improve doc, test=document_fix

26e5e540 · littletomatodonkey · Tingquan Gao · bba176e0 · 26e5e540 · 26e5e540
4 changed file
--- a/docs/en/tutorials/install_en.md
+++ b/docs/en/tutorials/install_en.md
@@ -16,13 +16,13 @@ Python 3.x, CUDA 10.0, CUDNN7.6.4 nccl2.1.2 and later version are required at fi
 If you want to use PaddlePaddle on GPU, you can use the following command to install PaddlePaddle.

 ```bash
-pip install paddlepaddle-gpu==2.0.0 --upgrade
+pip3 install paddlepaddle-gpu==2.0.0 --upgrade -i https://mirror.baidu.com/pypi/simple
 ```

 If you want to use PaddlePaddle on CPU, you can use the following command to install PaddlePaddle.

 ```bash
-pip install paddlepaddle==2.0.0 --upgrade
+pip3 install paddlepaddle==2.0.0 --upgrade -i https://mirror.baidu.com/pypi/simple
 ```

 ### Install PaddlePaddle from source code
@@ -39,7 +39,7 @@ paddle.utils.run_check()
 Check PaddlePaddle version：

 ```bash
-python -c "import paddle; print(paddle.__version__)"
+python3 -c "import paddle; print(paddle.__version__)"
 ```

 Note:
@@ -53,21 +53,25 @@ Note:
 **Clone PaddleClas: **

 ```
-cd path_to_clone_PaddleClas
-git clone https://github.com/PaddlePaddle/PaddleClas.git
+git clone https://github.com/PaddlePaddle/PaddleClas.git -b release/2.0
 ```

-**Install requirements**
+If it is too slow for you to download from github, you can download PaddleClas from gitee. The command is as follows.

+```bash
+git clone https://gitee.com/paddlepaddle/PaddleClas.git -b release/2.0
+```
+
+**Install requirements**

 ```
-pip install --upgrade -r requirements.txt
+pip3 install --upgrade -r requirements.txt -i https://mirror.baidu.com/pypi/simple
 ```

 If the install process of visualdl failed, you can try the following commands.

 ```
-pip3 install --upgrade visualdl==2.0.0b3 -i https://mirror.baidu.com/pypi/simple
+pip3 install --upgrade visualdl==2.1.1 -i https://mirror.baidu.com/pypi/simple

 ```


--- a/docs/en/tutorials/quick_start_en.md
+++ b/docs/en/tutorials/quick_start_en.md
 # Trial in 30mins

-Based on the flowers102 dataset, it takes only 30 mins to experience PaddleClas, include training varieties of backbone and pretrained model, SSLD distillation, and multiple data augmentation, Please refer to [Installation](install.md) to install at first.
+Based on the flowers102 dataset, it takes only 30 mins to experience PaddleClas, include training varieties of backbone and pretrained model, SSLD distillation, and multiple data augmentation, Please refer to [Installation](install_en.md) to install at first.


 ## Preparation
@@ -11,28 +11,17 @@ Based on the flowers102 dataset, it takes only 30 mins to experience PaddleClas,
 cd path_to_PaddleClas
 ```

-* enter `dataset/flowers102`, download and decompress flowers102 dataset.
+* Enter `dataset/flowers102`, download and decompress flowers102 dataset.

 ```shell
 cd dataset/flowers102
-wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz
-wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat
-wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat
-tar -xf 102flowers.tgz
+# If you want to download from the brower, you can copy the link, visit it
+# in the browser, download and then commpress.
+wget https://paddle-imagenet-models-name.bj.bcebos.com/data/flowers102.zip
+unzip flowers102.zip
 ```

-* create train/val/test label files
-
-```shell
-python generate_flowers102_list.py jpg train > train_list.txt
-python generate_flowers102_list.py jpg valid > val_list.txt
-python generate_flowers102_list.py jpg test > extra_list.txt
-cat train_list.txt extra_list.txt > train_extra_list.txt
-```
-
-**Note:** In order to offer more data to SSLD training task, train_list.txt and extra_list.txt will merge into train_extra_list.txft
-
-* return `PaddleClas` dir
+* Return `PaddleClas` dir

 ```
 cd ../../
@@ -67,11 +56,7 @@ cd ../

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd.yaml
-
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml
 ```

 The validation `Top1 Acc` curve is shown below.
@@ -85,11 +70,7 @@ The validation `Top1 Acc` curve is shown below.

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd_finetune.yaml
-
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_finetune.yaml
 ```

 The validation `Top1 Acc` curve is shown below
@@ -99,6 +80,31 @@ The validation `Top1 Acc` curve is shown below
 Compare with training from scratch, it improve by 65\% to 94.02\%


