init

TPSMM/LICENSE

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+MIT License
+
+Copyright (c) 2021 yoyo-nb
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

TPSMM/README.md

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+# [CVPR2022] Thin-Plate Spline Motion Model for Image Animation
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+![stars](https://img.shields.io/github/stars/yoyo-nb/Thin-Plate-Spline-Motion-Model.svg?style=flat)
+![GitHub repo size](https://img.shields.io/github/repo-size/yoyo-nb/Thin-Plate-Spline-Motion-Model.svg)
+
+Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"
+
+[**Paper**](https://arxiv.org/abs/2203.14367) **|** [**Supp**](https://cloud.tsinghua.edu.cn/f/f7b8573bb5b04583949f/?dl=1)
+
+### Example animation
+
+![vox](assets/vox.gif)
+![ted](assets/ted.gif)
+
+**PS**: The paper trains the model for 100 epochs for a fair comparison. You can use more data and train for more epochs to get better performance.
+
+
+### Web demo for animation
+- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model)
+- Try the web demo for animation here: [![Replicate](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model/badge)](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model)
+- Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DREfdpnaBhqISg0fuQlAAIwyGVn1loH_?usp=sharing)
+
+### Pre-trained models
+- [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/30ab8765da364fefa101/)
+- [Google Drive](https://drive.google.com/drive/folders/1pNDo1ODQIb5HVObRtCmubqJikmR7VVLT?usp=sharing)
+
+### Installation
+
+We support ```python3```.(Recommended version is Python 3.9).
+To install the dependencies run:
+```bash
+pip install -r requirements.txt
+```
+
+
+### YAML configs
+ 
+There are several configuration files one for each `dataset` in the `config` folder named as ```config/dataset_name.yaml```. 
+
+See description of the parameters in the ```config/taichi-256.yaml```.
+
+### Datasets
+
+1) **MGif**. Follow [Monkey-Net](https://github.com/AliaksandrSiarohin/monkey-net).
+
+2) **TaiChiHD** and **VoxCeleb**. Follow instructions from [video-preprocessing](https://github.com/AliaksandrSiarohin/video-preprocessing). 
+
+3) **TED-talks**. Follow instructions from [MRAA](https://github.com/snap-research/articulated-animation).
+
+
+### Training
+To train a model on specific dataset run:
+```
+CUDA_VISIBLE_DEVICES=0,1 python run.py --config config/dataset_name.yaml --device_ids 0,1
+```
+A log folder named after the timestamp will be created. Checkpoints, loss values, reconstruction results will be saved to this folder.
+
+
+#### Training AVD network
+To train a model on specific dataset run:
+```
+CUDA_VISIBLE_DEVICES=0 python run.py --mode train_avd --checkpoint '{checkpoint_folder}/checkpoint.pth.tar' --config config/dataset_name.yaml
+```
+Checkpoints, loss values, reconstruction results will be saved to `{checkpoint_folder}`.
+
+
+
+### Evaluation on video reconstruction
+
+To evaluate the reconstruction performance run:
+```
+CUDA_VISIBLE_DEVICES=0 python run.py --mode reconstruction --config config/dataset_name.yaml --checkpoint '{checkpoint_folder}/checkpoint.pth.tar'
+```
+The `reconstruction` subfolder will be created in `{checkpoint_folder}`.
+The generated video will be stored to this folder, also generated videos will be stored in ```png``` subfolder in loss-less '.png' format for evaluation.
+To compute metrics, follow instructions from [pose-evaluation](https://github.com/AliaksandrSiarohin/pose-evaluation).
+
+
+### Image animation demo
+- notebook: `demo.ipynb`, edit the config cell and run for image animation.
+- python:
+```bash
+CUDA_VISIBLE_DEVICES=0 python demo.py --config config/vox-256.yaml --checkpoint checkpoints/vox.pth.tar --source_image ./source.jpg --driving_video ./driving.mp4
+```
+
+# Acknowledgments
+The main code is based upon [FOMM](https://github.com/AliaksandrSiarohin/first-order-model) and [MRAA](https://github.com/snap-research/articulated-animation)
+
+Thanks for the excellent works!
+
+And Thanks to:
+
+- [@chenxwh](https://github.com/chenxwh): Add Web Demo & Docker environment [![Replicate](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model/badge)](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model) 
+
+- [@TalkUHulk](https://github.com/TalkUHulk): The C++/Python demo is provided in [Image-Animation-Turbo-Boost](https://github.com/TalkUHulk/Image-Animation-Turbo-Boost)
+
+- [@AK391](https://github.com/AK391): Add huggingface web demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model)

TPSMM/augmentation.py

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+"""
+Code from https://github.com/hassony2/torch_videovision
+"""
+
+import numbers
+
+import random
+import numpy as np
+import PIL
+
+from skimage.transform import resize, rotate
+import torchvision
+
+import warnings
+
+from skimage import img_as_ubyte, img_as_float
+
+
+def crop_clip(clip, min_h, min_w, h, w):
+    if isinstance(clip[0], np.ndarray):
+        cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip]
+
+    elif isinstance(clip[0], PIL.Image.Image):
+        cropped = [
+            img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip
+            ]
+    else:
+        raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                        'but got list of {0}'.format(type(clip[0])))
+    return cropped
+
+
+def pad_clip(clip, h, w):
+    im_h, im_w = clip[0].shape[:2]
+    pad_h = (0, 0) if h < im_h else ((h - im_h) // 2, (h - im_h + 1) // 2)
+    pad_w = (0, 0) if w < im_w else ((w - im_w) // 2, (w - im_w + 1) // 2)
+
+    return np.pad(clip, ((0, 0), pad_h, pad_w, (0, 0)), mode='edge')
+
+
+def resize_clip(clip, size, interpolation='bilinear'):
+    if isinstance(clip[0], np.ndarray):
+        if isinstance(size, numbers.Number):
+            im_h, im_w, im_c = clip[0].shape
+            # Min spatial dim already matches minimal size
+            if (im_w <= im_h and im_w == size) or (im_h <= im_w
+                                                   and im_h == size):
+                return clip
+            new_h, new_w = get_resize_sizes(im_h, im_w, size)
+            size = (new_w, new_h)
+        else:
+            size = size[1], size[0]
+
+        scaled = [
+            resize(img, size, order=1 if interpolation == 'bilinear' else 0, preserve_range=True,
+                   mode='constant', anti_aliasing=True) for img in clip
+            ]
+    elif isinstance(clip[0], PIL.Image.Image):
+        if isinstance(size, numbers.Number):
+            im_w, im_h = clip[0].size
+            # Min spatial dim already matches minimal size
+            if (im_w <= im_h and im_w == size) or (im_h <= im_w
+                                                   and im_h == size):
+                return clip
+            new_h, new_w = get_resize_sizes(im_h, im_w, size)
+            size = (new_w, new_h)
+        else:
+            size = size[1], size[0]
+        if interpolation == 'bilinear':
+            pil_inter = PIL.Image.NEAREST
+        else:
+            pil_inter = PIL.Image.BILINEAR
+        scaled = [img.resize(size, pil_inter) for img in clip]
+    else:
+        raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                        'but got list of {0}'.format(type(clip[0])))
+    return scaled
+
+
+def get_resize_sizes(im_h, im_w, size):
+    if im_w < im_h:
+        ow = size
+        oh = int(size * im_h / im_w)
+    else:
+        oh = size
+        ow = int(size * im_w / im_h)
+    return oh, ow
+
+
+class RandomFlip(object):
+    def __init__(self, time_flip=False, horizontal_flip=False):
+        self.time_flip = time_flip
+        self.horizontal_flip = horizontal_flip
+
+    def __call__(self, clip):
+        if random.random() < 0.5 and self.time_flip:
+            return clip[::-1]
+        if random.random() < 0.5 and self.horizontal_flip:
+            return [np.fliplr(img) for img in clip]
+
+        return clip
+
+
+class RandomResize(object):
+    """Resizes a list of (H x W x C) numpy.ndarray to the final size
+    The larger the original image is, the more times it takes to
+    interpolate
+    Args:
+    interpolation (str): Can be one of 'nearest', 'bilinear'
+    defaults to nearest
+    size (tuple): (widht, height)
+    """
+
+    def __init__(self, ratio=(3. / 4., 4. / 3.), interpolation='nearest'):
+        self.ratio = ratio
+        self.interpolation = interpolation
+
+    def __call__(self, clip):
+        scaling_factor = random.uniform(self.ratio[0], self.ratio[1])
+
+        if isinstance(clip[0], np.ndarray):
+            im_h, im_w, im_c = clip[0].shape
+        elif isinstance(clip[0], PIL.Image.Image):
+            im_w, im_h = clip[0].size
+
+        new_w = int(im_w * scaling_factor)
+        new_h = int(im_h * scaling_factor)
+        new_size = (new_w, new_h)
+        resized = resize_clip(
+            clip, new_size, interpolation=self.interpolation)
+
+        return resized
+
+
+class RandomCrop(object):
+    """Extract random crop at the same location for a list of videos
+    Args:
+    size (sequence or int): Desired output size for the
+    crop in format (h, w)
+    """
+
+    def __init__(self, size):
+        if isinstance(size, numbers.Number):
+            size = (size, size)
+
+        self.size = size
+
+    def __call__(self, clip):
+        """
+        Args:
+        img (PIL.Image or numpy.ndarray): List of videos to be cropped
+        in format (h, w, c) in numpy.ndarray
+        Returns:
+        PIL.Image or numpy.ndarray: Cropped list of videos
+        """
+        h, w = self.size
+        if isinstance(clip[0], np.ndarray):
+            im_h, im_w, im_c = clip[0].shape
+        elif isinstance(clip[0], PIL.Image.Image):
+            im_w, im_h = clip[0].size
+        else:
+            raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                            'but got list of {0}'.format(type(clip[0])))
+
+        clip = pad_clip(clip, h, w)
+        im_h, im_w = clip.shape[1:3]
+        x1 = 0 if h == im_h else random.randint(0, im_w - w)
+        y1 = 0 if w == im_w else random.randint(0, im_h - h)
+        cropped = crop_clip(clip, y1, x1, h, w)
+
+        return cropped
+
+
+class RandomRotation(object):
+    """Rotate entire clip randomly by a random angle within
+    given bounds
+    Args:
+    degrees (sequence or int): Range of degrees to select from
+    If degrees is a number instead of sequence like (min, max),
+    the range of degrees, will be (-degrees, +degrees).
+    """
+
+    def __init__(self, degrees):
+        if isinstance(degrees, numbers.Number):
+            if degrees < 0:
+                raise ValueError('If degrees is a single number,'
+                                 'must be positive')
+            degrees = (-degrees, degrees)
+        else:
+            if len(degrees) != 2:
+                raise ValueError('If degrees is a sequence,'
+                                 'it must be of len 2.')
+
+        self.degrees = degrees
+
+    def __call__(self, clip):
+        """
+        Args:
+        img (PIL.Image or numpy.ndarray): List of videos to be cropped
+        in format (h, w, c) in numpy.ndarray
+        Returns:
+        PIL.Image or numpy.ndarray: Cropped list of videos
+        """
+        angle = random.uniform(self.degrees[0], self.degrees[1])
+        if isinstance(clip[0], np.ndarray):
+            rotated = [rotate(image=img, angle=angle, preserve_range=True) for img in clip]
+        elif isinstance(clip[0], PIL.Image.Image):
+            rotated = [img.rotate(angle) for img in clip]
+        else:
+            raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                            'but got list of {0}'.format(type(clip[0])))
+
+        return rotated
+
+
+class ColorJitter(object):
+    """Randomly change the brightness, contrast and saturation and hue of the clip
+    Args:
+    brightness (float): How much to jitter brightness. brightness_factor
+    is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
+    contrast (float): How much to jitter contrast. contrast_factor
+    is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
+    saturation (float): How much to jitter saturation. saturation_factor
+    is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
+    hue(float): How much to jitter hue. hue_factor is chosen uniformly from
+    [-hue, hue]. Should be >=0 and <= 0.5.
+    """
+
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
+        self.brightness = brightness
+        self.contrast = contrast
+        self.saturation = saturation
+        self.hue = hue
+
+    def get_params(self, brightness, contrast, saturation, hue):
+        if brightness > 0:
+            brightness_factor = random.uniform(
+                max(0, 1 - brightness), 1 + brightness)
+        else:
+            brightness_factor = None
+
+        if contrast > 0:
+            contrast_factor = random.uniform(
+                max(0, 1 - contrast), 1 + contrast)
+        else:
+            contrast_factor = None
+
+        if saturation > 0:
+            saturation_factor = random.uniform(
+                max(0, 1 - saturation), 1 + saturation)
+        else:
+            saturation_factor = None
+
+        if hue > 0:
+            hue_factor = random.uniform(-hue, hue)
+        else:
+            hue_factor = None
+        return brightness_factor, contrast_factor, saturation_factor, hue_factor
+
+    def __call__(self, clip):
+        """
+        Args:
+        clip (list): list of PIL.Image
+        Returns:
+        list PIL.Image : list of transformed PIL.Image
+        """
+        if isinstance(clip[0], np.ndarray):
+            brightness, contrast, saturation, hue = self.get_params(
+                self.brightness, self.contrast, self.saturation, self.hue)
+
+            # Create img transform function sequence
+            img_transforms = []
+            if brightness is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness))
+            if saturation is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation))
+            if hue is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue))
+            if contrast is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast))
+            random.shuffle(img_transforms)
+            img_transforms = [img_as_ubyte, torchvision.transforms.ToPILImage()] + img_transforms + [np.array,
+                                                                                                     img_as_float]
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                jittered_clip = []
+                for img in clip:
+                    jittered_img = img
+                    for func in img_transforms:
+                        jittered_img = func(jittered_img)
+                    jittered_clip.append(jittered_img.astype('float32'))
+        elif isinstance(clip[0], PIL.Image.Image):
+            brightness, contrast, saturation, hue = self.get_params(
+                self.brightness, self.contrast, self.saturation, self.hue)
+
+            # Create img transform function sequence
+            img_transforms = []
+            if brightness is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness))
+            if saturation is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation))
+            if hue is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue))
+            if contrast is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast))
+            random.shuffle(img_transforms)
+
+            # Apply to all videos
+            jittered_clip = []
+            for img in clip:
+                for func in img_transforms:
+                    jittered_img = func(img)
+                jittered_clip.append(jittered_img)
+
+        else:
+            raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                            'but got list of {0}'.format(type(clip[0])))
+        return jittered_clip
+
+
+class AllAugmentationTransform:
+    def __init__(self, resize_param=None, rotation_param=None, flip_param=None, crop_param=None, jitter_param=None):
+        self.transforms = []
+
+        if flip_param is not None:
+            self.transforms.append(RandomFlip(**flip_param))
+
+        if rotation_param is not None:
+            self.transforms.append(RandomRotation(**rotation_param))
+
+        if resize_param is not None:
+            self.transforms.append(RandomResize(**resize_param))
+
+        if crop_param is not None:
+            self.transforms.append(RandomCrop(**crop_param))
+
+        if jitter_param is not None:
+            self.transforms.append(ColorJitter(**jitter_param))
+
+    def __call__(self, clip):
+        for t in self.transforms:
+            clip = t(clip)
+        return clip

TPSMM/cog.yaml

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+build:
+  cuda: "11.0"
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+    - "ninja-build"
+  python_packages:
+    - "ipython==7.21.0"
+    - "torch==1.10.1"
+    - "torchvision==0.11.2"
+    - "cffi==1.14.6"
+    - "cycler==0.10.0"
+    - "decorator==5.1.0"
+    - "face-alignment==1.3.5"
+    - "imageio==2.9.0"
+    - "imageio-ffmpeg==0.4.5"
+    - "kiwisolver==1.3.2"
+    - "matplotlib==3.4.3"
+    - "networkx==2.6.3"
+    - "numpy==1.20.3"
+    - "pandas==1.3.3"
+    - "Pillow==8.3.2"
+    - "pycparser==2.20"
+    - "pyparsing==2.4.7"
+    - "python-dateutil==2.8.2"
+    - "pytz==2021.1"
+    - "PyWavelets==1.1.1"
+    - "PyYAML==5.4.1"
+    - "scikit-image==0.18.3"
+    - "scikit-learn==1.0"
+    - "scipy==1.7.1"
+    - "six==1.16.0"
+    - "tqdm==4.62.3"
+    - "cmake==3.21.3"
+  run:
+    - pip install dlib
+
+predict: "predict.py:Predictor"

TPSMM/config/mgif-256.yaml

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+dataset_params:
+  root_dir: ../moving-gif
+  frame_shape: null
+  id_sampling: False
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    crop_param:
+      size: [256, 256]
+    resize_param:
+      ratio: [0.9, 1.1]
+    jitter_param:
+      hue: 0.5
+
+model_params:
+  common_params:
+    num_tps: 10
+    num_channels: 3
+    bg: False
+    multi_mask: True
+  generator_params:
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 3
+  dense_motion_params:
+    block_expansion: 64
+    max_features: 1024
+    num_blocks: 5
+    scale_factor: 0.25
+  avd_network_params:
+    id_bottle_size: 128
+    pose_bottle_size: 128
+
+
+train_params:
+  num_epochs: 100
+  num_repeats: 50
+  epoch_milestones: [70, 90]
+  lr_generator: 2.0e-4
+  batch_size: 28
+  scales: [1, 0.5, 0.25, 0.125]
+  dataloader_workers: 12
+  checkpoint_freq: 50
+  dropout_epoch: 35
+  dropout_maxp: 0.5
+  dropout_startp: 0.2
+  dropout_inc_epoch: 10
+  bg_start: 0
+  transform_params:
+    sigma_affine: 0.05
+    sigma_tps: 0.005
+    points_tps: 5
+  loss_weights:
+    perceptual: [10, 10, 10, 10, 10]
+    equivariance_value: 10
+    warp_loss: 10
+    bg: 10
+
+train_avd_params:
+  num_epochs: 100
+  num_repeats: 50
+  batch_size: 256
+  dataloader_workers: 24
+  checkpoint_freq: 10
+  epoch_milestones: [70, 90]
+  lr: 1.0e-3
+  lambda_shift: 1
+  lambda_affine: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

