barzah / tta_wrapper

Test Time image Augmentation (TTA) wrapper for Keras model.

TTA wrapper

Test time augmnentation wrapper for keras image segmentation and classification models.

Description

How it works?

Wrapper add augmentation layers to your Keras model like this:

          Input
            |           # input image; shape 1, H, W, C
       / / / \ \ \      # duplicate image for augmentation; shape N, H, W, C
      | | |   | | |     # apply augmentations (flips, rotation, shifts)
     your Keras model
      | | |   | | |     # reverse transformations
       \ \ \ / / /      # merge predictions (mean, max, gmean)
            |           # output mask; shape 1, H, W, C
          Output

Arguments

• h_flip - bool, horizontal flip augmentation
• v_flip - bool, vertical flip augmentation
• rotataion - list, allowable angles - 90, 180, 270
• h_shift - list of int, horizontal shift augmentation in pixels
• v_shift - list of int, vertical shift augmentation in pixels
• add - list of int/float, additive factor (aug_image = image + factor)
• mul - list of int/float, additive factor (aug_image = image * factor)
• contrast - list of int/float, contrast adjustment factor (aug_image = (image - mean) * factor + mean)
• merge - one of 'mean', 'gmean' and 'max' - mode of merging augmented predictions together

Constraints

1. model has to have 1 input and 1 output
2. inference batch_size == 1
3. image height == width if rotation augmentation is used

Installation

1. PyPI package:

$ pip install tta-wrapper

2. Latest version:

$ pip install git+https://github.com/qubvel/tta_wrapper/

Example

from keras.models import load_model
from tta_wrapper import tta_segmentation

model = load_model('path/to/model.h5')
tta_model = tta_segmentation(model, h_flip=True, rotation=(90, 270), 
                             h_shift=(-5, 5), merge='mean')
y = tta_model.predict(x)