[PaddleDetection] update GETTING_STARTED.md (#2982)

* update GETTING_STARTED.md

PaddleCV/PaddleDetection/docs/GETTING_STARTED.md

@@ -6,45 +6,122 @@ instructions](INSTALL.md).
 
 ## Training
 
-
 #### Single-GPU Training
 
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
 python tools/train.py -c configs/faster_rcnn_r50_1x.yml
 ```
 
 #### Multi-GPU Training
 
-
 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PYTHONPATH=$PYTHONPATH:.
+python tools/train.py -c configs/faster_rcnn_r50_1x.yml
+```
+
+#### CPU Training
+
+```bash
+export CPU_NUM=8
+export PYTHONPATH=$PYTHONPATH:.
 python tools/train.py -c configs/faster_rcnn_r50_1x.yml
 ```
 
-- Datasets is stored in `dataset/coco` by default (configurable).
-- Datasets will be downloaded automatically and cached in `~/.cache/paddle/dataset` if not be found locally.
+##### Optional arguments
+
+- `-r` or `--resume_checkpoint`: Checkpoint path for resuming training. Such as: `-r output/faster_rcnn_r50_1x/10000`
+- `--eval`: Whether to perform evaluation in training, default is `False`
+- `-p` or `--output_eval`: If perform evaluation in training, this edits evaluation directory, default is current directory.
+- `-d` or `--dataset_dir`: Dataset path, same as `dataset_dir` of configs. Such as: `-d dataset/coco`
+- `-o`: Set configuration options in config file. Such as: `-o weights=output/faster_rcnn_r50_1x/model_final`
+
+
+##### Examples
+
+- Perform evaluation in training
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PYTHONPATH=$PYTHONPATH:.
+python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml --eval
+```
+
+Alternating between training epoch and evaluation run is possible, simply pass
+in `--eval` to do so and evaluate at each snapshot_iter. It can be modified at `snapshot_iter` of the configuration file. If evaluation dataset is large and
+causes time-consuming in training, we suggest decreasing evaluation times or evaluating after training.
+
+
+- configuration options and assign Dataset path
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PYTHONPATH=$PYTHONPATH:.
+python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
+                         -o weights=output/faster_rcnn_r50_1x/model_final \
+                         -d dataset/coco
+```
+
+
+##### NOTES
+
+- `CUDA_VISIBLE_DEVICES` can specify different gpu numbers. Such as: `export CUDA_VISIBLE_DEVICES=0,1,2,3`. GPU calculation rules can refer [FAQ](#faq)
+- Dataset is stored in `dataset/coco` by default (configurable).
+- Dataset will be downloaded automatically and cached in `~/.cache/paddle/dataset` if not be found locally.
 - Pretrained model is downloaded automatically and cached in `~/.cache/paddle/weights`.
-- Model checkpoints is saved in `output` by default (configurable).
+- Model checkpoints are saved in `output` by default (configurable).
 - To check out hyper parameters used, please refer to the config file.
 - RCNN models training on CPU is not supported on PaddlePaddle<=1.5.1 and will be fixed on later version.
 
 
-Alternating between training epoch and evaluation run is possible, simply pass
-in `--eval` to do so and evaluate at each snapshot_iter. If evaluation dataset is large and
-causes time-consuming in training, we suggest decreasing evaluation times or evaluating after training.
 
 ## Evaluation
 
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
 # or run on CPU with:
 # export CPU_NUM=1
 python tools/eval.py -c configs/faster_rcnn_r50_1x.yml
 ```
 
+#### Optional arguments
+
+- `-d` or `--dataset_dir`: Dataset path, same as dataset_dir of configs. Such as: `-d dataset/coco`
+- `-p` or `--output_eval`: Evaluation directory, default is current directory.
+- `-o`: Set configuration options in config file. Such as: `-o weights=output/faster_rcnn_r50_1x/model_final`
+- `--json_eval`: Whether to eval with already existed bbox.json or mask.json. Default is `False`. Json file directory is assigned by `-f` argument.
+
+#### Examples
+
+- configuration options && assign Dataset path
+```bash
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
+# or run on CPU with:
+# export CPU_NUM=1
+python -u tools/eval.py -c configs/faster_rcnn_r50_1x.yml \
+                        -o weights=output/faster_rcnn_r50_1x/model_final \
+                        -d dataset/coco
+```
+
+- Evaluation with json
+```bash
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
+# or run on CPU with:
+# export CPU_NUM=1
+python tools/eval.py -c configs/faster_rcnn_r50_1x.yml \
+		     --json_eval \
+		     -f evaluation/
+```
+
+The json file must be named bbox.json or mask.json, placed in the `evaluation/` directory. Or without the `-f` parameter, default is the current directory.
+
+#### NOTES
+
 - Checkpoint is loaded from `output` by default (configurable)
 - Multi-GPU evaluation for R-CNN and SSD models is not supported at the
 moment, but it is a planned feature
@@ -57,30 +134,54 @@ moment, but it is a planned feature
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
 # or run on CPU with:
 # export CPU_NUM=1
 python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/000000570688.jpg
 ```
 
