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Repository: LeeDongYeun/FixNoise
Branch: main
Commit: ce84b0cd4e81
Files: 53
Total size: 10.9 MB

Directory structure:
gitextract_btpqzbte/

├── .gitignore
├── Dockerfile
├── LICENSE.txt
├── README.md
├── calc_metrics.py
├── dataset_resize.py
├── demo.ipynb
├── demo_colab.ipynb
├── dnnlib/
│   ├── __init__.py
│   └── util.py
├── docker_run.sh
├── generate.py
├── legacy.py
├── metrics/
│   ├── __init__.py
│   ├── frechet_inception_distance.py
│   ├── inception_score.py
│   ├── kernel_inception_distance.py
│   ├── lpips.py
│   ├── metric_main.py
│   ├── metric_utils.py
│   ├── perceptual_path_length.py
│   └── precision_recall.py
├── projector_z.py
├── scripts/
│   ├── aahq/
│   │   └── fm_05e-2.sh
│   ├── metfaces/
│   │   └── fm_05e-2.sh
│   └── wikiart/
│       └── fm_05e-2.sh
├── torch_utils/
│   ├── __init__.py
│   ├── custom_ops.py
│   ├── misc.py
│   ├── ops/
│   │   ├── __init__.py
│   │   ├── bias_act.cpp
│   │   ├── bias_act.cu
│   │   ├── bias_act.h
│   │   ├── bias_act.py
│   │   ├── conv2d_gradfix.py
│   │   ├── conv2d_resample.py
│   │   ├── fma.py
│   │   ├── grid_sample_gradfix.py
│   │   ├── upfirdn2d.cpp
│   │   ├── upfirdn2d.cu
│   │   ├── upfirdn2d.h
│   │   └── upfirdn2d.py
│   ├── persistence.py
│   └── training_stats.py
├── train.py
├── training/
│   ├── __init__.py
│   ├── augment.py
│   ├── dataset.py
│   ├── loss.py
│   ├── networks.py
│   └── training_loop.py
└── utils/
    ├── dataset_tool.py
    └── style_mixing.py

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FILE CONTENTS
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FILE: .gitignore
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__pycache__/
.cache/
logs/
_scripts/

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FILE: Dockerfile
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# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

FROM nvcr.io/nvidia/pytorch:20.12-py3

ENV PYTHONDONTWRITEBYTECODE 1
ENV PYTHONUNBUFFERED 1

RUN pip install imageio-ffmpeg==0.4.3 pyspng==0.1.0
RUN pip install lpips

WORKDIR /workspace

# Unset TORCH_CUDA_ARCH_LIST and exec.  This makes pytorch run-time
# extension builds significantly faster as we only compile for the
# currently active GPU configuration.
RUN (printf '#!/bin/bash\nunset TORCH_CUDA_ARCH_LIST\nexec \"$@\"\n' >> /entry.sh) && chmod a+x /entry.sh
ENTRYPOINT ["/entry.sh"]


================================================
FILE: LICENSE.txt
================================================
Copyright (c) 2021, NVIDIA Corporation. All rights reserved.


NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator Augmentation (ADA)


=======================================================================

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the Software that are made available under this License.

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"distribution" have the meaning as provided under U.S. copyright law;
provided, however, that for the purposes of this License, derivative
works shall not include works that remain separable from, or merely
link (or bind by name) to the interfaces of, the Work.

Works, including the Software, are "made available" under this License
by including in or with the Work either (a) a copyright notice
referencing the applicability of this License to the Work, or (b) a
copy of this License.

2. License Grants

    2.1 Copyright Grant. Subject to the terms and conditions of this
    License, each Licensor grants to you a perpetual, worldwide,
    non-exclusive, royalty-free, copyright license to reproduce,
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    works in any form.

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    3.2 Derivative Works. You may specify that additional or different
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4. Disclaimer of Warranty.

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THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
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=======================================================================


================================================
FILE: README.md
================================================
# Fix the Noise: Disentangling Source Feature for Controllable Domain Translation</sub>

[![arXiv](https://img.shields.io/badge/arXiv-2303.11545-b31b1b.svg)](https://arxiv.org/abs/2303.11545)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LeeDongYeun/FixNoise/blob/main/demo_colab.ipynb)

![Teaser image](./docs/figure_1.png)
**Fix the Noise: Disentangling Source Feature for Controllable Domain Translation**<br>
Dongyeun Lee, Jae Young Lee, Doyeon Kim, Jaehyun Choi, Jaejun Yoo, Junmo Kim<br>
https://arxiv.org/abs/2303.11545

>**Abstract**: 
*Recent studies show strong generative performance in domain translation especially by using transfer learning techniques on the unconditional generator. However, the control between different domain features using a single model is still challenging. Existing methods often require additional models, which is computationally demanding and leads to unsatisfactory visual quality. In addition, they have restricted control steps, which prevents a smooth transition. In this paper, we propose a new approach for high-quality domain translation with better controllability. The key idea is to preserve source features within a disentangled subspace of a target feature space. This allows our method to smoothly control the degree to which it preserves source features while generating images from an entirely new domain using only a single model. Our extensive experiments show that the proposed method can produce more consistent and realistic images than previous works and maintain precise controllability over different levels of transformation.*

## Recent Updates
* **`2023-05-09`** Add several useful code for inference. For detailed usage, refer to [Inference](#Inference).

* **`2023-05-15`** Add [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/LeeDongYeun/FixNoise/blob/main/demo_colab.ipynb)

## Requirements
Our code is highly based on the official implementation of [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch). Please refer to [requirements](https://github.com/NVlabs/stylegan2-ada-pytorch#requirements) for detailed requirements.
* Python libraries:
```bash
pip install click requests tqdm pyspng ninja imageio-ffmpeg==0.4.3 lpips
```
* Docker users:
```bash
docker build --tag sg2ada:latest .
docker run --gpus all --shm-size 64g -it -v /etc/localtime:/etc/localtime:ro -v /mnt:/mnt -v /data:/data --name sg2ada sg2ada /bin/bash
```


## Pretrained Checkpoints
You can download the pre-trained checkpoints used in our paper:
| Setting                 |   Resolution  | Config   |    Description   |
| :--------------------   | :------------ | :------- | :--------------- |
| [FFHQ &rarr; MetFaces](https://drive.google.com/file/d/1Eo4T9KjkzRYdnENXgTpqIUOvaY4-SDeD/view?usp=sharing)    |    256x256    | paper256 | Trained initialized with official [pre-trained model on FFHQ 256](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/) from Pytorch implementation of [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch). |
| [FFHQ &rarr; AAHQ](https://drive.google.com/file/d/1GzM3icWaSOSGcKfYoidjEaloqc_MyAxX/view?usp=sharing)        |    256x256    | paper256 | Trained initialized with official [pre-trained model on FFHQ 256](https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/) from Pytorch implementation of [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch). |
| [Church &rarr; Cityscape](https://drive.google.com/file/d/1YHa_g5xC_VM5MbHsr3VSfco1_PX1sRkA/view?usp=sharing) |    256x256    | stylegan2| Trained initialized with official [pre-trained model on LSUN Church config-f](https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/) from Tensorflow implementation of [stylegan2](https://github.com/NVlabs/stylegan2). |

## Datasets
We provide official dataset download pages and our processing code for reproducibility. You could alse use official processing code in [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch#preparing-datasets). However, doing so does not guarantee reported performance.

