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| author | CoprDistGit <infra@openeuler.org> | 2023-05-05 10:16:40 +0000 |
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| committer | CoprDistGit <infra@openeuler.org> | 2023-05-05 10:16:40 +0000 |
| commit | c8ab759f6c5d14f2691f73bf6f0f2fb4b24f39b6 (patch) | |
| tree | 78ae975c09604113b29517f2f328cdcb73a91631 | |
| parent | d9c5fc29b71a04573128413f8c012ac4e6cacd7f (diff) | |
automatic import of python-pywickopeneuler20.03
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| -rw-r--r-- | python-pywick.spec | 1281 | ||||
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@@ -0,0 +1 @@ +/pywick-0.6.5.tar.gz diff --git a/python-pywick.spec b/python-pywick.spec new file mode 100644 index 0000000..93ad551 --- /dev/null +++ b/python-pywick.spec @@ -0,0 +1,1281 @@ +%global _empty_manifest_terminate_build 0 +Name: python-pywick +Version: 0.6.5 +Release: 1 +Summary: High-level batteries-included neural network training library for Pytorch +License: MIT License +URL: https://github.com/achaiah/pywick +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/17/c5/a5b08dea4ce58acc6af2c71c14d8e19935a0792552111635445cf2d8187b/pywick-0.6.5.tar.gz +BuildArch: noarch + +Requires: python3-albumentations +Requires: python3-dill +Requires: python3-h5py +Requires: python3-numpy +Requires: python3-opencv-python-headless +Requires: python3-pandas +Requires: python3-pillow +Requires: python3-prodict +Requires: python3-pycm +Requires: python3-pyyaml +Requires: python3-scipy +Requires: python3-requests +Requires: python3-scikit-image +Requires: python3-setuptools +Requires: python3-six +Requires: python3-tabulate +Requires: python3-torch +Requires: python3-torchvision +Requires: python3-tqdm +Requires: python3-yacs +Requires: python3-wheel + +%description +# Pywick + +<div style="text-align:center"> + +[](https://pywick.readthedocs.io/en/latest/) +[](https://pywick.readthedocs.io/en/latest/) +[](https://pypi.org/project/pywick/) +[](https://pywick.readthedocs.io/en/latest/) +[](https://github.com/achaiah/pywick/blob/master/LICENSE.txt) + +</div> + +#### High-Level Training framework for Pytorch + +Pywick is a high-level Pytorch training framework that aims to get you +up and running quickly with state of the art neural networks. *Does the +world need another Pytorch framework?* Probably not. But we started this +project when no good frameworks were available and it just kept growing. +So here we are. + +Pywick tries to stay on the bleeding edge of research into neural networks. If you just wish to run a vanilla CNN, this is probably +going to be overkill. However, if you want to get lost in the world of neural networks, fine-tuning and hyperparameter optimization +for months on end then this is probably the right place for you :) + +Among other things Pywick includes: +- State of the art normalization, activation, loss functions and optimizers not included in the standard Pytorch library (AdaBelief, Addsign, Apollo, Eve, Lookahead, Radam, Ralamb, RangerLARS etc). +- A high-level module for training with callbacks, constraints, metrics, conditions and regularizers. +- Hundreds of popular object classification and semantic segmentation models! +- Comprehensive data loading, augmentation, transforms, and sampling capability. +- Utility tensor functions. +- Useful meters. +- Basic GridSearch (exhaustive and random). + +## Docs +Hey, [check this out](https://pywick.readthedocs.io/en/latest/), we now have [docs](https://pywick.readthedocs.io/en/latest/)! They're still a work in progress though so apologies for anything that's broken. + +## What's New (highlights) + +### v0.6.5 - Docker all the things! +Another great improvement to the framework - docker! You can now run the 17flowers demo right out of the box! + - Grab our docker image at [docker hub](https://hub.docker.com/repository/docker/achaiah/pywick): `docker pull achaiah/pywick:latest`. Pytorch 1.8 and cuda dependencies are pre-installed. + - Run 17flowers demo with: `docker run --rm -it --ipc=host -v your_local_out_dir:/jobs/17flowers --init -e demo=true achaiah/pywick:latest` + - Or run the container in standalone mode so you can use your own data (don't forget to map your local dir to container): + ```bash + docker run --rm -it \ + --ipc=host \ + -v <your_local_data_dir>:<container_data_dir> \ + -v <your_local_out_dir>:<container_out_dir> \ + --init \ + achaiah/pywick:latest + ``` + +### Older Notes +- **Oct. 11, 2021 - We thought ya might like YAML!** + - So you're saying you like **configuration files**? You're saying you like **examples** too? Well, we've got you covered! Huge release today with a configuration-based training example! All you have to do is: + - Get your favorite dataset (or download [17 flowers](https://www.robots.ox.ac.uk/~vgg/data/flowers/17/) to get started and `pywick/examples/17flowers_split.py` to convert) + - Adjust the `configs/train_classifier.yaml` file to fit your workspace + - Then simply run: `python3 train_classifier.py configs/train_classifier.yaml` and watch it train! +- **May 6, 2021** + - Many SoTA classification and segmentation models added: Swin-Transformer variants, NFNet variants (L0, L1), Halo nets, Lambda nets, ECA variants, Rexnet + others + - Many new loss functions added: RecallLoss, SoftInvDiceLoss, OhemBCEDicePenalizeBorderLoss, RMIBCEDicePenalizeBorderLoss + others + - Bug fixes +- **Jun. 15, 2020** + - 700+ models added from [rwightman's](https://github.com/rwightman/pytorch-image-models) repo via `torch.hub`! See docs for all the variants! + - Some minor bug fixes +- **Jan. 20, 2020** + - New release: 0.5.6 (minor fix from 0.5.5 for pypi) + - Mish activation function (SoTA) + - [rwightman's](https://github.com/rwightman/gen-efficientnet-pytorch) models of pretrained/ported variants for classification (44 total) + - efficientnet Tensorflow port b0-b8, with and without AP, el/em/es, cc + - mixnet L/M/S + - mobilenetv3 + - mnasnet + - spnasnet + - Additional loss functions +- **Aug. 1, 2019** + - New segmentation NNs: BiSeNet, DANet, DenseASPP, DUNet, OCNet, PSANet + - New Loss Functions: Focal Tversky Loss, OHEM CrossEntropy Loss, various combination losses + - Major restructuring and standardization of NN models and loading functionality + - General bug fixes and code improvements + +## Install +Pywick requires **pytorch >= 1.4** + +`pip install pywick` + +or specific version from git: + +`pip install git+https://github.com/achaiah/pywick.git@v0.6.5` + +## ModuleTrainer +The `ModuleTrainer` class provides a high-level training interface which abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, +and more. + +See the `train_classifier.py` example for a pretty complete configuration example. To get up and running with your own data quickly simply edit the `configs/train_classifier.yaml` file with your desired parameters and dataset location(s). + +Note: <i>Dataset needs to be organized for classification where each directory name is the name of a class and contains all images pertaining to that class</i> + +PyWick provides a wide range of <b>callbacks</b>, generally mimicking the interface found in `Keras`: + +- `CSVLogger` - Logs epoch-level metrics to a CSV file +- [`CyclicLRScheduler`](https://github.com/bckenstler/CLR) - Cycles through min-max learning rate +- `EarlyStopping` - Provides