Merge branch 'develop' into dev/doc2

docs/Makefile

+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/en/algorithm_introduction/image_classification_en.md

-## Contents
+## Catalogue
 
 - [1. Dataset Introduction](#1)
   - [1.1 ImageNet-1k](#1.1)

docs/en/quick_start/quick_start_multilabel_classification_en.md

@@ -2,7 +2,7 @@
 
 Experience the training, evaluation, and prediction of multi-label classification based on the [NUS-WIDE-SCENE](https://lms.comp.nus.edu.sg/wp-content/uploads/2019/research/nuswide/NUS-WIDE.html) dataset, which is a subset of the NUS-WIDE dataset. Please first install PaddlePaddle and PaddleClas, see [Paddle Installation](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/installation) and [PaddleClas installation](https://github.com/PaddlePaddle/PaddleClas/blob/develop/docs/zh_CN/installation/install_ paddleclas.md) for more details.
 
-## Contents
+## Catalogue
 
 - [1. Data and Model Preparation](#1)
 - [2. Model Training](#2)
@@ -114,4 +114,4 @@ Obtain an output silimar to the following:
 
 ```
 0517_2715693311.jpg:    class id(s): [6, 13, 17, 23, 26, 30], score(s): [0.96, 0.56, 0.55, 0.99, 0.59, 0.79], label_name(s): []
-```
\ No newline at end of file
+```

docs/make.bat

+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.https://www.sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd

docs/source/index.rst

+.. paddleclas documentation master file, created by
+   sphinx-quickstart on Wed Dec 15 16:13:23 2021.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to paddleclas's documentation!
+======================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents:
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`

docs/zh_CN/algorithm_introduction/DataAugmentation.md

 # 数据增强
 ------
-
 ## 目录
 
   - [1. 数据增强简介](#1)

docs/zh_CN/algorithm_introduction/image_classification.md

 # 图像分类任务介绍
 
 
-------
 ## 目录
 
 

docs/zh_CN/conf.py

@@ -15,7 +15,7 @@
 # sys.path.insert(0, os.path.abspath('.'))
 import sphinx_rtd_theme
 from recommonmark.parser import CommonMarkParser
-#import sphinx-markdown-tables
+import sphinx-markdown-tables
 # -- Project information -----------------------------------------------------
 
 project = 'PaddleClas'

docs/zh_CN/index.rst

@@ -16,8 +16,4 @@
    advanced_tutorials/index
    others/index
    faq_series/index
-   
-  
-   
-      
-   
+

docs/zh_CN/make.bat

@@ -7,6 +7,7 @@ REM Command file for Sphinx documentation
 if "%SPHINXBUILD%" == "" (
 	set SPHINXBUILD=sphinx-build
 )
+
 set SOURCEDIR=source
 set BUILDDIR=build
 

docs/zh_CN/others/course_link.md

+# 往期课程链接：
+---
+
+- [**【AI快车道PaddleClas系列直播课】**](https://aistudio.baidu.com/aistudio/course/introduce/24519)
+
+  - [图像识别系统解析](https://aistudio.baidu.com/aistudio/education/group/info/24519)
+    - 图像识别全能优势
+    - 整体架构及快速落地详解
+    - 个性化需求实现方案
+  - [商品识别系统全拆解](https://aistudio.baidu.com/aistudio/education/lessonvideo/1495317)
+    - 小样本多类别场景方案
+    - 图像检索技术及快速构建策略
+    - 动漫搜索趣味应用
+  - [车辆ReID核心技术方案](https://aistudio.baidu.com/aistudio/education/lessonvideo/1496537)
+    - ReID及跨境头场景应用
+    - Metric Learning——更鲁棒的检索特征
+    - Logo识别等方向延展
+  - [超轻量图像识别系统概览](https://aistudio.baidu.com/aistudio/education/lessonvideo/1890318)
+    - 图像识别技术选型策略 
+    - 推理速度提升8倍的秘籍
+    - 四大典型行业应用案例 
+  - [SOTA模型炼丹秘诀](https://aistudio.baidu.com/aistudio/education/lessonvideo/1890323)
+    - CPU定制模型PP-LCNet优化思路
+    - Vison Transformer模型的应用拓展
+  - [商品识别产业痛点剖析](https://aistudio.baidu.com/aistudio/education/lessonvideo/1896890)
+    - 特征提取技术详解
+    - 向量快速检索揭秘 
+  - [手把手教你玩转图像识别](https://aistudio.baidu.com/aistudio/education/lessonvideo/1911507)
+    - 产业应用十问十答 
+    - 智能零售下的应用案例
+    - 识别系统快速落地方案
+  
+  
+

docs/zh_CN/others/train_with_DALI.md

@@ -31,29 +31,29 @@
  <a name='3'></a>
 
 ## 3. 使用 DALI
-PaddleClas 支持在静态图训练方式中使用 DALI 加速，由于 DALI 仅支持 GPU 训练，因此需要设置 GPU，且 DALI 需要占用 GPU 显存，需要为 DALI 预留显存。使用 DALI 训练只需在训练配置文件中设置字段 `use_dali=True`，或通过以下命令启动训练即可：
+PaddleClas 支持使用 DALI 对图像预处理进行加速，由于 DALI 仅支持 GPU 训练，因此需要设置 GPU，且 DALI 需要占用 GPU 显存，需要为 DALI 预留显存。使用 DALI 训练只需在训练配置文件中设置字段 `use_dali=True`，或通过以下命令启动训练即可：
 