**MetFaces**: Download the [MetFaces dataset](https://github.com/NVlabs/metfaces-dataset) and unzip it.
```bash
# Resize MetFaces
python dataset_resize.py --source data/metfaces/images --dest data/metfaces/images256x256
```
**AAHQ**: Download the [AAHQ dataset](https://github.com/onion-liu/aahq-dataset) and process it following original instruction.
```bash
# Resize AAHQ
python dataset_resize.py --source data/aahq-dataset/aligned --dest data/aahq-dataset/images256x256
```
**Wikiart Cityscape**: Download cityscape from [Wikiart](https://www.kaggle.com/datasets/ipythonx/wikiart-gangogh-creating-art-gan) and unzip it.

```bash
# Resize Wikiart Cityscape
python dataset_resize.py --source data/wikiart_cityscape/images --dest data/wikiart_cityscape/images256x256
```

## Train new networks using FixNoise
Using FixNoise, base command for training stylegan2-ada network as follows:

**FFHQ &rarr; MetFaces**
```bash
python train.py --outdir=${OUTDIR} --data=${DATADIR} --cfg=paper256 --resume=ffhq256 --fm=0.05
```
**FFHQ &rarr; AAHQ**
```bash
python train.py --outdir=${OUTDIR} --data=${DATADIR} --cfg=paper256 --resume=ffhq256 --fm=0.05
```
**Church &rarr; Cityscape**
```bash
python train.py --outdir=${OUTDIR} --data=${DATADIR} --cfg=stylegan2 --resume=church256 --fm=0.05
```
Additionally, we provide detailed [training scripts](./scripts/) used in our experiments.

## Inference
### Generate interpolated images
To generate interpolated images according to different noise, run:
```bash
# Generate MetFaces images without truncation
python generate.py --cfg=paper256 --outdir=out --trunc_psi=1 --seeds=865-1000 \\
    --network=pretrained/metfaces-fm0.05-001612.pkl

# Generate MetFaces images with truncation
python generate.py --cfg=paper256 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 --seeds=865-1000 \\
    --network=pretrained/metfaces-fm0.05-001612.pkl

# Generate AAHQ images with truncation
python generate.py --cfg=paper256 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 --seeds=865-1000 \\
    --network=pretrained/aahq-fm0.05-010886.pkl

# Generate Wikiart images with truncation
python generate.py --cfg=stylegan2 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 --seeds=865-1000 \\
    --network=pretrained/wikiart-fm0.05-004032.pkl
```
You can change interpolation steps by modifying `--interp-step`.

### Projecting images to latent space
To find the matching latent code for a given image file, run:
```bash
python projector_z.py --outdir=${OUTDIR} --target_dir=${DATADIR} \
    --https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl
```
We modify [projector.py](https://github.com/NVlabs/stylegan2-ada-pytorch/blob/main/projector.py) to project image to z space of StyleGAN2. To use multiple gpus, add `--gpus` arguments. You can render the resulting latent vector by specifying `--projected-z-dir` for `generate.py`.
```bash
# Render an image from projected Z
python generate.py --cfg=paper256 --outdir=out --trunc_psi=0.7 --trunc_cutoff=8 \\
    --projected-z-dir=./projected --network=pretrained/aahq-fm0.05-010886.pkl
```

## Demo
We provide noise interpolation example code in [jupyter notebook](./demo.ipynb).

#### FFHQ &rarr; MetFaces
<img src="./docs/interpolation_video/metfaces/noise_interpolation_metfaces00.gif" width="45%"> &nbsp; <img src="./docs/interpolation_video/metfaces/noise_interpolation_metfaces01.gif" width="45%"> \
<img src="./docs/interpolation_video/metfaces/noise_interpolation_metfaces02.gif" width="45%"> &nbsp; <img src="./docs/interpolation_video/metfaces/noise_interpolation_metfaces03.gif" width="45%"> 

#### FFHQ &rarr; AAHQ
<img src="./docs/interpolation_video/aahq/noise_interpolation_aahq00.gif" width="45%"> &nbsp; <img src="./docs/interpolation_video/aahq/noise_interpolation_aahq01.gif" width="45%"> \
<img src="./docs/interpolation_video/aahq/noise_interpolation_aahq02.gif" width="45%"> &nbsp; <img src="./docs/interpolation_video/aahq/noise_interpolation_aahq03.gif" width="45%"> 

#### Church &rarr; Cityscape
<img src="./docs/interpolation_video/cityscape/noise_interpolation_cityscape00.gif" width="45%"> &nbsp; <img src="./docs/interpolation_video/cityscape/noise_interpolation_cityscape01.gif" width="45%"> \
<img src="./docs/interpolation_video/cityscape/noise_interpolation_cityscape02.gif" width="45%"> &nbsp; <img src="./docs/interpolation_video/cityscape/noise_interpolation_cityscape03.gif" width="45%"> 

## Citation
```
@InProceedings{Lee_2023_CVPR,
    author    = {Lee, Dongyeun and Lee, Jae Young and Kim, Doyeon and Choi, Jaehyun and Yoo, Jaejun and Kim, Junmo},
    title     = {Fix the Noise: Disentangling Source Feature for Controllable Domain Translation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {14224-14234}
}
```

## License
The majority of FixNoise is licensed under [CC-BY-NC](https://creativecommons.org/licenses/by-nc/4.0/), however, portions of this project are available under a separate license terms: all codes used or modified from [stylegan2-ada-pytorch](https://github.com/NVlabs/stylegan2-ada-pytorch) is under the [Nvidia Source Code License](https://nvlabs.github.io/stylegan2-ada-pytorch/license.html).


================================================
FILE: calc_metrics.py
================================================
# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto.  Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.

"""Calculate quality metrics for previous training run or pretrained network pickle."""

import os
import click
import json
import tempfile
import copy
import torch
import dnnlib

import legacy
from metrics import metric_main
from metrics import metric_utils
from torch_utils import training_stats
from torch_utils import custom_ops
from torch_utils import misc

#----------------------------------------------------------------------------

def subprocess_fn(rank, args, temp_dir):
    dnnlib.util.Logger(should_flush=True)

    # Init torch.distributed.
    if args.num_gpus > 1:
        init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
        if os.name == 'nt':
            init_method = 'file:///' + init_file.replace('\\', '/')
            torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus)
        else:
            init_method = f'file://{init_file}'
            torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus)

    # Init torch_utils.
    sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None
    training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
    if rank != 0 or not args.verbose:
        custom_ops.verbosity = 'none'