TPSMM/config/taichi-256.yaml

@@ -0,0 +1,134 @@
+# Dataset parameters
+# Each dataset should contain 2 folders train and test
+# Each video can be represented as:
+#   - an image of concatenated frames
+#   - '.mp4' or '.gif'
+#   - folder with all frames from a specific video
+# In case of Taichi. Same (youtube) video can be splitted in many parts (chunks). Each part has a following
+# format (id)#other#info.mp4. For example '12335#adsbf.mp4' has an id 12335. In case of TaiChi id stands for youtube
+# video id.
+dataset_params:
+  # Path to data, data can be stored in several formats: .mp4 or .gif videos, stacked .png images or folders with frames.
+  root_dir: ../taichi
+  # Image shape, needed for staked .png format.
+  frame_shape: null
+  # In case of TaiChi single video can be splitted in many chunks, or the maybe several videos for single person.
+  # In this case epoch can be a pass over different videos (if id_sampling=True) or over different chunks (if id_sampling=False)
+  # If the name of the video '12335#adsbf.mp4' the id is assumed to be 12335
+  id_sampling: True
+  # Augmentation parameters see augmentation.py for all posible augmentations
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    jitter_param:
+      brightness: 0.1
+      contrast: 0.1
+      saturation: 0.1
+      hue: 0.1
+
+# Defines model architecture
+model_params:
+  common_params:
+    # Number of TPS transformation
+    num_tps: 10
+    # Number of channels per image
+    num_channels: 3
+    # Whether to estimate affine background transformation
+    bg: True
+    # Whether to estimate the multi-resolution occlusion masks
+    multi_mask: True
+  generator_params:
+    # Number of features mutliplier
+    block_expansion: 64
+    # Maximum allowed number of features
+    max_features: 512
+    # Number of downsampling blocks and Upsampling blocks.
+    num_down_blocks: 3
+  dense_motion_params:
+    # Number of features mutliplier
+    block_expansion: 64
+    # Maximum allowed number of features
+    max_features: 1024
+    # Number of block in Unet. 
+    num_blocks: 5
+    # Optical flow is predicted on smaller images for better performance,
+    # scale_factor=0.25 means that 256x256 image will be resized to 64x64  
+    scale_factor: 0.25
+  avd_network_params:
+    # Bottleneck for identity branch
+    id_bottle_size: 128
+    # Bottleneck for pose branch
+    pose_bottle_size: 128
+
+# Parameters of training
+train_params:
+  # Number of training epochs 
+  num_epochs: 100
+  # For better i/o performance when number of videos is small number of epochs can be multiplied by this number.
+  # Thus effectivlly with num_repeats=100 each epoch is 100 times larger. 
+  num_repeats: 150
+  # Drop learning rate by 10 times after this epochs 
+  epoch_milestones: [70, 90]
+  # Initial learing rate for all modules
+  lr_generator: 2.0e-4
+  batch_size: 28
+  # Scales for perceptual pyramide loss. If scales = [1, 0.5, 0.25, 0.125] and image resolution is 256x256,
+  # than the loss will be computer on resolutions 256x256, 128x128, 64x64, 32x32.
+  scales: [1, 0.5, 0.25, 0.125]
+  # Dataset preprocessing cpu workers
+  dataloader_workers: 12
+  # Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs.
+  checkpoint_freq: 50
+  # Parameters of dropout
+  # The first dropout_epoch training uses dropout operation 
+  dropout_epoch: 35
+  # The probability P will linearly increase from dropout_startp to dropout_maxp in dropout_inc_epoch epochs
+  dropout_maxp: 0.7
+  dropout_startp: 0.0
+  dropout_inc_epoch: 10
+  # Estimate affine background transformation from the bg_start epoch.
+  bg_start: 0
+  # Parameters of random TPS transformation for equivariance loss
+  transform_params:
+    # Sigma for affine part
+    sigma_affine: 0.05
+    # Sigma for deformation part
+    sigma_tps: 0.005
+    # Number of point in the deformation grid
+    points_tps: 5
+  loss_weights:
+    # Weights for perceptual loss.
+    perceptual: [10, 10, 10, 10, 10]
+    # Weights for value equivariance.
+    equivariance_value: 10
+    # Weights for warp loss.
+    warp_loss: 10
+    # Weights for bg loss.
+    bg: 10
+
+# Parameters of training (animation-via-disentanglement)
+train_avd_params:
+  # Number of training epochs, visualization is produced after each epoch.
+  num_epochs: 100
+  # For better i/o performance when number of videos is small number of epochs can be multiplied by this number.
+  # Thus effectively with num_repeats=100 each epoch is 100 times larger.
+  num_repeats: 150
+  # Batch size.
+  batch_size: 256
+  # Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs.
+  checkpoint_freq: 10
+  # Dataset preprocessing cpu workers
+  dataloader_workers: 24
+  # Drop learning rate 10 times after this epochs
+  epoch_milestones: [70, 90]
+  # Initial learning rate
+  lr: 1.0e-3
+  # Weights for equivariance loss.
+  lambda_shift: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

TPSMM/config/ted-384.yaml

@@ -0,0 +1,73 @@
+dataset_params:
+  root_dir: ../TED384-v2
+  frame_shape: null
+  id_sampling: True
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    jitter_param:
+      brightness: 0.1
+      contrast: 0.1
+      saturation: 0.1
+      hue: 0.1
+
+model_params:
+  common_params:
+    num_tps: 10
+    num_channels: 3
+    bg: True
+    multi_mask: True
+  generator_params:
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 3
+  dense_motion_params:
+    block_expansion: 64
+    max_features: 1024
+    num_blocks: 5
+    scale_factor: 0.25
+  avd_network_params:
+    id_bottle_size: 128
+    pose_bottle_size: 128
+
+
+train_params:
+  num_epochs: 100
+  num_repeats: 150
+  epoch_milestones: [70, 90]
+  lr_generator: 2.0e-4
+  batch_size: 12
+  scales: [1, 0.5, 0.25, 0.125]
+  dataloader_workers: 6
+  checkpoint_freq: 50
+  dropout_epoch: 35
+  dropout_maxp: 0.5
+  dropout_startp: 0.0
+  dropout_inc_epoch: 10
+  bg_start: 0
+  transform_params:
+    sigma_affine: 0.05
+    sigma_tps: 0.005
+    points_tps: 5
+  loss_weights:
+    perceptual: [10, 10, 10, 10, 10]
+    equivariance_value: 10
+    warp_loss: 10
+    bg: 10
+
+train_avd_params:
+  num_epochs: 30
+  num_repeats: 500
+  batch_size: 256
+  dataloader_workers: 24
+  checkpoint_freq: 10
+  epoch_milestones: [20, 25]
+  lr: 1.0e-3
+  lambda_shift: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

TPSMM/config/vox-256.yaml

@@ -0,0 +1,74 @@
+dataset_params:
+  root_dir: ../vox
+  frame_shape: null
+  id_sampling: True
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    jitter_param:
+      brightness: 0.1
+      contrast: 0.1
+      saturation: 0.1
+      hue: 0.1
+
+
+model_params:
+  common_params:
+    num_tps: 10
+    num_channels: 3
+    bg: True
+    multi_mask: True
+  generator_params:
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 3
+  dense_motion_params:
+    block_expansion: 64
+    max_features: 1024
+    num_blocks: 5
+    scale_factor: 0.25
+  avd_network_params:
+    id_bottle_size: 128
+    pose_bottle_size: 128
+
+
+train_params:
+  num_epochs: 100
+  num_repeats: 75
+  epoch_milestones: [70, 90]
+  lr_generator: 2.0e-4
+  batch_size: 28
+  scales: [1, 0.5, 0.25, 0.125]
+  dataloader_workers: 12
+  checkpoint_freq: 50
+  dropout_epoch: 35
+  dropout_maxp: 0.3
+  dropout_startp: 0.1
+  dropout_inc_epoch: 10
+  bg_start: 10
+  transform_params:
+    sigma_affine: 0.05
+    sigma_tps: 0.005
+    points_tps: 5
+  loss_weights:
+    perceptual: [10, 10, 10, 10, 10]
+    equivariance_value: 10
+    warp_loss: 10
+    bg: 10
+
+train_avd_params:
+  num_epochs: 200
+  num_repeats: 300
+  batch_size: 256
+  dataloader_workers: 24
+  checkpoint_freq: 50
+  epoch_milestones: [140, 180]
+  lr: 1.0e-3
+  lambda_shift: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

TPSMM/demo.py

@@ -0,0 +1,180 @@
+import matplotlib
+matplotlib.use('Agg')
+import sys
+import yaml
+from argparse import ArgumentParser
+from tqdm import tqdm
+from scipy.spatial import ConvexHull
+import numpy as np
+import imageio
+from skimage.transform import resize
+from skimage import img_as_ubyte
+import torch
+from modules.inpainting_network import InpaintingNetwork
+from modules.keypoint_detector import KPDetector
+from modules.dense_motion import DenseMotionNetwork
+from modules.avd_network import AVDNetwork
+
+if sys.version_info[0] < 3:
+    raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
+
+def relative_kp(kp_source, kp_driving, kp_driving_initial):
+
+    source_area = ConvexHull(kp_source['fg_kp'][0].data.cpu().numpy()).volume
+    driving_area = ConvexHull(kp_driving_initial['fg_kp'][0].data.cpu().numpy()).volume
+    adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
+
+    kp_new = {k: v for k, v in kp_driving.items()}
+
+    kp_value_diff = (kp_driving['fg_kp'] - kp_driving_initial['fg_kp'])
+    kp_value_diff *= adapt_movement_scale
+    kp_new['fg_kp'] = kp_value_diff + kp_source['fg_kp']
+
+    return kp_new
+
+def load_checkpoints(config_path, checkpoint_path, device):
+    with open(config_path) as f:
+        config = yaml.full_load(f)
+
+    inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
+                                        **config['model_params']['common_params'])
+    kp_detector = KPDetector(**config['model_params']['common_params'])
+    dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
+                                              **config['model_params']['dense_motion_params'])
+    avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
+                             **config['model_params']['avd_network_params'])
+    kp_detector.to(device)
+    dense_motion_network.to(device)
+    inpainting.to(device)
+    avd_network.to(device)
+       
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+ 
+    inpainting.load_state_dict(checkpoint['inpainting_network'])
+    kp_detector.load_state_dict(checkpoint['kp_detector'])
+    dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
+    if 'avd_network' in checkpoint:
+        avd_network.load_state_dict(checkpoint['avd_network'])
+    
+    inpainting.eval()
+    kp_detector.eval()
+    dense_motion_network.eval()
+    avd_network.eval()
+    
+    return inpainting, kp_detector, dense_motion_network, avd_network
+
+
+def make_animation(source_image, driving_video, inpainting_network, kp_detector, dense_motion_network, avd_network, device, mode = 'relative'):
+    assert mode in ['standard', 'relative', 'avd']
+    with torch.no_grad():
+        predictions = []
+        source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
+        source = source.to(device)
+        driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3).to(device)
+        kp_source = kp_detector(source)
+        kp_driving_initial = kp_detector(driving[:, :, 0])
+
+        for frame_idx in tqdm(range(driving.shape[2])):
+            driving_frame = driving[:, :, frame_idx]
+            driving_frame = driving_frame.to(device)
+            kp_driving = kp_detector(driving_frame)
+            if mode == 'standard':
+                kp_norm = kp_driving
+            elif mode=='relative':
+                kp_norm = relative_kp(kp_source=kp_source, kp_driving=kp_driving,
+                                    kp_driving_initial=kp_driving_initial)
+            elif mode == 'avd':
+                kp_norm = avd_network(kp_source, kp_driving)
+            dense_motion = dense_motion_network(source_image=source, kp_driving=kp_norm,
+                                                    kp_source=kp_source, bg_param = None, 
+                                                    dropout_flag = False)
+            out = inpainting_network(source, dense_motion)
+
+            predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
+    return predictions
+
+
+def find_best_frame(source, driving, cpu):
+    import face_alignment
+
+    def normalize_kp(kp):
+        kp = kp - kp.mean(axis=0, keepdims=True)
+        area = ConvexHull(kp[:, :2]).volume
+        area = np.sqrt(area)
+        kp[:, :2] = kp[:, :2] / area
+        return kp
+
+    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
+                                      device= 'cpu' if cpu else 'cuda')
+    kp_source = fa.get_landmarks(255 * source)[0]
+    kp_source = normalize_kp(kp_source)
+    norm  = float('inf')
+    frame_num = 0
+    for i, image in tqdm(enumerate(driving)):
+        try:
+            kp_driving = fa.get_landmarks(255 * image)[0]
+            kp_driving = normalize_kp(kp_driving)
+            new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
+            if new_norm < norm:
+                norm = new_norm
+                frame_num = i
+        except:
+            pass
+    return frame_num
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--config", required=True, help="path to config")
+    parser.add_argument("--checkpoint", default='checkpoints/vox.pth.tar', help="path to checkpoint to restore")
+
+    parser.add_argument("--source_image", default='./assets/source.png', help="path to source image")
+    parser.add_argument("--driving_video", default='./assets/driving.mp4', help="path to driving video")
+    parser.add_argument("--result_video", default='./result.mp4', help="path to output")
+    
+    parser.add_argument("--img_shape", default="256,256", type=lambda x: list(map(int, x.split(','))),
+                        help='Shape of image, that the model was trained on.')
+    
+    parser.add_argument("--mode", default='relative', choices=['standard', 'relative', 'avd'], help="Animate mode: ['standard', 'relative', 'avd'], when use the relative mode to animate a face, use '--find_best_frame' can get better quality result")
+    
+    parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true", 
+                        help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)")
+
+    parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
+
+    opt = parser.parse_args()
+
+    source_image = imageio.imread(opt.source_image)
+    reader = imageio.get_reader(opt.driving_video)
+    fps = reader.get_meta_data()['fps']
+    driving_video = []
+    try:
+        for im in reader:
+            driving_video.append(im)
+    except RuntimeError:
+        pass
+    reader.close()
+    
+    if opt.cpu:
+        device = torch.device('cpu')
+    else:
+        device = torch.device('cuda')
+    
+    source_image = resize(source_image, opt.img_shape)[..., :3]
+    driving_video = [resize(frame, opt.img_shape)[..., :3] for frame in driving_video]
+    inpainting, kp_detector, dense_motion_network, avd_network = load_checkpoints(config_path = opt.config, checkpoint_path = opt.checkpoint, device = device)
+ 
+    if opt.find_best_frame:
+        i = find_best_frame(source_image, driving_video, opt.cpu)
+        print ("Best frame: " + str(i))
+        driving_forward = driving_video[i:]
+        driving_backward = driving_video[:(i+1)][::-1]
+        predictions_forward = make_animation(source_image, driving_forward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
+        predictions_backward = make_animation(source_image, driving_backward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
+        predictions = predictions_backward[::-1] + predictions_forward[1:]
+    else:
+        predictions = make_animation(source_image, driving_video, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
+    
+    predictions =[img_as_ubyte(frame) for frame in predictions]
+    imageio.mimsave(opt.result_video, predictions, fps=fps)
+

