├── .gitignore ├── InterFaceGAN++.ipynb ├── LICENSE ├── README.md ├── README_old.md ├── boundaries ├── stylegan2_ffhq_z │ ├── boundary_5_o_Clock_Shadow.npy │ ├── boundary_Arched_Eyebrows.npy │ ├── boundary_Attractive.npy │ ├── boundary_Bags_Under_Eyes.npy │ ├── boundary_Bald.npy │ ├── boundary_Bangs.npy │ ├── boundary_Big_Lips.npy │ ├── boundary_Big_Nose.npy │ ├── boundary_Black_Hair.npy │ ├── boundary_Blond_Hair.npy │ ├── boundary_Brown_Hair.npy │ ├── boundary_Bushy_Eyebrows.npy │ ├── boundary_Chubby.npy │ ├── boundary_Double_Chin.npy │ ├── boundary_Eyeglasses.npy │ ├── boundary_Gender.npy │ ├── boundary_Goatee.npy │ ├── boundary_Gray_Hair.npy │ ├── boundary_Heavy_Makeup.npy │ ├── boundary_High_Cheekbones.npy │ ├── boundary_Mouth_Slightly_Open.npy │ ├── boundary_Mustache.npy │ ├── boundary_Narrow_Eyes.npy │ ├── boundary_No_Beard.npy │ ├── boundary_Oval_Face.npy │ ├── boundary_Pale_Skin.npy │ ├── boundary_Pointy_Nose.npy │ ├── boundary_Receding_Hairline.npy │ ├── boundary_Rosy_Cheeks.npy │ ├── boundary_Sideburns.npy │ ├── boundary_Smiling.npy │ ├── boundary_Straight_Hair.npy │ ├── boundary_Wavy_Hair.npy │ ├── boundary_Wearing_Earrings.npy │ ├── boundary_Wearing_Hat.npy │ ├── boundary_Wearing_Lipstick.npy │ ├── boundary_Wearing_Necklace.npy │ ├── boundary_Wearing_Necktie.npy │ └── boundary_Young.npy ├── stylegan3_ffhq_z │ ├── boundary_5_o_Clock_Shadow.npy │ ├── boundary_Arched_Eyebrows.npy │ ├── boundary_Attractive.npy │ ├── boundary_Bags_Under_Eyes.npy │ ├── boundary_Bald.npy │ ├── boundary_Bangs.npy │ ├── boundary_Big_Lips.npy │ ├── boundary_Big_Nose.npy │ ├── boundary_Black_Hair.npy │ ├── boundary_Blond_Hair.npy │ ├── boundary_Brown_Hair.npy │ ├── boundary_Bushy_Eyebrows.npy │ ├── boundary_Chubby.npy │ ├── boundary_Double_Chin.npy │ ├── boundary_Eyeglasses.npy │ ├── boundary_Gender.npy │ ├── boundary_Goatee.npy │ ├── boundary_Gray_Hair.npy │ ├── boundary_Heavy_Makeup.npy │ ├── boundary_High_Cheekbones.npy │ ├── boundary_Mouth_Slightly_Open.npy │ ├── boundary_Mustache.npy │ ├── boundary_Narrow_Eyes.npy │ ├── boundary_No_Beard.npy │ ├── boundary_Oval_Face.npy │ ├── boundary_Pale_Skin.npy │ ├── boundary_Pointy_Nose.npy │ ├── boundary_Receding_Hairline.npy │ ├── boundary_Rosy_Cheeks.npy │ ├── boundary_Sideburns.npy │ ├── boundary_Smiling.npy │ ├── boundary_Straight_Hair.npy │ ├── boundary_Wavy_Hair.npy │ ├── boundary_Wearing_Earrings.npy │ ├── boundary_Wearing_Hat.npy │ ├── boundary_Wearing_Lipstick.npy │ ├── boundary_Wearing_Necklace.npy │ ├── boundary_Wearing_Necktie.npy │ └── boundary_Young.npy ├── stylegan_ffhq_w │ ├── boundary_5_o_Clock_Shadow.npy │ ├── boundary_Arched_Eyebrows.npy │ ├── boundary_Attractive.npy │ ├── boundary_Bags_Under_Eyes.npy │ ├── boundary_Bald.npy │ ├── boundary_Bangs.npy │ ├── boundary_Big_Lips.npy │ ├── boundary_Big_Nose.npy │ ├── boundary_Black_Hair.npy │ ├── boundary_Blond_Hair.npy │ ├── boundary_Brown_Hair.npy │ ├── boundary_Bushy_Eyebrows.npy │ ├── boundary_Chubby.npy │ ├── boundary_Double_Chin.npy │ ├── boundary_Eyeglasses.npy │ ├── boundary_Gender.npy │ ├── boundary_Goatee.npy │ ├── boundary_Gray_Hair.npy │ ├── boundary_Heavy_Makeup.npy │ ├── boundary_High_Cheekbones.npy │ ├── boundary_Mouth_Slightly_Open.npy │ ├── boundary_Mustache.npy │ ├── boundary_Narrow_Eyes.npy │ ├── boundary_No_Beard.npy │ ├── boundary_Oval_Face.npy │ ├── boundary_Pale_Skin.npy │ ├── boundary_Pointy_Nose.npy │ ├── boundary_Receding_Hairline.npy │ ├── boundary_Rosy_Cheeks.npy │ ├── boundary_Sideburns.npy │ ├── boundary_Smiling.npy │ ├── boundary_Straight_Hair.npy │ ├── boundary_Wavy_Hair.npy │ ├── boundary_Wearing_Earrings.npy │ ├── boundary_Wearing_Hat.npy │ ├── boundary_Wearing_Lipstick.npy │ ├── boundary_Wearing_Necklace.npy │ ├── boundary_Wearing_Necktie.npy │ └── boundary_Young.npy └── stylegan_ffhq_z │ ├── boundary_5_o_Clock_Shadow.npy │ ├── boundary_Arched_Eyebrows.npy │ ├── boundary_Attractive.npy │ ├── boundary_Bags_Under_Eyes.npy │ ├── boundary_Bald.npy │ ├── boundary_Bangs.npy │ ├── boundary_Big_Lips.npy │ ├── boundary_Big_Nose.npy │ ├── boundary_Black_Hair.npy │ ├── boundary_Blond_Hair.npy │ ├── boundary_Brown_Hair.npy │ ├── boundary_Bushy_Eyebrows.npy │ ├── boundary_Chubby.npy │ ├── boundary_Double_Chin.npy │ ├── boundary_Eyeglasses.npy │ ├── boundary_Gender.npy │ ├── boundary_Goatee.npy │ ├── boundary_Gray_Hair.npy │ ├── boundary_Heavy_Makeup.npy │ ├── boundary_High_Cheekbones.npy │ ├── boundary_Mouth_Slightly_Open.npy │ ├── boundary_Mustache.npy │ ├── boundary_Narrow_Eyes.npy │ ├── boundary_No_Beard.npy │ ├── boundary_Oval_Face.npy │ ├── boundary_Pale_Skin.npy │ ├── boundary_Pointy_Nose.npy │ ├── boundary_Receding_Hairline.npy │ ├── boundary_Rosy_Cheeks.npy │ ├── boundary_Sideburns.npy │ ├── boundary_Smiling.npy │ ├── boundary_Straight_Hair.npy │ ├── boundary_Wavy_Hair.npy │ ├── boundary_Wearing_Earrings.npy │ ├── boundary_Wearing_Hat.npy │ ├── boundary_Wearing_Lipstick.npy │ ├── boundary_Wearing_Necklace.npy │ ├── boundary_Wearing_Necktie.npy │ └── boundary_Young.npy ├── dnnlib ├── __init__.py └── util.py ├── docs ├── InterFaceGAN.ipynb ├── assets │ ├── age.gif │ ├── artifact.gif │ ├── bootstrap.min.css │ ├── expression.gif │ ├── eyeglasses.gif │ ├── font.css │ ├── ganalyze.jpg │ ├── gandissection.jpg │ ├── gender.gif │ ├── genforce.png │ ├── higan.jpg │ ├── pose.gif │ ├── steerability.jpg │ ├── style.css │ └── teaser.jpg └── index.html ├── edit.py ├── generate_data.py ├── images ├── bald1.gif ├── bald2.gif ├── blond.gif ├── gray_hair.gif ├── high_cheekbones.gif ├── makeup.gif ├── sg2.jpeg ├── sg2_not_young.jpeg ├── sg3_beard.jpeg ├── sg3_before.jpeg ├── sg_before.jpeg └── sg_grey_hair.jpeg ├── models ├── __init__.py ├── base_generator.py ├── model_settings.py ├── pggan_generator.py ├── pggan_generator_model.py ├── pggan_tf_official │ ├── LICENSE.txt │ ├── README.md │ ├── config.py │ ├── dataset.py │ ├── dataset_tool.py │ ├── legacy.py │ ├── loss.py │ ├── metrics │ │ ├── __init__.py │ │ ├── frechet_inception_distance.py │ │ ├── inception_score.py │ │ ├── ms_ssim.py │ │ └── sliced_wasserstein.py │ ├── misc.py │ ├── networks.py │ ├── requirements-pip.txt │ ├── tfutil.py │ ├── train.py │ └── util_scripts.py ├── pretrain │ └── Pretrained_Models_Should_Be_Placed_Here ├── stylegan2_generator.py ├── stylegan3_generator.py ├── stylegan3_official_network.py ├── stylegan_generator.py ├── stylegan_generator_model.py └── stylegan_tf_official │ ├── LICENSE.txt │ ├── README.md │ ├── config.py │ ├── dataset_tool.py │ ├── dnnlib │ ├── __init__.py │ ├── submission │ │ ├── __init__.py │ │ ├── _internal │ │ │ └── run.py │ │ ├── run_context.py │ │ └── submit.py │ ├── tflib │ │ ├── __init__.py │ │ ├── autosummary.py │ │ ├── network.py │ │ ├── optimizer.py │ │ └── tfutil.py │ └── util.py │ ├── generate_figures.py │ ├── metrics │ ├── __init__.py │ ├── frechet_inception_distance.py │ ├── linear_separability.py │ ├── metric_base.py │ └── perceptual_path_length.py │ ├── pretrained_example.py │ ├── run_metrics.py │ ├── train.py │ └── training │ ├── __init__.py │ ├── dataset.py │ ├── loss.py │ ├── misc.py │ ├── networks_progan.py │ ├── networks_stylegan.py │ └── training_loop.py ├── 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 │ ├── filtered_lrelu.cpp │ ├── filtered_lrelu.cu │ ├── filtered_lrelu.h │ ├── filtered_lrelu.py │ ├── filtered_lrelu_ns.cu │ ├── filtered_lrelu_rd.cu │ ├── filtered_lrelu_wr.cu │ ├── fma.py │ ├── grid_sample_gradfix.py │ ├── upfirdn2d.cpp │ ├── upfirdn2d.cu │ ├── upfirdn2d.h │ └── upfirdn2d.py ├── persistence.py └── training_stats.py ├── train_boundary.py └── utils ├── __init__.py ├── logger.py └── manipulator.py /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__/ 2 | *.py[cod] 3 | 4 | *.jpg 5 | *.png 6 | *.jpeg 7 | *.npy 8 | log.txt 9 | 10 | /results/ 11 | /models/pretrain/ 12 | /data/ 13 | !/boundaries/*.npy 14 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2022 Younes Belkada 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | from .util import EasyDict, make_cache_dir_path 10 | -------------------------------------------------------------------------------- /docs/assets/age.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/docs/assets/age.gif -------------------------------------------------------------------------------- /docs/assets/artifact.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/docs/assets/artifact.gif -------------------------------------------------------------------------------- /docs/assets/expression.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/docs/assets/expression.gif -------------------------------------------------------------------------------- /docs/assets/eyeglasses.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/docs/assets/eyeglasses.gif -------------------------------------------------------------------------------- /docs/assets/font.css: -------------------------------------------------------------------------------- 1 | /* Homepage Font */ 2 | 3 | /* latin-ext */ 4 | @font-face { 5 | font-family: 'Lato'; 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4 | color: #ffffff; 5 | font-family: 'Lato', Verdana, Helvetica, sans-serif; 6 | font-weight: 300; 7 | font-size: 14pt; 8 | } 9 | 10 | /* Hyperlinks */ 11 | a {text-decoration: none;} 12 | a:link {color: #1772d0;} 13 | a:visited {color: #1772d0;} 14 | a:active {color: red;} 15 | a:hover {color: #f09228;} 16 | 17 | /* Pre-formatted Text */ 18 | pre { 19 | margin: 5pt 0; 20 | border: 0; 21 | font-size: 12pt; 22 | background: #fcfcfc; 23 | } 24 | 25 | /* Project Page Style */ 26 | /* Section */ 27 | .section { 28 | width: 768pt; 29 | min-height: 100pt; 30 | margin: 15pt auto; 31 | padding: 20pt 30pt; 32 | border: 1pt hidden #000; 33 | text-align: justify; 34 | color: #000000; 35 | background: #ffffff; 36 | } 37 | 38 | /* Header (Title and Logo) */ 39 | .section .header { 40 | min-height: 80pt; 41 | margin-top: 30pt; 42 | } 43 | .section .header .logo { 44 | width: 80pt; 45 | margin-left: 10pt; 46 | float: left; 47 | } 48 | .section .header .logo img { 49 | width: 80pt; 50 | object-fit: cover; 51 | } 52 | .section .header .title { 53 | margin: 0 120pt; 54 | text-align: center; 55 | font-size: 22pt; 56 | } 57 | 58 | /* Author */ 59 | .section .author { 60 | margin: 5pt 0; 61 | text-align: center; 62 | font-size: 16pt; 63 | } 64 | 65 | /* Institution */ 66 | .section .institution { 67 | margin: 5pt 0; 68 | text-align: center; 69 | font-size: 16pt; 70 | } 71 | 72 | /* Hyperlink (such as Paper and Code) */ 73 | .section .link { 74 | margin: 5pt 0; 75 | text-align: center; 76 | font-size: 16pt; 77 | } 78 | 79 | /* Teaser */ 80 | .section .teaser { 81 | margin: 20pt 0; 82 | text-align: center; 83 | } 84 | .section .teaser img { 85 | width: 95%; 86 | } 87 | 88 | /* Section Title */ 89 | .section .title { 90 | text-align: center; 91 | font-size: 22pt; 92 | margin: 5pt 0 15pt 0; /* top right bottom left */ 93 | } 94 | 95 | /* Section Body */ 96 | .section .body { 97 | margin-bottom: 15pt; 98 | text-align: justify; 99 | font-size: 14pt; 100 | } 101 | 102 | /* BibTeX */ 103 | .section .bibtex { 104 | margin: 5pt 0; 105 | text-align: left; 106 | font-size: 22pt; 107 | } 108 | 109 | /* Related Work */ 110 | .section .ref { 111 | margin: 20pt 0 10pt 0; /* top right bottom left */ 112 | text-align: left; 113 | font-size: 18pt; 114 | font-weight: bold; 115 | } 116 | 117 | /* Citation */ 118 | .section .citation { 119 | min-height: 60pt; 120 | margin: 10pt 0; 121 | } 122 | .section .citation .image { 123 | width: 120pt; 124 | float: left; 125 | } 126 | .section .citation .image img { 127 | max-height: 60pt; 128 | width: 120pt; 129 | object-fit: cover; 130 | } 131 | .section .citation .comment{ 132 | margin-left: 130pt; 133 | text-align: left; 134 | font-size: 14pt; 135 | } 136 | -------------------------------------------------------------------------------- /docs/assets/teaser.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/docs/assets/teaser.jpg -------------------------------------------------------------------------------- /docs/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | InterFaceGAN 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 |
23 | 24 |
25 | 28 |
29 | Interpreting the Latent Space of GANs for Semantic Face Editing 30 |
31 |
32 | 33 |
34 | Yujun Shen1,  35 | Jinjin Gu2,  36 | Xiaoou Tang1,  37 | Bolei Zhou1  38 |
39 |
40 | 1The Chinese University of Hong Kong
41 | 2The Chinese University of Hong Kong, Shenzhen 42 |
43 |
44 | [Paper (CVPR)]  45 | [Paper (TPAMI)]  46 | [Code] 47 | [Colab] 48 |
49 |
50 | 51 |
52 |
53 | 54 | 55 | 56 | 57 |
58 |
Overview
59 |
60 | We find that the latent code for well-trained generative models, such as PGGAN and StyleGAN, 61 | actually learns a disentangled representation after some linear transformations. 62 | Based on our analysis, we propose a simple and general technique, called InterFaceGAN, 63 | for semantic face editing in latent space. 64 | We manage to control the pose as well as other facial attributes, such as gender, age, eyeglasses. 65 | More importantly, we are able to correct the artifacts made by GANs. 66 |
67 |
68 | 69 | 70 | 71 | 72 |
73 |
Results
74 |
75 | We manipulate the following attributes with PGGAN. 76 | 77 | 78 | 79 | 80 | 81 | 82 | 83 | 84 | 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | 94 | 95 | 96 | 97 | 98 |
PoseAgeGender
ExpressionEyeglassesArtifacts
99 | 100 | Check more results in the following video. 101 | 102 | 103 |
104 | 108 |
109 |
110 |
111 | 112 | 113 | 114 | 115 |
116 |
BibTeX
117 | 
118 | @inproceedings{shen2020interpreting,
119 |   title     = {Interpreting the Latent Space of GANs for Semantic Face Editing},
120 |   author    = {Shen, Yujun and Gu, Jinjin and Tang, Xiaoou and Zhou, Bolei},
121 |   booktitle = {CVPR},
122 |   year      = {2020}
123 | }
124 | 
125 | @article{shen2020interfacegan,
126 |   title   = {InterFaceGAN: Interpreting the Disentangled Face Representation Learned by GANs},
127 |   author  = {Shen, Yujun and Yang, Ceyuan and Tang, Xiaoou and Zhou, Bolei},
128 |   journal = {TPAMI},
129 |   year    = {2020}
130 | }
131 |
132 | 133 |
Related Work
134 |
135 |
136 |
137 | 138 | D. Bau, JY. Zhu, H. Strobelt, B. Zhou, JB. Tenenbaum, WT. Freeman, A. Torralba. 139 | GAN Dissection: Visualizing and Understanding Generative Adversarial Networks. 140 | ICLR 2019.
