Merge branch 'develop' into add_re_ranking

deploy/configs/inference_cartoon.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_det.yaml

@@ -5,7 +5,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 1
-  labe_list:
+  label_list:
   - foreground
 
   # inference engine config

deploy/configs/inference_drink.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_general.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_general_binary.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_logo.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/configs/inference_product.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   # inference engine config

deploy/configs/inference_vehicle.yaml

@@ -8,7 +8,7 @@ Global:
   image_shape: [3, 640, 640]
   threshold: 0.2
   max_det_results: 5
-  labe_list:
+  label_list:
   - foreground
 
   use_gpu: True

deploy/cpp_shitu/include/object_detector.h

@@ -33,106 +33,106 @@ using namespace paddle_infer;
 
 namespace Detection {
 // Object Detection Result
-    struct ObjectResult {
-        // Rectangle coordinates of detected object: left, right, top, down
-        std::vector<int> rect;
-        // Class id of detected object
-        int class_id;
-        // Confidence of detected object
-        float confidence;
-    };
+struct ObjectResult {
+  // Rectangle coordinates of detected object: left, right, top, down
+  std::vector<int> rect;
+  // Class id of detected object
+  int class_id;
+  // Confidence of detected object
+  float confidence;
+};
 
 // Generate visualization colormap for each class
-    std::vector<int> GenerateColorMap(int num_class);
+std::vector<int> GenerateColorMap(int num_class);
 
 // Visualiztion Detection Result
-    cv::Mat VisualizeResult(const cv::Mat &img,
-                            const std::vector <ObjectResult> &results,
-                            const std::vector <std::string> &lables,
-                            const std::vector<int> &colormap, const bool is_rbox);
-
-    class ObjectDetector {
-    public:
-        explicit ObjectDetector(const YAML::Node &config_file) {
-            this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
-            if (config_file["Global"]["gpu_id"].IsDefined())
-                this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
-            this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
-            this->cpu_math_library_num_threads_ =
-                    config_file["Global"]["cpu_num_threads"].as<int>();
-            this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
-            this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
-            this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
-            this->model_dir_ =
-                    config_file["Global"]["det_inference_model_dir"].as<std::string>();
-            this->threshold_ = config_file["Global"]["threshold"].as<float>();
-            this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
-            this->image_shape_ =
-                    config_file["Global"]["image_shape"].as < std::vector < int >> ();
-            this->label_list_ =
-                    config_file["Global"]["labe_list"].as < std::vector < std::string >> ();
-            this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
-            this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
-
-            preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
-            LoadModel(model_dir_, batch_size_, run_mode);
-        }
-
-        // Load Paddle inference model
-        void LoadModel(const std::string &model_dir, const int batch_size = 1,
-                       const std::string &run_mode = "fluid");
-
-        // Run predictor
-        void Predict(const std::vector <cv::Mat> imgs, const int warmup = 0,
-                     const int repeats = 1,
-                     std::vector <ObjectResult> *result = nullptr,
-                     std::vector<int> *bbox_num = nullptr,
-                     std::vector<double> *times = nullptr);
-
-        const std::vector <std::string> &GetLabelList() const {
-            return this->label_list_;
-        }
-
-        const float &GetThreshold() const { return this->threshold_; }
-
-    private:
-        bool use_gpu_ = true;
-        int gpu_id_ = 0;
-        int gpu_mem_ = 800;
-        int cpu_math_library_num_threads_ = 6;
-        std::string run_mode = "fluid";
-        bool use_mkldnn_ = false;
-        bool use_tensorrt_ = false;
-        bool batch_size_ = 1;
-        bool use_fp16_ = false;
-        std::string model_dir_;
-        float threshold_ = 0.5;
-        float max_det_results_ = 5;
-        std::vector<int> image_shape_ = {3, 640, 640};
-        std::vector <std::string> label_list_;
-        bool ir_optim_ = true;
-        bool det_permute_ = true;
-        bool det_postprocess_ = true;
-        int min_subgraph_size_ = 30;
-        bool use_dynamic_shape_ = false;
-        int trt_min_shape_ = 1;
-        int trt_max_shape_ = 1280;
-        int trt_opt_shape_ = 640;
-        bool trt_calib_mode_ = false;
-
-        // Preprocess image and copy data to input buffer
-        void Preprocess(const cv::Mat &image_mat);
-
-        // Postprocess result
-        void Postprocess(const std::vector <cv::Mat> mats,
-                         std::vector <ObjectResult> *result, std::vector<int> bbox_num,
-                         bool is_rbox);
-
-        std::shared_ptr <Predictor> predictor_;
-        Preprocessor preprocessor_;
-        ImageBlob inputs_;
-        std::vector<float> output_data_;
-        std::vector<int> out_bbox_num_data_;
-    };
+cv::Mat VisualizeResult(const cv::Mat &img,
+                        const std::vector<ObjectResult> &results,
+                        const std::vector<std::string> &lables,
+                        const std::vector<int> &colormap, const bool is_rbox);
+
+class ObjectDetector {
+public:
+  explicit ObjectDetector(const YAML::Node &config_file) {
+    this->use_gpu_ = config_file["Global"]["use_gpu"].as<bool>();
+    if (config_file["Global"]["gpu_id"].IsDefined())
+      this->gpu_id_ = config_file["Global"]["gpu_id"].as<int>();
+    this->gpu_mem_ = config_file["Global"]["gpu_mem"].as<int>();
+    this->cpu_math_library_num_threads_ =
+        config_file["Global"]["cpu_num_threads"].as<int>();
+    this->use_mkldnn_ = config_file["Global"]["enable_mkldnn"].as<bool>();
+    this->use_tensorrt_ = config_file["Global"]["use_tensorrt"].as<bool>();
+    this->use_fp16_ = config_file["Global"]["use_fp16"].as<bool>();
+    this->model_dir_ =
+        config_file["Global"]["det_inference_model_dir"].as<std::string>();
+    this->threshold_ = config_file["Global"]["threshold"].as<float>();
+    this->max_det_results_ = config_file["Global"]["max_det_results"].as<int>();
+    this->image_shape_ =
+        config_file["Global"]["image_shape"].as<std::vector<int>>();
+    this->label_list_ =
+        config_file["Global"]["label_list"].as<std::vector<std::string>>();
+    this->ir_optim_ = config_file["Global"]["ir_optim"].as<bool>();
+    this->batch_size_ = config_file["Global"]["batch_size"].as<int>();
+
+    preprocessor_.Init(config_file["DetPreProcess"]["transform_ops"]);
+    LoadModel(model_dir_, batch_size_, run_mode);
+  }
+
+  // Load Paddle inference model
+  void LoadModel(const std::string &model_dir, const int batch_size = 1,
+                 const std::string &run_mode = "fluid");
+
+  // Run predictor
+  void Predict(const std::vector<cv::Mat> imgs, const int warmup = 0,
+               const int repeats = 1,
+               std::vector<ObjectResult> *result = nullptr,
+               std::vector<int> *bbox_num = nullptr,
+               std::vector<double> *times = nullptr);
+
+  const std::vector<std::string> &GetLabelList() const {
+    return this->label_list_;
+  }
+
+  const float &GetThreshold() const { return this->threshold_; }
+
+private:
+  bool use_gpu_ = true;
+  int gpu_id_ = 0;
+  int gpu_mem_ = 800;
+  int cpu_math_library_num_threads_ = 6;
+  std::string run_mode = "fluid";
+  bool use_mkldnn_ = false;
+  bool use_tensorrt_ = false;
+  bool batch_size_ = 1;
+  bool use_fp16_ = false;
+  std::string model_dir_;
+  float threshold_ = 0.5;
+  float max_det_results_ = 5;
+  std::vector<int> image_shape_ = {3, 640, 640};
+  std::vector<std::string> label_list_;
+  bool ir_optim_ = true;
+  bool det_permute_ = true;
+  bool det_postprocess_ = true;
+  int min_subgraph_size_ = 30;
+  bool use_dynamic_shape_ = false;
+  int trt_min_shape_ = 1;
+  int trt_max_shape_ = 1280;
+  int trt_opt_shape_ = 640;
+  bool trt_calib_mode_ = false;
+
+  // Preprocess image and copy data to input buffer
+  void Preprocess(const cv::Mat &image_mat);
+
+  // Postprocess result
+  void Postprocess(const std::vector<cv::Mat> mats,
+                   std::vector<ObjectResult> *result, std::vector<int> bbox_num,
+                   bool is_rbox);
+
+  std::shared_ptr<Predictor> predictor_;
+  Preprocessor preprocessor_;
+  ImageBlob inputs_;
+  std::vector<float> output_data_;
+  std::vector<int> out_bbox_num_data_;
+};
 