    # Print network summary.
    device = torch.device('cuda', rank)
    torch.backends.cudnn.benchmark = True
    torch.backends.cuda.matmul.allow_tf32 = False
    torch.backends.cudnn.allow_tf32 = False
    G = copy.deepcopy(args.G).eval().requires_grad_(False).to(device)
    if rank == 0 and args.verbose:
        z = torch.empty([1, G.z_dim], device=device)
        c = torch.empty([1, G.c_dim], device=device)
        misc.print_module_summary(G, [z, c])

    # Calculate each metric.
    for metric in args.metrics:
        if rank == 0 and args.verbose:
            print(f'Calculating {metric}...')
        progress = metric_utils.ProgressMonitor(verbose=args.verbose)
        result_dict = metric_main.calc_metric(metric=metric, G=G, dataset_kwargs=args.dataset_kwargs,
            num_gpus=args.num_gpus, rank=rank, device=device, progress=progress)
        if rank == 0:
            metric_main.report_metric(result_dict, run_dir=args.run_dir, snapshot_pkl=args.network_pkl)
        if rank == 0 and args.verbose:
            print()

    # Done.
    if rank == 0 and args.verbose:
        print('Exiting...')

#----------------------------------------------------------------------------

class CommaSeparatedList(click.ParamType):
    name = 'list'

    def convert(self, value, param, ctx):
        _ = param, ctx
        if value is None or value.lower() == 'none' or value == '':
            return []
        return value.split(',')

#----------------------------------------------------------------------------

@click.command()
@click.pass_context
@click.option('network_pkl', '--network', help='Network pickle filename or URL', metavar='PATH', required=True)
@click.option('--metrics', help='Comma-separated list or "none"', type=CommaSeparatedList(), default='fid50k_full', show_default=True)
@click.option('--data', help='Dataset to evaluate metrics against (directory or zip) [default: same as training data]', metavar='PATH')
@click.option('--mirror', help='Whether the dataset was augmented with x-flips during training [default: look up]', type=bool, metavar='BOOL')
@click.option('--gpus', help='Number of GPUs to use', type=int, default=1, metavar='INT', show_default=True)
@click.option('--verbose', help='Print optional information', type=bool, default=True, metavar='BOOL', show_default=True)

def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):
    """Calculate quality metrics for previous training run or pretrained network pickle.

    Examples:

    \b
    # Previous training run: look up options automatically, save result to JSONL file.
    python calc_metrics.py --metrics=pr50k3_full \\
        --network=~/training-runs/00000-ffhq10k-res64-auto1/network-snapshot-000000.pkl

    \b
    # Pre-trained network pickle: specify dataset explicitly, print result to stdout.
    python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \\
        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl

    Available metrics:

    \b
      ADA paper:
        fid50k_full  Frechet inception distance against the full dataset.
        kid50k_full  Kernel inception distance against the full dataset.
        pr50k3_full  Precision and recall againt the full dataset.
        is50k        Inception score for CIFAR-10.

    \b
      StyleGAN and StyleGAN2 papers:
        fid50k       Frechet inception distance against 50k real images.
        kid50k       Kernel inception distance against 50k real images.
        pr50k3       Precision and recall against 50k real images.
        ppl2_wend    Perceptual path length in W at path endpoints against full image.
        ppl_zfull    Perceptual path length in Z for full paths against cropped image.
        ppl_wfull    Perceptual path length in W for full paths against cropped image.
        ppl_zend     Perceptual path length in Z at path endpoints against cropped image.
        ppl_wend     Perceptual path length in W at path endpoints against cropped image.
    """
    dnnlib.util.Logger(should_flush=True)

    # Validate arguments.
    args = dnnlib.EasyDict(metrics=metrics, num_gpus=gpus, network_pkl=network_pkl, verbose=verbose)
    if not all(metric_main.is_valid_metric(metric) for metric in args.metrics):
        ctx.fail('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
    if not args.num_gpus >= 1:
        ctx.fail('--gpus must be at least 1')

    # Load network.
    if not dnnlib.util.is_url(network_pkl, allow_file_urls=True) and not os.path.isfile(network_pkl):
        ctx.fail('--network must point to a file or URL')
    if args.verbose:
        print(f'Loading network from "{network_pkl}"...')
    with dnnlib.util.open_url(network_pkl, verbose=args.verbose) as f:
        network_dict = legacy.load_network_pkl(f)
        args.G = network_dict['G_ema'] # subclass of torch.nn.Module

    # Initialize dataset options.
    if data is not None:
        args.dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data)
    elif network_dict['training_set_kwargs'] is not None:
        args.dataset_kwargs = dnnlib.EasyDict(network_dict['training_set_kwargs'])
    else:
        ctx.fail('Could not look up dataset options; please specify --data')

    # Finalize dataset options.
    args.dataset_kwargs.resolution = args.G.img_resolution
    args.dataset_kwargs.use_labels = (args.G.c_dim != 0)
    if mirror is not None:
        args.dataset_kwargs.xflip = mirror

    # Print dataset options.
    if args.verbose:
        print('Dataset options:')
        print(json.dumps(args.dataset_kwargs, indent=2))

    # Locate run dir.
    args.run_dir = None
    if os.path.isfile(network_pkl):
        pkl_dir = os.path.dirname(network_pkl)
        if os.path.isfile(os.path.join(pkl_dir, 'training_options.json')):
            args.run_dir = pkl_dir

    # Launch processes.
    if args.verbose:
        print('Launching processes...')
    torch.multiprocessing.set_start_method('spawn')
    with tempfile.TemporaryDirectory() as temp_dir:
        if args.num_gpus == 1:
            subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
        else:
            torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus)

#----------------------------------------------------------------------------

if __name__ == "__main__":
    calc_metrics() # pylint: disable=no-value-for-parameter

#----------------------------------------------------------------------------


================================================
FILE: dataset_resize.py
================================================
import os
import click
import numpy as np
from PIL import Image

IMG_EXTENSIONS = [
    'jpg', 'jpeg', 'png', 'ppm', 'bmp',
    'pgm', 'tif', 'tiff', 'webp',
    'JPG', 'JPEG', 'PNG', 'PPM', 'BMP',
    'PGM', 'TIF', 'TIFF', 'WEBP'
]


def is_image_file(filename):
    return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)

    
def make_dataset(dir):
    images = []
    assert os.path.isdir(dir), '%s is not a valid directory' % dir
    for root, _, fnames in sorted(os.walk(dir)):
        for fname in fnames:
            if is_image_file(fname):
                path = os.path.join(root, fname)
                images.append(path)
    return images

@click.command()
@click.pass_context
@click.option('--source', help='Directory for input dataset', required=True, metavar='PATH')
@click.option('--dest', help='Output directory for output dataset', required=True, metavar='PATH')
@click.option('--width', help='Output width', default=256, type=int)
@click.option('--height', help='Output height', default=256, type=int)
@click.option('--crop', help='Whether to crop or not', default=False, type=bool)
def resize_dataset(ctx, source, dest, width, height, crop):
    """Resize dataset
    Ex)
        --source data/metfaces/images
        --dest data/metfaces/images256x256
        
        --source data/aahq-dataset/aligned
        --dest data/aahq-dataset/images256x256