TPSMM/frames_dataset.py

@@ -0,0 +1,173 @@
+import os
+from skimage import io, img_as_float32
+from skimage.color import gray2rgb
+from sklearn.model_selection import train_test_split
+from imageio import mimread
+from skimage.transform import resize
+import numpy as np
+from torch.utils.data import Dataset
+from augmentation import AllAugmentationTransform
+import glob
+from functools import partial
+
+
+def read_video(name, frame_shape):
+    """
+    Read video which can be:
+      - an image of concatenated frames
+      - '.mp4' and'.gif'
+      - folder with videos
+    """
+
+    if os.path.isdir(name):
+        frames = sorted(os.listdir(name))
+        num_frames = len(frames)
+        video_array = np.array(
+            [img_as_float32(io.imread(os.path.join(name, frames[idx]))) for idx in range(num_frames)])
+    elif name.lower().endswith('.png') or name.lower().endswith('.jpg'):
+        image = io.imread(name)
+
+        if len(image.shape) == 2 or image.shape[2] == 1:
+            image = gray2rgb(image)
+
+        if image.shape[2] == 4:
+            image = image[..., :3]
+
+        image = img_as_float32(image)
+
+        video_array = np.moveaxis(image, 1, 0)
+
+        video_array = video_array.reshape((-1,) + frame_shape)
+        video_array = np.moveaxis(video_array, 1, 2)
+    elif name.lower().endswith('.gif') or name.lower().endswith('.mp4') or name.lower().endswith('.mov'):
+        video = mimread(name)
+        if len(video[0].shape) == 2:
+            video = [gray2rgb(frame) for frame in video]
+        if frame_shape is not None:
+            video = np.array([resize(frame, frame_shape) for frame in video])
+        video = np.array(video)
+        if video.shape[-1] == 4:
+            video = video[..., :3]
+        video_array = img_as_float32(video)
+    else:
+        raise Exception("Unknown file extensions  %s" % name)
+
+    return video_array
+
+
+class FramesDataset(Dataset):
+    """
+    Dataset of videos, each video can be represented as:
+      - an image of concatenated frames
+      - '.mp4' or '.gif'
+      - folder with all frames
+    """
+
+    def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=False, is_train=True,
+                 random_seed=0, pairs_list=None, augmentation_params=None):
+        self.root_dir = root_dir
+        self.videos = os.listdir(root_dir)
+        self.frame_shape = frame_shape
+        print(self.frame_shape)
+        self.pairs_list = pairs_list
+        self.id_sampling = id_sampling
+
+        if os.path.exists(os.path.join(root_dir, 'train')):
+            assert os.path.exists(os.path.join(root_dir, 'test'))
+            print("Use predefined train-test split.")
+            if id_sampling:
+                train_videos = {os.path.basename(video).split('#')[0] for video in
+                                os.listdir(os.path.join(root_dir, 'train'))}
+                train_videos = list(train_videos)
+            else:
+                train_videos = os.listdir(os.path.join(root_dir, 'train'))
+            test_videos = os.listdir(os.path.join(root_dir, 'test'))
+            self.root_dir = os.path.join(self.root_dir, 'train' if is_train else 'test')
+        else:
+            print("Use random train-test split.")
+            train_videos, test_videos = train_test_split(self.videos, random_state=random_seed, test_size=0.2)
+
+        if is_train:
+            self.videos = train_videos
+        else:
+            self.videos = test_videos
+
+        self.is_train = is_train
+
+        if self.is_train:
+            self.transform = AllAugmentationTransform(**augmentation_params)
+        else:
+            self.transform = None
+
+    def __len__(self):
+        return len(self.videos)
+
+    def __getitem__(self, idx):
+            
+        if self.is_train and self.id_sampling:   
+            name = self.videos[idx]
+            path = np.random.choice(glob.glob(os.path.join(self.root_dir, name + '*.mp4')))
+        else:
+            name = self.videos[idx]
+            path = os.path.join(self.root_dir, name)
+
+        video_name = os.path.basename(path)
+        if self.is_train and os.path.isdir(path):
+
+            frames = os.listdir(path)
+            num_frames = len(frames)
+            frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2))
+            
+            if self.frame_shape is not None:
+                resize_fn = partial(resize, output_shape=self.frame_shape)
+            else:
+                resize_fn = img_as_float32
+
+            if type(frames[0]) is bytes:
+                video_array = [resize_fn(io.imread(os.path.join(path, frames[idx].decode('utf-8')))) for idx in
+                            frame_idx]
+            else:
+                video_array = [resize_fn(io.imread(os.path.join(path, frames[idx]))) for idx in frame_idx]
+        else:
+                 
+            video_array = read_video(path, frame_shape=self.frame_shape)
+            
+            num_frames = len(video_array)
+            frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) if self.is_train else range(
+                num_frames)
+            video_array = video_array[frame_idx]
+            
+
+        if self.transform is not None:
+            video_array = self.transform(video_array)
+
+        out = {}
+        if self.is_train:
+            source = np.array(video_array[0], dtype='float32')
+            driving = np.array(video_array[1], dtype='float32')
+
+            out['driving'] = driving.transpose((2, 0, 1))
+            out['source'] = source.transpose((2, 0, 1))
+        else:
+            video = np.array(video_array, dtype='float32')
+            out['video'] = video.transpose((3, 0, 1, 2))
+
+        out['name'] = video_name
+        return out
+
+
+class DatasetRepeater(Dataset):
+    """
+    Pass several times over the same dataset for better i/o performance
+    """
+
+    def __init__(self, dataset, num_repeats=100):
+        self.dataset = dataset
+        self.num_repeats = num_repeats
+
+    def __len__(self):
+        return self.num_repeats * self.dataset.__len__()
+
+    def __getitem__(self, idx):
+        return self.dataset[idx % self.dataset.__len__()]
+

TPSMM/logger.py

@@ -0,0 +1,212 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+import imageio
+
+import os
+from skimage.draw import circle
+
+import matplotlib.pyplot as plt
+import collections
+
+
+class Logger:
+    def __init__(self, log_dir, checkpoint_freq=50, visualizer_params=None, zfill_num=8, log_file_name='log.txt'):
+
+        self.loss_list = []
+        self.cpk_dir = log_dir
+        self.visualizations_dir = os.path.join(log_dir, 'train-vis')
+        if not os.path.exists(self.visualizations_dir):
+            os.makedirs(self.visualizations_dir)
+        self.log_file = open(os.path.join(log_dir, log_file_name), 'a')
+        self.zfill_num = zfill_num
+        self.visualizer = Visualizer(**visualizer_params)
+        self.checkpoint_freq = checkpoint_freq
+        self.epoch = 0
+        self.best_loss = float('inf')
+        self.names = None
+
+    def log_scores(self, loss_names):
+        loss_mean = np.array(self.loss_list).mean(axis=0)
+
+        loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)])
+        loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string
+
+        print(loss_string, file=self.log_file)
+        self.loss_list = []
+        self.log_file.flush()
+
+    def visualize_rec(self, inp, out):
+        image = self.visualizer.visualize(inp['driving'], inp['source'], out)
+        imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image)
+
+    def save_cpk(self, emergent=False):
+        cpk = {k: v.state_dict() for k, v in self.models.items()}
+        cpk['epoch'] = self.epoch
+        cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch).zfill(self.zfill_num)) 
+        if not (os.path.exists(cpk_path) and emergent):
+            torch.save(cpk, cpk_path)
+
+    @staticmethod
+    def load_cpk(checkpoint_path, inpainting_network=None, dense_motion_network =None, kp_detector=None, 
+                bg_predictor=None, avd_network=None, optimizer=None, optimizer_bg_predictor=None,
+                optimizer_avd=None):
+        checkpoint = torch.load(checkpoint_path)
+        if inpainting_network is not None:
+            inpainting_network.load_state_dict(checkpoint['inpainting_network'])
+        if kp_detector is not None:
+            kp_detector.load_state_dict(checkpoint['kp_detector'])
+        if bg_predictor is not None and 'bg_predictor' in checkpoint:
+            bg_predictor.load_state_dict(checkpoint['bg_predictor'])
+        if dense_motion_network is not None:
+            dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
+        if avd_network is not None:
+            if 'avd_network' in checkpoint:
+                avd_network.load_state_dict(checkpoint['avd_network'])
+        if optimizer_bg_predictor is not None and 'optimizer_bg_predictor' in checkpoint:
+            optimizer_bg_predictor.load_state_dict(checkpoint['optimizer_bg_predictor'])
+        if optimizer is not None and 'optimizer' in checkpoint:
+            optimizer.load_state_dict(checkpoint['optimizer'])
+        if optimizer_avd is not None:
+            if 'optimizer_avd' in checkpoint:
+                optimizer_avd.load_state_dict(checkpoint['optimizer_avd'])
+        epoch = -1
+        if 'epoch' in checkpoint:
+            epoch = checkpoint['epoch']
+        return epoch
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, tb):
+        if 'models' in self.__dict__:
+            self.save_cpk()
+        self.log_file.close()
+
+    def log_iter(self, losses):
+        losses = collections.OrderedDict(losses.items())
+        self.names = list(losses.keys())
+        self.loss_list.append(list(losses.values()))
+
+    def log_epoch(self, epoch, models, inp, out):
+        self.epoch = epoch
+        self.models = models
+        if (self.epoch + 1) % self.checkpoint_freq == 0:
+            self.save_cpk()
+        self.log_scores(self.names)
+        self.visualize_rec(inp, out)
+
+
+class Visualizer:
+    def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'):
+        self.kp_size = kp_size
+        self.draw_border = draw_border
+        self.colormap = plt.get_cmap(colormap)
+
+    def draw_image_with_kp(self, image, kp_array):
+        image = np.copy(image)
+        spatial_size = np.array(image.shape[:2][::-1])[np.newaxis]
+        kp_array = spatial_size * (kp_array + 1) / 2
+        num_kp = kp_array.shape[0]
+        for kp_ind, kp in enumerate(kp_array):
+            rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2])
+            image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3]
+        return image
+
+    def create_image_column_with_kp(self, images, kp):
+        image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)])
+        return self.create_image_column(image_array)
+
+    def create_image_column(self, images):
+        if self.draw_border:
+            images = np.copy(images)
+            images[:, :, [0, -1]] = (1, 1, 1)
+            images[:, :, [0, -1]] = (1, 1, 1)
+        return np.concatenate(list(images), axis=0)
+
+    def create_image_grid(self, *args):
+        out = []
+        for arg in args:
+            if type(arg) == tuple:
+                out.append(self.create_image_column_with_kp(arg[0], arg[1]))
+            else:
+                out.append(self.create_image_column(arg))
+        return np.concatenate(out, axis=1)
+
+    def visualize(self, driving, source, out):
+        images = []
+
+        # Source image with keypoints
+        source = source.data.cpu()
+        kp_source = out['kp_source']['fg_kp'].data.cpu().numpy()
+        source = np.transpose(source, [0, 2, 3, 1])
+        images.append((source, kp_source))
+
+        # Equivariance visualization
+        if 'transformed_frame' in out:
+            transformed = out['transformed_frame'].data.cpu().numpy()
+            transformed = np.transpose(transformed, [0, 2, 3, 1])
+            transformed_kp = out['transformed_kp']['fg_kp'].data.cpu().numpy()
+            images.append((transformed, transformed_kp))
+
+        # Driving image with keypoints
+        kp_driving = out['kp_driving']['fg_kp'].data.cpu().numpy()
+        driving = driving.data.cpu().numpy()
+        driving = np.transpose(driving, [0, 2, 3, 1])
+        images.append((driving, kp_driving))
+
+        # Deformed image
+        if 'deformed' in out:
+            deformed = out['deformed'].data.cpu().numpy()
+            deformed = np.transpose(deformed, [0, 2, 3, 1])
+            images.append(deformed)
+
+        # Result with and without keypoints
+        prediction = out['prediction'].data.cpu().numpy()
+        prediction = np.transpose(prediction, [0, 2, 3, 1])
+        if 'kp_norm' in out:
+            kp_norm = out['kp_norm']['fg_kp'].data.cpu().numpy()
+            images.append((prediction, kp_norm))
+        images.append(prediction)
+
+
+        ## Occlusion map
+        if 'occlusion_map' in out:
+            for i in range(len(out['occlusion_map'])):
+                occlusion_map = out['occlusion_map'][i].data.cpu().repeat(1, 3, 1, 1)
+                occlusion_map = F.interpolate(occlusion_map, size=source.shape[1:3]).numpy()
+                occlusion_map = np.transpose(occlusion_map, [0, 2, 3, 1])
+                images.append(occlusion_map)
+
+        # Deformed images according to each individual transform
+        if 'deformed_source' in out:
+            full_mask = []
+            for i in range(out['deformed_source'].shape[1]):
+                image = out['deformed_source'][:, i].data.cpu()
+                # import ipdb;ipdb.set_trace()
+                image = F.interpolate(image, size=source.shape[1:3])
+                mask = out['contribution_maps'][:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1)
+                mask = F.interpolate(mask, size=source.shape[1:3])
+                image = np.transpose(image.numpy(), (0, 2, 3, 1))
+                mask = np.transpose(mask.numpy(), (0, 2, 3, 1))
+
+                if i != 0:
+                    color = np.array(self.colormap((i - 1) / (out['deformed_source'].shape[1] - 1)))[:3]
+                else:
+                    color = np.array((0, 0, 0))
+
+                color = color.reshape((1, 1, 1, 3))
+
+                images.append(image)
+                if i != 0:
+                    images.append(mask * color)
+                else:
+                    images.append(mask)
+
+                full_mask.append(mask * color)
+
+            images.append(sum(full_mask))
+
+        image = self.create_image_grid(*images)
+        image = (255 * image).astype(np.uint8)
+        return image

TPSMM/modules/avd_network.py

@@ -0,0 +1,65 @@
+
+import torch
+from torch import nn
+
+
+class AVDNetwork(nn.Module):
+    """
+    Animation via Disentanglement network
+    """
+
+    def __init__(self, num_tps, id_bottle_size=64, pose_bottle_size=64):
+        super(AVDNetwork, self).__init__()
+        input_size = 5*2 * num_tps
+        self.num_tps = num_tps
+
+        self.id_encoder = nn.Sequential(
+            nn.Linear(input_size, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(inplace=True),
+            nn.Linear(512, 1024),
+            nn.BatchNorm1d(1024),
+            nn.ReLU(inplace=True),
+            nn.Linear(1024, id_bottle_size)
+        )
+
+        self.pose_encoder = nn.Sequential(
+            nn.Linear(input_size, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(inplace=True),
+            nn.Linear(512, 1024),
+            nn.BatchNorm1d(1024),
+            nn.ReLU(inplace=True),
+            nn.Linear(1024, pose_bottle_size)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(pose_bottle_size + id_bottle_size, 1024),
+            nn.BatchNorm1d(1024),
+            nn.ReLU(),
+            nn.Linear(1024, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            nn.Linear(256, input_size)
+        )
+
+    def forward(self, kp_source, kp_random):
+
+        bs = kp_source['fg_kp'].shape[0]
+        
+        pose_emb = self.pose_encoder(kp_random['fg_kp'].view(bs, -1))
+        id_emb = self.id_encoder(kp_source['fg_kp'].view(bs, -1))
+
+        rec = self.decoder(torch.cat([pose_emb, id_emb], dim=1))
+
+        rec =  {'fg_kp': rec.view(bs, self.num_tps*5, -1)}
+        return rec

TPSMM/modules/bg_motion_predictor.py

@@ -0,0 +1,24 @@
+from torch import nn
+import torch
+from torchvision import models
+
+class BGMotionPredictor(nn.Module):
+    """
+    Module for background estimation, return single transformation, parametrized as 3x3 matrix. The third row is [0 0 1]
+    """
+
+    def __init__(self):
+        super(BGMotionPredictor, self).__init__()
+        self.bg_encoder = models.resnet18(pretrained=False)
+        self.bg_encoder.conv1 = nn.Conv2d(6, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+        num_features = self.bg_encoder.fc.in_features
+        self.bg_encoder.fc = nn.Linear(num_features, 6)
+        self.bg_encoder.fc.weight.data.zero_()
+        self.bg_encoder.fc.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
+
+    def forward(self, source_image, driving_image):
+        bs = source_image.shape[0]
+        out = torch.eye(3).unsqueeze(0).repeat(bs, 1, 1).type(source_image.type())
+        prediction = self.bg_encoder(torch.cat([source_image, driving_image], dim=1))
+        out[:, :2, :] = prediction.view(bs, 2, 3)
+        return out