+You can use the trained model to infer the result of image `docs/images/quick_start/flowers102/image_06739.jpg`. The command is as follows.
+
+
+```shell
+python3 tools/infer/infer.py \
+    -i docs/images/quick_start/flowers102/image_06739.jpg \
+    --model=ResNet50_vd \
+    --pretrained_model="output/ResNet50_vd/best_model/ppcls" \
+    --class_num=102
+```
+
+The output is as follows. Top-5 class ids and their scores are printed.
+
+```
+Current image file: docs/images/quick_start/flowers102/image_06739.jpg
+    top1, class id: 0, probability: 0.5129
+    top2, class id: 50, probability: 0.0671
+    top3, class id: 18, probability: 0.0377
+    top4, class id: 82, probability: 0.0238
+    top5, class id: 54, probability: 0.0231
+```
+
+* Note: Results are different for different models, so you might get different results for the command.
+
+
 ### SSLD finetune - ResNet50_vd_ssld pretrained model (Acc 82.39\%)

 Note: when finetuning model, which has been trained by SSLD, please use smaller learning rate in the middle of net.
@@ -115,10 +121,7 @@ Tringing script

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd_ssld_finetune.yaml
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_ssld_finetune.yaml
 ```

 Compare with finetune on the 79.12% pretrained model, it improve by 0.9% to 95%.
@@ -130,10 +133,7 @@ Training script

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
+python3 tools/train.py -c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
 ```

 Compare with ResNet50_vd pretrained model, it decrease by 5% to 90%. Different architecture generates different performance, actually it is a task-oriented decision to apply the best performance model, should consider the inference time, storage, heterogeneous device, etc.
@@ -147,10 +147,7 @@ Training script

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
 ```

 It improves by 1.27\% to 96.27\%
@@ -184,10 +181,7 @@ Final training script

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
+python3 tools/train.py -c ./configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
 ```

 It significantly imporve by 6.47% to 96.47% with more unlabeled data and teacher model.
@@ -214,4 +208,4 @@ The whole accuracy curves are shown below

 * **NOTE**: As flowers102 is a small dataset, validatation accuracy maybe float 1%.

-* Please refer to [Getting_started](./getting_started) for more details
+* Please refer to [Getting_started](./getting_started_en.md) for more details
--- a/docs/zh_CN/tutorials/install.md
+++ b/docs/zh_CN/tutorials/install.md
@@ -16,13 +16,13 @@
 如果已经安装好了cuda、cudnn、nccl或者安装好了docker、nvidia-docker运行环境，可以pip安装最新GPU版本PaddlePaddle

 ```bash
-pip install paddlepaddle-gpu==2.0.0 --upgrade
+pip3 install paddlepaddle-gpu==2.0.0 --upgrade -i https://mirror.baidu.com/pypi/simple
 ```

 如果希望在CPU环境中使用PaddlePaddle，可以运行下面的命令安装PaddlePaddle。

 ```bash
-pip install paddlepaddle==2.0.0 --upgrade
+pip3 install paddlepaddle==2.0.0 --upgrade -i https://mirror.baidu.com/pypi/simple
 ```

 ### 源码编译PaddlePaddle
@@ -40,7 +40,7 @@ paddle.utils.run_check()
 查看PaddlePaddle版本的命令如下：

 ```bash
-python -c "import paddle; print(paddle.__version__)"
+python3 -c "import paddle; print(paddle.__version__)"
 ```

 注意：
@@ -60,24 +60,30 @@ python -c "import paddle; print(paddle.__version__)"

 **克隆PaddleClas模型库：**

+```bash
+git clone https://github.com/PaddlePaddle/PaddleClas.git -b release/2.0
 ```
-cd path_to_clone_PaddleClas
-git clone https://github.com/PaddlePaddle/PaddleClas.git
+
+如果从github上网速太慢，可以从gitee下载，下载命令如下：
+
+```bash
+git clone https://gitee.com/paddlepaddle/PaddleClas.git -b release/2.0
 ```

+
+
 **安装Python依赖库：**

-Python依赖库在[requirements.txt](https://github.com/PaddlePaddle/PaddleClas/blob/master/requirements.txt)中给出，可通过如下命令安装：
+Python依赖库在[requirements.txt](https://github.com/PaddlePaddle/PaddleClas/blob/release/2.0/requirements.txt)中给出，可通过如下命令安装：

-```
-pip install --upgrade -r requirements.txt
+```bash
+pip3 install --upgrade -r requirements.txt -i https://mirror.baidu.com/pypi/simple
 ```

 visualdl可能出现安装失败，请尝试

-```
-pip3 install --upgrade visualdl==2.0.0b3 -i https://mirror.baidu.com/pypi/simple
-
+```bash
+pip3 install --upgrade visualdl==2.1.1 -i https://mirror.baidu.com/pypi/simple
 ```

