |--imagenet1k_label_list.txt                类别映射文件
-|   |--libpaddle_light_api_shared.so    C++预测库文件
-|   |--config.txt                       分类预测超参数配置
-|-- config.txt                                  分类预测超参数配置
-|-- image_classfication.cpp                图像分类代码文件
-|-- Makefile                                    编译文件
+```shell
+adb shell mkdir -p /data/local/tmp/arm_cpu/
+adb push clas_system /data/local/tmp/arm_cpu/
+adb shell chmod +x /data/local/tmp/arm_cpu//clas_system
+adb push inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv/cxx/lib/libpaddle_light_api_shared.so /data/local/tmp/arm_cpu/
+adb push MobileNetV3_large_x1_0.nb /data/local/tmp/arm_cpu/
+adb push config.txt /data/local/tmp/arm_cpu/
+adb push ../../ppcls/utils/imagenet1k_label_list.txt /data/local/tmp/arm_cpu/
+adb push imgs/tabby_cat.jpg /data/local/tmp/arm_cpu/
 ```
 
 #### 注意：
-* 上述文件中，`imagenet1k_label_list.txt` 是 ImageNet1k 数据集的类别映射文件，如果使用自定义的类别，需要更换该类别映射文件。
+* 上述文件中，`imagenet1k_label_list.txt` 是ImageNet1k数据集的类别映射文件，如果使用自定义的类别，需要更换该类别映射文件。
 
-* `config.txt` 包含了分类器的超参数，如下：
+*  `config.txt` 包含了分类器的超参数，如下：
 
 ```shell
 clas_model_file ./MobileNetV3_large_x1_0.nb # 模型文件地址
-label_path ./imagenet1k_label_list.txt         # 类别映射文本文件
-resize_short_size 256                       # resize 之后的短边边长
-crop_size 224                                 # 裁剪后用于预测的边长 
-visualize 0       # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为 clas_result.png 的图像文件。
+label_path ./imagenet1k_label_list.txt 			# 类别映射文本文件
+resize_short_size 256 # resize之后的短边边长
+crop_size 224 				# 裁剪后用于预测的边长
+visualize 0 # 是否进行可视化，如果选择的话，会在当前文件夹下生成名为clas_result.png的图像文件
+num_threads 1 # 线程数，默认是1。
+precision FP32 # 精度类型，可以选择 FP32 或者 INT8，默认是 FP32。
+runtime_device arm_cpu # 设备类型，默认是 arm_cpu
+enable_benchmark 0 # 是否开启benchmark， 默认是 0
+tipc_benchmark 0 # 是否开启tipc_benchmark，默认是 0
 ```
 
-5. 启动调试，上述步骤完成后就可以使用 ADB 将文件夹 `debug/` push 到手机上运行，步骤如下：
-
-```shell
-# 执行编译，得到可执行文件 clas_system
-make -j
-
-# 将编译得到的可执行文件移动到 debug 文件夹中
-mv clas_system ./debug/
+5. 执行预测命令
 
-# 将上述 debug 文件夹 push 到手机上
-adb push debug /data/local/tmp/
+执行以下命令，可完成在手机上的预测。
 
-adb shell
-cd /data/local/tmp/debug
-export LD_LIBRARY_PATH=/data/local/tmp/debug:$LD_LIBRARY_PATH
-
-# clas_system 可执行文件的使用方式为:
-# ./clas_system 配置文件路径  测试图像路径
-./clas_system ./config.txt ./tabby_cat.jpg
+```shell
+adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/clas_system /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/tabby_cat.jpg'
 ```
 
-如果对代码做了修改，则需要重新编译并 push 到手机上。
-
 运行效果如下：
 
-![](../../images/inference_deployment/lite_demo_result.png)
+<div align="center">
+    <img src="./imgs/lite_demo_result.png" width="600">
+</div>
 
-<a name="3"></a>
-## 3. FAQ
+
+## FAQ
 Q1：如果想更换模型怎么办，需要重新按照流程走一遍吗？  
 A1：如果已经走通了上述步骤，更换模型只需要替换 `.nb` 模型文件即可，同时要注意修改下配置文件中的 `.nb` 文件路径以及类别映射文件（如有必要）。
 
 Q2：换一个图测试怎么做？  
 A2：替换 debug 下的测试图像为你想要测试的图像，使用 ADB 再次 push 到手机上即可。
+

docs/zh_CN/models/MobileViT.md

+# MobileviT
+---
+## 目录
+
+* [1. 概述](#1)
+* [2. 精度、FLOPs 和参数量](#2)
+
+<a name='1'></a>
+
+## 1. 概述
+
+MobileViT 是一个轻量级的视觉 Transformer 网络，可以用作计算机视觉领域的通用骨干网路。 MobileViT 结合了 CNN 和 Transformer 的优势，可以更好的处理全局特征和局部特征，更好地解决 Transformer 模型缺乏归纳偏置的问题，最终，在同样参数量下，与其他 SOTA 模型相比，在图像分类、目标检测、语义分割任务上都有大幅提升。[论文地址](https://arxiv.org/pdf/2110.02178.pdf)。
+
+<a name='2'></a>
+
+## 2. 精度、FLOPs 和参数量
+
+| Models           | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(M) | Params<br>(M) |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| MobileViT_XXS    | 0.6867 | 0.8878 | 0.690 | - | 1849.35  | 5.59   |
+| MobileViT_XS    | 0.7454 | 0.9227 | 0.747 | - | 930.75  | 2.33   |
+| MobileViT_S    | 0.7814 | 0.9413 | 0.783 | - | 337.24  | 1.28   |

docs/zh_CN/samples/Goods_Recognition/README.md

+**商品识别**，即在智能零售场景下精准快速的识别商品类别、商品属性等。当下零售市场存在降低人力及运营成本、实现24小时不间断营业等需求，商品识别可以帮助越来越多的零售门店实现智慧零售数字化转型。在智慧零售概念火爆的今天，商品识别具有非常广阔的应用场景，如**货架陈列分析**、**智能结算**、**仓库管理**、**以图搜图**等。  
+
+本案例使用了飞桨图像分类开发套件PaddleClas中的通用图像识别系统PP-ShiTu进行**商品识别**的实现。
+
+![result](./imgs/result.jpg)
+
+**注**: AI Studio在线运行代码请参考[智慧商超商品识别系统](https://aistudio.baidu.com/aistudio/projectdetail/3460304)
\ No newline at end of file

ppcls/arch/backbone/__init__.py

@@ -62,7 +62,9 @@ from ppcls.arch.backbone.model_zoo.tnt import TNT_small
 from ppcls.arch.backbone.model_zoo.hardnet import HarDNet68, HarDNet85, HarDNet39_ds, HarDNet68_ds
 from ppcls.arch.backbone.model_zoo.cspnet import CSPDarkNet53
 from ppcls.arch.backbone.model_zoo.pvt_v2 import PVT_V2_B0, PVT_V2_B1, PVT_V2_B2_Linear, PVT_V2_B2, PVT_V2_B3, PVT_V2_B4, PVT_V2_B5
+from ppcls.arch.backbone.model_zoo.mobilevit import MobileViT_XXS, MobileViT_XS, MobileViT_S
 from ppcls.arch.backbone.model_zoo.repvgg import RepVGG_A0, RepVGG_A1, RepVGG_A2, RepVGG_B0, RepVGG_B1, RepVGG_B2, RepVGG_B1g2, RepVGG_B1g4, RepVGG_B2g4, RepVGG_B3g4
+from ppcls.arch.backbone.model_zoo.van import VAN_tiny
 from ppcls.arch.backbone.variant_models.resnet_variant import ResNet50_last_stage_stride1
 from ppcls.arch.backbone.variant_models.vgg_variant import VGG19Sigmoid
 from ppcls.arch.backbone.variant_models.pp_lcnet_variant import PPLCNet_x2_5_Tanh

ppcls/arch/backbone/legendary_models/resnet.py

@@ -276,6 +276,7 @@ class ResNet(TheseusLayer):
                  config,
                  stages_pattern,
                  version="vb",
+                 stem_act="relu",
                  class_num=1000,
                  lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
                  data_format="NCHW",
@@ -310,7 +311,7 @@ class ResNet(TheseusLayer):
             [[input_image_channel, 32, 3, 2], [32, 32, 3, 1], [32, 64, 3, 1]]
         }
 
-        self.stem = nn.Sequential(* [
+        self.stem = nn.Sequential(*[
             ConvBNLayer(
                 num_channels=in_c,
                 num_filters=out_c,

ppcls/arch/backbone/model_zoo/mobilevit.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Code was based on https://github.com/BR-IDL/PaddleViT/blob/develop/image_classification/MobileViT/mobilevit.py
+# and https://github.com/apple/ml-cvnets/blob/main/cvnets/models/classification/mobilevit.py
+
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn.initializer import KaimingUniform, TruncatedNormal, Constant
+import math
+
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "MobileViT_XXS":
+    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams",
+    "MobileViT_XS":
+    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams",
+    "MobileViT_S":
+    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams",
+}
+
+
+def _init_weights_linear():
+    weight_attr = ParamAttr(initializer=TruncatedNormal(std=.02))
+    bias_attr = ParamAttr(initializer=Constant(0.0))
+    return weight_attr, bias_attr
+
+
+def _init_weights_layernorm():
+    weight_attr = ParamAttr(initializer=Constant(1.0))
+    bias_attr = ParamAttr(initializer=Constant(0.0))
+    return weight_attr, bias_attr
+
+
+class ConvBnAct(nn.Layer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 stride=1,
+                 padding=0,
+                 bias_attr=False,
+                 groups=1):
+        super().__init__()
+        self.in_channels = in_channels
+        self.conv = nn.Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            groups=groups,
+            weight_attr=ParamAttr(initializer=KaimingUniform()),
+            bias_attr=bias_attr)
+        self.norm = nn.BatchNorm2D(out_channels)
+        self.act = nn.Silu()
+
+    def forward(self, inputs):
+        out = self.conv(inputs)
+        out = self.norm(out)
+        out = self.act(out)
+        return out
+
+
+class Identity(nn.Layer):
+    """ Identity layer"""
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, inputs):
+        return inputs
+
+
+class Mlp(nn.Layer):
+    def __init__(self, embed_dim, mlp_ratio, dropout=0.1):
+        super().__init__()
+        w_attr_1, b_attr_1 = _init_weights_linear()
+        self.fc1 = nn.Linear(
+            embed_dim,
+            int(embed_dim * mlp_ratio),
+            weight_attr=w_attr_1,
+            bias_attr=b_attr_1)
+
+        w_attr_2, b_attr_2 = _init_weights_linear()
+        self.fc2 = nn.Linear(
+            int(embed_dim * mlp_ratio),
+            embed_dim,
+            weight_attr=w_attr_2,
+            bias_attr=b_attr_2)
+
+        self.act = nn.Silu()
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.dropout1(x)
+        x = self.fc2(x)
+        x = self.dropout2(x)
+        return x
+
+
+class Attention(nn.Layer):
+    def __init__(self,
+                 embed_dim,
+                 num_heads,
+                 qkv_bias=True,
+                 dropout=0.1,
+                 attention_dropout=0.):
+        super().__init__()
+        self.num_heads = num_heads
+        self.attn_head_dim = int(embed_dim / self.num_heads)
+        self.all_head_dim = self.attn_head_dim * self.num_heads
+
+        w_attr_1, b_attr_1 = _init_weights_linear()
+        self.qkv = nn.Linear(
+            embed_dim,
+            self.all_head_dim * 3,
+            weight_attr=w_attr_1,
+            bias_attr=b_attr_1 if qkv_bias else False)
+
+        self.scales = self.attn_head_dim**-0.5
+
+        w_attr_2, b_attr_2 = _init_weights_linear()
+        self.proj = nn.Linear(
+            embed_dim, embed_dim, weight_attr=w_attr_2, bias_attr=b_attr_2)
+
+        self.attn_dropout = nn.Dropout(attention_dropout)
+        self.proj_dropout = nn.Dropout(dropout)
+        self.softmax = nn.Softmax(axis=-1)
+
+    def transpose_multihead(self, x):
+        B, P, N, d = x.shape
+        x = x.reshape([B, P, N, self.num_heads, d // self.num_heads])
+        x = x.transpose([0, 1, 3, 2, 4])
+        return x
+
+    def forward(self, x):
+        b_sz, n_patches, in_channels = x.shape
+        qkv = self.qkv(x)
+        qkv = qkv.reshape([
+            b_sz, n_patches, 3, self.num_heads,
+            qkv.shape[-1] // self.num_heads // 3
+        ])
+        qkv = qkv.transpose([0, 3, 2, 1, 4])
+        query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+        query = query * self.scales
+        key = key.transpose([0, 1, 3, 2])
+        # QK^T
+        attn = paddle.matmul(query, key)
+        attn = self.softmax(attn)
+        attn = self.attn_dropout(attn)
+        # weighted sum
+        out = paddle.matmul(attn, value)
+        out = out.transpose([0, 2, 1, 3]).reshape(
+            [b_sz, n_patches, out.shape[1] * out.shape[3]])
+        out = self.proj(out)
+        out = self.proj_dropout(out)
+        return out
+
+
+class EncoderLayer(nn.Layer):
+    def __init__(self,
+                 embed_dim,
+                 num_heads=4,
+                 qkv_bias=True,
+                 mlp_ratio=2.0,
+                 dropout=0.1,
+                 attention_dropout=0.,
+                 droppath=0.):
+        super().__init__()
+        w_attr_1, b_attr_1 = _init_weights_layernorm()
+        w_attr_2, b_attr_2 = _init_weights_layernorm()
+
+        self.attn_norm = nn.LayerNorm(
+            embed_dim, weight_attr=w_attr_1, bias_attr=b_attr_1)
+        self.attn = Attention(embed_dim, num_heads, qkv_bias, dropout,
+                              attention_dropout)
+        self.drop_path = DropPath(droppath) if droppath > 0. else Identity()
+        self.mlp_norm = nn.LayerNorm(
+            embed_dim, weight_attr=w_attr_2, bias_attr=b_attr_2)
+        self.mlp = Mlp(embed_dim, mlp_ratio, dropout)
+
+    def forward(self, x):
+        h = x
+        x = self.attn_norm(x)
+        x = self.attn(x)
+        x = self.drop_path(x)
+        x = h + x
+        h = x
+        x = self.mlp_norm(x)
+        x = self.mlp(x)
+        x = self.drop_path(x)
+        x = x + h
+        return x
+
+
+class Transformer(nn.Layer):
+    """Transformer block for MobileViTBlock"""
+
+    def __init__(self,
+                 embed_dim,
+                 num_heads,
+                 depth,
+                 qkv_bias=True,
+                 mlp_ratio=2.0,
+                 dropout=0.1,
+                 attention_dropout=0.,
+                 droppath=0.):
+        super().__init__()
+        depth_decay = [x.item() for x in paddle.linspace(0, droppath, depth)]
+
+        layer_list = []
+        for i in range(depth):
+            layer_list.append(
+                EncoderLayer(embed_dim, num_heads, qkv_bias, mlp_ratio,
+                             dropout, attention_dropout, droppath))
+        self.layers = nn.LayerList(layer_list)
+
+        w_attr_1, b_attr_1 = _init_weights_layernorm()
+        self.norm = nn.LayerNorm(
+            embed_dim, weight_attr=w_attr_1, bias_attr=b_attr_1, epsilon=1e-6)
+
+    def forward(self, x):
+        for layer in self.layers:
+            x = layer(x)
+        out = self.norm(x)
+        return out
+
+
+class MobileV2Block(nn.Layer):
+    """Mobilenet v2 InvertedResidual block"""
+
+    def __init__(self, inp, oup, stride=1, expansion=4):
+        super().__init__()
+        self.stride = stride
+        assert stride in [1, 2]
+
+        hidden_dim = int(round(inp * expansion))
+        self.use_res_connect = self.stride == 1 and inp == oup
+
+        layers = []
+        if expansion != 1:
+            layers.append(ConvBnAct(inp, hidden_dim, kernel_size=1))
+
+        layers.extend([
+            # dw
+            ConvBnAct(
+                hidden_dim,
+                hidden_dim,
+                stride=stride,
+                groups=hidden_dim,
+                padding=1),
+            # pw-linear
+            nn.Conv2D(
+                hidden_dim, oup, 1, 1, 0, bias_attr=False),
+            nn.BatchNorm2D(oup),
+        ])
+
+        self.conv = nn.Sequential(*layers)
+        self.out_channels = oup
+
+    def forward(self, x):
+        if self.use_res_connect:
+            return x + self.conv(x)
+        return self.conv(x)
+
+
+class MobileViTBlock(nn.Layer):
+    """ MobileViTBlock for MobileViT"""
+
+    def __init__(self,
+                 dim,
+                 hidden_dim,
+                 depth,
+                 num_heads=4,
+                 qkv_bias=True,
+                 mlp_ratio=2.0,
+                 dropout=0.1,
+                 attention_dropout=0.,
+                 droppath=0.0,
+                 patch_size=(2, 2)):
+        super().__init__()
+        self.patch_h, self.patch_w = patch_size
+
+        # local representations
+        self.conv1 = ConvBnAct(dim, dim, padding=1)
+        self.conv2 = nn.Conv2D(
+            dim, hidden_dim, kernel_size=1, stride=1, bias_attr=False)
+        # global representations
+        self.transformer = Transformer(
+            embed_dim=hidden_dim,
+            num_heads=num_heads,
+            depth=depth,
+            qkv_bias=qkv_bias,
+            mlp_ratio=mlp_ratio,
+            dropout=dropout,
+            attention_dropout=attention_dropout,
+            droppath=droppath)
+
+        # fusion
+        self.conv3 = ConvBnAct(hidden_dim, dim, kernel_size=1)
+        self.conv4 = ConvBnAct(2 * dim, dim, padding=1)
+
+    def forward(self, x):
+        h = x
+        x = self.conv1(x)
+        x = self.conv2(x)
+
+        patch_h = self.patch_h
+        patch_w = self.patch_w
+        patch_area = int(patch_w * patch_h)
+        _, in_channels, orig_h, orig_w = x.shape
+        new_h = int(math.ceil(orig_h / self.patch_h) * self.patch_h)
+        new_w = int(math.ceil(orig_w / self.patch_w) * self.patch_w)
+        interpolate = False
+
+        if new_w != orig_w or new_h != orig_h:
+            x = F.interpolate(x, size=[new_h, new_w], mode="bilinear")
+            interpolate = True
+
+        num_patch_w, num_patch_h = new_w // patch_w, new_h // patch_h
+        num_patches = num_patch_h * num_patch_w
+        reshaped_x = x.reshape([-1, patch_h, num_patch_w, patch_w])
+        transposed_x = reshaped_x.transpose([0, 2, 1, 3])
+        reshaped_x = transposed_x.reshape(
+            [-1, in_channels, num_patches, patch_area])
+        transposed_x = reshaped_x.transpose([0, 3, 2, 1])
+
+        x = transposed_x.reshape([-1, num_patches, in_channels])
+        x = self.transformer(x)
+        x = x.reshape([-1, patch_h * patch_w, num_patches, in_channels])
+
+        _, pixels, num_patches, channels = x.shape
+        x = x.transpose([0, 3, 2, 1])
+        x = x.reshape([-1, num_patch_w, patch_h, patch_w])
+        x = x.transpose([0, 2, 1, 3])
+        x = x.reshape(
+            [-1, channels, num_patch_h * patch_h, num_patch_w * patch_w])
+
+        if interpolate:
+            x = F.interpolate(x, size=[orig_h, orig_w])
+        x = self.conv3(x)
+        x = paddle.concat((h, x), axis=1)
+        x = self.conv4(x)
+        return x
+
+
+class MobileViT(nn.Layer):
+    """ MobileViT
+        A PaddlePaddle impl of : `MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer`  -
+          https://arxiv.org/abs/2110.02178
+    """
+
+    def __init__(self,
+                 in_channels=3,
+                 dims=[16, 32, 48, 48, 48, 64, 80, 96, 384],
+                 hidden_dims=[96, 120, 144],
+                 mv2_expansion=4,
+                 class_num=1000):
+        super().__init__()
+        self.conv3x3 = ConvBnAct(
+            in_channels, dims[0], kernel_size=3, stride=2, padding=1)
+        self.mv2_block_1 = MobileV2Block(
+            dims[0], dims[1], expansion=mv2_expansion)
+        self.mv2_block_2 = MobileV2Block(
+            dims[1], dims[2], stride=2, expansion=mv2_expansion)
+        self.mv2_block_3 = MobileV2Block(
+            dims[2], dims[3], expansion=mv2_expansion)
+        self.mv2_block_4 = MobileV2Block(
+            dims[3], dims[4], expansion=mv2_expansion)
+
+        self.mv2_block_5 = MobileV2Block(
+            dims[4], dims[5], stride=2, expansion=mv2_expansion)
+        self.mvit_block_1 = MobileViTBlock(dims[5], hidden_dims[0], depth=2)
+
+        self.mv2_block_6 = MobileV2Block(
+            dims[5], dims[6], stride=2, expansion=mv2_expansion)
+        self.mvit_block_2 = MobileViTBlock(dims[6], hidden_dims[1], depth=4)
+
+        self.mv2_block_7 = MobileV2Block(
+            dims[6], dims[7], stride=2, expansion=mv2_expansion)
+        self.mvit_block_3 = MobileViTBlock(dims[7], hidden_dims[2], depth=3)
+        self.conv1x1 = ConvBnAct(dims[7], dims[8], kernel_size=1)
+
+        self.pool = nn.AdaptiveAvgPool2D(1)
+        self.dropout = nn.Dropout(0.1)
+        self.linear = nn.Linear(dims[8], class_num)
+
+    def forward(self, x):
+        x = self.conv3x3(x)
+        x = self.mv2_block_1(x)
+        x = self.mv2_block_2(x)
+        x = self.mv2_block_3(x)
+        x = self.mv2_block_4(x)
+
+        x = self.mv2_block_5(x)
+        x = self.mvit_block_1(x)
+
+        x = self.mv2_block_6(x)
+        x = self.mvit_block_2(x)
+
+        x = self.mv2_block_7(x)
+        x = self.mvit_block_3(x)
+        x = self.conv1x1(x)
+
+        x = self.pool(x)
+        x = x.reshape(x.shape[:2])
+
+        x = self.dropout(x)
+        x = self.linear(x)
+        return x
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld=False):
+    if pretrained is False:
+        pass
+    elif pretrained is True:
+        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
+    elif isinstance(pretrained, str):
+        load_dygraph_pretrain(model, pretrained)
+    else:
+        raise RuntimeError(
+            "pretrained type is not available. Please use `string` or `boolean` type."
+        )
+
+
+def MobileViT_XXS(pretrained=False, use_ssld=False, **kwargs):
+    model = MobileViT(
+        in_channels=3,
+        dims=[16, 16, 24, 24, 24, 48, 64, 80, 320],
+        hidden_dims=[64, 80, 96],
+        mv2_expansion=2,
+        **kwargs)
+
+    _load_pretrained(
+        pretrained, model, MODEL_URLS["MobileViT_XXS"], use_ssld=use_ssld)
+    return model
+
+
+def MobileViT_XS(pretrained=False, use_ssld=False, **kwargs):
+    model = MobileViT(
+        in_channels=3,
+        dims=[16, 32, 48, 48, 48, 64, 80, 96, 384],
+        hidden_dims=[96, 120, 144],
+        mv2_expansion=4,
+        **kwargs)
+    _load_pretrained(
+        pretrained, model, MODEL_URLS["MobileViT_XS"], use_ssld=use_ssld)
+    return model
+
+
+def MobileViT_S(pretrained=False, use_ssld=False, **kwargs):
+    model = MobileViT(
+        in_channels=3,
+        dims=[16, 32, 64, 64, 64, 96, 128, 160, 640],
+        hidden_dims=[144, 192, 240],
+        mv2_expansion=4,
+        **kwargs)
+    _load_pretrained(
+        pretrained, model, MODEL_URLS["MobileViT_S"], use_ssld=use_ssld)
+    return model