 } // namespace Detection

deploy/lite_shitu/generate_json_config.py

@@ -95,7 +95,7 @@ def main():
     config_json["Global"]["det_model_path"] = args.det_model_path
     config_json["Global"]["rec_model_path"] = args.rec_model_path
     config_json["Global"]["rec_label_path"] = args.rec_label_path
-    config_json["Global"]["label_list"] = config_yaml["Global"]["labe_list"]
+    config_json["Global"]["label_list"] = config_yaml["Global"]["label_list"]
     config_json["Global"]["rec_nms_thresold"] = config_yaml["Global"][
         "rec_nms_thresold"]
     config_json["Global"]["max_det_results"] = config_yaml["Global"][

deploy/python/predict_det.py

@@ -128,13 +128,10 @@ class DetPredictor(Predictor):
         results = []
         if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
             print('[WARNNING] No object detected.')
-            results = np.array([])
         else:
-            results = np_boxes
-
-        results = self.parse_det_results(results,
-                                         self.config["Global"]["threshold"],
-                                         self.config["Global"]["labe_list"])
+            results = self.parse_det_results(
+                np_boxes, self.config["Global"]["threshold"],
+                self.config["Global"]["label_list"])
         return results
 
 

docs/zh_CN/algorithm_introduction/ImageNet_models.md

@@ -10,7 +10,7 @@
   - [2.1 服务器端知识蒸馏模型](#2.1)
   - [2.2 移动端知识蒸馏模型](#2.2)
   - [2.3 Intel CPU 端知识蒸馏模型](#2.3)
-- [3. PP-LCNet 系列](#3)
+- [3. PP-LCNet & PP-LCNetV2 系列](#3)
 - [4. ResNet 系列](#4)
 - [5. 移动端系列](#5)
 - [6. SEResNeXt 与 Res2Net 系列](#6)
@@ -106,9 +106,9 @@
 
 <a name="3"></a>
 
-## 3. PP-LCNet 系列 <sup>[[28](#ref28)]</sup>
+## 3. PP-LCNet & PP-LCNetV2 系列 <sup>[[28](#ref28)]</sup>
 
-PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[PP-LCNet 系列模型文档](../models/PP-LCNet.md)。
+PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[PP-LCNet 系列模型文档](../models/PP-LCNet.md)，[PP-LCNetV2 系列模型文档](../models/PP-LCNetV2.md)。
 
 | 模型           | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)<br>bs=1 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
 |:--:|:--:|:--:|:--:|----|----|----|:--:|
@@ -121,6 +121,10 @@ PP-LCNet 系列模型的精度、速度指标如下表所示，更多关于该
 | PPLCNet_x2_0         |0.7518           | 0.9227   | 20.1667 | 590   | 6.54  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar) |
 | PPLCNet_x2_5         |0.7660           | 0.9300   | 29.595 | 906   | 9.04  | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar) |
 
+| 模型           | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6271C<br>bs=1<br>OpenVINO 2021.4.2<br>time(ms) | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
+|:--:|:--:|:--:|:--:|----|----|----|:--:|
+| PPLCNetV2_base  | 77.04 | 93.27 | 4.32 | 604 | 6.6 | https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams | https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar |
+
 <a name="4"></a>
 
 ## 4. ResNet 系列 <sup>[[1](#ref1)]</sup>

docs/zh_CN/models/PP-HGNet.md

+# PP-HGNet 系列
+---
+## 目录
+
+* [1. 概述](#1)
+* [2. 精度、FLOPs 和参数量](#2)
+
+<a name='1'></a>
+
+## 1. 概述
+
+PP-HGNet是百度自研的一个在 GPU 端上高性能的网络，该网络在 VOVNet 的基础上融合了 ResNet_vd、PPLCNet 的优点，使用了可学习的下采样层，组合成了一个在 GPU 设备上速度快、精度高的网络，超越其他 GPU 端 SOTA 模型。
+
+<a name='2'></a>
+
+## 2.精度、FLOPs 和参数量
+
+| Models | Top1 | Top5 | FLOPs<br>(G) | Params<br/>(M) |
+|:--:|:--:|:--:|:--:|:--:|
+| PPHGNet_tiny | 79.83 | 95.04 | 4.54 | 14.75 |
+| PPHGNet_tiny_ssld | 81.95 | 96.12 | 4.54 | 14.75 |
+| PPHGNet_small | 81.51 | 95.82 | 8.53 | 24.38 |
+
+关于 Inference speed 等信息，敬请期待。

