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News 👇👇

Most of my time now is focused on LLM/VLM Inference. Please check 📖Awesome-LLM-Inference , 📖Awesome-SD-Inference and 📖CUDA-Learn-Notes for more details.

🤗 Introduction

torchlm is aims to build a high level pipeline for face landmarks detection, it supports training, evaluating, exporting, inference(Python/C++) and 100+ data augmentations, can easily install via pip.

👋 Core Features

• High level pipeline for training and inference.
• Provides 30+ native landmarks data augmentations.
• Can bind 80+ transforms from torchvision and albumentations with one-line-code.
• Support PIPNet, YOLOX, ResNet, MobileNet and ShuffleNet for face landmarks detection.

🆕 What's New

• [2022/03/08]: Add PIPNet: Towards Efficient Facial Landmark Detection in the Wild, CVPR2021
• [2022/02/13]: Add 30+ transforms and bind 80+ transforms from torchvision and albumentations.

🔥🔥Performance(@NME)

🛠️Installation

you can install torchlm directly from pypi.

pip install torchlm>=0.1.6.10 # or install the latest pypi version `pip install torchlm`
pip install torchlm>=0.1.6.10 -i https://pypi.org/simple/ # or install from specific pypi mirrors use '-i'

or install from source if you want the latest torchlm and install it in editable mode with -e.

git clone --depth=1 https://github.com/DefTruth/torchlm.git 
cd torchlm && pip install -e .

🌟🌟Data Augmentation

torchlm provides 30+ native data augmentations for landmarks and can bind with 80+ transforms from torchvision and albumentations. The layout format of landmarks is xy with shape (N, 2).

Use almost 30+ native transforms from torchlm directly

import torchlm
transform = torchlm.LandmarksCompose([
    torchlm.LandmarksRandomScale(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)
])

Also, a user-friendly API build_default_transform is available to build a default transform pipeline.

transform = torchlm.build_default_transform(
    input_size=(input_size, input_size),
    mean=[0.485, 0.456, 0.406],
    std=[0.229, 0.224, 0.225],
    force_norm_before_mean_std=True,  # img/=255. first
    rotate=30,
    keep_aspect=False,
    to_tensor=True  # array -> Tensor & HWC -> CHW
)

See transforms.md for supported transforms sets and more example can be found at test/transforms.py.

torchlm provides 30+ native data augmentations for landmarks and can bind with 80+ transforms from torchvision and albumentations through torchlm.bind method. The layout format of landmarks is xy with shape (N, 2), N denotes the number of the input landmarks. 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. Yes, 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.

NOTE: Please install albumentations first if you want to bind albumentations's transforms. If you have the conflict problem between different installed version of opencv (opencv-python and opencv-python-headless, ablumentations need opencv-python-headless). Please uninstall the opencv-python and opencv-python-headless first, and then reinstall albumentations. See albumentations#1140 for more details.

# first uninstall conflict opencvs
pip uninstall opencv-python
pip uninstall opencv-python-headless
pip uninstall albumentations  # if you have installed albumentations
pip install albumentations # then reinstall albumentations, will also install deps, e.g opencv

Then, check if albumentations is available.

torchlm.albumentations_is_available()  # True or False

transform = torchlm.LandmarksCompose([
    torchlm.bind(torchvision.transforms.GaussianBlur(kernel_size=(5, 25)), prob=0.5),  
    torchlm.bind(albumentations.ColorJitter(p=0.5))
])

# 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([
        torchlm.bind(callable_array_noop, bind_type=torchlm.BindEnum.Callable_Array),
        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

# 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
Error at LandmarksRandomTranslate() Skip, Flag: False Error Info: LandmarksRandomTranslate() have 98 input landmarks, but got 96 output landmarks!
LandmarksRandomTranslate() 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.
  Yes, 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.

🎉🎉Training

In torchlm, each model have two high level and user-friendly APIs named apply_training and apply_freezing for training. apply_training handle the training process and apply_freezing decide whether to freeze the backbone for fune-tuning.

