improve install and quick_start doc (#631)

* improve doc, test=document_fix

* improve doc, test=document_fix

* fix doc

* fix config, test=document_fix

* add cpu note, test=document_fix

* fix, test=document_fix

configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml

@@ -3,6 +3,7 @@ ARCHITECTURE:
     name: 'MobileNetV3_large_x1_0'
 pretrained_model: "./pretrained/MobileNetV3_large_x1_0_pretrained"
 model_save_dir: "./output/"
+use_gpu: True
 classes_num: 102
 total_images: 1020
 save_interval: 1

configs/quick_start/R50_vd_distill_MV3_large_x1_0.yaml

@@ -6,6 +6,7 @@ pretrained_model:
     - "./pretrained/flowers102_R50_vd_final/ppcls"
     - "./pretrained/MobileNetV3_large_x1_0_pretrained"
 model_save_dir: "./output/"
+use_gpu: True
 classes_num: 102
 total_images: 7169
 save_interval: 1

configs/quick_start/ResNet50_vd.yaml

@@ -4,6 +4,7 @@ ARCHITECTURE:
 
 checkpoints: ""
 pretrained_model: ""
+use_gpu: True
 model_save_dir: "./output/"
 classes_num: 102
 total_images: 1020

configs/quick_start/ResNet50_vd_finetune.yaml

@@ -3,6 +3,7 @@ ARCHITECTURE:
     name: 'ResNet50_vd'
 pretrained_model: "./pretrained/ResNet50_vd_pretrained"
 model_save_dir: "./output/"
+use_gpu: True
 classes_num: 102
 total_images: 1020
 save_interval: 1

configs/quick_start/ResNet50_vd_ssld_finetune.yaml

@@ -5,6 +5,7 @@ ARCHITECTURE:
         lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
 pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
 model_save_dir: "./output/"
+use_gpu: True
 classes_num: 102
 total_images: 1020
 save_interval: 1

configs/quick_start/ResNet50_vd_ssld_random_erasing_finetune.yaml

@@ -5,6 +5,7 @@ ARCHITECTURE:
         lr_mult_list: [0.1, 0.1, 0.2, 0.2, 0.3]
 pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
 model_save_dir: "./output/"
+use_gpu: True
 classes_num: 102
 total_images: 1020
 save_interval: 1

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
 
 ```
 

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
 
-* enter insatallation dir
+* Enter insatallation dir.
 
 ```
 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 decommpress.
+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 ../../
@@ -60,20 +49,39 @@ cd ../
 ## Training
 
 * All experiments are running on the NVIDIA® Tesla® V100 single card.
+* First of all, use the following command to set visible device.
+
+If you use mac or linux, you can use the following command:
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+```
+
+* If you use windows, you can use the following command.
+
+```shell
+set CUDA_VISIBLE_DEVICES=0
+```
+
+* If you want to train on cpu device, you can modify the field `use_gpu: True` in the config file to `use_gpu: False`, or you can append `-o use_gpu=False` in the training command, which means override the value of `use_gpu` as False.
+
 
 ### Train from scratch
 
 * Train ResNet50_vd
 
 ```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
+```
+
+If you want to train on cpu device, the command is as follows.
 
+```shell
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml -o use_gpu=False
 ```
 
+Similarly, for the following commands, if you want to train on cpu device, you can append `-o use_gpu=False` in the command.
+
 The validation `Top1 Acc` curve is shown below.
 
 ![](../../images/quick_start/r50_vd_acc.png)
@@ -81,15 +89,10 @@ The validation `Top1 Acc` curve is shown below.
 
 ### Finetune - ResNet50_vd pretrained model (Acc 79.12\%)
 
-* finetune ResNet50_vd_ model pretrained on the 1000-class Imagenet dataset
+* Finetune ResNet50_vd model pretrained on the 1000-class Imagenet dataset
 
 ```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 +102,31 @@ 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.
@@ -114,14 +142,10 @@ pretrained_model: "./pretrained/ResNet50_vd_ssld_pretrained"
 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%.
+Compare with finetune on the 79.12% pretrained model, it improve by 0.98\% to 95\%.
 
 
 ### More architecture - MobileNetV3
@@ -129,11 +153,7 @@ Compare with finetune on the 79.12% pretrained model, it improve by 0.9% to 95%.
 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.
@@ -146,11 +166,7 @@ Data augmentation works when training data is small.
 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\%
@@ -183,11 +199,7 @@ TRAIN:
 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 +226,4 @@ 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

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`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。

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 ../
 ```
 
@@ -58,8 +46,21 @@ cd ../
 
 ### 2.2 环境说明
 
-* 下面所有的训练过程均在`单卡V100`机器上运行。
+* 下面所有的训练过程均在`单卡V100`机器上运行。首先需要设置可用的显卡设备id。
+
+如果使用mac或者linux，可以使用下面的命令进行设置。
+
+```shell
+export CUDA_VISIBLE_DEVICES=0
+```
+
+如果使用windows，可以使用下面的命令进行设置。
+
+```shell
+set CUDA_VISIBLE_DEVICES=0
+```
 
+* 如果希望在cpu上训练，可以将配置文件中的`use_gpu: True`修改为`use_gpu: False`，或者在训练脚本后面添加`-o use_gpu=False`，表示传入参数，覆盖默认的`use_gpu`值。
 
 ## 三、模型训练
 
@@ -68,14 +69,18 @@ cd ../
 * 基于ResNet50_vd模型，训练脚本如下所示。
 
 ```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
+```
 
+如果希望在cpu上训练，训练脚本如下所示。
+
+```shell
+python3 tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml -o use_gpu=False
 ```
 
+下面的训练任务中，如果希望使用cpu训练，也可以在训练脚本中添加`-o use_gpu=False`。
+
+
 验证集的`Top1 Acc`曲线如下所示，最高准确率为0.2735。
 
 ![](../../images/quick_start/r50_vd_acc.png)
@@ -86,21 +91,39 @@ python -m paddle.distributed.launch \
 * 基于ImageNet1k分类预训练模型进行微调，训练脚本如下所示。
 
 ```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。
 
 
@@ -113,15 +136,12 @@ 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\%。
+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0.98\%。
 
 
 ### 3.4 尝试更多的模型结构-MobileNetV3
@@ -129,11 +149,7 @@ python -m paddle.distributed.launch \
 训练脚本如下所示。
 
 ```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。
@@ -145,11 +161,7 @@ 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\%。
@@ -184,11 +196,7 @@ TRAIN:
 最终的训练脚本如下所示。
 
 ```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
 ```
 
 最终flowers102验证集上的精度为0.9647，结合更多的无标签数据，使用教师模型进行知识蒸馏，MobileNetV3的精度涨幅高达6.47\%。