https://github.com/qubvel/tta_wrapper

Test Time image Augmentation (TTA) wrapper for Keras model.
https://github.com/qubvel/tta_wrapper

augmentation image-classification image-segmentation keras tensorflow tta tta-wrapper

Last synced: about 2 months ago
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Test Time image Augmentation (TTA) wrapper for Keras model.

• Host: GitHub
• URL: https://github.com/qubvel/tta_wrapper
• Owner: qubvel
• Created: 2018-08-27T14:32:03.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2022-01-06T07:10:28.000Z (over 2 years ago)
• Last Synced: 2024-07-10T10:57:53.545Z (2 months ago)
• Topics: augmentation, image-classification, image-segmentation, keras, tensorflow, tta, tta-wrapper
• Language: Python
• Homepage:
• Size: 25.4 KB
• Stars: 110
• Watchers: 4
• Forks: 18
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
[![PyPI version](https://badge.fury.io/py/tta-wrapper.svg)](https://badge.fury.io/py/tta-wrapper)

# 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**:

```bash

$ pip install tta-wrapper

```

2) **Latest version**:

```bash

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

```

## Example

```python

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)

```