docs/zh_CN/models/PP-LCNetV2.md

+# PP-LCNetV2 系列
+
+---
+
+## 概述
+
+PP-LCNetV2 是在 [PP-LCNet 系列模型](./PP-LCNet.md)的基础上，所提出的针对 Intel CPU 硬件平台设计的计算机视觉骨干网络，该模型更为
+
+在不使用额外数据的前提下，PPLCNetV2_base 模型在图像分类 ImageNet 数据集上能够取得超过 77% 的 Top1 Acc，同时在 Intel CPU 平台仅有 4.4 ms 以下的延迟，如下表所示，其中延时测试基于 Intel(R) Xeon(R) Gold 6271C CPU @ 2.60GHz 硬件平台，OpenVINO 2021.4.2推理平台。
+
+| Model | Params(M) | FLOPs(M) | Top-1 Acc(\%) | Top-5 Acc(\%) | Latency(ms) |
+|-------|-----------|----------|---------------|---------------|-------------|
+| PPLCNetV2_base  | 6.6 | 604  | 77.04 | 93.27 | 4.32 |
+
+关于 PP-LCNetV2 系列模型的更多信息，敬请关注。

ppcls/arch/backbone/__init__.py

@@ -22,7 +22,9 @@ from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
 from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
 from ppcls.arch.backbone.legendary_models.hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W64_C
 from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
+from ppcls.arch.backbone.legendary_models.pp_lcnet_v2 import PPLCNetV2_base
 from ppcls.arch.backbone.legendary_models.esnet import ESNet_x0_25, ESNet_x0_5, ESNet_x0_75, ESNet_x1_0
+from ppcls.arch.backbone.legendary_models.pp_hgnet import PPHGNet_tiny, PPHGNet_small, PPHGNet_base
 
 from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
 from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d

