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Repository: AliaksandrSiarohin/first-order-model
Branch: master
Commit: c0274845cb2d
Files: 42
Total size: 1.5 MB
Directory structure:
gitextract_zo48uj8s/
├── .dockerignore
├── .gitignore
├── Dockerfile
├── LICENSE.md
├── README.md
├── animate.py
├── augmentation.py
├── config/
│ ├── bair-256.yaml
│ ├── fashion-256.yaml
│ ├── mgif-256.yaml
│ ├── nemo-256.yaml
│ ├── taichi-256.yaml
│ ├── taichi-adv-256.yaml
│ ├── vox-256.yaml
│ └── vox-adv-256.yaml
├── crop-video.py
├── data/
│ ├── bair256.csv
│ ├── taichi-loading/
│ │ ├── README.md
│ │ ├── load_videos.py
│ │ └── taichi-metadata.csv
│ └── taichi256.csv
├── demo.ipynb
├── demo.py
├── demo_jupyter.ipynb
├── frames_dataset.py
├── logger.py
├── modules/
│ ├── dense_motion.py
│ ├── discriminator.py
│ ├── generator.py
│ ├── keypoint_detector.py
│ ├── model.py
│ └── util.py
├── old_demo.ipynb
├── reconstruction.py
├── requirements.txt
├── run.py
├── sync_batchnorm/
│ ├── __init__.py
│ ├── batchnorm.py
│ ├── comm.py
│ ├── replicate.py
│ └── unittest.py
└── train.py
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FILE CONTENTS
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FILE: .dockerignore
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/venv
.git
__pycache__
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FILE: .gitignore
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/.vscode
__pycache__
/venv
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FILE: Dockerfile
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FROM nvcr.io/nvidia/pytorch:21.02-py3
RUN DEBIAN_FRONTEND=noninteractive apt-get -qq update \
&& DEBIAN_FRONTEND=noninteractive apt-get -qqy install python3-pip ffmpeg git less nano libsm6 libxext6 libxrender-dev \
&& rm -rf /var/lib/apt/lists/*
COPY . /app/
WORKDIR /app
RUN pip3 install --upgrade pip
RUN pip3 install \
git+https://github.com/1adrianb/face-alignment \
-r requirements.txt
================================================
FILE: LICENSE.md
================================================
MIT License
Copyright (c) 2019-2023 Aliaksandr Siarohin
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.
================================================
FILE: README.md
================================================
<b>!!! Check out our new [paper](https://arxiv.org/pdf/2104.11280.pdf) and [framework](https://github.com/snap-research/articulated-animation) improved for articulated objects</b>
# First Order Motion Model for Image Animation
This repository contains the source code for the paper [First Order Motion Model for Image Animation](https://papers.nips.cc/paper/8935-first-order-motion-model-for-image-animation) by Aliaksandr Siarohin, [Stéphane Lathuilière](http://stelat.eu), [Sergey Tulyakov](http://stulyakov.com), [Elisa Ricci](http://elisaricci.eu/) and [Nicu Sebe](http://disi.unitn.it/~sebe/).
[Hugging Face Spaces](https://huggingface.co/spaces/abhishek/first-order-motion-model)
## Example animations
The videos on the left show the driving videos. The first row on the right for each dataset shows the source videos. The bottom row contains the animated sequences with motion transferred from the driving video and object taken from the source image. We trained a separate network for each task.
### VoxCeleb Dataset
### Fashion Dataset
### MGIF Dataset
### Installation
We support ```python3```. To install the dependencies run:
```
pip install -r requirements.txt
```
### YAML configs
There are several configuration (```config/dataset_name.yaml```) files one for each `dataset`. See ```config/taichi-256.yaml``` to get description of each parameter.
### Pre-trained checkpoint
Checkpoints can be found under following link: [google-drive](https://drive.google.com/open?id=1PyQJmkdCsAkOYwUyaj_l-l0as-iLDgeH) or [yandex-disk](https://yadi.sk/d/lEw8uRm140L_eQ).
### Animation Demo
To run a demo, download checkpoint and run the following command:
```
python demo.py --config config/dataset_name.yaml --driving_video path/to/driving --source_image path/to/source --checkpoint path/to/checkpoint --relative --adapt_scale
```
The result will be stored in ```result.mp4```.
The driving videos and source images should be cropped before it can be used in our method. To obtain some semi-automatic crop suggestions you can use ```python crop-video.py --inp some_youtube_video.mp4```. It will generate commands for crops using ffmpeg. In order to use the script, face-alligment library is needed:
```
git clone https://github.com/1adrianb/face-alignment
cd face-alignment
pip install -r requirements.txt
python setup.py install
```
### Animation demo with Docker
If you are having trouble getting the demo to work because of library compatibility issues,
and you're running Linux, you might try running it inside a Docker container, which would
give you better control over the execution environment.
Requirements: Docker 19.03+ and [nvidia-docker](https://github.com/NVIDIA/nvidia-docker)
installed and able to successfully run the `nvidia-docker` usage tests.
We'll first build the container.
```
docker build -t first-order-model .
```
And now that we have the container available locally, we can use it to run the demo.
```
docker run -it --rm --gpus all \
-v $HOME/first-order-model:/app first-order-model \
python3 demo.py --config config/vox-256.yaml \
--driving_video driving.mp4 \
--source_image source.png \
--checkpoint vox-cpk.pth.tar \
--result_video result.mp4 \
--relative --adapt_scale
```
### Colab Demo
[](https://colab.research.google.com/github/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb) [](https://kaggle.com/kernels/welcome?src=https://github.com/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb)
@graphemecluster prepared a GUI demo for the Google Colab. It also works in Kaggle. For the source code, see [```demo.ipynb```](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/demo.ipynb).
For the old demo, see [```old_demo.ipynb```](https://github.com/AliaksandrSiarohin/first-order-model/blob/master/old_demo.ipynb).
### Face-swap
It is possible to modify the method to perform face-swap using supervised segmentation masks.
For both unsupervised and supervised video editing, such as face-swap, please refer to [Motion Co-Segmentation](https://github.com/AliaksandrSiarohin/motion-cosegmentation).
### Training
To train a model on specific dataset run:
```
CUDA_VISIBLE_DEVICES=0,1,2,3 python run.py --config config/dataset_name.yaml --device_ids 0,1,2,3
```
The code will create a folder in the log directory (each run will create a time-stamped new directory).
Checkpoints will be saved to this folder.
To check the loss values during training see ```log.txt```.
You can also check training data reconstructions in the ```train-vis``` subfolder.
By default the batch size is tuned to run on 2 or 4 Titan-X gpu (apart from speed it does not make much difference). You can change the batch size in the train_params in corresponding ```.yaml``` file.
### Evaluation on video reconstruction
To evaluate the reconstruction performance run:
```
CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode reconstruction --checkpoint path/to/checkpoint
```
You will need to specify the path to the checkpoint,
the ```reconstruction``` subfolder will be created in the 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.
Instructions for computing metrics from the paper can be found: https://github.com/AliaksandrSiarohin/pose-evaluation.
### Image animation
In order to animate videos run:
```
CUDA_VISIBLE_DEVICES=0 python run.py --config config/dataset_name.yaml --mode animate --checkpoint path/to/checkpoint
```
You will need to specify the path to the checkpoint,
the ```animation``` subfolder will be created in the same folder as the checkpoint.
You can find the generated video there and its loss-less version in the ```png``` subfolder.
By default video from test set will be randomly paired, but you can specify the "source,driving" pairs in the corresponding ```.csv``` files. The path to this file should be specified in corresponding ```.yaml``` file in pairs_list setting.
There are 2 different ways of performing animation:
by using **absolute** keypoint locations or by using **relative** keypoint locations.
1) <i>Animation using absolute coordinates:</i> the animation is performed using the absolute positions of the driving video and appearance of the source image.
In this way there are no specific requirements for the driving video and source appearance that is used.
However this usually leads to poor performance since irrelevant details such as shape is transferred.
Check animate parameters in ```taichi-256.yaml``` to enable this mode.
<img src="sup-mat/absolute-demo.gif" width="512">
2) <i>Animation using relative coordinates:</i> from the driving video we first estimate the relative movement of each keypoint,
then we add this movement to the absolute position of keypoints in the source image.
This keypoint along with source image is used for animation. This usually leads to better performance, however this requires
that the object in the first frame of the video and in the source image have the same pose
<img src="sup-mat/relative-demo.gif" width="512">
### Datasets
1) **Bair**. This dataset can be directly [downloaded](https://yadi.sk/d/Rr-fjn-PdmmqeA).
2) **Mgif**. This dataset can be directly [downloaded](https://yadi.sk/d/5VdqLARizmnj3Q).
3) **Fashion**. Follow the instruction on dataset downloading [from](https://vision.cs.ubc.ca/datasets/fashion/).
4) **Taichi**. Follow the instructions in [data/taichi-loading](data/taichi-loading/README.md) or instructions from https://github.com/AliaksandrSiarohin/video-preprocessing.
5) **Nemo**. Please follow the [instructions](https://www.uva-nemo.org/) on how to download the dataset. Then the dataset should be preprocessed using scripts from https://github.com/AliaksandrSiarohin/video-preprocessing.
6) **VoxCeleb**. Please follow the instruction from https://github.com/AliaksandrSiarohin/video-preprocessing.
### Training on your own dataset
1) Resize all the videos to the same size e.g 256x256, the videos can be in '.gif', '.mp4' or folder with images.
We recommend the later, for each video make a separate folder with all the frames in '.png' format. This format is loss-less, and it has better i/o performance.