TPSMM/modules/dense_motion.py

@@ -0,0 +1,164 @@
+from torch import nn
+import torch.nn.functional as F
+import torch
+from modules.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian
+from modules.util import to_homogeneous, from_homogeneous, UpBlock2d, TPS
+import math
+
+class DenseMotionNetwork(nn.Module):
+    """
+    Module that estimating an optical flow and multi-resolution occlusion masks 
+                        from K TPS transformations and an affine transformation.
+    """
+
+    def __init__(self, block_expansion, num_blocks, max_features, num_tps, num_channels, 
+                 scale_factor=0.25, bg = False, multi_mask = True, kp_variance=0.01):
+        super(DenseMotionNetwork, self).__init__()
+
+        if scale_factor != 1:
+            self.down = AntiAliasInterpolation2d(num_channels, scale_factor)
+        self.scale_factor = scale_factor
+        self.multi_mask = multi_mask
+
+        self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_channels * (num_tps+1) + num_tps*5+1),
+                                   max_features=max_features, num_blocks=num_blocks)
+
+        hourglass_output_size = self.hourglass.out_channels
+        self.maps = nn.Conv2d(hourglass_output_size[-1], num_tps + 1, kernel_size=(7, 7), padding=(3, 3))
+
+        if multi_mask:
+            up = []
+            self.up_nums = int(math.log(1/scale_factor, 2))
+            self.occlusion_num = 4
+            
+            channel = [hourglass_output_size[-1]//(2**i) for i in range(self.up_nums)]
+            for i in range(self.up_nums):
+                up.append(UpBlock2d(channel[i], channel[i]//2, kernel_size=3, padding=1))
+            self.up = nn.ModuleList(up)
+
+            channel = [hourglass_output_size[-i-1] for i in range(self.occlusion_num-self.up_nums)[::-1]]
+            for i in range(self.up_nums):
+                channel.append(hourglass_output_size[-1]//(2**(i+1)))
+            occlusion = []
+            
+            for i in range(self.occlusion_num):
+                occlusion.append(nn.Conv2d(channel[i], 1, kernel_size=(7, 7), padding=(3, 3)))
+            self.occlusion = nn.ModuleList(occlusion)
+        else:
+            occlusion = [nn.Conv2d(hourglass_output_size[-1], 1, kernel_size=(7, 7), padding=(3, 3))]
+            self.occlusion = nn.ModuleList(occlusion)
+
+        self.num_tps = num_tps
+        self.bg = bg
+        self.kp_variance = kp_variance
+
+        
+    def create_heatmap_representations(self, source_image, kp_driving, kp_source):
+
+        spatial_size = source_image.shape[2:]
+        gaussian_driving = kp2gaussian(kp_driving['fg_kp'], spatial_size=spatial_size, kp_variance=self.kp_variance)
+        gaussian_source = kp2gaussian(kp_source['fg_kp'], spatial_size=spatial_size, kp_variance=self.kp_variance)
+        heatmap = gaussian_driving - gaussian_source
+
+        zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type()).to(heatmap.device)
+        heatmap = torch.cat([zeros, heatmap], dim=1)
+
+        return heatmap
+
+    def create_transformations(self, source_image, kp_driving, kp_source, bg_param):
+        # K TPS transformaions
+        bs, _, h, w = source_image.shape
+        kp_1 = kp_driving['fg_kp']
+        kp_2 = kp_source['fg_kp']
+        kp_1 = kp_1.view(bs, -1, 5, 2)
+        kp_2 = kp_2.view(bs, -1, 5, 2)
+        trans = TPS(mode = 'kp', bs = bs, kp_1 = kp_1, kp_2 = kp_2)
+        driving_to_source = trans.transform_frame(source_image)
+
+        identity_grid = make_coordinate_grid((h, w), type=kp_1.type()).to(kp_1.device)
+        identity_grid = identity_grid.view(1, 1, h, w, 2)
+        identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1)
+
+        # affine background transformation
+        if not (bg_param is None):            
+            identity_grid = to_homogeneous(identity_grid)
+            identity_grid = torch.matmul(bg_param.view(bs, 1, 1, 1, 3, 3), identity_grid.unsqueeze(-1)).squeeze(-1)
+            identity_grid = from_homogeneous(identity_grid)
+
+        transformations = torch.cat([identity_grid, driving_to_source], dim=1)
+        return transformations
+
+    def create_deformed_source_image(self, source_image, transformations):
+
+        bs, _, h, w = source_image.shape
+        source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_tps + 1, 1, 1, 1, 1)
+        source_repeat = source_repeat.view(bs * (self.num_tps + 1), -1, h, w)
+        transformations = transformations.view((bs * (self.num_tps + 1), h, w, -1))
+        deformed = F.grid_sample(source_repeat, transformations, align_corners=True)
+        deformed = deformed.view((bs, self.num_tps+1, -1, h, w))
+        return deformed
+
+    def dropout_softmax(self, X, P):
+        '''
+        Dropout for TPS transformations. Eq(7) and Eq(8) in the paper.
+        '''
+        drop = (torch.rand(X.shape[0],X.shape[1]) < (1-P)).type(X.type()).to(X.device)
+        drop[..., 0] = 1
+        drop = drop.repeat(X.shape[2],X.shape[3],1,1).permute(2,3,0,1)
+
+        maxx = X.max(1).values.unsqueeze_(1)
+        X = X - maxx
+        X_exp = X.exp()
+        X[:,1:,...] /= (1-P)
+        mask_bool =(drop == 0)
+        X_exp = X_exp.masked_fill(mask_bool, 0)
+        partition = X_exp.sum(dim=1, keepdim=True) + 1e-6
+        return X_exp / partition  
+
+    def forward(self, source_image, kp_driving, kp_source, bg_param = None, dropout_flag=False, dropout_p = 0):
+        if self.scale_factor != 1:
+            source_image = self.down(source_image)
+
+        bs, _, h, w = source_image.shape
+
+        out_dict = dict()
+        heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source)
+        transformations = self.create_transformations(source_image, kp_driving, kp_source, bg_param)
+        deformed_source = self.create_deformed_source_image(source_image, transformations)
+        out_dict['deformed_source'] = deformed_source
+        # out_dict['transformations'] = transformations
+        deformed_source = deformed_source.view(bs,-1,h,w)
+        input = torch.cat([heatmap_representation, deformed_source], dim=1)
+        input = input.view(bs, -1, h, w)
+
+        prediction = self.hourglass(input, mode = 1)
+
+        contribution_maps = self.maps(prediction[-1]) 
+        if(dropout_flag):
+            contribution_maps = self.dropout_softmax(contribution_maps, dropout_p)
+        else:
+            contribution_maps = F.softmax(contribution_maps, dim=1)
+        out_dict['contribution_maps'] = contribution_maps
+
+        # Combine the K+1 transformations
+        # Eq(6) in the paper
+        contribution_maps = contribution_maps.unsqueeze(2)
+        transformations = transformations.permute(0, 1, 4, 2, 3)
+        deformation = (transformations * contribution_maps).sum(dim=1)
+        deformation = deformation.permute(0, 2, 3, 1)
+
+        out_dict['deformation'] = deformation # Optical Flow
+
+        occlusion_map = []
+        if self.multi_mask:
+            for i in range(self.occlusion_num-self.up_nums):
+                occlusion_map.append(torch.sigmoid(self.occlusion[i](prediction[self.up_nums-self.occlusion_num+i])))
+            prediction = prediction[-1]
+            for i in range(self.up_nums):
+                prediction = self.up[i](prediction)
+                occlusion_map.append(torch.sigmoid(self.occlusion[i+self.occlusion_num-self.up_nums](prediction)))
+        else:
+            occlusion_map.append(torch.sigmoid(self.occlusion[0](prediction[-1])))
+                
+        out_dict['occlusion_map'] = occlusion_map # Multi-resolution Occlusion Masks
+        return out_dict

TPSMM/modules/inpainting_network.py

@@ -0,0 +1,127 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d
+from modules.dense_motion import DenseMotionNetwork
+
+
+class InpaintingNetwork(nn.Module):
+    """
+    Inpaint the missing regions and reconstruct the Driving image.
+    """
+    def __init__(self, num_channels, block_expansion, max_features, num_down_blocks, multi_mask = True, **kwargs):
+        super(InpaintingNetwork, self).__init__()
+
+        self.num_down_blocks = num_down_blocks
+        self.multi_mask = multi_mask
+        self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3))
+
+        down_blocks = []
+        up_blocks = []
+        resblock = []
+        for i in range(num_down_blocks):
+            in_features = min(max_features, block_expansion * (2 ** i))
+            out_features = min(max_features, block_expansion * (2 ** (i + 1)))
+            down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
+            decoder_in_feature = out_features * 2
+            if i==num_down_blocks-1:
+                decoder_in_feature = out_features
+            up_blocks.append(UpBlock2d(decoder_in_feature, in_features, kernel_size=(3, 3), padding=(1, 1)))
+            resblock.append(ResBlock2d(decoder_in_feature, kernel_size=(3, 3), padding=(1, 1)))
+            resblock.append(ResBlock2d(decoder_in_feature, kernel_size=(3, 3), padding=(1, 1)))
+        self.down_blocks = nn.ModuleList(down_blocks)
+        self.up_blocks = nn.ModuleList(up_blocks[::-1])
+        self.resblock = nn.ModuleList(resblock[::-1])
+
+        self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3))
+        self.num_channels = num_channels
+
+    def deform_input(self, inp, deformation):
+        _, h_old, w_old, _ = deformation.shape
+        _, _, h, w = inp.shape
+        if h_old != h or w_old != w:
+            deformation = deformation.permute(0, 3, 1, 2)
+            deformation = F.interpolate(deformation, size=(h, w), mode='bilinear', align_corners=True)
+            deformation = deformation.permute(0, 2, 3, 1)
+        return F.grid_sample(inp, deformation,align_corners=True)
+
+    def occlude_input(self, inp, occlusion_map):
+        if not self.multi_mask:
+            if inp.shape[2] != occlusion_map.shape[2] or inp.shape[3] != occlusion_map.shape[3]:
+                occlusion_map = F.interpolate(occlusion_map, size=inp.shape[2:], mode='bilinear',align_corners=True)
+        out = inp * occlusion_map
+        return out
+
+    def forward(self, source_image, dense_motion):
+        out = self.first(source_image) 
+        encoder_map = [out]
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out)
+            encoder_map.append(out)
+
+        output_dict = {}
+        output_dict['contribution_maps'] = dense_motion['contribution_maps']
+        output_dict['deformed_source'] = dense_motion['deformed_source']
+
+        occlusion_map = dense_motion['occlusion_map']
+        output_dict['occlusion_map'] = occlusion_map
+
+        deformation = dense_motion['deformation']
+        out_ij = self.deform_input(out.detach(), deformation)
+        out = self.deform_input(out, deformation)
+
+        out_ij = self.occlude_input(out_ij, occlusion_map[0].detach())
+        out = self.occlude_input(out, occlusion_map[0])
+
+        warped_encoder_maps = []
+        warped_encoder_maps.append(out_ij)
+
+        for i in range(self.num_down_blocks):
+            
+            out = self.resblock[2*i](out)
+            out = self.resblock[2*i+1](out)
+            out = self.up_blocks[i](out)
+            
+            encode_i = encoder_map[-(i+2)]
+            encode_ij = self.deform_input(encode_i.detach(), deformation)
+            encode_i = self.deform_input(encode_i, deformation)
+            
+            occlusion_ind = 0
+            if self.multi_mask:
+                occlusion_ind = i+1
+            encode_ij = self.occlude_input(encode_ij, occlusion_map[occlusion_ind].detach())
+            encode_i = self.occlude_input(encode_i, occlusion_map[occlusion_ind])
+            warped_encoder_maps.append(encode_ij)
+
+            if(i==self.num_down_blocks-1):
+                break
+
+            out = torch.cat([out, encode_i], 1)
+
+        deformed_source = self.deform_input(source_image, deformation)
+        output_dict["deformed"] = deformed_source
+        output_dict["warped_encoder_maps"] = warped_encoder_maps
+
+        occlusion_last = occlusion_map[-1]
+        if not self.multi_mask:
+            occlusion_last = F.interpolate(occlusion_last, size=out.shape[2:], mode='bilinear',align_corners=True)
+
+        out = out * (1 - occlusion_last) + encode_i
+        out = self.final(out)
+        out = torch.sigmoid(out)
+        out = out * (1 - occlusion_last) + deformed_source * occlusion_last
+        output_dict["prediction"] = out
+
+        return output_dict
+
+    def get_encode(self, driver_image, occlusion_map):
+        out = self.first(driver_image)
+        encoder_map = []
+        encoder_map.append(self.occlude_input(out.detach(), occlusion_map[-1].detach()))
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out.detach())
+            out_mask = self.occlude_input(out.detach(), occlusion_map[2-i].detach())
+            encoder_map.append(out_mask.detach())
+
+        return encoder_map
+

TPSMM/modules/keypoint_detector.py

@@ -0,0 +1,27 @@
+from torch import nn
+import torch
+from torchvision import models
+
+class KPDetector(nn.Module):
+    """
+    Predict K*5 keypoints.
+    """
+
+    def __init__(self, num_tps, **kwargs):
+        super(KPDetector, self).__init__()
+        self.num_tps = num_tps
+
+        self.fg_encoder = models.resnet18(pretrained=False)
+        num_features = self.fg_encoder.fc.in_features
+        self.fg_encoder.fc = nn.Linear(num_features, num_tps*5*2)
+
+        
+    def forward(self, image):
+
+        fg_kp = self.fg_encoder(image)
+        bs, _, = fg_kp.shape
+        fg_kp = torch.sigmoid(fg_kp)
+        fg_kp = fg_kp * 2 - 1
+        out = {'fg_kp': fg_kp.view(bs, self.num_tps*5, -1)}
+
+        return out

TPSMM/modules/model.py

@@ -0,0 +1,182 @@
+from torch import nn
+import torch
+import torch.nn.functional as F
+from modules.util import AntiAliasInterpolation2d, TPS
+from torchvision import models
+import numpy as np
+
+
+class Vgg19(torch.nn.Module):
+    """
+    Vgg19 network for perceptual loss. See Sec 3.3.
+    """
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+
+        self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))),
+                                       requires_grad=False)
+        self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))),
+                                      requires_grad=False)
+
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        X = (X - self.mean) / self.std
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+class ImagePyramide(torch.nn.Module):
+    """
+    Create image pyramide for computing pyramide perceptual loss. See Sec 3.3
+    """
+    def __init__(self, scales, num_channels):
+        super(ImagePyramide, self).__init__()
+        downs = {}
+        for scale in scales:
+            downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale)
+        self.downs = nn.ModuleDict(downs)
+
+    def forward(self, x):
+        out_dict = {}
+        for scale, down_module in self.downs.items():
+            out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x)
+        return out_dict
+
+
+def detach_kp(kp):
+    return {key: value.detach() for key, value in kp.items()}
+
+
+class GeneratorFullModel(torch.nn.Module):
+    """
+    Merge all generator related updates into single model for better multi-gpu usage
+    """
+
+    def __init__(self, kp_extractor, bg_predictor, dense_motion_network, inpainting_network, train_params, *kwargs):
+        super(GeneratorFullModel, self).__init__()
+        self.kp_extractor = kp_extractor
+        self.inpainting_network = inpainting_network
+        self.dense_motion_network = dense_motion_network
+
+        self.bg_predictor = None
+        if bg_predictor:
+            self.bg_predictor = bg_predictor
+            self.bg_start = train_params['bg_start']
+
+        self.train_params = train_params
+        self.scales = train_params['scales']
+
+        self.pyramid = ImagePyramide(self.scales, inpainting_network.num_channels)
+        if torch.cuda.is_available():
+            self.pyramid = self.pyramid.cuda()
+
+        self.loss_weights = train_params['loss_weights']
+        self.dropout_epoch = train_params['dropout_epoch']
+        self.dropout_maxp = train_params['dropout_maxp']
+        self.dropout_inc_epoch = train_params['dropout_inc_epoch']
+        self.dropout_startp =train_params['dropout_startp']
+        
+        if sum(self.loss_weights['perceptual']) != 0:
+            self.vgg = Vgg19()
+            if torch.cuda.is_available():
+                self.vgg = self.vgg.cuda()
+
+
+    def forward(self, x, epoch):
+        kp_source = self.kp_extractor(x['source'])
+        kp_driving = self.kp_extractor(x['driving'])
+        bg_param = None
+        if self.bg_predictor:
+            if(epoch>=self.bg_start):
+                bg_param = self.bg_predictor(x['source'], x['driving'])
+          
+        if(epoch>=self.dropout_epoch):
+            dropout_flag = False
+            dropout_p = 0
+        else:
+            # dropout_p will linearly increase from dropout_startp to dropout_maxp 
+            dropout_flag = True
+            dropout_p = min(epoch/self.dropout_inc_epoch * self.dropout_maxp + self.dropout_startp, self.dropout_maxp)
+        
+        dense_motion = self.dense_motion_network(source_image=x['source'], kp_driving=kp_driving,
+                                                    kp_source=kp_source, bg_param = bg_param, 
+                                                    dropout_flag = dropout_flag, dropout_p = dropout_p)
+        generated = self.inpainting_network(x['source'], dense_motion)
+        generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
+
+        loss_values = {}
+
+        pyramide_real = self.pyramid(x['driving'])
+        pyramide_generated = self.pyramid(generated['prediction'])
+
+        # reconstruction loss
+        if sum(self.loss_weights['perceptual']) != 0:
+            value_total = 0
+            for scale in self.scales:
+                x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)])
+                y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)])
+
+                for i, weight in enumerate(self.loss_weights['perceptual']):
+                    value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean()
+                    value_total += self.loss_weights['perceptual'][i] * value
+            loss_values['perceptual'] = value_total
+
+        # equivariance loss
+        if self.loss_weights['equivariance_value'] != 0:
+            transform_random = TPS(mode = 'random', bs = x['driving'].shape[0], **self.train_params['transform_params'])
+            transform_grid = transform_random.transform_frame(x['driving'])
+            transformed_frame = F.grid_sample(x['driving'], transform_grid, padding_mode="reflection",align_corners=True)
+            transformed_kp = self.kp_extractor(transformed_frame)
+
+            generated['transformed_frame'] = transformed_frame
+            generated['transformed_kp'] = transformed_kp
+        
+            warped = transform_random.warp_coordinates(transformed_kp['fg_kp'])
+            kp_d = kp_driving['fg_kp']
+            value = torch.abs(kp_d - warped).mean()
+            loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value
+
+        # warp loss
+        if self.loss_weights['warp_loss'] != 0:
+            occlusion_map = generated['occlusion_map']
+            encode_map = self.inpainting_network.get_encode(x['driving'], occlusion_map)
+            decode_map = generated['warped_encoder_maps']
+            value = 0
+            for i in range(len(encode_map)):
+                value += torch.abs(encode_map[i]-decode_map[-i-1]).mean()
+
+            loss_values['warp_loss'] = self.loss_weights['warp_loss'] * value
+        
+        # bg loss
+        if self.bg_predictor and epoch >= self.bg_start and self.loss_weights['bg'] != 0:
+            bg_param_reverse = self.bg_predictor(x['driving'], x['source'])
+            value = torch.matmul(bg_param, bg_param_reverse)
+            eye = torch.eye(3).view(1, 1, 3, 3).type(value.type())
+            value = torch.abs(eye - value).mean()
+            loss_values['bg'] = self.loss_weights['bg'] * value
+
+        return loss_values, generated