ability to stop training early based on supplied criteria +- `History` - Keeps history of metrics etc. during the learning process +- `LambdaCallback` - Allows you to implement your own callbacks on the fly +- `LRScheduler` - Simple learning rate scheduler based on function or supplied schedule +- `ModelCheckpoint` - Comprehensive model saver +- `ReduceLROnPlateau` - Reduces learning rate (LR) when a plateau has been reached +- `SimpleModelCheckpoint` - Simple model saver +- Additionally, a `TensorboardLogger` is incredibly easy to implement via tensorboardX (now part of pytorch 1.1 release!) + + +```python +from pywick.callbacks import EarlyStopping + +callbacks = [EarlyStopping(monitor='val_loss', patience=5)] +trainer.set_callbacks(callbacks) +``` + +PyWick also provides <b>regularizers</b>: + +- `L1Regularizer` +- `L2Regularizer` +- `L1L2Regularizer` + + +and <b>constraints</b>: +- `UnitNorm` +- `MaxNorm` +- `NonNeg` + +Both regularizers and constraints can be selectively applied on layers using regular expressions and the `module_filter` +argument. Constraints can be explicit (hard) constraints applied at an arbitrary batch or +epoch frequency, or they can be implicit (soft) constraints similar to regularizers +where the constraint deviation is added as a penalty to the total model loss. + +```python +from pywick.constraints import MaxNorm, NonNeg +from pywick.regularizers import L1Regularizer + +# hard constraint applied every 5 batches +hard_constraint = MaxNorm(value=2., frequency=5, unit='batch', module_filter='*fc*') +# implicit constraint added as a penalty term to model loss +soft_constraint = NonNeg(lagrangian=True, scale=1e-3, module_filter='*fc*') +constraints = [hard_constraint, soft_constraint] +trainer.set_constraints(constraints) + +regularizers = [L1Regularizer(scale=1e-4, module_filter='*conv*')] +trainer.set_regularizers(regularizers) +``` + +You can also fit directly on a `torch.utils.data.DataLoader` and can have +a validation set as well : + +```python +from pywick import TensorDataset +from torch.utils.data import DataLoader + +train_dataset = TensorDataset(x_train, y_train) +train_loader = DataLoader(train_dataset, batch_size=32) + +val_dataset = TensorDataset(x_val, y_val) +val_loader = DataLoader(val_dataset, batch_size=32) + +trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100) +``` + +## Extensive Library of Image Classification Models (most are pretrained!) +- All standard models from Pytorch: + - [**Densenet**](https://arxiv.org/abs/1608.06993) + - [**Inception v3**](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf) + - [**MobileNet v2**](https://arxiv.org/abs/1801.04381) + - [**ResNet**](https://arxiv.org/abs/1512.03385) + - [**ShuffleNet v2**](https://arxiv.org/abs/1807.11164) + - [**SqueezeNet**](https://arxiv.org/abs/1602.07360) + - [**VGG**](https://arxiv.org/abs/1409.1556) +- [**BatchNorm Inception**](https://arxiv.org/abs/1502.03167) +- [**Deep High-Resolution Representation Learning for Human Pose Estimation**](https://arxiv.org/abs/1902.09212v1) +- [**Deep Layer Aggregation**](https://arxiv.org/abs/1707.06484) +- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629) +- [**EfficientNet variants (b0-b8, el, em, es, lite1-lite4, pruned, AP/NS)**](https://arxiv.org/abs/1905.11946) +- [**ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks**](https://arxiv.org/abs/1910.03151v4) +- [**FBResnet**](https://github.com/facebook/fb.resnet.torch) +- [**FBNet-C**](https://arxiv.org/abs/1812.03443) +- [**Inception v4**](http://arxiv.org/abs/1602.07261) +- [**InceptionResnet v2**](https://arxiv.org/abs/1602.07261) +- [**Mixnet variants (l, m, s, xl, xxl)**](https://arxiv.org/abs/1907.09595) +- [**MnasNet**](https://arxiv.org/abs/1807.11626) +- [**MobileNet V3**](https://arxiv.org/abs/1905.02244) +- [**NasNet variants (mnas, pnas, mobile)**](https://arxiv.org/abs/1707.07012) +- [**PNASNet**](https://arxiv.org/abs/1712.00559) +- [**Polynet**](https://arxiv.org/abs/1611.05725) +- [**Pyramid Resnet**](https://arxiv.org/abs/1610.02915) +- [**RegNet - Designing Network Design Spaces**](https://arxiv.org/abs/2003.13678) +- **[Resnet variants (gluon, res2net, se, ssl, tv, wide)](https://arxiv.org/abs/1512.03385)** +- [**ResNeSt: Split-Attention Networks**](https://arxiv.org/abs/2004.08955) +- [**ResNext variants (ig, se, ssl, swsl, tv)**](https://arxiv.org/abs/1611.05431) +- [**SE Net variants (gluon, resnet, resnext, inception)**](https://arxiv.org/pdf/1709.01507.pdf) +- [**SelecSLS Convolutional Net**](https://github.com/mehtadushy/SelecSLS-Pytorch) +- [**Selective Kernel Networks**](https://arxiv.org/abs/1903.06586) +- [**Semi-Supervised and Semi-Weakly Supervised ImageNet Models**](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models) +- [**Single-Pass NAS Net**](https://arxiv.org/abs/1904.02877) +- [**TResNet: High Performance GPU-Dedicated Architecture**](https://arxiv.org/abs/2003.13630) +- [**Wide Resnet**](https://arxiv.org/abs/1605.07146) +- [**XCeption**](https://arxiv.org/pdf/1610.02357.pdf) +- All the newest classification models (200+) from [rwightman's repo](https://github.com/rwightman/pytorch-image-models) ECA-NFNet, GERNet, RegNet, SKResnext, SWIN-Transformer, VIT etc.) + +## Image Segmentation Models +- **BiSeNet** ([Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)) +- **DANet** ([Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)) +- **Deeplab v2** ([DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)) +- **Deeplab v3** ([Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)) +- **DenseASPP** ([DenseASPP for Semantic Segmentation in Street Scenes](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf)) +- **DRNNet** ([Dilated Residual Networks](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Dilated_Residual_Networks_CVPR_2017_paper.pdf)) +- **DUC, HDC** ([understanding convolution for semantic segmentation](https://arxiv.org/abs/1702.08502)) +- **DUNet** ([Decoders Matter for Semantic Segmentation](https://arxiv.org/abs/1903.02120)) +- **ENet** ([ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://arxiv.org/abs/1606.02147)) +- **Vanilla FCN:** FCN32, FCN16, FCN8, in the versions of VGG, ResNet + and OptDenseNet respectively ([Fully convolutional networks for semantic segmentation](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)) +- **FRRN** ([Full Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://arxiv.org/abs/1611.08323)) +- **FusionNet** ([FusionNet in Tensorflow by Hyungjoo Andrew Cho](https://github.com/NySunShine/fusion-net)) +- **GALDNet** +- **GCN** ([Large Kernel Matters](https://arxiv.org/pdf/1703.02719)) +- **LinkNet** ([Link-Net](https://codeac29.github.io/projects/linknet/)) +- **OCNet** ([Object Context Network for Scene Parsing](https://arxiv.org/abs/1809.00916)) +- **PSPNet** ([Pyramid scene parsing network](https://arxiv.org/abs/1612.01105)) +- **RefineNet** ([RefineNet](https://arxiv.org/abs/1611.06612)) +- **SegNet** ([Segnet: A deep convolutional encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561)) +- **Tiramisu** ([The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/abs/1611.09326)) +- **U-Net** ([U-net: Convolutional networks for biomedical image segmentation](https://arxiv.org/abs/1505.04597)) +- Additional variations of many of the above + +###### To load one of these models: +[Read the docs](https://pywick.readthedocs.io/en/latest/api/pywick.models.html) +for useful details! Then dive in: +```python +# use the `get_model` utility +from pywick.models.model_utils import get_model, ModelType + +model = get_model(model_type=ModelType.CLASSIFICATION, model_name='resnet18', num_classes=1000, pretrained=True) +``` +For a complete list of models (including many experimental ones) you can call the `get_supported_models` method e.g. +`pywick.models.model_utils.get_supported_models(ModelType.SEGMENTATION)` + +## Data Augmentation and Datasets +The PyWick package provides wide variety of good data augmentation and transformation +tools which can be applied during data loading. The package also provides the flexible +`TensorDataset`, `FolderDataset` and `MultiFolderDataset` classes to handle most dataset needs. + +### Torch Transforms +##### These transforms work directly on torch tensors + +- `AddChannel` +- `ChannelsFirst` +- `ChannelsLast` +- `Compose` +- `ExpandAxis` +- `Pad` +- `PadNumpy` +- `RandomChoiceCompose` +- `RandomCrop` +- `RandomFlip` +- `RandomOrder` +- `RangeNormalize` +- `Slice2D` +- `SpecialCrop` +- `StdNormalize` +- `ToFile` +- `ToNumpyType` +- `ToTensor` +- `Transpose` +- `TypeCast` + +##### Additionally, we provide image-specific manipulations directly on tensors: + +- `Brightness` +- `Contrast` +- `Gamma` +- `Grayscale` +- `RandomBrightness` +- `RandomChoiceBrightness` +- `RandomChoiceContrast` +- `RandomChoiceGamma` +- `RandomChoiceSaturation` +- `RandomContrast` +- `RandomGamma` +- `RandomGrayscale` +- `RandomSaturation` +- `Saturation` + +##### Affine Transforms (perform affine or affine-like transforms on torch tensors) + +- `RandomAffine` +- `RandomChoiceRotate` +- `RandomChoiceShear` +- `RandomChoiceTranslate` +- `RandomChoiceZoom` +- `RandomRotate` +- `RandomShear` +- `RandomSquareZoom` +- `RandomTranslate` +- `RandomZoom` +- `Rotate` +- `Shear` +- `Translate` +- `Zoom` + +We also provide a class for stringing multiple affine transformations together so that only one interpolation takes place: + +- `Affine` +- `AffineCompose` + +##### Blur and Scramble transforms (for tensors) +- `Blur` +- `RandomChoiceBlur` +- `RandomChoiceScramble` +- `Scramble` + +### Datasets and Sampling +We provide the following datasets which provide general structure and iterators for sampling from and using transforms on in-memory or out-of-memory data. In particular, +the [FolderDataset](pywick/datasets/FolderDataset.py) has been designed to fit most of your dataset needs. It has extensive options for data filtering and manipulation. +It supports loading images for classification, segmentation and even arbitrary source/target mapping. Take a good look at its documentation for more info. + +- `ClonedDataset` +- `CSVDataset` +- `FolderDataset` +- `MultiFolderDataset` +- `TensorDataset` +- `tnt.BatchDataset` +- `tnt.ConcatDataset` +- `tnt.ListDataset` +- `tnt.MultiPartitionDataset` +- `tnt.ResampleDataset` +- `tnt.ShuffleDataset` +- `tnt.TensorDataset` +- `tnt.TransformDataset` + +### Imbalanced Datasets +In many scenarios it is important to ensure that your traing set is properly balanced, +however, it may not be practical in real life to obtain such a perfect dataset. In these cases +you can use the `ImbalancedDatasetSampler` as a drop-in replacement for the basic sampler provided +by the DataLoader. More information can be found [here](https://github.com/ufoym/imbalanced-dataset-sampler) + +```python +from pywick.samplers import ImbalancedDatasetSampler + +train_loader = torch.utils.data.DataLoader(train_dataset, + sampler=ImbalancedDatasetSampler(train_dataset), + batch_size=args.batch_size, **kwargs) +``` + +## Utility Functions +PyWick provides a few utility functions not commonly found: + +### Tensor Functions +- `th_iterproduct` (mimics itertools.product) +- `th_gather_nd` (N-dimensional version of torch.gather) +- `th_random_choice` (mimics np.random.choice) +- `th_pearsonr` (mimics scipy.stats.pearsonr) +- `th_corrcoef` (mimics np.corrcoef) +- `th_affine2d` and `th_affine3d` (affine transforms on torch.Tensors) + + +## Acknowledgements and References +We stand on the shoulders of (github?) giants and couldn't have done +this without the rich github ecosystem and community. This framework is +based in part on the excellent +[Torchsample](https://github.com/ncullen93/torchsample) framework +originally published by @ncullen93. Additionally, many models have been +gently borrowed/modified from @Cadene pretrained models +[repo](https://github.com/Cadene/pretrained-models.pytorch) as well as @Tramac segmentation [repo](https://github.com/Tramac/awesome-semantic-segmentation-pytorch). + +##### Thank you to the following people and the projects they maintain: +- @ncullen93 +- @cadene +- @deallynomore +- @recastrodiaz +- @zijundeng +- @Tramac +- And many others! (attributions listed in the codebase as they occur) + +##### Thank you to the following projects from which we gently borrowed code and models +- [PyTorchNet](https://github.com/pytorch/tnt) +- [pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch) +- [DeepLab_pytorch](https://github.com/doiken23/DeepLab_pytorch) +- [Pytorch for Semantic Segmentation](https://github.com/zijundeng/pytorch-semantic-segmentation) +- [Binseg Pytorch](https://github.com/saeedizadi/binseg_pytoch) +- [awesome-semantic-segmentation-pytorch](https://github.com/Tramac/awesome-semantic-segmentation-pytorch) +- And many others! (attributions listed in the codebase as they occur) + + + +| *Thangs are broken matey! Arrr!!!* | +|-----------------------| +| We're working on this project as time permits so you might discover bugs here and there. Feel free to report them, or better yet, to submit a pull request! | + + + +%package -n python3-pywick +Summary: High-level batteries-included neural network training library for Pytorch +Provides: python-pywick +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-pywick +# Pywick + +<div style="text-align:center"> + +[](https://pywick.readthedocs.io/en/latest/) +[](https://pywick.readthedocs.io/en/latest/) +[](https://pypi.org/project/pywick/) +[](https://pywick.readthedocs.io/en/latest/) +[](https://github.com/achaiah/pywick/blob/master/LICENSE.txt) + +</div> + +#### High-Level Training framework for Pytorch + +Pywick is a high-level Pytorch training framework that aims to get you +up and running quickly with state of the art neural networks. *Does the +world need another Pytorch framework?