ppcls/arch/backbone/model_zoo/van.py

+# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Code was heavily based on https://github.com/Visual-Attention-Network/VAN-Classification
+
+from functools import partial
+import math
+import paddle
+import paddle.nn as nn
+from paddle.nn.initializer import TruncatedNormal, Constant
+
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "VAN_tiny": "",  # TODO
+}
+
+__all__ = list(MODEL_URLS.keys())
+
+trunc_normal_ = TruncatedNormal(std=.02)
+zeros_ = Constant(value=0.)
+ones_ = Constant(value=1.)
+
+
+def drop_path(x, drop_prob=0., training=False):
+    """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
+    the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...
+    See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ...
+    """
+    if drop_prob == 0. or not training:
+        return x
+    keep_prob = paddle.to_tensor(1 - drop_prob)
+    shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
+    random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
+    random_tensor = paddle.floor(random_tensor)  # binarize
+    output = x.divide(keep_prob) * random_tensor
+    return output
+
+
+class DropPath(nn.Layer):
+    """Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).
+    """
+
+    def __init__(self, drop_prob=None):
+        super(DropPath, self).__init__()
+        self.drop_prob = drop_prob
+
+    def forward(self, x):
+        return drop_path(x, self.drop_prob, self.training)
+
+
+@paddle.jit.not_to_static
+def swapdim(x, dim1, dim2):
+    a = list(range(len(x.shape)))
+    a[dim1], a[dim2] = a[dim2], a[dim1]
+    return x.transpose(a)
+
+
+class Mlp(nn.Layer):
+    def __init__(self,
+                 in_features,
+                 hidden_features=None,
+                 out_features=None,
+                 act_layer=nn.GELU,
+                 drop=0.):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Conv2D(in_features, hidden_features, 1)
+        self.dwconv = DWConv(hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Conv2D(hidden_features, out_features, 1)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.dwconv(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+class LKA(nn.Layer):
+    def __init__(self, dim):
+        super().__init__()
+        self.conv0 = nn.Conv2D(dim, dim, 5, padding=2, groups=dim)
+        self.conv_spatial = nn.Conv2D(
+            dim, dim, 7, stride=1, padding=9, groups=dim, dilation=3)
+        self.conv1 = nn.Conv2D(dim, dim, 1)
+
+    def forward(self, x):
+        attn = self.conv0(x)
+        attn = self.conv_spatial(attn)
+        attn = self.conv1(attn)
+        return x * attn
+
+
+class Attention(nn.Layer):
+    def __init__(self, d_model):
+        super().__init__()
+        self.proj_1 = nn.Conv2D(d_model, d_model, 1)
+        self.activation = nn.GELU()
+        self.spatial_gating_unit = LKA(d_model)
+        self.proj_2 = nn.Conv2D(d_model, d_model, 1)
+
+    def forward(self, x):
+        shorcut = x
+        x = self.proj_1(x)
+        x = self.activation(x)
+        x = self.spatial_gating_unit(x)
+        x = self.proj_2(x)
+        x = x + shorcut
+        return x
+
+
+class Block(nn.Layer):
+    def __init__(self,
+                 dim,
+                 mlp_ratio=4.,
+                 drop=0.,
+                 drop_path=0.,
+                 act_layer=nn.GELU):
+        super().__init__()
+        self.norm1 = nn.BatchNorm2D(dim)
+        self.attn = Attention(dim)
+        self.drop_path = DropPath(
+            drop_path) if drop_path > 0. else nn.Identity()
+        self.norm2 = nn.BatchNorm2D(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(in_features=dim,
+                       hidden_features=mlp_hidden_dim,
+                       act_layer=act_layer,
+                       drop=drop)
+        layer_scale_init_value = 1e-2
+        self.layer_scale_1 = self.create_parameter(
+            shape=[dim, 1, 1],
+            default_initializer=Constant(value=layer_scale_init_value))
+        self.layer_scale_2 = self.create_parameter(
+            shape=[dim, 1, 1],
+            default_initializer=Constant(value=layer_scale_init_value))
+
+    def forward(self, x):
+        x = x + self.drop_path(self.layer_scale_1 * self.attn(self.norm1(x)))
+        x = x + self.drop_path(self.layer_scale_2 * self.mlp(self.norm2(x)))
+        return x
+
+
+class OverlapPatchEmbed(nn.Layer):
+    """ Image to Patch Embedding
+    """
+
+    def __init__(self,
+                 img_size=224,
+                 patch_size=7,
+                 stride=4,
+                 in_chans=3,
+                 embed_dim=768):
+        super().__init__()
+        self.proj = nn.Conv2D(
+            in_chans,
+            embed_dim,
+            kernel_size=patch_size,
+            stride=stride,
+            padding=patch_size // 2)
+        self.norm = nn.BatchNorm2D(embed_dim)
+
+    def forward(self, x):
+        x = self.proj(x)
+        _, _, H, W = x.shape
+        x = self.norm(x)
+        return x, H, W
+
+
+class VAN(nn.Layer):
+    r""" VAN
+    A PaddlePaddle impl of : `Visual Attention Network`  -
+      https://arxiv.org/pdf/2202.09741.pdf
+    """
+
+    def __init__(self,
+                 img_size=224,
+                 in_chans=3,
+                 class_num=1000,
+                 embed_dims=[64, 128, 256, 512],
+                 mlp_ratios=[4, 4, 4, 4],
+                 drop_rate=0.,
+                 drop_path_rate=0.,
+                 norm_layer=nn.LayerNorm,
+                 depths=[3, 4, 6, 3],
+                 num_stages=4,
+                 flag=False):
+        super().__init__()
+        if flag == False:
+            self.class_num = class_num
+        self.depths = depths
+        self.num_stages = num_stages
+
+        dpr = [x for x in paddle.linspace(0, drop_path_rate, sum(depths))
+               ]  # stochastic depth decay rule
+        cur = 0
+
+        for i in range(num_stages):
+            patch_embed = OverlapPatchEmbed(
+                img_size=img_size if i == 0 else img_size // (2**(i + 1)),
+                patch_size=7 if i == 0 else 3,
+                stride=4 if i == 0 else 2,
+                in_chans=in_chans if i == 0 else embed_dims[i - 1],
+                embed_dim=embed_dims[i])
+
+            block = nn.LayerList([
+                Block(
+                    dim=embed_dims[i],
+                    mlp_ratio=mlp_ratios[i],
+                    drop=drop_rate,
+                    drop_path=dpr[cur + j]) for j in range(depths[i])
+            ])
+            norm = norm_layer(embed_dims[i])
+            cur += depths[i]
+
+            setattr(self, f"patch_embed{i + 1}", patch_embed)
+            setattr(self, f"block{i + 1}", block)
+            setattr(self, f"norm{i + 1}", norm)
+
+        # classification head
+        self.head = nn.Linear(embed_dims[3],
+                              class_num) if class_num > 0 else nn.Identity()
+
+        self.apply(self._init_weights)
+
+    def _init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            trunc_normal_(m.weight)
+            if isinstance(m, nn.Linear) and m.bias is not None:
+                zeros_(m.bias)
+        elif isinstance(m, nn.LayerNorm):
+            zeros_(m.bias)
+            ones_(m.weight)
+        elif isinstance(m, nn.Conv2D):
+            fan_out = m._kernel_size[0] * m._kernel_size[1] * m._out_channels
+            fan_out //= m._groups
+            m.weight.set_value(
+                paddle.normal(
+                    std=math.sqrt(2.0 / fan_out), shape=m.weight.shape))
+            if m.bias is not None:
+                zeros_(m.bias)
+
+    def forward_features(self, x):
+        B = x.shape[0]
+
+        for i in range(self.num_stages):
+            patch_embed = getattr(self, f"patch_embed{i + 1}")
+            block = getattr(self, f"block{i + 1}")
+            norm = getattr(self, f"norm{i + 1}")
+            x, H, W = patch_embed(x)
+            for blk in block:
+                x = blk(x)
+            x = x.flatten(2)
+            x = swapdim(x, 1, 2)
+            x = norm(x)
+            if i != self.num_stages - 1:
+                x = x.reshape([B, H, W, x.shape[2]]).transpose([0, 3, 1, 2])
+
+        return x.mean(axis=1)
+
+    def forward(self, x):
+        x = self.forward_features(x)
+        x = self.head(x)
+
+        return x
+
+
+class DWConv(nn.Layer):
+    def __init__(self, dim=768):
+        super().__init__()
+        self.dwconv = nn.Conv2D(dim, dim, 3, 1, 1, bias_attr=True, groups=dim)
+
+    def forward(self, x):
+        x = self.dwconv(x)
+        return x
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld=False):
+    if pretrained is False:
+        pass
+    elif pretrained is True:
+        load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
+    elif isinstance(pretrained, str):
+        load_dygraph_pretrain(model, pretrained)
+    else:
+        raise RuntimeError(
+            "pretrained type is not available. Please use `string` or `boolean` type."
+        )
+
+
+def VAN_tiny(pretrained=False, use_ssld=False, **kwargs):
+    model = VAN(embed_dims=[32, 64, 160, 256],
+                mlp_ratios=[8, 8, 4, 4],
+                norm_layer=partial(
+                    nn.LayerNorm, epsilon=1e-6),
+                depths=[3, 3, 5, 2],
+                **kwargs)
+    _load_pretrained(
+        pretrained, model, MODEL_URLS["VAN_tiny"], use_ssld=use_ssld)
+    return model

ppcls/arch/gears/__init__.py

@@ -25,7 +25,8 @@ __all__ = ['build_gear']
 
 def build_gear(config):
     support_dict = [
-        'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck', 'Tanh', "BNNeck"
+        'ArcMargin', 'CosMargin', 'CircleMargin', 'FC', 'VehicleNeck', 'Tanh',
+        'BNNeck'
     ]
     module_name = config.pop('name')
     assert module_name in support_dict, Exception(

ppcls/arch/gears/bnneck.py

-import paddle
+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import absolute_import, division, print_function
 
+import paddle
+import paddle.nn as nn
 
-class BNNeck(paddle.nn.Layer):
-    def __init__(self, num_filters, trainable=False):
-        super(BNNeck, self).__init__()
-        self.num_filters = num_filters
 
-        self.bn = paddle.nn.BatchNorm1D(self.num_filters)
-        # TODO: freeze bn.bias
-        # if not trainable:
-        #     self.bn.bias.trainable = False
+class BNNeck(nn.Layer):
+    def __init__(self, num_features):
+        super().__init__()
+        weight_attr = paddle.ParamAttr(
+            initializer=paddle.nn.initializer.Constant(value=1.0))
+        bias_attr = paddle.ParamAttr(
+            initializer=paddle.nn.initializer.Constant(value=0.0),
+            trainable=False)
+        self.feat_bn = nn.BatchNorm1D(
+            num_features,
+            momentum=0.9,
+            epsilon=1e-05,
+            weight_attr=weight_attr,
+            bias_attr=bias_attr)
+        # TODO: set bnneck.bias learnable=False
+        self.flatten = nn.Flatten()
 
-    def forward(self, input, label=None):
-        out = self.bn(input)
-        return out
+    def forward(self, x):
+        x = self.flatten(x)
+        x = self.feat_bn(x)
+        return x

ppcls/arch/gears/fc.py

@@ -19,16 +19,25 @@ from __future__ import print_function
 import paddle
 import paddle.nn as nn
 
+from ppcls.arch.utils import get_param_attr_dict
+
 
 class FC(nn.Layer):
-    def __init__(self, embedding_size, class_num, bias_attr=None):
+    def __init__(self, embedding_size, class_num, **kwargs):
         super(FC, self).__init__()
         self.embedding_size = embedding_size
         self.class_num = class_num
-        # TODO: hard code for initializer
+
         weight_attr = paddle.ParamAttr(
-            initializer=paddle.nn.initializer.Normal(std=0.001))
-        self.fc = paddle.nn.Linear(
+            initializer=paddle.nn.initializer.XavierNormal())
+        if 'weight_attr' in kwargs:
+            weight_attr = get_param_attr_dict(kwargs['weight_attr'])
+
+        bias_attr = None
+        if 'bias_attr' in kwargs:
+            bias_attr = get_param_attr_dict(kwargs['bias_attr'])
+
+        self.fc = nn.Linear(
             self.embedding_size,
             self.class_num,
             weight_attr=weight_attr,

ppcls/arch/utils.py

@@ -14,9 +14,11 @@
 
 import six
 import types
+import paddle
 from difflib import SequenceMatcher
 
 from . import backbone
+from typing import Any, Dict, Union
 
 
 def get_architectures():
@@ -31,8 +33,8 @@ def get_architectures():
 
 
 def get_blacklist_model_in_static_mode():
-    from ppcls.arch.backbone import distilled_vision_transformer
-    from ppcls.arch.backbone import vision_transformer
+    from ppcls.arch.backbone import (distilled_vision_transformer,
+                                     vision_transformer)
     blacklist = distilled_vision_transformer.__all__ + vision_transformer.__all__
     return blacklist
 
@@ -51,3 +53,47 @@ def similar_architectures(name='', names=[], thresh=0.1, topk=10):
     scores.sort(key=lambda x: x[1], reverse=True)
     similar_names = [names[s[0]] for s in scores[:min(topk, len(scores))]]
     return similar_names
+
+
+def get_param_attr_dict(ParamAttr_config: Union[None, bool, Dict[str, Dict]]
+                        ) -> Union[None, bool, paddle.ParamAttr]:
+    """parse ParamAttr from an dict
+
+    Args:
+        ParamAttr_config (Union[bool, Dict[str, Dict]]): ParamAttr_config
+
+    Returns:
+        Union[bool, paddle.ParamAttr]: Generated ParamAttr
+    """
+    if ParamAttr_config is None:
+        return None
+    if isinstance(ParamAttr_config, bool):
+        return ParamAttr_config
+    ParamAttr_dict = {}
+    if 'initializer' in ParamAttr_config:
+        initializer_cfg = ParamAttr_config.get('initializer')
+        if 'name' in initializer_cfg:
+            initializer_name = initializer_cfg.pop('name')
+            ParamAttr_dict['initializer'] = getattr(
+                paddle.nn.initializer, initializer_name)(**initializer_cfg)
+        else:
+            raise ValueError(f"'name' must specified in initializer_cfg")
+    if 'learning_rate' in ParamAttr_config:
+        # NOTE: only support an single value now
+        learning_rate_value = ParamAttr_config.get('learning_rate')
+        if isinstance(learning_rate_value, (int, float)):
+            ParamAttr_dict['learning_rate'] = learning_rate_value
+        else:
+            raise ValueError(
+                f"learning_rate_value must be float or int, but got {type(learning_rate_value)}"
+            )
+    if 'regularizer' in ParamAttr_config:
+        regularizer_cfg = ParamAttr_config.get('regularizer')
+        if 'name' in regularizer_cfg:
+            # L1Decay or L2Decay
+            regularizer_name = regularizer_cfg.pop('name')
+            ParamAttr_dict['regularizer'] = getattr(
+                paddle.regularizer, regularizer_name)(**regularizer_cfg)
+        else:
+            raise ValueError(f"'name' must specified in regularizer_cfg")
+    return paddle.ParamAttr(**ParamAttr_dict)

ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 300
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 256, 256]
+  save_inference_dir: ./inference
+  use_dali: False
+# model architecture
+Arch:
+  name: MobileViT_S
+  class_num: 1000
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.01
+  no_weight_decay_name: .bias norm
+  one_dim_param_no_weight_decay: True
+  lr:
+    # for 8 cards
+    name: Cosine
+    learning_rate: 0.002
+    eta_min: 0.0002
+    warmup_epoch: 5
+    warmup_start_lr: 0.0002
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 256
+            interpolation: bilinear
+            backend: pil
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 8
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 292
+            interpolation: bilinear
+            backend: pil
+        - CropImage:
+            size: 256
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 292
+    - CropImage:
+        size: 256
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.0, 0.0, 0.0]
+        std: [1.0, 1.0, 1.0]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 300
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 256, 256]
+  save_inference_dir: ./inference
+  use_dali: False
+# model architecture
+Arch:
+  name: MobileViT_XS
+  class_num: 1000
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.01
+  no_weight_decay_name: .bias norm
+  one_dim_param_no_weight_decay: True
+  lr:
+    # for 8 cards
+    name: Cosine
+    learning_rate: 0.002
+    eta_min: 0.0002
+    warmup_epoch: 5
+    warmup_start_lr: 0.0002
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 256
+            interpolation: bilinear
+            backend: pil
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 8
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 292
+            interpolation: bilinear
+            backend: pil
+        - CropImage:
+            size: 256
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 292
+    - CropImage:
+        size: 256
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.0, 0.0, 0.0]
+        std: [1.0, 1.0, 1.0]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 300
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 256, 256]
+  save_inference_dir: ./inference
+  use_dali: False
+# model architecture
+Arch:
+  name: MobileViT_XXS
+  class_num: 1000
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.01
+  no_weight_decay_name: .bias norm
+  one_dim_param_no_weight_decay: True
+  lr:
+    # for 8 cards
+    name: Cosine
+    learning_rate: 0.002
+    eta_min: 0.0002
+    warmup_epoch: 5
+    warmup_start_lr: 0.0002
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 256
+            interpolation: bilinear
+            backend: pil
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 8
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: False
+            channel_first: False
+        - ResizeImage:
+            resize_short: 292
+            interpolation: bilinear
+            backend: pil
+        - CropImage:
+            size: 256
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.0, 0.0, 0.0]
+            std: [1.0, 1.0, 1.0]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 64
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg 
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 292
+    - CropImage:
+        size: 256
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.0, 0.0, 0.0]
+        std: [1.0, 1.0, 1.0]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Train:
+    - TopkAcc:
+        topk: [1, 5]
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d_amp_O2_ultra.yaml

@@ -34,10 +34,10 @@ Loss:
 
 # mixed precision training
 AMP:
-    scale_loss: 128.0
-    use_dynamic_loss_scaling: True
-    # O2: pure fp16
-    level: O2
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  # O2: pure fp16
+  level: O2
 
 Optimizer:
   name: Momentum

ppcls/configs/ImageNet/VAN/VAN_tiny.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 300
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+  # training model under @to_static
+  to_static: False
+
+# model architecture
+Arch:
+  name: VAN_tiny
+  class_num: 1000
+  drop_path_rate: 0.1
+  drop_rate: 0.0
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: AdamW
+  beta1: 0.9
+  beta2: 0.999
+  epsilon: 1e-8
+  weight_decay: 0.05
+  one_dim_param_no_weight_decay: True
+  lr:
+    name: Cosine
+    learning_rate: 1e-3
+    eta_min: 1e-6
+    warmup_epoch: 5
+    warmup_start_lr: 1e-6
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+            interpolation: random
+            backend: pil
+        - RandFlipImage:
+            flip_code: 1
+        - TimmAutoAugment:
+            config_str: rand-m9-mstd0.5-inc1
+            interpolation: random
+            img_size: 224
+            mean: [0.5, 0.5, 0.5]
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.5, 0.5, 0.5]
+            std: [0.5, 0.5, 0.5]
+            order: ''
+        - RandomErasing:
+            EPSILON: 0.25
+            sl: 0.02
+            sh: 1.0/3.0
+            r1: 0.3
+            attempt: 10
+            use_log_aspect: True
+            mode: pixel
+      batch_transform_ops:
+        - OpSampler:
+            MixupOperator:
+              alpha: 0.8
+              prob: 0.5
+            CutmixOperator:
+              alpha: 1.0
+              prob: 0.5
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256
+      drop_last: True
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    dataset: 
+      name: ImageNetDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - ResizeImage:
+            resize_short: 248
+            interpolation: bicubic
+            backend: pil
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.5, 0.5, 0.5]
+            std: [0.5, 0.5, 0.5]
+            order: ''
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 256
+      drop_last: False
+      shuffle: False
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+Infer:
+  infer_imgs: docs/images/inference_deployment/whl_demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 248
+        interpolation: bicubic
+        backend: pil
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.5, 0.5, 0.5]
+        std: [0.5, 0.5, 0.5]
+        order: ''
+    - ToCHWImage:
+  PostProcess:
+    name: Topk
+    topk: 5
+    class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+  Eval:
+    - TopkAcc:
+        topk: [1, 5]

ppcls/configs/PersonReID/ResNet50_strong_baseline_market1501.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  output_dir: "./output/"
+  device: "gpu"
+  save_interval: 40
+  eval_during_train: True
+  eval_interval: 10
+  epochs: 120
+  print_batch_step: 20
+  use_visualdl: False
+  warmup_by_epoch: True
+  eval_mode: "retrieval"
+  re_ranking: True
+  # used for static mode and model export
+  image_shape: [3, 256, 128]
+  save_inference_dir: "./inference"
+
+# model architecture
+Arch:
+  name: "RecModel"
+  infer_output_key: "features"
+  infer_add_softmax: False
+  Backbone:
+    name: "ResNet50_last_stage_stride1"
+    pretrained: True
+    stem_act: null
+  BackboneStopLayer:
+    name: "flatten"
+  Neck:
+    name: BNNeck
+    num_features: &feat_dim 2048
+  Head:
+    name: "FC"
+    embedding_size: *feat_dim
+    class_num: &class_num 751
+    weight_attr:
+      initializer:
+        name: Normal
+        std: 0.001
+    bias_attr: False
+
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+        epsilon: 0.1
+    - TripletLossV3:
+        weight: 1.0
+        margin: 0.3
+        normalize_feature: false
+    - CenterLoss:
+        weight: 0.0005
+        num_classes: *class_num
+        feat_dim: *feat_dim
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  - Adam:
+      scope: model
+      lr:
+        name: Piecewise
+        decay_epochs: [30, 60]
+        values: [0.00035, 0.000035, 0.0000035]
+        warmup_epoch: 10
+        warmup_start_lr: 0.0000035
+        warmup_by_epoch: True
+      regularizer:
+        name: 'L2'
+        coeff: 0.0005
+  - SGD:
+      scope: CenterLoss
+      lr:
+        name: Constant
+        learning_rate: 1000.0
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+        name: "Market1501"
+        image_root: "./dataset/Market-1501-v15.09.15"
+        cls_label_path: "bounding_box_train"
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: [128, 256]
+          - RandFlipImage:
+              flip_code: 1
+          - Pad:
+              padding: 10
+          - RandCropImage:
+              size: [128, 256]
+              scale: [ 0.8022, 0.8022 ]
+              ratio: [ 0.5, 0.5 ]
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+          - RandomErasing:
+              EPSILON: 0.5
+              sl: 0.02
+              sh: 0.4
+              r1: 0.3
+              mean: [0.4914, 0.4822, 0.4465]
+    sampler:
+        name: DistributedRandomIdentitySampler
+        batch_size: 64
+        num_instances: 4
+        drop_last: True
+        shuffle: True
+    loader:
+        num_workers: 4
+        use_shared_memory: True
+  Eval:
+    Query:
+      dataset:
+        name: "Market1501"
+        image_root: "./dataset/Market-1501-v15.09.15"
+        cls_label_path: "query"
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: [128, 256]
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 128
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 4
+        use_shared_memory: True
+
+    Gallery:
+      dataset:
+        name: "Market1501"
+        image_root: "./dataset/Market-1501-v15.09.15"
+        cls_label_path: "bounding_box_test"
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: [128, 256]
+          - NormalizeImage:
+              scale: 0.00392157
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 128
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 4
+        use_shared_memory: True
+
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]
+    - mAP: {}

ppcls/configs/ResNet50_UReID_infer.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: null
+  # pretrained_model: "./pd_model_trace/ISE/ISE_M_model" # pretrained ISE model for Market1501
+  # pretrained_model: "./pd_model_trace/ISE/ISE_MS_model" # pretrained ISE model for MSMT17
+  output_dir: "./output/"
+  device: "gpu"
+  save_interval: 10
+  eval_during_train: True
+  eval_interval: 10
+  epochs: 120
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 128, 256]
+  save_inference_dir: "./inference"
+  eval_mode: "retrieval"
+
+# model architecture
+Arch:
+  name: "RecModel"
+  infer_output_key: "features"
+  infer_add_softmax: False
+  Backbone: 
+    name: "ResNet50_last_stage_stride1"
+    pretrained: True
+    stem_act: null
+  BackboneStopLayer:
+    name: "avg_pool"
+  Neck:
+    name: "BNNeck"
+    num_features: 2048
+  Head:
+    name: "FC"  
+    embedding_size: 2048
+    class_num: 751
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+    - SupConLoss:
+        weight: 1.0
+        views: 2
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.04
+  regularizer:
+    name: 'L2'
+    coeff: 0.0005
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+        name: "Market1501" # ["Market1501", "MSMT17"]
+        image_root: "./dataset"
+        cls_label_path: "bounding_box_train"
+        transform_ops:
+          - ResizeImage:
+              size: [128, 256]
+              interpolation: 'bicubic'
+              backend: 'pil'
+          - RandFlipImage:
+              flip_code: 1
+          - Pad:
+              padding: 10
+              fill: 0
+          - RandomCrop:
+              size: [128, 256]
+              pad_if_needed: False
+          - NormalizeImage:
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+          - RandomErasing:
+              EPSILON: 0.5
+              sl: 0.02
+              sh: 0.4
+              r1: 0.3
+              mean: [0.485, 0.456, 0.406] 
+
+    sampler:
+        name: PKSampler
+        batch_size: 16
+        sample_per_id: 4
+        drop_last: True
+        shuffle: True
+    loader:
+        num_workers: 6
+        use_shared_memory: True
+  Eval:
+    Query:
+      dataset: 
+        name: "Market1501" # ["Market1501", "MSMT17"]
+        image_root: "./dataset"
+        cls_label_path: "query"
+        transform_ops:
+          - ResizeImage:
+              size: [128, 256]
+              interpolation: 'bicubic'
+              backend: 'pil'
+          - NormalizeImage:
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 128
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 6
+        use_shared_memory: True
+
+    Gallery:
+      dataset: 
+        name: "Market1501" # ["Market1501", "MSMT17"]
+        image_root: "./dataset"
+        cls_label_path: "bounding_box_test"
+        transform_ops:
+          - ResizeImage:
+              size: [128, 256]
+              interpolation: 'bicubic'
+              backend: 'pil'
+          - NormalizeImage:
+              mean: [0.485, 0.456, 0.406]
+              std: [0.229, 0.224, 0.225]
+              order: ''
+      sampler:
+        name: DistributedBatchSampler
+        batch_size: 128
+        drop_last: False
+        shuffle: False
+      loader:
+        num_workers: 6
+        use_shared_memory: True
+
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]
+    - mAP: {}
+

ppcls/configs/Pedestrian/strong_baseline_baseline.yaml → ppcls/configs/strong_baseline_baseline.yaml

@@ -2,6 +2,8 @@
 Global:
   checkpoints: null
   pretrained_model: null
+  # pretrained_model: "./pd_model_trace/ISE/ISE_M_model" # pretrained ISE model for Market1501
+  # pretrained_model: "./pd_model_trace/ISE/ISE_MS_model" # pretrained ISE model for MSMT17
   output_dir: "./output/"
   device: "gpu"
   save_interval: 10
@@ -11,7 +13,7 @@ Global:
   print_batch_step: 10
   use_visualdl: False
   # used for static mode and model export
-  image_shape: [3, 256, 128]
+  image_shape: [3, 128, 256]
   save_inference_dir: "./inference"
   eval_mode: "retrieval"
 
@@ -20,17 +22,16 @@ Arch:
   name: "RecModel"
   infer_output_key: "features"
   infer_add_softmax: False
-  Backbone:
-    name: "ResNet50"
+  Backbone: 
+    name: "ResNet50_last_stage_stride1"
     pretrained: True
     stem_act: null
   BackboneStopLayer:
     name: "flatten"
   Head:
-    name: "FC"
+    name: "FC"  
     embedding_size: 2048
     class_num: 751
-
 # loss function config for traing/eval process
 Loss:
   Train:
@@ -46,7 +47,8 @@ Loss:
         weight: 1.0
 
 Optimizer:
-  name: Adam
+  name: Momentum
+  momentum: 0.9
   lr:
     name: Piecewise
     decay_epochs: [30, 60]
@@ -65,9 +67,6 @@ DataLoader:
         image_root: "./dataset/market1501"
         cls_label_path: "./dataset/market1501/bounding_box_train.txt"
         transform_ops:
-          - DecodeImage:
-              to_rgb: True
-              channel_first: False
           - ResizeImage:
               size: [128, 256]
           - RandFlipImage:
@@ -79,7 +78,6 @@ DataLoader:
               scale: [0.8022, 0.8022]
               ratio: [0.5, 0.5]
           - NormalizeImage:
-              scale: 0.00392157
               mean: [0.485, 0.456, 0.406]
               std: [0.229, 0.224, 0.225]
               order: ''
@@ -99,13 +97,9 @@ DataLoader:
         image_root: "./dataset/market1501"
         cls_label_path: "./dataset/market1501/query.txt"
         transform_ops:
-          - DecodeImage:
-              to_rgb: True
-              channel_first: False
           - ResizeImage:
               size: [128, 256]
           - NormalizeImage:
-              scale: 0.00392157
               mean: [0.485, 0.456, 0.406]
               std: [0.229, 0.224, 0.225]
               order: ''
@@ -124,13 +118,9 @@ DataLoader:
         image_root: "./dataset/market1501"
         cls_label_path: "./dataset/market1501/bounding_box_test.txt"
         transform_ops:
-          - DecodeImage:
-              to_rgb: True
-              channel_first: False
           - ResizeImage:
               size: [128, 256]
           - NormalizeImage:
-              scale: 0.00392157
               mean: [0.485, 0.456, 0.406]
               std: [0.229, 0.224, 0.225]
               order: ''

ppcls/data/__init__.py

@@ -28,6 +28,7 @@ from ppcls.data.dataloader.vehicle_dataset import CompCars, VeriWild
 from ppcls.data.dataloader.logo_dataset import LogoDataset
 from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
 from ppcls.data.dataloader.mix_dataset import MixDataset
+from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
 
 # sampler
 from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler

ppcls/data/dataloader/__init__.py

@@ -7,3 +7,4 @@ from ppcls.data.dataloader.icartoon_dataset import ICartoonDataset
 from ppcls.data.dataloader.mix_dataset import MixDataset
 from ppcls.data.dataloader.mix_sampler import MixSampler
 from ppcls.data.dataloader.pk_sampler import PKSampler
+from ppcls.data.dataloader.person_dataset import Market1501, MSMT17

ppcls/data/dataloader/person_dataset.py

+#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import paddle
+from paddle.io import Dataset
+import os
+import cv2
+
+from ppcls.data import preprocess
+from ppcls.data.preprocess import transform
+from ppcls.utils import logger
+from .common_dataset import create_operators
+import os.path as osp
+import glob
+import re
+from PIL import Image
+
+
+class Market1501(Dataset):
+    """
+    Market1501
+    Reference:
+    Zheng et al. Scalable Person Re-identification: A Benchmark. ICCV 2015.
+    URL: http://www.liangzheng.org/Project/project_reid.html
+
+    Dataset statistics:
+    # identities: 1501 (+1 for background)
+    # images: 12936 (train) + 3368 (query) + 15913 (gallery)
+    """
+    _dataset_dir = 'market1501/Market-1501-v15.09.15'
+
+    def __init__(self, image_root, cls_label_path, transform_ops=None):
+        self._img_root = image_root
+        self._cls_path = cls_label_path  # the sub folder in the dataset
+        self._dataset_dir = osp.join(image_root, self._dataset_dir,
+                                     self._cls_path)
+        self._check_before_run()
+        if transform_ops:
+            self._transform_ops = create_operators(transform_ops)
+        self._dtype = paddle.get_default_dtype()
+        self._load_anno(relabel=True if 'train' in self._cls_path else False)
+
+    def _check_before_run(self):
+        """Check if the file is available before going deeper"""
+        if not osp.exists(self._dataset_dir):
+            raise RuntimeError("'{}' is not available".format(
+                self._dataset_dir))
+
+    def _load_anno(self, relabel=False):
+        img_paths = glob.glob(osp.join(self._dataset_dir, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d)')
+
+        self.images = []
+        self.labels = []
+        self.cameras = []
+        pid_container = set()
+
+        for img_path in sorted(img_paths):
+            pid, _ = map(int, pattern.search(img_path).groups())
+            if pid == -1: continue  # junk images are just ignored
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        for img_path in sorted(img_paths):
+            pid, camid = map(int, pattern.search(img_path).groups())
+            if pid == -1: continue  # junk images are just ignored
+            assert 0 <= pid <= 1501  # pid == 0 means background
+            assert 1 <= camid <= 6
+            camid -= 1  # index starts from 0
+            if relabel: pid = pid2label[pid]
+            self.images.append(img_path)
+            self.labels.append(pid)
+            self.cameras.append(camid)
+
+        self.num_pids, self.num_imgs, self.num_cams = get_imagedata_info(
+            self.images, self.labels, self.cameras, subfolder=self._cls_path)
+
+    def __getitem__(self, idx):
+        try:
+            img = Image.open(self.images[idx]).convert('RGB')
+            img = np.array(img, dtype="float32").astype(np.uint8)
+            if self._transform_ops:
+                img = transform(img, self._transform_ops)
+            img = img.transpose((2, 0, 1))
+            return (img, self.labels[idx], self.cameras[idx])
+        except Exception as ex:
+            logger.error("Exception occured when parse line: {} with msg: {}".
+                         format(self.images[idx], ex))
+            rnd_idx = np.random.randint(self.__len__())
+            return self.__getitem__(rnd_idx)
+
+    def __len__(self):
+        return len(self.images)
+
+    @property
+    def class_num(self):
+        return len(set(self.labels))
+
+
+class MSMT17(Dataset):
+    """
+    MSMT17
+
+    Reference:
+    Wei et al. Person Transfer GAN to Bridge Domain Gap for Person Re-Identification. CVPR 2018.
+
+    URL: http://www.pkuvmc.com/publications/msmt17.html
+
+    Dataset statistics:
+    # identities: 4101
+    # images: 32621 (train) + 11659 (query) + 82161 (gallery)
+    # cameras: 15
+    """
+    _dataset_dir = 'msmt17/MSMT17_V1'
+
+    def __init__(self, image_root, cls_label_path, transform_ops=None):
+        self._img_root = image_root
+        self._cls_path = cls_label_path  # the sub folder in the dataset
+        self._dataset_dir = osp.join(image_root, self._dataset_dir,
+                                     self._cls_path)
+        self._check_before_run()
+        if transform_ops:
+            self._transform_ops = create_operators(transform_ops)
+        self._dtype = paddle.get_default_dtype()
+        self._load_anno(relabel=True if 'train' in self._cls_path else False)
+
+    def _check_before_run(self):
+        """Check if the file is available before going deeper"""
+        if not osp.exists(self._dataset_dir):
+            raise RuntimeError("'{}' is not available".format(
+                self._dataset_dir))
+
+    def _load_anno(self, relabel=False):
+        img_paths = glob.glob(osp.join(self._dataset_dir, '*.jpg'))
+        pattern = re.compile(r'([-\d]+)_c(\d+)')
+
+        self.images = []
+        self.labels = []
+        self.cameras = []
+        pid_container = set()
+
+        for img_path in img_paths:
+            pid, _ = map(int, pattern.search(img_path).groups())
+            if pid == -1:
+                continue  # junk images are just ignored
+            pid_container.add(pid)
+        pid2label = {pid: label for label, pid in enumerate(pid_container)}
+
+        for img_path in img_paths:
+            pid, camid = map(int, pattern.search(img_path).groups())
+            if pid == -1:
+                continue  # junk images are just ignored
+            assert 1 <= camid <= 15
+            camid -= 1  # index starts from 0
+            if relabel:
+                pid = pid2label[pid]
+            self.images.append(img_path)
+            self.labels.append(pid)
+            self.cameras.append(camid)
+
+        self.num_pids, self.num_imgs, self.num_cams = get_imagedata_info(
+            self.images, self.labels, self.cameras, subfolder=self._cls_path)
+
+    def __getitem__(self, idx):
+        try:
+            img = Image.open(self.images[idx]).convert('RGB')
+            img = np.array(img, dtype="float32").astype(np.uint8)
+            if self._transform_ops:
+                img = transform(img, self._transform_ops)
+            img = img.transpose((2, 0, 1))
+            return (img, self.labels[idx], self.cameras[idx])
+        except Exception as ex:
+            logger.error("Exception occured when parse line: {} with msg: {}".
+                         format(self.images[idx], ex))
+            rnd_idx = np.random.randint(self.__len__())
+            return self.__getitem__(rnd_idx)
+
+    def __len__(self):
+        return len(self.images)
+
+    @property
+    def class_num(self):
+        return len(set(self.labels))
+
+
+def get_imagedata_info(data, labels, cameras, subfolder='train'):
+    pids, cams = [], []
+    for _, pid, camid in zip(data, labels, cameras):
+        pids += [pid]
+        cams += [camid]
+    pids = set(pids)
+    cams = set(cams)
+    num_pids = len(pids)
+    num_cams = len(cams)
+    num_imgs = len(data)
+    print("Dataset statistics:")
+    print("  ----------------------------------------")
+    print("  subset   | # ids | # images | # cameras")
+    print("  ----------------------------------------")
+    print("  {}    | {:5d} | {:8d} | {:9d}".format(subfolder, num_pids,
+                                                   num_imgs, num_cams))
+    print("  ----------------------------------------")
+    return num_pids, num_imgs, num_cams

