# PaTTA
**Repository Path**: AgentMaker/PaTTA
## Basic Information
- **Project Name**: PaTTA
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-05-11
- **Last Updated**: 2021-05-11
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Patta
[](https://github.com/AgentMaker/PaTTA/actions/workflows/python-publish.yml)
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](#quick-start)
- [Test](#Test)
- [Predict](#Predict)
- [Use Tools](#Use-Tools)
2. [Transforms](#Advanced-Examples (DIY Transforms))
3. [Aliases](#Aliases (Combos))
4. [Merge modes](#Merge-modes)
5. [Installation](#installation)
## Quick start (Default Transforms)
#### Test
We support that you can use the following to test after defining the network.
##### Segmentation model wrapping [[docstring](patta/wrappers.py#L8)]:
```python
import patta as tta
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
```
##### Classification model wrapping [[docstring](patta/wrappers.py#L52)]:
```python
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform())
```
##### Keypoints model wrapping [[docstring](patta/wrappers.py#L96)]:
```python
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](patta/load_model.py#L3)]:
```python
import patta as tta
model = tta.load_model(path='output/model')
```
##### Segmentation model wrapping [[docstring](patta/wrappers.py#L8)]:
```python
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
```
##### Classification model wrapping [[docstring](patta/wrappers.py#L52)]:
```python
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform())
```
##### Keypoints model wrapping [[docstring](patta/wrappers.py#L96)]:
```python
tta_model = tta.KeypointsTTAWrapper(model, tta.aliases.flip_transform(), scaled=True)
```
#### Use-Tools
##### Segmentation model [[docstring](tools/seg.py)]:
We recommend modifying the file `seg.py` according to your own model.
```python
python seg.py --model_path='output/model' \
--batch_size=16 \
--test_dataset='test.txt'
```
**Note**: Related to [paddleseg](https://github.com/PaddlePaddle/Paddleseg)
## Advanced-Examples (DIY Transforms)
##### Custom transform:
```python
# 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)
```python
# 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](https://www.kaggle.com/c/severstal-steel-defect-detection/discussion/107716#latest-624046) with t=0.5)
## Installation
PyPI:
```bash
# Use pip install PaTTA
$ pip install patta
```
or
```bash
# After downloading the whole dir
$ git clone https://github.com/AgentMaker/PaTTA.git
$ pip install PaTTA/
```
## Run tests
```bash
# run test_transforms.py and test_base.py for test
python test/test_transforms.py
python test/test_base.py
```