141 | Comment: 142 | Dissects neurons in GANs from the perspective of spatial feature map. 143 |
144 |
145 |
146 |
147 |
148 | 149 | L. Goetschalckx, A. Andonian, A. Oliva, P. Isola. 150 | GANalyze: Toward Visual Definitions of Cognitive Image Properties. 151 | ICCV, 2019.
152 | Comment: 153 | Navigates the manifold of GAN's latent space to increase memorability. 154 |
155 |
156 |
157 |
158 |
159 | 160 | A. Jahanian, L. Chai, P. Isola. 161 | On the "Steerability" of Generative Adversarial Networks. 162 | ICLR, 2020.
163 | Comment: 164 | Shifts the data distribution by steering the latent code to fit camera movements and color changes. 165 |
166 |
167 |
168 |
169 |
170 | 171 | C. Yang, Y. Shen, B. Zhou. 172 | Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis. 173 | arXiv preprint arXiv:1911.09267.
174 | Comment: 175 | Explores the emergent semantic hierarchy in scene synthesis models. 176 |
177 |
178 |
179 | 180 | 181 | 182 | 183 | 184 | -------------------------------------------------------------------------------- /edit.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Edits latent codes with respect to given boundary. 3 | 4 | Basically, this file takes latent codes and a semantic boundary as inputs, and 5 | then shows how the image synthesis will change if the latent codes is moved 6 | towards the given boundary. 7 | 8 | NOTE: If you want to use W or W+ space of StyleGAN, please do not randomly 9 | sample the latent code, since neither W nor W+ space is subject to Gaussian 10 | distribution. Instead, please use `generate_data.py` to get the latent vectors 11 | from W or W+ space first, and then use `--input_latent_codes_path` option to 12 | pass in the latent vectors. 13 | """ 14 | 15 | import os.path 16 | import argparse 17 | import cv2 18 | import numpy as np 19 | from tqdm import tqdm 20 | 21 | from models.model_settings import MODEL_POOL 22 | from models.pggan_generator import PGGANGenerator 23 | from models.stylegan_generator import StyleGANGenerator 24 | from models.stylegan3_generator import StyleGAN3Generator 25 | from models.stylegan2_generator import StyleGAN2Generator 26 | from utils.logger import setup_logger 27 | from utils.manipulator import linear_interpolate 28 | 29 | 30 | def parse_args(): 31 | """Parses arguments.""" 32 | parser = argparse.ArgumentParser( 33 | description='Edit image synthesis with given semantic boundary.') 34 | parser.add_argument('-m', '--model_name', type=str, required=True, 35 | choices=list(MODEL_POOL), 36 | help='Name of the model for generation. (required)') 37 | parser.add_argument('-o', '--output_dir', type=str, required=True, 38 | help='Directory to save the output results. (required)') 39 | parser.add_argument('-b', '--boundary_path', type=str, required=True, 40 | help='Path to the semantic boundary. (required)') 41 | parser.add_argument('-i', '--input_latent_codes_path', type=str, default='', 42 | help='If specified, will load latent codes from given ' 43 | 'path instead of randomly sampling. (optional)') 44 | parser.add_argument('-n', '--num', type=int, default=1, 45 | help='Number of images for editing. This field will be ' 46 | 'ignored if `input_latent_codes_path` is specified. ' 47 | '(default: 1)') 48 | parser.add_argument('-s', '--latent_space_type', type=str, default='z', 49 | choices=['z', 'Z', 'w', 'W', 'wp', 'wP', 'Wp', 'WP'], 50 | help='Latent space used in Style GAN. (default: `Z`)') 51 | parser.add_argument('--start_distance', type=float, default=-3.0, 52 | help='Start point for manipulation in latent space. ' 53 | '(default: -3.0)') 54 | parser.add_argument('--end_distance', type=float, default=3.0, 55 | help='End point for manipulation in latent space. ' 56 | '(default: 3.0)') 57 | parser.add_argument('--steps', type=int, default=10, 58 | help='Number of steps for image editing. (default: 10)') 59 | 60 | return parser.parse_args() 61 | 62 | 63 | def main(): 64 | """Main function.""" 65 | args = parse_args() 66 | logger = setup_logger(args.output_dir, logger_name='edit') 67 | 68 | logger.info(f'Initializing generator.') 69 | gan_type = MODEL_POOL[args.model_name]['gan_type'] 70 | if gan_type == 'pggan': 71 | model = PGGANGenerator(args.model_name, logger) 72 | kwargs = {} 73 | elif gan_type == 'stylegan': 74 | model = StyleGANGenerator(args.model_name, logger) 75 | kwargs = {'latent_space_type': args.latent_space_type} 76 | elif gan_type == 'stylegan3': 77 | model = StyleGAN3Generator(args.model_name, logger) 78 | kwargs = {'latent_space_type': args.latent_space_type} 79 | elif gan_type == 'stylegan2': 80 | model = StyleGAN2Generator(args.model_name, logger) 81 | kwargs = {'latent_space_type': args.latent_space_type} 82 | else: 83 | raise NotImplementedError(f'Not implemented GAN type `{gan_type}`!') 84 | 85 | logger.info(f'Preparing boundary.') 86 | if not os.path.isfile(args.boundary_path): 87 | raise ValueError(f'Boundary `{args.boundary_path}` does not exist!') 88 | boundary = np.load(args.boundary_path) 89 | np.save(os.path.join(args.output_dir, 'boundary.npy'), boundary) 90 | 91 | logger.info(f'Preparing latent codes.') 92 | if os.path.isfile(args.input_latent_codes_path): 93 | logger.info(f' Load latent codes from `{args.input_latent_codes_path}`.') 94 | latent_codes = np.load(args.input_latent_codes_path) 95 | latent_codes = model.preprocess(latent_codes, **kwargs) 96 | else: 97 | logger.info(f' Sample latent codes randomly.') 98 | latent_codes = model.easy_sample(args.num, **kwargs) 99 | np.save(os.path.join(args.output_dir, 'latent_codes.npy'), latent_codes) 100 | total_num = latent_codes.shape[0] 101 | 102 | logger.info(f'Editing {total_num} samples.') 103 | for sample_id in tqdm(range(total_num), leave=False): 104 | interpolations = linear_interpolate(latent_codes[sample_id:sample_id + 1], 105 | boundary, 106 | start_distance=args.start_distance, 107 | end_distance=args.end_distance, 108 | steps=args.steps) 109 | interpolation_id = 0 110 | for interpolations_batch in model.get_batch_inputs(interpolations): 111 | if gan_type == 'pggan': 112 | outputs = model.easy_synthesize(interpolations_batch) 113 | elif gan_type == 'stylegan': 114 | outputs = model.easy_synthesize(interpolations_batch, **kwargs) 115 | elif gan_type in ['stylegan3', 'stylegan2']: 116 | outputs = model.easy_synthesize(interpolations_batch, **kwargs) 117 | for image in outputs['image']: 118 | save_path = os.path.join(args.output_dir, 119 | f'{sample_id:03d}_{interpolation_id:03d}.jpg') 120 | cv2.imwrite(save_path, image[:, :, ::-1]) 121 | interpolation_id += 1 122 | assert interpolation_id == args.steps 123 | logger.debug(f' Finished sample {sample_id:3d}.') 124 | logger.info(f'Successfully edited {total_num} samples.') 125 | 126 | 127 | if __name__ == '__main__': 128 | main() 129 | -------------------------------------------------------------------------------- /generate_data.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Generates a collection of images with specified model. 3 | 4 | Commonly, this file is used for data preparation. More specifically, before 5 | exploring the hidden semantics from the latent space, user need to prepare a 6 | collection of images. These images can be used for further attribute prediction. 7 | In this way, it is able to build a relationship between input latent codes and 8 | the corresponding attribute scores. 9 | """ 10 | 11 | import os.path 12 | import argparse 13 | from collections import defaultdict 14 | import cv2 15 | import numpy as np 16 | from tqdm import tqdm 17 | 18 | from models.model_settings import MODEL_POOL 19 | from models.pggan_generator import PGGANGenerator 20 | from models.stylegan_generator import StyleGANGenerator 21 | from models.stylegan3_generator import StyleGAN3Generator 22 | from models.stylegan2_generator import StyleGAN2Generator 23 | from utils.logger import setup_logger 24 | 25 | 26 | def parse_args(): 27 | """Parses arguments.""" 28 | parser = argparse.ArgumentParser( 29 | description='Generate images with given model.') 30 | parser.add_argument('-m', '--model_name', type=str, required=True, 31 | choices=list(MODEL_POOL), 32 | help='Name of the model for generation. (required)') 33 | parser.add_argument('-o', '--output_dir', type=str, required=True, 34 | help='Directory to save the output results. (required)') 35 | parser.add_argument('-i', '--latent_codes_path', type=str, default='', 36 | help='If specified, will load latent codes from given ' 37 | 'path instead of randomly sampling. (optional)') 38 | parser.add_argument('-n', '--num', type=int, default=1, 39 | help='Number of images to generate. This field will be ' 40 | 'ignored if `latent_codes_path` is specified. ' 41 | '(default: 1)') 42 | parser.add_argument('-s', '--latent_space_type', type=str, default='z', 43 | choices=['z', 'Z', 'w', 'W', 'wp', 'wP', 'Wp', 'WP'], 44 | help='Latent space used in Style GAN. (default: `Z`)') 45 | parser.add_argument('-S', '--generate_style', action='store_true', 46 | help='If specified, will generate layer-wise style codes ' 47 | 'in Style GAN. (default: do not generate styles)') 48 | parser.add_argument('-I', '--generate_image', action='store_false', 49 | help='If specified, will skip generating images in ' 50 | 'Style GAN. (default: generate images)') 51 | 52 | parser.add_argument('-r', '--resize', type=int, default=224, 53 | help='Resize the image at the specified size') 54 | 55 | return parser.parse_args() 56 | 57 | 58 | def main(): 59 | """Main function.""" 60 | args = parse_args() 61 | logger = setup_logger(args.output_dir, logger_name='generate_data') 62 | 63 | logger.info(f'Initializing generator.') 64 | gan_type = MODEL_POOL[args.model_name]['gan_type'] 65 | if gan_type == 'pggan': 66 | model = PGGANGenerator(args.model_name, logger) 67 | kwargs = {} 68 | elif gan_type == 'stylegan': 69 | model = StyleGANGenerator(args.model_name, logger) 70 | kwargs = {'latent_space_type': args.latent_space_type} 71 | elif gan_type == 'stylegan3': 72 | model = StyleGAN3Generator(args.model_name, logger) 73 | kwargs = {'latent_space_type': args.latent_space_type} 74 | elif gan_type == 'stylegan2': 75 | model = StyleGAN2Generator(args.model_name, logger) 76 | kwargs = {'latent_space_type': args.latent_space_type} 77 | else: 78 | raise NotImplementedError(f'Not implemented GAN type `{gan_type}`!') 79 | 80 | logger.info(f'Preparing latent codes.') 81 | if os.path.isfile(args.latent_codes_path): 82 | logger.info(f' Load latent codes from `{args.latent_codes_path}`.') 83 | latent_codes = np.load(args.latent_codes_path) 84 | latent_codes = model.preprocess(latent_codes, **kwargs) 85 | else: 86 | logger.info(f' Sample latent codes randomly.') 87 | latent_codes = model.easy_sample(args.num, **kwargs) 88 | total_num = latent_codes.shape[0] 89 | 90 | logger.info(f'Generating {total_num} samples.') 91 | results = defaultdict(list) 92 | pbar = tqdm(total=total_num, leave=False) 93 | for latent_codes_batch in model.get_batch_inputs(latent_codes): 94 | if gan_type == 'pggan': 95 | outputs = model.easy_synthesize(latent_codes_batch) 96 | elif gan_type == 'stylegan': 97 | outputs = model.easy_synthesize(latent_codes_batch, 98 | **kwargs, 99 | generate_style=args.generate_style, 100 | generate_image=args.generate_image) 101 | elif gan_type == 'stylegan3' or gan_type == 'stylegan2': 102 | outputs = model.easy_synthesize(latent_codes_batch, 103 | **kwargs, 104 | generate_style=args.generate_style, 105 | generate_image=args.generate_image) 106 | for key, val in outputs.items(): 107 | if key == 'image': 108 | for image in val: 109 | image = image[:, :, ::-1] 110 | save_path = os.path.join(args.output_dir, f'{pbar.n:06d}.jpg') 111 | if args.resize: 112 | resize_dim = (args.resize, args.resize) 113 | image = cv2.resize(image, resize_dim, interpolation = cv2.INTER_CUBIC) 114 | cv2.imwrite(save_path, image) 115 | pbar.update(1) 116 | else: 117 | results[key].append(val) 118 | if 'image' not in outputs: 119 | pbar.update(latent_codes_batch.shape[0]) 120 | if pbar.n % 1000 == 0 or pbar.n == total_num: 121 | logger.debug(f' Finish {pbar.n:6d} samples.') 122 | pbar.close() 123 | 124 | logger.info(f'Saving results.') 125 | for key, val in results.items(): 126 | save_path = os.path.join(args.output_dir, f'{key}.npy') 127 | np.save(save_path, np.concatenate(val, axis=0)) 128 | 129 | 130 | if __name__ == '__main__': 131 | main() 132 | -------------------------------------------------------------------------------- /images/bald1.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/images/bald1.gif -------------------------------------------------------------------------------- /images/bald2.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/images/bald2.gif -------------------------------------------------------------------------------- /images/blond.gif: -------------------------------------------------------------------------------- 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StyleGAN: 10 | 11 | NOTE: Any new model should be registered in `MODEL_POOL` before using. 12 | """ 13 | 14 | import os.path 15 | 16 | BASE_DIR = os.path.dirname(os.path.relpath(__file__)) 17 | 18 | MODEL_DIR = BASE_DIR + '/pretrain' 19 | 20 | MODEL_POOL = { 21 | 'pggan_celebahq': { 22 | 'tf_model_path': MODEL_DIR + '/karras2018iclr-celebahq-1024x1024.pkl', 23 | 'model_path': MODEL_DIR + '/pggan_celebahq.pth', 24 | 'gan_type': 'pggan', 25 | 'dataset_name': 'celebahq', 26 | 'latent_space_dim': 512, 27 | 'resolution': 1024, 28 | 'min_val': -1.0, 29 | 'max_val': 1.0, 30 | 'output_channels': 3, 31 | 'channel_order': 'RGB', 32 | 'fused_scale': False, 33 | }, 34 | 'stylegan_celebahq': { 35 | 'tf_model_path': 36 | MODEL_DIR + '/karras2019stylegan-celebahq-1024x1024.pkl', 37 | 'model_path': MODEL_DIR + '/stylegan_celebahq.pth', 38 | 'gan_type': 'stylegan', 39 | 'dataset_name': 'celebahq', 40 | 'latent_space_dim': 512, 41 | 'w_space_dim': 512, 42 | 'resolution': 1024, 43 | 'min_val': -1.0, 44 | 'max_val': 1.0, 45 | 'output_channels': 3, 46 | 'channel_order': 'RGB', 47 | 'fused_scale': 'auto', 48 | }, 49 | 'stylegan_ffhq': { 50 | 'tf_model_path': MODEL_DIR + '/karras2019stylegan-ffhq-1024x1024.pkl', 51 | 'model_path': MODEL_DIR + '/stylegan_ffhq.pth', 52 | 'gan_type': 'stylegan', 53 | 'dataset_name': 'ffhq', 54 | 'latent_space_dim': 512, 55 | 'w_space_dim': 512, 56 | 'resolution': 1024, 57 | 'min_val': -1.0, 58 | 'max_val': 1.0, 59 | 'output_channels': 3, 60 | 'channel_order': 'RGB', 61 | 'fused_scale': 'auto', 62 | }, 63 | 'stylegan2_ffhq': { 64 | 'tf_model_path': MODEL_DIR + '/karras2019stylegan-ffhq-1024x1024.pkl', 65 | 'model_path': MODEL_DIR + '/stylegan2-ffhq-1024x1024.pkl', 66 | 'gan_type': 'stylegan2', 67 | 'dataset_name': 'ffhq', 68 | 'latent_space_dim': 512, 69 | 'w_space_dim': 512, 70 | 'c_space_dim': 512, 71 | 'resolution': 1024, 72 | 'min_val': -1.0, 73 | 'max_val': 1.0, 74 | 'output_channels': 3, 75 | 'channel_order': 'RGB', 76 | 'fused_scale': 'auto', 77 | }, 78 | 'stylegan3_ffhq': { 79 | 'model_path': MODEL_DIR + '/stylegan3-t-ffhq-1024x1024.pkl', 80 | 'gan_type': 'stylegan3', 81 | 'dataset_name': 'ffhq', 82 | 'latent_space_dim': 512, 83 | 'w_space_dim': 512, 84 | 'c_space_dim': 512, 85 | 'resolution': 1024, 86 | 'min_val': -1.0, 87 | 'max_val': 1.0, 88 | 'output_channels': 3, 89 | 'channel_order': 'RGB', 90 | 'fused_scale': 'auto', 91 | }, 92 | } 93 | 94 | # Settings for StyleGAN. 95 | STYLEGAN_TRUNCATION_PSI = 0.7 # 1.0 means no truncation 96 | STYLEGAN_TRUNCATION_LAYERS = 8 # 0 means no truncation 97 | STYLEGAN_RANDOMIZE_NOISE = False 98 | 99 | # Settings for model running. 100 | USE_CUDA = True 101 | 102 | MAX_IMAGES_ON_DEVICE = 8 103 | -------------------------------------------------------------------------------- /models/pggan_generator.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Contains the generator class of ProgressiveGAN. 3 | 4 | Basically, this class is derived from the `BaseGenerator` class defined in 5 | `base_generator.py`. 6 | """ 7 | 8 | import os 9 | import numpy as np 10 | 11 | import torch 12 | 13 | from . import model_settings 14 | from .pggan_generator_model import PGGANGeneratorModel 15 | from .base_generator import BaseGenerator 16 | 17 | __all__ = ['PGGANGenerator'] 18 | 19 | 20 | class PGGANGenerator(BaseGenerator): 21 | """Defines the generator class of ProgressiveGAN.""" 