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# TTA Related Notes
## Test Time Augmentation (TTA)
MMYOLO support for TTA in v0.5.0+, so that users can specify the `-tta` parameter to enable it during evaluation. Take `YOLOv5-s` as an example, its single GPU TTA test command is as follows
```shell
python tools/test.py configs/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco.py https://download.openmmlab.com/mmyolo/v0/yolov5/yolov5_n-v61_syncbn_fast_8xb16-300e_coco/yolov5_n-v61_syncbn_fast_8xb16-300e_coco_20220919_090739-b804c1ad.pth --tta
```
For TTA to work properly, you must ensure that the variables `tta_model` and `tta_pipeline` are present in the configuration, see [det_p5_tta.py](https://github.com/open-mmlab/mmyolo/blob/dev/configs/_base_/det_p5_tta.py) for details.
The default TTA in MMYOLO performs 3 multi-scale enhancements, followed by 2 horizontal flip enhancements, for a total of 6 parallel pipelines. take `YOLOv5-s` as an example, its TTA configuration is as follows
```python
img_scales = [(640, 640), (320, 320), (960, 960)]
_multiscale_resize_transforms = [
dict(
type='Compose',
transforms=[
dict(type='YOLOv5KeepRatioResize', scale=s),
dict(
type='LetterResize',
scale=s,
allow_scale_up=False,
pad_val=dict(img=114))
]) for s in img_scales
]
tta_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='TestTimeAug',
transforms=[
_multiscale_resize_transforms,
[
dict(type='mmdet.RandomFlip', prob=1.),
dict(type='mmdet.RandomFlip', prob=0.)
], [dict(type='mmdet.LoadAnnotations', with_bbox=True)],
[
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param', 'flip',
'flip_direction'))
]
])
]
```
The schematic diagram is shown below.
```text
LoadImageFromFile
/ | \
(RatioResize,LetterResize) (RatioResize,LetterResize) (RatioResize,LetterResize)
/ \ / \ / \
RandomFlip RandomFlip RandomFlip RandomFlip RandomFlip RandomFlip
| | | | | |
LoadAnn LoadAnn LoadAnn LoadAnn LoadAnn LoadAnn
| | | | | |
PackDetIn PackDetIn PackDetIn PackDetIn PackDetIn PackDetIn
```
You can modify `img_scales` to support different multi-scale enhancements, or you can insert a new pipeline to implement custom TTA requirements. Assuming you only want to do horizontal flip enhancements, the configuration should be modified as follows.
```python
tta_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='TestTimeAug',
transforms=[
[
dict(type='mmdet.RandomFlip', prob=1.),
dict(type='mmdet.RandomFlip', prob=0.)
], [dict(type='mmdet.LoadAnnotations', with_bbox=True)],
[
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'pad_param', 'flip',
'flip_direction'))
]
])
]
```
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