From d2eb33da0eb35f232a98c921411c20630779e9ca Mon Sep 17 00:00:00 2001 From: Feng Ni Date: Mon, 18 Apr 2022 19:50:45 +0800 Subject: [PATCH] add YOLOX doc and configs (#5731) * add yolox doc and configs * fix bn and update doc, test=document_fix --- configs/yolox/README.md | 81 +++++++++++++++++++++++ configs/yolox/_base_/optimizer_300e.yml | 20 ++++++ configs/yolox/_base_/yolox_cspdarknet.yml | 42 ++++++++++++ configs/yolox/_base_/yolox_reader.yml | 44 ++++++++++++ configs/yolox/yolox_l_300e_coco.yml | 13 ++++ configs/yolox/yolox_m_300e_coco.yml | 13 ++++ configs/yolox/yolox_nano_300e_coco.yml | 81 +++++++++++++++++++++++ configs/yolox/yolox_s_300e_coco.yml | 13 ++++ configs/yolox/yolox_tiny_300e_coco.yml | 69 +++++++++++++++++++ configs/yolox/yolox_x_300e_coco.yml | 13 ++++ ppdet/data/transform/operators.py | 2 + ppdet/engine/trainer.py | 4 +- 12 files changed, 393 insertions(+), 2 deletions(-) create mode 100644 configs/yolox/README.md create mode 100644 configs/yolox/_base_/optimizer_300e.yml create mode 100644 configs/yolox/_base_/yolox_cspdarknet.yml create mode 100644 configs/yolox/_base_/yolox_reader.yml create mode 100644 configs/yolox/yolox_l_300e_coco.yml create mode 100644 configs/yolox/yolox_m_300e_coco.yml create mode 100644 configs/yolox/yolox_nano_300e_coco.yml create mode 100644 configs/yolox/yolox_s_300e_coco.yml create mode 100644 configs/yolox/yolox_tiny_300e_coco.yml create mode 100644 configs/yolox/yolox_x_300e_coco.yml diff --git a/configs/yolox/README.md b/configs/yolox/README.md new file mode 100644 index 000000000..df4adccad --- /dev/null +++ b/configs/yolox/README.md @@ -0,0 +1,81 @@ +# YOLOX (YOLOX: Exceeding YOLO Series in 2021) + +## Model Zoo +### YOLOX on COCO + +| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 |推理时间(fps) | Box AP | 下载链接 | 配置文件 | +| :------------- | :------- | :-------: | :------: | :---------: | :-----: | :-------------: | :-----: | +| YOLOX-nano | 416 | 8 | 300e | ---- | 26.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_nano_300e_coco.pdparams) | [配置文件](./yolox_nano_300e_coco.yml) | +| YOLOX-tiny | 416 | 8 | 300e | ---- | 32.9 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_tiny_300e_coco.pdparams) | [配置文件](./yolox_tiny_300e_coco.yml) | +| YOLOX-s | 640 | 8 | 300e | ---- | 40.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams) | [配置文件](./yolox_s_300e_coco.yml) | +| YOLOX-m | 640 | 8 | 300e | ---- | 46.9 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_m_300e_coco.pdparams) | [配置文件](./yolox_m_300e_coco.yml) | +| YOLOX-l | 640 | 8 | 300e | ---- | 50.1 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_l_300e_coco.pdparams) | [配置文件](./yolox_l_300e_coco.yml) | +| YOLOX-x | 640 | 8 | 300e | ---- | 51.4 | [下载链接](https://paddledet.bj.bcebos.com/models/yolox_x_300e_coco.pdparams) | [配置文件](./yolox_x_300e_coco.yml) | + +**注意:** + - 以上模型默认采用8 GPUs训练，总batch_size为64，均训练300 epochs； + - 为保持高mAP的同时提高推理速度，可以将[yolox_cspdarknet.yml](_base_/yolox_cspdarknet.yml)中的`nms_top_k`修改为`1000`，将`keep_top_k`修改为`100`，mAP会下降约0.1~0.2%； + - 为快速的demo演示效果，可以将[yolox_cspdarknet.yml](_base_/yolox_cspdarknet.yml)中的`score_threshold`修改为`0.25`，将`nms_threshold`修改为`0.45`，但mAP会下降较多； + + +## 使用教程 + +### 1. 训练 +执行以下指令使用混合精度训练YOLOX +```bash +python -m paddle.distributed.launch --gpus 0,1,2,3,4,5,6,7 tools/train.py -c configs/yolox/yolox_s_300e_coco.yml --fleet --amp --eval +``` +**注意:** +使用默认配置训练需要设置`--fleet`，`--amp`最好也设置以避免显存溢出，`--eval`表示边训边验证。 + +### 2. 评估 +执行以下命令在单个GPU上评估COCO val2017数据集 +```bash +CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams +``` + +### 3. 推理 +使用以下命令在单张GPU上预测图片，使用`--infer_img`推理单张图片以及使用`--infer_dir`推理文件中的所有图片。 +```bash +# 推理单张图片 +CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams --infer_img=demo/000000014439_640x640.jpg + +# 推理文件中的所有图片 +CUDA_VISIBLE_DEVICES=0 python tools/infer.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams --infer_dir=demo +``` + +### 4. 部署 +YOLOX在GPU上推理部署或benchmark测速等需要通过`tools/export_model.py`导出模型。 +运行以下的命令进行导出： +```bash +python tools/export_model.py -c configs/yolox/yolox_s_300e_coco.yml -o weights=https://paddledet.bj.bcebos.com/models/yolox_s_300e_coco.pdparams +``` +`deploy/python/infer.py`使用上述导出后的Paddle Inference模型用于推理和benchnark测速. + +```bash +# 推理单张图片 +python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu + +# 推理文件夹下的所有图片 +python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_dir=demo/ --device=gpu + +# benchmark测速 +python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True + +# tensorRT-FP32测速 +python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True --trt_max_shape=640 --trt_min_shape=640 --trt_opt_shape=640 --run_mode=trt_fp32 + +# tensorRT-FP16测速 +python deploy/python/infer.py --model_dir=output_inference/yolox_s_300e_coco --image_file=demo/000000014439_640x640.jpg --device=gpu --run_benchmark=True --trt_max_shape=640 --trt_min_shape=640 --trt_opt_shape=640 --run_mode=trt_fp16 +``` + + +## Citations +``` + @article{yolox2021, + title={YOLOX: Exceeding YOLO Series in 2021}, + author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian}, + journal={arXiv preprint arXiv:2107.08430}, + year={2021} +} +``` diff --git a/configs/yolox/_base_/optimizer_300e.yml b/configs/yolox/_base_/optimizer_300e.yml new file mode 100644 index 000000000..1853ad61f --- /dev/null +++ b/configs/yolox/_base_/optimizer_300e.yml @@ -0,0 +1,20 @@ +epoch: 300 + +LearningRate: + base_lr: 0.01 + schedulers: + - !CosineDecay + max_epochs: 300 + min_lr_ratio: 0.05 + last_plateau_epochs: 15 + - !ExpWarmup + epochs: 5 + +OptimizerBuilder: + optimizer: + type: Momentum + momentum: 0.9 + use_nesterov: True + regularizer: + factor: 0.0005 + type: L2 diff --git a/configs/yolox/_base_/yolox_cspdarknet.yml b/configs/yolox/_base_/yolox_cspdarknet.yml new file mode 100644 index 000000000..24ef370c4 --- /dev/null +++ b/configs/yolox/_base_/yolox_cspdarknet.yml @@ -0,0 +1,42 @@ +architecture: YOLOX +norm_type: sync_bn +use_ema: True +ema_decay: 0.9999 +ema_decay_type: "exponential" +act: silu +find_unused_parameters: True + +depth_mult: 1.0 +width_mult: 1.0 + +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + size_stride: 32 + size_range: [15, 25] # multi-scale range [480*480 ~ 800*800] + +CSPDarkNet: + arch: "X" + return_idx: [2, 3, 4] + depthwise: False + +YOLOCSPPAN: + depthwise: False + +YOLOXHead: + l1_epoch: 285 + depthwise: False + loss_weight: {cls: 1.0, obj: 1.0, iou: 5.0, l1: 1.0} + assigner: + name: SimOTAAssigner + candidate_topk: 10 + use_vfl: False + nms: + name: MultiClassNMS + nms_top_k: 10000 + keep_top_k: 1000 + score_threshold: 0.001 + nms_threshold: 0.65 + # For speed while keep high mAP, you can modify 'nms_top_k' to 1000 and 'keep_top_k' to 100, the mAP will drop about 0.1%. + # For high speed demo, you can modify 'score_threshold' to 0.25 and 'nms_threshold' to 0.45, but the mAP will drop a lot. diff --git a/configs/yolox/_base_/yolox_reader.yml b/configs/yolox/_base_/yolox_reader.yml new file mode 100644 index 000000000..657638817 --- /dev/null +++ b/configs/yolox/_base_/yolox_reader.yml @@ -0,0 +1,44 @@ +worker_num: 4 +TrainReader: + sample_transforms: + - Decode: {} + - Mosaic: + prob: 1.0 + input_dim: [640, 640] + degrees: [-10, 10] + scale: [0.1, 2.0] + shear: [-2, 2] + translate: [-0.1, 0.1] + enable_mixup: True + mixup_prob: 1.0 + mixup_scale: [0.5, 