• Patta
• • Table of Contents
  • Quick start (Default Transforms)
  • • • Test
      • • Segmentation model wrapping [docstring]:
        • Classification model wrapping [docstring]:
        • Keypoints model wrapping [docstring]:
      • Predict
      • • Load model [docstring]:
        • Segmentation model wrapping [docstring]:
        • Classification model wrapping [docstring]:
        • Keypoints model wrapping [docstring]:
      • Use-Tools
      • • Segmentation model [docstring]:
  • Advanced-Examples (DIY Transforms)
  • • • • Custom transform:
        • Custom model (multi-input / multi-output)
  • Optional Transforms
  • Aliases (Combos)
  • Merge-modes
  • Installation
  • Run tests
  • Contact us

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

1. Quick Start

• Test
• Predict
• Use Tools

2. Transforms
3. Aliases
4. Merge modes
5. Installation

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]:

Note: Usually, we recommend that the picture's shape is [**, **, 3].

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	-	-
HorizontalShift	shifts	List[float]
VerticalShift	shifts	List[float]
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
AdjustContrast	factors	List[float]
AdjustBrightness	factors	List[float]
AverageBlur	kernel_sizes	List[Union[Tuple[int, int], int]]
GaussianBlur	kernel_sizes sigma	List[Union[Tuple[int, int], int]] Optional[Union[Tuple[float, float], float]]
Sharpen	kernel_sizes	List[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:

# Use pip install PaTTA
$ pip install patta

or

# After downloading the whole dir
$ git clone https://github.com/AgentMaker/PaTTA.git
$ pip install PaTTA/

Run tests

python -m pytest

Contact us

Email : agentmaker@163.com

QQ Group : 1005109853