22 | 23 | def __init__(self, model_name, logger=None): 24 | super().__init__(model_name, logger) 25 | assert self.gan_type == 'pggan' 26 | 27 | def build(self): 28 | self.check_attr('fused_scale') 29 | self.model = PGGANGeneratorModel(resolution=self.resolution, 30 | fused_scale=self.fused_scale, 31 | output_channels=self.output_channels) 32 | 33 | def load(self): 34 | self.logger.info(f'Loading pytorch model from `{self.model_path}`.') 35 | self.model.load_state_dict(torch.load(self.model_path)) 36 | self.logger.info(f'Successfully loaded!') 37 | self.lod = self.model.lod.to(self.cpu_device).tolist() 38 | self.logger.info(f' `lod` of the loaded model is {self.lod}.') 39 | 40 | def convert_tf_model(self, test_num=10): 41 | import sys 42 | import pickle 43 | import tensorflow as tf 44 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' 45 | sys.path.append(model_settings.BASE_DIR + '/pggan_tf_official') 46 | 47 | self.logger.info(f'Loading tensorflow model from `{self.tf_model_path}`.') 48 | tf.InteractiveSession() 49 | with open(self.tf_model_path, 'rb') as f: 50 | _, _, tf_model = pickle.load(f) 51 | self.logger.info(f'Successfully loaded!') 52 | 53 | self.logger.info(f'Converting tensorflow model to pytorch version.') 54 | tf_vars = dict(tf_model.__getstate__()['variables']) 55 | state_dict = self.model.state_dict() 56 | for pth_var_name, tf_var_name in self.model.pth_to_tf_var_mapping.items(): 57 | if 'ToRGB_lod' in tf_var_name: 58 | lod = int(tf_var_name[len('ToRGB_lod')]) 59 | lod_shift = 10 - int(np.log2(self.resolution)) 60 | tf_var_name = tf_var_name.replace(f'{lod}', f'{lod - lod_shift}') 61 | if tf_var_name not in tf_vars: 62 | self.logger.debug(f'Variable `{tf_var_name}` does not exist in ' 63 | f'tensorflow model.') 64 | continue 65 | self.logger.debug(f' Converting `{tf_var_name}` to `{pth_var_name}`.') 66 | var = torch.from_numpy(np.array(tf_vars[tf_var_name])) 67 | if 'weight' in pth_var_name: 68 | if 'layer0.conv' in pth_var_name: 69 | var = var.view(var.shape[0], -1, 4, 4).permute(1, 0, 2, 3).flip(2, 3) 70 | elif 'Conv0_up' in tf_var_name: 71 | var = var.permute(0, 1, 3, 2) 72 | else: 73 | var = var.permute(3, 2, 0, 1) 74 | state_dict[pth_var_name] = var 75 | self.logger.info(f'Successfully converted!') 76 | 77 | self.logger.info(f'Saving pytorch model to `{self.model_path}`.') 78 | torch.save(state_dict, self.model_path) 79 | self.logger.info(f'Successfully saved!') 80 | 81 | self.load() 82 | 83 | # Official tensorflow model can only run on GPU. 84 | if test_num <= 0 or not tf.test.is_built_with_cuda(): 85 | return 86 | self.logger.info(f'Testing conversion results.') 87 | self.model.eval().to(self.run_device) 88 | label_dim = tf_model.input_shapes[1][1] 89 | tf_fake_label = np.zeros((1, label_dim), np.float32) 90 | total_distance = 0.0 91 | for i in range(test_num): 92 | latent_code = self.easy_sample(1) 93 | tf_output = tf_model.run(latent_code, tf_fake_label) 94 | pth_output = self.synthesize(latent_code)['image'] 95 | distance = np.average(np.abs(tf_output - pth_output)) 96 | self.logger.debug(f' Test {i:03d}: distance {distance:.6e}.') 97 | total_distance += distance 98 | self.logger.info(f'Average distance is {total_distance / test_num:.6e}.') 99 | 100 | def sample(self, num): 101 | assert num > 0 102 | return np.random.randn(num, self.latent_space_dim).astype(np.float32) 103 | 104 | def preprocess(self, latent_codes): 105 | if not isinstance(latent_codes, np.ndarray): 106 | raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') 107 | 108 | latent_codes = latent_codes.reshape(-1, self.latent_space_dim) 109 | norm = np.linalg.norm(latent_codes, axis=1, keepdims=True) 110 | latent_codes = latent_codes / norm * np.sqrt(self.latent_space_dim) 111 | return latent_codes.astype(np.float32) 112 | 113 | def synthesize(self, latent_codes): 114 | if not isinstance(latent_codes, np.ndarray): 115 | raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') 116 | latent_codes_shape = latent_codes.shape 117 | if not (len(latent_codes_shape) == 2 and 118 | latent_codes_shape[0] <= self.batch_size and 119 | latent_codes_shape[1] == self.latent_space_dim): 120 | raise ValueError(f'Latent_codes should be with shape [batch_size, ' 121 | f'latent_space_dim], where `batch_size` no larger than ' 122 | f'{self.batch_size}, and `latent_space_dim` equal to ' 123 | f'{self.latent_space_dim}!\n' 124 | f'But {latent_codes_shape} received!') 125 | 126 | zs = torch.from_numpy(latent_codes).type(torch.FloatTensor) 127 | zs = zs.to(self.run_device) 128 | images = self.model(zs) 129 | results = { 130 | 'z': latent_codes, 131 | 'image': self.get_value(images), 132 | } 133 | return results 134 | -------------------------------------------------------------------------------- /models/pggan_tf_official/README.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/models/pggan_tf_official/README.md -------------------------------------------------------------------------------- /models/pggan_tf_official/legacy.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | import pickle 9 | import inspect 10 | import numpy as np 11 | 12 | import tfutil 13 | import networks 14 | 15 | #---------------------------------------------------------------------------- 16 | # Custom unpickler that is able to load network pickles produced by 17 | # the old Theano implementation. 18 | 19 | class LegacyUnpickler(pickle.Unpickler): 20 | def __init__(self, *args, **kwargs): 21 | super().__init__(*args, **kwargs) 22 | 23 | def find_class(self, module, name): 24 | if module == 'network' and name == 'Network': 25 | return tfutil.Network 26 | return super().find_class(module, name) 27 | 28 | #---------------------------------------------------------------------------- 29 | # Import handler for tfutil.Network that silently converts networks produced 30 | # by the old Theano implementation to a suitable format. 31 | 32 | theano_gan_remap = { 33 | 'G_paper': 'G_paper', 34 | 'G_progressive_8': 'G_paper', 35 | 'D_paper': 'D_paper', 36 | 'D_progressive_8': 'D_paper'} 37 | 38 | def patch_theano_gan(state): 39 | if 'version' in state or state['build_func_spec']['func'] not in theano_gan_remap: 40 | return state 41 | 42 | spec = dict(state['build_func_spec']) 43 | func = spec.pop('func') 44 | resolution = spec.get('resolution', 32) 45 | resolution_log2 = int(np.log2(resolution)) 46 | use_wscale = spec.get('use_wscale', True) 47 | 48 | assert spec.pop('label_size', 0) == 0 49 | assert spec.pop('use_batchnorm', False) == False 50 | assert spec.pop('tanh_at_end', None) is None 51 | assert spec.pop('mbstat_func', 'Tstdeps') == 'Tstdeps' 52 | assert spec.pop('mbstat_avg', 'all') == 'all' 53 | assert spec.pop('mbdisc_kernels', None) is None 54 | spec.pop( 'use_gdrop', True) # doesn't make a difference 55 | assert spec.pop('use_layernorm', False) == False 56 | spec[ 'fused_scale'] = False 57 | spec[ 'mbstd_group_size'] = 16 58 | 59 | vars = [] 60 | param_iter = iter(state['param_values']) 61 | relu = np.sqrt(2); linear = 1.0 62 | def flatten2(w): return w.reshape(w.shape[0], -1) 63 | def he_std(gain, w): return gain / np.sqrt(np.prod(w.shape[:-1])) 64 | def wscale(gain, w): return w * next(param_iter) / he_std(gain, w) if use_wscale else w 65 | def layer(name, gain, w): return [(name + '/weight', wscale(gain, w)), (name + '/bias', next(param_iter))] 66 | 67 | if func.startswith('G'): 68 | vars += layer('4x4/Dense', relu/4, flatten2(next(param_iter).transpose(1,0,2,3))) 69 | vars += layer('4x4/Conv', relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1]) 70 | for res in range(3, resolution_log2 + 1): 71 | vars += layer('%dx%d/Conv0' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1]) 72 | vars += layer('%dx%d/Conv1' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1]) 73 | for lod in range(0, resolution_log2 - 1): 74 | vars += layer('ToRGB_lod%d' % lod, linear, next(param_iter)[np.newaxis, np.newaxis]) 75 | 76 | if func.startswith('D'): 77 | vars += layer('FromRGB_lod0', relu, next(param_iter)[np.newaxis, np.newaxis]) 78 | for res in range(resolution_log2, 2, -1): 79 | vars += layer('%dx%d/Conv0' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1]) 80 | vars += layer('%dx%d/Conv1' % (2**res, 2**res), relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1]) 81 | vars += layer('FromRGB_lod%d' % (resolution_log2 - (res - 1)), relu, next(param_iter)[np.newaxis, np.newaxis]) 82 | vars += layer('4x4/Conv', relu, next(param_iter).transpose(2,3,1,0)[::-1,::-1]) 83 | vars += layer('4x4/Dense0', relu, flatten2(next(param_iter)[:,:,::-1,::-1]).transpose()) 84 | vars += layer('4x4/Dense1', linear, next(param_iter)) 85 | 86 | vars += [('lod', state['toplevel_params']['cur_lod'])] 87 | 88 | return { 89 | 'version': 2, 90 | 'name': func, 91 | 'build_module_src': inspect.getsource(networks), 92 | 'build_func_name': theano_gan_remap[func], 93 | 'static_kwargs': spec, 94 | 'variables': vars} 95 | 96 | tfutil.network_import_handlers.append(patch_theano_gan) 97 | 98 | #---------------------------------------------------------------------------- 99 | # Import handler for tfutil.Network that ignores unsupported/deprecated 100 | # networks produced by older versions of the code. 101 | 102 | def ignore_unknown_theano_network(state): 103 | if 'version' in state: 104 | return state 105 | 106 | print('Ignoring unknown Theano network:', state['build_func_spec']['func']) 107 | return { 108 | 'version': 2, 109 | 'name': 'Dummy', 110 | 'build_module_src': 'def dummy(input, **kwargs): input.set_shape([None, 1]); return input', 111 | 'build_func_name': 'dummy', 112 | 'static_kwargs': {}, 113 | 'variables': []} 114 | 115 | tfutil.network_import_handlers.append(ignore_unknown_theano_network) 116 | 117 | #---------------------------------------------------------------------------- 118 | -------------------------------------------------------------------------------- /models/pggan_tf_official/loss.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | import numpy as np 9 | import tensorflow as tf 10 | 11 | import tfutil 12 | 13 | #---------------------------------------------------------------------------- 14 | # Convenience func that casts all of its arguments to tf.float32. 15 | 16 | def fp32(*values): 17 | if len(values) == 1 and isinstance(values[0], tuple): 18 | values = values[0] 19 | values = tuple(tf.cast(v, tf.float32) for v in values) 20 | return values if len(values) >= 2 else values[0] 21 | 22 | #---------------------------------------------------------------------------- 23 | # Generator loss function used in the paper (WGAN + AC-GAN). 24 | 25 | def G_wgan_acgan(G, D, opt, training_set, minibatch_size, 26 | cond_weight = 1.0): # Weight of the conditioning term. 27 | 28 | latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:]) 29 | labels = training_set.get_random_labels_tf(minibatch_size) 30 | fake_images_out = G.get_output_for(latents, labels, is_training=True) 31 | fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True)) 32 | loss = -fake_scores_out 33 | 34 | if D.output_shapes[1][1] > 0: 35 | with tf.name_scope('LabelPenalty'): 36 | label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out) 37 | loss += label_penalty_fakes * cond_weight 38 | return loss 39 | 40 | #---------------------------------------------------------------------------- 41 | # Discriminator loss function used in the paper (WGAN-GP + AC-GAN). 42 | 43 | def D_wgangp_acgan(G, D, opt, training_set, minibatch_size, reals, labels, 44 | wgan_lambda = 10.0, # Weight for the gradient penalty term. 45 | wgan_epsilon = 0.001, # Weight for the epsilon term, \epsilon_{drift}. 46 | wgan_target = 1.0, # Target value for gradient magnitudes. 47 | cond_weight = 1.0): # Weight of the conditioning terms. 48 | 49 | latents = tf.random_normal([minibatch_size] + G.input_shapes[0][1:]) 50 | fake_images_out = G.get_output_for(latents, labels, is_training=True) 51 | real_scores_out, real_labels_out = fp32(D.get_output_for(reals, is_training=True)) 52 | fake_scores_out, fake_labels_out = fp32(D.get_output_for(fake_images_out, is_training=True)) 53 | real_scores_out = tfutil.autosummary('Loss/real_scores', real_scores_out) 54 | fake_scores_out = tfutil.autosummary('Loss/fake_scores', fake_scores_out) 55 | loss = fake_scores_out - real_scores_out 56 | 57 | with tf.name_scope('GradientPenalty'): 58 | mixing_factors = tf.random_uniform([minibatch_size, 1, 1, 1], 0.0, 1.0, dtype=fake_images_out.dtype) 59 | mixed_images_out = tfutil.lerp(tf.cast(reals, fake_images_out.dtype), fake_images_out, mixing_factors) 60 | mixed_scores_out, mixed_labels_out = fp32(D.get_output_for(mixed_images_out, is_training=True)) 61 | mixed_scores_out = tfutil.autosummary('Loss/mixed_scores', mixed_scores_out) 62 | mixed_loss = opt.apply_loss_scaling(tf.reduce_sum(mixed_scores_out)) 63 | mixed_grads = opt.undo_loss_scaling(fp32(tf.gradients(mixed_loss, [mixed_images_out])[0])) 64 | mixed_norms = tf.sqrt(tf.reduce_sum(tf.square(mixed_grads), axis=[1,2,3])) 65 | mixed_norms = tfutil.autosummary('Loss/mixed_norms', mixed_norms) 66 | gradient_penalty = tf.square(mixed_norms - wgan_target) 67 | loss += gradient_penalty * (wgan_lambda / (wgan_target**2)) 68 | 69 | with tf.name_scope('EpsilonPenalty'): 70 | epsilon_penalty = tfutil.autosummary('Loss/epsilon_penalty', tf.square(real_scores_out)) 71 | loss += epsilon_penalty * wgan_epsilon 72 | 73 | if D.output_shapes[1][1] > 0: 74 | with tf.name_scope('LabelPenalty'): 75 | label_penalty_reals = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=real_labels_out) 76 | label_penalty_fakes = tf.nn.softmax_cross_entropy_with_logits_v2(labels=labels, logits=fake_labels_out) 77 | label_penalty_reals = tfutil.autosummary('Loss/label_penalty_reals', label_penalty_reals) 78 | label_penalty_fakes = tfutil.autosummary('Loss/label_penalty_fakes', label_penalty_fakes) 79 | loss += (label_penalty_reals + label_penalty_fakes) * cond_weight 80 | return loss 81 | 82 | #---------------------------------------------------------------------------- 83 | -------------------------------------------------------------------------------- /models/pggan_tf_official/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | # empty 2 | -------------------------------------------------------------------------------- /models/pggan_tf_official/metrics/inception_score.py: -------------------------------------------------------------------------------- 1 | # Copyright 2016 Wojciech Zaremba 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | # Adapted from the original implementation by Wojciech Zaremba. 16 | # Source: https://github.com/openai/improved-gan/blob/master/inception_score/model.py 17 | 18 | from __future__ import absolute_import 19 | from __future__ import division 20 | from __future__ import print_function 21 | 22 | import os.path 23 | import sys 24 | import tarfile 25 | 26 | import numpy as np 27 | from six.moves import urllib 28 | import tensorflow as tf 29 | import glob 30 | import scipy.misc 31 | import math 32 | import sys 33 | 34 | MODEL_DIR = '/tmp/imagenet' 35 | 36 | DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz' 37 | softmax = None 38 | 39 | # Call this function with list of images. Each of elements should be a 40 | # numpy array with values ranging from 0 to 255. 41 | def get_inception_score(images, splits=10): 42 | assert(type(images) == list) 43 | assert(type(images[0]) == np.ndarray) 44 | assert(len(images[0].shape) == 3) 45 | #assert(np.max(images[0]) > 10) # EDIT: commented out 46 | #assert(np.min(images[0]) >= 0.0) 47 | inps = [] 48 | for img in images: 49 | img = img.astype(np.float32) 50 | inps.append(np.expand_dims(img, 0)) 51 | bs = 100 52 | with tf.Session() as sess: 53 | preds = [] 54 | n_batches = int(math.ceil(float(len(inps)) / float(bs))) 55 | for i in range(n_batches): 56 | #sys.stdout.write(".") # EDIT: commented out 57 | #sys.stdout.flush() 58 | inp = inps[(i * bs):min((i + 1) * bs, len(inps))] 59 | inp = np.concatenate(inp, 0) 60 | pred = sess.run(softmax, {'ExpandDims:0': inp}) 61 | preds.append(pred) 62 | preds = np.concatenate(preds, 0) 63 | scores = [] 64 | for i in range(splits): 65 | part = preds[(i * preds.shape[0] // splits):((i + 1) * preds.shape[0] // splits), :] 66 | kl = part * (np.log(part) - np.log(np.expand_dims(np.mean(part, 0), 0))) 67 | kl = np.mean(np.sum(kl, 1)) 68 | scores.append(np.exp(kl)) 69 | return np.mean(scores), np.std(scores) 70 | 71 | # This function is called automatically. 