diff --git a/PaddleCV/PaddleDetection/docs/GETTING_STARTED.md b/PaddleCV/PaddleDetection/docs/GETTING_STARTED.md index ab1d830d8a8409e2247ef4ea6a411fce0b18b7b3..4932fbfc32eea9a14ff71d914180bd84e86de5f1 100644 --- a/PaddleCV/PaddleDetection/docs/GETTING_STARTED.md +++ b/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?
**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:
+ +| 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?
**A:** Setting environment variable FLAGS_conv_workspace_size_limit to a smaller diff --git a/PaddleCV/PaddleDetection/docs/GETTING_STARTED_cn.md b/PaddleCV/PaddleDetection/docs/GETTING_STARTED_cn.md index a84162e1c0cedd0d0b919ed7ef7c414f1adbbefb..066911710cc5616928fb9f849e1a26c895a65c82 100644 --- a/PaddleCV/PaddleDetection/docs/GETTING_STARTED_cn.md +++ b/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`?
-**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:
+ + +| 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?