        --source data/wikiart_cityscape/images
        --dest data/wikiart_cityscape/images256x256
    """
    if not os.path.exists(dest):
        os.makedirs(dest)

    image_paths = make_dataset(source)
    print(len(image_paths))
    i = 0
    for img_path in image_paths:
        img = np.array(Image.open(img_path))
        if crop:
            crop_size = np.min(img.shape[:2])
            img = img[(img.shape[0] - crop_size) // 2 : (img.shape[0] + crop_size) // 2, (img.shape[1] - crop_size) // 2 : (img.shape[1] + crop_size) // 2]
        img = Image.fromarray(img, 'RGB')
        img = img.resize((width, height), Image.BILINEAR)

        img_name = img_path.split('/')[-1]
        new_img_path = os.path.join(dest, img_path.split('/')[-2])

        if not os.path.exists(new_img_path):
            os.makedirs(new_img_path)
        
        img.save(os.path.join(new_img_path, img_name))

        if i % 1000 == 0:
            print(i, "images done")
        i += 1

    print("Resize Done!!!")


if __name__ == '__main__':
	resize_dataset()

================================================
FILE: demo.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.utils import make_grid\n",
    "import os\n",
    "import torch\n",
    "import PIL.Image\n",
    "import imageio\n",
    "import numpy as np\n",
    "from IPython.display import Video\n",
    "\n",
    "from legacy import load_network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_blended_img(G_s, G_t, z=None, blend_weights=[0,0.25,0.5,0.75,1], truncation_psi=0.7, truncation_cutoff=8):\n",
    "    all_images = []\n",
    "    \n",
    "    if z == None:\n",
    "        z = torch.randn([1,512]).cuda()\n",
    "    assert z.shape == torch.Size([1, 512])\n",
    "    \n",
    "    c = torch.zeros(1,0).cuda()\n",
    "\n",
    "    img = G_s(z, c, truncation_psi, truncation_cutoff, noise_mode='const')\n",
    "    all_images.append(img)\n",
    "\n",
    "    for weight in blend_weights:\n",
    "        img = G_t(z, c, truncation_psi, truncation_cutoff, noise_mode='interpolate', blend_weight=weight)\n",
    "        all_images.append(img)\n",
    "\n",
    "    all_images = torch.cat(all_images)\n",
    "    images = make_grid(all_images, nrow=len(blend_weights)+1, padding=5, pad_value=0.99999)\n",
    "    images = (images.permute(1, 2, 0) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy()\n",
    "    images = PIL.Image.fromarray(images, 'RGB')\n",
    "    return images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_dim = 0\n",
    "img_resolution = 256\n",
    "img_channels = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download [pretrained weights](https://drive.google.com/drive/folders/1oJXMEHbZdU36CkRzgt_GxX2mgUgvHO_S?usp=sharing) and locate them to 'pretrained' directory."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metfaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset = 'metfaces'\n",
    "cfg = 'paper256'\n",
    "source_pkl = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl'\n",
    "target_pkl = 'pretrained/metfaces-fm0.05-001612.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AAHQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset = 'aahq'\n",
    "cfg = 'paper256'\n",
    "source_pkl = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl'\n",
    "target_pkl = 'pretrained/aahq-fm0.05-010886.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Wikiart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_dataset = 'wikiart'\n",
    "cfg = 'stylegan2'\n",
    "source_pkl = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-church-config-f.pkl'\n",
    "target_pkl = 'pretrained/wikiart-fm0.05-004032.pkl'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading networks from \"https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl\"\n",
      "Loading networks from \"pretrained/metfaces-fm0.05-001612.pkl\"\n"
     ]
    }
   ],
   "source": [
    "G_s = load_network(cfg, source_pkl, img_resolution, img_channels, c_dim).cuda()\n",
    "G_t = load_network(cfg, target_pkl, img_resolution, img_channels, c_dim).cuda()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Noise Interpolation result images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Setting up PyTorch plugin \"bias_act_plugin\"... Done.\n",
      "Setting up PyTorch plugin \"upfirdn2d_plugin\"... Done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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
Download .txt
gitextract_btpqzbte/

├── .gitignore
├── Dockerfile
├── LICENSE.txt
├── README.md
├── calc_metrics.py
├── dataset_resize.py
├── demo.ipynb
├── demo_colab.ipynb
├── dnnlib/
│   ├── __init__.py
│   └── util.py
├── docker_run.sh
├── generate.py
├── legacy.py
├── metrics/
│   ├── __init__.py
│   ├── frechet_inception_distance.py
│   ├── inception_score.py
│   ├── kernel_inception_distance.py
│   ├── lpips.py
│   ├── metric_main.py
│   ├── metric_utils.py
│   ├── perceptual_path_length.py
│   └── precision_recall.py
├── projector_z.py
├── scripts/
│   ├── aahq/
│   │   └── fm_05e-2.sh
│   ├── metfaces/
│   │   └── fm_05e-2.sh
│   └── wikiart/
│       └── fm_05e-2.sh
├── torch_utils/
│   ├── __init__.py
│   ├── custom_ops.py
│   ├── misc.py
│   ├── ops/
│   │   ├── __init__.py
│   │   ├── bias_act.cpp
│   │   ├── bias_act.cu
│   │   ├── bias_act.h
│   │   ├── bias_act.py
│   │   ├── conv2d_gradfix.py
│   │   ├── conv2d_resample.py
│   │   ├── fma.py
│   │   ├── grid_sample_gradfix.py
│   │   ├── upfirdn2d.cpp
│   │   ├── upfirdn2d.cu
│   │   ├── upfirdn2d.h
│   │   └── upfirdn2d.py
│   ├── persistence.py
│   └── training_stats.py
├── train.py
├── training/
│   ├── __init__.py
│   ├── augment.py
│   ├── dataset.py
│   ├── loss.py
│   ├── networks.py
│   └── training_loop.py
└── utils/
    ├── dataset_tool.py
    └── style_mixing.py
Download .txt
SYMBOL INDEX (313 symbols across 36 files)

FILE: calc_metrics.py
  function subprocess_fn (line 28) | def subprocess_fn(rank, args, temp_dir):
  class CommaSeparatedList (line 76) | class CommaSeparatedList(click.ParamType):
    method convert (line 79) | def convert(self, value, param, ctx):
  function calc_metrics (line 96) | def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):

FILE: dataset_resize.py
  function is_image_file (line 14) | def is_image_file(filename):
  function make_dataset (line 18) | def make_dataset(dir):
  function resize_dataset (line 35) | def resize_dataset(ctx, source, dest, width, height, crop):