Quick Start👇

Here is an example of PIPNet. You can freeze backbone before fine-tuning through apply_freezing.

from torchlm.models import pipnet
# will auto download pretrained weights from latest release if pretrained=True
model = pipnet(backbone="resnet18", pretrained=True, num_nb=10, num_lms=98, net_stride=32,
               input_size=256, meanface_type="wflw", backbone_pretrained=True)
model.apply_freezing(backbone=True)
model.apply_training(
    annotation_path="../data/WFLW/converted/train.txt",  # or fine-tuning your custom data
    num_epochs=10,
    learning_rate=0.0001,
    save_dir="./save/pipnet",
    save_prefix="pipnet-wflw-resnet18",
    save_interval=1,
    logging_interval=1,
    device="cuda",
    coordinates_already_normalized=True,
    batch_size=16,
    num_workers=4,
    shuffle=True
)

Please jump to the entry point of the function for the detail documentations of apply_training API for each defined models in torchlm, e.g pipnet/_impls.py#L166. You might see some logs if the training process is running:

Parameters for DataLoader:  {'batch_size': 16, 'num_workers': 4, 'shuffle': True}
Built _PIPTrainDataset: train count is 7500 !
Epoch 0/9
----------
[Epoch 0/9, Batch 1/468] <Total loss: 0.372885> <cls loss: 0.063186> <x loss: 0.078508> <y loss: 0.071679> <nbx loss: 0.086480> <nby loss: 0.073031>
[Epoch 0/9, Batch 2/468] <Total loss: 0.354169> <cls loss: 0.051672> <x loss: 0.075350> <y loss: 0.071229> <nbx loss: 0.083785> <nby loss: 0.072132>
[Epoch 0/9, Batch 3/468] <Total loss: 0.367538> <cls loss: 0.056038> <x loss: 0.078029> <y loss: 0.076432> <nbx loss: 0.083546> <nby loss: 0.073492>
[Epoch 0/9, Batch 4/468] <Total loss: 0.339656> <cls loss: 0.053631> <x loss: 0.073036> <y loss: 0.066723> <nbx loss: 0.080007> <nby loss: 0.066258>
[Epoch 0/9, Batch 5/468] <Total loss: 0.364556> <cls loss: 0.051094> <x loss: 0.077378> <y loss: 0.071951> <nbx loss: 0.086363> <nby loss: 0.077770>
[Epoch 0/9, Batch 6/468] <Total loss: 0.371356> <cls loss: 0.049117> <x loss: 0.079237> <y loss: 0.075729> <nbx loss: 0.086213> <nby loss: 0.081060>
...
[Epoch 0/9, Batch 33/468] <Total loss: 0.298983> <cls loss: 0.041368> <x loss: 0.069912> <y loss: 0.057667> <nbx loss: 0.072996> <nby loss: 0.057040>

Dataset Format👇

The annotation_path parameter is denotes the path to a custom annotation file, the format must be:

"img0_path x0 y0 x1 y1 ... xn-1,yn-1"
"img1_path x0 y0 x1 y1 ... xn-1,yn-1"
"img2_path x0 y0 x1 y1 ... xn-1,yn-1"
"img3_path x0 y0 x1 y1 ... xn-1,yn-1"
...

If the label in annotation_path is already normalized by image size, please set coordinates_already_normalized as True in apply_training API.

"img0_path x0/w y0/h x1/w y1/h ... xn-1/w,yn-1/h"
"img1_path x0/w y0/h x1/w y1/h ... xn-1/w,yn-1/h"
"img2_path x0/w y0/h x1/w y1/h ... xn-1/w,yn-1/h"
"img3_path x0/w y0/h x1/w y1/h ... xn-1/w,yn-1/h"
...

Here is an example of WFLW to show you how to prepare the dataset, also see test/data.py.

Additional Custom Settings👋

Some models in torchlm support additional custom settings beyond the num_lms of your custom dataset. For example, PIPNet also need to set a custom meanface generated by your custom dataset. Please jump the source code of each defined model in torchlm for the details about additional custom settings to get more flexibilities of training or fine-tuning processes. Here is an example of How to train PIPNet in your own dataset with custom meanface setting?

Set up your custom meanface and nearest-neighbor landmarks through pipnet.set_custom_meanface method, this method will calculate the Euclidean Distance between different landmarks in meanface and will auto set up the nearest-neighbors for each landmark. NOTE: The PIPNet will reshape the detection headers if the number of landmarks in custom dataset is not equal with the num_lms you initialized.

def set_custom_meanface(custom_meanface_file_or_string: str) -> bool:
    """
    :param custom_meanface_file_or_string: a long string or a file contains normalized
    or un-normalized meanface coords, the format is "x0,y0,x1,y1,x2,y2,...,xn-1,yn-1".
    :return: status, True if successful.
    """

Also, a generate_meanface API is available in torchlm to help you get meanface in custom dataset.