TPSMM/modules/util.py

@@ -0,0 +1,349 @@
+from torch import nn
+import torch.nn.functional as F
+import torch
+
+
+class TPS:
+    '''
+    TPS transformation, mode 'kp' for Eq(2) in the paper, mode 'random' for equivariance loss.
+    '''
+    def __init__(self, mode, bs, **kwargs):
+        self.bs = bs
+        self.mode = mode
+        if mode == 'random':
+            noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3]))
+            self.theta = noise + torch.eye(2, 3).view(1, 2, 3)
+            self.control_points = make_coordinate_grid((kwargs['points_tps'], kwargs['points_tps']), type=noise.type())
+            self.control_points = self.control_points.unsqueeze(0)
+            self.control_params = torch.normal(mean=0, 
+                        std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2]))
+        elif mode == 'kp':
+            kp_1 = kwargs["kp_1"]
+            kp_2 = kwargs["kp_2"]
+            device = kp_1.device
+            kp_type = kp_1.type()
+            self.gs = kp_1.shape[1]
+            n = kp_1.shape[2]
+            K = torch.norm(kp_1[:,:,:, None]-kp_1[:,:, None, :], dim=4, p=2)
+            K = K**2
+            K = K * torch.log(K+1e-9)
+            
+            one1 = torch.ones(self.bs, kp_1.shape[1], kp_1.shape[2], 1).to(device).type(kp_type)
+            kp_1p = torch.cat([kp_1,one1], 3)
+            
+            zero = torch.zeros(self.bs, kp_1.shape[1], 3, 3).to(device).type(kp_type)
+            P = torch.cat([kp_1p, zero],2)
+            L = torch.cat([K,kp_1p.permute(0,1,3,2)],2)
+            L = torch.cat([L,P],3)
+        
+            zero = torch.zeros(self.bs, kp_1.shape[1], 3, 2).to(device).type(kp_type)
+            Y = torch.cat([kp_2, zero], 2)
+            one = torch.eye(L.shape[2]).expand(L.shape).to(device).type(kp_type)*0.01
+            L = L + one
+
+            param = torch.matmul(torch.inverse(L),Y)
+            self.theta = param[:,:,n:,:].permute(0,1,3,2)
+
+            self.control_points = kp_1
+            self.control_params = param[:,:,:n,:]
+        else:
+            raise Exception("Error TPS mode")
+
+    def transform_frame(self, frame):
+        grid = make_coordinate_grid(frame.shape[2:], type=frame.type()).unsqueeze(0).to(frame.device)
+        grid = grid.view(1, frame.shape[2] * frame.shape[3], 2)
+        shape = [self.bs, frame.shape[2], frame.shape[3], 2]
+        if self.mode == 'kp':
+            shape.insert(1, self.gs)
+        grid = self.warp_coordinates(grid).view(*shape)
+        return grid
+
+    def warp_coordinates(self, coordinates):
+        theta = self.theta.type(coordinates.type()).to(coordinates.device)
+        control_points = self.control_points.type(coordinates.type()).to(coordinates.device)
+        control_params = self.control_params.type(coordinates.type()).to(coordinates.device)
+
+        if self.mode == 'kp':
+            transformed = torch.matmul(theta[:, :, :, :2], coordinates.permute(0, 2, 1)) + theta[:, :, :, 2:]
+
+            distances = coordinates.view(coordinates.shape[0], 1, 1, -1, 2) - control_points.view(self.bs, control_points.shape[1], -1, 1, 2)
+
+            distances = distances ** 2
+            result = distances.sum(-1)
+            result = result * torch.log(result + 1e-9)
+            result = torch.matmul(result.permute(0, 1, 3, 2), control_params)
+            transformed = transformed.permute(0, 1, 3, 2) + result
+
+        elif self.mode == 'random':
+            theta = theta.unsqueeze(1)
+            transformed = torch.matmul(theta[:, :, :, :2], coordinates.unsqueeze(-1)) + theta[:, :, :, 2:]
+            transformed = transformed.squeeze(-1)
+            ances = coordinates.view(coordinates.shape[0], -1, 1, 2) - control_points.view(1, 1, -1, 2)
+            distances = ances ** 2
+
+            result = distances.sum(-1)
+            result = result * torch.log(result + 1e-9)
+            result = result * control_params
+            result = result.sum(dim=2).view(self.bs, coordinates.shape[1], 1)
+            transformed = transformed + result
+        else:
+            raise Exception("Error TPS mode")
+
+        return transformed
+        
+
+def kp2gaussian(kp, spatial_size, kp_variance):
+    """
+    Transform a keypoint into gaussian like representation
+    """
+
+    coordinate_grid = make_coordinate_grid(spatial_size, kp.type()).to(kp.device)
+    number_of_leading_dimensions = len(kp.shape) - 1
+    shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
+    coordinate_grid = coordinate_grid.view(*shape)
+    repeats = kp.shape[:number_of_leading_dimensions] + (1, 1, 1)
+    coordinate_grid = coordinate_grid.repeat(*repeats)
+
+    # Preprocess kp shape
+    shape = kp.shape[:number_of_leading_dimensions] + (1, 1, 2)
+    kp = kp.view(*shape)
+
+    mean_sub = (coordinate_grid - kp)
+
+    out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
+
+    return out
+
+
+def make_coordinate_grid(spatial_size, type):
+    """
+    Create a meshgrid [-1,1] x [-1,1] of given spatial_size.
+    """
+    h, w = spatial_size
+    x = torch.arange(w).type(type)
+    y = torch.arange(h).type(type)
+
+    x = (2 * (x / (w - 1)) - 1)
+    y = (2 * (y / (h - 1)) - 1)
+
+    yy = y.view(-1, 1).repeat(1, w)
+    xx = x.view(1, -1).repeat(h, 1)
+
+    meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
+
+    return meshed
+
+
+class ResBlock2d(nn.Module):
+    """
+    Res block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, kernel_size, padding):
+        super(ResBlock2d, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.norm1 = nn.InstanceNorm2d(in_features, affine=True)
+        self.norm2 = nn.InstanceNorm2d(in_features, affine=True)
+
+    def forward(self, x):
+        out = self.norm1(x)
+        out = F.relu(out)
+        out = self.conv1(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+        out = self.conv2(out)
+        out += x
+        return out
+
+
+class UpBlock2d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(UpBlock2d, self).__init__()
+
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = nn.InstanceNorm2d(out_features, affine=True)
+
+    def forward(self, x):
+        out = F.interpolate(x, scale_factor=2)
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class DownBlock2d(nn.Module):
+    """
+    Downsampling block for use in encoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(DownBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = nn.InstanceNorm2d(out_features, affine=True)
+        self.pool = nn.AvgPool2d(kernel_size=(2, 2))
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        out = self.pool(out)
+        return out
+
+
+class SameBlock2d(nn.Module):
+    """
+    Simple block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1):
+        super(SameBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,
+                              kernel_size=kernel_size, padding=padding, groups=groups)
+        self.norm = nn.InstanceNorm2d(out_features, affine=True)
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class Encoder(nn.Module):
+    """
+    Hourglass Encoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Encoder, self).__init__()
+
+        down_blocks = []
+        for i in range(num_blocks):
+            down_blocks.append(DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
+                                           min(max_features, block_expansion * (2 ** (i + 1))),
+                                           kernel_size=3, padding=1))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+    def forward(self, x):
+        outs = [x]
+        #print('encoder:' ,outs[-1].shape)
+        for down_block in self.down_blocks:
+            outs.append(down_block(outs[-1]))
+            #print('encoder:' ,outs[-1].shape)
+        return outs
+
+
+class Decoder(nn.Module):
+    """
+    Hourglass Decoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Decoder, self).__init__()
+
+        up_blocks = []
+        self.out_channels = []
+        for i in range(num_blocks)[::-1]:
+            in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
+            self.out_channels.append(in_filters)
+            out_filters = min(max_features, block_expansion * (2 ** i))
+            up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1))
+
+        self.up_blocks = nn.ModuleList(up_blocks)
+        self.out_channels.append(block_expansion + in_features)
+        # self.out_filters = block_expansion + in_features
+
+    def forward(self, x, mode = 0):
+        out = x.pop()
+        outs = []
+        for up_block in self.up_blocks:
+            out = up_block(out)
+            skip = x.pop()
+            out = torch.cat([out, skip], dim=1)
+            outs.append(out)
+        if(mode == 0):
+            return out
+        else:
+            return outs
+
+
+class Hourglass(nn.Module):
+    """
+    Hourglass architecture.
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Hourglass, self).__init__()
+        self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
+        self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
+        self.out_channels = self.decoder.out_channels
+        # self.out_filters = self.decoder.out_filters
+
+    def forward(self, x, mode = 0):
+        return self.decoder(self.encoder(x), mode)
+
+
+class AntiAliasInterpolation2d(nn.Module):
+    """
+    Band-limited downsampling, for better preservation of the input signal.
+    """
+    def __init__(self, channels, scale):
+        super(AntiAliasInterpolation2d, self).__init__()
+        sigma = (1 / scale - 1) / 2
+        kernel_size = 2 * round(sigma * 4) + 1
+        self.ka = kernel_size // 2
+        self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
+
+        kernel_size = [kernel_size, kernel_size]
+        sigma = [sigma, sigma]
+        # The gaussian kernel is the product of the
+        # gaussian function of each dimension.
+        kernel = 1
+        meshgrids = torch.meshgrid(
+            [
+                torch.arange(size, dtype=torch.float32)
+                for size in kernel_size
+                ]
+        )
+        for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
+            mean = (size - 1) / 2
+            kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))
+
+        # Make sure sum of values in gaussian kernel equals 1.
+        kernel = kernel / torch.sum(kernel)
+        # Reshape to depthwise convolutional weight
+        kernel = kernel.view(1, 1, *kernel.size())
+        kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
+
+        self.register_buffer('weight', kernel)
+        self.groups = channels
+        self.scale = scale
+
+    def forward(self, input):
+        if self.scale == 1.0:
+            return input
+
+        out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
+        out = F.conv2d(out, weight=self.weight, groups=self.groups)
+        out = F.interpolate(out, scale_factor=(self.scale, self.scale))
+
+        return out
+
+
+def to_homogeneous(coordinates):
+    ones_shape = list(coordinates.shape)
+    ones_shape[-1] = 1
+    ones = torch.ones(ones_shape).type(coordinates.type())
+
+    return torch.cat([coordinates, ones], dim=-1)
+
+def from_homogeneous(coordinates):
+    return coordinates[..., :2] / coordinates[..., 2:3]

TPSMM/pkgs/tpsmm.py

@@ -0,0 +1,80 @@
+import os
+import sys
+import torch
+import cv2
+import numpy as np
+from skimage import img_as_ubyte
+from skimage.transform import resize
+pwd = os.path.dirname(os.path.realpath(__file__))
+sys.path.insert(1, os.path.join(pwd, ".."))
+
+from demo import relative_kp, load_checkpoints
+
+
+class TPSMM:
+    def __init__(self):
+        self.device = torch.device("cuda")
+        self.inpainting, self.kp_detector, self.dense_motion_network, self.avd_network = load_checkpoints(
+                config_path=os.path.join(pwd, "../config/vox-256.yaml"),
+                checkpoint_path=os.path.join(pwd, "../pretrained/vox.pth.tar"),
+                device=self.device
+            )
+        self.kp_driving_initial = None
+
+    def process_source(self, src_img):
+        with torch.no_grad():
+            src_img = cv2.cvtColor(src_img, cv2.COLOR_BGR2RGB)
+            src_img = resize(src_img, (256, 256))
+            source_tensor = torch.tensor(src_img[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2).to(self.device)
+            kp_source = self.kp_detector(source_tensor)
+        
+        return source_tensor, kp_source
+        
+    
+    def gen_image(self, driving_img, source_tensor, kp_source):
+        with torch.no_grad():
+            driving_img = cv2.cvtColor(driving_img, cv2.COLOR_BGR2RGB)
+            driving_img = resize(driving_img, (256, 256))
+            driving_frame = torch.tensor(driving_img[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2).to(self.device)
+
+            kp_driving = self.kp_detector(driving_frame)
+            if self.kp_driving_initial is None:
+                self.kp_driving_initial = kp_driving
+            kp_norm = relative_kp(kp_source=kp_source, kp_driving=kp_driving,
+                                kp_driving_initial=self.kp_driving_initial)
+            dense_motion = self.dense_motion_network(source_image=source_tensor, 
+                                                kp_driving=kp_norm,
+                                                kp_source=kp_source, bg_param=None, 
+                                                dropout_flag=False)
+            out = self.inpainting(source_tensor, dense_motion)
+            out = np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0]
+            out = img_as_ubyte(out)
+            out = cv2.cvtColor(out, cv2.COLOR_RGB2BGR)
+        
+        return out
+
+
+if __name__ == "__main__":
+    tpsmm = TPSMM()
+    source_image = cv2.imread(os.path.join(pwd, "../assets/source1.png"))
+    cap = cv2.VideoCapture("/research/GAN/git/CVPR2022-DaGAN/assets/video1.mp4")
+
+    source_tensor, kp_source = tpsmm.process_source(source_image)
+    
+    while True:
+        ret, frame = cap.read()
+        if frame is None:
+            break
+        
+        output = tpsmm.gen_image(frame, source_tensor, kp_source)
+        cv2.imshow("output", output)
+        key = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            break
+        # cv2.imwrite("./tmp.jpg", output)
+    
+    cv2.destroyAllWindows()
+        
+    
+    
+    

TPSMM/predict.py

@@ -0,0 +1,125 @@
+import os
+import sys
+sys.path.insert(0, "stylegan-encoder")
+import tempfile
+import warnings
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+from skimage.transform import resize
+from skimage import img_as_ubyte
+import torch
+import torchvision.transforms as transforms
+import dlib
+from cog import BasePredictor, Path, Input
+
+from demo import load_checkpoints
+from demo import make_animation
+from ffhq_dataset.face_alignment import image_align
+from ffhq_dataset.landmarks_detector import LandmarksDetector
+
+
+warnings.filterwarnings("ignore")
+
+
+PREDICTOR = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+LANDMARKS_DETECTOR = LandmarksDetector("shape_predictor_68_face_landmarks.dat")
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+
+        self.device = torch.device("cuda:0")
+        datasets = ["vox", "taichi", "ted", "mgif"]
+        (
+            self.inpainting,
+            self.kp_detector,
+            self.dense_motion_network,
+            self.avd_network,
+        ) = ({}, {}, {}, {})
+        for d in datasets:
+            (
+                self.inpainting[d],
+                self.kp_detector[d],
+                self.dense_motion_network[d],
+                self.avd_network[d],
+            ) = load_checkpoints(
+                config_path=f"config/{d}-384.yaml"
+                if d == "ted"
+                else f"config/{d}-256.yaml",
+                checkpoint_path=f"checkpoints/{d}.pth.tar",
+                device=self.device,
+            )
+
+    def predict(
+        self,
+        source_image: Path = Input(
+            description="Input source image.",
+        ),
+        driving_video: Path = Input(
+            description="Choose a micromotion.",
+        ),
+        dataset_name: str = Input(
+            choices=["vox", "taichi", "ted", "mgif"],
+            default="vox",
+            description="Choose a dataset.",
+        ),
+    ) -> Path:
+
+        predict_mode = "relative"  # ['standard', 'relative', 'avd']
+        # find_best_frame = False
+
+        pixel = 384 if dataset_name == "ted" else 256
+
+        if dataset_name == "vox":
+            # first run face alignment
+            align_image(str(source_image), 'aligned.png')
+            source_image = imageio.imread('aligned.png')
+        else:
+            source_image = imageio.imread(str(source_image))
+        reader = imageio.get_reader(str(driving_video))
+        fps = reader.get_meta_data()["fps"]
+        source_image = resize(source_image, (pixel, pixel))[..., :3]
+
+        driving_video = []
+        try:
+            for im in reader:
+                driving_video.append(im)
+        except RuntimeError:
+            pass
+        reader.close()
+
+        driving_video = [
+            resize(frame, (pixel, pixel))[..., :3] for frame in driving_video
+        ]
+
+        inpainting, kp_detector, dense_motion_network, avd_network = (
+            self.inpainting[dataset_name],
+            self.kp_detector[dataset_name],
+            self.dense_motion_network[dataset_name],
+            self.avd_network[dataset_name],
+        )
+
+        predictions = make_animation(
+            source_image,
+            driving_video,
+            inpainting,
+            kp_detector,
+            dense_motion_network,
+            avd_network,
+            device="cuda:0",
+            mode=predict_mode,
+        )
+
+        # save resulting video
+        out_path = Path(tempfile.mkdtemp()) / "output.mp4"
+        imageio.mimsave(
+            str(out_path), [img_as_ubyte(frame) for frame in predictions], fps=fps
+        )
+        return out_path
+
+
+def align_image(raw_img_path, aligned_face_path):
+    for i, face_landmarks in enumerate(LANDMARKS_DETECTOR.get_landmarks(raw_img_path), start=1):
+        image_align(raw_img_path, aligned_face_path, face_landmarks)

TPSMM/pretrained/vox.pth.tar

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:52ad8c848e2a1d91b621de96fea83faf57ce3b8c1c06424e317f4df1d3998204
+size 350993469

TPSMM/reconstruction.py

@@ -0,0 +1,69 @@
+import os
+from tqdm import tqdm
+import torch
+from torch.utils.data import DataLoader
+from logger import Logger, Visualizer
+import numpy as np
+import imageio
+
+
+def reconstruction(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
+    png_dir = os.path.join(log_dir, 'reconstruction/png')
+    log_dir = os.path.join(log_dir, 'reconstruction')
+
+    if checkpoint is not None:
+        Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
+                         bg_predictor=bg_predictor, dense_motion_network=dense_motion_network)
+    else:
+        raise AttributeError("Checkpoint should be specified for mode='reconstruction'.")
+    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
+
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+
+    if not os.path.exists(png_dir):
+        os.makedirs(png_dir)
+    
+    loss_list = []
+
+    inpainting_network.eval()
+    kp_detector.eval()
+    dense_motion_network.eval()
+    if bg_predictor:
+        bg_predictor.eval()
+
+    for it, x in tqdm(enumerate(dataloader)):
+        with torch.no_grad():
+            predictions = []
+            visualizations = []
+            if torch.cuda.is_available():
+                x['video'] = x['video'].cuda()
+            kp_source = kp_detector(x['video'][:, :, 0])
+            for frame_idx in range(x['video'].shape[2]):
+                source = x['video'][:, :, 0]
+                driving = x['video'][:, :, frame_idx]
+                kp_driving = kp_detector(driving)
+                bg_params = None
+                if bg_predictor:
+                    bg_params = bg_predictor(source, driving)
+                
+                dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
+                                                    kp_source=kp_source, bg_param = bg_params, 
+                                                    dropout_flag = False)
+                out = inpainting_network(source, dense_motion)
+                out['kp_source'] = kp_source
+                out['kp_driving'] = kp_driving
+
+                predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
+
+                visualization = Visualizer(**config['visualizer_params']).visualize(source=source,
+                                                                                   driving=driving, out=out)
+                visualizations.append(visualization)
+                loss = torch.abs(out['prediction'] - driving).mean().cpu().numpy()
+                
+                loss_list.append(loss)
+            # print(np.mean(loss_list))
+            predictions = np.concatenate(predictions, axis=1)
+            imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * predictions).astype(np.uint8))
+
+    print("Reconstruction loss: %s" % np.mean(loss_list))