ppcls/data/preprocess/ops/operators.py

@@ -39,7 +39,8 @@ class UnifiedResize(object):
             'bilinear': cv2.INTER_LINEAR,
             'area': cv2.INTER_AREA,
             'bicubic': cv2.INTER_CUBIC,
-            'lanczos': cv2.INTER_LANCZOS4
+            'lanczos': cv2.INTER_LANCZOS4,
+            'random': (cv2.INTER_LINEAR, cv2.INTER_CUBIC)
         }
         _pil_interp_from_str = {
             'nearest': Image.NEAREST,
@@ -47,10 +48,18 @@ class UnifiedResize(object):
             'bicubic': Image.BICUBIC,
             'box': Image.BOX,
             'lanczos': Image.LANCZOS,
-            'hamming': Image.HAMMING
+            'hamming': Image.HAMMING,
+            'random': (Image.BILINEAR, Image.BICUBIC)
         }
 
+        def _cv2_resize(src, size, resample):
+            if isinstance(resample, tuple):
+                resample = random.choice(resample)
+            return cv2.resize(src, size, interpolation=resample)
+
         def _pil_resize(src, size, resample):
+            if isinstance(resample, tuple):
+                resample = random.choice(resample)
             pil_img = Image.fromarray(src)
             pil_img = pil_img.resize(size, resample)
             return np.asarray(pil_img)
@@ -61,7 +70,7 @@ class UnifiedResize(object):
             # compatible with opencv < version 4.4.0
             elif interpolation is None:
                 interpolation = cv2.INTER_LINEAR
-            self.resize_func = partial(cv2.resize, interpolation=interpolation)
+            self.resize_func = partial(_cv2_resize, resample=interpolation)
         elif backend.lower() == "pil":
             if isinstance(interpolation, str):
                 interpolation = _pil_interp_from_str[interpolation.lower()]
@@ -93,14 +102,15 @@ class DecodeImage(object):
         self.channel_first = channel_first  # only enabled when to_np is True
 
     def __call__(self, img):
-        if six.PY2:
-            assert type(img) is str and len(
-                img) > 0, "invalid input 'img' in DecodeImage"
-        else:
-            assert type(img) is bytes and len(
-                img) > 0, "invalid input 'img' in DecodeImage"
-        data = np.frombuffer(img, dtype='uint8')
-        img = cv2.imdecode(data, 1)
+        if not isinstance(img, np.ndarray):
+            if six.PY2:
+                assert type(img) is str and len(
+                    img) > 0, "invalid input 'img' in DecodeImage"
+            else:
+                assert type(img) is bytes and len(
+                    img) > 0, "invalid input 'img' in DecodeImage"
+            data = np.frombuffer(img, dtype='uint8')
+            img = cv2.imdecode(data, 1)
         if self.to_rgb:
             assert img.shape[2] == 3, 'invalid shape of image[%s]' % (
                 img.shape)

ppcls/engine/engine.py

@@ -214,21 +214,19 @@ class Engine(object):
         if self.config["Global"]["pretrained_model"] is not None:
             if self.config["Global"]["pretrained_model"].startswith("http"):
                 load_dygraph_pretrain_from_url(
-                    self.model, self.config["Global"]["pretrained_model"])
+                    [self.model, getattr(self, 'train_loss_func', None)],
+                    self.config["Global"]["pretrained_model"])
             else:
                 load_dygraph_pretrain(
-                    self.model, self.config["Global"]["pretrained_model"])
+                    [self.model, getattr(self, 'train_loss_func', None)],
+                    self.config["Global"]["pretrained_model"])
 
         # build optimizer
         if self.mode == 'train':
             self.optimizer, self.lr_sch = build_optimizer(
-                self.config["Optimizer"], self.config["Global"]["epochs"],
-                len(self.train_dataloader), [
-                    self.model, * [
-                        m for m in self.train_loss_func.loss_func
-                        if len(m.parameters()) > 0
-                    ]
-                ])
+                self.config, self.config["Global"]["epochs"],
+                len(self.train_dataloader),
+                [self.model, self.train_loss_func])
 
         # for amp training
         if self.amp:
@@ -246,6 +244,11 @@ class Engine(object):
                 optimizers=self.optimizer,
                 level=amp_level,
                 save_dtype='float32')
+            if len(self.train_loss_func.parameters()) > 0:
+                self.train_loss_func = paddle.amp.decorate(
+                    models=self.train_loss_func,
+                    level=amp_level,
+                    save_dtype='float32')
 
         # for distributed
         world_size = dist.get_world_size()
@@ -256,12 +259,10 @@ class Engine(object):
         if self.config["Global"]["distributed"]:
             dist.init_parallel_env()
             self.model = paddle.DataParallel(self.model)
-            # NOTE: parallelize loss which has parameters, such as CenterLoss
-            for i in range(len(self.train_loss_func.loss_func)):
-                if len(self.train_loss_func.loss_func[i].parameters()) > 0:
-                    self.train_loss_func.loss_func[i] = paddle.DataParallel(
-                        self.train_loss_func.loss_func[i])
-
+            if self.mode == 'train' and len(self.train_loss_func.parameters(
+            )) > 0:
+                self.train_loss_func = paddle.DataParallel(
+                    self.train_loss_func)
         # build postprocess for infer
         if self.mode == 'infer':
             self.preprocess_func = create_operators(self.config["Infer"][
@@ -289,20 +290,32 @@ class Engine(object):
         # global iter counter
         self.global_step = 0
 
-        if self.config["Global"]["checkpoints"] is not None:
-            metric_info = init_model(self.config["Global"], self.model,
-                                     self.optimizer)
+        if self.config.Global.checkpoints is not None:
+            metric_info = init_model(self.config.Global, self.model,
+                                     self.optimizer, self.train_loss_func)
             if metric_info is not None:
                 best_metric.update(metric_info)
 
         self.max_iter = len(self.train_dataloader) - 1 if platform.system(
         ) == "Windows" else len(self.train_dataloader)
+
+        if self.config["Global"].get("warmup_by_epoch", False):
+            for i in range(len(self.lr_sch)):
+                self.lr_sch[i].step()
+            logger.info(
+                "lr_sch step once before first epoch, when Global.warmup_by_epoch=True"
+            )
+
         for epoch_id in range(best_metric["epoch"] + 1,
                               self.config["Global"]["epochs"] + 1):
             acc = 0.0
             # for one epoch train
             self.train_epoch_func(self, epoch_id, print_batch_step)
 
+            if self.config["Global"].get("warmup_by_epoch", False):
+                for i in range(len(self.lr_sch)):
+                    self.lr_sch[i].step()
+
             if self.use_dali:
                 self.train_dataloader.reset()
             metric_msg = ", ".join([
@@ -327,7 +340,8 @@ class Engine(object):
                         best_metric,
                         self.output_dir,
                         model_name=self.config["Arch"]["name"],
-                        prefix="best_model")
+                        prefix="best_model",
+                        loss=self.train_loss_func)
                 logger.info("[Eval][Epoch {}][best metric: {}]".format(
                     epoch_id, best_metric["metric"]))
                 logger.scaler(
@@ -346,7 +360,8 @@ class Engine(object):
                                      "epoch": epoch_id},
                     self.output_dir,
                     model_name=self.config["Arch"]["name"],
-                    prefix="epoch_{}".format(epoch_id))
+                    prefix="epoch_{}".format(epoch_id),
+                    loss=self.train_loss_func)
             # save the latest model
             save_load.save_model(
                 self.model,
@@ -354,7 +369,8 @@ class Engine(object):
                                  "epoch": epoch_id},
                 self.output_dir,
                 model_name=self.config["Arch"]["name"],
-                prefix="latest")
+                prefix="latest",
+                loss=self.train_loss_func)
 
         if self.vdl_writer is not None:
             self.vdl_writer.close()

ppcls/engine/evaluation/classification.py

@@ -53,13 +53,20 @@ def classification_eval(engine, epoch_id=0):
             ]
         time_info["reader_cost"].update(time.time() - tic)
         batch_size = batch[0].shape[0]
-        batch[0] = paddle.to_tensor(batch[0]).astype("float32")
+        batch[0] = paddle.to_tensor(batch[0])
         if not engine.config["Global"].get("use_multilabel", False):
             batch[1] = batch[1].reshape([-1, 1]).astype("int64")
 
         # image input
-        if engine.amp:
+        if engine.amp and (
+                engine.config['AMP'].get("level", "O1").upper() == "O2" or
+                engine.config["AMP"].get("use_fp16_test", False)):
             amp_level = engine.config['AMP'].get("level", "O1").upper()
+
+            if amp_level == "O2":
+                msg = "Only support FP16 evaluation when AMP O2 is enabled."
+                logger.warning(msg)
+
             with paddle.amp.auto_cast(
                     custom_black_list={
                         "flatten_contiguous_range", "greater_than"

ppcls/engine/evaluation/retrieval.py

@@ -16,6 +16,8 @@ from __future__ import division
 from __future__ import print_function
 
 import platform
+
+import numpy as np
 import paddle
 from ppcls.utils import logger
 
@@ -49,34 +51,51 @@ def retrieval_eval(engine, epoch_id=0):
         metric_dict = {metric_key: 0.}
     else:
         metric_dict = dict()
-        for block_idx, block_fea in enumerate(fea_blocks):
-            similarity_matrix = paddle.matmul(
-                block_fea, gallery_feas, transpose_y=True)
-            if query_query_id is not None:
-                query_id_block = query_id_blocks[block_idx]
-                query_id_mask = (query_id_block != gallery_unique_id.t())
-
-                image_id_block = image_id_blocks[block_idx]
-                image_id_mask = (image_id_block != gallery_img_id.t())
-
-                keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
-                similarity_matrix = similarity_matrix * keep_mask.astype(
-                    "float32")
-            else:
-                keep_mask = None
-
-            metric_tmp = engine.eval_metric_func(similarity_matrix,
-                                                 image_id_blocks[block_idx],
-                                                 gallery_img_id, keep_mask)
+        reranking_flag = engine.config['Global'].get('re_ranking', False)
+        logger.info(f"re_ranking={reranking_flag}")
+        if not reranking_flag:
+            for block_idx, block_fea in enumerate(fea_blocks):
+                similarity_matrix = paddle.matmul(
+                    block_fea, gallery_feas, transpose_y=True)
+                if query_query_id is not None:
+                    query_id_block = query_id_blocks[block_idx]
+                    query_id_mask = (query_id_block != gallery_unique_id.t())
 
-            for key in metric_tmp:
-                if key not in metric_dict:
-                    metric_dict[key] = metric_tmp[key] * block_fea.shape[
-                        0] / len(query_feas)
+                    image_id_block = image_id_blocks[block_idx]
+                    image_id_mask = (image_id_block != gallery_img_id.t())
+
+                    keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
+                    similarity_matrix = similarity_matrix * keep_mask.astype(
+                        "float32")
                 else:
-                    metric_dict[key] += metric_tmp[key] * block_fea.shape[
-                        0] / len(query_feas)
+                    keep_mask = None
+
+                metric_tmp = engine.eval_metric_func(
+                    similarity_matrix, image_id_blocks[block_idx],
+                    gallery_img_id, keep_mask)
 
+                for key in metric_tmp:
+                    if key not in metric_dict:
+                        metric_dict[key] = metric_tmp[key] * block_fea.shape[
+                            0] / len(query_feas)
+                    else:
+                        metric_dict[key] += metric_tmp[key] * block_fea.shape[
+                            0] / len(query_feas)
+        else:
+            distmat = re_ranking(
+                query_feas, gallery_feas, k1=20, k2=6, lambda_value=0.3)
+            cmc, mAP = eval_func(distmat,
+                                 np.squeeze(query_img_id.numpy()),
+                                 np.squeeze(gallery_img_id.numpy()),
+                                 np.squeeze(query_query_id.numpy()),
+                                 np.squeeze(gallery_unique_id.numpy()))
+            metric_dict["recall1(RK)"] = cmc[0]
+            metric_dict["recall5(RK)"] = cmc[4]
+            metric_dict["mAP(RK)"] = mAP
+
+            for key in metric_tmp:
+                metric_dict[key] = metric_tmp[key] * block_fea.shape[0] / len(
+                    query_feas)
     metric_info_list = []
     for key in metric_dict:
         if metric_key is None:
@@ -88,6 +107,159 @@ def retrieval_eval(engine, epoch_id=0):
     return metric_dict[metric_key]
 
 
+def re_ranking(queFea,
+               galFea,
+               k1=20,
+               k2=6,
+               lambda_value=0.5,
+               local_distmat=None,
+               only_local=False):
+    # if feature vector is numpy, you should use 'paddle.tensor' transform it to tensor
+    query_num = queFea.shape[0]
+    all_num = query_num + galFea.shape[0]
+    if only_local:
+        original_dist = local_distmat
+    else:
+        feat = paddle.concat([queFea, galFea])
+        logger.info('using GPU to compute original distance')
+
+        # L2 distance
+        distmat = paddle.pow(feat, 2).sum(axis=1, keepdim=True).expand([all_num, all_num]) + \
+                  paddle.pow(feat, 2).sum(axis=1, keepdim=True).expand([all_num, all_num]).t()
+        distmat = distmat.addmm(x=feat, y=feat.t(), alpha=-2.0, beta=1.0)
+        # Cosine distance
+        # distmat = paddle.matmul(queFea, galFea, transpose_y=True)
+        # if query_query_id is not None:
+        #     query_id_mask = (queCid != galCid.t())
+        #     image_id_mask = (queId != galId.t())
+        #     keep_mask = paddle.logical_or(query_id_mask, image_id_mask)
+        #     distmat = distmat * keep_mask.astype("float32")
+
+        original_dist = distmat.cpu().numpy()
+        del feat
+        if local_distmat is not None:
+            original_dist = original_dist + local_distmat
+
+    gallery_num = original_dist.shape[0]
+    original_dist = np.transpose(original_dist / np.max(original_dist, axis=0))
+    V = np.zeros_like(original_dist).astype(np.float16)
+    initial_rank = np.argsort(original_dist).astype(np.int32)
+    logger.info('starting re_ranking')
+    for i in range(all_num):
+        # k-reciprocal neighbors
+        forward_k_neigh_index = initial_rank[i, :k1 + 1]
+        backward_k_neigh_index = initial_rank[forward_k_neigh_index, :k1 + 1]
+        fi = np.where(backward_k_neigh_index == i)[0]
+        k_reciprocal_index = forward_k_neigh_index[fi]
+        k_reciprocal_expansion_index = k_reciprocal_index
+        for j in range(len(k_reciprocal_index)):
+            candidate = k_reciprocal_index[j]
+            candidate_forward_k_neigh_index = initial_rank[candidate, :int(
+                np.around(k1 / 2)) + 1]
+            candidate_backward_k_neigh_index = initial_rank[
+                candidate_forward_k_neigh_index, :int(np.around(k1 / 2)) + 1]
+            fi_candidate = np.where(
+                candidate_backward_k_neigh_index == candidate)[0]
+            candidate_k_reciprocal_index = candidate_forward_k_neigh_index[
+                fi_candidate]
+            if len(
+                    np.intersect1d(candidate_k_reciprocal_index,
+                                   k_reciprocal_index)) > 2 / 3 * len(
+                                       candidate_k_reciprocal_index):
+                k_reciprocal_expansion_index = np.append(
+                    k_reciprocal_expansion_index, candidate_k_reciprocal_index)
+
+        k_reciprocal_expansion_index = np.unique(k_reciprocal_expansion_index)
+        weight = np.exp(-original_dist[i, k_reciprocal_expansion_index])
+        V[i, k_reciprocal_expansion_index] = weight / np.sum(weight)
+    original_dist = original_dist[:query_num, ]
+    if k2 != 1:
+        V_qe = np.zeros_like(V, dtype=np.float16)
+        for i in range(all_num):
+            V_qe[i, :] = np.mean(V[initial_rank[i, :k2], :], axis=0)
+        V = V_qe
+        del V_qe
+    del initial_rank
+    invIndex = []
+    for i in range(gallery_num):
+        invIndex.append(np.where(V[:, i] != 0)[0])
+
+    jaccard_dist = np.zeros_like(original_dist, dtype=np.float16)
+    for i in range(query_num):
+        temp_min = np.zeros(shape=[1, gallery_num], dtype=np.float16)
+        indNonZero = np.where(V[i, :] != 0)[0]
+        indImages = [invIndex[ind] for ind in indNonZero]
+        for j in range(len(indNonZero)):
+            temp_min[0, indImages[j]] = temp_min[0, indImages[j]] + np.minimum(
+                V[i, indNonZero[j]], V[indImages[j], indNonZero[j]])
+        jaccard_dist[i] = 1 - temp_min / (2 - temp_min)
+
+    final_dist = jaccard_dist * (1 - lambda_value
+                                 ) + original_dist * lambda_value
+    del original_dist
+    del V
+    del jaccard_dist
+    final_dist = final_dist[:query_num, query_num:]
+    return final_dist
+
+
+def eval_func(distmat, q_pids, g_pids, q_camids, g_camids, max_rank=50):
+    """Evaluation with market1501 metric
+        Key: for each query identity, its gallery images from the same camera view are discarded.
+        """
+    num_q, num_g = distmat.shape
+    if num_g < max_rank:
+        max_rank = num_g
+        print("Note: number of gallery samples is quite small, got {}".format(
+            num_g))
+    indices = np.argsort(distmat, axis=1)
+    matches = (g_pids[indices] == q_pids[:, np.newaxis]).astype(np.int32)
+
+    # compute cmc curve for each query
+    all_cmc = []
+    all_AP = []
+    num_valid_q = 0.  # number of valid query
+    for q_idx in range(num_q):
+        # get query pid and camid
+        q_pid = q_pids[q_idx]
+        q_camid = q_camids[q_idx]
+
+        # remove gallery samples that have the same pid and camid with query
+        order = indices[q_idx]
+        remove = (g_pids[order] == q_pid) & (g_camids[order] == q_camid)
+        keep = np.invert(remove)
+
+        # compute cmc curve
+        # binary vector, positions with value 1 are correct matches
+        orig_cmc = matches[q_idx][keep]
+        if not np.any(orig_cmc):
+            # this condition is true when query identity does not appear in gallery
+            continue
+
+        cmc = orig_cmc.cumsum()
+        cmc[cmc > 1] = 1
+
+        all_cmc.append(cmc[:max_rank])
+        num_valid_q += 1.
+
+        # compute average precision
+        # reference: https://en.wikipedia.org/wiki/Evaluation_measures_(information_retrieval)#Average_precision
+        num_rel = orig_cmc.sum()
+        tmp_cmc = orig_cmc.cumsum()
+        tmp_cmc = [x / (i + 1.) for i, x in enumerate(tmp_cmc)]
+        tmp_cmc = np.asarray(tmp_cmc) * orig_cmc
+        AP = tmp_cmc.sum() / num_rel
+        all_AP.append(AP)
+
+    assert num_valid_q > 0, "Error: all query identities do not appear in gallery"
+
+    all_cmc = np.asarray(all_cmc).astype(np.float32)
+    all_cmc = all_cmc.sum(0) / num_valid_q
+    mAP = np.mean(all_AP)
+
+    return all_cmc, mAP
+
+
 def cal_feature(engine, name='gallery'):
     all_feas = None
     all_image_id = None