# generate your custom meanface.
custom_meanface, custom_meanface_string = torchlm.data.annotools.generate_meanface(
  annotation_path="../data/WFLW/converted/train.txt",
  coordinates_already_normalized=True)
# check your generated meanface.
rendered_meanface = torchlm.data.annotools.draw_meanface(
  meanface=custom_meanface, coordinates_already_normalized=True)
cv2.imwrite("./logs/wflw_meanface.jpg", rendered_meanface)
# setting up your custom meanface
model.set_custom_meanface(custom_meanface_file_or_string=custom_meanface_string)

Benchmarks Dataset Converters👇

In torchlm, some pre-defined dataset converters for common use benchmark datasets are available, such as 300W, COFW, WFLW and AFLW. These converters will help you to convert the common use dataset to the standard annotation format that torchlm need. Here is an example of WFLW.

from torchlm.data import LandmarksWFLWConverter
# setup your path to the original downloaded dataset from official 
converter = LandmarksWFLWConverter(
    data_dir="../data/WFLW", save_dir="../data/WFLW/converted",
    extend=0.2, rebuild=True, target_size=256, keep_aspect=False,
    force_normalize=True, force_absolute_path=True
)
converter.convert()
converter.show(count=30)  # show you some converted images with landmarks for debugging

Then, the output's layout in ../data/WFLW/converted would be look like:

├── image
│   ├── test
│   └── train
├── show
│   ├── 16--Award_Ceremony_16_Award_Ceremony_Awards_Ceremony_16_589x456y91.jpg
│   ├── 20--Family_Group_20_Family_Group_Family_Group_20_118x458y58.jpg
...
├── test.txt
└── train.txt

🛸🚵‍️ Inference

C++ APIs👀

The ONNXRuntime(CPU/GPU), MNN, NCNN and TNN C++ inference of torchlm will be release in lite.ai.toolkit. Here is an example of 1000 Facial Landmarks Detection using FaceLandmarks1000. Download model from Model-Zoo².

#include "lite/lite.h"

static void test_default()
{
  std::string onnx_path = "../../../hub/onnx/cv/FaceLandmark1000.onnx";
  std::string test_img_path = "../../../examples/lite/resources/test_lite_face_landmarks_0.png";
  std::string save_img_path = "../../../logs/test_lite_face_landmarks_1000.jpg";
    
  auto *face_landmarks_1000 = new lite::cv::face::align::FaceLandmark1000(onnx_path);

  lite::types::Landmarks landmarks;
  cv::Mat img_bgr = cv::imread(test_img_path);
  face_landmarks_1000->detect(img_bgr, landmarks);
  lite::utils::draw_landmarks_inplace(img_bgr, landmarks);
  cv::imwrite(save_img_path, img_bgr);
  
  delete face_landmarks_1000;
}

The output is:

More classes for face alignment (68 points, 98 points, 106 points, 1000 points)

auto *align = new lite::cv::face::align::PFLD(onnx_path);  // 106 landmarks, 1.0Mb only!
auto *align = new lite::cv::face::align::PFLD98(onnx_path);  // 98 landmarks, 4.8Mb only!
auto *align = new lite::cv::face::align::PFLD68(onnx_path);  // 68 landmarks, 2.8Mb only!
auto *align = new lite::cv::face::align::MobileNetV268(onnx_path);  // 68 landmarks, 9.4Mb only!
auto *align = new lite::cv::face::align::MobileNetV2SE68(onnx_path);  // 68 landmarks, 11Mb only!
auto *align = new lite::cv::face::align::FaceLandmark1000(onnx_path);  // 1000 landmarks, 2.0Mb only!
auto *align = new lite::cv::face::align::PIPNet98(onnx_path);  // 98 landmarks, CVPR2021!
auto *align = new lite::cv::face::align::PIPNet68(onnx_path);  // 68 landmarks, CVPR2021!
auto *align = new lite::cv::face::align::PIPNet29(onnx_path);  // 29 landmarks, CVPR2021!
auto *align = new lite::cv::face::align::PIPNet19(onnx_path);  // 19 landmarks, CVPR2021!

More details of C++ APIs, please check lite.ai.toolkit.