TPSMM/requirements.txt

@@ -0,0 +1,25 @@
+cffi==1.14.6
+cycler==0.10.0
+decorator==5.1.0
+face-alignment==1.3.5
+imageio==2.9.0
+imageio-ffmpeg==0.4.5
+kiwisolver==1.3.2
+matplotlib==3.4.3
+networkx==2.6.3
+numpy==1.20.3
+pandas==1.3.3
+Pillow==8.3.2
+pycparser==2.20
+pyparsing==2.4.7
+python-dateutil==2.8.2
+pytz==2021.1
+PyWavelets==1.1.1
+PyYAML==5.4.1
+scikit-image==0.18.3
+scikit-learn==1.0
+scipy==1.7.1
+six==1.16.0
+torch==1.10.0+cu113
+torchvision==0.11.0+cu113
+tqdm==4.62.3

TPSMM/run.py

@@ -0,0 +1,89 @@
+import matplotlib
+matplotlib.use('Agg')
+
+import os, sys
+import yaml
+from argparse import ArgumentParser
+from time import gmtime, strftime
+from shutil import copy
+from frames_dataset import FramesDataset
+
+from modules.inpainting_network import InpaintingNetwork
+from modules.keypoint_detector import KPDetector
+from modules.bg_motion_predictor import BGMotionPredictor
+from modules.dense_motion import DenseMotionNetwork
+from modules.avd_network import AVDNetwork
+import torch
+from train import train
+from train_avd import train_avd
+from reconstruction import reconstruction
+import os 
+
+
+if __name__ == "__main__":
+    
+    if sys.version_info[0] < 3:
+        raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
+
+    parser = ArgumentParser()
+    parser.add_argument("--config", default="config/vox-256.yaml", help="path to config")
+    parser.add_argument("--mode", default="train", choices=["train", "reconstruction", "train_avd"])
+    parser.add_argument("--log_dir", default='log', help="path to log into")
+    parser.add_argument("--checkpoint", default=None, help="path to checkpoint to restore")
+    parser.add_argument("--device_ids", default="0,1", type=lambda x: list(map(int, x.split(','))),
+                        help="Names of the devices comma separated.")
+
+    opt = parser.parse_args()
+    with open(opt.config) as f:
+        config = yaml.load(f)
+
+    if opt.checkpoint is not None:
+        log_dir = os.path.join(*os.path.split(opt.checkpoint)[:-1])
+    else:
+        log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0])
+        log_dir += ' ' + strftime("%d_%m_%y_%H.%M.%S", gmtime())
+
+    inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
+                                        **config['model_params']['common_params'])
+
+    if torch.cuda.is_available():
+        cuda_device = torch.device('cuda:'+str(opt.device_ids[0]))
+        inpainting.to(cuda_device)
+
+    kp_detector = KPDetector(**config['model_params']['common_params'])
+    dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
+                                              **config['model_params']['dense_motion_params'])
+                                                           
+    if torch.cuda.is_available():
+        kp_detector.to(opt.device_ids[0])
+        dense_motion_network.to(opt.device_ids[0])
+
+    bg_predictor = None
+    if (config['model_params']['common_params']['bg']):
+        bg_predictor = BGMotionPredictor()
+        if torch.cuda.is_available():
+            bg_predictor.to(opt.device_ids[0])
+
+    avd_network = None
+    if opt.mode == "train_avd":
+        avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
+                             **config['model_params']['avd_network_params'])
+        if torch.cuda.is_available():
+            avd_network.to(opt.device_ids[0])
+
+    dataset = FramesDataset(is_train=(opt.mode.startswith('train')), **config['dataset_params'])
+
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))):
+        copy(opt.config, log_dir)
+
+    if opt.mode == 'train':
+        print("Training...")
+        train(config, inpainting, kp_detector, bg_predictor, dense_motion_network, opt.checkpoint, log_dir, dataset)
+    elif opt.mode == 'train_avd':
+        print("Training Animation via Disentaglement...")
+        train_avd(config, inpainting, kp_detector, bg_predictor, dense_motion_network, avd_network, opt.checkpoint, log_dir, dataset)
+    elif opt.mode == 'reconstruction':
+        print("Reconstruction...")
+        reconstruction(config, inpainting, kp_detector, bg_predictor, dense_motion_network, opt.checkpoint, log_dir, dataset)

TPSMM/tmp.py

@@ -0,0 +1,14 @@
+import cv2
+
+
+cap = cv2.VideoCapture("/research/GAN/git/CVPR2022-DaGAN/assets/video1.mp4")
+while True:
+    ret, frame = cap.read()
+    if frame is None:
+        break
+    cv2.imshow("output", frame)
+    key = cv2.waitKey(1) & 0xff
+    if key == ord("q"):
+        break
+
+cv2.destroyAllWindows()

TPSMM/train.py

@@ -0,0 +1,94 @@
+from tqdm import trange
+import torch
+from torch.utils.data import DataLoader
+from logger import Logger
+from modules.model import GeneratorFullModel
+from torch.optim.lr_scheduler import MultiStepLR
+from torch.nn.utils import clip_grad_norm_
+from frames_dataset import DatasetRepeater
+import math
+
+def train(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
+    train_params = config['train_params']
+    optimizer = torch.optim.Adam(
+        [{'params': list(inpainting_network.parameters()) +
+                    list(dense_motion_network.parameters()) +
+                    list(kp_detector.parameters()), 'initial_lr': train_params['lr_generator']}],lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4)
+    
+    optimizer_bg_predictor = None
+    if bg_predictor:
+        optimizer_bg_predictor = torch.optim.Adam(
+            [{'params':bg_predictor.parameters(),'initial_lr': train_params['lr_generator']}], 
+            lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4)
+
+    if checkpoint is not None:
+        start_epoch = Logger.load_cpk(
+            checkpoint, inpainting_network = inpainting_network, dense_motion_network = dense_motion_network,       
+            kp_detector = kp_detector, bg_predictor = bg_predictor,
+            optimizer = optimizer, optimizer_bg_predictor = optimizer_bg_predictor)
+        print('load success:', start_epoch)
+        start_epoch += 1
+    else:
+        start_epoch = 0
+
+    scheduler_optimizer = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1,
+                                      last_epoch=start_epoch - 1)
+    if bg_predictor:
+        scheduler_bg_predictor = MultiStepLR(optimizer_bg_predictor, train_params['epoch_milestones'],
+                                              gamma=0.1, last_epoch=start_epoch - 1)
+
+    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
+        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
+    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, 
+                            num_workers=train_params['dataloader_workers'], drop_last=True)
+
+    generator_full = GeneratorFullModel(kp_detector, bg_predictor, dense_motion_network, inpainting_network, train_params)
+
+    if torch.cuda.is_available():
+        generator_full = torch.nn.DataParallel(generator_full).cuda()  
+        
+    bg_start = train_params['bg_start']
+    
+    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], 
+                checkpoint_freq=train_params['checkpoint_freq']) as logger:
+        for epoch in trange(start_epoch, train_params['num_epochs']):
+            for x in dataloader:
+                if(torch.cuda.is_available()):
+                    x['driving'] = x['driving'].cuda()
+                    x['source'] = x['source'].cuda()
+
+                losses_generator, generated = generator_full(x, epoch)
+                loss_values = [val.mean() for val in losses_generator.values()]
+                loss = sum(loss_values)
+                loss.backward()
+
+                clip_grad_norm_(kp_detector.parameters(), max_norm=10, norm_type = math.inf)
+                clip_grad_norm_(dense_motion_network.parameters(), max_norm=10, norm_type = math.inf)
+                if bg_predictor and epoch>=bg_start:
+                    clip_grad_norm_(bg_predictor.parameters(), max_norm=10, norm_type = math.inf)
+                
+                optimizer.step()
+                optimizer.zero_grad()
+                if bg_predictor and epoch>=bg_start:
+                    optimizer_bg_predictor.step()
+                    optimizer_bg_predictor.zero_grad()
+                
+                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
+                logger.log_iter(losses=losses)
+
+            scheduler_optimizer.step()
+            if bg_predictor:
+                scheduler_bg_predictor.step()
+            
+            model_save = {
+                'inpainting_network': inpainting_network,
+                'dense_motion_network': dense_motion_network,
+                'kp_detector': kp_detector,
+                'optimizer': optimizer,
+            }
+            if bg_predictor and epoch>=bg_start:
+                model_save['bg_predictor'] = bg_predictor
+                model_save['optimizer_bg_predictor'] = optimizer_bg_predictor
+            
+            logger.log_epoch(epoch, model_save, inp=x, out=generated)
+

TPSMM/train_avd.py

@@ -0,0 +1,91 @@
+from tqdm import trange
+import torch
+from torch.utils.data import DataLoader
+from logger import Logger
+from torch.optim.lr_scheduler import MultiStepLR
+from frames_dataset import DatasetRepeater
+
+
+def random_scale(kp_params, scale):
+    theta = torch.rand(kp_params['fg_kp'].shape[0], 2) * (2 * scale) + (1 - scale)
+    theta = torch.diag_embed(theta).unsqueeze(1).type(kp_params['fg_kp'].type())
+    new_kp_params = {'fg_kp': torch.matmul(theta, kp_params['fg_kp'].unsqueeze(-1)).squeeze(-1)}
+    return new_kp_params
+
+
+def train_avd(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, 
+              avd_network, checkpoint, log_dir, dataset):
+    train_params = config['train_avd_params']
+
+    optimizer = torch.optim.Adam(avd_network.parameters(), lr=train_params['lr'], betas=(0.5, 0.999))
+
+    if checkpoint is not None:
+        Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
+                                      bg_predictor=bg_predictor, avd_network=avd_network,
+                                      dense_motion_network= dense_motion_network,optimizer_avd=optimizer)
+        start_epoch = 0
+    else:
+        raise AttributeError("Checkpoint should be specified for mode='train_avd'.")
+
+    scheduler = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1)
+
+    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
+        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
+
+    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True,
+                            num_workers=train_params['dataloader_workers'], drop_last=True)
+
+    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], 
+                checkpoint_freq=train_params['checkpoint_freq']) as logger:
+        for epoch in trange(start_epoch, train_params['num_epochs']):
+            avd_network.train()
+            for x in dataloader:
+                with torch.no_grad():
+                    kp_source = kp_detector(x['source'].cuda())
+                    kp_driving_gt = kp_detector(x['driving'].cuda())
+                    kp_driving_random = random_scale(kp_driving_gt, scale=train_params['random_scale'])
+                rec = avd_network(kp_source, kp_driving_random)
+
+                reconstruction_kp = train_params['lambda_shift'] * \
+                                       torch.abs(kp_driving_gt['fg_kp'] - rec['fg_kp']).mean()
+                
+                loss_dict = {'rec_kp': reconstruction_kp}
+                loss = reconstruction_kp
+                
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+
+                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in loss_dict.items()}
+                logger.log_iter(losses=losses)
+
+            # Visualization
+            avd_network.eval()
+            with torch.no_grad():
+                source = x['source'][:6].cuda()
+                driving = torch.cat([x['driving'][[0, 1]].cuda(), source[[2, 3, 2, 1]]], dim=0)
+                kp_source = kp_detector(source)
+                kp_driving = kp_detector(driving)
+
+                out = avd_network(kp_source, kp_driving)
+                kp_driving = out
+                dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
+                                            kp_source=kp_source)
+                generated = inpainting_network(source, dense_motion)
+
+                generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
+
+            scheduler.step(epoch)
+            model_save = {
+                'inpainting_network': inpainting_network,
+                'dense_motion_network': dense_motion_network,
+                'kp_detector': kp_detector,
+                'avd_network': avd_network,
+                'optimizer_avd': optimizer
+            }
+            if bg_predictor :
+                model_save['bg_predictor'] = bg_predictor
+
+            logger.log_epoch(epoch, model_save,
+                             inp={'source': source, 'driving': driving},
+                             out=generated)

app.py

@@ -0,0 +1,122 @@
+import av
+import sys
+import numpy as np
+import cv2
+import streamlit as st
+from PIL import Image
+from streamlit_webrtc import WebRtcMode, webrtc_streamer
+
+sys.path.insert(1, "./retinaface")
+sys.path.insert(1, "./TPSMM/pkgs")
+from tpsmm import TPSMM
+from detect import Detect
+from turn import get_ice_servers
+
+
+def parse_roi_box_from_bbox(bbox, shape):
+    img_h, img_w = shape[:2]
+    left, top, right, bottom = bbox[:4]
+    old_size = (right - left + bottom - top) / 2
+    center_x = right - (right - left) / 2.0
+    center_y = bottom - (bottom - top) / 2.0 + old_size * 0.14
+    
+    size = int(min((old_size * 2.0) / 2, center_x, img_w-center_x, center_y, img_h-center_y) * 2.0)
+
+    roi_box = [0] * 4
+    roi_box[0] = center_x - size / 2
+    roi_box[1] = center_y - size / 2
+    roi_box[2] = roi_box[0] + size
+    roi_box[3] = roi_box[1] + size
+
+    return roi_box
+
+cache_key = "retinaface"
+if cache_key in st.session_state:
+    detector = st.session_state[cache_key]
+else:
+    detector = Detect("./retinaface/weights/mobilenet0.25_epoch_842.pth", net_inshape=(486, 864))
+    st.session_state[cache_key] = detector
+
+cache_key = "tpsmm"
+if cache_key in st.session_state:
+    generator = st.session_state[cache_key]
+else:
+    generator = TPSMM()
+    st.session_state[cache_key] = generator
+
+
+@st.cache_resource  # type: ignore
+def get_images():
+    images = [
+        cv2.imread("assets/0.jpg"),
+        cv2.imread("assets/1.jpg"),
+        cv2.imread("assets/2.jpg"),
+        cv2.imread("assets/3.jpg"),
+    ]
+    item_list = [str(i) for i in range(len(images))]
+    images = [generator.process_source(src_img) for src_img in images]
+
+    return dict(zip(item_list, images))
+images = get_images()
+user_option = st.selectbox("Choose an item", list(images.keys()))
+
+uploaded_file = st.file_uploader("Or upload your file here...", type=['png', 'jpeg', 'jpg'])
+@st.cache_resource
+def process_file(uploaded_file):
+    img = Image.open(uploaded_file)
+    img = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
+    dets = detector(img)
+    for i, b in enumerate(dets):
+        bbox = parse_roi_box_from_bbox(b[:4], img.shape)
+        bbox = [int(i) for i in bbox]
+
+        face_img = img[bbox[1]:bbox[3], bbox[0]:bbox[2]].copy()
+        # cv2.imwrite("./tmp.jpg", face_img)
+        return generator.process_source(face_img)
+
+    return None
+if uploaded_file is not None:
+    uploaded_file = process_file(uploaded_file)
+
+def callback(frame: av.VideoFrame) -> av.VideoFrame:
+    img = frame.to_ndarray(format="bgr24")
+    
+    try:
+        dets = detector(img)
+        output = None
+        for i, b in enumerate(dets):
+            text = "{:.4f}".format(b[4])
+            b = b.astype(np.int32)
+            cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+            bbox = parse_roi_box_from_bbox(b[:4], img.shape)
+            bbox = [int(i) for i in bbox]
+            cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (255, 0, 0), 2)
+
+            face_img = img[bbox[1]:bbox[3], bbox[0]:bbox[2]].copy()
+            if uploaded_file is None:
+                source_tensor, kp_source = images[user_option]
+            else:
+                source_tensor, kp_source = uploaded_file
+            output = generator.gen_image(face_img, source_tensor, kp_source)
+            
+            landm = b[5:15]
+            landm = landm.reshape((5, 2))
+            cv2.circle(img, tuple(landm[0]), 1, (0, 0, 255), 2)
+            cv2.circle(img, tuple(landm[1]), 1, (0, 255, 255), 2)
+            cv2.circle(img, tuple(landm[2]), 1, (255, 0, 255), 2)
+            cv2.circle(img, tuple(landm[3]), 1, (0, 255, 0), 2)
+            cv2.circle(img, tuple(landm[4]), 1, (255, 0, 0), 2)
+        
+        if output is not None:
+            img[:256, :256] = output
+    except Exception as e:
+        print(e)
+    
+    return av.VideoFrame.from_ndarray(img, format="bgr24")
+
+webrtc_streamer(
+    key="sample",
+    rtc_configuration={"iceServers": get_ice_servers()},
+    video_frame_callback=callback,
+    media_stream_constraints={"video": True, "audio": False},
+)

requirements.txt

@@ -0,0 +1,12 @@
+streamlit-webrtc
+twilio
+altair<5
+numpy==1.23.1
+opencv-python==4.8.0.74
+imutils
+scikit-image==0.21.0
+matplotlib==3.7.1
+pyaml==23.5.9
+tqdm
+torch
+torchvision

retinaface/change_batch_onnx.py

@@ -0,0 +1,43 @@
+import onnx
+
+
+model = onnx.load('weights/faceDetector_243_432_b1_sim.onnx')
+
+# # for fixed batchsize
+# model.graph.input[0].type.tensor_type.shape.dim[0].dim_value = 32
+# model.graph.output[0].type.tensor_type.shape.dim[0].dim_value = 32
+# model.graph.output[1].type.tensor_type.shape.dim[0].dim_value = 32
+# model.graph.output[2].type.tensor_type.shape.dim[0].dim_value = 32
+# onnx.save(model, 'faceDetector_640_b32.onnx')
+
+
+model.graph.input[0].type.tensor_type.shape.dim[0].dim_param = 'batch'  # for dynamic batchsize
+model.graph.output[0].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[1].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[2].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[3].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[4].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[5].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[6].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[7].type.tensor_type.shape.dim[0].dim_param = 'batch'
+model.graph.output[8].type.tensor_type.shape.dim[0].dim_param = 'batch'
+onnx.save(model, 'weights/faceDetector_243_432_batch_sim.onnx')
+
+
+####################################################
+# SHow model onnx
+
+# import onnxruntime as rt
+# ort_session = rt.InferenceSession("faceDetector_180_320_batch_sim.onnx")
+# print("====INPUT====")
+# for i in ort_session.get_inputs():
+#     print("Name: {}, Shape: {}, Dtype: {}".format(i.name, i.shape, i.type))
+# print("====OUTPUT====")
+# for i in ort_session.get_outputs():
+#     print("Name: {}, Shape: {}, Dtype: {}".format(i.name, i.shape, i.type))
+
+# import numpy as np
+# input_name = ort_session.get_inputs()[0].name
+# img = np.random.randn(4, 3, 180, 320).astype(np.float32)
+# data = ort_session.run(None, {input_name: img})
+# print("Done")

