torchlm 0.1.3

A PyTorch landmarks-only library with 100+ data augmentations, training and inference, can easily install with pip and compatible with albumentations and torchvision.

🤗 Introduction

torchlm is a PyTorch landmarks-only library with 100+ data augmentations, support training and inference. torchlm is aims at only focus on any landmark detection, such as face landmarks, hand keypoints and body keypoints, etc. It provides 30+ native data augmentations and can bind with 80+ transforms from torchvision and albumentations, no matter the input is a np.ndarray or a torch Tensor, torchlm will automatically be compatible with different data types and then wrap it back to the original type through a autodtype wrapper. Further, torchlm will add modules for training and inference in the future.

🆕 What's New

• [2022/02/13]: Add 30+ native data augmentations and bind 80+ transforms from torchvision and albumentations.

🛠️ Usage

Requirements

• opencv-python-headless>=4.5.2
• numpy>=1.14.4
• torch>=1.6.0
• torchvision>=0.9.0
• albumentations>=1.1.0

Installation

you can install torchlm directly from pypi.

pip3 install torchlm
# install from specific pypi mirrors use '-i'
pip3 install torchlm -i https://pypi.org/simple/

or install from source.

# clone torchlm repository locally
git clone --depth=1 https://github.com/DefTruth/torchlm.git 
cd torchlm
# install in editable mode
pip install -e .

Data Augmentation

torchlm provides 30+ native data augmentations for landmarks and can bind with 80+ transforms from torchvision and albumentations through torchlm.bind method. Further, torchlm.bind provide a prob param at bind-level to force any transform or callable be a random-style augmentation. The data augmentations in torchlm are safe and simplest. Any transform operations at runtime cause landmarks outside will be auto dropped to keep the number of landmarks unchanged. The layout format of landmarks is xy with shape (N, 2), N denotes the number of the input landmarks. No matter the input is a np.ndarray or a torch Tensor, torchlm will automatically be compatible with different data types and then wrap it back to the original type through a autodtype wrapper.

• use almost 30+ native transforms from torchlm directly

import torchlm
transform = torchlm.LandmarksCompose([
        # use native torchlm transforms
        torchlm.LandmarksRandomScale(prob=0.5),
        torchlm.LandmarksRandomTranslate(prob=0.5),
        torchlm.LandmarksRandomShear(prob=0.5),
        torchlm.LandmarksRandomMask(prob=0.5),
        torchlm.LandmarksRandomBlur(kernel_range=(5, 25), prob=0.5),
        torchlm.LandmarksRandomBrightness(prob=0.),
        torchlm.LandmarksRandomRotate(40, prob=0.5, bins=8),
        torchlm.LandmarksRandomCenterCrop((0.5, 1.0), (0.5, 1.0), prob=0.5),
        # ...
    ])

• bind torchvision and albumentations's transforms through torchlm.bind

import torchvision
import albumentations
import torchlm
transform = torchlm.LandmarksCompose([
        # use native torchlm transforms
        torchlm.LandmarksRandomScale(prob=0.5),
        # bind torchvision image only transforms, bind with a given prob
        torchlm.bind(torchvision.transforms.GaussianBlur(kernel_size=(5, 25)), prob=0.5),  
        torchlm.bind(torchvision.transforms.RandomAutocontrast(p=0.5)),
        # bind albumentations image only transforms
        torchlm.bind(albumentations.ColorJitter(p=0.5)),
        torchlm.bind(albumentations.GlassBlur(p=0.5)),
        # bind albumentations dual transforms
        torchlm.bind(albumentations.RandomCrop(height=200, width=200, p=0.5)),
        torchlm.bind(albumentations.Rotate(p=0.5)),
        # ...
    ])

• bind custom callable array or Tensor functions through torchlm.bind

# First, defined your custom functions
def callable_array_noop(img: np.ndarray, landmarks: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
    # do some transform here ...
    return img.astype(np.uint32), landmarks.astype(np.float32)

def callable_tensor_noop(img: Tensor, landmarks: Tensor) -> Tuple[Tensor, Tensor]:
    # do some transform here ...
    return img, landmarks

# Then, bind your functions and put it into the transforms pipeline.
transform = torchlm.LandmarksCompose([
        # use native torchlm transforms
        torchlm.LandmarksRandomScale(prob=0.5),
        # bind custom callable array functions
        torchlm.bind(callable_array_noop, bind_type=torchlm.BindEnum.Callable_Array),
        # bind custom callable Tensor functions with a given prob
        torchlm.bind(callable_tensor_noop, bind_type=torchlm.BindEnum.Callable_Tensor, prob=0.5),  
        # ...
    ])

• setup logging mode as True globally might help you figure out the runtime details

import torchlm
# some global setting
torchlm.set_transforms_debug(True)
torchlm.set_transforms_logging(True)
torchlm.set_autodtype_logging(True)

some detail information will show you at each runtime, the infos might look like

LandmarksRandomScale() AutoDtype Info: AutoDtypeEnum.Array_InOut
LandmarksRandomScale() Execution Flag: False
BindTorchVisionTransform(GaussianBlur())() AutoDtype Info: AutoDtypeEnum.Tensor_InOut
BindTorchVisionTransform(GaussianBlur())() Execution Flag: True
BindAlbumentationsTransform(ColorJitter())() AutoDtype Info: AutoDtypeEnum.Array_InOut
BindAlbumentationsTransform(ColorJitter())() Execution Flag: True
BindTensorCallable(callable_tensor_noop())() AutoDtype Info: AutoDtypeEnum.Tensor_InOut
BindTensorCallable(callable_tensor_noop())() Execution Flag: False

• Execution Flag: True means current transform was executed successful, False means it was not executed because of the random probability or some Runtime Exceptions(torchlm will should the error infos if debug mode is True).

• AutoDtype Info:

  • Array_InOut means current transform need a np.ndnarray as input and then output a np.ndarray.
  • Tensor_InOut means current transform need a torch Tensor as input and then output a torch Tensor.
  • Array_In means current transform needs a np.ndarray input and then output a torch Tensor.
  • Tensor_In means current transform needs a torch Tensor input and then output a np.ndarray.

  But, is ok if you pass a Tensor to a np.ndarray-like transform, torchlm will automatically be compatible with different data types and then wrap it back to the original type through a autodtype wrapper.

• Supported Transforms Sets, see transforms.md. A detail example can be found at test/transforms.py.

Training(TODO)

• YOLOX
• YOLOv5
• NanoDet
• PIPNet
• ResNet
• MobileNet
• ShuffleNet
• ...

Inference

The ONNXRuntime(CPU/GPU), MNN, NCNN and TNN C++ inference of torchlm will be release at lite.ai.toolkit.

📖 Documentations

• Data Augmentation's API (TODO)
• ...

🎓 License

The code of torchlm is released under the MIT License.

❤️ Contribution

Please consider ⭐ this repo if you like it, as it is the simplest way to support me.

👋 Acknowledgement

The implementation of torchlm's transforms borrow the code from Paperspace .