2) Create a folder ```data/dataset_name``` with 2 subfolders ```train``` and ```test```, put training videos in the ```train``` and testing in the ```test```.
3) Create a config ```config/dataset_name.yaml```, in dataset_params specify the root dir the ```root_dir: data/dataset_name```. Also adjust the number of epoch in train_params.
#### Additional notes
Citation:
```
@InProceedings{Siarohin_2019_NeurIPS,
author={Siarohin, Aliaksandr and Lathuilière, Stéphane and Tulyakov, Sergey and Ricci, Elisa and Sebe, Nicu},
title={First Order Motion Model for Image Animation},
booktitle = {Conference on Neural Information Processing Systems (NeurIPS)},
month = {December},
year = {2019}
}
```
================================================
FILE: animate.py
================================================
import os
from tqdm import tqdm
import torch
from torch.utils.data import DataLoader
from frames_dataset import PairedDataset
from logger import Logger, Visualizer
import imageio
from scipy.spatial import ConvexHull
import numpy as np
from sync_batchnorm import DataParallelWithCallback
def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_movement_scale=False,
use_relative_movement=False, use_relative_jacobian=False):
if adapt_movement_scale:
source_area = ConvexHull(kp_source['value'][0].data.cpu().numpy()).volume
driving_area = ConvexHull(kp_driving_initial['value'][0].data.cpu().numpy()).volume
adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
else:
adapt_movement_scale = 1
kp_new = {k: v for k, v in kp_driving.items()}
if use_relative_movement:
kp_value_diff = (kp_driving['value'] - kp_driving_initial['value'])
kp_value_diff *= adapt_movement_scale
kp_new['value'] = kp_value_diff + kp_source['value']
if use_relative_jacobian:
jacobian_diff = torch.matmul(kp_driving['jacobian'], torch.inverse(kp_driving_initial['jacobian']))
kp_new['jacobian'] = torch.matmul(jacobian_diff, kp_source['jacobian'])
return kp_new
def animate(config, generator, kp_detector, checkpoint, log_dir, dataset):
log_dir = os.path.join(log_dir, 'animation')
png_dir = os.path.join(log_dir, 'png')
animate_params = config['animate_params']
dataset = PairedDataset(initial_dataset=dataset, number_of_pairs=animate_params['num_pairs'])
dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
if checkpoint is not None:
Logger.load_cpk(checkpoint, generator=generator, kp_detector=kp_detector)
else:
raise AttributeError("Checkpoint should be specified for mode='animate'.")
if not os.path.exists(log_dir):
os.makedirs(log_dir)
if not os.path.exists(png_dir):
os.makedirs(png_dir)
if torch.cuda.is_available():
generator = DataParallelWithCallback(generator)
kp_detector = DataParallelWithCallback(kp_detector)
generator.eval()
kp_detector.eval()
for it, x in tqdm(enumerate(dataloader)):
with torch.no_grad():
predictions = []
visualizations = []
driving_video = x['driving_video']
source_frame = x['source_video'][:, :, 0, :, :]
kp_source = kp_detector(source_frame)
kp_driving_initial = kp_detector(driving_video[:, :, 0])
for frame_idx in range(driving_video.shape[2]):
driving_frame = driving_video[:, :, frame_idx]
kp_driving = kp_detector(driving_frame)
kp_norm = normalize_kp(kp_source=kp_source, kp_driving=kp_driving,
kp_driving_initial=kp_driving_initial, **animate_params['normalization_params'])
out = generator(source_frame, kp_source=kp_source, kp_driving=kp_norm)
out['kp_driving'] = kp_driving
out['kp_source'] = kp_source
out['kp_norm'] = kp_norm
del out['sparse_deformed']
predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
visualization = Visualizer(**config['visualizer_params']).visualize(source=source_frame,
driving=driving_frame, out=out)
visualization = visualization
visualizations.append(visualization)
predictions = np.concatenate(predictions, axis=1)
result_name = "-".join([x['driving_name'][0], x['source_name'][0]])
imageio.imsave(os.path.join(png_dir, result_name + '.png'), (255 * predictions).astype(np.uint8))
image_name = result_name + animate_params['format']
imageio.mimsave(os.path.join(log_dir, image_name), visualizations)
================================================
FILE: augmentation.py
================================================
"""
Code from https://github.com/hassony2/torch_videovision
"""
import numbers
import random
import numpy as np
import PIL
from skimage.transform import resize, rotate
from numpy import pad
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 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
================================================
FILE: config/bair-256.yaml
================================================
dataset_params:
root_dir: data/bair
frame_shape: [256, 256, 3]
id_sampling: False
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_kp: 10
num_channels: 3
estimate_jacobian: True
kp_detector_params:
temperature: 0.1
block_expansion: 32
max_features: 1024
scale_factor: 0.25
num_blocks: 5
generator_params:
block_expansion: 64
max_features: 512
num_down_blocks: 2
num_bottleneck_blocks: 6
estimate_occlusion_map: True
dense_motion_params:
block_expansion: 64
max_features: 1024
num_blocks: 5
scale_factor: 0.25
discriminator_params:
scales: [1]
block_expansion: 32
max_features: 512
num_blocks: 4
sn: True
train_params:
num_epochs: 20
num_repeats: 1
epoch_milestones: [12, 18]
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 36
scales: [1, 0.5, 0.25, 0.125]
checkpoint_freq: 10
transform_params:
sigma_affine: 0.05
sigma_tps: 0.005
points_tps: 5
loss_weights:
generator_gan: 1
discriminator_gan: 1
feature_matching: [10, 10, 10, 10]
perceptual: [10, 10, 10, 10, 10]
equivariance_value: 10
equivariance_jacobian: 10
reconstruction_params:
num_videos: 1000
format: '.mp4'
animate_params:
num_pairs: 50
format: '.mp4'
normalization_params:
adapt_movement_scale: False
use_relative_movement: True
use_relative_jacobian: True
visualizer_params:
kp_size: 5
draw_border: True
colormap: 'gist_rainbow'
================================================
FILE: config/fashion-256.yaml
================================================
dataset_params:
root_dir: data/fashion-png
frame_shape: [256, 256, 3]
id_sampling: False
augmentation_params:
flip_param:
horizontal_flip: True
time_flip: True
jitter_param:
hue: 0.1
model_params:
common_params:
num_kp: 10
num_channels: 3
estimate_jacobian: True
kp_detector_params:
temperature: 0.1
block_expansion: 32
max_features: 1024
scale_factor: 0.25
num_blocks: 5
generator_params:
block_expansion: 64
max_features: 512
num_down_blocks: 2
num_bottleneck_blocks: 6
estimate_occlusion_map: True
dense_motion_params:
block_expansion: 64
max_features: 1024
num_blocks: 5
scale_factor: 0.25
discriminator_params:
scales: [1]
block_expansion: 32
max_features: 512
num_blocks: 4
train_params:
num_epochs: 100
num_repeats: 50
epoch_milestones: [60, 90]
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 27
scales: [1, 0.5, 0.25, 0.125]
checkpoint_freq: 50
transform_params:
sigma_affine: 0.05
sigma_tps: 0.005
points_tps: 5
loss_weights:
generator_gan: 1
discriminator_gan: 1
feature_matching: [10, 10, 10, 10]
perceptual: [10, 10, 10, 10, 10]
equivariance_value: 10
equivariance_jacobian: 10
reconstruction_params:
num_videos: 1000
format: '.mp4'
animate_params:
num_pairs: 50
format: '.mp4'
normalization_params:
adapt_movement_scale: False
use_relative_movement: True
use_relative_jacobian: True
visualizer_params:
kp_size: 5
draw_border: True
colormap: 'gist_rainbow'
================================================
FILE: config/mgif-256.yaml
================================================
dataset_params:
root_dir: data/moving-gif
frame_shape: [256, 256, 3]
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_kp: 10
num_channels: 3
estimate_jacobian: True
kp_detector_params:
temperature: 0.1
block_expansion: 32
max_features: 1024
scale_factor: 0.25
num_blocks: 5
single_jacobian_map: True
generator_params:
block_expansion: 64
max_features: 512
num_down_blocks: 2
num_bottleneck_blocks: 6
estimate_occlusion_map: True
dense_motion_params:
block_expansion: 64
max_features: 1024
num_blocks: 5
scale_factor: 0.25
discriminator_params:
scales: [1]
block_expansion: 32
max_features: 512
num_blocks: 4
sn: True
train_params:
num_epochs: 100
num_repeats: 25
epoch_milestones: [60, 90]
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 36
scales: [1, 0.5, 0.25, 0.125]
checkpoint_freq: 100
transform_params:
sigma_affine: 0.05
sigma_tps: 0.005
points_tps: 5
loss_weights:
generator_gan: 1
discriminator_gan: 1
feature_matching: [10, 10, 10, 10]
perceptual: [10, 10, 10, 10, 10]
equivariance_value: 10
equivariance_jacobian: 10
reconstruction_params:
num_videos: 1000
format: '.mp4'
animate_params:
num_pairs: 50
format: '.mp4'
normalization_params:
adapt_movement_scale: False
use_relative_movement: True
use_relative_jacobian: True
visualizer_params:
kp_size: 5
draw_border: True
colormap: 'gist_rainbow'
================================================
FILE: config/nemo-256.yaml
================================================
dataset_params:
root_dir: data/nemo-png
frame_shape: [256, 256, 3]
id_sampling: False
augmentation_params:
flip_param:
horizontal_flip: True
time_flip: True
model_params:
common_params:
num_kp: 10
num_channels: 3
estimate_jacobian: True
kp_detector_params:
temperature: 0.1
block_expansion: 32
max_features: 1024
scale_factor: 0.25
num_blocks: 5
generator_params:
block_expansion: 64
max_features: 512
num_down_blocks: 2
num_bottleneck_blocks: 6
estimate_occlusion_map: True
dense_motion_params:
block_expansion: 64
max_features: 1024
num_blocks: 5
scale_factor: 0.25
discriminator_params:
scales: [1]
block_expansion: 32
max_features: 512
num_blocks: 4
sn: True
train_params:
num_epochs: 100
num_repeats: 8
epoch_milestones: [60, 90]
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 36
scales: [1, 0.5, 0.25, 0.125]
checkpoint_freq: 50
transform_params:
sigma_affine: 0.05
sigma_tps: 0.005
points_tps: 5
loss_weights:
generator_gan: 1
discriminator_gan: 1
feature_matching: [10, 10, 10, 10]
perceptual: [10, 10, 10, 10, 10]
equivariance_value: 10
equivariance_jacobian: 10
reconstruction_params:
num_videos: 1000
format: '.mp4'
animate_params:
num_pairs: 50
format: '.mp4'
normalization_params:
adapt_movement_scale: False
use_relative_movement: True
use_relative_jacobian: True
visualizer_params:
kp_size: 5
draw_border: True
colormap: 'gist_rainbow'