ppcls/engine/train/train.py

@@ -21,7 +21,6 @@ from ppcls.utils import profiler
 
 def train_epoch(engine, epoch_id, print_batch_step):
     tic = time.time()
-    v_current = [int(i) for i in paddle.__version__.split(".")]
     for iter_id, batch in enumerate(engine.train_dataloader):
         if iter_id >= engine.max_iter:
             break
@@ -58,10 +57,16 @@ def train_epoch(engine, epoch_id, print_batch_step):
         if engine.amp:
             scaled = engine.scaler.scale(loss_dict["loss"])
             scaled.backward()
+            # set BNneck.bias grad to zero
+            engine.model.neck.feat_bn.bias.grad.set_value(
+                paddle.zeros_like(engine.model.neck.feat_bn.bias.grad))
             for i in range(len(engine.optimizer)):
                 engine.scaler.minimize(engine.optimizer[i], scaled)
         else:
             loss_dict["loss"].backward()
+            # set BNneck.bias grad to zero
+            engine.model.neck.feat_bn.bias.grad.set_value(
+                paddle.zeros_like(engine.model.neck.feat_bn.bias.grad))
             for i in range(len(engine.optimizer)):
                 engine.optimizer[i].step()
 
@@ -70,8 +75,9 @@ def train_epoch(engine, epoch_id, print_batch_step):
             engine.optimizer[i].clear_grad()
 
         # step lr
-        for i in range(len(engine.lr_sch)):
-            engine.lr_sch[i].step()
+        if engine.config["Global"].get("warmup_by_epoch", False) is False:
+            for i in range(len(engine.lr_sch)):
+                engine.lr_sch[i].step()
 
         # below code just for logging
         # update metric_for_logger

ppcls/engine/train/utils.py

@@ -38,12 +38,10 @@ def update_loss(trainer, loss_dict, batch_size):
 
 
 def log_info(trainer, batch_size, epoch_id, iter_id):
-    if len(trainer.lr_sch) <= 1:
-        lr_msg = "lr: {:.8f}".format(trainer.lr_sch[0].get_lr())
-    else:
-        lr_msg = "lr_model: {:.8f}".format(trainer.lr_sch[0].get_lr())
-        lr_msg += ", lr_loss: {:.8f}".format(trainer.lr_sch[1].get_lr())
-
+    lr_msg = ", ".join([
+        "lr_{}: {:.8f}".format(i + 1, lr.get_lr())
+        for i, lr in enumerate(trainer.lr_sch)
+    ])
     metric_msg = ", ".join([
         "{}: {:.5f}".format(key, trainer.output_info[key].avg)
         for key in trainer.output_info
@@ -63,21 +61,11 @@ def log_info(trainer, batch_size, epoch_id, iter_id):
         epoch_id, trainer.config["Global"]["epochs"], iter_id,
         len(trainer.train_dataloader), lr_msg, metric_msg, time_msg, ips_msg,
         eta_msg))
-    if len(trainer.lr_sch) <= 1:
-        logger.scaler(
-            name="lr",
-            value=trainer.lr_sch[0].get_lr(),
-            step=trainer.global_step,
-            writer=trainer.vdl_writer)
-    else:
-        logger.scaler(
-            name="lr_model",
-            value=trainer.lr_sch[0].get_lr(),
-            step=trainer.global_step,
-            writer=trainer.vdl_writer)
+
+    for i, lr in enumerate(trainer.lr_sch):
         logger.scaler(
-            name="lr_loss",
-            value=trainer.lr_sch[1].get_lr(),
+            name="lr_{}".format(i + 1),
+            value=lr.get_lr(),
             step=trainer.global_step,
             writer=trainer.vdl_writer)
     for key in trainer.output_info:

ppcls/loss/__init__.py

@@ -11,7 +11,7 @@ from .emlloss import EmlLoss
 from .msmloss import MSMLoss
 from .npairsloss import NpairsLoss
 from .trihardloss import TriHardLoss
-from .triplet import TripletLoss, TripletLossV2
+from .triplet import TripletLoss, TripletLossV2, TripletLossV3
 from .supconloss import SupConLoss
 from .pairwisecosface import PairwiseCosface
 from .dmlloss import DMLLoss
@@ -47,6 +47,7 @@ class CombinedLoss(nn.Layer):
                 param.keys())
             self.loss_weight.append(param.pop("weight"))
             self.loss_func.append(eval(name)(**param))
+            self.loss_func = nn.LayerList(self.loss_func)
 
     def __call__(self, input, batch):
         loss_dict = {}

ppcls/loss/centerloss.py

@@ -20,7 +20,6 @@ from typing import Dict
 
 import paddle
 import paddle.nn as nn
-from paddle import Tensor
 
 
 class CenterLoss(nn.Layer):
@@ -42,16 +41,16 @@ class CenterLoss(nn.Layer):
             default_initializer=nn.initializer.Assign(random_init_centers))
         self.add_parameter("centers", self.centers)
 
-    def __call__(self, input: Dict[str, Tensor],
-                 target: Tensor) -> Dict[str, Tensor]:
+    def __call__(self, input: Dict[str, paddle.Tensor],
+                 target: paddle.Tensor) -> Dict[str, paddle.Tensor]:
         """compute center loss.
 
         Args:
-            input (Dict[str, Tensor]): {'features': (batch_size, feature_dim), ...}.
-            target (Tensor): ground truth label with shape (batch_size, ).
+            input (Dict[str, paddle.Tensor]): {'features': (batch_size, feature_dim), ...}.
+            target (paddle.Tensor): ground truth label with shape (batch_size, ).
 
         Returns:
-            Dict[str, Tensor]: {'CenterLoss': loss}.
+            Dict[str, paddle.Tensor]: {'CenterLoss': loss}.
         """
         feats = input['backbone']
         labels = target
@@ -61,31 +60,15 @@ class CenterLoss(nn.Layer):
             labels = paddle.squeeze(labels, axis=[-1])
 
         batch_size = feats.shape[0]
-        # calc feat * feat
-        dist1 = paddle.sum(paddle.square(feats), axis=1, keepdim=True)
-        dist1 = paddle.expand(dist1, [batch_size, self.num_classes])
+        distmat = paddle.pow(feats, 2).sum(axis=1, keepdim=True).expand([batch_size, self.num_classes]) + \
+            paddle.pow(self.centers, 2).sum(axis=1, keepdim=True).expand([self.num_classes, batch_size]).t()
+        distmat = distmat.addmm(x=feats, y=self.centers.t(), beta=1, alpha=-2)
 
-        # dist2 of centers
-        dist2 = paddle.sum(paddle.square(self.centers), axis=1,
-                           keepdim=True)  # num_classes
-        dist2 = paddle.expand(dist2, [self.num_classes, batch_size])
-        dist2 = paddle.transpose(dist2, [1, 0])
+        classes = paddle.arange(self.num_classes).astype(labels.dtype)
+        labels = labels.unsqueeze(1).expand([batch_size, self.num_classes])
+        mask = labels.equal(classes.expand([batch_size, self.num_classes]))
 
-        # first x * x + y * y
-        distmat = paddle.add(dist1, dist2)
-
-        tmp = paddle.matmul(feats, paddle.transpose(self.centers, [1, 0]))
-        distmat = distmat - 2.0 * tmp
-
-        # generate the mask
-        classes = paddle.arange(self.num_classes)
-        labels = paddle.expand(
-            paddle.unsqueeze(labels, 1), (batch_size, self.num_classes))
-        mask = paddle.equal(
-            paddle.expand(classes, [batch_size, self.num_classes]),
-            labels).astype("float32")  # get mask
-
-        dist = paddle.multiply(distmat, mask)
-        loss = paddle.sum(paddle.clip(dist, min=1e-12, max=1e+12)) / batch_size
+        dist = distmat * mask.astype(feats.dtype)
+        loss = dist.clip(min=1e-12, max=1e+12).sum() / batch_size
         # return loss
         return {'CenterLoss': loss}

ppcls/loss/triplet.py

 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
+from typing import Tuple
 
 import paddle
 import paddle.nn as nn
@@ -135,3 +136,122 @@ class TripletLoss(nn.Layer):
         y = paddle.ones_like(dist_an)
         loss = self.ranking_loss(dist_an, dist_ap, y)
         return {"TripletLoss": loss}
+
+
+class TripletLossV3(nn.Layer):
+    """Modified from Tong Xiao's open-reid (https://github.com/Cysu/open-reid).
+    Related Triplet Loss theory can be found in paper 'In Defense of the Triplet
+    Loss for Person Re-Identification'."""
+
+    def __init__(self, margin=None, normalize_feature=False):
+        super(TripletLossV3, self).__init__()
+        self.normalize_feature = normalize_feature
+        self.margin = margin
+        if margin is not None:
+            self.ranking_loss = nn.MarginRankingLoss(margin=margin)
+        else:
+            self.ranking_loss = nn.SoftMarginLoss()
+
+    def forward(self, input, target):
+        global_feat = input["backbone"]
+        if self.normalize_feature:
+            global_feat = self._normalize(global_feat, axis=-1)
+        dist_mat = self._euclidean_dist(global_feat, global_feat)
+        dist_ap, dist_an = self._hard_example_mining(dist_mat, target)
+        y = paddle.ones_like(dist_an)
+        if self.margin is not None:
+            loss = self.ranking_loss(dist_an, dist_ap, y)
+
+        return {"TripletLossV3": loss}
+
+    def _normalize(self, x: paddle.Tensor, axis: int=-1) -> paddle.Tensor:
+        """Normalizing to unit length along the specified dimension.
+
+        Args:
+            x (paddle.Tensor): (batch_size, feature_dim)
+            axis (int, optional): normalization dim. Defaults to -1.
+
+        Returns:
+            paddle.Tensor: (batch_size, feature_dim)
+        """
+        x = 1. * x / (paddle.norm(
+            x, 2, axis, keepdim=True).expand_as(x) + 1e-12)
+        return x
+
+    def _euclidean_dist(self, x: paddle.Tensor,
+                        y: paddle.Tensor) -> paddle.Tensor:
+        """compute euclidean distance between two batched vectors
+
+        Args:
+            x (paddle.Tensor): (N, feature_dim)
+            y (paddle.Tensor): (M, feature_dim)
+
+        Returns:
+            paddle.Tensor: (N, M)
+        """
+        m, n = x.shape[0], y.shape[0]
+        d = x.shape[1]
+        xx = paddle.pow(x, 2).sum(1, keepdim=True).expand([m, n])
+        yy = paddle.pow(y, 2).sum(1, keepdim=True).expand([n, m]).t()
+        dist = xx + yy
+        dist = dist.addmm(x, y.t(), alpha=-2, beta=1)
+        # dist = dist - 2*(x@y.t())
+        dist = dist.clip(min=1e-12).sqrt()  # for numerical stability
+        return dist
+
+    def _hard_example_mining(
+            self,
+            dist_mat: paddle.Tensor,
+            labels: paddle.Tensor,
+            return_inds: bool=False) -> Tuple[paddle.Tensor, paddle.Tensor]:
+        """For each anchor, find the hardest positive and negative sample.
+
+        Args:
+            dist_mat (paddle.Tensor): pair wise distance between samples, [N, N]
+            labels (paddle.Tensor): labels, [N, ]
+            return_inds (bool, optional): whether to return the indices . Defaults to False.
+
+        Returns:
+            Tuple[paddle.Tensor, paddle.Tensor]: [(N, ), (N, )]
+
+        NOTE: Only consider the case in which all labels have same num of samples,
+        thus we can cope with all anchors in parallel.
+        """
+        assert len(dist_mat.shape) == 2
+        assert dist_mat.shape[0] == dist_mat.shape[1]
+        N = dist_mat.shape[0]
+
+        # shape [N, N]
+        is_pos = labels.expand([N, N]).equal(labels.expand([N, N]).t())
+        is_neg = labels.expand([N, N]).not_equal(labels.expand([N, N]).t())
+
+        # `dist_ap` means distance(anchor, positive)
+        # both `dist_ap` and `relative_p_inds` with shape [N, 1]
+        dist_ap = paddle.max(dist_mat[is_pos].reshape([N, -1]),
+                             1,
+                             keepdim=True)
+        # `dist_an` means distance(anchor, negative)
+        # both `dist_an` and `relative_n_inds` with shape [N, 1]
+        dist_an = paddle.min(dist_mat[is_neg].reshape([N, -1]),
+                             1,
+                             keepdim=True)
+        # shape [N]
+        dist_ap = dist_ap.squeeze(1)
+        dist_an = dist_an.squeeze(1)
+
+        if return_inds:
+            # shape [N, N]
+            ind = (labels.new().resize_as_(labels)
+                   .copy_(paddle.arange(0, N).long())
+                   .unsqueeze(0).expand(N, N))
+            # shape [N, 1]
+            p_inds = paddle.gather(ind[is_pos].reshape([N, -1]), 1,
+                                   relative_p_inds.data)
+            n_inds = paddle.gather(ind[is_neg].reshape([N, -1]), 1,
+                                   relative_n_inds.data)
+            # shape [N]
+            p_inds = p_inds.squeeze(1)
+            n_inds = n_inds.squeeze(1)
+            return dist_ap, dist_an, p_inds, n_inds
+
+        return dist_ap, dist_an