retinaface/convert_to_onnx.py

@@ -0,0 +1,135 @@
+# python convert_to_onnx.py --network mobile0.25 --trained_model weights/mobilenet0.25_Final.pth
+from __future__ import print_function
+import os
+import argparse
+import torch
+import torch.backends.cudnn as cudnn
+import numpy as np
+from data import cfg_mnet, cfg_slim, cfg_rfb
+from layers.functions.prior_box import PriorBox
+from utils.nms.py_cpu_nms import py_cpu_nms
+import cv2
+from models.retinaface import RetinaFace
+from models.net_slim import Slim
+from models.net_rfb import RFB
+from utils.box_utils import decode, decode_landm
+from utils.timer import Timer
+
+
+parser = argparse.ArgumentParser(description='Test')
+parser.add_argument('-m', '--trained_model', default='./weights/RBF_Final.pth',
+                    type=str, help='Trained state_dict file path to open')
+parser.add_argument('--network', default='RFB', help='Backbone network mobile0.25 or slim or RFB')
+parser.add_argument('--long_side', default=320, help='when origin_size is false, long_side is scaled size(320 or 640 for long side)')
+parser.add_argument('--cpu', action="store_true", help='Use cpu inference')
+
+args = parser.parse_args()
+
+
+def check_keys(model, pretrained_state_dict):
+    ckpt_keys = set(pretrained_state_dict.keys())
+    model_keys = set(model.state_dict().keys())
+    used_pretrained_keys = model_keys & ckpt_keys
+    unused_pretrained_keys = ckpt_keys - model_keys
+    missing_keys = model_keys - ckpt_keys
+    print('Missing keys:{}'.format(len(missing_keys)))
+    print('Unused checkpoint keys:{}'.format(len(unused_pretrained_keys)))
+    print('Used keys:{}'.format(len(used_pretrained_keys)))
+    assert len(used_pretrained_keys) > 0, 'load NONE from pretrained checkpoint'
+    return True
+
+
+def remove_prefix(state_dict, prefix):
+    ''' Old style model is stored with all names of parameters sharing common prefix 'module.' '''
+    print('remove prefix \'{}\''.format(prefix))
+    f = lambda x: x.split(prefix, 1)[-1] if x.startswith(prefix) else x
+    return {f(key): value for key, value in state_dict.items()}
+
+
+def load_model(model, pretrained_path, load_to_cpu):
+    print('Loading pretrained model from {}'.format(pretrained_path))
+    if load_to_cpu:
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage)
+    else:
+        device = torch.cuda.current_device()
+        pretrained_dict = torch.load(pretrained_path, map_location=lambda storage, loc: storage.cuda(device))
+    if "state_dict" in pretrained_dict.keys():
+        pretrained_dict = remove_prefix(pretrained_dict['state_dict'], 'module.')
+    else:
+        pretrained_dict = remove_prefix(pretrained_dict, 'module.')
+    check_keys(model, pretrained_dict)
+    model.load_state_dict(pretrained_dict, strict=False)
+    return model
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+
+    cfg = None
+    net = None
+    # long_side = int(args.long_side)
+    net_inshape = (243, 432)
+    device = torch.device("cpu" if args.cpu else "cuda")
+    print(device)
+    if args.network == "mobile0.25":
+        cfg = cfg_mnet
+        # net_inshape = (long_side, long_side)  # h, w
+        priorbox = PriorBox(cfg, image_size=net_inshape)
+        priors = priorbox.forward()
+        prior_data = priors.to(device)
+        net = RetinaFace(cfg=cfg, phase='test')
+    elif args.network == "slim":
+        cfg = cfg_slim
+        net = Slim(cfg = cfg, phase = 'test')
+    elif args.network == "RFB":
+        cfg = cfg_rfb
+        net = RFB(cfg = cfg, phase = 'test')
+    else:
+        print("Don't support network!")
+        exit(0)
+
+    # load weight
+    net = load_model(net, args.trained_model, args.cpu)
+    net.eval()
+    print('Finished loading model!')
+    print(net)
+    net = net.to(device)
+
+    ##################export###############
+    output_onnx = f'weights/faceDetector_{net_inshape[0]}_{net_inshape[1]}_b1.onnx'
+    print("==> Exporting model to ONNX format at '{}'".format(output_onnx))
+    input_names = ['input_1']
+    output_names = ['box_1', 'box_2', 'box_3']
+
+    # import torch.onnx.symbolic_opset9 as onnx_symbolic
+    # def upsample_nearest2d(g, input, output_size, *args):
+    #     # Currently, TRT 5.1/6.0/7.0 ONNX Parser does not support all ONNX ops
+    #     # needed to support dynamic upsampling ONNX forumlation
+    #     # Here we hardcode scale=2 as a temporary workaround
+    #     scales = g.op("Constant", value_t=torch.tensor([1., 1., 2., 2.]))
+    #     return g.op("Resize", input, scales, mode_s="nearest")
+    
+
+    # onnx_symbolic.upsample_nearest2d = upsample_nearest2d
+
+    # import io
+    # onnx_bytes = io.BytesIO()
+    # zero_input = torch.zeros([1, 3, net_inshape[0], net_inshape[1]]).cuda()
+    # dynamic_axes = {input_names[0]: {0:'batch'}}
+    # for _, name in enumerate(output_names):
+    #     dynamic_axes[name] = dynamic_axes[input_names[0]]
+    # extra_args = {'opset_version': 10, 'verbose': False,
+    #                 'input_names': input_names, 'output_names': output_names,
+    #                 'dynamic_axes': dynamic_axes}
+    # torch.onnx.export(net, zero_input, onnx_bytes, **extra_args)
+    # with open(output_onnx, 'wb') as out:
+    #     out.write(onnx_bytes.getvalue())
+
+    inputs = torch.randn(1, 3, net_inshape[0], net_inshape[1]).to(device)
+    torch_out = torch.onnx._export(net, inputs, output_onnx, export_params=True, verbose=False, opset_version=9,
+                                   input_names=input_names, output_names=output_names)
+    ################end###############
+
+
+
+

retinaface/data/__init__.py

@@ -0,0 +1,3 @@
+from .wider_face import WiderFaceDetection, detection_collate
+from .data_augment import *
+from .config import *

retinaface/data/config.py

@@ -0,0 +1,55 @@
+# config.py
+cfg_mnet = {
+    'name': 'mobilenet0.25',
+    'min_sizes': [[10, 20], [32, 64], [128, 256]],
+    'steps': [8, 16, 32],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 32,
+    'ngpu': 1,
+    'epoch': 250,
+    'decay1': 190,
+    'decay2': 220,
+    'image_size': 300,
+    "net_inshape": (320, 320),   # h, w
+    'pretrain': False,
+    'return_layers': {'stage1': 1, 'stage2': 2, 'stage3': 3},
+    'in_channel': 32,
+    'out_channel': 64
+}
+
+cfg_slim = {
+    'name': 'slim',
+    'min_sizes': [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]],
+    'steps': [8, 16, 32, 64],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 32,
+    'ngpu': 1,
+    'epoch': 250,
+    'decay1': 190,
+    'decay2': 220,
+    'image_size': 300
+}
+
+cfg_rfb = {
+    'name': 'RFB',
+    'min_sizes': [[10, 16, 24], [32, 48], [64, 96], [128, 192, 256]],
+    'steps': [8, 16, 32, 64],
+    'variance': [0.1, 0.2],
+    'clip': False,
+    'loc_weight': 2.0,
+    'gpu_train': True,
+    'batch_size': 32,
+    'ngpu': 1,
+    'epoch': 250,
+    'decay1': 190,
+    'decay2': 220,
+    'image_size': 300
+}
+
+

retinaface/data/data_augment.py

@@ -0,0 +1,235 @@
+import cv2
+import numpy as np
+import random
+from utils.box_utils import matrix_iof
+
+
+def _crop(image, boxes, labels, landm, img_dim):
+    height, width, _ = image.shape
+    pad_image_flag = True
+
+    for _ in range(250):
+        if random.uniform(0, 1) <= 0.2:
+            scale = 1.0
+        else:
+            scale = random.uniform(0.3, 1.0)
+        # PRE_SCALES = [0.3, 0.45, 0.6, 0.8, 1.0]
+        # scale = random.choice(PRE_SCALES)
+        short_side = min(width, height)
+        w = int(scale * short_side)
+        h = w
+
+        if width == w:
+            l = 0
+        else:
+            l = random.randrange(width - w)
+        if height == h:
+            t = 0
+        else:
+            t = random.randrange(height - h)
+        roi = np.array((l, t, l + w, t + h))
+
+        value = matrix_iof(boxes, roi[np.newaxis])
+        flag = (value >= 1)
+        if not flag.any():
+            continue
+
+        centers = (boxes[:, :2] + boxes[:, 2:]) / 2
+        mask_a = np.logical_and(roi[:2] < centers, centers < roi[2:]).all(axis=1)
+        boxes_t = boxes[mask_a].copy()
+        labels_t = labels[mask_a].copy()
+        landms_t = landm[mask_a].copy()
+        landms_t = landms_t.reshape([-1, 5, 2])
+
+        if boxes_t.shape[0] == 0:
+            continue
+
+        image_t = image[roi[1]:roi[3], roi[0]:roi[2]]
+
+        boxes_t[:, :2] = np.maximum(boxes_t[:, :2], roi[:2])
+        boxes_t[:, :2] -= roi[:2]
+        boxes_t[:, 2:] = np.minimum(boxes_t[:, 2:], roi[2:])
+        boxes_t[:, 2:] -= roi[:2]
+
+        # landm
+        landms_t[:, :, :2] = landms_t[:, :, :2] - roi[:2]
+        landms_t[:, :, :2] = np.maximum(landms_t[:, :, :2], np.array([0, 0]))
+        landms_t[:, :, :2] = np.minimum(landms_t[:, :, :2], roi[2:] - roi[:2])
+        landms_t = landms_t.reshape([-1, 10])
+
+
+	# make sure that the cropped image contains at least one face > 16 pixel at training image scale
+        b_w_t = (boxes_t[:, 2] - boxes_t[:, 0] + 1) / w * img_dim
+        b_h_t = (boxes_t[:, 3] - boxes_t[:, 1] + 1) / h * img_dim
+        mask_b = np.minimum(b_w_t, b_h_t) > 5
+        boxes_t = boxes_t[mask_b]
+        labels_t = labels_t[mask_b]
+        landms_t = landms_t[mask_b]
+
+        if boxes_t.shape[0] == 0:
+            continue
+
+        pad_image_flag = False
+
+        return image_t, boxes_t, labels_t, landms_t, pad_image_flag
+    return image, boxes, labels, landm, pad_image_flag
+
+
+def _distort(image):
+
+    def _convert(image, alpha=1, beta=0):
+        tmp = image.astype(float) * alpha + beta
+        tmp[tmp < 0] = 0
+        tmp[tmp > 255] = 255
+        image[:] = tmp
+
+    image = image.copy()
+
+    if random.randrange(2):
+
+        #brightness distortion
+        if random.randrange(2):
+            _convert(image, beta=random.uniform(-32, 32))
+
+        #contrast distortion
+        if random.randrange(2):
+            _convert(image, alpha=random.uniform(0.5, 1.5))
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+        #saturation distortion
+        if random.randrange(2):
+            _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
+
+        #hue distortion
+        if random.randrange(2):
+            tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
+            tmp %= 180
+            image[:, :, 0] = tmp
+
+        image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+
+    else:
+
+        #brightness distortion
+        if random.randrange(2):
+            _convert(image, beta=random.uniform(-32, 32))
+
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
+
+        #saturation distortion
+        if random.randrange(2):
+            _convert(image[:, :, 1], alpha=random.uniform(0.5, 1.5))
+
+        #hue distortion
+        if random.randrange(2):
+            tmp = image[:, :, 0].astype(int) + random.randint(-18, 18)
+            tmp %= 180
+            image[:, :, 0] = tmp
+
+        image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)
+
+        #contrast distortion
+        if random.randrange(2):
+            _convert(image, alpha=random.uniform(0.5, 1.5))
+
+    return image
+
+
+def _expand(image, boxes, fill, p):
+    if random.randrange(2):
+        return image, boxes
+
+    height, width, depth = image.shape
+
+    scale = random.uniform(1, p)
+    w = int(scale * width)
+    h = int(scale * height)
+
+    left = random.randint(0, w - width)
+    top = random.randint(0, h - height)
+
+    boxes_t = boxes.copy()
+    boxes_t[:, :2] += (left, top)
+    boxes_t[:, 2:] += (left, top)
+    expand_image = np.empty(
+        (h, w, depth),
+        dtype=image.dtype)
+    expand_image[:, :] = fill
+    expand_image[top:top + height, left:left + width] = image
+    image = expand_image
+
+    return image, boxes_t
+
+
+def _mirror(image, boxes, landms):
+    _, width, _ = image.shape
+    if random.randrange(2):
+        image = image[:, ::-1]
+        boxes = boxes.copy()
+        boxes[:, 0::2] = width - boxes[:, 2::-2]
+
+        # landm
+        landms = landms.copy()
+        landms = landms.reshape([-1, 5, 2])
+        landms[:, :, 0] = width - landms[:, :, 0]
+        tmp = landms[:, 1, :].copy()
+        landms[:, 1, :] = landms[:, 0, :]
+        landms[:, 0, :] = tmp
+        tmp1 = landms[:, 4, :].copy()
+        landms[:, 4, :] = landms[:, 3, :]
+        landms[:, 3, :] = tmp1
+        landms = landms.reshape([-1, 10])
+
+    return image, boxes, landms
+
+
+def _pad_to_square(image, rgb_mean, pad_image_flag):
+    if not pad_image_flag:
+        return image
+    height, width, _ = image.shape
+    long_side = max(width, height)
+    image_t = np.empty((long_side, long_side, 3), dtype=image.dtype)
+    image_t[:, :] = rgb_mean
+    image_t[0:0 + height, 0:0 + width] = image
+    return image_t
+
+
+def _resize_subtract_mean(image, insize, rgb_mean):
+    interp_methods = [cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_NEAREST, cv2.INTER_LANCZOS4]
+    interp_method = interp_methods[random.randrange(5)]
+    image = cv2.resize(image, (insize, insize), interpolation=interp_method)
+    image = image.astype(np.float32)
+    image -= rgb_mean
+    return image.transpose(2, 0, 1)
+
+
+class preproc(object):
+
+    def __init__(self, img_dim, rgb_means):
+        self.img_dim = img_dim
+        self.rgb_means = rgb_means
+
+    def __call__(self, image, targets):
+        assert targets.shape[0] > 0, "this image does not have gt"
+
+        boxes = targets[:, :4].copy()
+        labels = targets[:, -1].copy()
+        landm = targets[:, 4:-1].copy()
+
+        image_t, boxes_t, labels_t, landm_t, pad_image_flag = _crop(image, boxes, labels, landm, self.img_dim)
+        image_t = _distort(image_t)
+        image_t = _pad_to_square(image_t,self.rgb_means, pad_image_flag)
+        image_t, boxes_t, landm_t = _mirror(image_t, boxes_t, landm_t)
+        height, width, _ = image_t.shape
+        image_t = _resize_subtract_mean(image_t, self.img_dim, self.rgb_means)
+        boxes_t[:, 0::2] /= width
+        boxes_t[:, 1::2] /= height
+
+        landm_t[:, 0::2] /= width
+        landm_t[:, 1::2] /= height
+
+        labels_t = np.expand_dims(labels_t, 1)
+        targets_t = np.hstack((boxes_t, landm_t, labels_t))
+
+        return image_t, targets_t

retinaface/data/wider_face.py

@@ -0,0 +1,101 @@
+import os
+import os.path
+import sys
+import torch
+import torch.utils.data as data
+import cv2
+import numpy as np
+
+class WiderFaceDetection(data.Dataset):
+    def __init__(self, txt_path, preproc=None):
+        self.preproc = preproc
+        self.imgs_path = []
+        self.words = []
+        f = open(txt_path,'r')
+        lines = f.readlines()
+        isFirst = True
+        labels = []
+        for line in lines:
+            line = line.rstrip()
+            if line.startswith('#'):
+                if isFirst is True:
+                    isFirst = False
+                else:
+                    labels_copy = labels.copy()
+                    self.words.append(labels_copy)
+                    labels.clear()
+                path = line[2:]
+                path = txt_path.replace('label.txt','images/') + path
+                self.imgs_path.append(path)
+            else:
+                line = line.split(' ')
+                label = [float(x) for x in line]
+                labels.append(label)
+
+        self.words.append(labels)
+
+    def __len__(self):
+        return len(self.imgs_path)
+
+    def __getitem__(self, index):
+        img = cv2.imread(self.imgs_path[index])
+        height, width, _ = img.shape
+
+        labels = self.words[index]
+        annotations = np.zeros((0, 15))
+        if len(labels) == 0:
+            return annotations
+        for idx, label in enumerate(labels):
+            annotation = np.zeros((1, 15))
+            # bbox
+            annotation[0, 0] = label[0]  # x1
+            annotation[0, 1] = label[1]  # y1
+            annotation[0, 2] = label[0] + label[2]  # x2
+            annotation[0, 3] = label[1] + label[3]  # y2
+
+            # landmarks
+            annotation[0, 4] = label[4]    # l0_x
+            annotation[0, 5] = label[5]    # l0_y
+            annotation[0, 6] = label[7]    # l1_x
+            annotation[0, 7] = label[8]    # l1_y
+            annotation[0, 8] = label[10]   # l2_x
+            annotation[0, 9] = label[11]   # l2_y
+            annotation[0, 10] = label[13]  # l3_x
+            annotation[0, 11] = label[14]  # l3_y
+            annotation[0, 12] = label[16]  # l4_x
+            annotation[0, 13] = label[17]  # l4_y
+            if (annotation[0, 4]<0):
+                annotation[0, 14] = -1
+            else:
+                annotation[0, 14] = 1
+
+            annotations = np.append(annotations, annotation, axis=0)
+        target = np.array(annotations)
+        if self.preproc is not None:
+            img, target = self.preproc(img, target)
+
+        return torch.from_numpy(img), target
+
+def detection_collate(batch):
+    """Custom collate fn for dealing with batches of images that have a different
+    number of associated object annotations (bounding boxes).
+
+    Arguments:
+        batch: (tuple) A tuple of tensor images and lists of annotations
+
+    Return:
+        A tuple containing:
+            1) (tensor) batch of images stacked on their 0 dim
+            2) (list of tensors) annotations for a given image are stacked on 0 dim
+    """
+    targets = []
+    imgs = []
+    for _, sample in enumerate(batch):
+        for _, tup in enumerate(sample):
+            if torch.is_tensor(tup):
+                imgs.append(tup)
+            elif isinstance(tup, type(np.empty(0))):
+                annos = torch.from_numpy(tup).float()
+                targets.append(annos)
+
+    return (torch.stack(imgs, 0), targets)