FILE: dnnlib/util.py
  class EasyDict (line 40) | class EasyDict(dict):
    method __getattr__ (line 43) | def __getattr__(self, name: str) -> Any:
    method __setattr__ (line 49) | def __setattr__(self, name: str, value: Any) -> None:
    method __delattr__ (line 52) | def __delattr__(self, name: str) -> None:
  class Logger (line 56) | class Logger(object):
    method __init__ (line 59) | def __init__(self, file_name: str = None, file_mode: str = "w", should...
    method __enter__ (line 72) | def __enter__(self) -> "Logger":
    method __exit__ (line 75) | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> N...
    method write (line 78) | def write(self, text: Union[str, bytes]) -> None:
    method flush (line 93) | def flush(self) -> None:
    method close (line 100) | def close(self) -> None:
  function set_cache_dir (line 120) | def set_cache_dir(path: str) -> None:
  function make_cache_dir_path (line 124) | def make_cache_dir_path(*paths: str) -> str:
  function format_time (line 139) | def format_time(seconds: Union[int, float]) -> str:
  function ask_yes_no (line 153) | def ask_yes_no(question: str) -> bool:
  function tuple_product (line 163) | def tuple_product(t: Tuple) -> Any:
  function get_dtype_and_ctype (line 187) | def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
  function is_pickleable (line 210) | def is_pickleable(obj: Any) -> bool:
  function get_module_from_obj_name (line 222) | def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, s...
  function get_obj_from_module (line 263) | def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
  function get_obj_by_name (line 273) | def get_obj_by_name(name: str) -> Any:
  function call_func_by_name (line 279) | def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
  function construct_class_by_name (line 287) | def construct_class_by_name(*args, class_name: str = None, **kwargs) -> ...
  function get_module_dir_by_obj_name (line 292) | def get_module_dir_by_obj_name(obj_name: str) -> str:
  function is_top_level_function (line 298) | def is_top_level_function(obj: Any) -> bool:
  function get_top_level_function_name (line 303) | def get_top_level_function_name(obj: Any) -> str:
  function list_dir_recursively_with_ignore (line 315) | def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] =...
  function copy_files_and_create_dirs (line 348) | def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
  function is_url (line 364) | def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
  function open_url (line 382) | def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, ve...

FILE: generate.py
  function make_dataset (line 25) | def make_dataset(dir, extension='npz'):
  function num_range (line 37) | def num_range(s: str) -> List[int]:
  function generate_blended_img (line 49) | def generate_blended_img(G_t, z=None, blend_weights=[0,0.25,0.5,0.75,1],...
  function generate_images (line 81) | def generate_images(

FILE: legacy.py
  function load_network (line 20) | def load_network(cfg, network_pkl, img_resolution, img_channels, c_dim):
  function load_network_pkl (line 53) | def load_network_pkl(f, force_fp16=False):
  class _TFNetworkStub (line 97) | class _TFNetworkStub(dnnlib.EasyDict):
  class _LegacyUnpickler (line 100) | class _LegacyUnpickler(pickle.Unpickler):
    method find_class (line 101) | def find_class(self, module, name):
  function _collect_tf_params (line 108) | def _collect_tf_params(tf_net):
  function _populate_module_params (line 121) | def _populate_module_params(module, *patterns):
  function convert_tf_generator (line 142) | def convert_tf_generator(tf_G):
  function convert_tf_discriminator (line 241) | def convert_tf_discriminator(tf_D):
  function convert_network_pickle (line 328) | def convert_network_pickle(source, dest, force_fp16):

FILE: metrics/frechet_inception_distance.py
  function compute_fid (line 20) | def compute_fid(opts, max_real, num_gen):

FILE: metrics/inception_score.py
  function compute_is (line 18) | def compute_is(opts, num_gen, num_splits):

FILE: metrics/kernel_inception_distance.py
  function compute_kid (line 18) | def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size):

FILE: metrics/lpips.py
  class LPIPSSampler (line 20) | class LPIPSSampler(torch.nn.Module):
    method __init__ (line 21) | def __init__(self, G, G_orig, G_kwargs, epsilon, crop):
    method forward (line 30) | def forward(self, c):
  function compute_lpips (line 59) | def compute_lpips(opts, num_samples, epsilon, crop, batch_size, resume='...

FILE: metrics/metric_main.py
  function register_metric (line 27) | def register_metric(fn):
  function is_valid_metric (line 32) | def is_valid_metric(metric):
  function list_valid_metrics (line 35) | def list_valid_metrics():
  function calc_metric (line 40) | def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for ...
  function report_metric (line 68) | def report_metric(result_dict, run_dir=None, snapshot_pkl=None):
  function fid50k_full (line 84) | def fid50k_full(opts):
  function kid50k_full (line 90) | def kid50k_full(opts):
  function pr50k3_full (line 96) | def pr50k3_full(opts):
  function ppl2_wend (line 102) | def ppl2_wend(opts):
  function is50k (line 107) | def is50k(opts):
  function fid50k (line 116) | def fid50k(opts):
  function kid50k (line 122) | def kid50k(opts):
  function pr50k3 (line 128) | def pr50k3(opts):
  function ppl_zfull (line 134) | def ppl_zfull(opts):
  function ppl_wfull (line 139) | def ppl_wfull(opts):
  function ppl_zend (line 144) | def ppl_zend(opts):
  function ppl_wend (line 149) | def ppl_wend(opts):
  function lpips2k (line 157) | def lpips2k(opts):
  function lpips_church2k (line 162) | def lpips_church2k(opts):

FILE: metrics/metric_utils.py
  class MetricOptions (line 21) | class MetricOptions:
    method __init__ (line 22) | def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1,...
  function get_feature_detector_name (line 37) | def get_feature_detector_name(url):
  function get_feature_detector (line 40) | def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, ra...
  class FeatureStats (line 55) | class FeatureStats:
    method __init__ (line 56) | def __init__(self, capture_all=False, capture_mean_cov=False, max_item...
    method set_num_features (line 66) | def set_num_features(self, num_features):
    method is_full (line 75) | def is_full(self):
    method append (line 78) | def append(self, x):
    method append_torch (line 95) | def append_torch(self, x, num_gpus=1, rank=0):
    method get_all (line 107) | def get_all(self):
    method get_all_torch (line 111) | def get_all_torch(self):
    method get_mean_cov (line 114) | def get_mean_cov(self):
    method save (line 121) | def save(self, pkl_file):
    method load (line 126) | def load(pkl_file):
  class ProgressMonitor (line 135) | class ProgressMonitor:
    method __init__ (line 136) | def __init__(self, tag=None, num_items=None, flush_interval=1000, verb...
    method update (line 151) | def update(self, cur_items):
    method sub (line 166) | def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0,...
  function compute_feature_stats_for_dataset (line 180) | def compute_feature_stats_for_dataset(opts, detector_url, detector_kwarg...
  function compute_feature_stats_for_generator (line 232) | def compute_feature_stats_for_generator(opts, detector_url, detector_kwa...

FILE: metrics/perceptual_path_length.py
  function slerp (line 23) | def slerp(a, b, t):
  class PPLSampler (line 36) | class PPLSampler(torch.nn.Module):
    method __init__ (line 37) | def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16):
    method forward (line 49) | def forward(self, c):
  function compute_ppl (line 95) | def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch...

FILE: metrics/precision_recall.py
  function compute_distances (line 19) | def compute_distances(row_features, col_features, num_gpus, rank, col_ba...
  function compute_pr (line 36) | def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_...

