path: root/python-ttach.spec

%global _empty_manifest_terminate_build 0
Name:		python-ttach
Version:	0.0.3
Release:	1
Summary:	Images test time augmentation with PyTorch.
License:	MIT
URL:		https://github.com/qubvel/ttach
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/91/5d/4c49e0eca4206bc25eff4ba89cee51b781466e2e3aad2f1057fd5d2634be/ttach-0.0.3.tar.gz
BuildArch:	noarch

Requires:	python3-pytest

%description

# TTAch
Image Test Time Augmentation with PyTorch!

Similar to what Data Augmentation is doing to the training set, the purpose of Test Time Augmentation is to perform random modifications to the test images. Thus, instead of showing the regular, “clean” images, only once to the trained model, we will show it the augmented images several times. We will then average the predictions of each corresponding image and take that as our final guess [[1](https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d)].  
```
           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)
2. [Transforms](#transforms)
3. [Aliases](#aliases)
4. [Merge modes](#merge-modes)
5. [Installation](#installation)

## Quick start

#####  Segmentation model wrapping:
```python
import ttach as tta
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
```
#####  Classification model wrapping:
```python
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform())
```

#####  Keypoints model wrapping:
```python
tta_model = tta.KeypointsTTAWrapper(model, tta.aliases.flip_transform(), scaled=True)
```
**Note**: the model must return keypoints in the format `torch([x1, y1, ..., xn, yn])`

## Advanced Examples
#####  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)
```

## Transforms

| Transform      | Parameters                | Values                            |
|----------------|:-------------------------:|:---------------------------------:|
| HorizontalFlip | -                         | -                                 |
| VerticalFlip   | -                         | -                                 |
| Rotate90       | angles                    | List\[0, 90, 180, 270]            |
| Scale          | scales<br>interpolation   | List\[float]<br>"nearest"/"linear"|
| Resize         | sizes<br>original_size<br>interpolation   | List\[Tuple\[int, int]]<br>Tuple\[int,int]<br>"nearest"/"linear"|
| Add            | values                    | List\[float]                      |
| Multiply       | factors                   | List\[float]                      |
| FiveCrops      | crop_height<br>crop_width | int<br>int                        |

## Aliases

  - 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
$ pip install ttach
```
Source:
```bash
$ pip install git+https://github.com/qubvel/ttach
```

## Run tests

```bash
docker build -f Dockerfile.dev -t ttach:dev . && docker run --rm ttach:dev pytest -p no:cacheprovider
```




%package -n python3-ttach
Summary:	Images test time augmentation with PyTorch.
Provides:	python-ttach
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-ttach

# TTAch
Image Test Time Augmentation with PyTorch!

Similar to what Data Augmentation is doing to the training set, the purpose of Test Time Augmentation is to perform random modifications to the test images. Thus, instead of showing the regular, “clean” images, only once to the trained model, we will show it the augmented images several times. We will then average the predictions of each corresponding image and take that as our final guess [[1](https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d)].  
```
           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)
2. [Transforms](#transforms)
3. [Aliases](#aliases)
4. [Merge modes](#merge-modes)
5. [Installation](#installation)

## Quick start

#####  Segmentation model wrapping:
```python
import ttach as tta
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
```
#####  Classification model wrapping:
```python
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform())
```

#####  Keypoints model wrapping:
```python
tta_model = tta.KeypointsTTAWrapper(model, tta.aliases.flip_transform(), scaled=True)
```
**Note**: the model must return keypoints in the format `torch([x1, y1, ..., xn, yn])`

## Advanced Examples
#####  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)
```

## Transforms

| Transform      | Parameters                | Values                            |
|----------------|:-------------------------:|:---------------------------------:|
| HorizontalFlip | -                         | -                                 |
| VerticalFlip   | -                         | -                                 |
| Rotate90       | angles                    | List\[0, 90, 180, 270]            |
| Scale          | scales<br>interpolation   | List\[float]<br>"nearest"/"linear"|
| Resize         | sizes<br>original_size<br>interpolation   | List\[Tuple\[int, int]]<br>Tuple\[int,int]<br>"nearest"/"linear"|
| Add            | values                    | List\[float]                      |
| Multiply       | factors                   | List\[float]                      |
| FiveCrops      | crop_height<br>crop_width | int<br>int                        |

## Aliases

  - 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
$ pip install ttach
```
Source:
```bash
$ pip install git+https://github.com/qubvel/ttach
```

## Run tests

```bash
docker build -f Dockerfile.dev -t ttach:dev . && docker run --rm ttach:dev pytest -p no:cacheprovider
```




%package help
Summary:	Development documents and examples for ttach
Provides:	python3-ttach-doc
%description help

# TTAch
Image Test Time Augmentation with PyTorch!

Similar to what Data Augmentation is doing to the training set, the purpose of Test Time Augmentation is to perform random modifications to the test images. Thus, instead of showing the regular, “clean” images, only once to the trained model, we will show it the augmented images several times. We will then average the predictions of each corresponding image and take that as our final guess [[1](https://towardsdatascience.com/test-time-augmentation-tta-and-how-to-perform-it-with-keras-4ac19b67fb4d)].  
```
           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)
2. [Transforms](#transforms)
3. [Aliases](#aliases)
4. [Merge modes](#merge-modes)
5. [Installation](#installation)

## Quick start

#####  Segmentation model wrapping:
```python
import ttach as tta
tta_model = tta.SegmentationTTAWrapper(model, tta.aliases.d4_transform(), merge_mode='mean')
```
#####  Classification model wrapping:
```python
tta_model = tta.ClassificationTTAWrapper(model, tta.aliases.five_crop_transform())
```

#####  Keypoints model wrapping:
```python
tta_model = tta.KeypointsTTAWrapper(model, tta.aliases.flip_transform(), scaled=True)
```
**Note**: the model must return keypoints in the format `torch([x1, y1, ..., xn, yn])`

## Advanced Examples
#####  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)
```

## Transforms

| Transform      | Parameters                | Values                            |
|----------------|:-------------------------:|:---------------------------------:|
| HorizontalFlip | -                         | -                                 |
| VerticalFlip   | -                         | -                                 |
| Rotate90       | angles                    | List\[0, 90, 180, 270]            |
| Scale          | scales<br>interpolation   | List\[float]<br>"nearest"/"linear"|
| Resize         | sizes<br>original_size<br>interpolation   | List\[Tuple\[int, int]]<br>Tuple\[int,int]<br>"nearest"/"linear"|
| Add            | values                    | List\[float]                      |
| Multiply       | factors                   | List\[float]                      |
| FiveCrops      | crop_height<br>crop_width | int<br>int                        |

## Aliases

  - 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
$ pip install ttach
```
Source:
```bash
$ pip install git+https://github.com/qubvel/ttach
```

## Run tests

```bash
docker build -f Dockerfile.dev -t ttach:dev . && docker run --rm ttach:dev pytest -p no:cacheprovider
```




%prep
%autosetup -n ttach-0.0.3

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-ttach -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Wed Apr 12 2023 Python_Bot <Python_Bot@openeuler.org> - 0.0.3-1
- Package Spec generated