ppcls/arch/backbone/legendary_models/pp_hgnet.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.
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn.initializer import KaimingNormal, Constant
+from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
+from paddle.regularizer import L2Decay
+from paddle import ParamAttr
+
+from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "PPHGNet_tiny":
+    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams",
+    "PPHGNet_small":
+    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams"
+}
+
+__all__ = list(MODEL_URLS.keys())
+
+kaiming_normal_ = KaimingNormal()
+zeros_ = Constant(value=0.)
+ones_ = Constant(value=1.)
+
+
+class ConvBNAct(TheseusLayer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 groups=1,
+                 use_act=True):
+        super().__init__()
+        self.use_act = use_act
+        self.conv = Conv2D(
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            bias_attr=False)
+        self.bn = BatchNorm2D(
+            out_channels,
+            weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
+            bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
+        if self.use_act:
+            self.act = ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.use_act:
+            x = self.act(x)
+        return x
+
+
+class ESEModule(TheseusLayer):
+    def __init__(self, channels):
+        super().__init__()
+        self.avg_pool = AdaptiveAvgPool2D(1)
+        self.conv = Conv2D(
+            in_channels=channels,
+            out_channels=channels,
+            kernel_size=1,
+            stride=1,
+            padding=0)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        identity = x
+        x = self.avg_pool(x)
+        x = self.conv(x)
+        x = self.sigmoid(x)
+        return paddle.multiply(x=identity, y=x)
+
+
+class HG_Block(TheseusLayer):
+    def __init__(
+            self,
+            in_channels,
+            mid_channels,
+            out_channels,
+            layer_num,
+            identity=False, ):
+        super().__init__()
+        self.identity = identity
+
+        self.layers = nn.LayerList()
+        self.layers.append(
+            ConvBNAct(
+                in_channels=in_channels,
+                out_channels=mid_channels,
+                kernel_size=3,
+                stride=1))
+        for _ in range(layer_num - 1):
+            self.layers.append(
+                ConvBNAct(
+                    in_channels=mid_channels,
+                    out_channels=mid_channels,
+                    kernel_size=3,
+                    stride=1))
+
+        # feature aggregation
+        total_channels = in_channels + layer_num * mid_channels
+        self.aggregation_conv = ConvBNAct(
+            in_channels=total_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1)
+        self.att = ESEModule(out_channels)
+
+    def forward(self, x):
+        identity = x
+        output = []
+        output.append(x)
+        for layer in self.layers:
+            x = layer(x)
+            output.append(x)
+        x = paddle.concat(output, axis=1)
+        x = self.aggregation_conv(x)
+        x = self.att(x)
+        if self.identity:
+            x += identity
+        return x
+
+
+class HG_Stage(TheseusLayer):
+    def __init__(self,
+                 in_channels,
+                 mid_channels,
+                 out_channels,
+                 block_num,
+                 layer_num,
+                 downsample=True):
+        super().__init__()
+        self.downsample = downsample
+        if downsample:
+            self.downsample = ConvBNAct(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                kernel_size=3,
+                stride=2,
+                groups=in_channels,
+                use_act=False)
+
+        blocks_list = []
+        blocks_list.append(
+            HG_Block(
+                in_channels,
+                mid_channels,
+                out_channels,
+                layer_num,
+                identity=False))
+        for _ in range(block_num - 1):
+            blocks_list.append(
+                HG_Block(
+                    out_channels,
+                    mid_channels,
+                    out_channels,
+                    layer_num,
+                    identity=True))
+        self.blocks = nn.Sequential(*blocks_list)
+
+    def forward(self, x):
+        if self.downsample:
+            x = self.downsample(x)
+        x = self.blocks(x)
+        return x
+
+
+class PPHGNet(TheseusLayer):
+    """
+    PPHGNet
+    Args:
+        stem_channels: list. Stem channel list of PPHGNet.
+        stage_config: dict. The configuration of each stage of PPHGNet. such as the number of channels, stride, etc.
+        layer_num: int. Number of layers of HG_Block.
+        use_last_conv: boolean. Whether to use a 1x1 convolutional layer before the classification layer.
+        class_expand: int=2048. Number of channels for the last 1x1 convolutional layer.
+        dropout_prob: float. Parameters of dropout, 0.0 means dropout is not used.
+        class_num: int=1000. The number of classes.
+    Returns:
+        model: nn.Layer. Specific PPHGNet model depends on args.
+    """
+    def __init__(self,
+                 stem_channels,
+                 stage_config,
+                 layer_num,
+                 use_last_conv=True,
+                 class_expand=2048,
+                 dropout_prob=0.0,
+                 class_num=1000):
+        super().__init__()
+        self.use_last_conv = use_last_conv
+        self.class_expand = class_expand
+
+        # stem
+        stem_channels.insert(0, 3)
+        self.stem = nn.Sequential(* [
+            ConvBNAct(
+                in_channels=stem_channels[i],
+                out_channels=stem_channels[i + 1],
+                kernel_size=3,
+                stride=2 if i == 0 else 1) for i in range(
+                    len(stem_channels) - 1)
+        ])
+        self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
+
+        # stages
+        self.stages = nn.LayerList()
+        for k in stage_config:
+            in_channels, mid_channels, out_channels, block_num, downsample = stage_config[
+                k]
+            self.stages.append(
+                HG_Stage(in_channels, mid_channels, out_channels, block_num,
+                          layer_num, downsample))
+
+        self.avg_pool = AdaptiveAvgPool2D(1)
+        if self.use_last_conv:
+            self.last_conv = Conv2D(
+                in_channels=out_channels,
+                out_channels=self.class_expand,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias_attr=False)
+            self.act = nn.ReLU()
+            self.dropout = nn.Dropout(
+                p=dropout_prob, mode="downscale_in_infer")
+
+        self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
+        self.fc = nn.Linear(self.class_expand
+                            if self.use_last_conv else out_channels, class_num)
+
+        self._init_weights()
+
+    def _init_weights(self):
+        for m in self.sublayers():
+            if isinstance(m, nn.Conv2D):
+                kaiming_normal_(m.weight)
+            elif isinstance(m, (nn.BatchNorm2D)):
+                ones_(m.weight)
+                zeros_(m.bias)
+            elif isinstance(m, nn.Linear):
+                zeros_(m.bias)
+
+    def forward(self, x):
+        x = self.stem(x)
+        x = self.pool(x)
+
+        for stage in self.stages:
+            x = stage(x)
+
+        x = self.avg_pool(x)
+        if self.use_last_conv:
+            x = self.last_conv(x)
+            x = self.act(x)
+            x = self.dropout(x)
+        x = self.flatten(x)
+        x = self.fc(x)
+        return x
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld):
+    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 PPHGNet_tiny(pretrained=False, use_ssld=False, **kwargs):
+    """
+    PPHGNet_tiny
+    Args:
+        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+                    If str, means the path of the pretrained model.
+        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+    Returns:
+        model: nn.Layer. Specific `PPHGNet_tiny` model depends on args.
+    """
+    stage_config = {
+        # in_channels, mid_channels, out_channels, blocks, downsample
+        "stage1": [96, 96, 224, 1, False],
+        "stage2": [224, 128, 448, 1, True],
+        "stage3": [448, 160, 512, 2, True],
+        "stage4": [512, 192, 768, 1, True],
+    }
+
+    model = PPHGNet(
+        stem_channels=[48, 48, 96],
+        stage_config=stage_config,
+        layer_num=5,
+        **kwargs)
+    _load_pretrained(pretrained, model, MODEL_URLS["PPHGNet_tiny"], use_ssld)
+    return model
+
+
+def PPHGNet_small(pretrained=False, use_ssld=False, **kwargs):
+    """
+    PPHGNet_small
+    Args:
+        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+                    If str, means the path of the pretrained model.
+        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+    Returns:
+        model: nn.Layer. Specific `PPHGNet_small` model depends on args.
+    """
+    stage_config = {
+        # in_channels, mid_channels, out_channels, blocks, downsample
+        "stage1": [128, 128, 256, 1, False],
+        "stage2": [256, 160, 512, 1, True],
+        "stage3": [512, 192, 768, 2, True],
+        "stage4": [768, 224, 1024, 1, True],
+    }
+
+    model = PPHGNet(
+        stem_channels=[64, 64, 128],
+        stage_config=stage_config,
+        layer_num=6,
+        **kwargs)
+    _load_pretrained(pretrained, model, MODEL_URLS["PPHGNet_small"], use_ssld)
+    return model
+
+
+def PPHGNet_base(pretrained=False, use_ssld=False, **kwargs):
+    """
+    PPHGNet_base
+    Args:
+        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+                    If str, means the path of the pretrained model.
+        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+    Returns:
+        model: nn.Layer. Specific `PPHGNet_base` model depends on args.
+    """
+    stage_config = {
+        # in_channels, mid_channels, out_channels, blocks, downsample
+        "stage1": [160, 192, 320, 1, False],
+        "stage2": [320, 224, 640, 2, True],
+        "stage3": [640, 256, 960, 3, True],
+        "stage4": [960, 288, 1280, 2, True],
+    }
+
+    model = PPHGNet(
+        stem_channels=[96, 96, 160],
+        stage_config=stage_config,
+        layer_num=7,
+        dropout_prob=0.2,
+        **kwargs)
+    _load_pretrained(pretrained, model, MODEL_URLS["PPHGNet_base"], use_ssld)
+    return model