72 | def _init_inception(): 73 | global softmax 74 | if not os.path.exists(MODEL_DIR): 75 | os.makedirs(MODEL_DIR) 76 | filename = DATA_URL.split('/')[-1] 77 | filepath = os.path.join(MODEL_DIR, filename) 78 | if not os.path.exists(filepath): 79 | def _progress(count, block_size, total_size): 80 | sys.stdout.write('\r>> Downloading %s %.1f%%' % ( 81 | filename, float(count * block_size) / float(total_size) * 100.0)) 82 | sys.stdout.flush() 83 | filepath, _ = urllib.request.urlretrieve(DATA_URL, filepath, _progress) 84 | print() 85 | statinfo = os.stat(filepath) 86 | print('Succesfully downloaded', filename, statinfo.st_size, 'bytes.') 87 | tarfile.open(filepath, 'r:gz').extractall(MODEL_DIR) # EDIT: increased indent 88 | with tf.gfile.FastGFile(os.path.join( 89 | MODEL_DIR, 'classify_image_graph_def.pb'), 'rb') as f: 90 | graph_def = tf.GraphDef() 91 | graph_def.ParseFromString(f.read()) 92 | _ = tf.import_graph_def(graph_def, name='') 93 | # Works with an arbitrary minibatch size. 94 | with tf.Session() as sess: 95 | pool3 = sess.graph.get_tensor_by_name('pool_3:0') 96 | ops = pool3.graph.get_operations() 97 | for op_idx, op in enumerate(ops): 98 | for o in op.outputs: 99 | shape = o.get_shape() 100 | shape = [s.value for s in shape] 101 | new_shape = [] 102 | for j, s in enumerate(shape): 103 | if s == 1 and j == 0: 104 | new_shape.append(None) 105 | else: 106 | new_shape.append(s) 107 | try: 108 | o._shape = tf.TensorShape(new_shape) 109 | except ValueError: 110 | o._shape_val = tf.TensorShape(new_shape) # EDIT: added for compatibility with tensorflow 1.6.0 111 | w = sess.graph.get_operation_by_name("softmax/logits/MatMul").inputs[1] 112 | logits = tf.matmul(tf.squeeze(pool3), w) 113 | softmax = tf.nn.softmax(logits) 114 | 115 | #if softmax is None: # EDIT: commented out 116 | # _init_inception() # EDIT: commented out 117 | 118 | #---------------------------------------------------------------------------- 119 | # EDIT: added 120 | 121 | class API: 122 | def __init__(self, num_images, image_shape, image_dtype, minibatch_size): 123 | import config 124 | globals()['MODEL_DIR'] = os.path.join(config.result_dir, '_inception') 125 | self.sess = tf.get_default_session() 126 | _init_inception() 127 | 128 | def get_metric_names(self): 129 | return ['IS_mean', 'IS_std'] 130 | 131 | def get_metric_formatting(self): 132 | return ['%-10.4f', '%-10.4f'] 133 | 134 | def begin(self, mode): 135 | assert mode in ['warmup', 'reals', 'fakes'] 136 | self.images = [] 137 | 138 | def feed(self, mode, minibatch): 139 | self.images.append(minibatch.transpose(0, 2, 3, 1)) 140 | 141 | def end(self, mode): 142 | images = list(np.concatenate(self.images)) 143 | with self.sess.as_default(): 144 | mean, std = get_inception_score(images) 145 | return [mean, std] 146 | 147 | #---------------------------------------------------------------------------- 148 | -------------------------------------------------------------------------------- /models/pggan_tf_official/metrics/sliced_wasserstein.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | import numpy as np 9 | import scipy.ndimage 10 | 11 | #---------------------------------------------------------------------------- 12 | 13 | def get_descriptors_for_minibatch(minibatch, nhood_size, nhoods_per_image): 14 | S = minibatch.shape # (minibatch, channel, height, width) 15 | assert len(S) == 4 and S[1] == 3 16 | N = nhoods_per_image * S[0] 17 | H = nhood_size // 2 18 | nhood, chan, x, y = np.ogrid[0:N, 0:3, -H:H+1, -H:H+1] 19 | img = nhood // nhoods_per_image 20 | x = x + np.random.randint(H, S[3] - H, size=(N, 1, 1, 1)) 21 | y = y + np.random.randint(H, S[2] - H, size=(N, 1, 1, 1)) 22 | idx = ((img * S[1] + chan) * S[2] + y) * S[3] + x 23 | return minibatch.flat[idx] 24 | 25 | #---------------------------------------------------------------------------- 26 | 27 | def finalize_descriptors(desc): 28 | if isinstance(desc, list): 29 | desc = np.concatenate(desc, axis=0) 30 | assert desc.ndim == 4 # (neighborhood, channel, height, width) 31 | desc -= np.mean(desc, axis=(0, 2, 3), keepdims=True) 32 | desc /= np.std(desc, axis=(0, 2, 3), keepdims=True) 33 | desc = desc.reshape(desc.shape[0], -1) 34 | return desc 35 | 36 | #---------------------------------------------------------------------------- 37 | 38 | def sliced_wasserstein(A, B, dir_repeats, dirs_per_repeat): 39 | assert A.ndim == 2 and A.shape == B.shape # (neighborhood, descriptor_component) 40 | results = [] 41 | for repeat in range(dir_repeats): 42 | dirs = np.random.randn(A.shape[1], dirs_per_repeat) # (descriptor_component, direction) 43 | dirs /= np.sqrt(np.sum(np.square(dirs), axis=0, keepdims=True)) # normalize descriptor components for each direction 44 | dirs = dirs.astype(np.float32) 45 | projA = np.matmul(A, dirs) # (neighborhood, direction) 46 | projB = np.matmul(B, dirs) 47 | projA = np.sort(projA, axis=0) # sort neighborhood projections for each direction 48 | projB = np.sort(projB, axis=0) 49 | dists = np.abs(projA - projB) # pointwise wasserstein distances 50 | results.append(np.mean(dists)) # average over neighborhoods and directions 51 | return np.mean(results) # average over repeats 52 | 53 | #---------------------------------------------------------------------------- 54 | 55 | def downscale_minibatch(minibatch, lod): 56 | if lod == 0: 57 | return minibatch 58 | t = minibatch.astype(np.float32) 59 | for i in range(lod): 60 | t = (t[:, :, 0::2, 0::2] + t[:, :, 0::2, 1::2] + t[:, :, 1::2, 0::2] + t[:, :, 1::2, 1::2]) * 0.25 61 | return np.round(t).clip(0, 255).astype(np.uint8) 62 | 63 | #---------------------------------------------------------------------------- 64 | 65 | gaussian_filter = np.float32([ 66 | [1, 4, 6, 4, 1], 67 | [4, 16, 24, 16, 4], 68 | [6, 24, 36, 24, 6], 69 | [4, 16, 24, 16, 4], 70 | [1, 4, 6, 4, 1]]) / 256.0 71 | 72 | def pyr_down(minibatch): # matches cv2.pyrDown() 73 | assert minibatch.ndim == 4 74 | return scipy.ndimage.convolve(minibatch, gaussian_filter[np.newaxis, np.newaxis, :, :], mode='mirror')[:, :, ::2, ::2] 75 | 76 | def pyr_up(minibatch): # matches cv2.pyrUp() 77 | assert minibatch.ndim == 4 78 | S = minibatch.shape 79 | res = np.zeros((S[0], S[1], S[2] * 2, S[3] * 2), minibatch.dtype) 80 | res[:, :, ::2, ::2] = minibatch 81 | return scipy.ndimage.convolve(res, gaussian_filter[np.newaxis, np.newaxis, :, :] * 4.0, mode='mirror') 82 | 83 | def generate_laplacian_pyramid(minibatch, num_levels): 84 | pyramid = [np.float32(minibatch)] 85 | for i in range(1, num_levels): 86 | pyramid.append(pyr_down(pyramid[-1])) 87 | pyramid[-2] -= pyr_up(pyramid[-1]) 88 | return pyramid 89 | 90 | def reconstruct_laplacian_pyramid(pyramid): 91 | minibatch = pyramid[-1] 92 | for level in pyramid[-2::-1]: 93 | minibatch = pyr_up(minibatch) + level 94 | return minibatch 95 | 96 | #---------------------------------------------------------------------------- 97 | 98 | class API: 99 | def __init__(self, num_images, image_shape, image_dtype, minibatch_size): 100 | self.nhood_size = 7 101 | self.nhoods_per_image = 128 102 | self.dir_repeats = 4 103 | self.dirs_per_repeat = 128 104 | self.resolutions = [] 105 | res = image_shape[1] 106 | while res >= 16: 107 | self.resolutions.append(res) 108 | res //= 2 109 | 110 | def get_metric_names(self): 111 | return ['SWDx1e3_%d' % res for res in self.resolutions] + ['SWDx1e3_avg'] 112 | 113 | def get_metric_formatting(self): 114 | return ['%-13.4f'] * len(self.get_metric_names()) 115 | 116 | def begin(self, mode): 117 | assert mode in ['warmup', 'reals', 'fakes'] 118 | self.descriptors = [[] for res in self.resolutions] 119 | 120 | def feed(self, mode, minibatch): 121 | for lod, level in enumerate(generate_laplacian_pyramid(minibatch, len(self.resolutions))): 122 | desc = get_descriptors_for_minibatch(level, self.nhood_size, self.nhoods_per_image) 123 | self.descriptors[lod].append(desc) 124 | 125 | def end(self, mode): 126 | desc = [finalize_descriptors(d) for d in self.descriptors] 127 | del self.descriptors 128 | if mode in ['warmup', 'reals']: 129 | self.desc_real = desc 130 | dist = [sliced_wasserstein(dreal, dfake, self.dir_repeats, self.dirs_per_repeat) for dreal, dfake in zip(self.desc_real, desc)] 131 | del desc 132 | dist = [d * 1e3 for d in dist] # multiply by 10^3 133 | return dist + [np.mean(dist)] 134 | 135 | #---------------------------------------------------------------------------- 136 | -------------------------------------------------------------------------------- /models/pggan_tf_official/requirements-pip.txt: -------------------------------------------------------------------------------- 1 | numpy>=1.13.3 2 | scipy>=1.0.0 3 | tensorflow-gpu>=1.6.0 4 | moviepy>=0.2.3.2 5 | Pillow>=3.1.1 6 | lmdb>=0.93 7 | opencv-python>=3.4.0.12 8 | cryptography>=2.1.4 9 | h5py>=2.7.1 10 | six>=1.11.0 11 | -------------------------------------------------------------------------------- /models/pretrain/Pretrained_Models_Should_Be_Placed_Here: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/models/pretrain/Pretrained_Models_Should_Be_Placed_Here -------------------------------------------------------------------------------- /models/stylegan2_generator.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Contains the generator class of StyleGAN. 3 | 4 | Basically, this class is derived from the `BaseGenerator` class defined in 5 | `base_generator.py`. 6 | """ 7 | 8 | import os 9 | import numpy as np 10 | import pickle 11 | from PIL import Image 12 | 13 | from typing import List, Optional, Tuple, Union 14 | 15 | import torch 16 | 17 | from . import model_settings 18 | from .stylegan3_official_network import StyleGAN3GeneratorModel 19 | from .base_generator import BaseGenerator 20 | 21 | __all__ = ['StyleGANGenerator'] 22 | 23 | def make_transform(translate: Tuple[float,float], angle: float): 24 | m = np.eye(3) 25 | s = np.sin(angle/360.0*np.pi*2) 26 | c = np.cos(angle/360.0*np.pi*2) 27 | m[0][0] = c 28 | m[0][1] = s 29 | m[0][2] = translate[0] 30 | m[1][0] = -s 31 | m[1][1] = c 32 | m[1][2] = translate[1] 33 | return m 34 | 35 | class StyleGAN2Generator(BaseGenerator): 36 | """Defines the generator class of StyleGAN. 37 | 38 | Different from conventional GAN, StyleGAN introduces a disentangled latent 39 | space (i.e., W space) besides the normal latent space (i.e., Z space). Then, 40 | the disentangled latent code, w, is fed into each convolutional layer to 41 | modulate the `style` of the synthesis through AdaIN (Adaptive Instance 42 | Normalization) layer. Normally, the w's fed into all layers are the same. But, 43 | they can actually be different to make different layers get different styles. 44 | Accordingly, an extended space (i.e. W+ space) is used to gather all w's 45 | together. Taking the official StyleGAN model trained on FF-HQ dataset as an 46 | instance, there are 47 | (1) Z space, with dimension (512,) 48 | (2) W space, with dimension (512,) 49 | (3) W+ space, with dimension (18, 512) 50 | """ 51 | 52 | def __init__(self, model_name, logger=None): 53 | self.truncation_psi = model_settings.STYLEGAN_TRUNCATION_PSI 54 | self.truncation_layers = model_settings.STYLEGAN_TRUNCATION_LAYERS 55 | self.randomize_noise = model_settings.STYLEGAN_RANDOMIZE_NOISE 56 | self.model_specific_vars = ['truncation.truncation'] 57 | super().__init__(model_name, logger) 58 | self.num_layers = (int(np.log2(self.resolution)) - 1) * 2 59 | assert self.gan_type in ['stylegan3', 'stylegan2'] 60 | 61 | def build(self): 62 | self.check_attr('w_space_dim') 63 | self.check_attr('fused_scale') 64 | self.model = StyleGAN3GeneratorModel( 65 | img_resolution=self.resolution, 66 | w_dim=self.w_space_dim, 67 | z_dim=self.latent_space_dim, 68 | c_dim=self.c_space_dim, 69 | img_channels=3 70 | ) 71 | 72 | 73 | def load(self): 74 | self.logger.info(f'Loading pytorch model from `{self.model_path}`.') 75 | with open(self.model_path, 'rb') as f: 76 | self.model = pickle.load(f)['G_ema'] 77 | self.logger.info(f'Successfully loaded!') 78 | # self.lod = self.model.synthesis.lod.to(self.cpu_device).tolist() 79 | # self.logger.info(f' `lod` of the loaded model is {self.lod}.') 80 | 81 | 82 | def sample(self, num, latent_space_type='Z'): 83 | """Samples latent codes randomly. 84 | 85 | Args: 86 | num: Number of latent codes to sample. Should be positive. 87 | latent_space_type: Type of latent space from which to sample latent code. 88 | Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) 89 | 90 | Returns: 91 | A `numpy.ndarray` as sampled latend codes. 92 | 93 | Raises: 94 | ValueError: If the given `latent_space_type` is not supported. 95 | """ 96 | latent_space_type = latent_space_type.upper() 97 | if latent_space_type == 'Z': 98 | latent_codes = np.random.randn(num, self.latent_space_dim) 99 | elif latent_space_type == 'W': 100 | latent_codes = np.random.randn(num, self.w_space_dim) 101 | elif latent_space_type == 'WP': 102 | latent_codes = np.random.randn(num, self.num_layers, self.w_space_dim) 103 | else: 104 | raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') 105 | 106 | return latent_codes.astype(np.float32) 107 | 108 | def preprocess(self, latent_codes, latent_space_type='Z'): 109 | """Preprocesses the input latent code if needed. 110 | 111 | Args: 112 | latent_codes: The input latent codes for preprocessing. 113 | latent_space_type: Type of latent space to which the latent codes belong. 114 | Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) 115 | 116 | Returns: 117 | The preprocessed latent codes which can be used as final input for the 118 | generator. 119 | 120 | Raises: 121 | ValueError: If the given `latent_space_type` is not supported. 122 | """ 123 | if not isinstance(latent_codes, np.ndarray): 124 | raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') 125 | 126 | latent_space_type = latent_space_type.upper() 127 | if latent_space_type == 'Z': 128 | latent_codes = latent_codes.reshape(-1, self.latent_space_dim) 129 | norm = np.linalg.norm(latent_codes, axis=1, keepdims=True) 130 | latent_codes = latent_codes / norm * np.sqrt(self.latent_space_dim) 131 | elif latent_space_type == 'W': 132 | latent_codes = latent_codes.reshape(-1, self.w_space_dim) 133 | elif latent_space_type == 'WP': 134 | latent_codes = latent_codes.reshape(-1, self.num_layers, self.w_space_dim) 135 | else: 136 | raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') 137 | 138 | return latent_codes.astype(np.float32) 139 | 140 | def easy_sample(self, num, latent_space_type='Z'): 141 | return self.sample(num, latent_space_type) 142 | 143 | def synthesize(self, 144 | latent_codes, 145 | latent_space_type='Z', 146 | generate_style=False, 147 | generate_image=True): 148 | """Synthesizes images with given latent codes. 149 | 150 | One can choose whether to generate the layer-wise style codes. 151 | 152 | Args: 153 | latent_codes: Input latent codes for image synthesis. 154 | latent_space_type: Type of latent space to which the latent codes belong. 155 | Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) 156 | generate_style: Whether to generate the layer-wise style codes. (default: 157 | False) 158 | generate_image: Whether to generate the final image synthesis. (default: 159 | True) 160 | 161 | Returns: 162 | A dictionary whose values are raw outputs from the generator. 