-- Batch inference:
+- Multi-image inference:
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
 # or run on CPU with:
 # export CPU_NUM=1
 python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_dir=demo
 ```
 
+#### Optional arguments
+
+- `--output_dir`: Directory for storing the output visualization files.
+- `--draw_threshold`: Threshold to reserve the result for visualization. Default is 0.5.
+- `--save_inference_model`: Save inference model in output_dir if True.
+
+#### Examples
+
+- Output specified directory && Set up threshold
+```bash
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
+# or run on CPU with:
+# export CPU_NUM=1
+python tools/infer.py -c configs/faster_rcnn_r50_1x.yml \
+                      --infer_img=demo/000000570688.jpg \
+                      --output_dir=infer_output/ \
+                      --draw_threshold=0.5
+```
 The visualization files are saved in `output` by default, to specify a different
-path, simply add a `--output_dir=` flag.
+path, simply add a `--output_dir=` flag.         
+`--draw_threshold` is an optional argument. Default is 0.5. Different thresholds will produce different results depending on the calculation of [NMS](https://ieeexplore.ieee.org/document/1699659)
 
 - Save inference model
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
 # or run on CPU with:
 # export CPU_NUM=1
-python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/000000570688.jpg \
+python tools/infer.py -c configs/faster_rcnn_r50_1x.yml \
+                      --infer_img=demo/000000570688.jpg \
                       --save_inference_model
 ```
 
@@ -91,8 +192,15 @@ Save inference model by set `--save_inference_model`, which can be loaded by Pad
 
 **Q:**  Why do I get `NaN` loss values during single GPU training? </br>
 **A:**  The default learning rate is tuned to multi-GPU training (8x GPUs), it must
-be adapted for single GPU training accordingly (e.g., divide by 8).
+be adapted for single GPU training accordingly (e.g., divide by 8).       
+The calculation rules are as follows，they are equivalent: </br>     
+
 
+| GPU number  | Learning rate  | Max_iters | Milestones       |       
+| :---------: | :------------: | :-------: | :--------------: |     
+| 2           | 0.0025         | 720000    | [480000, 640000] |
+| 4           | 0.005          | 360000    | [240000, 320000] |
+| 8           | 0.01           | 180000    | [120000, 160000] |
 
 **Q:**  How to reduce GPU memory usage? </br>
 **A:**  Setting environment variable FLAGS_conv_workspace_size_limit to a smaller

PaddleCV/PaddleDetection/docs/GETTING_STARTED_cn.md

@@ -11,6 +11,7 @@
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
 python tools/train.py -c configs/faster_rcnn_r50_1x.yml
 ```
 
@@ -19,11 +20,49 @@ python tools/train.py -c configs/faster_rcnn_r50_1x.yml
 
 ```bash
 export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
-# or run on CPU with:
-# export CPU_NUM=8
+export PYTHONPATH=$PYTHONPATH:.
 python tools/train.py -c configs/faster_rcnn_r50_1x.yml
 ```
 
+#### CPU训练
+
+```bash
+export CPU_NUM=8
+export PYTHONPATH=$PYTHONPATH:.
+python tools/train.py -c configs/faster_rcnn_r50_1x.yml
+```
+
+##### 可选参数
+
+- `-r` or `--resume_checkpoint`: 从某一检查点恢复训练，例如: `-r output/faster_rcnn_r50_1x/10000`
+- `--eval`: 是否边训练边测试，默认是 `False`
+- `-p` or `--output_eval`: 如果边训练边测试, 这个参数可以编辑评测保存json路径, 默认是当前目录。
+- `-d` or `--dataset_dir`: 数据集路径, 同配置文件里的`dataset_dir`. 例如: `-d dataset/coco`
+- `-o`: 设置配置文件里的参数内容。 例如: `-o weights=output/faster_rcnn_r50_1x/model_final`
+
+##### 例子
+
+- 边训练边测试
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PYTHONPATH=$PYTHONPATH:.
+python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml --eval
+```
+可通过设置`--eval`在训练epoch中交替执行评估, 评估在每个snapshot_iter时开始。可在配置文件的`snapshot_iter`处修改。
+如果验证集很大，测试将会比较耗时，影响训练速度，建议减少评估次数，或训练完再进行评估。
+
+- 设置配置文件参数 && 指定数据集路径 
+```bash
+export CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7
+export PYTHONPATH=$PYTHONPATH:.
+python -u tools/train.py -c configs/faster_rcnn_r50_1x.yml \
+                         -o weights=output/faster_rcnn_r50_1x/model_final \
+                         -d dataset/coco
+```
+
+##### 提示
+
+- `CUDA_VISIBLE_DEVICES` 参数可以指定不同的GPU。例如: `export CUDA_VISIBLE_DEVICES=0,1,2,3`. GPU计算规则可以参考 [FAQ](#faq)
 - 数据集默认存储在`dataset/coco`中（可配置）。
 - 若本地未找到数据集，将自动下载数据集并保存在`~/.cache/paddle/dataset`中。
 - 预训练模型自动下载并保存在`〜/.cache/paddle/weights`中。
@@ -32,9 +71,6 @@ python tools/train.py -c configs/faster_rcnn_r50_1x.yml
 - RCNN系列模型CPU训练在PaddlePaddle 1.5.1及以下版本暂不支持，将在下个版本修复。
 
 
-可通过设置`--eval`在训练epoch中交替执行评估, 评估在每个snapshot_iter时开始。
-如果验证集很大，测试将会比较耗时，影响训练速度，建议减少评估次数，或训练完再进行评估。
-
 ## 评估
 
 
@@ -45,6 +81,41 @@ export CUDA_VISIBLE_DEVICES=0
 python tools/eval.py -c configs/faster_rcnn_r50_1x.yml
 ```
 