* Probably not. But we started this +project when no good frameworks were available and it just kept growing. +So here we are. + +Pywick tries to stay on the bleeding edge of research into neural networks. If you just wish to run a vanilla CNN, this is probably +going to be overkill. However, if you want to get lost in the world of neural networks, fine-tuning and hyperparameter optimization +for months on end then this is probably the right place for you :) + +Among other things Pywick includes: +- State of the art normalization, activation, loss functions and optimizers not included in the standard Pytorch library (AdaBelief, Addsign, Apollo, Eve, Lookahead, Radam, Ralamb, RangerLARS etc). +- A high-level module for training with callbacks, constraints, metrics, conditions and regularizers. +- Hundreds of popular object classification and semantic segmentation models! +- Comprehensive data loading, augmentation, transforms, and sampling capability. +- Utility tensor functions. +- Useful meters. +- Basic GridSearch (exhaustive and random). + +## Docs +Hey, [check this out](https://pywick.readthedocs.io/en/latest/), we now have [docs](https://pywick.readthedocs.io/en/latest/)! They're still a work in progress though so apologies for anything that's broken. + +## What's New (highlights) + +### v0.6.5 - Docker all the things! +Another great improvement to the framework - docker! You can now run the 17flowers demo right out of the box! + - Grab our docker image at [docker hub](https://hub.docker.com/repository/docker/achaiah/pywick): `docker pull achaiah/pywick:latest`. Pytorch 1.8 and cuda dependencies are pre-installed. + - Run 17flowers demo with: `docker run --rm -it --ipc=host -v your_local_out_dir:/jobs/17flowers --init -e demo=true achaiah/pywick:latest` + - Or run the container in standalone mode so you can use your own data (don't forget to map your local dir to container): + ```bash + docker run --rm -it \ + --ipc=host \ + -v <your_local_data_dir>:<container_data_dir> \ + -v <your_local_out_dir>:<container_out_dir> \ + --init \ + achaiah/pywick:latest + ``` + +### Older Notes +- **Oct. 11, 2021 - We thought ya might like YAML!** + - So you're saying you like **configuration files**? You're saying you like **examples** too? Well, we've got you covered! Huge release today with a configuration-based training example! All you have to do is: + - Get your favorite dataset (or download [17 flowers](https://www.robots.ox.ac.uk/~vgg/data/flowers/17/) to get started and `pywick/examples/17flowers_split.py` to convert) + - Adjust the `configs/train_classifier.yaml` file to fit your workspace + - Then simply run: `python3 train_classifier.py configs/train_classifier.yaml` and watch it train! +- **May 6, 2021** + - Many SoTA classification and segmentation models added: Swin-Transformer variants, NFNet variants (L0, L1), Halo nets, Lambda nets, ECA variants, Rexnet + others + - Many new loss functions added: RecallLoss, SoftInvDiceLoss, OhemBCEDicePenalizeBorderLoss, RMIBCEDicePenalizeBorderLoss + others + - Bug fixes +- **Jun. 15, 2020** + - 700+ models added from [rwightman's](https://github.com/rwightman/pytorch-image-models) repo via `torch.hub`! See docs for all the variants! + - Some minor bug fixes +- **Jan. 20, 2020** + - New release: 0.5.6 (minor fix from 0.5.5 for pypi) + - Mish activation function (SoTA) + - [rwightman's](https://github.com/rwightman/gen-efficientnet-pytorch) models of pretrained/ported variants for classification (44 total) + - efficientnet Tensorflow port b0-b8, with and without AP, el/em/es, cc + - mixnet L/M/S + - mobilenetv3 + - mnasnet + - spnasnet + - Additional loss functions +- **Aug. 1, 2019** + - New segmentation NNs: BiSeNet, DANet, DenseASPP, DUNet, OCNet, PSANet + - New Loss Functions: Focal Tversky Loss, OHEM CrossEntropy Loss, various combination losses + - Major restructuring and standardization of NN models and loading functionality + - General bug fixes and code improvements + +## Install +Pywick requires **pytorch >= 1.4** + +`pip install pywick` + +or specific version from git: + +`pip install git+https://github.com/achaiah/pywick.git@v0.6.5` + +## ModuleTrainer +The `ModuleTrainer` class provides a high-level training interface which abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, +and more. + +See the `train_classifier.py` example for a pretty complete configuration example. To get up and running with your own data quickly simply edit the `configs/train_classifier.yaml` file with your desired parameters and dataset location(s). + +Note: <i>Dataset needs to be organized for classification where each directory name is the name of a class and contains all images pertaining to that class</i> + +PyWick provides a wide range of <b>callbacks</b>, generally mimicking the interface found in `Keras`: + +- `CSVLogger` - Logs epoch-level metrics to a CSV file +- [`CyclicLRScheduler`](https://github.com/bckenstler/CLR) - Cycles through min-max learning rate +- `EarlyStopping` - Provides ability to stop training early based on supplied criteria +- `History` - Keeps history of metrics etc. during the learning process +- `LambdaCallback` - Allows you to implement your own callbacks on the fly +- `LRScheduler` - Simple learning rate scheduler based on function or supplied schedule +- `ModelCheckpoint` - Comprehensive model saver +- `ReduceLROnPlateau` - Reduces learning rate (LR) when a plateau has been reached +- `SimpleModelCheckpoint` - Simple model saver +- Additionally, a `TensorboardLogger` is incredibly easy to implement via tensorboardX (now part of pytorch 1.1 release!) + + +```python +from pywick.callbacks import EarlyStopping + +callbacks = [EarlyStopping(monitor='val_loss', patience=5)] +trainer.set_callbacks(callbacks) +``` + +PyWick also provides <b>regularizers</b>: + +- `L1Regularizer` +- `L2Regularizer` +- `L1L2Regularizer` + + +and <b>constraints</b>: +- `UnitNorm` +- `MaxNorm` +- `NonNeg` + +Both regularizers and constraints can be selectively applied on layers using regular expressions and the `module_filter` +argument. Constraints can be explicit (hard) constraints applied at an arbitrary batch or +epoch frequency, or they can be implicit (soft) constraints similar to regularizers +where the constraint deviation is added as a penalty to the total model loss. + +```python +from pywick.constraints import MaxNorm, NonNeg +from pywick.regularizers import L1Regularizer + +# hard constraint applied every 5 batches +hard_constraint = MaxNorm(value=2., frequency=5, unit='batch', module_filter='*fc*') +# implicit constraint added as a penalty term to model loss +soft_constraint = NonNeg(lagrangian=True, scale=1e-3, module_filter='*fc*') +constraints = [hard_constraint, soft_constraint] +trainer.set_constraints(constraints) + +regularizers = [L1Regularizer(scale=1e-4, module_filter='*conv*')] +trainer.set_regularizers(regularizers) +``` + +You can also fit directly on a `torch.utils.data.DataLoader` and can have +a validation set as well : + +```python +from pywick import TensorDataset +from torch.utils.data import DataLoader + +train_dataset = TensorDataset(x_train, y_train) +train_loader = DataLoader(train_dataset, batch_size=32) + +val_dataset = TensorDataset(x_val, y_val) +val_loader = DataLoader(val_dataset, batch_size=32) + +trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100) +``` + +## Extensive Library of Image Classification Models (most are pretrained!) +- All standard models from Pytorch: + - [**Densenet**](https://arxiv.org/abs/1608.06993) + - [**Inception v3**](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf) + - [**MobileNet v2**](https://arxiv.org/abs/1801.04381) + - [**ResNet**](https://arxiv.org/abs/1512.03385) + - [**ShuffleNet v2**](https://arxiv.org/abs/1807.11164) + - [**SqueezeNet**](https://arxiv.org/abs/1602.07360) + - [**VGG**](https://arxiv.org/abs/1409.1556) +- [**BatchNorm Inception**](https://arxiv.org/abs/1502.03167) +- [**Deep High-Resolution Representation Learning for Human Pose Estimation**](https://arxiv.org/abs/1902.09212v1) +- [**Deep Layer Aggregation**](https://arxiv.org/abs/1707.06484) +- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629) +- [**EfficientNet