FILE: projector_z.py
  function is_image_file (line 34) | def is_image_file(filename):
  function make_dataset (line 38) | def make_dataset(dir):
  function project (line 49) | def project(
  function project_loop (line 159) | def project_loop(
  function subprocess_fn (line 218) | def subprocess_fn(rank, args, temp_dir):
  function main (line 243) | def main(

FILE: torch_utils/custom_ops.py
  function _find_compiler_bindir (line 28) | def _find_compiler_bindir():
  function get_plugin (line 46) | def get_plugin(module_name, sources, **build_kwargs):

FILE: torch_utils/misc.py
  function constant (line 22) | def constant(value, shape=None, dtype=None, device=None, memory_format=N...
  function nan_to_num (line 49) | def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): #...
  class suppress_tracer_warnings (line 69) | class suppress_tracer_warnings(warnings.catch_warnings):
    method __enter__ (line 70) | def __enter__(self):
  function assert_shape (line 80) | def assert_shape(tensor, ref_shape):
  function profiled_function (line 98) | def profiled_function(fn):
  class InfiniteSampler (line 109) | class InfiniteSampler(torch.utils.data.Sampler):
    method __init__ (line 110) | def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed...
    method __iter__ (line 123) | def __iter__(self):
  function params_and_buffers (line 145) | def params_and_buffers(module):
  function named_params_and_buffers (line 149) | def named_params_and_buffers(module):
  function copy_params_and_buffers (line 153) | def copy_params_and_buffers(src_module, dst_module, require_all=False):
  function ddp_sync (line 167) | def ddp_sync(module, sync):
  function check_ddp_consistency (line 178) | def check_ddp_consistency(module, ignore_regex=None):
  function print_module_summary (line 192) | def print_module_summary(module, inputs, max_nesting=3, skip_redundant=T...

FILE: torch_utils/ops/bias_act.cpp
  function has_same_layout (line 16) | static bool has_same_layout(torch::Tensor x, torch::Tensor y)
  function bias_act (line 32) | static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::T...
  function PYBIND11_MODULE (line 94) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)

FILE: torch_utils/ops/bias_act.h
  type bias_act_kernel_params (line 12) | struct bias_act_kernel_params

FILE: torch_utils/ops/bias_act.py
  function _init (line 41) | def _init():
  function bias_act (line 55) | def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clam...
  function _bias_act_ref (line 94) | def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None,...
  function _bias_act_cuda (line 129) | def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):

FILE: torch_utils/ops/conv2d_gradfix.py
  function no_weight_gradients (line 26) | def no_weight_gradients():
  function conv2d (line 35) | def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, gr...
  function conv_transpose2d (line 40) | def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, outp...
  function _should_use_custom_op (line 47) | def _should_use_custom_op(input):
  function _tuple_of_ints (line 58) | def _tuple_of_ints(xs, ndim):
  function _conv2d_gradfix (line 68) | def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_pad...

FILE: torch_utils/ops/conv2d_resample.py
  function _get_weight_shape (line 21) | def _get_weight_shape(w):
  function _conv2d_wrapper (line 29) | def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False...
  function conv2d_resample (line 59) | def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, fli...

FILE: torch_utils/ops/fma.py
  function fma (line 15) | def fma(a, b, c): # => a * b + c
  class _FusedMultiplyAdd (line 20) | class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
    method forward (line 22) | def forward(ctx, a, b, c): # pylint: disable=arguments-differ
    method backward (line 29) | def backward(ctx, dout): # pylint: disable=arguments-differ
  function _unbroadcast (line 49) | def _unbroadcast(x, shape):

FILE: torch_utils/ops/grid_sample_gradfix.py
  function grid_sample (line 27) | def grid_sample(input, grid):
  function _should_use_custom_op (line 34) | def _should_use_custom_op():
  class _GridSample2dForward (line 44) | class _GridSample2dForward(torch.autograd.Function):
    method forward (line 46) | def forward(ctx, input, grid):
    method backward (line 54) | def backward(ctx, grad_output):
  class _GridSample2dBackward (line 61) | class _GridSample2dBackward(torch.autograd.Function):
    method forward (line 63) | def forward(ctx, grad_output, input, grid):
    method backward (line 70) | def backward(ctx, grad2_grad_input, grad2_grad_grid):

FILE: torch_utils/ops/upfirdn2d.cpp
  function upfirdn2d (line 16) | static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx...
  function PYBIND11_MODULE (line 98) | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)

FILE: torch_utils/ops/upfirdn2d.h
  type upfirdn2d_kernel_params (line 14) | struct upfirdn2d_kernel_params
  type upfirdn2d_kernel_spec (line 45) | struct upfirdn2d_kernel_spec

FILE: torch_utils/ops/upfirdn2d.py
  function _init (line 26) | def _init():
  function _parse_scaling (line 37) | def _parse_scaling(scaling):
  function _parse_padding (line 46) | def _parse_padding(padding):
  function _get_filter_size (line 57) | def _get_filter_size(f):
  function setup_filter (line 72) | def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_fil...
  function upfirdn2d (line 120) | def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, ...
  function _upfirdn2d_ref (line 169) | def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gai...
  function _upfirdn2d_cuda (line 214) | def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
  function filter2d (line 272) | def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
  function upsample2d (line 308) | def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='c...
  function downsample2d (line 347) | def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, imp...

FILE: torch_utils/persistence.py
  function persistent_class (line 35) | def persistent_class(orig_class):
  function is_persistent (line 134) | def is_persistent(obj):
  function import_hook (line 147) | def import_hook(hook):
  function _reconstruct_persistent_obj (line 179) | def _reconstruct_persistent_obj(meta):
  function _module_to_src (line 206) | def _module_to_src(module):
  function _src_to_module (line 216) | def _src_to_module(src):
  function _check_pickleable (line 231) | def _check_pickleable(obj):

FILE: torch_utils/training_stats.py
  function init_multiprocessing (line 34) | def init_multiprocessing(rank, sync_device):
  function report (line 56) | def report(name, value):
  function report0 (line 103) | def report0(name, value):
  class Collector (line 113) | class Collector:
    method __init__ (line 133) | def __init__(self, regex='.*', keep_previous=True):
    method names (line 141) | def names(self):
    method update (line 147) | def update(self):
    method _get_delta (line 170) | def _get_delta(self, name):
    method num (line 180) | def num(self, name):
    method mean (line 188) | def mean(self, name):
    method std (line 198) | def std(self, name):
    method as_dict (line 212) | def as_dict(self):
    method __getitem__ (line 226) | def __getitem__(self, name):
  function _sync (line 234) | def _sync(names):

FILE: train.py
  class UserError (line 27) | class UserError(Exception):
  function setup_training_loop_kwargs (line 32) | def setup_training_loop_kwargs(
  function subprocess_fn (line 420) | def subprocess_fn(rank, args, temp_dir):
  class CommaSeparatedList (line 444) | class CommaSeparatedList(click.ParamType):
    method convert (line 447) | def convert(self, value, param, ctx):
  function main (line 505) | def main(ctx, outdir, dry_run, **config_kwargs):

