tta_wrapper - GitLite

KO | EN

gitlite — search

#ai #typescript #llm #go #android #rust #macos #nodejs #windows #awesome #kubernetes #pytorch #ios #open-source

tta_wrapper

(★ 112)

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

File Explorer

Download Latest Version (.zip)

- __init__.py
- wrapper_test.py
- __init__.py
- __version__.py
- augmentation.py
- functional.py
- layers.py
- wrappers.py
.gitignore
__init__.py
MANIFEST.in
README.md
requirements.txt
setup.py

# Installation Guide

1. Get the code

git clone https://github.com/qubvel/tta_wrapper

Downloads the entire project code from GitHub to your computer.

cd tta_wrapper

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

$ pip install tta-wrapper

Installs the Python libraries listed in requirements.txt (or similar).

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

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

[![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)
```