ppcls/arch/backbone/legendary_models/pp_lcnet_v2.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.
+
+from __future__ import absolute_import, division, print_function
+
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle import ParamAttr
+from paddle.nn import AdaptiveAvgPool2D, BatchNorm2D, Conv2D, Dropout, Linear
+from paddle.regularizer import L2Decay
+from paddle.nn.initializer import KaimingNormal
+from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+    "PPLCNetV2_base":
+    "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams",
+}
+
+__all__ = list(MODEL_URLS.keys())
+
+NET_CONFIG = {
+    # in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut
+    "stage1": [64, 3, False, False, False, False],
+    "stage2": [128, 3, False, False, False, False],
+    "stage3": [256, 5, True, True, True, False],
+    "stage4": [512, 5, False, True, False, True],
+}
+
+
+def make_divisible(v, divisor=8, min_value=None):
+    if min_value is None:
+        min_value = divisor
+    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+    if new_v < 0.9 * v:
+        new_v += divisor
+    return new_v
+
+
+class ConvBNLayer(TheseusLayer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 groups=1,
+                 use_act=True):
+        super().__init__()
+        self.use_act = use_act
+        self.conv = Conv2D(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=(kernel_size - 1) // 2,
+            groups=groups,
+            weight_attr=ParamAttr(initializer=KaimingNormal()),
+            bias_attr=False)
+
+        self.bn = BatchNorm2D(
+            out_channels,
+            weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
+            bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
+        if self.use_act:
+            self.act = nn.ReLU()
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.bn(x)
+        if self.use_act:
+            x = self.act(x)
+        return x
+
+
+class SEModule(TheseusLayer):
+    def __init__(self, channel, reduction=4):
+        super().__init__()
+        self.avg_pool = AdaptiveAvgPool2D(1)
+        self.conv1 = Conv2D(
+            in_channels=channel,
+            out_channels=channel // reduction,
+            kernel_size=1,
+            stride=1,
+            padding=0)
+        self.relu = nn.ReLU()
+        self.conv2 = Conv2D(
+            in_channels=channel // reduction,
+            out_channels=channel,
+            kernel_size=1,
+            stride=1,
+            padding=0)
+        self.hardsigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        identity = x
+        x = self.avg_pool(x)
+        x = self.conv1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.hardsigmoid(x)
+        x = paddle.multiply(x=identity, y=x)
+        return x
+
+
+class RepDepthwiseSeparable(TheseusLayer):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride,
+                 dw_size=3,
+                 split_pw=False,
+                 use_rep=False,
+                 use_se=False,
+                 use_shortcut=False):
+        super().__init__()
+        self.is_repped = False
+
+        self.dw_size = dw_size
+        self.split_pw = split_pw
+        self.use_rep = use_rep
+        self.use_se = use_se
+        self.use_shortcut = True if use_shortcut and stride == 1 and in_channels == out_channels else False
+
+        if self.use_rep:
+            self.dw_conv_list = nn.LayerList()
+            for kernel_size in range(self.dw_size, 0, -2):
+                if kernel_size == 1 and stride != 1:
+                    continue
+                dw_conv = ConvBNLayer(
+                    in_channels=in_channels,
+                    out_channels=in_channels,
+                    kernel_size=kernel_size,
+                    stride=stride,
+                    groups=in_channels,
+                    use_act=False)
+                self.dw_conv_list.append(dw_conv)
+            self.dw_conv = nn.Conv2D(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                kernel_size=dw_size,
+                stride=stride,
+                padding=(dw_size - 1) // 2,
+                groups=in_channels)
+        else:
+            self.dw_conv = ConvBNLayer(
+                in_channels=in_channels,
+                out_channels=in_channels,
+                kernel_size=dw_size,
+                stride=stride,
+                groups=in_channels)
+
+        self.act = nn.ReLU()
+
+        if use_se:
+            self.se = SEModule(in_channels)
+
+        if self.split_pw:
+            pw_ratio = 0.5
+            self.pw_conv_1 = ConvBNLayer(
+                in_channels=in_channels,
+                kernel_size=1,
+                out_channels=int(out_channels * pw_ratio),
+                stride=1)
+            self.pw_conv_2 = ConvBNLayer(
+                in_channels=int(out_channels * pw_ratio),
+                kernel_size=1,
+                out_channels=out_channels,
+                stride=1)
+        else:
+            self.pw_conv = ConvBNLayer(
+                in_channels=in_channels,
+                kernel_size=1,
+                out_channels=out_channels,
+                stride=1)
+
+    def forward(self, x):
+        if self.use_rep:
+            input_x = x
+            if self.is_repped:
+                x = self.act(self.dw_conv(x))
+            else:
+                y = self.dw_conv_list[0](x)
+                for dw_conv in self.dw_conv_list[1:]:
+                    y += dw_conv(x)
+                x = self.act(y)
+        else:
+            x = self.dw_conv(x)
+
+        if self.use_se:
+            x = self.se(x)
+        if self.split_pw:
+            x = self.pw_conv_1(x)
+            x = self.pw_conv_2(x)
+        else:
+            x = self.pw_conv(x)
+        if self.use_shortcut:
+            x = x + input_x
+        return x
+
+    def rep(self):
+        if self.use_rep:
+            self.is_repped = True
+            kernel, bias = self._get_equivalent_kernel_bias()
+            self.dw_conv.weight.set_value(kernel)
+            self.dw_conv.bias.set_value(bias)
+
+    def _get_equivalent_kernel_bias(self):
+        kernel_sum = 0
+        bias_sum = 0
+        for dw_conv in self.dw_conv_list:
+            kernel, bias = self._fuse_bn_tensor(dw_conv)
+            kernel = self._pad_tensor(kernel, to_size=self.dw_size)
+            kernel_sum += kernel
+            bias_sum += bias
+        return kernel_sum, bias_sum
+
+    def _fuse_bn_tensor(self, branch):
+        kernel = branch.conv.weight
+        running_mean = branch.bn._mean
+        running_var = branch.bn._variance
+        gamma = branch.bn.weight
+        beta = branch.bn.bias
+        eps = branch.bn._epsilon
+        std = (running_var + eps).sqrt()
+        t = (gamma / std).reshape((-1, 1, 1, 1))
+        return kernel * t, beta - running_mean * gamma / std
+
+    def _pad_tensor(self, tensor, to_size):
+        from_size = tensor.shape[-1]
+        if from_size == to_size:
+            return tensor
+        pad = (to_size - from_size) // 2
+        return F.pad(tensor, [pad, pad, pad, pad])
+
+
+class PPLCNetV2(TheseusLayer):
+    def __init__(self,
+                 scale,
+                 depths,
+                 class_num=1000,
+                 dropout_prob=0,
+                 use_last_conv=True,
+                 class_expand=1280):
+        super().__init__()
+        self.scale = scale
+        self.use_last_conv = use_last_conv
+        self.class_expand = class_expand
+
+        self.stem = nn.Sequential(* [
+            ConvBNLayer(
+                in_channels=3,
+                kernel_size=3,
+                out_channels=make_divisible(32 * scale),
+                stride=2), RepDepthwiseSeparable(
+                    in_channels=make_divisible(32 * scale),
+                    out_channels=make_divisible(64 * scale),
+                    stride=1,
+                    dw_size=3)
+        ])
+
+        # stages
+        self.stages = nn.LayerList()
+        for depth_idx, k in enumerate(NET_CONFIG):
+            in_channels, kernel_size, split_pw, use_rep, use_se, use_shortcut = NET_CONFIG[
+                k]
+            self.stages.append(
+                nn.Sequential(* [
+                    RepDepthwiseSeparable(
+                        in_channels=make_divisible((in_channels if i == 0 else
+                                                    in_channels * 2) * scale),
+                        out_channels=make_divisible(in_channels * 2 * scale),
+                        stride=2 if i == 0 else 1,
+                        dw_size=kernel_size,
+                        split_pw=split_pw,
+                        use_rep=use_rep,
+                        use_se=use_se,
+                        use_shortcut=use_shortcut)
+                    for i in range(depths[depth_idx])
+                ]))
+
+        self.avg_pool = AdaptiveAvgPool2D(1)
+
+        if self.use_last_conv:
+            self.last_conv = Conv2D(
+                in_channels=make_divisible(NET_CONFIG["stage4"][0] * 2 *
+                                           scale),
+                out_channels=self.class_expand,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                bias_attr=False)
+            self.act = nn.ReLU()
+            self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
+
+        self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
+        in_features = self.class_expand if self.use_last_conv else NET_CONFIG[
+            "stage4"][0] * 2 * scale
+        self.fc = Linear(in_features, class_num)
+
+    def forward(self, x):
+        x = self.stem(x)
+        for stage in self.stages:
+            x = stage(x)
+        x = self.avg_pool(x)
+        if self.use_last_conv:
+            x = self.last_conv(x)
+            x = self.act(x)
+            x = self.dropout(x)
+        x = self.flatten(x)
+        x = self.fc(x)
+        return x
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld):
+    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 PPLCNetV2_base(pretrained=False, use_ssld=False, **kwargs):
+    """
+    PPLCNetV2_base
+    Args:
+        pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+                    If str, means the path of the pretrained model.
+        use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+    Returns:
+        model: nn.Layer. Specific `PPLCNetV2_base` model depends on args.
+    """
+    model = PPLCNetV2(
+        scale=1.0, depths=[2, 2, 6, 2], dropout_prob=0.2, **kwargs)
+    _load_pretrained(pretrained, model, MODEL_URLS["PPLCNetV2_base"], use_ssld)
+    return model

