TTAch
Image Test Time Augmentation with PyTorch!
Similar to what Data Augmentation is doing to the training set, the purpose of Test Time Augmentation is to perform random modifications to the test images. Thus, instead of showing the regular, βcleanβ images, only once to the trained model, we will show it the augmented images several times. We will then average the predictions of each corresponding image and take that as our final guess [1].
Input
| # input batch of images
/ / /|\ \ \ # apply augmentations (flips, rotation, scale, etc.)
| | | | | | | # pass augmented batches through model
| | | | | | | # reverse transformations for each batch of masks/labels
\ \ \ / / / # merge predictions (mean, max, gmean, etc.)
| # output batch of masks/labels
Output
Table of Contents
Quick start
Segmentation model wrapping [docstring]:
import ttach as tta
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')Classification model wrapping [docstring]:
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform())Keypoints model wrapping [docstring]:
tta_model = tta.KeypointsTTAWrapper(model, tta.aliases.flip_transform(), scaled=True)Note: the model must return keypoints in the format torch([x1, y1, ..., xn, yn])
Advanced Examples
Custom transform:
# defined 2 * 2 * 3 * 3 = 36 augmentations !
transforms = tta.Compose(
[
tta.HorizontalFlip(),
tta.Rotate90(angles=[0, 180]),
tta.Scale(scales=[1, 2, 4]),
tta.Multiply(factors=[0.9, 1, 1.1]),
]
)
tta_model = tta.SegmentationTTAWrapper(model, transforms)Custom model (multi-input / multi-output)
# Example how to process ONE batch on images with TTA
# Here `image`/`mask` are 4D tensors (B, C, H, W), `label` is 2D tensor (B, N)
for transformer in transforms: # custom transforms or e.g. tta.aliases.d4_transform()
# augment image
augmented_image = transformer.augment_image(image)
# pass to model
model_output = model(augmented_image, another_input_data)
# reverse augmentation for mask and label
deaug_mask = transformer.deaugment_mask(model_output['mask'])
deaug_label = transformer.deaugment_label(model_output['label'])
# save results
labels.append(deaug_mask)
masks.append(deaug_label)
# reduce results as you want, e.g mean/max/min
label = mean(labels)
mask = mean(masks)Transforms
| Transform | Parameters | Values |
|---|---|---|
| HorizontalFlip | - | - |
| VerticalFlip | - | - |
| Rotate90 | angles | List[0, 90, 180, 270] |
| Scale | scales interpolation |
List[float] "nearest"/"linear" |
| Resize | sizes original_size interpolation |
List[Tuple[int, int]] Tuple[int,int] "nearest"/"linear" |
| Add | values | List[float] |
| Multiply | factors | List[float] |
| FiveCrops | crop_height crop_width |
int int |
Aliases
- flip_transform (horizontal + vertical flips)
- hflip_transform (horizontal flip)
- d4_transform (flips + rotation 0, 90, 180, 270)
- multiscale_transform (scale transform, take scales as input parameter)
- five_crop_transform (corner crops + center crop)
- ten_crop_transform (five crops + five crops on horizontal flip)
Merge modes
- mean
- gmean (geometric mean)
- sum
- max
- min
- tsharpen (temperature sharpen with t=0.5)
Installation
PyPI:
$ pip install ttachSource:
$ pip install git+https://github.com/qubvel/ttachRun tests
docker build -f Dockerfile.dev -t ttach:dev . && docker run --rm ttach:dev pytest -p no:cacheprovider