 ```shell
 # 设置用于训练的 GPU 卡号
 export CUDA_VISIBLE_DEVICES="0"
 
-python ppcls/static/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o use_dali=True
+python ppcls/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.use_dali=True
 ```
 
 也可以使用多卡训练：
 
 ```shell
 # 设置用于训练的 GPU 卡号
-export CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"
+export CUDA_VISIBLE_DEVICES="0,1,2,3"
 
 # 设置用于神经网络训练的显存大小，可根据具体情况设置，一般可设置为 0.8 或 0.7，剩余显存则预留 DALI 使用
 export FLAGS_fraction_of_gpu_memory_to_use=0.80
 
 python -m paddle.distributed.launch \
-    --gpus="0,1,2,3,4,5,6,7" \
-    ppcls/static/train.py \
+    --gpus="0,1,2,3" \
+    ppcls/train.py \
         -c ./ppcls/configs/ImageNet/ResNet/ResNet50.yaml \
-        -o use_dali=True
+        -o Global.use_dali=True
 ```
 
 <a name='4'></a>
@@ -62,11 +62,11 @@ python -m paddle.distributed.launch \
 在上述基础上，使用 FP16 半精度训练，可以进一步提高速度，可以参考下面的配置与运行命令。
 
 ```shell
-export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export CUDA_VISIBLE_DEVICES=0,1,2,3
 export FLAGS_fraction_of_gpu_memory_to_use=0.8
 