1.5] + - AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30} + - PadResize: {target_size: 640} + - RandomFlip: {} + batch_transforms: + - Permute: {} + batch_size: 8 + shuffle: True + drop_last: True + collate_batch: False + mosaic_epoch: 285 + + +EvalReader: + sample_transforms: + - Decode: {} + - Resize: {target_size: 640, keep_ratio: True} + - Pad: {size: 640, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 4 + + +TestReader: + inputs_def: + image_shape: [3, 640, 640] + sample_transforms: + - Decode: {} + - Resize: {target_size: 640, keep_ratio: True} + - Pad: {size: 640, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 1 diff --git a/configs/yolox/yolox_l_300e_coco.yml b/configs/yolox/yolox_l_300e_coco.yml new file mode 100644 index 000000000..79cffd5e5 --- /dev/null +++ b/configs/yolox/yolox_l_300e_coco.yml @@ -0,0 +1,13 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + './_base_/optimizer_300e.yml', + './_base_/yolox_cspdarknet.yml', + './_base_/yolox_reader.yml' +] +depth_mult: 1.0 +width_mult: 1.0 + +log_iter: 100 +snapshot_epoch: 10 +weights: output/yolox_l_300e_coco/model_final diff --git a/configs/yolox/yolox_m_300e_coco.yml b/configs/yolox/yolox_m_300e_coco.yml new file mode 100644 index 000000000..4c25d7e25 --- /dev/null +++ b/configs/yolox/yolox_m_300e_coco.yml @@ -0,0 +1,13 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + './_base_/optimizer_300e.yml', + './_base_/yolox_cspdarknet.yml', + './_base_/yolox_reader.yml' +] +depth_mult: 0.67 +width_mult: 0.75 + +log_iter: 100 +snapshot_epoch: 10 +weights: output/yolox_m_300e_coco/model_final diff --git a/configs/yolox/yolox_nano_300e_coco.yml b/configs/yolox/yolox_nano_300e_coco.yml new file mode 100644 index 000000000..79f961317 --- /dev/null +++ b/configs/yolox/yolox_nano_300e_coco.yml @@ -0,0 +1,81 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + './_base_/optimizer_300e.yml', + './_base_/yolox_cspdarknet.yml', + './_base_/yolox_reader.yml' +] +depth_mult: 0.33 +width_mult: 0.25 + +log_iter: 100 +snapshot_epoch: 10 +weights: output/yolox_nano_300e_coco/model_final + + +### model config: +# Note: YOLOX-nano use depthwise conv in backbone, neck and head. +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + size_stride: 32 + size_range: [10, 20] # multi-scale range [320*320 ~ 640*640] + +CSPDarkNet: + arch: "X" + return_idx: [2, 3, 4] + depthwise: True + +YOLOCSPPAN: + depthwise: True + +YOLOXHead: + depthwise: True + + +### reader config: +# Note: YOLOX-tiny/nano uses 416*416 for evaluation and inference. +# And multi-scale training setting is in model config, TrainReader's operators use 640*640 as default. +worker_num: 4 +TrainReader: + sample_transforms: + - Decode: {} + - Mosaic: + prob: 0.5 # 1.0 in YOLOX-tiny/s/m/l/x + input_dim: [640, 640] + degrees: [-10, 10] + scale: [0.5, 1.5] # [0.1, 2.0] in YOLOX-s/m/l/x + shear: [-2, 2] + translate: [-0.1, 0.1] + enable_mixup: False # True in YOLOX-s/m/l/x + - AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30} + - PadResize: {target_size: 640} + - RandomFlip: {} + batch_transforms: + - Permute: {} + batch_size: 8 + shuffle: True + drop_last: True + collate_batch: False + mosaic_epoch: 285 + + +EvalReader: + sample_transforms: + - Decode: {} + - Resize: {target_size: 416, keep_ratio: True} + - Pad: {size: 416, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 8 + + +TestReader: + inputs_def: + image_shape: [3, 416, 416] + sample_transforms: + - Decode: {} + - Resize: {target_size: 416, keep_ratio: True} + - Pad: {size: 416, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 1 diff --git a/configs/yolox/yolox_s_300e_coco.yml b/configs/yolox/yolox_s_300e_coco.yml new file mode 100644 