================================================
FILE: config/taichi-256.yaml
================================================
# 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: data/taichi-png
# Image shape, needed for staked .png format.
frame_shape: [256, 256, 3]
# 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
# List with pairs for animation, None for random pairs
pairs_list: data/taichi256.csv
# 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 keypoint
num_kp: 10
# Number of channels per image
num_channels: 3
# Using first or zero order model
estimate_jacobian: True
kp_detector_params:
# Softmax temperature for keypoint heatmaps
temperature: 0.1
# Number of features mutliplier
block_expansion: 32
# Maximum allowed number of features
max_features: 1024
# Number of block in Unet. Can be increased or decreased depending or resolution.
num_blocks: 5
# Keypioint 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
generator_params:
# Number of features mutliplier
block_expansion: 64
# Maximum allowed number of features
max_features: 512
# Number of downsampling blocks in Jonson architecture.
# Can be increased or decreased depending or resolution.
num_down_blocks: 2
# Number of ResBlocks in Jonson architecture.
num_bottleneck_blocks: 6
# Use occlusion map or not
estimate_occlusion_map: True
dense_motion_params:
# Number of features mutliplier
block_expansion: 64
# Maximum allowed number of features
max_features: 1024
# Number of block in Unet. Can be increased or decreased depending or resolution.
num_blocks: 5
# Dense motion 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
discriminator_params:
# Discriminator can be multiscale, if you want 2 discriminator on original
# resolution and half of the original, specify scales: [1, 0.5]
scales: [1]
# Number of features mutliplier
block_expansion: 32
# Maximum allowed number of features
max_features: 512
# Number of blocks. Can be increased or decreased depending or resolution.
num_blocks: 4
# 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: [60, 90]
# Initial learing rate for all modules
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 30
# 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]
# Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs.
checkpoint_freq: 50
# Parameters of transform 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:
# Weight for LSGAN loss in generator, 0 for no adversarial loss.
generator_gan: 0
# Weight for LSGAN loss in discriminator
discriminator_gan: 1
# Weights for feature matching loss, the number should be the same as number of blocks in discriminator.
feature_matching: [10, 10, 10, 10]
# Weights for perceptual loss.
perceptual: [10, 10, 10, 10, 10]
# Weights for value equivariance.
equivariance_value: 10
# Weights for jacobian equivariance.
equivariance_jacobian: 10
# Parameters of reconstruction
reconstruction_params:
# Maximum number of videos for reconstruction
num_videos: 1000
# Format for visualization, note that results will be also stored in staked .png.
format: '.mp4'
# Parameters of animation
animate_params:
# Maximum number of pairs for animation, the pairs will be either taken from pairs_list or random.
num_pairs: 50
# Format for visualization, note that results will be also stored in staked .png.
format: '.mp4'
# Normalization of diriving keypoints
normalization_params:
# Increase or decrease relative movement scale depending on the size of the object
adapt_movement_scale: False
# Apply only relative displacement of the keypoint
use_relative_movement: True
# Apply only relative change in jacobian
use_relative_jacobian: True
# Visualization parameters
visualizer_params:
# Draw keypoints of this size, increase or decrease depending on resolution
kp_size: 5
# Draw white border around images
draw_border: True
# Color map for keypoints
colormap: 'gist_rainbow'
================================================
FILE: config/taichi-adv-256.yaml
================================================
# Dataset parameters
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: data/taichi-png
# Image shape, needed for staked .png format.
frame_shape: [256, 256, 3]
# 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
# List with pairs for animation, None for random pairs
pairs_list: data/taichi256.csv
# 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 keypoint
num_kp: 10
# Number of channels per image
num_channels: 3
# Using first or zero order model
estimate_jacobian: True
kp_detector_params:
# Softmax temperature for keypoint heatmaps
temperature: 0.1
# Number of features mutliplier
block_expansion: 32
# Maximum allowed number of features
max_features: 1024
# Number of block in Unet. Can be increased or decreased depending or resolution.
num_blocks: 5
# Keypioint 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
generator_params:
# Number of features mutliplier
block_expansion: 64
# Maximum allowed number of features
max_features: 512
# Number of downsampling blocks in Jonson architecture.
# Can be increased or decreased depending or resolution.
num_down_blocks: 2
# Number of ResBlocks in Jonson architecture.
num_bottleneck_blocks: 6
# Use occlusion map or not
estimate_occlusion_map: True
dense_motion_params:
# Number of features mutliplier
block_expansion: 64
# Maximum allowed number of features
max_features: 1024
# Number of block in Unet. Can be increased or decreased depending or resolution.
num_blocks: 5
# Dense motion 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
discriminator_params:
# Discriminator can be multiscale, if you want 2 discriminator on original
# resolution and half of the original, specify scales: [1, 0.5]
scales: [1]
# Number of features mutliplier
block_expansion: 32
# Maximum allowed number of features
max_features: 512
# Number of blocks. Can be increased or decreased depending or resolution.
num_blocks: 4
use_kp: True
# Parameters of training
train_params:
# Number of training epochs
num_epochs: 150
# 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: []
# Initial learing rate for all modules
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 0
batch_size: 27
# 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]
# Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs.
checkpoint_freq: 50
# Parameters of transform 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:
# Weight for LSGAN loss in generator
generator_gan: 1
# Weight for LSGAN loss in discriminator
discriminator_gan: 1
# Weights for feature matching loss, the number should be the same as number of blocks in discriminator.
feature_matching: [10, 10, 10, 10]
# Weights for perceptual loss.
perceptual: [10, 10, 10, 10, 10]
# Weights for value equivariance.
equivariance_value: 10
# Weights for jacobian equivariance.
equivariance_jacobian: 10
# Parameters of reconstruction
reconstruction_params:
# Maximum number of videos for reconstruction
num_videos: 1000
# Format for visualization, note that results will be also stored in staked .png.
format: '.mp4'
# Parameters of animation
animate_params:
# Maximum number of pairs for animation, the pairs will be either taken from pairs_list or random.
num_pairs: 50
# Format for visualization, note that results will be also stored in staked .png.
format: '.mp4'
# Normalization of diriving keypoints
normalization_params:
# Increase or decrease relative movement scale depending on the size of the object
adapt_movement_scale: False
# Apply only relative displacement of the keypoint
use_relative_movement: True
# Apply only relative change in jacobian
use_relative_jacobian: True
# Visualization parameters
visualizer_params:
# Draw keypoints of this size, increase or decrease depending on resolution
kp_size: 5
# Draw white border around images
draw_border: True
# Color map for keypoints
colormap: 'gist_rainbow'
================================================
FILE: config/vox-256.yaml
================================================
dataset_params:
root_dir: data/vox-png
frame_shape: [256, 256, 3]
id_sampling: True
pairs_list: data/vox256.csv
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_kp: 10
num_channels: 3
estimate_jacobian: True
kp_detector_params:
temperature: 0.1
block_expansion: 32
max_features: 1024
scale_factor: 0.25
num_blocks: 5
generator_params:
block_expansion: 64
max_features: 512
num_down_blocks: 2
num_bottleneck_blocks: 6
estimate_occlusion_map: True
dense_motion_params:
block_expansion: 64
max_features: 1024
num_blocks: 5
scale_factor: 0.25
discriminator_params:
scales: [1]
block_expansion: 32
max_features: 512
num_blocks: 4
sn: True
train_params:
num_epochs: 100
num_repeats: 75
epoch_milestones: [60, 90]
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 40
scales: [1, 0.5, 0.25, 0.125]
checkpoint_freq: 50
transform_params:
sigma_affine: 0.05
sigma_tps: 0.005
points_tps: 5
loss_weights:
generator_gan: 0
discriminator_gan: 1
feature_matching: [10, 10, 10, 10]
perceptual: [10, 10, 10, 10, 10]
equivariance_value: 10
equivariance_jacobian: 10
reconstruction_params:
num_videos: 1000
format: '.mp4'
animate_params:
num_pairs: 50
format: '.mp4'
normalization_params:
adapt_movement_scale: False
use_relative_movement: True
use_relative_jacobian: True
visualizer_params:
kp_size: 5
draw_border: True
colormap: 'gist_rainbow'
================================================
FILE: config/vox-adv-256.yaml
================================================
dataset_params:
root_dir: data/vox-png
frame_shape: [256, 256, 3]
id_sampling: True
pairs_list: data/vox256.csv
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_kp: 10
num_channels: 3
estimate_jacobian: True
kp_detector_params:
temperature: 0.1
block_expansion: 32
max_features: 1024
scale_factor: 0.25
num_blocks: 5
generator_params:
block_expansion: 64
max_features: 512
num_down_blocks: 2
num_bottleneck_blocks: 6
estimate_occlusion_map: True
dense_motion_params:
block_expansion: 64
max_features: 1024
num_blocks: 5
scale_factor: 0.25
discriminator_params:
scales: [1]
block_expansion: 32
max_features: 512
num_blocks: 4
use_kp: True
train_params:
num_epochs: 150
num_repeats: 75
epoch_milestones: []
lr_generator: 2.0e-4
lr_discriminator: 2.0e-4
lr_kp_detector: 2.0e-4
batch_size: 36
scales: [1, 0.5, 0.25, 0.125]
checkpoint_freq: 50
transform_params:
sigma_affine: 0.05
sigma_tps: 0.005
points_tps: 5
loss_weights:
generator_gan: 1
discriminator_gan: 1
feature_matching: [10, 10, 10, 10]
perceptual: [10, 10, 10, 10, 10]
equivariance_value: 10
equivariance_jacobian: 10
reconstruction_params:
num_videos: 1000
format: '.mp4'
animate_params:
num_pairs: 50
format: '.mp4'
normalization_params:
adapt_movement_scale: False
use_relative_movement: True
use_relative_jacobian: True
visualizer_params:
kp_size: 5
draw_border: True
colormap: 'gist_rainbow'
================================================
FILE: crop-video.py
================================================
import face_alignment
import skimage.io
import numpy
from argparse import ArgumentParser
from skimage import img_as_ubyte
from skimage.transform import resize
from tqdm import tqdm
import os
import imageio
import numpy as np
import warnings
warnings.filterwarnings("ignore")
def extract_bbox(frame, fa):
if max(frame.shape[0], frame.shape[1]) > 640:
scale_factor = max(frame.shape[0], frame.shape[1]) / 640.0
frame = resize(frame, (int(frame.shape[0] / scale_factor), int(frame.shape[1] / scale_factor)))
frame = img_as_ubyte(frame)
else:
scale_factor = 1
frame = frame[..., :3]
bboxes = fa.face_detector.detect_from_image(frame[..., ::-1])
if len(bboxes) == 0:
return []
return np.array(bboxes)[:, :-1] * scale_factor
def bb_intersection_over_union(boxA, boxB):
xA = max(boxA[0], boxB[0])
yA = max(boxA[1], boxB[1])
xB = min(boxA[2], boxB[2])
yB = min(boxA[3], boxB[3])
interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)
boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)
boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)
iou = interArea / float(boxAArea + boxBArea - interArea)
return iou
def join(tube_bbox, bbox):
xA = min(tube_bbox[0], bbox[0])
yA = min(tube_bbox[1], bbox[1])
xB = max(tube_bbox[2], bbox[2])
yB = max(tube_bbox[3], bbox[3])
return (xA, yA, xB, yB)
def compute_bbox(start, end, fps, tube_bbox, frame_shape, inp, image_shape, increase_area=0.1):
left, top, right, bot = tube_bbox
width = right - left
height = bot - top
#Computing aspect preserving bbox
width_increase = max(increase_area, ((1 + 2 * increase_area) * height - width) / (2 * width))
height_increase = max(increase_area, ((1 + 2 * increase_area) * width - height) / (2 * height))
left = int(left - width_increase * width)
top = int(top - height_increase * height)
right = int(right + width_increase * width)
bot = int(bot + height_increase * height)
top, bot, left, right = max(0, top), min(bot, frame_shape[0]), max(0, left), min(right, frame_shape[1])
h, w = bot - top, right - left
start = start / fps
end = end / fps
time = end - start
scale = f'{image_shape[0]}:{image_shape[1]}'
return f'ffmpeg -i {inp} -ss {start} -t {time} -filter:v "crop={w}:{h}:{left}:{top}, scale={scale}" crop.mp4'
def compute_bbox_trajectories(trajectories, fps, frame_shape, args):
commands = []
for i, (bbox, tube_bbox, start, end) in enumerate(trajectories):
if (end - start) > args.min_frames:
command = compute_bbox(start, end, fps, tube_bbox, frame_shape, inp=args.inp, image_shape=args.image_shape, increase_area=args.increase)
commands.append(command)
return commands
def process_video(args):
device = 'cpu' if args.cpu else 'cuda'
fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=False, device=device)
video = imageio.get_reader(args.inp)
trajectories = []
previous_frame = None
fps = video.get_meta_data()['fps']
commands = []
try:
for i, frame in tqdm(enumerate(video)):
frame_shape = frame.shape
bboxes = extract_bbox(frame, fa)
## For each trajectory check the criterion
not_valid_trajectories = []
valid_trajectories = []
for trajectory in trajectories:
tube_bbox = trajectory[0]
intersection = 0
for bbox in bboxes:
intersection = max(intersection, bb_intersection_over_union(tube_bbox, bbox))
if intersection > args.iou_with_initial:
valid_trajectories.append(trajectory)
else:
not_valid_trajectories.append(trajectory)
commands += compute_bbox_trajectories(not_valid_trajectories, fps, frame_shape, args)
trajectories = valid_trajectories
## Assign bbox to trajectories, create new trajectories
for bbox in bboxes:
intersection = 0
current_trajectory = None
for trajectory in trajectories:
tube_bbox = trajectory[0]
current_intersection = bb_intersection_over_union(tube_bbox, bbox)
if intersection < current_intersection and current_intersection > args.iou_with_initial:
intersection = bb_intersection_over_union(tube_bbox, bbox)
current_trajectory = trajectory
## Create new trajectory
if current_trajectory is None:
trajectories.append([bbox, bbox, i, i])
else:
current_trajectory[3] = i
current_trajectory[1] = join(current_trajectory[1], bbox)
except IndexError as e:
raise (e)
commands += compute_bbox_trajectories(trajectories, fps, frame_shape, args)
return commands
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--image_shape", default=(256, 256), type=lambda x: tuple(map(int, x.split(','))),
help="Image shape")
parser.add_argument("--increase", default=0.1, type=float, help='Increase bbox by this amount')
parser.add_argument("--iou_with_initial", type=float, default=0.25, help="The minimal allowed iou with inital bbox")
parser.add_argument("--inp", required=True, help='Input image or video')
parser.add_argument("--min_frames", type=int, default=150, help='Minimum number of frames')
parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
args = parser.parse_args()
commands = process_video(args)
for command in commands:
print (command)
================================================
FILE: data/bair256.csv
================================================
distance,source,driving,frame
0,000054.mp4,000048.mp4,0
0,000050.mp4,000063.mp4,0
0,000073.mp4,000007.mp4,0
0,000021.mp4,000010.mp4,0
0,000084.mp4,000046.mp4,0
0,000031.mp4,000102.mp4,0
0,000029.mp4,000111.mp4,0
0,000090.mp4,000112.mp4,0
0,000039.mp4,000010.mp4,0
0,000008.mp4,000069.mp4,0
0,000068.mp4,000076.mp4,0
0,000051.mp4,000052.mp4,0
0,000022.mp4,000098.mp4,0
0,000096.mp4,000032.mp4,0
0,000032.mp4,000099.mp4,0
0,000006.mp4,000053.mp4,0
0,000098.mp4,000020.mp4,0
0,000029.mp4,000066.mp4,0
0,000022.mp4,000007.mp4,0
0,000027.mp4,000065.mp4,0
0,000026.mp4,000059.mp4,0
0,000015.mp4,000112.mp4,0
0,000086.mp4,000123.mp4,0
0,000103.mp4,000052.mp4,0
0,000123.mp4,000103.mp4,0
0,000051.mp4,000005.mp4,0
0,000062.mp4,000125.mp4,0
0,000126.mp4,000111.mp4,0
0,000066.mp4,000090.mp4,0
0,000075.mp4,000106.mp4,0
0,000020.mp4,000010.mp4,0
0,000076.mp4,000028.mp4,0
0,000062.mp4,000002.mp4,0
0,000095.mp4,000127.mp4,0
0,000113.mp4,000072.mp4,0
0,000027.mp4,000104.mp4,0
0,000054.mp4,000124.mp4,0
0,000019.mp4,000089.mp4,0
0,000052.mp4,000072.mp4,0
0,000108.mp4,000033.mp4,0
0,000044.mp4,000118.mp4,0
0,000029.mp4,000086.mp4,0
0,000068.mp4,000066.mp4,0
0,000014.mp4,000036.mp4,0
0,000053.mp4,000071.mp4,0
0,000022.mp4,000094.mp4,0
0,000000.mp4,000121.mp4,0
0,000071.mp4,000079.mp4,0
0,000127.mp4,000005.mp4,0
0,000085.mp4,000023.mp4,0
================================================
FILE: data/taichi-loading/README.md
================================================
# TaiChi dataset
The scripst for loading the TaiChi dataset.
We provide only the id of the corresponding video and the bounding box. Following script will download videos from youtube and crop them according to the provided bounding boxes.
1) Load youtube-dl:
```
wget https://yt-dl.org/downloads/latest/youtube-dl -O youtube-dl
chmod a+rx youtube-dl
```
2) Run script to download videos, there are 2 formats that can be used for storing videos one is .mp4 and another is folder with .png images. While .png images occupy significantly more space, the format is loss-less and have better i/o performance when training.
```
python load_videos.py --metadata taichi-metadata.csv --format .mp4 --out_folder taichi --workers 8
```
select number of workers based on number of cpu avaliable. Note .png format take aproximatly 80GB.