ppcls/optimizer/__init__.py

@@ -18,6 +18,7 @@ from __future__ import print_function
 
 import copy
 import paddle
+from typing import Dict, List
 
 from ppcls.utils import logger
 
@@ -44,97 +45,86 @@ def build_lr_scheduler(lr_config, epochs, step_each_epoch):
 # model_list is None in static graph
 def build_optimizer(config, epochs, step_each_epoch, model_list=None):
     config = copy.deepcopy(config)
-    if 'name' in config:
-        # NOTE: build optimizer and lr for model only.
+    optim_config = config["Optimizer"]
+    if isinstance(optim_config, dict):
+        # convert {'name': xxx, **optim_cfg} to [{'name': {'scope': xxx, **optim_cfg}}]
+        optim_name = optim_config.pop("name")
+        optim_config: List[Dict[str, Dict]] = [{
+            optim_name: {
+                'scope': "all",
+                **
+                optim_config
+            }
+        }]
+    optim_list = []
+    lr_list = []
+    """NOTE:
+    Currently only support optim objets below.
+    1. single optimizer config.
+    2. next level uner Arch, such as Arch.backbone, Arch.neck, Arch.head.
+    3. loss which has parameters, such as CenterLoss.
+    """
+    for optim_item in optim_config:
+        # optim_cfg = {optim_name: {'scope': xxx, **optim_cfg}}
         # step1 build lr
-        lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)
-        logger.debug("build model's lr ({}) success..".format(lr))
+        optim_name = list(optim_item.keys())[0]  # get optim_name
+        optim_scope = optim_item[optim_name].pop('scope')  # get optim_scope
+        optim_cfg = optim_item[optim_name]  # get optim_cfg
+
+        lr = build_lr_scheduler(optim_cfg.pop('lr'), epochs, step_each_epoch)
+        logger.info("build lr ({}) for scope ({}) success..".format(
+            lr.__class__.__name__, optim_scope))
         # step2 build regularization
-        if 'regularizer' in config and config['regularizer'] is not None:
-            if 'weight_decay' in config:
+        if 'regularizer' in optim_cfg and optim_cfg['regularizer'] is not None:
+            if 'weight_decay' in optim_cfg:
                 logger.warning(
                     "ConfigError: Only one of regularizer and weight_decay can be set in Optimizer Config. \"weight_decay\" has been ignored."
                 )
-            reg_config = config.pop('regularizer')
+            reg_config = optim_cfg.pop('regularizer')
             reg_name = reg_config.pop('name') + 'Decay'
             reg = getattr(paddle.regularizer, reg_name)(**reg_config)
-            config["weight_decay"] = reg
-            logger.debug("build model's regularizer ({}) success..".format(
-                reg))
+            optim_cfg["weight_decay"] = reg
+            logger.info("build regularizer ({}) for scope ({}) success..".
+                        format(reg.__class__.__name__, optim_scope))
         # step3 build optimizer
-        optim_name = config.pop('name')
-        if 'clip_norm' in config:
-            clip_norm = config.pop('clip_norm')
+        if 'clip_norm' in optim_cfg:
+            clip_norm = optim_cfg.pop('clip_norm')
             grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
         else:
             grad_clip = None
+        optim_model = []
+        for i in range(len(model_list)):
+            if len(model_list[i].parameters()) == 0:
+                continue
+            if optim_scope == "all":
+                # optimizer for all
+                optim_model.append(model_list[i])
+            else:
+                if "Loss" in optim_scope:
+                    # optimizer for loss
+                    if hasattr(model_list[i], 'loss_func'):
+                        for j in range(len(model_list[i].loss_func)):
+                            if model_list[i].loss_func[
+                                    j].__class__.__name__ == optim_scope:
+                                optim_model.append(model_list[i].loss_func[j])
+                elif optim_scope == "model":
+                    # opmizer for entire model
+                    if not model_list[i].__class__.__name__.lower().endswith(
+                            "loss"):
+                        optim_model.append(model_list[i])
+                else:
+                    # opmizer for module in model, such as backbone, neck, head...
+                    if hasattr(model_list[i], optim_scope):
+                        optim_model.append(getattr(model_list[i], optim_scope))
+
+        assert len(optim_model) == 1, \
+            "Invalid optim model for optim scope({}), number of optim_model={}".\
+                format(optim_scope, [m.__class__.__name__ for m in optim_model])
         optim = getattr(optimizer, optim_name)(
             learning_rate=lr, grad_clip=grad_clip,
-            **config)(model_list=model_list[0:1])
-        optim = [optim, ]
-        lr = [lr, ]
-        logger.debug("build model's optimizer ({}) success..".format(optim))
-    else:
-        # NOTE: build optimizer and lr for model and loss.
-        config_model = config['model']
-        config_loss = config['loss']
-        # step1 build lr
-        lr_model = build_lr_scheduler(
-            config_model.pop('lr'), epochs, step_each_epoch)
-        logger.debug("build model's lr ({}) success..".format(lr_model))
-        # step2 build regularization
-        if 'regularizer' in config_model and config_model[
-                'regularizer'] is not None:
-            if 'weight_decay' in config_model:
-                logger.warning(
-                    "ConfigError: Only one of regularizer and weight_decay can be set in Optimizer Config. \"weight_decay\" has been ignored."
-                )
-            reg_config = config_model.pop('regularizer')
-            reg_name = reg_config.pop('name') + 'Decay'
-            reg_model = getattr(paddle.regularizer, reg_name)(**reg_config)
-            config_model["weight_decay"] = reg_model
-            logger.debug("build model's regularizer ({}) success..".format(
-                reg_model))
-        # step3 build optimizer
-        optim_name = config_model.pop('name')
-        if 'clip_norm' in config_model:
-            clip_norm = config_model.pop('clip_norm')
-            grad_clip_model = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
-        else:
-            grad_clip_model = None
-        optim_model = getattr(optimizer, optim_name)(
-            learning_rate=lr_model, grad_clip=grad_clip_model,
-            **config_model)(model_list=model_list[0:1])
-
-        # step4 build lr for loss
-        lr_loss = build_lr_scheduler(
-            config_loss.pop('lr'), epochs, step_each_epoch)
-        logger.debug("build loss's lr ({}) success..".format(lr_loss))
-        # step5 build regularization for loss
-        if 'regularizer' in config_loss and config_loss[
-                'regularizer'] is not None:
-            if 'weight_decay' in config_loss:
-                logger.warning(
-                    "ConfigError: Only one of regularizer and weight_decay can be set in Optimizer Config. \"weight_decay\" has been ignored."
-                )
-            reg_config = config_loss.pop('regularizer')
-            reg_name = reg_config.pop('name') + 'Decay'
-            reg_loss = getattr(paddle.regularizer, reg_name)(**reg_config)
-            config_loss["weight_decay"] = reg_loss
-            logger.debug("build loss's regularizer ({}) success..".format(
-                reg_loss))
-        # step6 build optimizer for loss
-        optim_name = config_loss.pop('name')
-        if 'clip_norm' in config_loss:
-            clip_norm = config_loss.pop('clip_norm')
-            grad_clip_loss = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
-        else:
-            grad_clip_loss = None
-        optim_loss = getattr(optimizer, optim_name)(
-            learning_rate=lr_loss, grad_clip=grad_clip_loss,
-            **config_loss)(model_list=model_list[1:2])
-
-        optim = [optim_model, optim_loss]
-        lr = [lr_model, lr_loss]
-        logger.debug("build loss's optimizer ({}) success..".format(optim))
-    return optim, lr
+            **optim_cfg)(model_list=optim_model)
+        logger.info("build optimizer ({}) for scope ({}) success..".format(
+            optim.__class__.__name__, optim_scope))
+        optim_list.append(optim)
+        lr_list.append(lr)
+    return optim_list, lr_list

ppcls/optimizer/learning_rate.py

@@ -215,6 +215,7 @@ class Piecewise(object):
                  epochs,
                  warmup_epoch=0,
                  warmup_start_lr=0.0,
+                 warmup_by_epoch=False,
                  last_epoch=-1,
                  **kwargs):
         super().__init__()
@@ -222,27 +223,61 @@ class Piecewise(object):
             msg = f"When using warm up, the value of \"Global.epochs\" must be greater than value of \"Optimizer.lr.warmup_epoch\". The value of \"Optimizer.lr.warmup_epoch\" has been set to {epochs}."
             logger.warning(msg)
             warmup_epoch = epochs
-        self.boundaries = [step_each_epoch * e for e in decay_epochs]
+        self.boundaries_steps = [step_each_epoch * e for e in decay_epochs]
+        self.boundaries_epoch = decay_epochs
         self.values = values
         self.last_epoch = last_epoch
         self.warmup_steps = round(warmup_epoch * step_each_epoch)
+        self.warmup_epoch = warmup_epoch
         self.warmup_start_lr = warmup_start_lr
+        self.warmup_by_epoch = warmup_by_epoch
 
     def __call__(self):
-        learning_rate = lr.PiecewiseDecay(
-            boundaries=self.boundaries,
-            values=self.values,
-            last_epoch=self.last_epoch)
-        if self.warmup_steps > 0:
-            learning_rate = lr.LinearWarmup(
-                learning_rate=learning_rate,
-                warmup_steps=self.warmup_steps,
-                start_lr=self.warmup_start_lr,
-                end_lr=self.values[0],
+        if self.warmup_by_epoch is False:
+            learning_rate = lr.PiecewiseDecay(
+                boundaries=self.boundaries_steps,
+                values=self.values,
                 last_epoch=self.last_epoch)
+            if self.warmup_steps > 0:
+                learning_rate = lr.LinearWarmup(
+                    learning_rate=learning_rate,
+                    warmup_steps=self.warmup_steps,
+                    start_lr=self.warmup_start_lr,
+                    end_lr=self.values[0],
+                    last_epoch=self.last_epoch)
+        else:
+            learning_rate = lr.PiecewiseDecay(
+                boundaries=self.boundaries_epoch,
+                values=self.values,
+                last_epoch=self.last_epoch)
+            if self.warmup_epoch > 0:
+                learning_rate = lr.LinearWarmup(
+                    learning_rate=learning_rate,
+                    warmup_steps=self.warmup_epoch,
+                    start_lr=self.warmup_start_lr,
+                    end_lr=self.values[0],
+                    last_epoch=self.last_epoch)
         return learning_rate
 
 
+class Constant(LRScheduler):
+    """
+    Constant learning rate
+    Args:
+        lr (float): The initial learning rate. It is a python float number.
+        last_epoch (int, optional):  The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
+    """
+
+    def __init__(self, learning_rate, last_epoch=-1, by_epoch=False, **kwargs):
+        self.learning_rate = learning_rate
+        self.last_epoch = last_epoch
+        self.by_epoch = by_epoch
+        super().__init__()
+
+    def get_lr(self):
+        return self.learning_rate
+
+
 class MultiStepDecay(LRScheduler):
     """
     Update the learning rate by ``gamma`` once ``epoch`` reaches one of the milestones.

ppcls/static/train.py

@@ -161,12 +161,22 @@ def main(args):
     # load pretrained models or checkpoints
     init_model(global_config, train_prog, exe)
 
-    if 'AMP' in config and config.AMP.get("level", "O1") == "O2":
+    if 'AMP' in config:
+        if config["AMP"].get("level", "O1").upper() == "O2":
+            use_fp16_test = True
+            msg = "Only support FP16 evaluation when AMP O2 is enabled."
+            logger.warning(msg)
+        elif "use_fp16_test" in config["AMP"]:
+            use_fp16_test = config["AMP"].get["use_fp16_test"]
+        else:
+            use_fp16_test = False
+
         optimizer.amp_init(
             device,
             scope=paddle.static.global_scope(),
             test_program=eval_prog
-            if global_config["eval_during_train"] else None)
+            if global_config["eval_during_train"] else None,
+            use_fp16_test=use_fp16_test)
 
     if not global_config.get("is_distributed", True):
         compiled_train_prog = program.compile(
@@ -182,7 +192,7 @@ def main(args):
         program.run(train_dataloader, exe, compiled_train_prog, train_feeds,
                     train_fetchs, epoch_id, 'train', config, vdl_writer,
                     lr_scheduler, args.profiler_options)
-        # 2. evaate with eval dataset
+        # 2. evaluate with eval dataset
         if global_config["eval_during_train"] and epoch_id % global_config[
                 "eval_interval"] == 0:
             top1_acc = program.run(eval_dataloader, exe, compiled_eval_prog,

ppcls/utils/config.py

@@ -137,9 +137,12 @@ def override(dl, ks, v):
         if len(ks) == 1:
             # assert ks[0] in dl, ('{} is not exist in {}'.format(ks[0], dl))
             if not ks[0] in dl:
-                print('A new filed ({}) detected!'.format(ks[0], dl))
+                print('A new field ({}) detected!'.format(ks[0], dl))
             dl[ks[0]] = str2num(v)
         else:
+            if ks[0] not in dl.keys():
+                dl[ks[0]] = {}
+                print("A new Series field ({}) detected!".format(ks[0], dl))
             override(dl[ks[0]], ks[1:], v)
 
 

ppcls/utils/save_load.py

@@ -18,9 +18,6 @@ from __future__ import print_function
 
 import errno
 import os
-import re
-import shutil
-import tempfile
 
 import paddle
 from ppcls.utils import logger
@@ -47,10 +44,15 @@ def _mkdir_if_not_exist(path):
 
 def load_dygraph_pretrain(model, path=None):
     if not (os.path.isdir(path) or os.path.exists(path + '.pdparams')):
-        raise ValueError("Model pretrain path {} does not "
-                         "exists.".format(path + '.pdparams'))
+        raise ValueError("Model pretrain path {}.pdparams does not "
+                         "exists.".format(path))
     param_state_dict = paddle.load(path + ".pdparams")
-    model.set_dict(param_state_dict)
+    if isinstance(model, list):
+        for m in model:
+            if hasattr(m, 'set_dict'):
+                m.set_dict(param_state_dict)
+    else:
+        model.set_dict(param_state_dict)
     return
 
 
@@ -85,7 +87,7 @@ def load_distillation_model(model, pretrained_model):
             pretrained_model))
 
 
-def init_model(config, net, optimizer=None):
+def init_model(config, net, optimizer=None, loss: paddle.nn.Layer=None):
     """
     load model from checkpoint or pretrained_model
     """
@@ -95,10 +97,13 @@ def init_model(config, net, optimizer=None):
             "Given dir {}.pdparams not exist.".format(checkpoints)
         assert os.path.exists(checkpoints + ".pdopt"), \
             "Given dir {}.pdopt not exist.".format(checkpoints)
-        para_dict = paddle.load(checkpoints + ".pdparams")
+        # load state dict
         opti_dict = paddle.load(checkpoints + ".pdopt")
+        para_dict = paddle.load(checkpoints + ".pdparams")
         metric_dict = paddle.load(checkpoints + ".pdstates")
-        net.set_dict(para_dict)
+        # set state dict
+        net.set_state_dict(para_dict)
+        loss.set_state_dict(para_dict)
         for i in range(len(optimizer)):
             optimizer[i].set_state_dict(opti_dict)
         logger.info("Finish load checkpoints from {}".format(checkpoints))
@@ -121,7 +126,8 @@ def save_model(net,
                metric_info,
                model_path,
                model_name="",
-               prefix='ppcls'):
+               prefix='ppcls',
+               loss: paddle.nn.Layer=None):
     """
     save model to the target path
     """
@@ -131,7 +137,14 @@ def save_model(net,
     _mkdir_if_not_exist(model_path)
     model_path = os.path.join(model_path, prefix)
 
-    paddle.save(net.state_dict(), model_path + ".pdparams")
+    params_state_dict = net.state_dict()
+    loss_state_dict = loss.state_dict()
+    keys_inter = set(params_state_dict.keys()) & set(loss_state_dict.keys())
+    assert len(keys_inter) == 0, \
+        f"keys in model and loss state_dict must be unique, but got intersection {keys_inter}"
+    params_state_dict.update(loss_state_dict)
+
+    paddle.save(params_state_dict, model_path + ".pdparams")
     paddle.save([opt.state_dict() for opt in optimizer], model_path + ".pdopt")
     paddle.save(metric_info, model_path + ".pdstates")
     logger.info("Already save model in {}".format(model_path))

test_tipc/common_func.sh

@@ -16,6 +16,14 @@ function func_parser_value(){
     echo ${tmp}
 }
 
+function func_parser_value_lite(){
+    strs=$1
+    IFS=$2
+    array=(${strs})
+    tmp=${array[1]}
+    echo ${tmp}
+}
+
 function func_set_params(){
     key=$1
     value=$2

test_tipc/config/AlexNet/AlexNet_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:AlexNet
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/AlexNet/AlexNet.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/AlexNet/AlexNet.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/AlexNet/AlexNet.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/AlexNet_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/CSPNet/CSPDarkNet53_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:CSPDarkNet53
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/CSPNet/CSPDarkNet53.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/CSPDarkNet53_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=288 -o PreProcess.transform_ops.1.CropImage.size=256
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/CSWinTransformer/CSWinTransformer_base_224_train_infer_python.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:32
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/CSWinTransformer/CSWinTransformer_base_384_train_infer_python.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:32
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/CSWinTransformer/CSWinTransformer_large_224_train_infer_python.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:32
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/CSWinTransformer/CSWinTransformer_large_384_train_infer_python.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:8
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/CSWinTransformer/CSWinTransformer_small_224_train_infer_python.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:64
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/CSWinTransformer/CSWinTransformer_tiny_224_train_infer_python.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/DLA/DLA102_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA102
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA102.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA102.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA102.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA102x2_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA102x2
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA102x2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA102x2.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA102x2.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x2_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA102x_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA102x
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA102x.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA102x.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA102x.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA102x_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA169_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA169
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA169.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA169.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA169.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA169_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA34_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA34
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA34.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA34.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA34.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA34_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA46_c_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA46_c
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA46_c.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA46_c.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA46_c.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46_c_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA46x_c_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA46x_c
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA46x_c.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA46x_c.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA46x_c.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA46x_c_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA60_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA60
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA60.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA60.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA60.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA60x_c_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA60x_c
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA60x_c.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA60x_c.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA60x_c.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_c_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DLA/DLA60x_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DLA60x
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DLA/DLA60x.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DLA/DLA60x.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DLA/DLA60x.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DLA60x_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DPN/DPN107_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DPN107
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DPN/DPN107.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DPN/DPN107.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DPN/DPN107.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN107_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DPN/DPN131_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DPN131
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DPN/DPN131.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DPN/DPN131.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DPN/DPN131.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN131_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DPN/DPN68_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DPN68
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DPN/DPN68.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DPN/DPN68.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DPN/DPN68.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN68_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DPN/DPN92_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DPN92
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DPN/DPN92.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DPN/DPN92.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DPN/DPN92.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN92_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DPN/DPN98_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DPN98
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DPN/DPN98.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DPN/DPN98.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DPN/DPN98.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DPN98_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DarkNet/DarkNet53_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DarkNet53
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DarkNet/DarkNet53.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=292 -o PreProcess.transform_ops.1.CropImage.size=256
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DeiT/DeiT_base_patch16_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DeiT_base_patch16_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DeiT/DeiT_base_patch16_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DeiT_base_patch16_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DeiT/DeiT_base_patch16_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_base_patch16_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384 
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DeiT/DeiT_small_patch16_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DeiT_small_patch16_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DeiT/DeiT_small_patch16_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_small_patch16_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DeiT/DeiT_tiny_patch16_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DeiT_tiny_patch16_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DeiT/DeiT_tiny_patch16_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DeiT_tiny_patch16_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DenseNet/DenseNet121_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DenseNet121
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DenseNet/DenseNet121.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet121_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DenseNet/DenseNet161_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DenseNet161
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DenseNet/DenseNet161.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet161_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DenseNet/DenseNet169_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DenseNet169
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DenseNet/DenseNet169.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet169_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DenseNet/DenseNet201_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DenseNet201
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DenseNet/DenseNet201.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet201_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/DenseNet/DenseNet264_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:DenseNet264
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/DenseNet/DenseNet264.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DenseNet264_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ESNet/ESNet_x0_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ESNet_x0_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ESNet/ESNet_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ESNet_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ESNet/ESNet_x0_75_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ESNet_x0_75
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ESNet/ESNet_x0_75.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x0_75_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ESNet/ESNet_x1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ESNet_x1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ESNet/ESNet_x1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ESNet_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB1_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB1
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB1.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB1_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=272 -o PreProcess.transform_ops.1.CropImage.size=240
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB2_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB2
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB2.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB2_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=292 -o PreProcess.transform_ops.1.CropImage.size=260
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB3_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB3
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB3.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB3_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=332 -o PreProcess.transform_ops.1.CropImage.size=300
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB4_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB4
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB4.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB4_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=412 -o PreProcess.transform_ops.1.CropImage.size=380
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=488 -o PreProcess.transform_ops.1.CropImage.size=456
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB6_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB6
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB6.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB6_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=560 -o PreProcess.transform_ops.1.CropImage.size=528
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/EfficientNet/EfficientNetB7_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:EfficientNetB7
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/EfficientNet/EfficientNetB7.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/EfficientNetB7_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml  -o PreProcess.transform_ops.0.ResizeImage.resize_short=632 -o PreProcess.transform_ops.1.CropImage.size=600
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:GeneralRecognition_PPLCNet_x2_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_rec.py -c configs/inference_rec.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.rec_inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/GhostNet/GhostNet_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:GhostNet_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/GhostNet/GhostNet_x1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:GhostNet_x1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/GhostNet/GhostNet_x1_3_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:GhostNet_x1_3
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/GhostNet/GhostNet_x1_3.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W18_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W18_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W18_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W30_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W30_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W30_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W30_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W32_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W32_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W32_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W32_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W40_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W40_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W40_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W40_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W44_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W44_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W44_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W44_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W48_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W48_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W48_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HRNet/HRNet_W64_C_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HRNet_W64_C
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HRNet/HRNet_W64_C.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W64_C_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HarDNet/HarDNet39_ds_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HarDNet39_ds
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HarDNet/HarDNet39_ds.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet39_ds_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HarDNet/HarDNet68_ds_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HarDNet68_ds
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HarDNet/HarDNet68_ds.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_ds_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HarDNet/HarDNet68_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HarDNet68
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HarDNet/HarDNet68.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet68_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/HarDNet/HarDNet85_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:HarDNet85
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/HarDNet/HarDNet85.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/HarDNet85_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Inception/GoogLeNet_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:GoogLeNet
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Inception/GoogLeNet.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Inception/GoogLeNet.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Inception/GoogLeNet.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GoogLeNet_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Inception/InceptionV3_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:InceptionV3
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Inception/InceptionV3.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Inception/InceptionV3.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Inception/InceptionV3.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV3_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml  -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Inception/InceptionV4_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:InceptionV4
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Inception/InceptionV4.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Inception/InceptionV4.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Inception/InceptionV4.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/InceptionV4_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml  -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/LeViT/LeViT_128S_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:LeViT_128S
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/LeViT/LeViT_128S.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128S_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/LeViT/LeViT_128_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:LeViT_128
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/LeViT/LeViT_128.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/LeViT/LeViT_128.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/LeViT/LeViT_128.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_128_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/LeViT/LeViT_192_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:LeViT_192
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/LeViT/LeViT_192.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/LeViT/LeViT_192.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/LeViT/LeViT_192.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_192_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/LeViT/LeViT_256_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:LeViT_256
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/LeViT/LeViT_256.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/LeViT/LeViT_256.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/LeViT/LeViT_256.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_256_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/LeViT/LeViT_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:LeViT_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/LeViT/LeViT_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/LeViT/LeViT_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/LeViT/LeViT_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/LeViT_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=256 -o PreProcess.transform_ops.1.CropImage.size=224
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MixNet/MixNet_L_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MixNet_L
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MixNet/MixNet_L.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MixNet/MixNet_L.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_L_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MixNet/MixNet_M_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MixNet_M
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MixNet/MixNet_M.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MixNet/MixNet_M.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_M_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MixNet/MixNet_S_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MixNet_S
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MixNet/MixNet_S.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MixNet/MixNet_S.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MixNet_S_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV1/MobileNetV1_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV1
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+to_static_train:-o Global.to_static=True
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV1/MobileNetV1_x0_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV1_x0_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV1/MobileNetV1_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV1_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV1/MobileNetV1_x0_75_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV1_x0_75
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV1/MobileNetV1_x0_75.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_x0_75_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV2/MobileNetV2_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV2
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+to_static_train:-o Global.to_static=True
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV2/MobileNetV2_x0_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV2_x0_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV2/MobileNetV2_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV2_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV2/MobileNetV2_x0_75_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV2_x0_75
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x0_75.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x0_75_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV2/MobileNetV2_x1_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV2_x1_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x1_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x1_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV2/MobileNetV2_x2_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV2_x2_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV2/MobileNetV2_x2_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_x2_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x0_35_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x0_35
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_35.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_35_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x0_75_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x0_75
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x0_75.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x0_75_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_FPGM_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x1_0_FPGM
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./inference
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_PACT_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x1_0_PACT
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./inference
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:MobileNetV3_large_x1_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./inference
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_large_x1_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_large_x1_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_small_x0_35_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_small_x0_35
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_35.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_small_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_small_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_small_x0_75_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_small_x0_75
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x0_75.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_75_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_small_x1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_small_x1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileNetV3/MobileNetV3_small_x1_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:MobileNetV3_small_x1_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_small_x1_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/MobileViT/MobileViT_S_train_infer_python.txt

