Patta
Image Test Time Augmentation with Paddle2.0!
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 (Default Transforms)
Test
We support that you can use the following to test after defining the network.
Segmentation model wrapping [docstring]:
import patta 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 Tensor([x1, y1, ..., xn, yn])
Predict
We support that you can use the following to test when you have the static model: *.pdmodel、*.pdiparams、*.pdiparams.info.
Load model [docstring]:
import patta as tta
model = tta.load_model(path='output/model')
Segmentation model wrapping [docstring]:
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)
Use-Tools
Segmentation model [docstring]:
We recommend modifying the file seg.py according to your own model.
python seg.py --model_path='output/model' \
--batch_size=16 \
--test_dataset='test.txt'
Note: Related to paddleseg
Advanced-Examples (DIY Transforms)
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)
Optional 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 (Combos)
- 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:
# After downloading the whole dir
$ git clone https://github.com/AgentMaker/PaTTA.git
$ pip install PaTTA/
# or
$ pip install git+https://github.com/AgentMaker/PaTTA.git
Run tests
# run test_transforms.py and test_base.py for test
python test/test_transforms.py
python test/test_base.py
32 Dec 14, 2022
690 Jan 04, 2023
13 Oct 10, 2022
395 Jan 03, 2023
47 Aug 14, 2022
63 Jun 24, 2022
13 Jan 06, 2023
0 Feb 05, 2022
15 Sep 12, 2022
2 Nov 28, 2021
29 Nov 26, 2022
3 Dec 22, 2021
102 Dec 19, 2022
100 Dec 19, 2022
7 Apr 01, 2022
61 Jan 01, 2023
901 Jan 06, 2023
6.4k Jan 09, 2023
47 Dec 15, 2022
1 Aug 19, 2021