variants (b0-b8, el, em, es, lite1-lite4, pruned, AP/NS)**](https://arxiv.org/abs/1905.11946) +- [**ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks**](https://arxiv.org/abs/1910.03151v4) +- [**FBResnet**](https://github.com/facebook/fb.resnet.torch) +- [**FBNet-C**](https://arxiv.org/abs/1812.03443) +- [**Inception v4**](http://arxiv.org/abs/1602.07261) +- [**InceptionResnet v2**](https://arxiv.org/abs/1602.07261) +- [**Mixnet variants (l, m, s, xl, xxl)**](https://arxiv.org/abs/1907.09595) +- [**MnasNet**](https://arxiv.org/abs/1807.11626) +- [**MobileNet V3**](https://arxiv.org/abs/1905.02244) +- [**NasNet variants (mnas, pnas, mobile)**](https://arxiv.org/abs/1707.07012) +- [**PNASNet**](https://arxiv.org/abs/1712.00559) +- [**Polynet**](https://arxiv.org/abs/1611.05725) +- [**Pyramid Resnet**](https://arxiv.org/abs/1610.02915) +- [**RegNet - Designing Network Design Spaces**](https://arxiv.org/abs/2003.13678) +- **[Resnet variants (gluon, res2net, se, ssl, tv, wide)](https://arxiv.org/abs/1512.03385)** +- [**ResNeSt: Split-Attention Networks**](https://arxiv.org/abs/2004.08955) +- [**ResNext variants (ig, se, ssl, swsl, tv)**](https://arxiv.org/abs/1611.05431) +- [**SE Net variants (gluon, resnet, resnext, inception)**](https://arxiv.org/pdf/1709.01507.pdf) +- [**SelecSLS Convolutional Net**](https://github.com/mehtadushy/SelecSLS-Pytorch) +- [**Selective Kernel Networks**](https://arxiv.org/abs/1903.06586) +- [**Semi-Supervised and Semi-Weakly Supervised ImageNet Models**](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models) +- [**Single-Pass NAS Net**](https://arxiv.org/abs/1904.02877) +- [**TResNet: High Performance GPU-Dedicated Architecture**](https://arxiv.org/abs/2003.13630) +- [**Wide Resnet**](https://arxiv.org/abs/1605.07146) +- [**XCeption**](https://arxiv.org/pdf/1610.02357.pdf) +- All the newest classification models (200+) from [rwightman's repo](https://github.com/rwightman/pytorch-image-models) ECA-NFNet, GERNet, RegNet, SKResnext, SWIN-Transformer, VIT etc.) + +## Image Segmentation Models +- **BiSeNet** ([Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)) +- **DANet** ([Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)) +- **Deeplab v2** ([DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)) +- **Deeplab v3** ([Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)) +- **DenseASPP** ([DenseASPP for Semantic Segmentation in Street Scenes](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf)) +- **DRNNet** ([Dilated Residual Networks](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Dilated_Residual_Networks_CVPR_2017_paper.pdf)) +- **DUC, HDC** ([understanding convolution for semantic segmentation](https://arxiv.org/abs/1702.08502)) +- **DUNet** ([Decoders Matter for Semantic Segmentation](https://arxiv.org/abs/1903.02120)) +- **ENet** ([ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://arxiv.org/abs/1606.02147)) +- **Vanilla FCN:** FCN32, FCN16, FCN8, in the versions of VGG, ResNet + and OptDenseNet respectively ([Fully convolutional networks for semantic segmentation](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)) +- **FRRN** ([Full Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://arxiv.org/abs/1611.08323)) +- **FusionNet** ([FusionNet in Tensorflow by Hyungjoo Andrew Cho](https://github.com/NySunShine/fusion-net)) +- **GALDNet** +- **GCN** ([Large Kernel Matters](https://arxiv.org/pdf/1703.02719)) +- **LinkNet** ([Link-Net](https://codeac29.github.io/projects/linknet/)) +- **OCNet** ([Object Context Network for Scene Parsing](https://arxiv.org/abs/1809.00916)) +- **PSPNet** ([Pyramid scene parsing network](https://arxiv.org/abs/1612.01105)) +- **RefineNet** ([RefineNet](https://arxiv.org/abs/1611.06612)) +- **SegNet** ([Segnet: A deep convolutional encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561)) +- **Tiramisu** ([The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/abs/1611.09326)) +- **U-Net** ([U-net: Convolutional networks for biomedical image segmentation](https://arxiv.org/abs/1505.04597)) +- Additional variations of many of the above + +###### To load one of these models: +[Read the docs](https://pywick.readthedocs.io/en/latest/api/pywick.models.html) +for useful details! Then dive in: +```python +# use the `get_model` utility +from pywick.models.model_utils import get_model, ModelType + +model = get_model(model_type=ModelType.CLASSIFICATION, model_name='resnet18', num_classes=1000, pretrained=True) +``` +For a complete list of models (including many experimental ones) you can call the `get_supported_models` method e.g. +`pywick.models.model_utils.get_supported_models(ModelType.SEGMENTATION)` + +## Data Augmentation and Datasets +The PyWick package provides wide variety of good data augmentation and transformation +tools which can be applied during data loading. The package also provides the flexible +`TensorDataset`, `FolderDataset` and `MultiFolderDataset` classes to handle most dataset needs. + +### Torch Transforms +##### These transforms work directly on torch tensors + +- `AddChannel` +- `ChannelsFirst` +- `ChannelsLast` +- `Compose` +- `ExpandAxis` +- `Pad` +- `PadNumpy` +- `RandomChoiceCompose` +- `RandomCrop` +- `RandomFlip` +- `RandomOrder` +- `RangeNormalize` +- `Slice2D` +- `SpecialCrop` +- `StdNormalize` +- `ToFile` +- `ToNumpyType` +- `ToTensor` +- `Transpose` +- `TypeCast` + +##### Additionally, we provide image-specific manipulations directly on tensors: + +- `Brightness` +- `Contrast` +- `Gamma` +- `Grayscale` +- `RandomBrightness` +- `RandomChoiceBrightness` +- `RandomChoiceContrast` +- `RandomChoiceGamma` +- `RandomChoiceSaturation` +- `RandomContrast` +- `RandomGamma` +- `RandomGrayscale` +- `RandomSaturation` +- `Saturation` + +##### Affine Transforms (perform affine or affine-like transforms on torch tensors) + +- `RandomAffine` +- `RandomChoiceRotate` +- `RandomChoiceShear` +- `RandomChoiceTranslate` +- `RandomChoiceZoom` +- `RandomRotate` +- `RandomShear` +- `RandomSquareZoom` +- `RandomTranslate` +- `RandomZoom` +- `Rotate` +- `Shear` +- `Translate` +- `Zoom` + +We also provide a class for stringing multiple affine transformations together so that only one interpolation takes place: + +- `Affine` +- `AffineCompose` + +##### Blur and Scramble transforms (for tensors) +- `Blur` +- `RandomChoiceBlur` +- `RandomChoiceScramble` +- `Scramble` + +### Datasets and Sampling +We provide the following datasets which provide general structure and iterators for sampling from and using transforms on in-memory or out-of-memory data. In particular, +the [FolderDataset](pywick/datasets/FolderDataset.py) has been designed to fit most of your dataset needs. It has extensive options for data filtering and manipulation. +It supports loading images for classification, segmentation and even arbitrary source/target mapping. Take a good look at its documentation for more info. + +- `ClonedDataset` +- `CSVDataset` +- `FolderDataset` +- `MultiFolderDataset` +- `TensorDataset` +- `tnt.BatchDataset` +- `tnt.ConcatDataset` +- `tnt.ListDataset` +- `tnt.MultiPartitionDataset` +- `tnt.ResampleDataset` +- `tnt.ShuffleDataset` +- `tnt.TensorDataset` +- `tnt.TransformDataset` + +### Imbalanced Datasets +In many scenarios it is important to ensure that your traing set is properly balanced, +however, it may not be practical in real