+#### 可选参数
+
+- `-d` or `--dataset_dir`: 数据集路径, 同配置文件里的`dataset_dir`。例如: `-d dataset/coco`
+- `-p` or `--output_eval`: 这个参数可以编辑评测保存json路径, 默认是当前目录。
+- `-o`: 设置配置文件里的参数内容。 例如: `-o weights=output/faster_rcnn_r50_1x/model_final`
+- `--json_eval`: 是否通过已存在的bbox.json或者mask.json进行评估。默认是`False`。json文件路径通过`-f`指令来设置。
+
+#### 例子
+
+- 设置配置文件参数 && 指定数据集路径 
+```bash
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
+# or run on CPU with:
+# export CPU_NUM=1
+python -u tools/eval.py -c configs/faster_rcnn_r50_1x.yml \
+                        -o weights=output/faster_rcnn_r50_1x/model_final \
+                        -d dataset/coco
+```
+
+- 通过json文件评估
+```bash
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
+# or run on CPU with:
+# export CPU_NUM=1
+python tools/eval.py -c configs/faster_rcnn_r50_1x.yml \
+		     --json_eval \
+		     -f evaluation/
+```
+
+json文件必须命名为bbox.json或者mask.json，放在`evaluation/`目录下，或者不加`-f`参数，默认为当前目录。
+
+#### 提示
+
 - 默认从`output`加载checkpoint（可配置）
 - R-CNN和SSD模型目前暂不支持多GPU评估，将在后续版本支持
 
@@ -70,7 +141,29 @@ export CUDA_VISIBLE_DEVICES=0
 python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_dir=demo
 ```
 
-可视化文件默认保存在`output`中，可通过`--output_dir=`指定不同的输出路径。
+#### 可选参数
+
+- `--output_dir`: 输出推断后可视化文件。
+- `--draw_threshold`: 设置推断的阈值。默认是0.5.
+- `--save_inference_model`: Save inference model in output_dir if True.
+
+#### 例子
+
+- 设置输出路径 && 设置推断阈值
+```bash
+export CUDA_VISIBLE_DEVICES=0
+export PYTHONPATH=$PYTHONPATH:.
+# or run on CPU with:
+# export CPU_NUM=1
+python tools/infer.py -c configs/faster_rcnn_r50_1x.yml \
+                      --infer_img=demo/000000570688.jpg \
+                      --output_dir=infer_output/ \
+                      --draw_threshold=0.5
+```
+
+
+可视化文件默认保存在`output`中，可通过`--output_dir=`指定不同的输出路径。       
+`--draw_threshold` 是个可选参数. 根据 [NMS](https://ieeexplore.ieee.org/document/1699659) 的计算，不同阈值会产生不同的结果。
 
 - 保存推断模型
 
@@ -88,7 +181,15 @@ python tools/infer.py -c configs/faster_rcnn_r50_1x.yml --infer_img=demo/0000005
 ## FAQ
 
 **Q:**  为什么我使用单GPU训练loss会出`NaN`? </br>
-**A:**  默认学习率是适配多GPU训练(8x GPU)，若使用单GPU训练，须对应调整学习率（例如，除以8）。
+**A:**  默认学习率是适配多GPU训练(8x GPU)，若使用单GPU训练，须对应调整学习率（例如，除以8）。       
+计算规则表如下所示，它们是等价的: </br>          
+
+
+| GPU数  | 学习率  | 最大轮数 | 变化节点       |       
+| :---------: | :------------: | :-------: | :--------------: |     
+| 2           | 0.0025         | 720000    | [480000, 640000] |
+| 4           | 0.005          | 360000    | [240000, 320000] |
+| 8           | 0.01           | 180000    | [120000, 160000] |
 
 
 **Q:**  如何减少GPU显存使用率? </br>