 此外，visualdl目前只支持在python3下运行，因此如果希望使用visualdl，需要使用python3。
--- a/docs/zh_CN/tutorials/quick_start.md
+++ b/docs/zh_CN/tutorials/quick_start.md
@@ -11,27 +11,16 @@
 cd path_to_PaddleClas
 ```

-* 进入`dataset/flowers102`目录，下载并解压flowers102数据集.
+* 进入`dataset/flowers102`目录，下载并解压flowers102数据集。

-```shell
-cd dataset/flowers102
-wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/102flowers.tgz
-wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/imagelabels.mat
-wget https://www.robots.ox.ac.uk/~vgg/data/flowers/102/setid.mat
-tar -xf 102flowers.tgz
-```
-
-* 制作train/val/test标签文件

 ```shell
-python generate_flowers102_list.py jpg train > train_list.txt
-python generate_flowers102_list.py jpg valid > val_list.txt
-python generate_flowers102_list.py jpg test > extra_list.txt
-cat train_list.txt extra_list.txt > train_extra_list.txt
+cd dataset/flowers102
+# 如果希望从浏览器中直接下载，可以复制该链接并访问，然后下载解压即可
+wget https://paddle-imagenet-models-name.bj.bcebos.com/data/flowers102.zip
+unzip flowers102.zip
 ```

-**注意**：这里将train_list.txt和extra_list.txt合并成train_extra_list.txt，是为了之后在进行知识蒸馏时，使用更多的数据提升无标签知识蒸馏任务的效果。
-
 * 返回`PaddleClas`根目录

 ```
@@ -50,7 +39,6 @@ cd pretrained
 wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
 wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
 wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams
-
 cd ../
 ```

@@ -69,11 +57,7 @@ cd ../

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd.yaml
-
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml
 ```

 验证集的`Top1 Acc`曲线如下所示，最高准确率为0.2735。
@@ -87,19 +71,39 @@ python -m paddle.distributed.launch \

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd_finetune.yaml
-
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_finetune.yaml
 ```

 验证集的`Top1 Acc`曲线如下所示，最高准确率为0.9402，加载预训练模型之后，flowers102数据集精度大幅提升，绝对精度涨幅超过65\%。

 ![](../../images/quick_start/r50_vd_pretrained_acc.png)

-### 3.3 SSLD模型微调-基于ResNet50_vd_ssld预训练模型(准确率82.39\%)
+使用训练完的预训练模型对图片`docs/images/quick_start/flowers102/image_06739.jpg`进行预测，预测命令为
+

+```shell
+python3 tools/infer/infer.py \
+    -i docs/images/quick_start/flowers102/image_06739.jpg \
+    --model=ResNet50_vd \
+    --pretrained_model="output/ResNet50_vd/best_model/ppcls" \
+    --class_num=102
+```
+
+最终可以得到如下结果，打印出了Top-5对应的class id以及score。
+
+```
+Current image file: docs/images/quick_start/flowers102/image_06739.jpg
+	top1, class id: 0, probability: 0.5129
+	top2, class id: 50, probability: 0.0671
+	top3, class id: 18, probability: 0.0377
+	top4, class id: 82, probability: 0.0238
+	top5, class id: 54, probability: 0.0231
+```
+
+* 注意：这里每个模型的训练结果都不相同，因此结果可能稍有不同。
+
+
+### 3.3 SSLD模型微调-基于ResNet50_vd_ssld预训练模型(准确率82.39\%)

 需要注意的是，在使用通过知识蒸馏得到的预训练模型进行微调时，我们推荐使用相对较小的网络中间层学习率。

@@ -113,12 +117,10 @@ pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
 ```

 训练脚本如下。
+
 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd_ssld_finetune.yaml
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_ssld_finetune.yaml
 ```

 最终flowers102验证集上精度指标为0.95，相对于79.12\%预训练模型的微调结构，新数据集指标可以再次提升0.9\%。
@@ -130,10 +132,7 @@ python -m paddle.distributed.launch \

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
+python3 tools/train.py -c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
 ```

 最终flowers102验证集上的精度为0.90，比加载了预训练模型的ResNet50_vd的精度差了5\%。不同模型结构的网络在相同数据集上的性能表现不同，需要根据预测耗时以及存储的需求选择合适的模型。
@@ -146,10 +145,7 @@ python -m paddle.distributed.launch \

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
-        -c ./configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml
 ```

 最终flowers102验证集上的精度为0.9627，使用数据增广可以使得模型精度再次提升1.27\%。
@@ -185,9 +181,7 @@ TRAIN:

 ```shell
 export CUDA_VISIBLE_DEVICES=0
-python -m paddle.distributed.launch \
-    --gpus="0" \
-    tools/train.py \
+python3 tools/train.py \
         -c ./configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml
 ```