================================================
FILE: data/taichi-loading/load_videos.py
================================================
import numpy as np
import pandas as pd
import imageio
import os
import subprocess
from multiprocessing import Pool
from itertools import cycle
import warnings
import glob
import time
from tqdm import tqdm
from argparse import ArgumentParser
from skimage import img_as_ubyte
from skimage.transform import resize
warnings.filterwarnings("ignore")
DEVNULL = open(os.devnull, 'wb')
def save(path, frames, format):
if format == '.mp4':
imageio.mimsave(path, frames)
elif format == '.png':
if os.path.exists(path):
print ("Warning: skiping video %s" % os.path.basename(path))
return
else:
os.makedirs(path)
for j, frame in enumerate(frames):
imageio.imsave(os.path.join(path, str(j).zfill(7) + '.png'), frames[j])
else:
print ("Unknown format %s" % format)
exit()
def download(video_id, args):
video_path = os.path.join(args.video_folder, video_id + ".mp4")
subprocess.call([args.youtube, '-f', "''best/mp4''", '--write-auto-sub', '--write-sub',
'--sub-lang', 'en', '--skip-unavailable-fragments',
"https://www.youtube.com/watch?v=" + video_id, "--output",
video_path], stdout=DEVNULL, stderr=DEVNULL)
return video_path
def run(data):
video_id, args = data
if not os.path.exists(os.path.join(args.video_folder, video_id.split('#')[0] + '.mp4')):
download(video_id.split('#')[0], args)
if not os.path.exists(os.path.join(args.video_folder, video_id.split('#')[0] + '.mp4')):
print ('Can not load video %s, broken link' % video_id.split('#')[0])
return
reader = imageio.get_reader(os.path.join(args.video_folder, video_id.split('#')[0] + '.mp4'))
fps = reader.get_meta_data()['fps']
df = pd.read_csv(args.metadata)
df = df[df['video_id'] == video_id]
all_chunks_dict = [{'start': df['start'].iloc[j], 'end': df['end'].iloc[j],
'bbox': list(map(int, df['bbox'].iloc[j].split('-'))), 'frames':[]} for j in range(df.shape[0])]
ref_fps = df['fps'].iloc[0]
ref_height = df['height'].iloc[0]
ref_width = df['width'].iloc[0]
partition = df['partition'].iloc[0]
try:
for i, frame in enumerate(reader):
for entry in all_chunks_dict:
if (i * ref_fps >= entry['start'] * fps) and (i * ref_fps < entry['end'] * fps):
left, top, right, bot = entry['bbox']
left = int(left / (ref_width / frame.shape[1]))
top = int(top / (ref_height / frame.shape[0]))
right = int(right / (ref_width / frame.shape[1]))
bot = int(bot / (ref_height / frame.shape[0]))
crop = frame[top:bot, left:right]
if args.image_shape is not None:
crop = img_as_ubyte(resize(crop, args.image_shape, anti_aliasing=True))
entry['frames'].append(crop)
except imageio.core.format.CannotReadFrameError:
None
for entry in all_chunks_dict:
first_part = '#'.join(video_id.split('#')[::-1])
path = first_part + '#' + str(entry['start']).zfill(6) + '#' + str(entry['end']).zfill(6) + '.mp4'
save(os.path.join(args.out_folder, partition, path), entry['frames'], args.format)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("--video_folder", default='youtube-taichi', help='Path to youtube videos')
parser.add_argument("--metadata", default='taichi-metadata-new.csv', help='Path to metadata')
parser.add_argument("--out_folder", default='taichi-png', help='Path to output')
parser.add_argument("--format", default='.png', help='Storing format')
parser.add_argument("--workers", default=1, type=int, help='Number of workers')
parser.add_argument("--youtube", default='./youtube-dl', help='Path to youtube-dl')
parser.add_argument("--image_shape", default=(256, 256), type=lambda x: tuple(map(int, x.split(','))),
help="Image shape, None for no resize")
args = parser.parse_args()
if not os.path.exists(args.video_folder):
os.makedirs(args.video_folder)
if not os.path.exists(args.out_folder):
os.makedirs(args.out_folder)
for partition in ['test', 'train']:
if not os.path.exists(os.path.join(args.out_folder, partition)):
os.makedirs(os.path.join(args.out_folder, partition))
df = pd.read_csv(args.metadata)
video_ids = set(df['video_id'])
pool = Pool(processes=args.workers)
args_list = cycle([args])
for chunks_data in tqdm(pool.imap_unordered(run, zip(video_ids, args_list))):
None
================================================
FILE: data/taichi-loading/taichi-metadata.csv
================================================
video_id,start,end,bbox,fps,width,height,partition
u6rJiF-gPx8,80,300,118-82-508-472,29.97,640,480,train
u6rJiF-gPx8,575,717,168-99-488-419,29.97,640,480,train
u6rJiF-gPx8,1257,1550,132-46-539-453,29.97,640,480,train
u6rJiF-gPx8,1551,1808,151-59-547-455,29.97,640,480,train
u6rJiF-gPx8,1888,2161,124-37-541-454,29.97,640,480,train
u6rJiF-gPx8,2484,2719,150-40-570-460,29.97,640,480,train
u6rJiF-gPx8,3000,3129,89-69-473-453,29.97,640,480,train
u6rJiF-gPx8,3300,3450,124-95-491-462,29.97,640,480,train
u6rJiF-gPx8,3600,3750,117-104-443-430,29.97,640,480,train
u6rJiF-gPx8,4157,4350,150-87-525-462,29.97,640,480,train
NBGys-uxScs,134,312,152-118-440-406,29.97,640,480,train
NBGys-uxScs,403,587,145-136-410-401,29.97,640,480,train
NBGys-uxScs,898,1092,95-79-409-393,29.97,640,480,train
NBGys-uxScs,1262,1535,49-57-391-399,29.97,640,480,train
NBGys-uxScs,1923,2165,232-80-554-402,29.97,640,480,train
NBGys-uxScs,2165,2324,138-63-465-390,29.97,640,480,train
NBGys-uxScs,2324,2503,88-72-410-394,29.97,640,480,train
NBGys-uxScs,2698,2840,154-73-486-405,29.97,640,480,train
NBGys-uxScs,2840,3222,191-53-537-399,29.97,640,480,train
NBGys-uxScs,3634,4177,186-67-521-402,29.97,640,480,train
NBGys-uxScs,4222,4367,255-80-575-400,29.97,640,480,train
NBGys-uxScs,5361,5656,165-67-501-403,29.97,640,480,train
NBGys-uxScs,5719,5923,160-78-485-403,29.97,640,480,train
NBGys-uxScs,6359,6577,152-51-499-398,29.97,640,480,train
NBGys-uxScs,7477,7830,131-64-476-409,29.97,640,480,train
uEqWZ9S_-Lw,89,581,51-38-489-476,25.0,600,480,test
uEqWZ9S_-Lw,660,1470,69-51-492-474,25.0,600,480,test
uEqWZ9S_-Lw,1470,1773,7-45-437-475,25.0,600,480,test
uEqWZ9S_-Lw,1773,1905,92-74-490-472,25.0,600,480,test
uEqWZ9S_-Lw,2600,2868,106-60-517-471,25.0,600,480,test
uEqWZ9S_-Lw,3142,3350,59-50-478-469,25.0,600,480,test
uEqWZ9S_-Lw,3414,3576,37-56-456-475,25.0,600,480,test
uEqWZ9S_-Lw,3662,3864,103-45-523-465,25.0,600,480,test
uEqWZ9S_-Lw,4340,4738,75-75-472-472,25.0,600,480,test
uEqWZ9S_-Lw,4947,5107,49-34-484-469,25.0,600,480,test
uEqWZ9S_-Lw,5252,5990,44-70-438-464,25.0,600,480,test
uEqWZ9S_-Lw,7083,7713,72-51-499-478,25.0,600,480,test
uEqWZ9S_-Lw,9388,9729,71-55-483-467,25.0,600,480,test
uEqWZ9S_-Lw,9881,10091,46-62-458-474,25.0,600,480,test
ctzMBulw1lI,320,577,85-71-478-464,30.0,640,480,train
ctzMBulw1lI,669,871,162-56-544-438,30.0,640,480,train
ctzMBulw1lI,1191,1338,144-55-533-444,30.0,640,480,train
ctzMBulw1lI,1822,1962,62-55-448-441,30.0,640,480,train
ctzMBulw1lI,2016,2175,125-61-498-434,30.0,640,480,train
ctzMBulw1lI,2509,2680,66-66-440-440,30.0,640,480,train
ctzMBulw1lI,2680,2823,152-107-491-446,30.0,640,480,train
ctzMBulw1lI,2832,3121,141-88-499-446,30.0,640,480,train
ctzMBulw1lI,4044,4326,108-16-546-454,30.0,640,480,train
ctzMBulw1lI,4326,4496,101-40-520-459,30.0,640,480,train
ctzMBulw1lI,4516,4755,56-35-483-462,30.0,640,480,train
ctzMBulw1lI,4796,4972,37-19-477-459,30.0,640,480,train
ctzMBulw1lI,5008,5236,37-2-503-468,30.0,640,480,train
ctzMBulw1lI,5548,5684,66-46-455-435,30.0,640,480,train
ctzMBulw1lI,6610,6754,78-44-491-457,30.0,640,480,train
ctzMBulw1lI,6811,7033,113-41-514-442,30.0,640,480,train
ctzMBulw1lI,7350,7511,203-66-589-452,30.0,640,480,train
ctzMBulw1lI,7659,7916,66-60-448-442,30.0,640,480,train
ctzMBulw1lI,9007,9148,94-39-492-437,30.0,640,480,train
ctzMBulw1lI,9643,9910,18-47-426-455,30.0,640,480,train