 python -m paddle.distributed.launch \
-    --gpus="0,1,2,3,4,5,6,7" \
-    ppcls/static/train.py \
-    -c ./ppcls/configs/ImageNet/ResNet/ResNet50_fp16.yaml
+    --gpus="0,1,2,3" \
+    ppcls/train.py \
+    -c ./ppcls/configs/ImageNet/ResNet/ResNet50_fp16_dygraph.yaml
 ```

ppcls/configs/slim/GeneralRecognition_PPLCNet_x2_5_quantization.yaml

+# global configs
+Global:
+  checkpoints: null
+  pretrained_model: https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0.pdparams
+  output_dir: ./output/
+  device: gpu
+  save_interval: 1
+  eval_during_train: True
+  eval_interval: 1
+  epochs: 30
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+  eval_mode: retrieval
+  use_dali: False
+  to_static: False
+
+# for quantizaiton or prune model
+Slim:
+  ## for prune
+  quant:
+    name: pact
+
+# model architecture
+Arch:
+  name: RecModel
+  infer_output_key: features
+  infer_add_softmax: False
+
+  Backbone: 
+    name: PPLCNet_x2_5
+    pretrained: False
+    use_ssld: True
+  BackboneStopLayer:
+    name: flatten_0
+  Neck:
+    name: FC
+    embedding_size: 1280
+    class_num: 512
+  Head:
+    name: ArcMargin 
+    embedding_size: 512
+    class_num: 185341
+    margin: 0.2
+    scale: 30
+
+# 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: Cosine
+    learning_rate: 0.002
+    warmup_epoch: 5
+  regularizer:
+    name: 'L2'
+    coeff: 0.00001
+
+
+# data loader for train and eval
+DataLoader:
+  Train:
+    dataset:
+      name: ImageNetDataset
+      image_root: ./dataset/
+      cls_label_path: ./dataset/train_reg_all_data.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: ''
+
+    sampler:
+      name: DistributedBatchSampler
+      batch_size: 128
+      drop_last: False
+      shuffle: True
+    loader:
+      num_workers: 4
+      use_shared_memory: True
+
+  Eval:
+    Query:
+      dataset: 
+        name: VeriWild
+        image_root: ./dataset/Aliproduct/
+        cls_label_path: ./dataset/Aliproduct/val_list.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 0.00392157
+              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
+
+    Gallery:
+      dataset: 
+        name: VeriWild
+        image_root: ./dataset/Aliproduct/
+        cls_label_path: ./dataset/Aliproduct/val_list.txt
+        transform_ops:
+          - DecodeImage:
+              to_rgb: True
+              channel_first: False
+          - ResizeImage:
+              size: 224
+          - NormalizeImage:
+              scale: 0.00392157
+              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
+
+Metric:
+  Eval:
+    - Recallk:
+        topk: [1, 5]

ppcls/configs/slim/PPLCNet_x1_0_quantization.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: 60
+  print_batch_step: 10
+  use_visualdl: False
+  # used for static mode and model export
+  image_shape: [3, 224, 224]
+  save_inference_dir: ./inference
+
+# for quantalization or prune model 
+Slim:
+  ## for quantization
+  quant:
+    name: pact
+
+# model architecture
+Arch:
+  name: PPLCNet_x1_0
+  class_num: 1000
+  pretrained: True
+ 
+# 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: 0.02
+    warmup_epoch: 0
+  regularizer:
+    name: 'L2'
+    coeff: 0.00003
+
+
+# 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
+        - RandFlipImage:
+            flip_code: 1
+        - AutoAugment:
+        - 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: 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/engine/engine.py

@@ -250,6 +250,8 @@ class Engine(object):
             self.scaler = paddle.amp.GradScaler(
                 init_loss_scaling=self.scale_loss,
                 use_dynamic_loss_scaling=self.use_dynamic_loss_scaling)
+            if self.config['AMP']['use_pure_fp16'] is True:
+                self.model = paddle.amp.decorate(models=self.model, level='O2')
 
         self.max_iter = len(self.train_dataloader) - 1 if platform.system(
         ) == "Windows" else len(self.train_dataloader)

ppcls/engine/train/train.py

@@ -21,6 +21,7 @@ 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
@@ -41,14 +42,15 @@ def train_epoch(engine, epoch_id, print_batch_step):
 
         # image input
         if engine.amp:
-            with paddle.amp.auto_cast(custom_black_list={
-                    "flatten_contiguous_range", "greater_than"
-            }):
+            amp_level = 'O1'
+            if engine.config['AMP']['use_pure_fp16'] is True:
+                amp_level = 'O2'
+            with paddle.amp.auto_cast(custom_black_list={"flatten_contiguous_range", "greater_than"}, level=amp_level):
                 out = forward(engine, batch)
+                loss_dict = engine.train_loss_func(out, batch[1])
         else:
             out = forward(engine, batch)
-
-        loss_dict = engine.train_loss_func(out, batch[1])
+            loss_dict = engine.train_loss_func(out, batch[1])
 
         # step opt and lr
         if engine.amp:

ppcls/optimizer/optimizer.py

@@ -17,6 +17,7 @@ from __future__ import division
 from __future__ import print_function
 
 from paddle import optimizer as optim
+import paddle
 
 from ppcls.utils import logger
 
@@ -36,7 +37,7 @@ class Momentum(object):
                  momentum,
                  weight_decay=None,
                  grad_clip=None,
-                 multi_precision=False):
+                 multi_precision=True):
         super().__init__()
         self.learning_rate = learning_rate
         self.momentum = momentum
@@ -55,6 +56,15 @@ class Momentum(object):
             grad_clip=self.grad_clip,
             multi_precision=self.multi_precision,