+===========================train_params===========================
+model_name:MobileViT_S
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_S.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=292 -o PreProcess.transform_ops.1.CropImage.size=256 -o PreProcess.transform_ops.2.NormalizeImage.mean=[0.,0.,0.] -o PreProcess.transform_ops.2.NormalizeImage.std=[1.,1.,1.]
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/MobileViT/MobileViT_XS_train_infer_python.txt

+===========================train_params===========================
+model_name:MobileViT_XS
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_XS.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=292 -o PreProcess.transform_ops.1.CropImage.size=256 -o PreProcess.transform_ops.2.NormalizeImage.mean=[0.,0.,0.] -o PreProcess.transform_ops.2.NormalizeImage.std=[1.,1.,1.]
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/MobileViT/MobileViT_XXS_train_infer_python.txt

+===========================train_params===========================
+model_name:MobileViT_XXS
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileViT/MobileViT_XXS.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=292 -o PreProcess.transform_ops.1.CropImage.size=256 -o PreProcess.transform_ops.2.NormalizeImage.mean=[0.,0.,0.] -o PreProcess.transform_ops.2.NormalizeImage.std=[1.,1.,1.]
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PPLCNet/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:MobileNetV3_large_x1_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_25_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x0_25
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x0_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x0_35_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x0_35
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x0_5_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x0_5
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x0_75_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x0_75
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x0_75_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x0_75
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x1_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x1_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x1_5_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x1_5
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x1_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x1_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x2_0_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x2_0
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x2_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x2_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PPLCNet/PPLCNet_x2_5_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:PPLCNet_x2_5
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/PPLCNet/PPLCNet_x2_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNet_x2_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null

test_tipc/config/PVTV2/PVT_V2_B0.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PVTV2/PVT_V2_B1.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PVTV2/PVT_V2_B2.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PVTV2/PVT_V2_B2_Linear.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PVTV2/PVT_V2_B3.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PVTV2/PVT_V2_B4.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/PVTV2/PVT_V2_B5.txt

@@ -50,3 +50,9 @@ inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.tran
 -o Global.benchmark:True
 null:null
 null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/ReXNet/ReXNet_1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ReXNet_1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ReXNet/ReXNet_1_3_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ReXNet_1_3
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_3.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_3_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ReXNet/ReXNet_1_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ReXNet_1_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_1_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_1_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ReXNet/ReXNet_2_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ReXNet_2_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_2_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_2_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ReXNet/ReXNet_3_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ReXNet_3_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ReXNet/ReXNet_3_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ReXNet_3_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/RedNet/RedNet101_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:RedNet101
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/RedNet/RedNet101.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/RedNet/RedNet101.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/RedNet/RedNet101.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet101_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/RedNet/RedNet152_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:RedNet152
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/RedNet/RedNet152.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/RedNet/RedNet152.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/RedNet/RedNet152.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet152_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/RedNet/RedNet26_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:RedNet26
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/RedNet/RedNet26.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/RedNet/RedNet26.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/RedNet/RedNet26.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet26_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/RedNet/RedNet38_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:RedNet38
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/RedNet/RedNet38.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/RedNet/RedNet38.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/RedNet/RedNet38.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet38_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/RedNet/RedNet50_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:RedNet50
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/RedNet/RedNet50.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/RedNet/RedNet50.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/RedNet/RedNet50.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/RedNet50_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Res2Net/Res2Net101_vd_26w_4s_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Res2Net101_vd_26w_4s
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Res2Net/Res2Net101_vd_26w_4s.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Res2Net/Res2Net200_vd_26w_4s_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Res2Net200_vd_26w_4s
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Res2Net/Res2Net200_vd_26w_4s.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Res2Net/Res2Net50_14w_8s_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Res2Net50_14w_8s
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_14w_8s.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_14w_8s_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Res2Net/Res2Net50_26w_4s_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Res2Net50_26w_4s
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_26w_4s.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_26w_4s_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Res2Net/Res2Net50_vd_26w_4s_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Res2Net50_vd_26w_4s
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Res2Net/Res2Net50_vd_26w_4s.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeSt/ResNeSt50_fast_1s1x64d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeSt50_fast_1s1x64d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeSt/ResNeSt50_fast_1s1x64d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_fast_1s1x64d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeSt/ResNeSt50_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeSt50
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeSt/ResNeSt50.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeSt50_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt101_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt101_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt101_64x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt101_64x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_64x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_64x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt101_vd_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt101_vd_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt101_vd_64x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt101_vd_64x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_vd_64x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_vd_64x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt152_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt101_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt101_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt101_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt152_64x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt152_64x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_64x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_64x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt152_vd_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt152_vd_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt152_vd_64x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt152_vd_64x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt152_vd_64x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt152_vd_64x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt50_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt50_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt50_64x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt50_64x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_64x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_64x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt50_vd_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt50_vd_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNeXt/ResNeXt50_vd_64x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNeXt50_vd_64x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNeXt/ResNeXt50_vd_64x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNeXt50_vd_64x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet101_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet101
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet101.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet101.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet101.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet101_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet101_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet101_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet152_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet152
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet152.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+to_static_train:-o Global.to_static=True
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet152.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet152.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet152_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet152_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet152_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet152_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet18_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet18
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet18.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet18.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet18.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet18_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet18_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet18_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet18_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet200_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet200_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet200_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet200_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet34_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet34
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet34.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet34.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet34.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet34_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet34_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet34_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet50_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:ResNet50
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/ResNet/ResNet50_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet50
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+to_static_train:-o Global.to_static=True
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet50_vd_FPGM_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet50_vd_FPGM
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.save_inference_dir=./inference
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet50_vd_PACT_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet50_vd_PACT
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.save_inference_dir=./inference
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ResNet/ResNet50_vd_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:ResNet50_vd
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/ResNet/ResNet50_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ResNet50_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml
+quant_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_quantization.yaml
+fpgm_export:tools/export_model.py -c ppcls/configs/slim/ResNet50_vd_prune.yaml
+distill_export:null
+kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50_vd.yaml -o Global.save_inference_dir=./inference
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SENet154_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SENet154_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SENet154_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SENet154_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SENet154_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SENet154_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SE_ResNeXt101_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SE_ResNeXt101_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt101_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt101_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SE_ResNeXt50_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SE_ResNeXt50_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt50_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SE_ResNeXt50_vd_32x4d_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SE_ResNeXt50_vd_32x4d
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SE_ResNeXt50_vd_32x4d.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNeXt50_vd_32x4d_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SE_ResNet18_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SE_ResNet18_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SE_ResNet18_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet18_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SE_ResNet34_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SE_ResNet34_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SE_ResNet34_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet34_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SENet/SE_ResNet50_vd_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SE_ResNet50_vd
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SENet/SE_ResNet50_vd.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SE_ResNet50_vd_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_swish_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_swish
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_swish.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_swish_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_x0_25_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_x0_25
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_25.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_x0_33_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_x0_33
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_33.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_33_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_x0_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_x0_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x0_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_x1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_x1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_x1_5_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_x1_5
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x1_5.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x1_5_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/ShuffleNet/ShuffleNetV2_x2_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ShuffleNetV2_x2_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ShuffleNet/ShuffleNetV2_x2_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x2_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SqueezeNet/SqueezeNet1_0_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SqueezeNet1_0
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_0.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_0_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SqueezeNet/SqueezeNet1_1_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SqueezeNet1_1
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SqueezeNet/SqueezeNet1_1.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SqueezeNet1_1_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SwinTransformer/SwinTransformer_base_patch4_window12_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_base_patch4_window12_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window12_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window12_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SwinTransformer/SwinTransformer_base_patch4_window7_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_base_patch4_window7_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_base_patch4_window7_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_base_patch4_window7_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window12_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_large_patch4_window12_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window12_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window12_384_22kto1k_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window7_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_large_patch4_window7_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_large_patch4_window7_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_large_patch4_window7_224_22kto1k_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SwinTransformer/SwinTransformer_small_patch4_window7_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_small_patch4_window7_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_small_patch4_window7_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_small_patch4_window7_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_lite_arm_cpu_cpp.txt

+runtime_device:arm_cpu
+lite_arm_work_path:/data/local/tmp/arm_cpu/
+lite_arm_so_path:inference_lite_lib.android.armv8/cxx/lib/libpaddle_light_api_shared.so
+clas_model_file:SwinTransformer_tiny_patch4_window7_224
+inference_cmd:clas_system config.txt tabby_cat.jpg
+--num_threads_list:1
+--batch_size_list:1
+--precision_list:FP32

test_tipc/config/SwinTransformer/SwinTransformer_tiny_patch4_window7_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:SwinTransformer_tiny_patch4_window7_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/SwinTransformer/SwinTransformer_tiny_patch4_window7_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/SwinTransformer_tiny_patch4_window7_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/TNT/TNT_small_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:TNT_small
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/TNT/TNT_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/TNT/TNT_small.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/TNT/TNT_small.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Twins/alt_gvt_base_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:alt_gvt_base
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Twins/alt_gvt_base.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_base_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Twins/alt_gvt_large_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:alt_gvt_large
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Twins/alt_gvt_large.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_large_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Twins/alt_gvt_small_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:alt_gvt_small
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Twins/alt_gvt_small.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/alt_gvt_small_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Twins/pcpvt_base_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:pcpvt_base
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Twins/pcpvt_base.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Twins/pcpvt_base.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Twins/pcpvt_base.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_base_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Twins/pcpvt_large_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:pcpvt_large
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Twins/pcpvt_large.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Twins/pcpvt_large.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Twins/pcpvt_large.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_large_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Twins/pcpvt_small_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:pcpvt_small
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Twins/pcpvt_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Twins/pcpvt_small.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Twins/pcpvt_small.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/pcpvt_small_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VAN/VAN_tiny.txt

+===========================train_params===========================
+model_name:VAN_tiny
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/VAN/VAN_tiny.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VAN/VAN_tiny.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VAN/VAN_tiny.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+inference_dir:null
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=248 -o PreProcess.transform_ops.2.NormalizeImage.mean=[0.5,0.5,0.5] -o PreProcess.transform_ops.2.NormalizeImage.std=[0.5,0.5,0.5]
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null
+===========================train_benchmark_params==========================
+batch_size:128
+fp_items:fp32
+epoch:1
+--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
+flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096

test_tipc/config/VGG/VGG11_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:VGG11
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VGG/VGG11.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VGG/VGG11.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VGG/VGG11.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG11_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VGG/VGG13_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:VGG13
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VGG/VGG13.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VGG/VGG13.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VGG/VGG13.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG13_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VGG/VGG16_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:VGG16
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VGG/VGG16.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VGG/VGG16.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VGG/VGG16.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG16_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VGG/VGG19_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:VGG19
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VGG/VGG19.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VGG/VGG19.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VGG/VGG19.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/VGG19_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_base_patch16_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_base_patch16_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_base_patch16_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_base_patch16_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch16_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch16_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_base_patch32_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_base_patch32_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_base_patch32_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_base_patch32_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_large_patch16_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_large_patch16_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_large_patch16_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_large_patch16_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch16_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch16_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_large_patch32_384_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_large_patch32_384
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:2
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_large_patch32_384.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_large_patch32_384_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=384 -o PreProcess.transform_ops.1.CropImage.size=384
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/VisionTransformer/ViT_small_patch16_224_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:ViT_small_patch16_224
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/VisionTransformer/ViT_small_patch16_224.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ViT_small_patch16_224_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Xception/Xception41_deeplab_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Xception41_deeplab
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Xception/Xception41_deeplab.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_deeplab_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Xception/Xception41_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Xception41
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Xception/Xception41.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Xception/Xception41.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Xception/Xception41.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception41_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Xception/Xception65_deeplab_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Xception65_deeplab
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Xception/Xception65_deeplab.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_deeplab_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Xception/Xception65_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Xception65
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Xception/Xception65.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Xception/Xception65.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Xception/Xception65.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception65_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/config/Xception/Xception71_train_amp_infer_python.txt

+===========================train_params===========================
+model_name:Xception71
+python:python3.7
+gpu_list:0|0,1
+-o Global.device:gpu
+-o Global.auto_cast:null
+-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
+-o Global.output_dir:./output/
+-o DataLoader.Train.sampler.batch_size:8
+-o Global.pretrained_model:null
+train_model_name:latest
+train_infer_img_dir:./dataset/ILSVRC2012/val
+null:null
+##
+trainer:amp_train
+amp_train:tools/train.py -c ppcls/configs/ImageNet/Xception/Xception71.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/Xception/Xception71.yaml
+null:null
+##
+===========================infer_params==========================
+-o Global.save_inference_dir:./inference
+-o Global.pretrained_model:
+norm_export:tools/export_model.py -c ppcls/configs/ImageNet/Xception/Xception71.yaml
+quant_export:null
+fpgm_export:null
+distill_export:null
+kl_quant:null
+export2:null
+pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Xception71_pretrained.pdparams
+infer_model:../inference/
+infer_export:True
+infer_quant:Fasle
+inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=320 -o PreProcess.transform_ops.1.CropImage.size=299
+-o Global.use_gpu:True|False
+-o Global.enable_mkldnn:True|False
+-o Global.cpu_num_threads:1|6
+-o Global.batch_size:1|16
+-o Global.use_tensorrt:True|False
+-o Global.use_fp16:True|False
+-o Global.inference_model_dir:../inference
+-o Global.infer_imgs:../dataset/ILSVRC2012/val
+-o Global.save_log_path:null
+-o Global.benchmark:True
+null:null
+null:null

test_tipc/docs/test_lite_arm_cpu_cpp.md

+# Lite_arm_cpp_cpu 预测功能测试
+
+Lite_arm_cpp_cpu 预测功能测试的主程序为`test_lite_arm_cpu_cpp.sh`，可以测试基于 Paddle-Lite 预测库的模型推理功能。
+
+## 1. 测试结论汇总
+
+| 模型类型 |device | batchsize | 精度类型| 线程数 |
+|  :----:   |  :----: |   :----:   |  :----:  | :----: |
+| 正常模型 | arm_cpu | 1 | FP32 | 1 |
+
+## 2. 测试流程
+运行环境配置请参考[文档](https://github.com/PaddlePaddle/models/blob/release/2.2/tutorials/mobilenetv3_prod/Step6/deploy/lite_infer_cpp_arm_cpu/README.md) 的内容配置 TIPC Lite 的运行环境。
+
+### 2.1 功能测试
+先运行 `prepare_lite_arm_cpu_cpp.sh` 准备数据和模型，然后运行 `test_lite_arm_cpu_cpp.sh` 进行测试，最终在 `./output` 目录下生成 `lite_*.log` 后缀的日志文件。
+
+```shell
+bash test_tipc/prepare_lite_arm_cpu_cpp.sh test_tipc/config/MobileNetV3/MobileNetV3_large_x1_0_lite_arm_cpu_cpp.txt
+```
+
+运行预测指令后，在`./output`文件夹下自动会保存运行日志，包括以下文件：
+
+```shell
+test_tipc/output/
+|- results.log    # 运行指令状态的日志
+|- lite_MobileNetV3_large_x1_0_runtime_device_arm_cpu_precision_FP32_batchsize_1_threads_1.log  # ARM_CPU 上 FP32 状态下，线程数设置为1，测试batch_size=1条件下的预测运行日志
+......
+```
+其中results.log中包含了每条指令的运行状态，如果运行成功会输出：
+
+```
+Run successfully with command - adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/mobilenet_v3 /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/demo.jpg'  > ./output/lite_MobileNetV3_large_x1_0_runtime_device_arm_cpu_precision_FP32_batchsize_1_threads_1.log 2>&1!
+......
+```
+如果运行失败，会输出：
+```
+Run failed with command - adb shell 'export LD_LIBRARY_PATH=/data/local/tmp/arm_cpu/; /data/local/tmp/arm_cpu/mobilenet_v3 /data/local/tmp/arm_cpu/config.txt /data/local/tmp/arm_cpu/demo.jpg'  > ./output/lite_MobileNetV3_large_x1_0_runtime_device_arm_cpu_precision_FP32_batchsize_1_threads_1.log 2>&1!
+......
+```
+可以很方便的根据results.log中的内容判定哪一个指令运行错误。
+
+## 3. 更多教程
+
+本文档为功能测试用，更详细的 Lite 预测使用教程请参考：[PaddleLite 推理部署](../../docs/zh_CN/inference_deployment/paddle_lite_deploy.md)  。