ctzMBulw1lI,11072,11285,98-44-489-435,30.0,640,480,train
ctzMBulw1lI,11701,11904,45-40-431-426,30.0,640,480,train
cpDT3xFbP4g,7,1031,133-87-482-436,29.97,640,480,train
cpDT3xFbP4g,1031,1301,127-97-467-437,29.97,640,480,train
cpDT3xFbP4g,1395,2419,112-92-473-453,29.97,640,480,train
cpDT3xFbP4g,2419,2837,136-92-484-440,29.97,640,480,train
cpDT3xFbP4g,2837,3861,92-89-460-457,29.97,640,480,train
cpDT3xFbP4g,3861,4805,172-104-508-440,29.97,640,480,train
cpDT3xFbP4g,4805,5829,153-88-514-449,29.97,640,480,train
cpDT3xFbP4g,5829,6020,148-101-487-440,29.97,640,480,train
oYlrB4ddEpM,1167,1646,277-23-962-708,25.0,1280,720,train
oYlrB4ddEpM,2815,2985,401-119-992-710,25.0,1280,720,train
oYlrB4ddEpM,3016,3228,483-105-1068-690,25.0,1280,720,train
oYlrB4ddEpM,5318,5453,235-84-846-695,25.0,1280,720,train
oYlrB4ddEpM,5656,5964,450-52-1101-703,25.0,1280,720,train
YonmpJvwmKM,2264,2578,340-67-963-690,29.97,1280,720,train
YonmpJvwmKM,4852,5063,231-9-920-698,29.97,1280,720,train
YonmpJvwmKM,5597,5854,435-48-1078-691,29.97,1280,720,train
Dn0mNZmAh2k,308,450,95-63-498-466,29.97,640,480,train
Dn0mNZmAh2k,495,975,125-114-481-470,29.97,640,480,train
Dn0mNZmAh2k,1369,1557,129-131-461-463,29.97,640,480,train
Dn0mNZmAh2k,1710,1900,35-101-401-467,29.97,640,480,train
Dn0mNZmAh2k,2085,2388,42-94-417-469,29.97,640,480,train
Dn0mNZmAh2k,3180,3377,108-134-445-471,29.97,640,480,train
Dn0mNZmAh2k,4815,5175,180-70-573-463,29.97,640,480,train
Dn0mNZmAh2k,6165,6379,127-109-498-480,29.97,640,480,train
Qlbxd48tQg8,0,263,346-187-617-458,29.97,854,480,train
Qlbxd48tQg8,358,810,251-79-624-452,29.97,854,480,train
Qlbxd48tQg8,941,1096,290-97-651-458,29.97,854,480,train
Qlbxd48tQg8,2777,3009,269-65-659-455,29.97,854,480,train
Qlbxd48tQg8,4052,4190,231-74-606-449,29.97,854,480,train
Qlbxd48tQg8,5244,5418,258-76-634-452,29.97,854,480,train
Qlbxd48tQg8,7183,7329,216-50-616-450,29.97,854,480,train
Qlbxd48tQg8,7329,8248,228-28-657-457,29.97,854,480,train
Qlbxd48tQg8,8248,9272,213-0-670-457,29.97,854,480,train
Qlbxd48tQg8,9429,9934,265-107-609-451,29.97,854,480,train
Qlbxd48tQg8,10527,10655,252-100-599-447,29.97,854,480,train
Qlbxd48tQg8,11211,11405,295-79-661-445,29.97,854,480,train
Qlbxd48tQg8,11431,11616,288-77-659-448,29.97,854,480,train
_XRyc2kiTlM,3844,4023,458-54-1402-998,25.0,1920,1080,train
3vcpk5cMDzo,170,1194,582-46-1565-1029,25.0,1920,1080,train
3vcpk5cMDzo,1410,1943,721-289-1409-977,25.0,1920,1080,train
3vcpk5cMDzo,1943,2157,576-332-1221-977,25.0,1920,1080,train
3vcpk5cMDzo,2226,2483,726-321-1430-1025,25.0,1920,1080,train
3vcpk5cMDzo,2678,3073,530-273-1311-1054,25.0,1920,1080,train
3vcpk5cMDzo,3119,3261,754-384-1303-933,25.0,1920,1080,train
3vcpk5cMDzo,3406,3710,723-377-1269-923,25.0,1920,1080,train
3vcpk5cMDzo,3755,3911,516-331-1159-974,25.0,1920,1080,train
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gitextract_zo48uj8s/ ├── .dockerignore ├── .gitignore ├── Dockerfile ├── LICENSE.md ├── README.md ├── animate.py ├── augmentation.py ├── config/ │ ├── bair-256.yaml │ ├── fashion-256.yaml │ ├── mgif-256.yaml │ ├── nemo-256.yaml │ ├── taichi-256.yaml │ ├── taichi-adv-256.yaml │ ├── vox-256.yaml │ └── vox-adv-256.yaml ├── crop-video.py ├── data/ │ ├── bair256.csv │ ├── taichi-loading/ │ │ ├── README.md │ │ ├── load_videos.py │ │ └── taichi-metadata.csv │ └── taichi256.csv ├── demo.ipynb ├── demo.py ├── demo_jupyter.ipynb ├── frames_dataset.py ├── logger.py ├── modules/ │ ├── dense_motion.py │ ├── discriminator.py │ ├── generator.py │ ├── keypoint_detector.py │ ├── model.py │ └── util.py ├── old_demo.ipynb ├── reconstruction.py ├── requirements.txt ├── run.py ├── sync_batchnorm/ │ ├── __init__.py │ ├── batchnorm.py │ ├── comm.py │ ├── replicate.py │ └── unittest.py └── train.py
SYMBOL INDEX (171 symbols across 19 files)
FILE: animate.py
function normalize_kp (line 16) | def normalize_kp(kp_source, kp_driving, kp_driving_initial, adapt_moveme...
function animate (line 39) | def animate(config, generator, kp_detector, checkpoint, log_dir, dataset):
FILE: augmentation.py
function crop_clip (line 20) | def crop_clip(clip, min_h, min_w, h, w):
function pad_clip (line 34) | def pad_clip(clip, h, w):
function resize_clip (line 42) | def resize_clip(clip, size, interpolation='bilinear'):
function get_resize_sizes (line 81) | def get_resize_sizes(im_h, im_w, size):
class RandomFlip (line 91) | class RandomFlip(object):
method __init__ (line 92) | def __init__(self, time_flip=False, horizontal_flip=False):
method __call__ (line 96) | def __call__(self, clip):
class RandomResize (line 105) | class RandomResize(object):
method __init__ (line 115) | def __init__(self, ratio=(3. / 4., 4. / 3.), interpolation='nearest'):
method __call__ (line 119) | def __call__(self, clip):
class RandomCrop (line 136) | class RandomCrop(object):
method __init__ (line 143) | def __init__(self, size):
method __call__ (line 149) | def __call__(self, clip):
class RandomRotation (line 175) | class RandomRotation(object):
method __init__ (line 184) | def __init__(self, degrees):
method __call__ (line 197) | def __call__(self, clip):
class ColorJitter (line 217) | class ColorJitter(object):
method __init__ (line 230) | def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
method get_params (line 236) | def get_params(self, brightness, contrast, saturation, hue):
method __call__ (line 261) | def __call__(self, clip):
class AllAugmentationTransform (line 323) | class AllAugmentationTransform:
method __init__ (line 324) | def __init__(self, resize_param=None, rotation_param=None, flip_param=...
method __call__ (line 342) | def __call__(self, clip):
FILE: crop-video.py
function extract_bbox (line 14) | def extract_bbox(frame, fa):
function bb_intersection_over_union (line 29) | def bb_intersection_over_union(boxA, boxB):
function join (line 41) | def join(tube_bbox, bbox):
function compute_bbox (line 49) | def compute_bbox(start, end, fps, tube_bbox, frame_shape, inp, image_sha...
function compute_bbox_trajectories (line 75) | def compute_bbox_trajectories(trajectories, fps, frame_shape, args):
function process_video (line 84) | def process_video(args):
FILE: data/taichi-loading/load_videos.py
function save (line 20) | def save(path, frames, format):
function download (line 36) | def download(video_id, args):
function run (line 45) | def run(data):
FILE: demo.py
function load_checkpoints (line 25) | def load_checkpoints(config_path, checkpoint_path, cpu=False):
function make_animation (line 58) | def make_animation(source_image, driving_video, generator, kp_detector, ...
function find_best_frame (line 81) | def find_best_frame(source, driving, cpu=False):
FILE: frames_dataset.py
function read_video (line 14) | def read_video(name, frame_shape):
class FramesDataset (line 55) | class FramesDataset(Dataset):
method __init__ (line 63) | def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=Fa...
method __len__ (line 97) | def __len__(self):
method __getitem__ (line 100) | def __getitem__(self, idx):
class DatasetRepeater (line 141) | class DatasetRepeater(Dataset):
method __init__ (line 146) | def __init__(self, dataset, num_repeats=100):
method __len__ (line 150) | def __len__(self):
method __getitem__ (line 153) | def __getitem__(self, idx):
class PairedDataset (line 157) | class PairedDataset(Dataset):
method __init__ (line 162) | def __init__(self, initial_dataset, number_of_pairs, seed=0):
method __len__ (line 187) | def __len__(self):
method __getitem__ (line 190) | def __getitem__(self, idx):
FILE: logger.py
class Logger (line 13) | class Logger:
method __init__ (line 14) | def __init__(self, log_dir, checkpoint_freq=100, visualizer_params=Non...
