update-mosaic-to-how-to (open-mmlab#7507)

docs/en/tutorials/how_to.md

@@ -66,3 +66,49 @@ optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
 `type='mmcls.TIMMBackbone'` means use the `TIMMBackbone` class from MMClassification in MMDetection, and the model used is `EfficientNet-B1`, where `mmcls` means the MMClassification repo and `TIMMBackbone` means the TIMMBackbone wrapper implemented in MMClassification.
 
 For the principle of the Hierarchy Registry, please refer to the [MMCV document](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/registry.md#hierarchy-registry). For how to use other backbones in MMClassification, you can refer to the [MMClassification document](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/config.md).
+
+## Use Mosaic augmentation
+
+If you want to use `Mosaic` in training, please make sure that you use `MultiImageMixDataset` at the same time. Taking the 'Faster R-CNN' algorithm as an example, you should modify the values of `train_pipeline` and `train_dataset` in the config as below:
+
+```python
+# Open configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py directly and add the following fields
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+img_scale=(1333, 800)​
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)), # The image will be enlarged by 4 times after Mosaic processing,so we use affine transformation to restore the image size.
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    _delete_ = True, # remove unnecessary Settings
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline
+    )
+​
+data = dict(
+    train=train_dataset
+    )
+```

docs/zh_cn/tutorials/how_to.md

@@ -64,3 +64,49 @@ optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
 `type='mmcls.TIMMBackbone'` 表示在 MMDetection 中使用 MMClassification 中的 `TIMMBackbone` 类，并且使用的模型为` EfficientNet-B1`，其中 `mmcls` 表示 MMClassification 库，而 `TIMMBackbone ` 表示 MMClassification 中实现的 TIMMBackbone 包装器。
 
 关于层次注册器的具体原理可以参考 [MMCV 文档](https://github.com/open-mmlab/mmcv/blob/master/docs/zh_cn/understand_mmcv/registry.md#%E6%B3%A8%E5%86%8C%E5%99%A8%E5%B1%82%E7%BB%93%E6%9E%84)，关于如何使用 MMClassification 中的其他 backbone，可以参考 [MMClassification 文档](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/config.md)。
+
+## 使用马赛克数据增强
+
+如果你想在训练中使用 `Mosaic`，那么请确保你同时使用 `MultiImageMixDataset`。以 `Faster R-CNN` 算法为例，你可以通过如下做法实现：
+
+```python
+# 直接打开 configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py ,增添如下字段
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+img_scale=(1333, 800)​
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)), # 图像经过马赛克处理后会放大4倍，所以我们使用仿射变换来恢复图像的大小。
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    _delete_ = True, # 删除不必要的设置
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline
+    )
+​
+data = dict(
+    train=train_dataset
+    )
+```