163 | """ 164 | if not isinstance(latent_codes, np.ndarray): 165 | raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') 166 | 167 | results = {} 168 | translate = (0,0) 169 | rotate=0.0 170 | z = torch.from_numpy(latent_codes).to(self.run_device) 171 | label = torch.zeros([1, self.c_space_dim]).to(self.run_device) 172 | 173 | if hasattr(self.model.synthesis, 'input'): 174 | m = make_transform(translate, rotate) 175 | m = np.linalg.inv(m) 176 | self.model.synthesis.input.transform.copy_(torch.from_numpy(m)) 177 | 178 | ws = self.model.mapping(z, label) 179 | #wps = self.model.truncation(w) 180 | img = self.model(z, label) 181 | img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) 182 | img = img.cpu().numpy() 183 | 184 | results['image'] = img 185 | results['z'] = latent_codes 186 | results['w'] = ws.detach().cpu().numpy() 187 | #results['wp'] = wps.detach().cpu().numpy() 188 | 189 | return results 190 | -------------------------------------------------------------------------------- /models/stylegan3_generator.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Contains the generator class of StyleGAN. 3 | 4 | Basically, this class is derived from the `BaseGenerator` class defined in 5 | `base_generator.py`. 6 | """ 7 | 8 | import os 9 | import numpy as np 10 | import pickle 11 | from PIL import Image 12 | 13 | from typing import List, Optional, Tuple, Union 14 | 15 | import torch 16 | 17 | from . import model_settings 18 | from .stylegan3_official_network import StyleGAN3GeneratorModel 19 | from .base_generator import BaseGenerator 20 | 21 | __all__ = ['StyleGANGenerator'] 22 | 23 | def make_transform(translate: Tuple[float,float], angle: float): 24 | m = np.eye(3) 25 | s = np.sin(angle/360.0*np.pi*2) 26 | c = np.cos(angle/360.0*np.pi*2) 27 | m[0][0] = c 28 | m[0][1] = s 29 | m[0][2] = translate[0] 30 | m[1][0] = -s 31 | m[1][1] = c 32 | m[1][2] = translate[1] 33 | return m 34 | 35 | class StyleGAN3Generator(BaseGenerator): 36 | """Defines the generator class of StyleGAN. 37 | 38 | Different from conventional GAN, StyleGAN introduces a disentangled latent 39 | space (i.e., W space) besides the normal latent space (i.e., Z space). Then, 40 | the disentangled latent code, w, is fed into each convolutional layer to 41 | modulate the `style` of the synthesis through AdaIN (Adaptive Instance 42 | Normalization) layer. Normally, the w's fed into all layers are the same. But, 43 | they can actually be different to make different layers get different styles. 44 | Accordingly, an extended space (i.e. W+ space) is used to gather all w's 45 | together. Taking the official StyleGAN model trained on FF-HQ dataset as an 46 | instance, there are 47 | (1) Z space, with dimension (512,) 48 | (2) W space, with dimension (512,) 49 | (3) W+ space, with dimension (18, 512) 50 | """ 51 | 52 | def __init__(self, model_name, logger=None): 53 | self.truncation_psi = model_settings.STYLEGAN_TRUNCATION_PSI 54 | self.truncation_layers = model_settings.STYLEGAN_TRUNCATION_LAYERS 55 | self.randomize_noise = model_settings.STYLEGAN_RANDOMIZE_NOISE 56 | self.model_specific_vars = ['truncation.truncation'] 57 | super().__init__(model_name, logger) 58 | self.num_layers = (int(np.log2(self.resolution)) - 1) * 2 59 | assert self.gan_type in ['stylegan3', 'stylegan2'] 60 | 61 | def build(self): 62 | self.check_attr('w_space_dim') 63 | self.check_attr('fused_scale') 64 | self.model = StyleGAN3GeneratorModel( 65 | img_resolution=self.resolution, 66 | w_dim=self.w_space_dim, 67 | z_dim=self.latent_space_dim, 68 | c_dim=self.c_space_dim, 69 | img_channels=3 70 | ) 71 | 72 | 73 | def load(self): 74 | self.logger.info(f'Loading pytorch model from `{self.model_path}`.') 75 | with open(self.model_path, 'rb') as f: 76 | self.model = pickle.load(f)['G_ema'] 77 | self.logger.info(f'Successfully loaded!') 78 | # self.lod = self.model.synthesis.lod.to(self.cpu_device).tolist() 79 | # self.logger.info(f' `lod` of the loaded model is {self.lod}.') 80 | 81 | 82 | def sample(self, num, latent_space_type='Z'): 83 | """Samples latent codes randomly. 84 | 85 | Args: 86 | num: Number of latent codes to sample. Should be positive. 87 | latent_space_type: Type of latent space from which to sample latent code. 88 | Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) 89 | 90 | Returns: 91 | A `numpy.ndarray` as sampled latend codes. 92 | 93 | Raises: 94 | ValueError: If the given `latent_space_type` is not supported. 95 | """ 96 | latent_space_type = latent_space_type.upper() 97 | if latent_space_type == 'Z': 98 | latent_codes = np.random.randn(num, self.latent_space_dim) 99 | elif latent_space_type == 'W': 100 | latent_codes = np.random.randn(num, self.w_space_dim) 101 | elif latent_space_type == 'WP': 102 | latent_codes = np.random.randn(num, self.num_layers, self.w_space_dim) 103 | else: 104 | raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') 105 | 106 | return latent_codes.astype(np.float32) 107 | 108 | def preprocess(self, latent_codes, latent_space_type='Z'): 109 | """Preprocesses the input latent code if needed. 110 | 111 | Args: 112 | latent_codes: The input latent codes for preprocessing. 113 | latent_space_type: Type of latent space to which the latent codes belong. 114 | Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) 115 | 116 | Returns: 117 | The preprocessed latent codes which can be used as final input for the 118 | generator. 119 | 120 | Raises: 121 | ValueError: If the given `latent_space_type` is not supported. 122 | """ 123 | if not isinstance(latent_codes, np.ndarray): 124 | raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') 125 | 126 | latent_space_type = latent_space_type.upper() 127 | if latent_space_type == 'Z': 128 | latent_codes = latent_codes.reshape(-1, self.latent_space_dim) 129 | norm = np.linalg.norm(latent_codes, axis=1, keepdims=True) 130 | latent_codes = latent_codes / norm * np.sqrt(self.latent_space_dim) 131 | elif latent_space_type == 'W': 132 | latent_codes = latent_codes.reshape(-1, self.w_space_dim) 133 | elif latent_space_type == 'WP': 134 | latent_codes = latent_codes.reshape(-1, self.num_layers, self.w_space_dim) 135 | else: 136 | raise ValueError(f'Latent space type `{latent_space_type}` is invalid!') 137 | 138 | return latent_codes.astype(np.float32) 139 | 140 | def easy_sample(self, num, latent_space_type='Z'): 141 | return self.sample(num, latent_space_type) 142 | 143 | def synthesize(self, 144 | latent_codes, 145 | latent_space_type='Z', 146 | generate_style=False, 147 | generate_image=True): 148 | """Synthesizes images with given latent codes. 149 | 150 | One can choose whether to generate the layer-wise style codes. 151 | 152 | Args: 153 | latent_codes: Input latent codes for image synthesis. 154 | latent_space_type: Type of latent space to which the latent codes belong. 155 | Only [`Z`, `W`, `WP`] are supported. Case insensitive. (default: `Z`) 156 | generate_style: Whether to generate the layer-wise style codes. (default: 157 | False) 158 | generate_image: Whether to generate the final image synthesis. (default: 159 | True) 160 | 161 | Returns: 162 | A dictionary whose values are raw outputs from the generator. 163 | """ 164 | if not isinstance(latent_codes, np.ndarray): 165 | raise ValueError(f'Latent codes should be with type `numpy.ndarray`!') 166 | 167 | results = {} 168 | translate = (0,0) 169 | rotate=0.0 170 | z = torch.from_numpy(latent_codes).to(self.run_device) 171 | label = torch.zeros([1, self.c_space_dim]).to(self.run_device) 172 | 173 | if hasattr(self.model.synthesis, 'input'): 174 | m = make_transform(translate, rotate) 175 | m = np.linalg.inv(m) 176 | self.model.synthesis.input.transform.copy_(torch.from_numpy(m)) 177 | 178 | ws = self.model.mapping(z, label) 179 | #wps = self.model.truncation(w) 180 | img = self.model(z, label) 181 | img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) 182 | img = img.cpu().numpy() 183 | 184 | results['image'] = img 185 | results['z'] = latent_codes 186 | results['w'] = ws.detach().cpu().numpy() 187 | #results['wp'] = wps.detach().cpu().numpy() 188 | 189 | return results 190 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/config.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Global configuration.""" 9 | 10 | #---------------------------------------------------------------------------- 11 | # Paths. 12 | 13 | result_dir = 'results' 14 | data_dir = 'datasets' 15 | cache_dir = 'cache' 16 | run_dir_ignore = ['results', 'datasets', 'cache'] 17 | 18 | #---------------------------------------------------------------------------- 19 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/dnnlib/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | from . import submission 9 | 10 | from .submission.run_context import RunContext 11 | 12 | from .submission.submit import SubmitTarget 13 | from .submission.submit import PathType 14 | from .submission.submit import SubmitConfig 15 | from .submission.submit import get_path_from_template 16 | from .submission.submit import submit_run 17 | 18 | from .util import EasyDict 19 | 20 | submit_config: SubmitConfig = None # Package level variable for SubmitConfig which is only valid when inside the run function. 21 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/dnnlib/submission/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | from . import run_context 9 | from . import submit 10 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/dnnlib/submission/_internal/run.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Helper for launching run functions in computing clusters. 9 | 10 | During the submit process, this file is copied to the appropriate run dir. 11 | When the job is launched in the cluster, this module is the first thing that 12 | is run inside the docker container. 13 | """ 14 | 15 | import os 16 | import pickle 17 | import sys 18 | 19 | # PYTHONPATH should have been set so that the run_dir/src is in it 20 | import dnnlib 21 | 22 | def main(): 23 | if not len(sys.argv) >= 4: 24 | raise RuntimeError("This script needs three arguments: run_dir, task_name and host_name!") 25 | 26 | run_dir = str(sys.argv[1]) 27 | task_name = str(sys.argv[2]) 28 | host_name = str(sys.argv[3]) 29 | 30 | submit_config_path = os.path.join(run_dir, "submit_config.pkl") 31 | 32 | # SubmitConfig should have been pickled to the run dir 33 | if not os.path.exists(submit_config_path): 34 | raise RuntimeError("SubmitConfig pickle file does not exist!") 35 | 36 | submit_config: dnnlib.SubmitConfig = pickle.load(open(submit_config_path, "rb")) 37 | dnnlib.submission.submit.set_user_name_override(submit_config.user_name) 38 | 39 | submit_config.task_name = task_name 40 | submit_config.host_name = host_name 41 | 42 | dnnlib.submission.submit.run_wrapper(submit_config) 43 | 44 | if __name__ == "__main__": 45 | main() 46 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/dnnlib/submission/run_context.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Helpers for managing the run/training loop.""" 9 | 10 | import datetime 11 | import json 12 | import os 13 | import pprint 14 | import time 15 | import types 16 | 17 | from typing import Any 18 | 19 | from . import submit 20 | 21 | 22 | class RunContext(object): 23 | """Helper class for managing the run/training loop. 24 | 25 | The context will hide the implementation details of a basic run/training loop. 26 | It will set things up properly, tell if run should be stopped, and then cleans up. 27 | User should call update periodically and use should_stop to determine if run should be stopped. 28 | 29 | Args: 30 | submit_config: The SubmitConfig that is used for the current run. 31 | config_module: The whole config module that is used for the current run. 32 | max_epoch: Optional cached value for the max_epoch variable used in update. 33 | """ 34 | 35 | def __init__(self, submit_config: submit.SubmitConfig, config_module: types.ModuleType = None, max_epoch: Any = None): 36 | self.submit_config = submit_config 37 | self.should_stop_flag = False 38 | self.has_closed = False 39 | self.start_time = time.time() 40 | self.last_update_time = time.time() 41 | self.last_update_interval = 0.0 42 | self.max_epoch = max_epoch 43 | 44 | # pretty print the all the relevant content of the config module to a text file 45 | if config_module is not None: 46 | with open(os.path.join(submit_config.run_dir, "config.txt"), "w") as f: 47 | filtered_dict = {k: v for k, v in config_module.__dict__.items() if not k.startswith("_") and not isinstance(v, (types.ModuleType, types.FunctionType, types.LambdaType, submit.SubmitConfig, type))} 48 | pprint.pprint(filtered_dict, stream=f, indent=4, width=200, compact=False) 49 | 50 | # write out details about the run to a text file 51 | self.run_txt_data = {"task_name": submit_config.task_name, "host_name": submit_config.host_name, "start_time": datetime.datetime.now().isoformat(sep=" ")} 52 | with open(os.path.join(submit_config.run_dir, "run.txt"), "w") as f: 53 | pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False) 54 | 55 | def __enter__(self) -> "RunContext": 56 | return self 57 | 58 | def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None: 59 | self.close() 60 | 61 | def update(self, loss: Any = 0, cur_epoch: Any = 0, max_epoch: Any = None) -> None: 62 | """Do general housekeeping and keep the state of the context up-to-date. 63 | Should be called often enough but not in a tight loop.""" 64 | assert not self.has_closed 65 | 66 | self.last_update_interval = time.time() - self.last_update_time 67 | self.last_update_time = time.time() 68 | 69 | if os.path.exists(os.path.join(self.submit_config.run_dir, "abort.txt")): 70 | self.should_stop_flag = True 71 | 72 | max_epoch_val = self.max_epoch if max_epoch is None else max_epoch 73 | 74 | def should_stop(self) -> bool: 75 | """Tell whether a stopping condition has been triggered one way or another.""" 76 | return self.should_stop_flag 77 | 78 | def get_time_since_start(self) -> float: 79 | """How much time has passed since the creation of the context.""" 80 | return time.time() - self.start_time 81 | 82 | def get_time_since_last_update(self) -> float: 83 | """How much time has passed since the last call to update.""" 84 | return time.time() - self.last_update_time 85 | 86 | def get_last_update_interval(self) -> float: 87 | """How much time passed between the previous two calls to update.""" 88 | return self.last_update_interval 89 | 90 | def close(self) -> None: 91 | """Close the context and clean up. 92 | Should only be called once.""" 93 | if not self.has_closed: 94 | # update the run.txt with stopping time 95 | self.run_txt_data["stop_time"] = datetime.datetime.now().isoformat(sep=" ") 96 | with open(os.path.join(self.submit_config.run_dir, "run.txt"), "w") as f: 97 | pprint.pprint(self.run_txt_data, stream=f, indent=4, width=200, compact=False) 98 | 99 | self.has_closed = True 100 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/dnnlib/tflib/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | from . import autosummary 9 | from . import network 10 | from . import optimizer 11 | from . import tfutil 12 | 13 | from .tfutil import * 14 | from .network import Network 15 | 16 | from .optimizer import Optimizer 17 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/metrics/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | # empty 9 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/metrics/frechet_inception_distance.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Frechet Inception Distance (FID).""" 9 | 10 | import os 11 | import numpy as np 12 | import scipy 13 | import tensorflow as tf 14 | import dnnlib.tflib as tflib 15 | 16 | from metrics import metric_base 17 | from training import misc 18 | 19 | #---------------------------------------------------------------------------- 20 | 21 | class FID(metric_base.MetricBase): 22 | def __init__(self, num_images, minibatch_per_gpu, **kwargs): 23 | super().__init__(**kwargs) 24 | self.num_images = num_images 25 | self.minibatch_per_gpu = minibatch_per_gpu 26 | 27 | def _evaluate(self, Gs, num_gpus): 28 | minibatch_size = num_gpus * self.minibatch_per_gpu 29 | inception = misc.load_pkl('https://drive.google.com/uc?id=1MzTY44rLToO5APn8TZmfR7_ENSe5aZUn') # inception_v3_features.pkl 30 | activations = np.empty([self.num_images, inception.output_shape[1]], dtype=np.float32) 31 | 32 | # Calculate statistics for reals. 