method log_scores (line 29) | def log_scores(self, loss_names):
method visualize_rec (line 39) | def visualize_rec(self, inp, out):
method save_cpk (line 43) | def save_cpk(self, emergent=False):
method load_cpk (line 51) | def load_cpk(checkpoint_path, generator=None, discriminator=None, kp_d...
method __enter__ (line 79) | def __enter__(self):
method __exit__ (line 82) | def __exit__(self, exc_type, exc_val, exc_tb):
method log_iter (line 87) | def log_iter(self, losses):
method log_epoch (line 93) | def log_epoch(self, epoch, models, inp, out):
class Visualizer (line 102) | class Visualizer:
method __init__ (line 103) | def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbo...
method draw_image_with_kp (line 108) | def draw_image_with_kp(self, image, kp_array):
method create_image_column_with_kp (line 118) | def create_image_column_with_kp(self, images, kp):
method create_image_column (line 122) | def create_image_column(self, images):
method create_image_grid (line 128) | def create_image_grid(self, *args):
method visualize (line 137) | def visualize(self, driving, source, out):
FILE: modules/dense_motion.py
class DenseMotionNetwork (line 7) | class DenseMotionNetwork(nn.Module):
method __init__ (line 12) | def __init__(self, block_expansion, num_blocks, max_features, num_kp, ...
method create_heatmap_representations (line 32) | def create_heatmap_representations(self, source_image, kp_driving, kp_...
method create_sparse_motions (line 47) | def create_sparse_motions(self, source_image, kp_driving, kp_source):
method create_deformed_source_image (line 69) | def create_deformed_source_image(self, source_image, sparse_motions):
method forward (line 81) | def forward(self, source_image, kp_driving, kp_source):
FILE: modules/discriminator.py
class DownBlock2d (line 7) | class DownBlock2d(nn.Module):
method __init__ (line 12) | def __init__(self, in_features, out_features, norm=False, kernel_size=...
method forward (line 25) | def forward(self, x):
class Discriminator (line 36) | class Discriminator(nn.Module):
method __init__ (line 41) | def __init__(self, num_channels=3, block_expansion=64, num_blocks=4, m...
method forward (line 59) | def forward(self, x, kp=None):
class MultiScaleDiscriminator (line 74) | class MultiScaleDiscriminator(nn.Module):
method __init__ (line 79) | def __init__(self, scales=(), **kwargs):
method forward (line 87) | def forward(self, x, kp=None):
FILE: modules/generator.py
class OcclusionAwareGenerator (line 8) | class OcclusionAwareGenerator(nn.Module):
method __init__ (line 14) | def __init__(self, num_channels, num_kp, block_expansion, max_features...
method deform_input (line 50) | def deform_input(self, inp, deformation):
method forward (line 59) | def forward(self, source_image, kp_driving, kp_source):
FILE: modules/keypoint_detector.py
class KPDetector (line 7) | class KPDetector(nn.Module):
method __init__ (line 12) | def __init__(self, block_expansion, num_kp, num_channels, max_features,
method gaussian2kp (line 37) | def gaussian2kp(self, heatmap):
method forward (line 49) | def forward(self, x):
FILE: modules/model.py
class Vgg19 (line 10) | class Vgg19(torch.nn.Module):
method __init__ (line 14) | def __init__(self, requires_grad=False):
method forward (line 42) | def forward(self, X):
class ImagePyramide (line 53) | class ImagePyramide(torch.nn.Module):
method __init__ (line 57) | def __init__(self, scales, num_channels):
method forward (line 64) | def forward(self, x):
class Transform (line 71) | class Transform:
method __init__ (line 75) | def __init__(self, bs, **kwargs):
method transform_frame (line 89) | def transform_frame(self, frame):
method warp_coordinates (line 95) | def warp_coordinates(self, coordinates):
method jacobian (line 115) | def jacobian(self, coordinates):
function detach_kp (line 123) | def detach_kp(kp):
class GeneratorFullModel (line 127) | class GeneratorFullModel(torch.nn.Module):
method __init__ (line 132) | def __init__(self, kp_extractor, generator, discriminator, train_params):
method forward (line 151) | def forward(self, x):
class DiscriminatorFullModel (line 225) | class DiscriminatorFullModel(torch.nn.Module):
method __init__ (line 230) | def __init__(self, kp_extractor, generator, discriminator, train_params):
method forward (line 243) | def forward(self, x, generated):
FILE: modules/util.py
function kp2gaussian (line 9) | def kp2gaussian(kp, spatial_size, kp_variance):
function make_coordinate_grid (line 33) | def make_coordinate_grid(spatial_size, type):
class ResBlock2d (line 52) | class ResBlock2d(nn.Module):
method __init__ (line 57) | def __init__(self, in_features, kernel_size, padding):
method forward (line 66) | def forward(self, x):
class UpBlock2d (line 77) | class UpBlock2d(nn.Module):
method __init__ (line 82) | def __init__(self, in_features, out_features, kernel_size=3, padding=1...
method forward (line 89) | def forward(self, x):
class DownBlock2d (line 97) | class DownBlock2d(nn.Module):
method __init__ (line 102) | def __init__(self, in_features, out_features, kernel_size=3, padding=1...
method forward (line 109) | def forward(self, x):
class SameBlock2d (line 117) | class SameBlock2d(nn.Module):
method __init__ (line 122) | def __init__(self, in_features, out_features, groups=1, kernel_size=3,...
method forward (line 128) | def forward(self, x):
class Encoder (line 135) | class Encoder(nn.Module):
method __init__ (line 140) | def __init__(self, block_expansion, in_features, num_blocks=3, max_fea...
method forward (line 150) | def forward(self, x):
class Decoder (line 157) | class Decoder(nn.Module):
method __init__ (line 162) | def __init__(self, block_expansion, in_features, num_blocks=3, max_fea...
method forward (line 175) | def forward(self, x):
class Hourglass (line 184) | class Hourglass(nn.Module):
method __init__ (line 189) | def __init__(self, block_expansion, in_features, num_blocks=3, max_fea...
method forward (line 195) | def forward(self, x):
class AntiAliasInterpolation2d (line 199) | class AntiAliasInterpolation2d(nn.Module):
method __init__ (line 203) | def __init__(self, channels, scale):
method forward (line 237) | def forward(self, input):
FILE: reconstruction.py
function reconstruction (line 11) | def reconstruction(config, generator, kp_detector, checkpoint, log_dir, ...
FILE: sync_batchnorm/batchnorm.py
function _sum_ft (line 24) | def _sum_ft(tensor):
function _unsqueeze_ft (line 29) | def _unsqueeze_ft(tensor):
class _SynchronizedBatchNorm (line 38) | class _SynchronizedBatchNorm(_BatchNorm):
method __init__ (line 39) | def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True):
method forward (line 48) | def forward(self, input):
method __data_parallel_replicate__ (line 80) | def __data_parallel_replicate__(self, ctx, copy_id):
method _data_parallel_master (line 90) | def _data_parallel_master(self, intermediates):
method _compute_mean_std (line 113) | def _compute_mean_std(self, sum_, ssum, size):
class SynchronizedBatchNorm1d (line 128) | class SynchronizedBatchNorm1d(_SynchronizedBatchNorm):
method _check_input_dim (line 184) | def _check_input_dim(self, input):
class SynchronizedBatchNorm2d (line 191) | class SynchronizedBatchNorm2d(_SynchronizedBatchNorm):
method _check_input_dim (line 247) | def _check_input_dim(self, input):
class SynchronizedBatchNorm3d (line 254) | class SynchronizedBatchNorm3d(_SynchronizedBatchNorm):
method _check_input_dim (line 311) | def _check_input_dim(self, input):
FILE: sync_batchnorm/comm.py
class FutureResult (line 18) | class FutureResult(object):
method __init__ (line 21) | def __init__(self):
method put (line 26) | def put(self, result):
method get (line 32) | def get(self):
class SlavePipe (line 46) | class SlavePipe(_SlavePipeBase):
method run_slave (line 49) | def run_slave(self, msg):
class SyncMaster (line 56) | class SyncMaster(object):
method __init__ (line 67) | def __init__(self, master_callback):
method __getstate__ (line 78) | def __getstate__(self):
method __setstate__ (line 81) | def __setstate__(self, state):
method register_slave (line 84) | def register_slave(self, identifier):
method run_master (line 102) | def run_master(self, master_msg):
method nr_slaves (line 136) | def nr_slaves(self):
FILE: sync_batchnorm/replicate.py
class CallbackContext (line 23) | class CallbackContext(object):
function execute_replication_callbacks (line 27) | def execute_replication_callbacks(modules):
class DataParallelWithCallback (line 50) | class DataParallelWithCallback(DataParallel):
method replicate (line 64) | def replicate(self, module, device_ids):
function patch_replication_callback (line 70) | def patch_replication_callback(data_parallel):
FILE: sync_batchnorm/unittest.py
function as_numpy (line 17) | def as_numpy(v):
class TorchTestCase (line 23) | class TorchTestCase(unittest.TestCase):
method assertTensorClose (line 24) | def assertTensorClose(self, a, b, atol=1e-3, rtol=1e-3):
FILE: train.py
function train (line 16) | def train(config, generator, discriminator, kp_detector, checkpoint, log...
Condensed preview — 42 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (1,613K chars).