ppcls/arch/backbone/model_zoo/repvgg.py

@@ -124,13 +124,7 @@ class RepVGGBlock(nn.Layer):
             groups=groups)
 
     def forward(self, inputs):
-        if not self.training and not self.is_repped:
-            self.rep()
-            self.is_repped = True
-        if self.training and self.is_repped:
-            self.is_repped = False
-
-        if not self.training:
+        if self.is_repped:
             return self.nonlinearity(self.rbr_reparam(inputs))
 
         if self.rbr_identity is None:
@@ -154,6 +148,7 @@ class RepVGGBlock(nn.Layer):
         kernel, bias = self.get_equivalent_kernel_bias()
         self.rbr_reparam.weight.set_value(kernel)
         self.rbr_reparam.bias.set_value(bias)
+        self.is_repped = True
 
     def get_equivalent_kernel_bias(self):
         kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense)

ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.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: 600
+  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
+  use_dali: False
+
+# mixed precision training
+AMP:
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  # O1: mixed fp16
+  level: O1
+
+# model architecture
+Arch:
+  name: PPHGNet_small
+  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: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.5
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.00004
+
+
+# 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: bicubic
+            backend: pil
+        - RandFlipImage:
+            flip_code: 1
+        - TimmAutoAugment:
+            config_str: rand-m7-mstd0.5-inc1
+            interpolation: bicubic
+            img_size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            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: 0.2
+              prob: 0.5
+            CutmixOperator:
+              alpha: 1.0
+              prob: 0.5
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 16
+      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: 236
+            interpolation: bicubic
+            backend: pil
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            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: 16
+      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: 236
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        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/PPHGNet/PPHGNet_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: 600
+  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
+  use_dali: False
+
+# mixed precision training
+AMP:
+  scale_loss: 128.0
+  use_dynamic_loss_scaling: True
+  # O1: mixed fp16
+  level: O1
+
+# model architecture
+Arch:
+  name: PPHGNet_tiny
+  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: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.5
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.00004
+
+
+# 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: bicubic
+            backend: pil
+        - RandFlipImage:
+            flip_code: 1
+        - TimmAutoAugment:
+            config_str: rand-m7-mstd0.5-inc1
+            interpolation: bicubic
+            img_size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            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: 0.2
+              prob: 0.5
+            CutmixOperator:
+              alpha: 1.0
+              prob: 0.5
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 16
+      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: 232
+            interpolation: bicubic
+            backend: pil
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            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: 16
+      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: 232
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        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/PPLCNetV2/PPLCNetV2_base.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: 480
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+# model architecture
+Arch:
+  name: PPLCNetV2_base
+  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: Momentum
+  momentum: 0.9
+  lr:
+    name: Cosine
+    learning_rate: 0.8
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.00004
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: MultiScaleDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+
+    # support to specify width and height respectively:
+    # scales: [(160,160), (192,192), (224,224) (288,288) (320,320)]
+    sampler:
+      name: MultiScaleSampler
+      scales: [160, 192, 224, 288, 320]
+      # first_bs: batch size for the first image resolution in the scales list
+      # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
+      first_bs: 500
+      divided_factor: 32
+      is_training: 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: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            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: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        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/multi_scale/MobileNetV1_multi_scale.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: 120
+  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: MobileNetV1
+  class_num: 1000
+ 
+# loss function config for traing/eval process
+Loss:
+  Train:
+    - CELoss:
+        weight: 1.0
+  Eval:
+    - CELoss:
+        weight: 1.0
+
+
+Optimizer:
+  name: Momentum
+  momentum: 0.9
+  lr:
+    name: Piecewise
+    learning_rate: 0.1
+    decay_epochs: [30, 60, 90]
+    values: [0.1, 0.01, 0.001, 0.0001]
+  regularizer:
+    name: 'L2'
+    coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: MultiScaleDataset
+      image_root: ./dataset/ILSVRC2012/
+      cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+      transform_ops:
+        - DecodeImage:
+            to_rgb: True
+            channel_first: False
+        - RandCropImage:
+            size: 224
+        - RandFlipImage:
+            flip_code: 1
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            order: ''
+            
+    # support to specify width and height respectively:
+    # scales: [(160,160), (192,192), (224,224) (288,288) (320,320)]
+    sampler:
+      name: MultiScaleSampler
+      scales: [160, 192, 224, 288, 320]
+      # first_bs: batch size for the first image resolution in the scales list
+      # divide_factor: to ensure the width and height dimensions can be devided by downsampling multiple
+      first_bs: 64
+      divided_factor: 32
+      is_training: 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: 256
+        - CropImage:
+            size: 224
+        - NormalizeImage:
+            scale: 1.0/255.0
+            mean: [0.485, 0.456, 0.406]
+            std: [0.229, 0.224, 0.225]
+            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/whl/demo.jpg
+  batch_size: 10
+  transforms:
+    - DecodeImage:
+        to_rgb: True
+        channel_first: False
+    - ResizeImage:
+        resize_short: 256
+    - CropImage:
+        size: 224
+    - NormalizeImage:
+        scale: 1.0/255.0
+        mean: [0.485, 0.456, 0.406]
+        std: [0.229, 0.224, 0.225]
+        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/Pedestrian/strong_baseline_baseline.yaml → ppcls/configs/reid/strong_baseline/baseline.yaml