FILE: training/augment.py
  function matrix (line 43) | def matrix(*rows, device=None):
  function translate2d (line 53) | def translate2d(tx, ty, **kwargs):
  function translate3d (line 60) | def translate3d(tx, ty, tz, **kwargs):
  function scale2d (line 68) | def scale2d(sx, sy, **kwargs):
  function scale3d (line 75) | def scale3d(sx, sy, sz, **kwargs):
  function rotate2d (line 83) | def rotate2d(theta, **kwargs):
  function rotate3d (line 90) | def rotate3d(v, theta, **kwargs):
  function translate2d_inv (line 100) | def translate2d_inv(tx, ty, **kwargs):
  function scale2d_inv (line 103) | def scale2d_inv(sx, sy, **kwargs):
  function rotate2d_inv (line 106) | def rotate2d_inv(theta, **kwargs):
  class AugmentPipe (line 117) | class AugmentPipe(torch.nn.Module):
    method __init__ (line 118) | def __init__(self,
    method forward (line 181) | def forward(self, images, debug_percentile=None):

FILE: training/dataset.py
  class Dataset (line 24) | class Dataset(torch.utils.data.Dataset):
    method __init__ (line 25) | def __init__(self,
    method _get_raw_labels (line 51) | def _get_raw_labels(self):
    method close (line 64) | def close(self): # to be overridden by subclass
    method _load_raw_image (line 67) | def _load_raw_image(self, raw_idx): # to be overridden by subclass
    method _load_raw_labels (line 70) | def _load_raw_labels(self): # to be overridden by subclass
    method __getstate__ (line 73) | def __getstate__(self):
    method __del__ (line 76) | def __del__(self):
    method __len__ (line 82) | def __len__(self):
    method __getitem__ (line 85) | def __getitem__(self, idx):
    method get_label (line 95) | def get_label(self, idx):
    method get_details (line 103) | def get_details(self, idx):
    method name (line 111) | def name(self):
    method image_shape (line 115) | def image_shape(self):
    method num_channels (line 119) | def num_channels(self):
    method resolution (line 124) | def resolution(self):
    method label_shape (line 130) | def label_shape(self):
    method label_dim (line 140) | def label_dim(self):
    method has_labels (line 145) | def has_labels(self):
    method has_onehot_labels (line 149) | def has_onehot_labels(self):
  class ImageFolderDataset (line 154) | class ImageFolderDataset(Dataset):
    method __init__ (line 155) | def __init__(self,
    method _file_ext (line 184) | def _file_ext(fname):
    method _get_zipfile (line 187) | def _get_zipfile(self):
    method _open_file (line 193) | def _open_file(self, fname):
    method close (line 200) | def close(self):
    method __getstate__ (line 207) | def __getstate__(self):
    method _load_raw_image (line 210) | def _load_raw_image(self, raw_idx):
    method _load_raw_labels (line 222) | def _load_raw_labels(self):

FILE: training/loss.py
  class FMLoss (line 27) | class FMLoss(torch.nn.Module):
    method __init__ (line 31) | def __init__(self,
    method _select_features (line 55) | def _select_features(self, img, block_features, block_rgbs):
    method forward (line 68) | def forward(self, z, c, sync):
  class Loss (line 104) | class Loss:
    method accumulate_gradients (line 105) | def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, ...
  class StyleGAN2Loss (line 110) | class StyleGAN2Loss(Loss):
    method __init__ (line 111) | def __init__(self, device, G_mapping, G_synthesis, D, augment_pipe=Non...
    method run_G (line 142) | def run_G(self, z, c, sync):
    method run_D (line 154) | def run_D(self, img, c, sync):
    method accumulate_gradients (line 161) | def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, ...

FILE: training/networks.py
  function normalize_2nd_moment (line 21) | def normalize_2nd_moment(x, dim=1, eps=1e-8):
  function modulated_conv2d (line 27) | def modulated_conv2d(
  class FullyConnectedLayer (line 89) | class FullyConnectedLayer(torch.nn.Module):
    method __init__ (line 90) | def __init__(self,
    method forward (line 116) | def forward(self, x):
  class Conv2dLayer (line 134) | class Conv2dLayer(torch.nn.Module):
    method __init__ (line 135) | def __init__(self,
    method forward (line 171) | def forward(self, x, gain=1):
  class MappingNetwork (line 185) | class MappingNetwork(torch.nn.Module):
    method __init__ (line 186) | def __init__(self,
    method forward (line 226) | def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip...
  class SynthesisLayer (line 266) | class SynthesisLayer(torch.nn.Module):
    method __init__ (line 267) | def __init__(self,
    method forward (line 312) | def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=...
    method sample_noise (line 335) | def sample_noise(self):
  class ToRGBLayer (line 341) | class ToRGBLayer(torch.nn.Module):
    method __init__ (line 342) | def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, co...
    method forward (line 359) | def forward(self, x, w, fused_modconv=True):
  class SynthesisBlock (line 368) | class SynthesisBlock(torch.nn.Module):
    method __init__ (line 369) | def __init__(self,
    method forward (line 435) | def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **...
    method sample_noise (line 477) | def sample_noise(self):
  class SynthesisNetwork (line 487) | class SynthesisNetwork(torch.nn.Module):
    method __init__ (line 488) | def __init__(self,
    method forward (line 522) | def forward(self, ws, return_features=False, **block_kwargs):
    method sample_noise (line 548) | def sample_noise(self):
  class Generator (line 556) | class Generator(torch.nn.Module):
    method __init__ (line 557) | def __init__(self,
    method forward (line 576) | def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, retu...
    method sample_noise (line 586) | def sample_noise(self):
  class DiscriminatorBlock (line 592) | class DiscriminatorBlock(torch.nn.Module):
    method __init__ (line 593) | def __init__(self,
    method forward (line 643) | def forward(self, x, img, force_fp32=False):
  class MinibatchStdLayer (line 676) | class MinibatchStdLayer(torch.nn.Module):
    method __init__ (line 677) | def __init__(self, group_size, num_channels=1):
    method forward (line 682) | def forward(self, x):
  class DiscriminatorEpilogue (line 702) | class DiscriminatorEpilogue(torch.nn.Module):
    method __init__ (line 703) | def __init__(self,
    method forward (line 729) | def forward(self, x, img, cmap, force_fp32=False):
  class Discriminator (line 760) | class Discriminator(torch.nn.Module):
    method __init__ (line 761) | def __init__(self,
    method forward (line 804) | def forward(self, img, c, **block_kwargs):

FILE: training/training_loop.py
  function setup_snapshot_image_grid (line 29) | def setup_snapshot_image_grid(training_set, random_seed=0):
  function save_image_grid (line 70) | def save_image_grid(img, fname, drange, grid_size):
  function training_loop (line 90) | def training_loop(