test_tipc/prepare_lite_arm_cpu_cpp.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+
+BASIC_CONFIG="./config.txt"
+CONFIG=$1
+
+# parser tipc config
+IFS=$'\n'
+TIPC_CONFIG=$1
+tipc_dataline=$(cat $TIPC_CONFIG)
+tipc_lines=(${tipc_dataline})
+
+runtime_device=$(func_parser_value_lite "${tipc_lines[0]}" ":")
+lite_arm_work_path=$(func_parser_value_lite "${tipc_lines[1]}" ":")
+lite_arm_so_path=$(func_parser_value_lite "${tipc_lines[2]}" ":")
+clas_model_name=$(func_parser_value_lite "${tipc_lines[3]}" ":")
+inference_cmd=$(func_parser_value_lite "${tipc_lines[4]}" ":")
+num_threads_list=$(func_parser_value_lite "${tipc_lines[5]}" ":")
+batch_size_list=$(func_parser_value_lite "${tipc_lines[6]}" ":")
+precision_list=$(func_parser_value_lite "${tipc_lines[7]}" ":")
+
+
+# Prepare config and test.sh
+work_path="./deploy/lite"
+cp ${CONFIG} ${work_path}
+cp test_tipc/test_lite_arm_cpu_cpp.sh ${work_path}
+
+# Prepare model
+cd ${work_path}
+pip3 install paddlelite==2.10
+model_url="https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/${clas_model_name}_infer.tar"
+wget --no-proxy ${model_url}
+model_tar=$(echo ${model_url} | awk -F "/" '{print $NF}')
+tar -xf ${model_tar}
+paddle_lite_opt --model_dir=${clas_model_name}_infer --model_file=${clas_model_name}_infer/inference.pdmodel --param_file=${clas_model_name}_infer/inference.pdiparams --valid_targets=arm --optimize_out=${clas_model_name}
+rm -rf ${clas_model_name}_infer*
+
+# Prepare paddlelite library
+paddlelite_lib_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10/inference_lite_lib.android.armv8.clang.c++_static.with_extra.with_cv.tar.gz"
+wget ${paddlelite_lib_url}
+paddlelite_lib_file=$(echo ${paddlelite_lib_url} | awk -F "/" '{print $NF}')
+tar -xzf ${paddlelite_lib_file}
+mv ${paddlelite_lib_file%*.tar.gz} inference_lite_lib.android.armv8
+rm -rf ${paddlelite_lib_file%*.tar.gz}*
+
+# Compile and obtain executable binary file
+git clone https://github.com/LDOUBLEV/AutoLog.git
+make
+
+# push executable binary, library, lite model, data, etc. to arm device
+adb shell mkdir -p ${lite_arm_work_path}
+adb push $(echo ${inference_cmd} | awk '{print $1}') ${lite_arm_work_path}
+adb shell chmod +x ${lite_arm_work_path}/$(echo ${inference_cmd} | awk '{print $1}')
+adb push ${lite_arm_so_path} ${lite_arm_work_path}
+adb push ${clas_model_name}.nb ${lite_arm_work_path}
+adb push ${BASIC_CONFIG} ${lite_arm_work_path}
+adb push ../../ppcls/utils/imagenet1k_label_list.txt ${lite_arm_work_path}
+adb push imgs/$(echo ${inference_cmd} | awk '{print $3}') ${lite_arm_work_path}

test_tipc/prepare_lite_cpp.sh

-#!/bin/bash
-source ./test_tipc/common_func.sh
-FILENAME=$1
-dataline=$(cat ${FILENAME})
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-IFS=$'\n'
-
-inference_cmd=$(func_parser_value "${lines[1]}")
-DEVICE=$(func_parser_value "${lines[2]}")
-det_lite_model_list=$(func_parser_value "${lines[3]}")
-rec_lite_model_list=$(func_parser_value "${lines[4]}")
-cls_lite_model_list=$(func_parser_value "${lines[5]}")
-
-if [[ $inference_cmd =~ "det" ]];then
-    lite_model_list=${det_lite_model_list}
-elif [[ $inference_cmd =~ "rec" ]];then
-    lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-elif [[ $inference_cmd =~ "system" ]];then
-    lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-else
-    echo "inference_cmd is wrong, please check."
-    exit 1
-fi
-
-if [ ${DEVICE} = "ARM_CPU" ];then
-    valid_targets="arm"
-    paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.android.armv8.gcc.c++_shared.with_extra.with_cv.tar.gz"
-    end_index="66"
-elif [ ${DEVICE} = "ARM_GPU_OPENCL" ];then
-    valid_targets="opencl"
-    paddlelite_url="https://github.com/PaddlePaddle/Paddle-Lite/releases/download/v2.10-rc/inference_lite_lib.armv8.clang.with_exception.with_extra.with_cv.opencl.tar.gz"
-    end_index="71"
-else
-    echo "DEVICE only suport ARM_CPU, ARM_GPU_OPENCL."
-    exit 2    
-fi
-
-# prepare lite .nb model
-pip install paddlelite==2.10-rc
-current_dir=${PWD}
-IFS="|"
-model_path=./inference_models
-
-for model in ${lite_model_list[*]}; do
-    if [[ $model =~ "PP-OCRv2" ]];then
-        inference_model_url=https://paddleocr.bj.bcebos.com/PP-OCRv2/chinese/${model}.tar
-    elif [[ $model =~ "v2.0" ]];then
-        inference_model_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/ch/${model}.tar
-    else 
-        echo "Model is wrong, please check."
-        exit 3
-    fi
-    inference_model=${inference_model_url##*/}
-    wget -nc  -P ${model_path} ${inference_model_url}
-    cd ${model_path} && tar -xf ${inference_model} && cd ../
-    model_dir=${model_path}/${inference_model%.*}
-    model_file=${model_dir}/inference.pdmodel
-    param_file=${model_dir}/inference.pdiparams
-    paddle_lite_opt --model_dir=${model_dir} --model_file=${model_file} --param_file=${param_file} --valid_targets=${valid_targets} --optimize_out=${model_dir}_opt
-done
-
-# prepare test data
-data_url=https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015_lite.tar
-model_path=./inference_models
-inference_model=${inference_model_url##*/}
-data_file=${data_url##*/}
-wget -nc  -P ./inference_models ${inference_model_url}
-wget -nc  -P ./test_data ${data_url}
-cd ./inference_models && tar -xf ${inference_model} && cd ../
-cd ./test_data && tar -xf ${data_file} && rm ${data_file} && cd ../
-
-# prepare lite env
-paddlelite_zipfile=$(echo $paddlelite_url | awk -F "/" '{print $NF}')
-paddlelite_file=${paddlelite_zipfile:0:${end_index}}
-wget ${paddlelite_url} && tar -xf ${paddlelite_zipfile}
-mkdir -p  ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp -r ${model_path}/*_opt.nb test_data ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp ppocr/utils/ppocr_keys_v1.txt deploy/lite/config.txt ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp -r ./deploy/lite/* ${paddlelite_file}/demo/cxx/ocr/
-cp ${paddlelite_file}/cxx/lib/libpaddle_light_api_shared.so ${paddlelite_file}/demo/cxx/ocr/test_lite
-cp ${FILENAME} test_tipc/test_lite_arm_cpp.sh test_tipc/common_func.sh ${paddlelite_file}/demo/cxx/ocr/test_lite
-cd ${paddlelite_file}/demo/cxx/ocr/
-git clone https://github.com/cuicheng01/AutoLog.git
-
-# make
-make -j
-sleep 1
-make -j
-cp ocr_db_crnn test_lite && cp test_lite/libpaddle_light_api_shared.so test_lite/libc++_shared.so
-tar -cf test_lite.tar ./test_lite && cp test_lite.tar ${current_dir} && cd ${current_dir}
-rm -rf ${paddlelite_file}* && rm -rf ${model_path}

test_tipc/test_lite_arm_cpp.sh

-#!/bin/bash
-source ./common_func.sh
-export LD_LIBRARY_PATH=${PWD}:$LD_LIBRARY_PATH
-
-FILENAME=$1
-dataline=$(cat $FILENAME)
-# parser params
-IFS=$'\n'
-lines=(${dataline})
-
-# parser lite inference
-inference_cmd=$(func_parser_value "${lines[1]}")
-runtime_device=$(func_parser_value "${lines[2]}")
-det_model_list=$(func_parser_value "${lines[3]}")
-rec_model_list=$(func_parser_value "${lines[4]}")
-cls_model_list=$(func_parser_value "${lines[5]}")
-cpu_threads_list=$(func_parser_value "${lines[6]}")
-det_batch_size_list=$(func_parser_value "${lines[7]}")
-rec_batch_size_list=$(func_parser_value "${lines[8]}")
-infer_img_dir_list=$(func_parser_value "${lines[9]}")
-config_dir=$(func_parser_value "${lines[10]}")
-rec_dict_dir=$(func_parser_value "${lines[11]}")
-benchmark_value=$(func_parser_value "${lines[12]}")
-
-if [[ $inference_cmd =~ "det" ]]; then
-    lite_model_list=${det_lite_model_list}
-elif [[ $inference_cmd =~ "rec" ]]; then
-    lite_model_list=(${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-elif [[ $inference_cmd =~ "system" ]]; then
-    lite_model_list=(${det_lite_model_list[*]} ${rec_lite_model_list[*]} ${cls_lite_model_list[*]})
-else
-    echo "inference_cmd is wrong, please check."
-    exit 1
-fi
-
-LOG_PATH="./output"
-mkdir -p ${LOG_PATH}
-status_log="${LOG_PATH}/results.log"
-
-
-function func_test_det(){
-    IFS='|'
-    _script=$1
-    _det_model=$2
-    _log_path=$3
-    _img_dir=$4
-    _config=$5
-    if [[ $_det_model =~ "slim" ]]; then
-        precision="INT8"
-    else
-        precision="FP32"
-    fi
-
-    # lite inference
-    for num_threads in ${cpu_threads_list[*]}; do
-	for det_batchsize in ${det_batch_size_list[*]}; do
-            _save_log_path="${_log_path}/lite_${_det_model}_runtime_device_${runtime_device}_precision_${precision}_det_batchsize_${det_batchsize}_threads_${num_threads}.log"
-            command="${_script} ${_det_model} ${runtime_device} ${precision} ${num_threads} ${det_batchsize}  ${_img_dir} ${_config} ${benchmark_value} > ${_save_log_path} 2>&1"
-            eval ${command}
-            status_check $? "${command}" "${status_log}"
-        done
-    done
-}
-
-function func_test_rec(){
-    IFS='|'
-    _script=$1
-    _rec_model=$2
-    _cls_model=$3
-    _log_path=$4
-    _img_dir=$5
-    _config=$6
-    _rec_dict_dir=$7
-
-    if [[ $_det_model =~ "slim" ]]; then
-        _precision="INT8"
-    else
-        _precision="FP32"
-    fi
-
-    # lite inference
-    for num_threads in ${cpu_threads_list[*]}; do
-	for rec_batchsize in ${rec_batch_size_list[*]}; do
-            _save_log_path="${_log_path}/lite_${_rec_model}_${cls_model}_runtime_device_${runtime_device}_precision_${_precision}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
-            command="${_script} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${rec_batchsize}  ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
-            eval ${command}
-            status_check $? "${command}" "${status_log}"
-        done
-    done
-}
-
-function func_test_system(){
-    IFS='|'
-    _script=$1
-    _det_model=$2
-    _rec_model=$3
-    _cls_model=$4
-    _log_path=$5
-    _img_dir=$6
-    _config=$7
-    _rec_dict_dir=$8
-    if [[ $_det_model =~ "slim" ]]; then
-        _precision="INT8"
-    else
-        _precision="FP32"
-    fi
-
-    # lite inference
-    for num_threads in ${cpu_threads_list[*]}; do
-	for det_batchsize in ${det_batch_size_list[*]}; do
-	   for rec_batchsize in ${rec_batch_size_list[*]}; do
-                _save_log_path="${_log_path}/lite_${_det_model}_${_rec_model}_${_cls_model}_runtime_device_${runtime_device}_precision_${_precision}_det_batchsize_${det_batchsize}_rec_batchsize_${rec_batchsize}_threads_${num_threads}.log"
-                command="${_script} ${_det_model} ${_rec_model} ${_cls_model} ${runtime_device} ${_precision} ${num_threads} ${det_batchsize}  ${_img_dir} ${_config} ${_rec_dict_dir} ${benchmark_value} > ${_save_log_path} 2>&1"
-               eval ${command}
-               status_check $? "${command}" "${status_log}"
-	    done
-        done
-    done
-}
-
-
-echo "################### run test ###################"
-
-if [[ $inference_cmd =~ "det" ]]; then
-    IFS="|"
-    det_model_list=(${det_model_list[*]})
-
-    for i in {0..1}; do
-        #run lite inference
-        for img_dir in ${infer_img_dir_list[*]}; do
-            func_test_det "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}"
-        done
-    done
-
-elif [[ $inference_cmd =~ "rec" ]]; then
-    IFS="|"
-    rec_model_list=(${rec_model_list[*]})
-    cls_model_list=(${cls_model_list[*]})
-
-    for i in {0..1}; do
-        #run lite inference
-        for img_dir in ${infer_img_dir_list[*]}; do
-            func_test_rec "${inference_cmd}" "${rec_model}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${rec_dict_dir}" "${config_dir}"
-        done
-    done
-
-elif [[ $inference_cmd =~ "system" ]]; then
-    IFS="|"
-    det_model_list=(${det_model_list[*]})
-    rec_model_list=(${rec_model_list[*]})
-    cls_model_list=(${cls_model_list[*]})
-
-    for i in {0..1}; do
-	#run lite inference
-        for img_dir in ${infer_img_dir_list[*]}; do
-            func_test_system "${inference_cmd}" "${det_model_list[i]}_opt.nb" "${rec_model_list[i]}_opt.nb" "${cls_model_list[i]}_opt.nb" "${LOG_PATH}" "${img_dir}" "${config_dir}" "${rec_dict_dir}"
-        done
-    done
-fi

test_tipc/test_lite_arm_cpu_cpp.sh

+#!/bin/bash
+source test_tipc/common_func.sh
+current_path=$PWD
+
+IFS=$'\n'
+
+TIPC_CONFIG=$1
+tipc_dataline=$(cat $TIPC_CONFIG)
+tipc_lines=(${tipc_dataline})
+
+work_path="./deploy/lite"
+cd ${work_path}
+
+BASIC_CONFIG="config.txt"
+basic_dataline=$(cat $BASIC_CONFIG)
+basic_lines=(${basic_dataline})
+
+# parser basic config
+label_path=$(func_parser_value_lite "${basic_lines[1]}" " ")
+resize_short_size=$(func_parser_value_lite "${basic_lines[2]}" " ")
+crop_size=$(func_parser_value_lite "${basic_lines[3]}" " ")
+visualize=$(func_parser_value_lite "${basic_lines[4]}" " ")
+enable_benchmark=$(func_parser_value_lite "${basic_lines[9]}" " ")
+tipc_benchmark=$(func_parser_value_lite "${basic_lines[10]}" " ")
+
+# parser tipc config
+runtime_device=$(func_parser_value_lite "${tipc_lines[0]}" ":")
+lite_arm_work_path=$(func_parser_value_lite "${tipc_lines[1]}" ":")
+lite_arm_so_path=$(func_parser_value_lite "${tipc_lines[2]}" ":")
+clas_model_name=$(func_parser_value_lite "${tipc_lines[3]}" ":")
+inference_cmd=$(func_parser_value_lite "${tipc_lines[4]}" ":")
+num_threads_list=$(func_parser_value_lite "${tipc_lines[5]}" ":")
+batch_size_list=$(func_parser_value_lite "${tipc_lines[6]}" ":")
+precision_list=$(func_parser_value_lite "${tipc_lines[7]}" ":")
+
+LOG_PATH=${current_path}"/output"
+mkdir -p ${LOG_PATH}
+status_log="${LOG_PATH}/results.log"
+
+#run Lite TIPC
+function func_test_tipc(){
+    IFS="|"
+    _basic_config=$1
+    _model_name=$2
+    _log_path=$3
+    for num_threads in ${num_threads_list[*]}; do
+        if [ $(uname) = "Darwin" ]; then
+            sed -i " " "s/num_threads.*/num_threads ${num_threads}/" ${_basic_config}
+        elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+            sed -i "s/num_threads.*/num_threads ${num_threads}/" ${_basic_config}
+        fi
+        for batch_size in ${batch_size_list[*]}; do
+            if [ $(uname) = "Darwin" ]; then
+                sed -i " " "s/batch_size.*/batch_size ${batch_size}/" ${_basic_config}
+            elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+                sed -i "s/batch_size.*/batch_size ${batch_size}/" ${_basic_config}
+            fi
+            for precision in ${precision_list[*]}; do
+                if [ $(uname) = "Darwin" ]; then
+                    sed -i " " "s/precision.*/precision ${precision}/" ${_basic_config}
+                elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+                    sed -i "s/precision.*/precision ${precision}/" ${_basic_config}
+                fi
+                _save_log_path="${_log_path}/lite_${_model_name}_runtime_device_${runtime_device}_precision_${precision}_batchsize_${batch_size}_threads_${num_threads}.log"
+                real_inference_cmd=$(echo ${inference_cmd} | awk -F " " '{print path $1" "path $2" "path $3}' path="$lite_arm_work_path")
+                command1="adb push ${_basic_config} ${lite_arm_work_path}"
+                eval ${command1}
+                command2="adb shell 'export LD_LIBRARY_PATH=${lite_arm_work_path}; ${real_inference_cmd}'  > ${_save_log_path} 2>&1"
+                eval ${command2}
+                status_check $? "${command2}" "${status_log}"
+            done
+        done
+    done
+}
+
+
+echo "################### run test tipc ###################"
+label_map=$(echo ${label_path} | awk -F "/" '{print $NF}')
+
+if [ $(uname) = "Darwin" ]; then
+    # for Mac
+    sed -i " " "s/runtime_device.*/runtime_device arm_cpu/" ${BASIC_CONFIG}
+    escape_lite_arm_work_path=$(echo ${lite_arm_work_path//\//\\\/})
+    sed -i " " "s/clas_model_file.*/clas_model_file ${escape_lite_arm_work_path}${clas_model_name}.nb/" ${BASIC_CONFIG}
+    sed -i " " "s/label_path.*/label_path ${escape_lite_arm_work_path}${label_map}/" ${BASIC_CONFIG}
+    sed -i " " "s/tipc_benchmark.*/tipc_benchmark 1/" ${BASIC_CONFIG}
+elif [ $(expr substr $(uname -s) 1 5) = "Linux"]; then
+    # for Linux
+    sed -i "s/runtime_device.*/runtime_device arm_cpu/" ${BASIC_CONFIG}
+    escape_lite_arm_work_path=$(echo ${lite_arm_work_path//\//\\\/})
+    sed -i "s/clas_model_file.*/clas_model_file ${escape_lite_arm_work_path}${clas_model_name}/" ${BASIC_CONFIG}
+    sed -i "s/label_path.*/label_path ${escape_lite_arm_work_path}${label_path}/" ${BASIC_CONFIG}
+    sed -i "s/tipc_benchmark.*/tipc_benchmark 1/" ${BASIC_CONFIG}
+fi
+func_test_tipc ${BASIC_CONFIG} ${clas_model_name} ${LOG_PATH}