Python APIs👇

In torchlm, we provide pipelines for deploying models with PyTorch and ONNXRuntime. A high level API named runtime.bind can bind face detection and landmarks models together, then you can run the runtime.forward API to get the output landmarks and bboxes. Here is an example of PIPNet. Pretrained weights of PIPNet, Download.

Inference on PyTorch Backend

import torchlm
from torchlm.tools import faceboxesv2
from torchlm.models import pipnet

torchlm.runtime.bind(faceboxesv2(device="cpu"))  # set device="cuda" if you want to run with CUDA
# set map_location="cuda" if you want to run with CUDA
torchlm.runtime.bind(
  pipnet(backbone="resnet18", pretrained=True,  
         num_nb=10, num_lms=98, net_stride=32, input_size=256,
         meanface_type="wflw", map_location="cpu", checkpoint=None) 
) # will auto download pretrained weights from latest release if pretrained=True
landmarks, bboxes = torchlm.runtime.forward(image)
image = torchlm.utils.draw_bboxes(image, bboxes=bboxes)
image = torchlm.utils.draw_landmarks(image, landmarks=landmarks)

Inference on ONNXRuntime Backend

import torchlm
from torchlm.runtime import faceboxesv2_ort, pipnet_ort

torchlm.runtime.bind(faceboxesv2_ort())
torchlm.runtime.bind(
  pipnet_ort(onnx_path="pipnet_resnet18.onnx",num_nb=10,
             num_lms=98, net_stride=32,input_size=256, meanface_type="wflw")
)
landmarks, bboxes = torchlm.runtime.forward(image)
image = torchlm.utils.draw_bboxes(image, bboxes=bboxes)
image = torchlm.utils.draw_landmarks(image, landmarks=landmarks)

🤠🎯 Evaluating

In torchlm, each model have a high level and user-friendly API named apply_evaluating for evaluation. This method will calculate the NME, FR and AUC for eval dataset. Here is an example of PIPNet.

from torchlm.models import pipnet
# will auto download pretrained weights from latest release if pretrained=True
model = pipnet(backbone="resnet18", pretrained=True, num_nb=10, num_lms=98, net_stride=32,
               input_size=256, meanface_type="wflw", backbone_pretrained=True)
NME, FR, AUC = model.apply_evaluating(
    annotation_path="../data/WFLW/convertd/test.txt",
    norm_indices=[60, 72],  # the indexes of two eyeballs.
    coordinates_already_normalized=True, 
    eval_normalized_coordinates=False
)
print(f"NME: {NME}, FR: {FR}, AUC: {AUC}")

Then, you will get the Performance(@NME@FR@AUC) results.

Built _PIPEvalDataset: eval count is 2500 !
Evaluating PIPNet: 100%|██████████| 2500/2500 [02:53<00:00, 14.45it/s]
NME: 0.04453323229181989, FR: 0.04200000000000004, AUC: 0.5732673333333334

⚙️⚔️ Exporting

In torchlm, each model have a high level and user-friendly API named apply_exporting for ONNX export. Here is an example of PIPNet.

from torchlm.models import pipnet
# will auto download pretrained weights from latest release if pretrained=True
model = pipnet(backbone="resnet18", pretrained=True, num_nb=10, num_lms=98, net_stride=32,
               input_size=256, meanface_type="wflw", backbone_pretrained=True)
model.apply_exporting(
    onnx_path="./save/pipnet/pipnet_resnet18.onnx",
    opset=12, simplify=True, output_names=None  # use default output names.
)

Then, you will get a Static ONNX model file if the exporting process was done.

  ...
  %195 = Add(%259, %189)
  %196 = Relu(%195)
  %outputs_cls = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%196, %cls_layer.weight, %cls_layer.bias)
  %outputs_x = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%196, %x_layer.weight, %x_layer.bias)
  %outputs_y = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%196, %y_layer.weight, %y_layer.bias)
  %outputs_nb_x = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%196, %nb_x_layer.weight, %nb_x_layer.bias)
  %outputs_nb_y = Conv[dilations = [1, 1], group = 1, kernel_shape = [1, 1], pads = [0, 0, 0, 0], strides = [1, 1]](%196, %nb_y_layer.weight, %nb_y_layer.bias)
  return %outputs_cls, %outputs_x, %outputs_y, %outputs_nb_x, %outputs_nb_y
}
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📖 Documentations

• Data Augmentation's API

🎓 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.
• PIPNet: Towards Efficient Facial Landmark Detection in the Wild, CVPR2021