[
{
"path": ".dockerignore",
"chars": 23,
"preview": "/venv\n.git\n__pycache__\n"
},
{
"path": ".gitignore",
"chars": 27,
"preview": "/.vscode\n__pycache__\n/venv\n"
},
{
"path": "Dockerfile",
"chars": 400,
"preview": "FROM nvcr.io/nvidia/pytorch:21.02-py3\n\nRUN DEBIAN_FRONTEND=noninteractive apt-get -qq update \\\n && DEBIAN_FRONTEND=nonin"
},
{
"path": "LICENSE.md",
"chars": 1081,
"preview": "MIT License\n\nCopyright (c) 2019-2023 Aliaksandr Siarohin\n\nPermission is hereby granted, free of charge, to any person ob"
},
{
"path": "README.md",
"chars": 9361,
"preview": "<b>!!! Check out our new [paper](https://arxiv.org/pdf/2104.11280.pdf) and [framework](https://github.com/snap-research/"
},
{
"path": "animate.py",
"chars": 4061,
"preview": "import os\nfrom tqdm import tqdm\n\nimport torch\nfrom torch.utils.data import DataLoader\n\nfrom frames_dataset import Paired"
},
{
"path": "augmentation.py",
"chars": 12540,
"preview": "\"\"\"\nCode from https://github.com/hassony2/torch_videovision\n\"\"\"\n\nimport numbers\n\nimport random\nimport numpy as np\nimport"
},
{
"path": "config/bair-256.yaml",
"chars": 1706,
"preview": "dataset_params:\n root_dir: data/bair\n frame_shape: [256, 256, 3]\n id_sampling: False\n augmentation_params:\n flip_"
},
{
"path": "config/fashion-256.yaml",
"chars": 1637,
"preview": "dataset_params:\n root_dir: data/fashion-png\n frame_shape: [256, 256, 3]\n id_sampling: False\n augmentation_params:\n "
},
{
"path": "config/mgif-256.yaml",
"chars": 1764,
"preview": "dataset_params:\n root_dir: data/moving-gif\n frame_shape: [256, 256, 3]\n id_sampling: False\n augmentation_params:\n "
},
{
"path": "config/nemo-256.yaml",
"chars": 1614,
"preview": "dataset_params:\n root_dir: data/nemo-png \n frame_shape: [256, 256, 3]\n id_sampling: False\n augmentation_params:\n "
},
{
"path": "config/taichi-256.yaml",
"chars": 6108,
"preview": "# Dataset parameters\n# Each dataset should contain 2 folders train and test\n# Each video can be represented as:\n# - an"
},
{
"path": "config/taichi-adv-256.yaml",
"chars": 5646,
"preview": "# Dataset parameters\ndataset_params:\n # Path to data, data can be stored in several formats: .mp4 or .gif videos, stack"
},
{
"path": "config/vox-256.yaml",
"chars": 1740,
"preview": "dataset_params:\n root_dir: data/vox-png\n frame_shape: [256, 256, 3]\n id_sampling: True\n pairs_list: data/vox256.csv\n"
},
{
"path": "config/vox-adv-256.yaml",
"chars": 1739,
"preview": "dataset_params:\n root_dir: data/vox-png\n frame_shape: [256, 256, 3]\n id_sampling: True\n pairs_list: data/vox256.csv\n"
},
{
"path": "crop-video.py",
"chars": 5876,
"preview": "import face_alignment\nimport skimage.io\nimport numpy\nfrom argparse import ArgumentParser\nfrom skimage import img_as_ubyt"
},
{
"path": "data/bair256.csv",
"chars": 1330,
"preview": "distance,source,driving,frame\n0,000054.mp4,000048.mp4,0\n0,000050.mp4,000063.mp4,0\n0,000073.mp4,000007.mp4,0\n0,000021.mp4"
},
{
"path": "data/taichi-loading/README.md",
"chars": 831,
"preview": "# TaiChi dataset\n\nThe scripst for loading the TaiChi dataset. \n\nWe provide only the id of the corresponding video and th"
},
{
"path": "data/taichi-loading/load_videos.py",
"chars": 4744,
"preview": "import numpy as np\nimport pandas as pd\nimport imageio\nimport os\nimport subprocess\nfrom multiprocessing import Pool\nfrom "
},
{
"path": "data/taichi-loading/taichi-metadata.csv",
"chars": 196418,
"preview": "video_id,start,end,bbox,fps,width,height,partition\nu6rJiF-gPx8,80,300,118-82-508-472,29.97,640,480,train\nu6rJiF-gPx8,575"
},
{
"path": "data/taichi256.csv",
"chars": 4051,
"preview": "distance,source,driving,frame\n3.54437869822485,ab28GAufK8o#000261#000596.mp4,aDyyTMUBoLE#000164#000351.mp4,0\n2.863905325"
},
{
"path": "demo.ipynb",
"chars": 22220,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"first-order-model-demo\",\n \""
},
{
"path": "demo.py",
"chars": 7462,
"preview": "import sys\nimport yaml\nfrom argparse import ArgumentParser\nfrom tqdm.auto import tqdm\n\nimport imageio\nimport numpy as np"
},
{
"path": "demo_jupyter.ipynb",
"chars": 28316,
"preview": "{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"metadata\": {\n \"colab_type\": \"text\",\n \"id\": \"view-in-github\"\n }"
},
{
"path": "frames_dataset.py",
"chars": 7027,
"preview": "import os\nfrom skimage import io, img_as_float32\nfrom skimage.color import gray2rgb\nfrom sklearn.model_selection import "
},
{
"path": "logger.py",
"chars": 8363,
"preview": "import numpy as np\nimport torch\nimport torch.nn.functional as F\nimport imageio\n\nimport os\nfrom skimage.draw import disk\n"
},
{
"path": "modules/dense_motion.py",
"chars": 5005,
"preview": "from torch import nn\nimport torch.nn.functional as F\nimport torch\nfrom modules.util import Hourglass, AntiAliasInterpola"
},
{
"path": "modules/discriminator.py",
"chars": 3156,
"preview": "from torch import nn\nimport torch.nn.functional as F\nfrom modules.util import kp2gaussian\nimport torch\n\n\nclass DownBlock"
},
{
"path": "modules/generator.py",
"chars": 4564,
"preview": "import torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom modules.util import ResBlock2d, SameBlock2d, UpBl"
},
{
"path": "modules/keypoint_detector.py",
"chars": 2962,
"preview": "from torch import nn\nimport torch\nimport torch.nn.functional as F\nfrom modules.util import Hourglass, make_coordinate_gr"
},
{
"path": "modules/model.py",
"chars": 11338,
"preview": "from torch import nn\nimport torch\nimport torch.nn.functional as F\nfrom modules.util import AntiAliasInterpolation2d, mak"
},
{
"path": "modules/util.py",
"chars": 7904,
"preview": "from torch import nn\n\nimport torch.nn.functional as F\nimport torch\n\nfrom sync_batchnorm import SynchronizedBatchNorm2d a"
},
{
"path": "old_demo.ipynb",
"chars": 1143822,
"preview": "{\n \"nbformat\": 4,\n \"nbformat_minor\": 0,\n \"metadata\": {\n \"colab\": {\n \"name\": \"first-order-model-demo.ipynb\",\n "
},
{
"path": "reconstruction.py",
"chars": 2772,
"preview": "import os\nfrom tqdm import tqdm\nimport torch\nfrom torch.utils.data import DataLoader\nfrom logger import Logger, Visualiz"
},
{
"path": "requirements.txt",
"chars": 256,
"preview": "ffmpeg-python==0.2.0\nimageio==2.22.0\nimageio-ffmpeg==0.4.7\nmatplotlib==3.6.0\nnumpy==1.23.3\npandas==1.5.0\npython-dateutil"
},
{
"path": "run.py",
"chars": 3299,
"preview": "import matplotlib\n\nmatplotlib.use('Agg')\n\nimport os, sys\nimport yaml\nfrom argparse import ArgumentParser\nfrom time impor"
},
{
"path": "sync_batchnorm/__init__.py",
"chars": 449,
"preview": "# -*- coding: utf-8 -*-\n# File : __init__.py\n# Author : Jiayuan Mao\n# Email : maojiayuan@gmail.com\n# Date : 27/01/2"
},
{
"path": "sync_batchnorm/batchnorm.py",
"chars": 12973,
"preview": "# -*- coding: utf-8 -*-\n# File : batchnorm.py\n# Author : Jiayuan Mao\n# Email : maojiayuan@gmail.com\n# Date : 27/01/"
},
{
"path": "sync_batchnorm/comm.py",
"chars": 4449,
"preview": "# -*- coding: utf-8 -*-\n# File : comm.py\n# Author : Jiayuan Mao\n# Email : maojiayuan@gmail.com\n# Date : 27/01/2018\n"
},
{
"path": "sync_batchnorm/replicate.py",
"chars": 3226,
"preview": "# -*- coding: utf-8 -*-\n# File : replicate.py\n# Author : Jiayuan Mao\n# Email : maojiayuan@gmail.com\n# Date : 27/01/"
},
{
"path": "sync_batchnorm/unittest.py",
"chars": 835,
"preview": "# -*- coding: utf-8 -*-\n# File : unittest.py\n# Author : Jiayuan Mao\n# Email : maojiayuan@gmail.com\n# Date : 27/01/2"
},
{
"path": "train.py",
"chars": 4479,
"preview": "from tqdm import trange\nimport torch\n\nfrom torch.utils.data import DataLoader\n\nfrom logger import Logger\nfrom modules.mo"
}
]
About this extraction
This page contains the full source code of the AliaksandrSiarohin/first-order-model GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 42 files (1.5 MB), approximately 825.6k tokens, and a symbol index with 171 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.