life to obtain such a perfect dataset. In these cases +you can use the `ImbalancedDatasetSampler` as a drop-in replacement for the basic sampler provided +by the DataLoader. More information can be found [here](https://github.com/ufoym/imbalanced-dataset-sampler) + +```python +from pywick.samplers import ImbalancedDatasetSampler + +train_loader = torch.utils.data.DataLoader(train_dataset, + sampler=ImbalancedDatasetSampler(train_dataset), + batch_size=args.batch_size, **kwargs) +``` + +## Utility Functions +PyWick provides a few utility functions not commonly found: + +### Tensor Functions +- `th_iterproduct` (mimics itertools.product) +- `th_gather_nd` (N-dimensional version of torch.gather) +- `th_random_choice` (mimics np.random.choice) +- `th_pearsonr` (mimics scipy.stats.pearsonr) +- `th_corrcoef` (mimics np.corrcoef) +- `th_affine2d` and `th_affine3d` (affine transforms on torch.Tensors) + + +## Acknowledgements and References +We stand on the shoulders of (github?) giants and couldn't have done +this without the rich github ecosystem and community. This framework is +based in part on the excellent +[Torchsample](https://github.com/ncullen93/torchsample) framework +originally published by @ncullen93. Additionally, many models have been +gently borrowed/modified from @Cadene pretrained models +[repo](https://github.com/Cadene/pretrained-models.pytorch) as well as @Tramac segmentation [repo](https://github.com/Tramac/awesome-semantic-segmentation-pytorch). + +##### Thank you to the following people and the projects they maintain: +- @ncullen93 +- @cadene +- @deallynomore +- @recastrodiaz +- @zijundeng +- @Tramac +- And many others! (attributions listed in the codebase as they occur) + +##### Thank you to the following projects from which we gently borrowed code and models +- [PyTorchNet](https://github.com/pytorch/tnt) +- [pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch) +- [DeepLab_pytorch](https://github.com/doiken23/DeepLab_pytorch) +- [Pytorch for Semantic Segmentation](https://github.com/zijundeng/pytorch-semantic-segmentation) +- [Binseg Pytorch](https://github.com/saeedizadi/binseg_pytoch) +- [awesome-semantic-segmentation-pytorch](https://github.com/Tramac/awesome-semantic-segmentation-pytorch) +- And many others! (attributions listed in the codebase as they occur) + + + +| *Thangs are broken matey! Arrr!!!* | +|-----------------------| +| We're working on this project as time permits so you might discover bugs here and there. Feel free to report them, or better yet, to submit a pull request! | + + + +%package help +Summary: Development documents and examples for pywick +Provides: python3-pywick-doc +%description help +# Pywick + +<div style="text-align:center"> + +[](https://pywick.readthedocs.io/en/latest/) +[](https://pywick.readthedocs.io/en/latest/) +[](https://pypi.org/project/pywick/) +[](https://pywick.readthedocs.io/en/latest/) +[](https://github.com/achaiah/pywick/blob/master/LICENSE.txt) + +</div> + +#### High-Level Training framework for Pytorch + +Pywick is a high-level Pytorch training framework that aims to get you +up and running quickly with state of the art neural networks. *Does the +world need another Pytorch framework?* Probably not. But we started this +project when no good frameworks were available and it just kept growing. +So here we are. + +Pywick tries to stay on the bleeding edge of research into neural networks. If you just wish to run a vanilla CNN, this is probably +going to be overkill. However, if you want to get lost in the world of neural networks, fine-tuning and hyperparameter optimization +for months on end then this is probably the right place for you :) + +Among other things Pywick includes: +- State of the art normalization, activation, loss functions and optimizers not included in the standard Pytorch library (AdaBelief, Addsign, Apollo, Eve, Lookahead, Radam, Ralamb, RangerLARS etc). +- A high-level module for training with callbacks, constraints, metrics, conditions and regularizers. +- Hundreds of popular object classification and semantic segmentation models! +- Comprehensive data loading, augmentation, transforms, and sampling capability. +- Utility tensor functions. +- Useful meters. +- Basic GridSearch (exhaustive and random). + +## Docs +Hey, [check this out](https://pywick.readthedocs.io/en/latest/), we now have [docs](https://pywick.readthedocs.io/en/latest/)! They're still a work in progress though so apologies for anything that's broken. + +## What's New (highlights) + +### v0.6.5 - Docker all the things! +Another great improvement to the framework - docker! You can now run the 17flowers demo right out of the box! + - Grab our docker image at [docker hub](https://hub.docker.com/repository/docker/achaiah/pywick): `docker pull achaiah/pywick:latest`. Pytorch 1.8 and cuda dependencies are pre-installed. + - Run 17flowers demo with: `docker run --rm -it --ipc=host -v your_local_out_dir:/jobs/17flowers --init -e demo=true achaiah/pywick:latest` + - Or run the container in standalone mode so you can use your own data (don't forget to map your local dir to container): + ```bash + docker run --rm -it \ + --ipc=host \ + -v <your_local_data_dir>:<container_data_dir> \ + -v <your_local_out_dir>:<container_out_dir> \ + --init \ + achaiah/pywick:latest + ``` + +### Older Notes +- **Oct. 11, 2021 - We thought ya might like YAML!** + - So you're saying you like **configuration files**? You're saying you like **examples** too? Well, we've got you covered! Huge release today with a configuration-based training example! All you have to do is: + - Get your favorite dataset (or download [17 flowers](https://www.robots.ox.ac.uk/~vgg/data/flowers/17/) to get started and `pywick/examples/17flowers_split.py` to convert) + - Adjust the `configs/train_classifier.yaml` file to fit your workspace + - Then simply run: `python3 train_classifier.py configs/train_classifier.yaml` and watch it train! +- **May 6, 2021** + - Many SoTA classification and segmentation models added: Swin-Transformer variants, NFNet variants (L0, L1), Halo nets, Lambda nets, ECA variants, Rexnet + others + - Many new loss functions added: RecallLoss, SoftInvDiceLoss, OhemBCEDicePenalizeBorderLoss, RMIBCEDicePenalizeBorderLoss + others + - Bug fixes +- **Jun. 15, 2020** + - 700+ models added from [rwightman's](https://github.com/rwightman/pytorch-image-models) repo via `torch.hub`! See docs for all the variants! + - Some minor bug fixes +- **Jan. 20, 2020** + - New release: 0.5.6 (minor fix from 0.5.5 for pypi) + - Mish activation function (SoTA) + - [rwightman's](https://github.com/rwightman/gen-efficientnet-pytorch) models of pretrained/ported variants for classification (44 total) + - efficientnet Tensorflow port b0-b8, with and without AP, el/em/es, cc + - mixnet L/M/S + - mobilenetv3 + - mnasnet + - spnasnet + - Additional loss functions +- **Aug. 1, 2019** + - New segmentation NNs: BiSeNet, DANet, DenseASPP, DUNet, OCNet, PSANet + - New Loss Functions: Focal Tversky Loss, OHEM CrossEntropy Loss, various combination losses + - Major restructuring and standardization of NN models and loading functionality + - General bug fixes and code improvements + +## Install +Pywick requires **pytorch >= 1.4** + +`pip install pywick` + +or specific version from git: + +`pip install