index 000000000..9ba6120a9 --- /dev/null +++ b/configs/yolox/yolox_s_300e_coco.yml @@ -0,0 +1,13 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + './_base_/optimizer_300e.yml', + './_base_/yolox_cspdarknet.yml', + './_base_/yolox_reader.yml' +] +depth_mult: 0.33 +width_mult: 0.50 + +log_iter: 100 +snapshot_epoch: 10 +weights: output/yolox_s_300e_coco/model_final diff --git a/configs/yolox/yolox_tiny_300e_coco.yml b/configs/yolox/yolox_tiny_300e_coco.yml new file mode 100644 index 000000000..6e32c4851 --- /dev/null +++ b/configs/yolox/yolox_tiny_300e_coco.yml @@ -0,0 +1,69 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + './_base_/optimizer_300e.yml', + './_base_/yolox_cspdarknet.yml', + './_base_/yolox_reader.yml' +] +depth_mult: 0.33 +width_mult: 0.375 + +log_iter: 100 +snapshot_epoch: 10 +weights: output/yolox_tiny_300e_coco/model_final + + +### model config: +YOLOX: + backbone: CSPDarkNet + neck: YOLOCSPPAN + head: YOLOXHead + size_stride: 32 + size_range: [10, 20] # multi-scale ragne [320*320 ~ 640*640] + + +### reader config: +# Note: YOLOX-tiny/nano uses 416*416 for evaluation and inference. +# And multi-scale training setting is in model config, TrainReader's operators use 640*640 as default. +worker_num: 4 +TrainReader: + sample_transforms: + - Decode: {} + - Mosaic: + prob: 1.0 + input_dim: [640, 640] + degrees: [-10, 10] + scale: [0.5, 1.5] # [0.1, 2.0] in YOLOX-s/m/l/x + shear: [-2, 2] + translate: [-0.1, 0.1] + enable_mixup: False # True in YOLOX-s/m/l/x + - AugmentHSV: {is_bgr: False, hgain: 5, sgain: 30, vgain: 30} + - PadResize: {target_size: 640} + - RandomFlip: {} + batch_transforms: + - Permute: {} + batch_size: 8 + shuffle: True + drop_last: True + collate_batch: False + mosaic_epoch: 285 + + +EvalReader: + sample_transforms: + - Decode: {} + - Resize: {target_size: 416, keep_ratio: True} + - Pad: {size: 416, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 8 + + +TestReader: + inputs_def: + image_shape: [3, 416, 416] + sample_transforms: + - Decode: {} + - Resize: {target_size: 416, keep_ratio: True} + - Pad: {size: 416, fill_value: [114., 114., 114.]} + - Permute: {} + batch_size: 1 diff --git a/configs/yolox/yolox_x_300e_coco.yml b/configs/yolox/yolox_x_300e_coco.yml new file mode 100644 index 000000000..fd8e0d2eb --- /dev/null +++ b/configs/yolox/yolox_x_300e_coco.yml @@ -0,0 +1,13 @@ +_BASE_: [ + '../datasets/coco_detection.yml', + '../runtime.yml', + './_base_/optimizer_300e.yml', + './_base_/yolox_cspdarknet.yml', + './_base_/yolox_reader.yml' +] +depth_mult: 1.33 +width_mult: 1.25 + +log_iter: 100 +snapshot_epoch: 10 +weights: output/yolox_x_300e_coco/model_final diff --git a/ppdet/data/transform/operators.py b/ppdet/data/transform/operators.py index c3fb37e26..a17f1c30b 100644 --- a/ppdet/data/transform/operators.py +++ b/ppdet/data/transform/operators.py @@ -3083,6 +3083,8 @@ class Mosaic(BaseOperator): remove_outside_box=False): super(Mosaic, self).__init__() self.prob = prob + if isinstance(input_dim, Integral): + input_dim = [input_dim, input_dim] self.input_dim = input_dim self.degrees = degrees self.translate = translate diff --git a/ppdet/engine/trainer.py b/ppdet/engine/trainer.py index f15b99211..0f2f21359 100644 --- a/ppdet/engine/trainer.py +++ b/ppdet/engine/trainer.py @@ -103,8 +103,8 @@ class Trainer(object): if cfg.architecture == 'YOLOX': for k, m in self.model.named_sublayers(): if isinstance(m, nn.BatchNorm2D): - m.epsilon = 1e-3 # for amp(fp16) - m.momentum = 0.97 # 0.03 in pytorch + m._epsilon = 1e-3 # for amp(fp16) + m._momentum = 0.97 # 0.03 in pytorch #normalize params for deploy if 'slim' in cfg and cfg['slim_type'] == 'OFA': -- GitLab