@@ -79,6 +79,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - RandFlipImage:
               flip_code: 1
@@ -110,6 +111,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - ToTensor:
           - Normalize:
@@ -134,6 +136,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - ToTensor:
           - Normalize:

ppcls/configs/Pedestrian/strong_baseline_m1.yaml → ppcls/configs/reid/strong_baseline/softmax_triplet.yaml

@@ -91,6 +91,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - RandFlipImage:
               flip_code: 1
@@ -128,6 +129,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - ToTensor:
           - Normalize:
@@ -152,6 +154,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - ToTensor:
           - Normalize:

ppcls/configs/Pedestrian/strong_baseline_m1_centerloss.yaml → ppcls/configs/reid/strong_baseline/softmax_triplet_with_center.yaml

@@ -102,6 +102,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - RandFlipImage:
               flip_code: 1
@@ -139,6 +140,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - ToTensor:
           - Normalize:
@@ -163,6 +165,7 @@ DataLoader:
           - ResizeImage:
               size: [128, 256]
               return_numpy: False
+              interpolation: 'bilinear'
               backend: "pil"
           - ToTensor:
           - Normalize:

ppcls/data/__init__.py

@@ -28,12 +28,15 @@ 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.multi_scale_dataset import MultiScaleDataset
 from ppcls.data.dataloader.person_dataset import Market1501, MSMT17
 
+
 # sampler
 from ppcls.data.dataloader.DistributedRandomIdentitySampler import DistributedRandomIdentitySampler
 from ppcls.data.dataloader.pk_sampler import PKSampler
 from ppcls.data.dataloader.mix_sampler import MixSampler
+from ppcls.data.dataloader.multi_scale_sampler import MultiScaleSampler
 from ppcls.data import preprocess
 from ppcls.data.preprocess import transform
 

ppcls/data/dataloader/__init__.py

@@ -5,6 +5,8 @@ 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.multi_scale_dataset import MultiScaleDataset
 from ppcls.data.dataloader.mix_sampler import MixSampler
+from ppcls.data.dataloader.multi_scale_sampler import MultiScaleSampler
 from ppcls.data.dataloader.pk_sampler import PKSampler
 from ppcls.data.dataloader.person_dataset import Market1501, MSMT17

ppcls/data/dataloader/dali.py

@@ -230,7 +230,7 @@ def dali_dataloader(config, mode, device, seed=None):
         lower = ratio[0]
         upper = ratio[1]
 
-        if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env:
+        if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env and 'FLAGS_selected_gpus' in env:
             shard_id = int(env['PADDLE_TRAINER_ID'])
             num_shards = int(env['PADDLE_TRAINERS_NUM'])
             device_id = int(env['FLAGS_selected_gpus'])
@@ -282,7 +282,7 @@ def dali_dataloader(config, mode, device, seed=None):
     else:
         resize_shorter = transforms["ResizeImage"].get("resize_short", 256)
         crop = transforms["CropImage"]["size"]
-        if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env and sampler_name == "DistributedBatchSampler":
+        if 'PADDLE_TRAINER_ID' in env and 'PADDLE_TRAINERS_NUM' in env and 'FLAGS_selected_gpus' in env and sampler_name == "DistributedBatchSampler":
             shard_id = int(env['PADDLE_TRAINER_ID'])
             num_shards = int(env['PADDLE_TRAINERS_NUM'])
             device_id = int(env['FLAGS_selected_gpus'])

ppcls/data/dataloader/multi_scale_dataset.py

+#   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 print_function
+
+import numpy as np
+import os
+
+from paddle.io import Dataset
+from paddle.vision import transforms
+import cv2
+import warnings
+
+from ppcls.data import preprocess
+from ppcls.data.preprocess import transform
+from ppcls.data.preprocess.ops.operators import DecodeImage
+from ppcls.utils import logger
+from ppcls.data.dataloader.common_dataset import create_operators
+
+
+class MultiScaleDataset(Dataset):
+    def __init__(
+            self,
+            image_root,
+            cls_label_path,
+            transform_ops=None, ):
+        self._img_root = image_root
+        self._cls_path = cls_label_path
+        self.transform_ops = transform_ops
+        self.images = []
+        self.labels = []
+        self._load_anno()
+        self.has_crop_flag = 1
+
+    def _load_anno(self, seed=None):
+        assert os.path.exists(self._cls_path)
+        assert os.path.exists(self._img_root)
+        self.images = []
+        self.labels = []
+
+        with open(self._cls_path) as fd:
+            lines = fd.readlines()
+            if seed is not None:
+                np.random.RandomState(seed).shuffle(lines)
+            for l in lines:
+                l = l.strip().split(" ")
+                self.images.append(os.path.join(self._img_root, l[0]))
+                self.labels.append(np.int64(l[1]))
+                assert os.path.exists(self.images[-1])
+
+    def __getitem__(self, properties):
+        # properites is a tuple, contains (width, height, index)
+        img_width = properties[0]
+        img_height = properties[1]
+        index = properties[2]
+        has_crop = False
+        if self.transform_ops:
+            for i in range(len(self.transform_ops)):
+                op = self.transform_ops[i]
+                resize_op = ['RandCropImage', 'ResizeImage', 'CropImage']
+                for resize in resize_op:
+                    if resize in op:
+                        if self.has_crop_flag:
+                            logger.warning(
+                                "Multi scale dataset will crop image according to the multi scale resolution"
+                            )
+                        self.transform_ops[i][resize] = {
+                            'size': (img_width, img_height)
+                        }
+                        has_crop = True
+                        self.has_crop_flag = 0
+        if has_crop == False:
+            logger.error("Multi scale dateset requests RandCropImage")
+            raise RuntimeError("Multi scale dateset requests RandCropImage")
+        self._transform_ops = create_operators(self.transform_ops)
+
+        try:
+            with open(self.images[index], 'rb') as f:
+                img = f.read()
+            if self._transform_ops:
+                img = transform(img, self._transform_ops)
+            img = img.transpose((2, 0, 1))
+            return (img, self.labels[index])
+
+        except Exception as ex:
+            logger.error("Exception occured when parse line: {} with msg: {}".
+                         format(self.images[index], 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))