FILE: utils/dataset_tool.py
  function error (line 28) | def error(msg):
  function maybe_min (line 34) | def maybe_min(a: int, b: Optional[int]) -> int:
  function file_ext (line 41) | def file_ext(name: Union[str, Path]) -> str:
  function is_image_ext (line 46) | def is_image_ext(fname: Union[str, Path]) -> bool:
  function open_image_folder (line 52) | def open_image_folder(source_dir, *, max_images: Optional[int]):
  function open_image_zip (line 80) | def open_image_zip(source, *, max_images: Optional[int]):
  function open_lmdb (line 109) | def open_lmdb(lmdb_dir: str, *, max_images: Optional[int]):
  function open_cifar10 (line 137) | def open_cifar10(tarball: str, *, max_images: Optional[int]):
  function open_mnist (line 169) | def open_mnist(images_gz: str, *, max_images: Optional[int]):
  function make_transform (line 199) | def make_transform(
  function open_dataset (line 252) | def open_dataset(source, *, max_images: Optional[int]):
  function open_dest (line 272) | def open_dest(dest: str) -> Tuple[str, Callable[[str, Union[bytes, str]]...
  function convert_dataset (line 313) | def convert_dataset(

FILE: utils/style_mixing.py
  function num_range (line 25) | def num_range(s: str) -> List[int]:
  function generate_style_mix (line 45) | def generate_style_mix(
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Condensed preview — 53 files, each showing path, character count, and a content snippet. Download the .json file for the full structured content (11,397K chars).
[
  {
    "path": ".gitignore",
    "chars": 36,
    "preview": "__pycache__/\n.cache/\nlogs/\n_scripts/"
  },
  {
    "path": "Dockerfile",
    "chars": 919,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "LICENSE.txt",
    "chars": 4417,
    "preview": "Copyright (c) 2021, NVIDIA Corporation. All rights reserved.\n\n\nNVIDIA Source Code License for StyleGAN2 with Adaptive Di"
  },
  {
    "path": "README.md",
    "chars": 9614,
    "preview": "# Fix the Noise: Disentangling Source Feature for Controllable Domain Translation</sub>\n\n[![arXiv](https://img.shields.i"
  },
  {
    "path": "calc_metrics.py",
    "chars": 8336,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "dataset_resize.py",
    "chars": 2446,
    "preview": "import os\nimport click\nimport numpy as np\nfrom PIL import Image\n\nIMG_EXTENSIONS = [\n    'jpg', 'jpeg', 'png', 'ppm', 'bm"
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    "path": "demo.ipynb",
    "chars": 2245281,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"code\",\n   \"execution_count\": 31,\n   \"metadata\": {},\n   \"outputs\": [],\n   \"source\": [\n"
  },
  {
    "path": "demo_colab.ipynb",
    "chars": 8730978,
    "preview": "{\n  \"nbformat\": 4,\n  \"nbformat_minor\": 0,\n  \"metadata\": {\n    \"colab\": {\n      \"provenance\": [],\n      \"gpuType\": \"T4\",\n"
  },
  {
    "path": "dnnlib/__init__.py",
    "chars": 476,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "dnnlib/util.py",
    "chars": 16625,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "docker_run.sh",
    "chars": 1196,
    "preview": "#!/bin/bash\n\n# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors re"
  },
  {
    "path": "generate.py",
    "chars": 6845,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "legacy.py",
    "chars": 18556,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/__init__.py",
    "chars": 435,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "metrics/frechet_inception_distance.py",
    "chars": 2040,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/inception_score.py",
    "chars": 1874,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/kernel_inception_distance.py",
    "chars": 2302,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/lpips.py",
    "chars": 4063,
    "preview": "import copy\nimport numpy as np\nimport torch\nimport dnnlib\nimport legacy\nfrom . import metric_utils\nfrom torch_utils impo"
  },
  {
    "path": "metrics/metric_main.py",
    "chars": 6114,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/metric_utils.py",
    "chars": 11806,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/perceptual_path_length.py",
    "chars": 5538,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "metrics/precision_recall.py",
    "chars": 3617,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "projector_z.py",
    "chars": 10457,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "scripts/aahq/fm_05e-2.sh",
    "chars": 429,
    "preview": "python train.py --outdir=./logs/aahq/ \\\n                --gpus=2 \\\n                --batch=64 \\\n                --kimg=1"
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    "path": "scripts/metfaces/fm_05e-2.sh",
    "chars": 428,
    "preview": "python train.py --outdir=./logs/metfaces/ \\\n                --gpus=2 \\\n                --batch=64 \\\n                --ki"
  },
  {
    "path": "scripts/wikiart/fm_05e-2.sh",
    "chars": 419,
    "preview": "python train.py --outdir=./logs/wikiart/ \\\n                --gpus=2 \\\n                --batch=64 \\\n                --kim"
  },
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    "path": "torch_utils/__init__.py",
    "chars": 436,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "torch_utils/custom_ops.py",
    "chars": 5644,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/misc.py",
    "chars": 10992,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "torch_utils/ops/__init__.py",
    "chars": 436,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "torch_utils/ops/bias_act.cpp",
    "chars": 4376,
    "preview": "// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n//\n// NVIDIA CORPORATION and its licensors retain all i"
  },
  {
    "path": "torch_utils/ops/bias_act.cu",
    "chars": 6147,
    "preview": "// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n//\n// NVIDIA CORPORATION and its licensors retain all i"
  },
  {
    "path": "torch_utils/ops/bias_act.h",
    "chars": 1280,
    "preview": "// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n//\n// NVIDIA CORPORATION and its licensors retain all i"
  },
  {
    "path": "torch_utils/ops/bias_act.py",
    "chars": 10047,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/ops/conv2d_gradfix.py",
    "chars": 7685,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/ops/conv2d_resample.py",
    "chars": 7591,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/ops/fma.py",
    "chars": 2034,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/ops/grid_sample_gradfix.py",
    "chars": 3307,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/ops/upfirdn2d.cpp",
    "chars": 4559,
    "preview": "// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n//\n// NVIDIA CORPORATION and its licensors retain all i"
  },
  {
    "path": "torch_utils/ops/upfirdn2d.cu",
    "chars": 21050,
    "preview": "// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n//\n// NVIDIA CORPORATION and its licensors retain all i"
  },
  {
    "path": "torch_utils/ops/upfirdn2d.h",
    "chars": 1836,
    "preview": "// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n//\n// NVIDIA CORPORATION and its licensors retain all i"
  },
  {
    "path": "torch_utils/ops/upfirdn2d.py",
    "chars": 16287,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "torch_utils/persistence.py",
    "chars": 9708,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "torch_utils/training_stats.py",
    "chars": 10707,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "train.py",
    "chars": 27045,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "training/__init__.py",
    "chars": 435,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
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  {
    "path": "training/augment.py",
    "chars": 26373,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
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  {
    "path": "training/dataset.py",
    "chars": 8551,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "training/loss.py",
    "chars": 12389,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "training/networks.py",
    "chars": 40937,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "training/training_loop.py",
    "chars": 22428,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all int"
  },
  {
    "path": "utils/dataset_tool.py",
    "chars": 17876,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  },
  {
    "path": "utils/style_mixing.py",
    "chars": 4891,
    "preview": "# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.\n#\n# NVIDIA CORPORATION and its licensors retain all inte"
  }
]

About this extraction

This page contains the full source code of the LeeDongYeun/FixNoise GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 53 files (10.9 MB), approximately 2.8M tokens, and a symbol index with 313 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.

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