33 | cache_file = self._get_cache_file_for_reals(num_images=self.num_images) 34 | os.makedirs(os.path.dirname(cache_file), exist_ok=True) 35 | if os.path.isfile(cache_file): 36 | mu_real, sigma_real = misc.load_pkl(cache_file) 37 | else: 38 | for idx, images in enumerate(self._iterate_reals(minibatch_size=minibatch_size)): 39 | begin = idx * minibatch_size 40 | end = min(begin + minibatch_size, self.num_images) 41 | activations[begin:end] = inception.run(images[:end-begin], num_gpus=num_gpus, assume_frozen=True) 42 | if end == self.num_images: 43 | break 44 | mu_real = np.mean(activations, axis=0) 45 | sigma_real = np.cov(activations, rowvar=False) 46 | misc.save_pkl((mu_real, sigma_real), cache_file) 47 | 48 | # Construct TensorFlow graph. 49 | result_expr = [] 50 | for gpu_idx in range(num_gpus): 51 | with tf.device('/gpu:%d' % gpu_idx): 52 | Gs_clone = Gs.clone() 53 | inception_clone = inception.clone() 54 | latents = tf.random_normal([self.minibatch_per_gpu] + Gs_clone.input_shape[1:]) 55 | images = Gs_clone.get_output_for(latents, None, is_validation=True, randomize_noise=True) 56 | images = tflib.convert_images_to_uint8(images) 57 | result_expr.append(inception_clone.get_output_for(images)) 58 | 59 | # Calculate statistics for fakes. 60 | for begin in range(0, self.num_images, minibatch_size): 61 | end = min(begin + minibatch_size, self.num_images) 62 | activations[begin:end] = np.concatenate(tflib.run(result_expr), axis=0)[:end-begin] 63 | mu_fake = np.mean(activations, axis=0) 64 | sigma_fake = np.cov(activations, rowvar=False) 65 | 66 | # Calculate FID. 67 | m = np.square(mu_fake - mu_real).sum() 68 | s, _ = scipy.linalg.sqrtm(np.dot(sigma_fake, sigma_real), disp=False) # pylint: disable=no-member 69 | dist = m + np.trace(sigma_fake + sigma_real - 2*s) 70 | self._report_result(np.real(dist)) 71 | 72 | #---------------------------------------------------------------------------- 73 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/metrics/metric_base.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Common definitions for GAN metrics.""" 9 | 10 | import os 11 | import time 12 | import hashlib 13 | import numpy as np 14 | import tensorflow as tf 15 | import dnnlib 16 | import dnnlib.tflib as tflib 17 | 18 | import config 19 | from training import misc 20 | from training import dataset 21 | 22 | #---------------------------------------------------------------------------- 23 | # Standard metrics. 24 | 25 | fid50k = dnnlib.EasyDict(func_name='metrics.frechet_inception_distance.FID', name='fid50k', num_images=50000, minibatch_per_gpu=8) 26 | ppl_zfull = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_zfull', num_samples=100000, epsilon=1e-4, space='z', sampling='full', minibatch_per_gpu=16) 27 | ppl_wfull = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_wfull', num_samples=100000, epsilon=1e-4, space='w', sampling='full', minibatch_per_gpu=16) 28 | ppl_zend = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_zend', num_samples=100000, epsilon=1e-4, space='z', sampling='end', minibatch_per_gpu=16) 29 | ppl_wend = dnnlib.EasyDict(func_name='metrics.perceptual_path_length.PPL', name='ppl_wend', num_samples=100000, epsilon=1e-4, space='w', sampling='end', minibatch_per_gpu=16) 30 | ls = dnnlib.EasyDict(func_name='metrics.linear_separability.LS', name='ls', num_samples=200000, num_keep=100000, attrib_indices=range(40), minibatch_per_gpu=4) 31 | dummy = dnnlib.EasyDict(func_name='metrics.metric_base.DummyMetric', name='dummy') # for debugging 32 | 33 | #---------------------------------------------------------------------------- 34 | # Base class for metrics. 35 | 36 | class MetricBase: 37 | def __init__(self, name): 38 | self.name = name 39 | self._network_pkl = None 40 | self._dataset_args = None 41 | self._mirror_augment = None 42 | self._results = [] 43 | self._eval_time = None 44 | 45 | def run(self, network_pkl, run_dir=None, dataset_args=None, mirror_augment=None, num_gpus=1, tf_config=None, log_results=True): 46 | self._network_pkl = network_pkl 47 | self._dataset_args = dataset_args 48 | self._mirror_augment = mirror_augment 49 | self._results = [] 50 | 51 | if (dataset_args is None or mirror_augment is None) and run_dir is not None: 52 | run_config = misc.parse_config_for_previous_run(run_dir) 53 | self._dataset_args = dict(run_config['dataset']) 54 | self._dataset_args['shuffle_mb'] = 0 55 | self._mirror_augment = run_config['train'].get('mirror_augment', False) 56 | 57 | time_begin = time.time() 58 | with tf.Graph().as_default(), tflib.create_session(tf_config).as_default(): # pylint: disable=not-context-manager 59 | _G, _D, Gs = misc.load_pkl(self._network_pkl) 60 | self._evaluate(Gs, num_gpus=num_gpus) 61 | self._eval_time = time.time() - time_begin 62 | 63 | if log_results: 64 | result_str = self.get_result_str() 65 | if run_dir is not None: 66 | log = os.path.join(run_dir, 'metric-%s.txt' % self.name) 67 | with dnnlib.util.Logger(log, 'a'): 68 | print(result_str) 69 | else: 70 | print(result_str) 71 | 72 | def get_result_str(self): 73 | network_name = os.path.splitext(os.path.basename(self._network_pkl))[0] 74 | if len(network_name) > 29: 75 | network_name = '...' + network_name[-26:] 76 | result_str = '%-30s' % network_name 77 | result_str += ' time %-12s' % dnnlib.util.format_time(self._eval_time) 78 | for res in self._results: 79 | result_str += ' ' + self.name + res.suffix + ' ' 80 | result_str += res.fmt % res.value 81 | return result_str 82 | 83 | def update_autosummaries(self): 84 | for res in self._results: 85 | tflib.autosummary.autosummary('Metrics/' + self.name + res.suffix, res.value) 86 | 87 | def _evaluate(self, Gs, num_gpus): 88 | raise NotImplementedError # to be overridden by subclasses 89 | 90 | def _report_result(self, value, suffix='', fmt='%-10.4f'): 91 | self._results += [dnnlib.EasyDict(value=value, suffix=suffix, fmt=fmt)] 92 | 93 | def _get_cache_file_for_reals(self, extension='pkl', **kwargs): 94 | all_args = dnnlib.EasyDict(metric_name=self.name, mirror_augment=self._mirror_augment) 95 | all_args.update(self._dataset_args) 96 | all_args.update(kwargs) 97 | md5 = hashlib.md5(repr(sorted(all_args.items())).encode('utf-8')) 98 | dataset_name = self._dataset_args['tfrecord_dir'].replace('\\', '/').split('/')[-1] 99 | return os.path.join(config.cache_dir, '%s-%s-%s.%s' % (md5.hexdigest(), self.name, dataset_name, extension)) 100 | 101 | def _iterate_reals(self, minibatch_size): 102 | dataset_obj = dataset.load_dataset(data_dir=config.data_dir, **self._dataset_args) 103 | while True: 104 | images, _labels = dataset_obj.get_minibatch_np(minibatch_size) 105 | if self._mirror_augment: 106 | images = misc.apply_mirror_augment(images) 107 | yield images 108 | 109 | def _iterate_fakes(self, Gs, minibatch_size, num_gpus): 110 | while True: 111 | latents = np.random.randn(minibatch_size, *Gs.input_shape[1:]) 112 | fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) 113 | images = Gs.run(latents, None, output_transform=fmt, is_validation=True, num_gpus=num_gpus, assume_frozen=True) 114 | yield images 115 | 116 | #---------------------------------------------------------------------------- 117 | # Group of multiple metrics. 118 | 119 | class MetricGroup: 120 | def __init__(self, metric_kwarg_list): 121 | self.metrics = [dnnlib.util.call_func_by_name(**kwargs) for kwargs in metric_kwarg_list] 122 | 123 | def run(self, *args, **kwargs): 124 | for metric in self.metrics: 125 | metric.run(*args, **kwargs) 126 | 127 | def get_result_str(self): 128 | return ' '.join(metric.get_result_str() for metric in self.metrics) 129 | 130 | def update_autosummaries(self): 131 | for metric in self.metrics: 132 | metric.update_autosummaries() 133 | 134 | #---------------------------------------------------------------------------- 135 | # Dummy metric for debugging purposes. 136 | 137 | class DummyMetric(MetricBase): 138 | def _evaluate(self, Gs, num_gpus): 139 | _ = Gs, num_gpus 140 | self._report_result(0.0) 141 | 142 | #---------------------------------------------------------------------------- 143 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/metrics/perceptual_path_length.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Perceptual Path Length (PPL).""" 9 | 10 | import numpy as np 11 | import tensorflow as tf 12 | import dnnlib.tflib as tflib 13 | 14 | from metrics import metric_base 15 | from training import misc 16 | 17 | #---------------------------------------------------------------------------- 18 | 19 | # Normalize batch of vectors. 20 | def normalize(v): 21 | return v / tf.sqrt(tf.reduce_sum(tf.square(v), axis=-1, keepdims=True)) 22 | 23 | # Spherical interpolation of a batch of vectors. 24 | def slerp(a, b, t): 25 | a = normalize(a) 26 | b = normalize(b) 27 | d = tf.reduce_sum(a * b, axis=-1, keepdims=True) 28 | p = t * tf.math.acos(d) 29 | c = normalize(b - d * a) 30 | d = a * tf.math.cos(p) + c * tf.math.sin(p) 31 | return normalize(d) 32 | 33 | #---------------------------------------------------------------------------- 34 | 35 | class PPL(metric_base.MetricBase): 36 | def __init__(self, num_samples, epsilon, space, sampling, minibatch_per_gpu, **kwargs): 37 | assert space in ['z', 'w'] 38 | assert sampling in ['full', 'end'] 39 | super().__init__(**kwargs) 40 | self.num_samples = num_samples 41 | self.epsilon = epsilon 42 | self.space = space 43 | self.sampling = sampling 44 | self.minibatch_per_gpu = minibatch_per_gpu 45 | 46 | def _evaluate(self, Gs, num_gpus): 47 | minibatch_size = num_gpus * self.minibatch_per_gpu 48 | 49 | # Construct TensorFlow graph. 50 | distance_expr = [] 51 | for gpu_idx in range(num_gpus): 52 | with tf.device('/gpu:%d' % gpu_idx): 53 | Gs_clone = Gs.clone() 54 | noise_vars = [var for name, var in Gs_clone.components.synthesis.vars.items() if name.startswith('noise')] 55 | 56 | # Generate random latents and interpolation t-values. 57 | lat_t01 = tf.random_normal([self.minibatch_per_gpu * 2] + Gs_clone.input_shape[1:]) 58 | lerp_t = tf.random_uniform([self.minibatch_per_gpu], 0.0, 1.0 if self.sampling == 'full' else 0.0) 59 | 60 | # Interpolate in W or Z. 61 | if self.space == 'w': 62 | dlat_t01 = Gs_clone.components.mapping.get_output_for(lat_t01, None, is_validation=True) 63 | dlat_t0, dlat_t1 = dlat_t01[0::2], dlat_t01[1::2] 64 | dlat_e0 = tflib.lerp(dlat_t0, dlat_t1, lerp_t[:, np.newaxis, np.newaxis]) 65 | dlat_e1 = tflib.lerp(dlat_t0, dlat_t1, lerp_t[:, np.newaxis, np.newaxis] + self.epsilon) 66 | dlat_e01 = tf.reshape(tf.stack([dlat_e0, dlat_e1], axis=1), dlat_t01.shape) 67 | else: # space == 'z' 68 | lat_t0, lat_t1 = lat_t01[0::2], lat_t01[1::2] 69 | lat_e0 = slerp(lat_t0, lat_t1, lerp_t[:, np.newaxis]) 70 | lat_e1 = slerp(lat_t0, lat_t1, lerp_t[:, np.newaxis] + self.epsilon) 71 | lat_e01 = tf.reshape(tf.stack([lat_e0, lat_e1], axis=1), lat_t01.shape) 72 | dlat_e01 = Gs_clone.components.mapping.get_output_for(lat_e01, None, is_validation=True) 73 | 74 | # Synthesize images. 75 | with tf.control_dependencies([var.initializer for var in noise_vars]): # use same noise inputs for the entire minibatch 76 | images = Gs_clone.components.synthesis.get_output_for(dlat_e01, is_validation=True, randomize_noise=False) 77 | 78 | # Crop only the face region. 79 | c = int(images.shape[2] // 8) 80 | images = images[:, :, c*3 : c*7, c*2 : c*6] 81 | 82 | # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images. 83 | if images.shape[2] > 256: 84 | factor = images.shape[2] // 256 85 | images = tf.reshape(images, [-1, images.shape[1], images.shape[2] // factor, factor, images.shape[3] // factor, factor]) 86 | images = tf.reduce_mean(images, axis=[3,5]) 87 | 88 | # Scale dynamic range from [-1,1] to [0,255] for VGG. 89 | images = (images + 1) * (255 / 2) 90 | 91 | # Evaluate perceptual distance. 92 | img_e0, img_e1 = images[0::2], images[1::2] 93 | distance_measure = misc.load_pkl('https://drive.google.com/uc?id=1N2-m9qszOeVC9Tq77WxsLnuWwOedQiD2') # vgg16_zhang_perceptual.pkl 94 | distance_expr.append(distance_measure.get_output_for(img_e0, img_e1) * (1 / self.epsilon**2)) 95 | 96 | # Sampling loop. 97 | all_distances = [] 98 | for _ in range(0, self.num_samples, minibatch_size): 99 | all_distances += tflib.run(distance_expr) 100 | all_distances = np.concatenate(all_distances, axis=0) 101 | 102 | # Reject outliers. 103 | lo = np.percentile(all_distances, 1, interpolation='lower') 104 | hi = np.percentile(all_distances, 99, interpolation='higher') 105 | filtered_distances = np.extract(np.logical_and(lo <= all_distances, all_distances <= hi), all_distances) 106 | self._report_result(np.mean(filtered_distances)) 107 | 108 | #---------------------------------------------------------------------------- 109 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/pretrained_example.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Minimal script for generating an image using pre-trained StyleGAN generator.""" 9 | 10 | import os 11 | import pickle 12 | import numpy as np 13 | import PIL.Image 14 | import dnnlib 15 | import dnnlib.tflib as tflib 16 | import config 17 | 18 | def main(): 19 | # Initialize TensorFlow. 20 | tflib.init_tf() 21 | 22 | # Load pre-trained network. 23 | url = 'https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ' # karras2019stylegan-ffhq-1024x1024.pkl 24 | with dnnlib.util.open_url(url, cache_dir=config.cache_dir) as f: 25 | _G, _D, Gs = pickle.load(f) 26 | # _G = Instantaneous snapshot of the generator. Mainly useful for resuming a previous training run. 27 | # _D = Instantaneous snapshot of the discriminator. Mainly useful for resuming a previous training run. 28 | # Gs = Long-term average of the generator. Yields higher-quality results than the instantaneous snapshot. 29 | 30 | # Print network details. 31 | Gs.print_layers() 32 | 33 | # Pick latent vector. 34 | rnd = np.random.RandomState(5) 35 | latents = rnd.randn(1, Gs.input_shape[1]) 36 | 37 | # Generate image. 38 | fmt = dict(func=tflib.convert_images_to_uint8, nchw_to_nhwc=True) 39 | images = Gs.run(latents, None, truncation_psi=0.7, randomize_noise=True, output_transform=fmt) 40 | 41 | # Save image. 42 | os.makedirs(config.result_dir, exist_ok=True) 43 | png_filename = os.path.join(config.result_dir, 'example.png') 44 | PIL.Image.fromarray(images[0], 'RGB').save(png_filename) 45 | 46 | if __name__ == "__main__": 47 | main() 48 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/run_metrics.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | """Main entry point for training StyleGAN and ProGAN networks.""" 9 | 10 | import dnnlib 11 | from dnnlib import EasyDict 12 | import dnnlib.tflib as tflib 13 | 14 | import config 15 | from metrics import metric_base 16 | from training import misc 17 | 18 | #---------------------------------------------------------------------------- 19 | 20 | def run_pickle(submit_config, metric_args, network_pkl, dataset_args, mirror_augment): 21 | ctx = dnnlib.RunContext(submit_config) 22 | tflib.init_tf() 23 | print('Evaluating %s metric on network_pkl "%s"...' % (metric_args.name, network_pkl)) 24 | metric = dnnlib.util.call_func_by_name(**metric_args) 25 | print() 26 | metric.run(network_pkl, dataset_args=dataset_args, mirror_augment=mirror_augment, num_gpus=submit_config.num_gpus) 27 | print() 28 | ctx.close() 29 | 30 | #---------------------------------------------------------------------------- 31 | 32 | def run_snapshot(submit_config, metric_args, run_id, snapshot): 33 | ctx = dnnlib.RunContext(submit_config) 34 | tflib.init_tf() 35 | print('Evaluating %s metric on run_id %s, snapshot %s...' % (metric_args.name, run_id, snapshot)) 36 | run_dir = misc.locate_run_dir(run_id) 37 | network_pkl = misc.locate_network_pkl(run_dir, snapshot) 38 | metric = dnnlib.util.call_func_by_name(**metric_args) 39 | print() 40 | metric.run(network_pkl, run_dir=run_dir, num_gpus=submit_config.num_gpus) 41 | print() 42 | ctx.close() 43 | 44 | #---------------------------------------------------------------------------- 45 | 46 | def run_all_snapshots(submit_config, metric_args, run_id): 47 | ctx = dnnlib.RunContext(submit_config) 48 | tflib.init_tf() 49 | print('Evaluating %s metric on all snapshots of run_id %s...' % (metric_args.name, run_id)) 50 | run_dir = misc.locate_run_dir(run_id) 51 | network_pkls = misc.list_network_pkls(run_dir) 52 | metric = dnnlib.util.call_func_by_name(**metric_args) 53 | print() 54 | for idx, network_pkl in enumerate(network_pkls): 55 | ctx.update('', idx, len(network_pkls)) 56 | metric.run(network_pkl, run_dir=run_dir, num_gpus=submit_config.num_gpus) 57 | print() 58 | ctx.close() 59 | 60 | #---------------------------------------------------------------------------- 61 | 62 | def main(): 63 | submit_config = dnnlib.SubmitConfig() 64 | 65 | # Which metrics to evaluate? 