             parameters=parameters)
+        if hasattr(opt, '_use_multi_tensor'):
+            opt = optim.Momentum(
+                learning_rate=self.learning_rate,
+                momentum=self.momentum,
+                weight_decay=self.weight_decay,
+                grad_clip=self.grad_clip,
+                multi_precision=self.multi_precision,
+                parameters=parameters,
+                use_multi_tensor=True)
         return opt
 
 

readthedoc指南.md

+1. 注册ReadtheDocs并连接到github
+
+2. 在github上将项目克隆到本地
+
+3. 在本地仓库中安装Sphinx
+
+   ```shell
+   pip install sphinx
+   ```
+
+4. 创建工程
+
+   ```shell
+   sphinx-quickstart
+   ```
+
+5. 对工程进行配置
+
+   5.1 更改主题
+
+   在source/conf.py中更改或添加如下代码
+
+   ```python
+   import sphinx_rtd_theme
+   html_theme = "sphinx_rtd_theme"
+   html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+   ```
+
+   5.2 添加markdown支持和markdown表格支持
+
+   首先需要安装recommonmark和sphinx_markdown_tables
+
+   ```shell
+   pip install recommonmark
+   pip install sphinx_markdown_tables
+   ```
+
+   在source/conf.py中更改或添加如下代码
+
+   ```python
+   from recommonmark.parser import CommonMarkParser
+   source_parsers = {
+       '.md': CommonMarkParser,
+   }
+   source_suffix = ['.rst', '.md']
+   extensions = [
+        'recommonmark',
+        'sphinx_markdown_tables'
+    ]
+   ```
+
+   以上五步具体效果可以参考https://www.jianshu.com/p/d1d59d0cd58c
+
+6. 在创建好项目以后，根目录下应该有如下几个文件：
+
+   - **Makefile**：在使用 `make` 命令时，可以使用这些指令（e.g. `sphinx-build`）来构建文档输出。
+   - **_build**：这是触发特定输出后用来存放所生成的文件的目录。
+   - **_static**：所有不属于源代码（e.g. 图片）一部分的文件均存放于此处，稍后会在构建目录中将它们链接在一起。
+   - **conf.py**：用于存放 Sphinx 的配置值，包括在终端执行 `sphinx-quickstart`时选中的那些值。
+   - **index.rst**：文档项目的 root 目录。如果将文档划分为其他文件，该目录会连接这些文件
+
+7. **编写文档**：在 index.rst 文件中的主标题之后，有一个内容清单，其中包括 `toctree` 声明，它将所有文档链接都汇集到 Index。
+
+   以根目录下的index.rst为例：
+
+   ```rst
+   欢迎使用PaddleClas图像分类库！
+   ================================
+   
+   .. toctree::
+      :maxdepth: 1
+   
+      models_training/index
+      introduction/index
+      image_recognition_pipeline/index
+      others/index
+      faq_series/index
+      data_preparation/index
+      installation/index
+      models/index
+      advanced_tutorials/index
+      algorithm_introduction/index
+      inference_deployment/index
+      quick_start/index
+   ```
+
+   可以用下面的python代码实现根目录和各个子目录下的`index.rst`文件的编写
+
+   注意：此代码应该在需要生成文档书的文件夹根目录上运行
+
+   ```python
+   import os
+   
+   def file_name(file_dir):
+       temp = []
+       for root, dirs, files in os.walk(file_dir):
+           print(dirs) #当前路径下所有子目录
+           temp = dirs #存储需要的子目录
+           break
+       
+       # 删除不需要的子目录
+       temp.remove('images')
+       temp.remove('_templates')
+       temp.remove('_build')
+       temp.remove('_static')
+       chinese_name = ['模型训练', '介绍', '图像识别流程', '其他', 'FAQ系列', '数据准备', '安装', '模型库', '高级教程', '算法介绍', '推理部署', '快速开始']
+       # 写根目录下的rst文件
+       with open('./index.rst', 'w') as f:
+           f.write('欢迎使用PaddleClas图像分类库！\n')
+           f.write('================================\n\n')
+           f.write('.. toctree::\n')
+           f.write('   :maxdepth: 1\n\n')
+           for dir in temp:
+               f.write('   ' + dir + '/index\n')
+           f.close()
+   
+       # 写各个子目录下的rst文件
+       for dir in temp:
+          for root, dirs, files in os.walk(dir):
+           print(root) #当前目录路径
+           
+           files.remove('index.rst')
+           print(files) #当前路径下所有非目录子文件
+           curDir = os.path.join(file_dir, dir)
+           filename = curDir + '/index.rst'
+           idx = temp.index(dir)
+           ch_name = chinese_name[idx]
+           with open(filename, 'w') as f:
+               f.write(ch_name+'\n')
+               f.write('================================\n\n')
+               f.write('.. toctree::\n')
+               f.write('   :maxdepth: 2\n\n')
+              
+               for f1 in files:
+                   f.write('   ' + f1 + '\n')
+               
+               f.close()
+   
+   
+   def readfile(filename):
+       file = open(filename)
+       i = 0
+       while 1:
+           line = file.readline()
+           print(i)
+           print(line)
+           i += 1
+           if not line:
+               break
+           pass # do something
+       file.close()
+   
+   
+   file_name('./')
+   # filename = './index.rst'
+   # readfile(filename)
+   ```
+
+8. 生成文档
+
+   运行 `make html` 命令
+
+9. 使用浏览器查看在build/html目录下的 `index.html`文件可以查看静态网页
+
+   
+

test_tipc/config/LeViT/LeViT_384_train_infer_python.txt

@@ -6,7 +6,7 @@ gpu_list:0|0,1
 -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 DataLoader.Train.sampler.batch_size:2
 -o Global.pretrained_model:null
 train_model_name:latest
 train_infer_img_dir:./dataset/ILSVRC2012/val
@@ -37,7 +37,7 @@ pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/L
 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
+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

test_tipc/config/RedNet/RedNet101_train_infer_python.txt

@@ -6,7 +6,7 @@ gpu_list:0|0,1
 -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 DataLoader.Train.sampler.batch_size:2
 -o Global.pretrained_model:null
 train_model_name:latest
 train_infer_img_dir:./dataset/ILSVRC2012/val

test_tipc/config/SwinTransformer/SwinTransformer_large_patch4_window12_384_train_infer_python.txt

@@ -6,7 +6,7 @@ gpu_list:0|0,1
 -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 DataLoader.Train.sampler.batch_size:2
 -o Global.pretrained_model:null
 train_model_name:latest
 train_infer_img_dir:./dataset/ILSVRC2012/val