add YOLOX doc and configs (#5741)

* add yolox doc and configs, test=document_fix

* fix bn and update doc, test=document_fix

configs/yolox/README.md

+# 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}
+}
+```

configs/yolox/_base_/optimizer_300e.yml

+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

configs/yolox/_base_/yolox_cspdarknet.yml

+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.

configs/yolox/_base_/yolox_reader.yml

+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

configs/yolox/yolox_l_300e_coco.yml

+_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

configs/yolox/yolox_m_300e_coco.yml

+_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

configs/yolox/yolox_nano_300e_coco.yml

+_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

configs/yolox/yolox_s_300e_coco.yml

+_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

configs/yolox/yolox_tiny_300e_coco.yml

+_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

configs/yolox/yolox_x_300e_coco.yml

+_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

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

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':