git+https://github.com/achaiah/pywick.git@v0.6.5` + +## ModuleTrainer +The `ModuleTrainer` class provides a high-level training interface which abstracts away the training loop while providing callbacks, constraints, initializers, regularizers, +and more. + +See the `train_classifier.py` example for a pretty complete configuration example. To get up and running with your own data quickly simply edit the `configs/train_classifier.yaml` file with your desired parameters and dataset location(s). + +Note: <i>Dataset needs to be organized for classification where each directory name is the name of a class and contains all images pertaining to that class</i> + +PyWick provides a wide range of <b>callbacks</b>, generally mimicking the interface found in `Keras`: + +- `CSVLogger` - Logs epoch-level metrics to a CSV file +- [`CyclicLRScheduler`](https://github.com/bckenstler/CLR) - Cycles through min-max learning rate +- `EarlyStopping` - Provides ability to stop training early based on supplied criteria +- `History` - Keeps history of metrics etc. during the learning process +- `LambdaCallback` - Allows you to implement your own callbacks on the fly +- `LRScheduler` - Simple learning rate scheduler based on function or supplied schedule +- `ModelCheckpoint` - Comprehensive model saver +- `ReduceLROnPlateau` - Reduces learning rate (LR) when a plateau has been reached +- `SimpleModelCheckpoint` - Simple model saver +- Additionally, a `TensorboardLogger` is incredibly easy to implement via tensorboardX (now part of pytorch 1.1 release!) + + +```python +from pywick.callbacks import EarlyStopping + +callbacks = [EarlyStopping(monitor='val_loss', patience=5)] +trainer.set_callbacks(callbacks) +``` + +PyWick also provides <b>regularizers</b>: + +- `L1Regularizer` +- `L2Regularizer` +- `L1L2Regularizer` + + +and <b>constraints</b>: +- `UnitNorm` +- `MaxNorm` +- `NonNeg` + +Both regularizers and constraints can be selectively applied on layers using regular expressions and the `module_filter` +argument. Constraints can be explicit (hard) constraints applied at an arbitrary batch or +epoch frequency, or they can be implicit (soft) constraints similar to regularizers +where the constraint deviation is added as a penalty to the total model loss. + +```python +from pywick.constraints import MaxNorm, NonNeg +from pywick.regularizers import L1Regularizer + +# hard constraint applied every 5 batches +hard_constraint = MaxNorm(value=2., frequency=5, unit='batch', module_filter='*fc*') +# implicit constraint added as a penalty term to model loss +soft_constraint = NonNeg(lagrangian=True, scale=1e-3, module_filter='*fc*') +constraints = [hard_constraint, soft_constraint] +trainer.set_constraints(constraints) + +regularizers = [L1Regularizer(scale=1e-4, module_filter='*conv*')] +trainer.set_regularizers(regularizers) +``` + +You can also fit directly on a `torch.utils.data.DataLoader` and can have +a validation set as well : + +```python +from pywick import TensorDataset +from torch.utils.data import DataLoader + +train_dataset = TensorDataset(x_train, y_train) +train_loader = DataLoader(train_dataset, batch_size=32) + +val_dataset = TensorDataset(x_val, y_val) +val_loader = DataLoader(val_dataset, batch_size=32) + +trainer.fit_loader(loader, val_loader=val_loader, num_epoch=100) +``` + +## Extensive Library of Image Classification Models (most are pretrained!) +- All standard models from Pytorch: + - [**Densenet**](https://arxiv.org/abs/1608.06993) + - [**Inception v3**](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Szegedy_Rethinking_the_Inception_CVPR_2016_paper.pdf) + - [**MobileNet v2**](https://arxiv.org/abs/1801.04381) + - [**ResNet**](https://arxiv.org/abs/1512.03385) + - [**ShuffleNet v2**](https://arxiv.org/abs/1807.11164) + - [**SqueezeNet**](https://arxiv.org/abs/1602.07360) + - [**VGG**](https://arxiv.org/abs/1409.1556) +- [**BatchNorm Inception**](https://arxiv.org/abs/1502.03167) +- [**Deep High-Resolution Representation Learning for Human Pose Estimation**](https://arxiv.org/abs/1902.09212v1) +- [**Deep Layer Aggregation**](https://arxiv.org/abs/1707.06484) +- [**Dual Path Networks**](https://arxiv.org/abs/1707.01629) +- [**EfficientNet variants (b0-b8, el, em, es, lite1-lite4, pruned, AP/NS)**](https://arxiv.org/abs/1905.11946) +- [**ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks**](https://arxiv.org/abs/1910.03151v4) +- [**FBResnet**](https://github.com/facebook/fb.resnet.torch) +- [**FBNet-C**](https://arxiv.org/abs/1812.03443) +- [**Inception v4**](http://arxiv.org/abs/1602.07261) +- [**InceptionResnet v2**](https://arxiv.org/abs/1602.07261) +- [**Mixnet variants (l, m, s, xl, xxl)**](https://arxiv.org/abs/1907.09595) +- [**MnasNet**](https://arxiv.org/abs/1807.11626) +- [**MobileNet V3**](https://arxiv.org/abs/1905.02244) +- [**NasNet variants (mnas, pnas, mobile)**](https://arxiv.org/abs/1707.07012) +- [**PNASNet**](https://arxiv.org/abs/1712.00559) +- [**Polynet**](https://arxiv.org/abs/1611.05725) +- [**Pyramid Resnet**](https://arxiv.org/abs/1610.02915) +- [**RegNet - Designing Network Design Spaces**](https://arxiv.org/abs/2003.13678) +- **[Resnet variants (gluon, res2net, se, ssl, tv, wide)](https://arxiv.org/abs/1512.03385)** +- [**ResNeSt: Split-Attention Networks**](https://arxiv.org/abs/2004.08955) +- [**ResNext variants (ig, se, ssl, swsl, tv)**](https://arxiv.org/abs/1611.05431) +- [**SE Net variants (gluon, resnet, resnext, inception)**](https://arxiv.org/pdf/1709.01507.pdf) +- [**SelecSLS Convolutional Net**](https://github.com/mehtadushy/SelecSLS-Pytorch) +- [**Selective Kernel Networks**](https://arxiv.org/abs/1903.06586) +- [**Semi-Supervised and Semi-Weakly Supervised ImageNet Models**](https://github.com/facebookresearch/semi-supervised-ImageNet1K-models) +- [**Single-Pass NAS Net**](https://arxiv.org/abs/1904.02877) +- [**TResNet: High Performance GPU-Dedicated Architecture**](https://arxiv.org/abs/2003.13630) +- [**Wide Resnet**](https://arxiv.org/abs/1605.07146) +- [**XCeption**](https://arxiv.org/pdf/1610.02357.pdf) +- All the newest classification models (200+) from [rwightman's repo](https://github.com/rwightman/pytorch-image-models) ECA-NFNet, GERNet, RegNet, SKResnext, SWIN-Transformer, VIT etc.) + +## Image Segmentation Models +- **BiSeNet** ([Bilateral Segmentation Network for Real-time Semantic Segmentation](https://arxiv.org/abs/1808.00897)) +- **DANet** ([Dual Attention Network for Scene Segmentation](https://arxiv.org/abs/1809.02983)) +- **Deeplab v2** ([DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://arxiv.org/abs/1606.00915)) +- **Deeplab v3** ([Rethinking Atrous Convolution for Semantic Image Segmentation](https://arxiv.org/abs/1706.05587)) +- **DenseASPP** ([DenseASPP for Semantic Segmentation in Street Scenes](http://openaccess.thecvf.com/content_cvpr_2018/papers/Yang_DenseASPP_for_Semantic_CVPR_2018_paper.pdf)) +- **DRNNet** ([Dilated Residual Networks](http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_Dilated_Residual_Networks_CVPR_2017_paper.pdf)) +- **DUC, HDC** ([understanding convolution for semantic segmentation](https://arxiv.org/abs/1702.08502)) +- **DUNet** ([Decoders Matter for Semantic Segmentation](https://arxiv.org/abs/1903.02120)) +- **ENet** ([ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://arxiv.org/abs/1606.02147)) +- **Vanilla FCN:** FCN32, FCN16, FCN8, in the versions