ppcls/data/dataloader/multi_scale_sampler.py

+from paddle.io import Sampler
+import paddle.distributed as dist
+
+import math
+import random
+import numpy as np
+
+from ppcls import data
+
+
+class MultiScaleSampler(Sampler):
+    def __init__(self,
+                 data_source,
+                 scales,
+                 first_bs,
+                 divided_factor=32,
+                 is_training=True,
+                 seed=None):
+        """
+            multi scale samper
+            Args:
+                data_source(dataset)
+                scales(list): several scales for image resolution
+                first_bs(int): batch size for the first scale in scales
+                divided_factor(int): ImageNet models down-sample images by a factor, ensure that width and height dimensions are multiples are multiple of devided_factor.
+                is_training(boolean): mode 
+        """
+        # min. and max. spatial dimensions
+        self.data_source = data_source
+        self.n_data_samples = len(self.data_source)
+
+        if isinstance(scales[0], tuple):
+            width_dims = [i[0] for i in scales]
+            height_dims = [i[1] for i in scales]
+        elif isinstance(scales[0], int):
+            width_dims = scales
+            height_dims = scales
+        base_im_w = width_dims[0]
+        base_im_h = height_dims[0]
+        base_batch_size = first_bs
+
+        # Get the GPU and node related information
+        num_replicas = dist.get_world_size()
+        rank = dist.get_rank()
+        # adjust the total samples to avoid batch dropping
+        num_samples_per_replica = int(
+            math.ceil(self.n_data_samples * 1.0 / num_replicas))
+        img_indices = [idx for idx in range(self.n_data_samples)]
+
+        self.shuffle = False
+        if is_training:
+            # compute the spatial dimensions and corresponding batch size
+            # ImageNet models down-sample images by a factor of 32.
+            # Ensure that width and height dimensions are multiples are multiple of 32.
+            width_dims = [
+                int((w // divided_factor) * divided_factor) for w in width_dims
+            ]
+            height_dims = [
+                int((h // divided_factor) * divided_factor)
+                for h in height_dims
+            ]
+
+            img_batch_pairs = list()
+            base_elements = base_im_w * base_im_h * base_batch_size
+            for (h, w) in zip(height_dims, width_dims):
+                batch_size = int(max(1, (base_elements / (h * w))))
+                img_batch_pairs.append((w, h, batch_size))
+            self.img_batch_pairs = img_batch_pairs
+            self.shuffle = True
+        else:
+            self.img_batch_pairs = [(base_im_w, base_im_h, base_batch_size)]
+
+        self.img_indices = img_indices
+        self.n_samples_per_replica = num_samples_per_replica
+        self.epoch = 0
+        self.rank = rank
+        self.num_replicas = num_replicas
+        self.seed = seed
+        self.batch_list = []
+        self.current = 0
+        indices_rank_i = self.img_indices[self.rank:len(self.img_indices):
+                                          self.num_replicas]
+        while self.current < self.n_samples_per_replica:
+            curr_w, curr_h, curr_bsz = random.choice(self.img_batch_pairs)
+
+            end_index = min(self.current + curr_bsz,
+                            self.n_samples_per_replica)
+
+            batch_ids = indices_rank_i[self.current:end_index]
+            n_batch_samples = len(batch_ids)
+            if n_batch_samples != curr_bsz:
+                batch_ids += indices_rank_i[:(curr_bsz - n_batch_samples)]
+            self.current += curr_bsz
+
+            if len(batch_ids) > 0:
+                batch = [curr_w, curr_h, len(batch_ids)]
+                self.batch_list.append(batch)
+        self.length = len(self.batch_list)
+
+    def __iter__(self):
+        if self.shuffle:
+            if self.seed is not None:
+                random.seed(self.seed)
+            else:
+                random.seed(self.epoch)
+            random.shuffle(self.img_indices)
+            random.shuffle(self.img_batch_pairs)
+            indices_rank_i = self.img_indices[self.rank:len(self.img_indices):
+                                              self.num_replicas]
+        else:
+            indices_rank_i = self.img_indices[self.rank:len(self.img_indices):
+                                              self.num_replicas]
+
+        start_index = 0
+        for batch_tuple in self.batch_list:
+            curr_w, curr_h, curr_bsz = batch_tuple
+            end_index = min(start_index + curr_bsz, self.n_samples_per_replica)
+            batch_ids = indices_rank_i[start_index:end_index]
+            n_batch_samples = len(batch_ids)
+            if n_batch_samples != curr_bsz:
+                batch_ids += indices_rank_i[:(curr_bsz - n_batch_samples)]
+            start_index += curr_bsz
+
+            if len(batch_ids) > 0:
+                batch = [(curr_w, curr_h, b_id) for b_id in batch_ids]
+                yield batch
+
+    def set_epoch(self, epoch: int):
+        self.epoch = epoch
+
+    def __len__(self):
+        return self.length

ppcls/engine/engine.py

@@ -452,6 +452,12 @@ class Engine(object):
                                   self.config["Global"]["pretrained_model"])
 
         model.eval()
+
+        # for rep nets
+        for layer in self.model.sublayers():
+            if hasattr(layer, "rep"):
+                layer.rep()
+
         save_path = os.path.join(self.config["Global"]["save_inference_dir"],
                                  "inference")
         if model.quanter:

ppcls/utils/save_load.py

@@ -116,9 +116,8 @@ def init_model(config, net, optimizer=None, loss: paddle.nn.Layer=None):
             load_distillation_model(net, pretrained_model)
         else:  # common load
             load_dygraph_pretrain(net, path=pretrained_model)
-            logger.info(
-                logger.coloring("Finish load pretrained model from {}".format(
-                    pretrained_model), "HEADER"))
+            logger.info("Finish load pretrained model from {}".format(
+                    pretrained_model))
 
 
 def save_model(net,

test_tipc/config/PPHGNet/PPHGNet_small_train_infer_python.txt

+===========================train_params===========================
+model_name:PPHGNet_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:norm_train
+norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_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
+pact_train:null
+fpgm_train:null
+distill_train:null
+null:null
+null:null
+##
+===========================eval_params=========================== 
+eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_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/PPHGNet/PPHGNet_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/legendary_models/PPHGNet_small_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=236
+-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
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]

test_tipc/config/PPHGNet/PPHGNet_tiny_train_infer_python.txt

+===========================train_params===========================
+model_name:PPHGNet_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/PPHGNet/PPHGNet_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/PPHGNet/PPHGNet_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/PPHGNet/PPHGNet_tiny.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/PPHGNet_tiny_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=232
+-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
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]

test_tipc/config/PPLCNetV2/PPLCNetV2_base_train_infer_python.txt

+===========================train_params===========================
+model_name:PPLCNetV2_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.first_bs: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/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -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/PPLCNetV2/PPLCNetV2_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/PPLCNetV2/PPLCNetV2_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/legendary_models/PPLCNetV2_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
+===========================infer_benchmark_params==========================
+random_infer_input:[{float32,[3,224,224]}]