retinaface/detect.py

@@ -0,0 +1,152 @@
+import torch
+import torch.nn.functional as F
+import cv2
+import numpy as np
+import timeit
+
+import imutils
+from utils.infer_utils import load_model
+from data import cfg_mnet as cfg
+from models.retinaface import RetinaFace
+from layers.functions.prior_box import PriorBox
+from utils.box_utils import decode, decode_landm
+from utils.nms.py_cpu_nms import py_cpu_nms
+from utils.infer_utils import align_face
+torch.set_grad_enabled(False)
+
+
+class Detect:
+    def __init__(self, weight_path, net_inshape=(180, 320)):
+        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+        self.net_inshape = net_inshape
+        im_height, im_width = net_inshape
+        self.box_scale = np.array([im_width, im_height] * 2)
+        self.lmk_scale = np.array([im_width, im_height] * 5)
+
+        priorbox = PriorBox(cfg, image_size=net_inshape)
+        priors = priorbox.forward()
+        self.prior_data = priors.to(self.device)
+        self.net = RetinaFace(cfg=cfg, phase='test')
+        self.net = load_model(self.net, weight_path, False)
+        self.net.eval()
+        self.net = self.net.to(self.device)
+    
+
+    def _preprocess(self, image):
+        rgb_mean = (104, 117, 123)  # bgr order
+        h, w = image.shape[:2]
+        dx = int(self.net_inshape[1] * h / self.net_inshape[0] - w)
+        dy = 0
+        if dx < 0:
+            dx = 0
+            dy = int(self.net_inshape[0] * w / self.net_inshape[1] - h)
+        img = cv2.copyMakeBorder(image, 0, dy, 0, dx, borderType=cv2.BORDER_CONSTANT, value=rgb_mean)
+        img = cv2.copyMakeBorder(img, 0, img.shape[0], 0, img.shape[1], borderType=cv2.BORDER_CONSTANT, value=rgb_mean)
+        
+        h, w = img.shape[:2]
+        resize = float(self.net_inshape[1]) / float(w)
+        img = cv2.resize(img, self.net_inshape[::-1])
+        img = np.float32(img)
+        img -= rgb_mean
+        
+        return img, resize
+
+
+    def __call__(self, img, verbose=False):
+        '''
+        bgr image
+        '''
+        t0 = timeit.default_timer()
+        img, resize = self._preprocess(img)
+        img = img.transpose(2, 0, 1)
+        img = torch.from_numpy(img).unsqueeze(0)
+        img = img.to(self.device)
+
+        t1 = timeit.default_timer()
+        loc, conf, landms = self.net(img)
+        loc = [i.permute(0,2,3,1).contiguous().view(i.shape[0], -1, 4) for i in loc]
+        loc = torch.cat(loc, dim=1)
+        conf = [i.permute(0,2,3,1).contiguous().view(i.shape[0], -1, 3) for i in conf]
+        conf = torch.cat(conf, dim=1)
+        landms = [i.permute(0,2,3,1).contiguous().view(i.shape[0], -1, 10) for i in landms]
+        landms = torch.cat(landms, dim=1)
+        conf = F.softmax(conf, dim=-1)
+        
+        t2 = timeit.default_timer()
+        conf = conf[0]
+        scores = conf.squeeze(0).detach().cpu().numpy()[:, 1:]
+        scores = np.amax(scores, axis=1)
+
+        boxes = decode(loc[0], self.prior_data, cfg['variance'])  # loc[0] 
+        boxes = boxes.detach().cpu().numpy()
+        boxes = boxes * self.box_scale / resize
+        
+        landms = decode_landm(landms[0], self.prior_data, cfg['variance'])
+        landms = landms.detach().cpu().numpy()
+        landms = landms * self.lmk_scale / resize
+        
+        # ignore low scores
+        inds = np.where(scores > 0.02)[0]
+        boxes = boxes[inds]
+        landms = landms[inds]
+        scores = scores[inds]
+        
+        # keep top-K before NMS
+        order = scores.argsort()[::-1][:5000]
+        boxes = boxes[order]
+        landms = landms[order]
+        scores = scores[order]
+        
+        # do NMS
+        dets = np.hstack((boxes, scores[:, np.newaxis])).astype(np.float32, copy=False)
+        keep = py_cpu_nms(dets, 0.4)
+        # keep = nms(dets, args.nms_threshold,force_cpu=args.cpu)
+        dets = dets[keep, :]
+        landms = landms[keep]
+        
+        # keep top-K faster NMS
+        dets = dets[:750, :]
+        landms = landms[:750, :]
+        
+        dets = np.concatenate((dets, landms), axis=1)
+        dets = dets[dets[:, 4] > 0.5]
+        dets = dets[np.argsort(dets, axis=0)[:, 0]]
+
+        t3 = timeit.default_timer()
+        if verbose:
+            print(t1 - t0, t2 - t1, t3 - t2)
+        
+        return dets   # (n, 15), box=0-3, cls=4, lmk=5-10
+
+
+if __name__ == "__main__":
+    net_inshape = (486, 864)  # h, w
+    model = Detect("/mnt/nvme0n1p2/ExternalHardrive/research/object_detection/face/Face-Detector-1MB-with-landmark-clear/weights/mobilenet0.25_epoch_842.pth", net_inshape=net_inshape)
+    image_path = "/mnt/nvme0n1p2/datasets/face/dyno/mytelpay230626/mytelpay230626_raw/data_2nd/၁၀ကတန(နိုင်)၀၀၁၀၀၁/mytel_ekyc_1m2_65160cc1802b6183d87fca091cab4c2faa93a9b1614106b5911ca778_front_image.jpg"
+    img = cv2.imread(image_path)
+    
+    dets = model(img)
+    for i, b in enumerate(dets):
+        text = "{:.4f}".format(b[4])
+        b = b.astype(np.int32)
+        landm = b[5:15]
+        landm = landm.reshape((5, 2))
+
+        alighed_face = align_face(img, landm.copy())
+        # cv2.imshow(str(i), alighed_face)
+
+        # landms
+        landm = landm.astype(np.int32)
+        cv2.circle(img, tuple(landm[0]), 1, (0, 0, 255), 2)
+        cv2.circle(img, tuple(landm[1]), 1, (0, 255, 255), 2)
+        cv2.circle(img, tuple(landm[2]), 1, (255, 0, 255), 2)
+        cv2.circle(img, tuple(landm[3]), 1, (0, 255, 0), 2)
+        cv2.circle(img, tuple(landm[4]), 1, (255, 0, 0), 2)
+
+        cv2.rectangle(img, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+        cx = b[0]
+        cy = b[1] + 20
+        cv2.putText(img, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX, 1.3, (255, 255, 255))
+
+    cv2.imwrite("./output.jpg", img)
+    

retinaface/detect_video_raw.py

@@ -0,0 +1,66 @@
+import cv2
+import numpy as np
+import imutils
+
+from utils.fps import FPS
+from utils.infer_utils import LoadStream, align_face
+from detect import Detect
+
+
+net_inshape = (486, 864)  # h, w
+model = Detect("/mnt/nvme0n1p2/ExternalHardrive/research/object_detection/face/Face-Detector-1MB-with-landmark-clear/weights/mobilenet0.25_epoch_842.pth", net_inshape=net_inshape)
+# dataloader = LoadStream("rtsp://admin:meditech123@192.168.100.90:555/")
+dataloader = LoadStream("../30Shine_1.mp4")
+fps = FPS().start()
+
+for frame in dataloader:
+    # frame = imutils.resize(frame, width=640)
+    frame = frame.copy()
+    frame_raw = frame.copy()
+
+    
+    dets = model(frame)
+    for i, b in enumerate(dets):
+        text = "{:.4f}".format(b[4])
+        b = b.astype(np.int32)
+        landm = b[5:15]
+        landm = landm.reshape((5, 2))
+
+        alighed_face = align_face(frame, landm.copy())
+        # cv2.imshow(str(i), alighed_face)
+
+        # landms
+        landm = landm.astype(np.int32)
+        cv2.circle(frame, tuple(landm[0]), 1, (0, 0, 255), 2)
+        cv2.circle(frame, tuple(landm[1]), 1, (0, 255, 255), 2)
+        cv2.circle(frame, tuple(landm[2]), 1, (255, 0, 255), 2)
+        cv2.circle(frame, tuple(landm[3]), 1, (0, 255, 0), 2)
+        cv2.circle(frame, tuple(landm[4]), 1, (255, 0, 0), 2)
+
+        cv2.rectangle(frame, (b[0], b[1]), (b[2], b[3]), (0, 0, 255), 2)
+        cx = b[0]
+        cy = b[1] + 20
+        cv2.putText(frame, text, (cx, cy), cv2.FONT_HERSHEY_DUPLEX, 1.3, (255, 255, 255))
+
+    fps.update()
+    text_fps = "FPS: {:.3f}".format(fps.get_fps_n())
+    cv2.putText(frame, text_fps, (5, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+    cv2.imshow("frame", imutils.resize(frame, width=1700))
+    key = cv2.waitKey(1) & 0xff
+    if key == ord("q"):
+        break
+    elif key == ord("c"):
+        while True:
+            cv2.imshow("frame", imutils.resize(frame, width=1700))
+            key = cv2.waitKey(1) & 0xff
+            if key == ord("q"):
+                break
+        # cv2.imwrite(f"{i}.jpg", alighed_face)
+        # i += 1
+        # # break
+
+print(text_fps)
+cv2.destroyAllWindows()
+fps.stop()
+print("Total FPS: {}".format(fps.fps()))
+dataloader.close()

retinaface/layers/__init__.py

@@ -0,0 +1,2 @@
+from .functions import *
+from .modules import *

retinaface/layers/functions/prior_box.py

@@ -0,0 +1,33 @@
+import torch
+from itertools import product as product
+import numpy as np
+from math import ceil
+
+
+class PriorBox(object):
+    def __init__(self, cfg, image_size=None, phase='train'):
+        super(PriorBox, self).__init__()
+        self.min_sizes = cfg['min_sizes']
+        self.steps = cfg['steps']
+        self.clip = cfg['clip']
+        self.image_size = image_size
+        self.feature_maps = [[ceil(self.image_size[0]/step), ceil(self.image_size[1]/step)] for step in self.steps]
+
+    def forward(self):
+        anchors = []
+        for k, f in enumerate(self.feature_maps):
+            min_sizes = self.min_sizes[k]
+            for i, j in product(range(f[0]), range(f[1])):
+                for min_size in min_sizes:
+                    s_kx = min_size / self.image_size[1]
+                    s_ky = min_size / self.image_size[0]
+                    dense_cx = [x * self.steps[k] / self.image_size[1] for x in [j + 0.5]]
+                    dense_cy = [y * self.steps[k] / self.image_size[0] for y in [i + 0.5]]
+                    for cy, cx in product(dense_cy, dense_cx):
+                        anchors += [cx, cy, s_kx, s_ky]
+
+        # back to torch land
+        output = torch.Tensor(anchors).view(-1, 4)
+        if self.clip:
+            output.clamp_(max=1, min=0)
+        return output

retinaface/layers/modules/__init__.py

@@ -0,0 +1,3 @@
+from .multibox_loss import MultiBoxLoss
+
+__all__ = ['MultiBoxLoss']

retinaface/layers/modules/multibox_loss.py

@@ -0,0 +1,125 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Variable
+from utils.box_utils import match, log_sum_exp
+from data import cfg_mnet
+GPU = cfg_mnet['gpu_train']
+
+class MultiBoxLoss(nn.Module):
+    """SSD Weighted Loss Function
+    Compute Targets:
+        1) Produce Confidence Target Indices by matching  ground truth boxes
+           with (default) 'priorboxes' that have jaccard index > threshold parameter
+           (default threshold: 0.5).
+        2) Produce localization target by 'encoding' variance into offsets of ground
+           truth boxes and their matched  'priorboxes'.
+        3) Hard negative mining to filter the excessive number of negative examples
+           that comes with using a large number of default bounding boxes.
+           (default negative:positive ratio 3:1)
+    Objective Loss:
+        L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
+        Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss
+        weighted by α which is set to 1 by cross val.
+        Args:
+            c: class confidences,
+            l: predicted boxes,
+            g: ground truth boxes
+            N: number of matched default boxes
+        See: https://arxiv.org/pdf/1512.02325.pdf for more details.
+    """
+
+    def __init__(self, num_classes, overlap_thresh, prior_for_matching, bkg_label, neg_mining, neg_pos, neg_overlap, encode_target):
+        super(MultiBoxLoss, self).__init__()
+        self.num_classes = num_classes
+        self.threshold = overlap_thresh
+        self.background_label = bkg_label
+        self.encode_target = encode_target
+        self.use_prior_for_matching = prior_for_matching
+        self.do_neg_mining = neg_mining
+        self.negpos_ratio = neg_pos
+        self.neg_overlap = neg_overlap
+        self.variance = [0.1, 0.2]
+
+    def forward(self, predictions, priors, targets):
+        """Multibox Loss
+        Args:
+            predictions (tuple): A tuple containing loc preds, conf preds,
+            and prior boxes from SSD net.
+                conf shape: torch.size(batch_size,num_priors,num_classes)
+                loc shape: torch.size(batch_size,num_priors,4)
+                priors shape: torch.size(num_priors,4)
+
+            ground_truth (tensor): Ground truth boxes and labels for a batch,
+                shape: [batch_size,num_objs,5] (last idx is the label).
+        """
+
+        loc_data, conf_data, landm_data = predictions
+        priors = priors
+        num = loc_data.size(0)
+        num_priors = (priors.size(0))
+
+        # match priors (default boxes) and ground truth boxes
+        loc_t = torch.Tensor(num, num_priors, 4)
+        landm_t = torch.Tensor(num, num_priors, 10)
+        conf_t = torch.LongTensor(num, num_priors)
+        for idx in range(num):
+            truths = targets[idx][:, :4].data
+            labels = targets[idx][:, -1].data
+            landms = targets[idx][:, 4:14].data
+            defaults = priors.data
+            match(self.threshold, truths, defaults, self.variance, labels, landms, loc_t, conf_t, landm_t, idx)
+        if GPU:
+            loc_t = loc_t.cuda()
+            conf_t = conf_t.cuda()
+            landm_t = landm_t.cuda()
+
+        zeros = torch.tensor(0).cuda()
+        # landm Loss (Smooth L1)
+        # Shape: [batch,num_priors,10]
+        pos1 = conf_t > zeros
+        num_pos_landm = pos1.long().sum(1, keepdim=True)
+        N1 = max(num_pos_landm.data.sum().float(), 1)
+        pos_idx1 = pos1.unsqueeze(pos1.dim()).expand_as(landm_data)
+        landm_p = landm_data[pos_idx1].view(-1, 10)
+        landm_t = landm_t[pos_idx1].view(-1, 10)
+        loss_landm = F.smooth_l1_loss(landm_p, landm_t, reduction='sum')
+
+
+        pos = conf_t != zeros
+        conf_t[pos] = 1
+
+        # Localization Loss (Smooth L1)
+        # Shape: [batch,num_priors,4]
+        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)
+        loc_p = loc_data[pos_idx].view(-1, 4)
+        loc_t = loc_t[pos_idx].view(-1, 4)
+        loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')
+
+        # Compute max conf across batch for hard negative mining
+        batch_conf = conf_data.view(-1, self.num_classes)
+        loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))
+
+        # Hard Negative Mining
+        loss_c[pos.view(-1, 1)] = 0 # filter out pos boxes for now
+        loss_c = loss_c.view(num, -1)
+        _, loss_idx = loss_c.sort(1, descending=True)
+        _, idx_rank = loss_idx.sort(1)
+        num_pos = pos.long().sum(1, keepdim=True)
+        num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)
+        neg = idx_rank < num_neg.expand_as(idx_rank)
+
+        # Confidence Loss Including Positive and Negative Examples
+        pos_idx = pos.unsqueeze(2).expand_as(conf_data)
+        neg_idx = neg.unsqueeze(2).expand_as(conf_data)
+        conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1,self.num_classes)
+        targets_weighted = conf_t[(pos+neg).gt(0)]
+        loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')
+
+        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N
+        N = max(num_pos.data.sum().float(), 1)
+        loss_l /= N
+        loss_c /= N
+        loss_landm /= N1
+
+        return loss_l, loss_c, loss_landm