of VGG, ResNet + and OptDenseNet respectively ([Fully convolutional networks for semantic segmentation](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)) +- **FRRN** ([Full Resolution Residual Networks for Semantic Segmentation in Street Scenes](https://arxiv.org/abs/1611.08323)) +- **FusionNet** ([FusionNet in Tensorflow by Hyungjoo Andrew Cho](https://github.com/NySunShine/fusion-net)) +- **GALDNet** +- **GCN** ([Large Kernel Matters](https://arxiv.org/pdf/1703.02719)) +- **LinkNet** ([Link-Net](https://codeac29.github.io/projects/linknet/)) +- **OCNet** ([Object Context Network for Scene Parsing](https://arxiv.org/abs/1809.00916)) +- **PSPNet** ([Pyramid scene parsing network](https://arxiv.org/abs/1612.01105)) +- **RefineNet** ([RefineNet](https://arxiv.org/abs/1611.06612)) +- **SegNet** ([Segnet: A deep convolutional encoder-decoder architecture for image segmentation](https://arxiv.org/pdf/1511.00561)) +- **Tiramisu** ([The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation](https://arxiv.org/abs/1611.09326)) +- **U-Net** ([U-net: Convolutional networks for biomedical image segmentation](https://arxiv.org/abs/1505.04597)) +- Additional variations of many of the above + +###### To load one of these models: +[Read the docs](https://pywick.readthedocs.io/en/latest/api/pywick.models.html) +for useful details! Then dive in: +```python +# use the `get_model` utility +from pywick.models.model_utils import get_model, ModelType + +model = get_model(model_type=ModelType.CLASSIFICATION, model_name='resnet18', num_classes=1000, pretrained=True) +``` +For a complete list of models (including many experimental ones) you can call the `get_supported_models` method e.g. +`pywick.models.model_utils.get_supported_models(ModelType.SEGMENTATION)` + +## Data Augmentation and Datasets +The PyWick package provides wide variety of good data augmentation and transformation +tools which can be applied during data loading. The package also provides the flexible +`TensorDataset`, `FolderDataset` and `MultiFolderDataset` classes to handle most dataset needs. + +### Torch Transforms +##### These transforms work directly on torch tensors + +- `AddChannel` +- `ChannelsFirst` +- `ChannelsLast` +- `Compose` +- `ExpandAxis` +- `Pad` +- `PadNumpy` +- `RandomChoiceCompose` +- `RandomCrop` +- `RandomFlip` +- `RandomOrder` +- `RangeNormalize` +- `Slice2D` +- `SpecialCrop` +- `StdNormalize` +- `ToFile` +- `ToNumpyType` +- `ToTensor` +- `Transpose` +- `TypeCast` + +##### Additionally, we provide image-specific manipulations directly on tensors: + +- `Brightness` +- `Contrast` +- `Gamma` +- `Grayscale` +- `RandomBrightness` +- `RandomChoiceBrightness` +- `RandomChoiceContrast` +- `RandomChoiceGamma` +- `RandomChoiceSaturation` +- `RandomContrast` +- `RandomGamma` +- `RandomGrayscale` +- `RandomSaturation` +- `Saturation` + +##### Affine Transforms (perform affine or affine-like transforms on torch tensors) + +- `RandomAffine` +- `RandomChoiceRotate` +- `RandomChoiceShear` +- `RandomChoiceTranslate` +- `RandomChoiceZoom` +- `RandomRotate` +- `RandomShear` +- `RandomSquareZoom` +- `RandomTranslate` +- `RandomZoom` +- `Rotate` +- `Shear` +- `Translate` +- `Zoom` + +We also provide a class for stringing multiple affine transformations together so that only one interpolation takes place: + +- `Affine` +- `AffineCompose` + +##### Blur and Scramble transforms (for tensors) +- `Blur` +- `RandomChoiceBlur` +- `RandomChoiceScramble` +- `Scramble` + +### Datasets and Sampling +We provide the following datasets which provide general structure and iterators for sampling from and using transforms on in-memory or out-of-memory data. In particular, +the [FolderDataset](pywick/datasets/FolderDataset.py) has been designed to fit most of your dataset needs. It has extensive options for data filtering and manipulation. +It supports loading images for classification, segmentation and even arbitrary source/target mapping. Take a good look at its documentation for more info. + +- `ClonedDataset` +- `CSVDataset` +- `FolderDataset` +- `MultiFolderDataset` +- `TensorDataset` +- `tnt.BatchDataset` +- `tnt.ConcatDataset` +- `tnt.ListDataset` +- `tnt.MultiPartitionDataset` +- `tnt.ResampleDataset` +- `tnt.ShuffleDataset` +- `tnt.TensorDataset` +- `tnt.TransformDataset` + +### Imbalanced Datasets +In many scenarios it is important to ensure that your traing set is properly balanced, +however, it may not be practical in real life to obtain such a perfect dataset. In these cases +you can use the `ImbalancedDatasetSampler` as a drop-in replacement for the basic sampler provided +by the DataLoader. More information can be found [here](https://github.com/ufoym/imbalanced-dataset-sampler) + +```python +from pywick.samplers import ImbalancedDatasetSampler + +train_loader = torch.utils.data.DataLoader(train_dataset, + sampler=ImbalancedDatasetSampler(train_dataset), + batch_size=args.batch_size, **kwargs) +``` + +## Utility Functions +PyWick provides a few utility functions not commonly found: + +### Tensor Functions +- `th_iterproduct` (mimics itertools.product) +- `th_gather_nd` (N-dimensional version of torch.gather) +- `th_random_choice` (mimics np.random.choice) +- `th_pearsonr` (mimics scipy.stats.pearsonr) +- `th_corrcoef` (mimics np.corrcoef) +- `th_affine2d` and `th_affine3d` (affine transforms on torch.Tensors) + + +## Acknowledgements and References +We stand on the shoulders of (github?) giants and couldn't have done +this without the rich github ecosystem and community. This framework is +based in part on the excellent +[Torchsample](https://github.com/ncullen93/torchsample) framework +originally published by @ncullen93. Additionally, many models have been +gently borrowed/modified from @Cadene pretrained models +[repo](https://github.com/Cadene/pretrained-models.pytorch) as well as @Tramac segmentation [repo](https://github.com/Tramac/awesome-semantic-segmentation-pytorch). + +##### Thank you to the following people and the projects they maintain: +- @ncullen93 +- @cadene +- @deallynomore +- @recastrodiaz +- @zijundeng +- @Tramac +- And many others! (attributions listed in the codebase as they occur) + +##### Thank you to the following projects from which we gently borrowed code and models +- [PyTorchNet](https://github.com/pytorch/tnt) +- [pretrained-models.pytorch](https://github.com/Cadene/pretrained-models.pytorch) +- [DeepLab_pytorch](https://github.com/doiken23/DeepLab_pytorch) +- [Pytorch for Semantic Segmentation](https://github.com/zijundeng/pytorch-semantic-segmentation) +- [Binseg Pytorch](https://github.com/saeedizadi/binseg_pytoch) +- [awesome-semantic-segmentation-pytorch](https://github.com/Tramac/awesome-semantic-segmentation-pytorch) +- And many others! (attributions listed in the codebase as they occur) + + + +| *Thangs are broken matey! Arrr!!!* | +|-----------------------| +| We're working on this project as time permits so you might discover bugs here and there. Feel free to report them, or better yet, to submit a pull request! | + + + +%prep +%autosetup -n pywick-0.6.5 + +%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-pywick -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Fri May 05 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.5-1 +- Package Spec generated @@ -0,0 +1 @@ +b37335fe8023e744fa915aa288b5d66d pywick-0.6.5.tar.gz |