66 | metrics = [] 67 | metrics += [metric_base.fid50k] 68 | #metrics += [metric_base.ppl_zfull] 69 | #metrics += [metric_base.ppl_wfull] 70 | #metrics += [metric_base.ppl_zend] 71 | #metrics += [metric_base.ppl_wend] 72 | #metrics += [metric_base.ls] 73 | #metrics += [metric_base.dummy] 74 | 75 | # Which networks to evaluate them on? 76 | tasks = [] 77 | tasks += [EasyDict(run_func_name='run_metrics.run_pickle', network_pkl='https://drive.google.com/uc?id=1MEGjdvVpUsu1jB4zrXZN7Y4kBBOzizDQ', dataset_args=EasyDict(tfrecord_dir='ffhq', shuffle_mb=0), mirror_augment=True)] # karras2019stylegan-ffhq-1024x1024.pkl 78 | #tasks += [EasyDict(run_func_name='run_metrics.run_snapshot', run_id=100, snapshot=25000)] 79 | #tasks += [EasyDict(run_func_name='run_metrics.run_all_snapshots', run_id=100)] 80 | 81 | # How many GPUs to use? 82 | submit_config.num_gpus = 1 83 | #submit_config.num_gpus = 2 84 | #submit_config.num_gpus = 4 85 | #submit_config.num_gpus = 8 86 | 87 | # Execute. 88 | submit_config.run_dir_root = dnnlib.submission.submit.get_template_from_path(config.result_dir) 89 | submit_config.run_dir_ignore += config.run_dir_ignore 90 | for task in tasks: 91 | for metric in metrics: 92 | submit_config.run_desc = '%s-%s' % (task.run_func_name, metric.name) 93 | if task.run_func_name.endswith('run_snapshot'): 94 | submit_config.run_desc += '-%s-%s' % (task.run_id, task.snapshot) 95 | if task.run_func_name.endswith('run_all_snapshots'): 96 | submit_config.run_desc += '-%s' % task.run_id 97 | submit_config.run_desc += '-%dgpu' % submit_config.num_gpus 98 | dnnlib.submit_run(submit_config, metric_args=metric, **task) 99 | 100 | #---------------------------------------------------------------------------- 101 | 102 | if __name__ == "__main__": 103 | main() 104 | 105 | #---------------------------------------------------------------------------- 106 | -------------------------------------------------------------------------------- /models/stylegan_tf_official/training/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved. 2 | # 3 | # This work is licensed under the Creative Commons Attribution-NonCommercial 4 | # 4.0 International License. To view a copy of this license, visit 5 | # http://creativecommons.org/licenses/by-nc/4.0/ or send a letter to 6 | # Creative Commons, PO Box 1866, Mountain View, CA 94042, USA. 7 | 8 | # empty 9 | -------------------------------------------------------------------------------- /torch_utils/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | # empty 10 | -------------------------------------------------------------------------------- /torch_utils/custom_ops.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | import glob 10 | import hashlib 11 | import importlib 12 | import os 13 | import re 14 | import shutil 15 | import uuid 16 | 17 | import torch 18 | import torch.utils.cpp_extension 19 | from torch.utils.file_baton import FileBaton 20 | 21 | #---------------------------------------------------------------------------- 22 | # Global options. 23 | 24 | verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full' 25 | 26 | #---------------------------------------------------------------------------- 27 | # Internal helper funcs. 28 | 29 | def _find_compiler_bindir(): 30 | patterns = [ 31 | 'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64', 32 | 'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64', 33 | 'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64', 34 | 'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin', 35 | ] 36 | for pattern in patterns: 37 | matches = sorted(glob.glob(pattern)) 38 | if len(matches): 39 | return matches[-1] 40 | return None 41 | 42 | #---------------------------------------------------------------------------- 43 | 44 | def _get_mangled_gpu_name(): 45 | name = torch.cuda.get_device_name().lower() 46 | out = [] 47 | for c in name: 48 | if re.match('[a-z0-9_-]+', c): 49 | out.append(c) 50 | else: 51 | out.append('-') 52 | return ''.join(out) 53 | 54 | #---------------------------------------------------------------------------- 55 | # Main entry point for compiling and loading C++/CUDA plugins. 56 | 57 | _cached_plugins = dict() 58 | 59 | def get_plugin(module_name, sources, headers=None, source_dir=None, **build_kwargs): 60 | assert verbosity in ['none', 'brief', 'full'] 61 | if headers is None: 62 | headers = [] 63 | if source_dir is not None: 64 | sources = [os.path.join(source_dir, fname) for fname in sources] 65 | headers = [os.path.join(source_dir, fname) for fname in headers] 66 | 67 | # Already cached? 68 | if module_name in _cached_plugins: 69 | return _cached_plugins[module_name] 70 | 71 | # Print status. 72 | if verbosity == 'full': 73 | print(f'Setting up PyTorch plugin "{module_name}"...') 74 | elif verbosity == 'brief': 75 | print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True) 76 | verbose_build = (verbosity == 'full') 77 | 78 | # Compile and load. 79 | try: # pylint: disable=too-many-nested-blocks 80 | # Make sure we can find the necessary compiler binaries. 81 | if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0: 82 | compiler_bindir = _find_compiler_bindir() 83 | if compiler_bindir is None: 84 | raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".') 85 | os.environ['PATH'] += ';' + compiler_bindir 86 | 87 | # Some containers set TORCH_CUDA_ARCH_LIST to a list that can either 88 | # break the build or unnecessarily restrict what's available to nvcc. 89 | # Unset it to let nvcc decide based on what's available on the 90 | # machine. 91 | os.environ['TORCH_CUDA_ARCH_LIST'] = '' 92 | 93 | # Incremental build md5sum trickery. Copies all the input source files 94 | # into a cached build directory under a combined md5 digest of the input 95 | # source files. Copying is done only if the combined digest has changed. 96 | # This keeps input file timestamps and filenames the same as in previous 97 | # extension builds, allowing for fast incremental rebuilds. 98 | # 99 | # This optimization is done only in case all the source files reside in 100 | # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR 101 | # environment variable is set (we take this as a signal that the user 102 | # actually cares about this.) 103 | # 104 | # EDIT: We now do it regardless of TORCH_EXTENSIOS_DIR, in order to work 105 | # around the *.cu dependency bug in ninja config. 106 | # 107 | all_source_files = sorted(sources + headers) 108 | all_source_dirs = set(os.path.dirname(fname) for fname in all_source_files) 109 | if len(all_source_dirs) == 1: # and ('TORCH_EXTENSIONS_DIR' in os.environ): 110 | 111 | # Compute combined hash digest for all source files. 112 | hash_md5 = hashlib.md5() 113 | for src in all_source_files: 114 | with open(src, 'rb') as f: 115 | hash_md5.update(f.read()) 116 | 117 | # Select cached build directory name. 118 | source_digest = hash_md5.hexdigest() 119 | build_top_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access 120 | cached_build_dir = os.path.join(build_top_dir, f'{source_digest}-{_get_mangled_gpu_name()}') 121 | 122 | if not os.path.isdir(cached_build_dir): 123 | tmpdir = f'{build_top_dir}/srctmp-{uuid.uuid4().hex}' 124 | os.makedirs(tmpdir) 125 | for src in all_source_files: 126 | shutil.copyfile(src, os.path.join(tmpdir, os.path.basename(src))) 127 | try: 128 | os.replace(tmpdir, cached_build_dir) # atomic 129 | except OSError: 130 | # source directory already exists, delete tmpdir and its contents. 131 | shutil.rmtree(tmpdir) 132 | if not os.path.isdir(cached_build_dir): raise 133 | 134 | # Compile. 135 | cached_sources = [os.path.join(cached_build_dir, os.path.basename(fname)) for fname in sources] 136 | torch.utils.cpp_extension.load(name=module_name, build_directory=cached_build_dir, 137 | verbose=verbose_build, sources=cached_sources, **build_kwargs) 138 | else: 139 | torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs) 140 | 141 | # Load. 142 | module = importlib.import_module(module_name) 143 | 144 | except: 145 | if verbosity == 'brief': 146 | print('Failed!') 147 | raise 148 | 149 | # Print status and add to cache dict. 150 | if verbosity == 'full': 151 | print(f'Done setting up PyTorch plugin "{module_name}".') 152 | elif verbosity == 'brief': 153 | print('Done.') 154 | _cached_plugins[module_name] = module 155 | return module 156 | 157 | #---------------------------------------------------------------------------- 158 | -------------------------------------------------------------------------------- /torch_utils/ops/__init__.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | # empty 10 | -------------------------------------------------------------------------------- /torch_utils/ops/bias_act.cpp: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include 10 | #include 11 | #include 12 | #include "bias_act.h" 13 | 14 | //------------------------------------------------------------------------ 15 | 16 | static bool has_same_layout(torch::Tensor x, torch::Tensor y) 17 | { 18 | if (x.dim() != y.dim()) 19 | return false; 20 | for (int64_t i = 0; i < x.dim(); i++) 21 | { 22 | if (x.size(i) != y.size(i)) 23 | return false; 24 | if (x.size(i) >= 2 && x.stride(i) != y.stride(i)) 25 | return false; 26 | } 27 | return true; 28 | } 29 | 30 | //------------------------------------------------------------------------ 31 | 32 | static torch::Tensor bias_act(torch::Tensor x, torch::Tensor b, torch::Tensor xref, torch::Tensor yref, torch::Tensor dy, int grad, int dim, int act, float alpha, float gain, float clamp) 33 | { 34 | // Validate arguments. 35 | TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); 36 | TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x"); 37 | TORCH_CHECK(xref.numel() == 0 || (xref.sizes() == x.sizes() && xref.dtype() == x.dtype() && xref.device() == x.device()), "xref must have the same shape, dtype, and device as x"); 38 | TORCH_CHECK(yref.numel() == 0 || (yref.sizes() == x.sizes() && yref.dtype() == x.dtype() && yref.device() == x.device()), "yref must have the same shape, dtype, and device as x"); 39 | TORCH_CHECK(dy.numel() == 0 || (dy.sizes() == x.sizes() && dy.dtype() == x.dtype() && dy.device() == x.device()), "dy must have the same dtype and device as x"); 40 | TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); 41 | TORCH_CHECK(b.dim() == 1, "b must have rank 1"); 42 | TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds"); 43 | TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements"); 44 | TORCH_CHECK(grad >= 0, "grad must be non-negative"); 45 | 46 | // Validate layout. 47 | TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense"); 48 | TORCH_CHECK(b.is_contiguous(), "b must be contiguous"); 49 | TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x"); 50 | TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x"); 51 | TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x"); 52 | 53 | // Create output tensor. 54 | const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); 55 | torch::Tensor y = torch::empty_like(x); 56 | TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x"); 57 | 58 | // Initialize CUDA kernel parameters. 59 | bias_act_kernel_params p; 60 | p.x = x.data_ptr(); 61 | p.b = (b.numel()) ? b.data_ptr() : NULL; 62 | p.xref = (xref.numel()) ? xref.data_ptr() : NULL; 63 | p.yref = (yref.numel()) ? yref.data_ptr() : NULL; 64 | p.dy = (dy.numel()) ? dy.data_ptr() : NULL; 65 | p.y = y.data_ptr(); 66 | p.grad = grad; 67 | p.act = act; 68 | p.alpha = alpha; 69 | p.gain = gain; 70 | p.clamp = clamp; 71 | p.sizeX = (int)x.numel(); 72 | p.sizeB = (int)b.numel(); 73 | p.stepB = (b.numel()) ? (int)x.stride(dim) : 1; 74 | 75 | // Choose CUDA kernel. 76 | void* kernel; 77 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] 78 | { 79 | kernel = choose_bias_act_kernel(p); 80 | }); 81 | TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func"); 82 | 83 | // Launch CUDA kernel. 84 | p.loopX = 4; 85 | int blockSize = 4 * 32; 86 | int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1; 87 | void* args[] = {&p}; 88 | AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); 89 | return y; 90 | } 91 | 92 | //------------------------------------------------------------------------ 93 | 94 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) 95 | { 96 | m.def("bias_act", &bias_act); 97 | } 98 | 99 | //------------------------------------------------------------------------ 100 | -------------------------------------------------------------------------------- /torch_utils/ops/bias_act.cu: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include 10 | #include "bias_act.h" 11 | 12 | //------------------------------------------------------------------------ 13 | // Helpers. 14 | 15 | template struct InternalType; 16 | template <> struct InternalType { typedef double scalar_t; }; 17 | template <> struct InternalType { typedef float scalar_t; }; 18 | template <> struct InternalType { typedef float scalar_t; }; 19 | 20 | //------------------------------------------------------------------------ 21 | // CUDA kernel. 22 | 23 | template 24 | __global__ void bias_act_kernel(bias_act_kernel_params p) 25 | { 26 | typedef typename InternalType::scalar_t scalar_t; 27 | int G = p.grad; 28 | scalar_t alpha = (scalar_t)p.alpha; 29 | scalar_t gain = (scalar_t)p.gain; 30 | scalar_t clamp = (scalar_t)p.clamp; 31 | scalar_t one = (scalar_t)1; 32 | scalar_t two = (scalar_t)2; 33 | scalar_t expRange = (scalar_t)80; 34 | scalar_t halfExpRange = (scalar_t)40; 35 | scalar_t seluScale = (scalar_t)1.0507009873554804934193349852946; 36 | scalar_t seluAlpha = (scalar_t)1.6732632423543772848170429916717; 37 | 38 | // Loop over elements. 39 | int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x; 40 | for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x) 41 | { 42 | // Load. 43 | scalar_t x = (scalar_t)((const T*)p.x)[xi]; 44 | scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0; 45 | scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0; 46 | scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0; 47 | scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one; 48 | scalar_t yy = (gain != 0) ? yref / gain : 0; 49 | scalar_t y = 0; 50 | 51 | // Apply bias. 52 | ((G == 0) ? x : xref) += b; 53 | 54 | // linear 55 | if (A == 1) 56 | { 57 | if (G == 0) y = x; 58 | if (G == 1) y = x; 59 | } 60 | 61 | // relu 62 | if (A == 2) 63 | { 64 | if (G == 0) y = (x > 0) ? x : 0; 65 | if (G == 1) y = (yy > 0) ? x : 0; 66 | } 67 | 68 | // lrelu 69 | if (A == 3) 70 | { 71 | if (G == 0) y = (x > 0) ? x : x * alpha; 72 | if (G == 1) y = (yy > 0) ? x : x * alpha; 73 | } 74 | 75 | // tanh 76 | if (A == 4) 77 | { 78 | if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); } 79 | if (G == 1) y = x * (one - yy * yy); 80 | if (G == 2) y = x * (one - yy * yy) * (-two * yy); 81 | } 82 | 83 | // sigmoid 84 | if (A == 5) 85 | { 86 | if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one); 87 | if (G == 1) y = x * yy * (one - yy); 88 | if (G == 2) y = x * yy * (one - yy) * (one - two * yy); 89 | } 90 | 91 | // elu 92 | if (A == 6) 93 | { 94 | if (G == 0) y = (x >= 0) ? x : exp(x) - one; 95 | if (G == 1) y = (yy >= 0) ? x : x * (yy + one); 96 | if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one); 97 | } 98 | 99 | // selu 100 | if (A == 7) 101 | { 102 | if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one); 103 | if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha); 104 | if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha); 105 | } 106 | 107 | // softplus 108 | if (A == 8) 109 | { 110 | if (G == 0) y = (x > expRange) ? x : log(exp(x) + one); 111 | if (G == 1) y = x * (one - exp(-yy)); 112 | if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); } 113 | } 114 | 115 | // swish 116 | if (A == 9) 117 | { 118 | if (G == 0) 119 | y = (x < -expRange) ? 0 : x / (exp(-x) + one); 120 | else 121 | { 122 | scalar_t c = exp(xref); 123 | scalar_t d = c + one; 124 | if (G == 1) 125 | y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d); 126 | else 127 | y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d); 128 | yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain; 129 | } 130 | } 131 | 132 | // Apply gain. 133 | y *= gain * dy; 134 | 135 | // Clamp. 136 | if (clamp >= 0) 137 | { 138 | if (G == 0) 139 | y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp; 140 | else 141 | y = (yref > -clamp & yref < clamp) ? y : 0; 142 | } 143 | 144 | // Store. 145 | ((T*)p.y)[xi] = (T)y; 146 | } 147 | } 148 | 149 | //------------------------------------------------------------------------ 150 | // CUDA kernel selection. 151 | 152 | template void* choose_bias_act_kernel(const bias_act_kernel_params& p) 153 | { 154 | if (p.act == 1) return (void*)bias_act_kernel; 155 | if (p.act == 2) return (void*)bias_act_kernel; 156 | if (p.act == 3) return (void*)bias_act_kernel; 157 | if (p.act == 4) return (void*)bias_act_kernel; 158 | if (p.act == 5) return (void*)bias_act_kernel; 159 | if (p.act == 6) return (void*)bias_act_kernel; 160 | if (p.act == 7) return (void*)bias_act_kernel; 161 | if (p.act == 8) return (void*)bias_act_kernel; 162 | if (p.act == 9) return (void*)bias_act_kernel; 163 | return NULL; 164 | } 165 | 166 | //------------------------------------------------------------------------ 167 | // Template specializations. 168 | 169 | template void* choose_bias_act_kernel (const bias_act_kernel_params& p); 170 | template void* choose_bias_act_kernel (const bias_act_kernel_params& p); 171 | template void* choose_bias_act_kernel (const bias_act_kernel_params& p); 172 | 173 | //------------------------------------------------------------------------ 174 | -------------------------------------------------------------------------------- /torch_utils/ops/bias_act.h: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | //------------------------------------------------------------------------ 10 | // CUDA kernel parameters. 11 | 12 | struct bias_act_kernel_params 13 | { 14 | const void* x; // [sizeX] 15 | const void* b; // [sizeB] or NULL 16 | const void* xref; // [sizeX] or NULL 17 | const void* yref; // [sizeX] or NULL 18 | const void* dy; // [sizeX] or NULL 19 | void* y; // [sizeX] 20 | 21 | int grad; 22 | int act; 23 | float alpha; 24 | float gain; 25 | float clamp; 26 | 27 | int sizeX; 28 | int sizeB; 29 | int stepB; 30 | int loopX; 31 | }; 32 | 33 | //------------------------------------------------------------------------ 34 | // CUDA kernel selection. 35 | 36 | template void* choose_bias_act_kernel(const bias_act_kernel_params& p); 37 | 38 | //------------------------------------------------------------------------ 39 | -------------------------------------------------------------------------------- /torch_utils/ops/conv2d_resample.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | """2D convolution with optional up/downsampling.""" 10 | 11 | import torch 12 | 13 | from .. import misc 14 | from . import conv2d_gradfix 15 | from . import upfirdn2d 16 | from .upfirdn2d import _parse_padding 17 | from .upfirdn2d import _get_filter_size 18 | 19 | #---------------------------------------------------------------------------- 20 | 21 | def _get_weight_shape(w): 22 | with misc.suppress_tracer_warnings(): # this value will be treated as a constant 23 | shape = [int(sz) for sz in w.shape] 24 | misc.assert_shape(w, shape) 25 | return shape 26 | 27 | #---------------------------------------------------------------------------- 28 | 29 | def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True): 30 | """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations. 31 | """ 32 | _out_channels, _in_channels_per_group, kh, kw = _get_weight_shape(w) 33 | 34 | # Flip weight if requested. 35 | # Note: conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False). 36 | if not flip_weight and (kw > 1 or kh > 1): 37 | w = w.flip([2, 3]) 38 | 39 | # Execute using conv2d_gradfix. 40 | op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d 41 | return op(x, w, stride=stride, padding=padding, groups=groups) 42 | 43 | #---------------------------------------------------------------------------- 44 | 45 | @misc.profiled_function 46 | def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False): 47 | r"""2D convolution with optional up/downsampling. 48 | 49 | Padding is performed only once at the beginning, not between the operations. 50 | 51 | Args: 52 | x: Input tensor of shape 53 | `[batch_size, in_channels, in_height, in_width]`. 54 | w: Weight tensor of shape 55 | `[out_channels, in_channels//groups, kernel_height, kernel_width]`. 56 | f: Low-pass filter for up/downsampling. Must be prepared beforehand by 57 | calling upfirdn2d.setup_filter(). None = identity (default). 58 | up: Integer upsampling factor (default: 1). 59 | down: Integer downsampling factor (default: 1). 60 | padding: Padding with respect to the upsampled image. Can be a single number 61 | or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]` 62 | (default: 0). 63 | groups: Split input channels into N groups (default: 1). 64 | flip_weight: False = convolution, True = correlation (default: True). 65 | flip_filter: False = convolution, True = correlation (default: False). 66 | 67 | Returns: 68 | Tensor of the shape `[batch_size, num_channels, out_height, out_width]`. 69 | """ 70 | # Validate arguments. 71 | assert isinstance(x, torch.Tensor) and (x.ndim == 4) 72 | assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype) 73 | assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32) 74 | assert isinstance(up, int) and (up >= 1) 75 | assert isinstance(down, int) and (down >= 1) 76 | assert isinstance(groups, int) and (groups >= 1) 77 | out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w) 78 | fw, fh = _get_filter_size(f) 79 | px0, px1, py0, py1 = _parse_padding(padding) 80 | 81 | # Adjust padding to account for up/downsampling. 82 | if up > 1: 83 | px0 += (fw + up - 1) // 2 84 | px1 += (fw - up) // 2 85 | py0 += (fh + up - 1) // 2 86 | py1 += (fh - up) // 2 87 | if down > 1: 88 | px0 += (fw - down + 1) // 2 89 | px1 += (fw - down) // 2 90 | py0 += (fh - down + 1) // 2 91 | py1 += (fh - down) // 2 92 | 93 | # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve. 94 | if kw == 1 and kh == 1 and (down > 1 and up == 1): 95 | x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter) 96 | x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) 97 | return x 98 | 99 | # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample. 100 | if kw == 1 and kh == 1 and (up > 1 and down == 1): 101 | x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) 102 | x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) 103 | return x 104 | 105 | # Fast path: downsampling only => use strided convolution. 106 | if down > 1 and up == 1: 107 | x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter) 108 | x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight) 109 | return x 110 | 111 | # Fast path: upsampling with optional downsampling => use transpose strided convolution. 112 | if up > 1: 113 | if groups == 1: 114 | w = w.transpose(0, 1) 115 | else: 116 | w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw) 117 | w = w.transpose(1, 2) 118 | w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw) 119 | px0 -= kw - 1 120 | px1 -= kw - up 121 | py0 -= kh - 1 122 | py1 -= kh - up 123 | pxt = max(min(-px0, -px1), 0) 124 | pyt = max(min(-py0, -py1), 0) 125 | x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight)) 126 | x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter) 127 | if down > 1: 128 | x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) 129 | return x 130 | 131 | # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d. 132 | if up == 1 and down == 1: 133 | if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0: 134 | return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight) 135 | 136 | # Fallback: Generic reference implementation. 137 | x = upfirdn2d.upfirdn2d(x=x, f=(f if up > 1 else None), up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter) 138 | x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight) 139 | if down > 1: 140 | x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter) 141 | return x 142 | 143 | #---------------------------------------------------------------------------- 144 | -------------------------------------------------------------------------------- /torch_utils/ops/filtered_lrelu.h: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include 10 | 11 | //------------------------------------------------------------------------ 12 | // CUDA kernel parameters. 13 | 14 | struct filtered_lrelu_kernel_params 15 | { 16 | // These parameters decide which kernel to use. 17 | int up; // upsampling ratio (1, 2, 4) 18 | int down; // downsampling ratio (1, 2, 4) 19 | int2 fuShape; // [size, 1] | [size, size] 20 | int2 fdShape; // [size, 1] | [size, size] 21 | 22 | int _dummy; // Alignment. 23 | 24 | // Rest of the parameters. 25 | const void* x; // Input tensor. 26 | void* y; // Output tensor. 27 | const void* b; // Bias tensor. 28 | unsigned char* s; // Sign tensor in/out. NULL if unused. 29 | const float* fu; // Upsampling filter. 30 | const float* fd; // Downsampling filter. 31 | 32 | int2 pad0; // Left/top padding. 33 | float gain; // Additional gain factor. 34 | float slope; // Leaky ReLU slope on negative side. 35 | float clamp; // Clamp after nonlinearity. 36 | int flip; // Filter kernel flip for gradient computation. 37 | 38 | int tilesXdim; // Original number of horizontal output tiles. 39 | int tilesXrep; // Number of horizontal tiles per CTA. 40 | int blockZofs; // Block z offset to support large minibatch, channel dimensions. 41 | 42 | int4 xShape; // [width, height, channel, batch] 43 | int4 yShape; // [width, height, channel, batch] 44 | int2 sShape; // [width, height] - width is in bytes. Contiguous. Zeros if unused. 45 | int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. 46 | int swLimit; // Active width of sign tensor in bytes. 47 | 48 | longlong4 xStride; // Strides of all tensors except signs, same component order as shapes. 49 | longlong4 yStride; // 50 | int64_t bStride; // 51 | longlong3 fuStride; // 52 | longlong3 fdStride; // 53 | }; 54 | 55 | struct filtered_lrelu_act_kernel_params 56 | { 57 | void* x; // Input/output, modified in-place. 58 | unsigned char* s; // Sign tensor in/out. NULL if unused. 59 | 60 | float gain; // Additional gain factor. 61 | float slope; // Leaky ReLU slope on negative side. 62 | float clamp; // Clamp after nonlinearity. 63 | 64 | int4 xShape; // [width, height, channel, batch] 65 | longlong4 xStride; // Input/output tensor strides, same order as in shape. 66 | int2 sShape; // [width, height] - width is in elements. Contiguous. Zeros if unused. 67 | int2 sOfs; // [ofs_x, ofs_y] - offset between upsampled data and sign tensor. 68 | }; 69 | 70 | //------------------------------------------------------------------------ 71 | // CUDA kernel specialization. 72 | 73 | struct filtered_lrelu_kernel_spec 74 | { 75 | void* setup; // Function for filter kernel setup. 76 | void* exec; // Function for main operation. 77 | int2 tileOut; // Width/height of launch tile. 78 | int numWarps; // Number of warps per thread block, determines launch block size. 79 | int xrep; // For processing multiple horizontal tiles per thread block. 80 | int dynamicSharedKB; // How much dynamic shared memory the exec kernel wants. 81 | }; 82 | 83 | //------------------------------------------------------------------------ 84 | // CUDA kernel selection. 85 | 86 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 87 | template void* choose_filtered_lrelu_act_kernel(void); 88 | template cudaError_t copy_filters(cudaStream_t stream); 89 | 90 | //------------------------------------------------------------------------ 91 | -------------------------------------------------------------------------------- /torch_utils/ops/filtered_lrelu_ns.cu: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include "filtered_lrelu.cu" 10 | 11 | // Template/kernel specializations for no signs mode (no gradients required). 12 | 13 | // Full op, 32-bit indexing. 14 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 15 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 16 | 17 | // Full op, 64-bit indexing. 18 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 19 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 20 | 21 | // Activation/signs only for generic variant. 64-bit indexing. 22 | template void* choose_filtered_lrelu_act_kernel(void); 23 | template void* choose_filtered_lrelu_act_kernel(void); 24 | template void* choose_filtered_lrelu_act_kernel(void); 25 | 26 | // Copy filters to constant memory. 27 | template cudaError_t copy_filters(cudaStream_t stream); 28 | -------------------------------------------------------------------------------- /torch_utils/ops/filtered_lrelu_rd.cu: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include "filtered_lrelu.cu" 10 | 11 | // Template/kernel specializations for sign read mode. 12 | 13 | // Full op, 32-bit indexing. 14 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 15 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 16 | 17 | // Full op, 64-bit indexing. 18 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 19 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 20 | 21 | // Activation/signs only for generic variant. 64-bit indexing. 22 | template void* choose_filtered_lrelu_act_kernel(void); 23 | template void* choose_filtered_lrelu_act_kernel(void); 24 | template void* choose_filtered_lrelu_act_kernel(void); 25 | 26 | // Copy filters to constant memory. 27 | template cudaError_t copy_filters(cudaStream_t stream); 28 | -------------------------------------------------------------------------------- /torch_utils/ops/filtered_lrelu_wr.cu: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include "filtered_lrelu.cu" 10 | 11 | // Template/kernel specializations for sign write mode. 12 | 13 | // Full op, 32-bit indexing. 14 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 15 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 16 | 17 | // Full op, 64-bit indexing. 18 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 19 | template filtered_lrelu_kernel_spec choose_filtered_lrelu_kernel(const filtered_lrelu_kernel_params& p, int sharedKB); 20 | 21 | // Activation/signs only for generic variant. 64-bit indexing. 22 | template void* choose_filtered_lrelu_act_kernel(void); 23 | template void* choose_filtered_lrelu_act_kernel(void); 24 | template void* choose_filtered_lrelu_act_kernel(void); 25 | 26 | // Copy filters to constant memory. 27 | template cudaError_t copy_filters(cudaStream_t stream); 28 | -------------------------------------------------------------------------------- /torch_utils/ops/fma.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | """Fused multiply-add, with slightly faster gradients than `torch.addcmul()`.""" 10 | 11 | import torch 12 | 13 | #---------------------------------------------------------------------------- 14 | 15 | def fma(a, b, c): # => a * b + c 16 | return _FusedMultiplyAdd.apply(a, b, c) 17 | 18 | #---------------------------------------------------------------------------- 19 | 20 | class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c 21 | @staticmethod 22 | def forward(ctx, a, b, c): # pylint: disable=arguments-differ 23 | out = torch.addcmul(c, a, b) 24 | ctx.save_for_backward(a, b) 25 | ctx.c_shape = c.shape 26 | return out 27 | 28 | @staticmethod 29 | def backward(ctx, dout): # pylint: disable=arguments-differ 30 | a, b = ctx.saved_tensors 31 | c_shape = ctx.c_shape 32 | da = None 33 | db = None 34 | dc = None 35 | 36 | if ctx.needs_input_grad[0]: 37 | da = _unbroadcast(dout * b, a.shape) 38 | 39 | if ctx.needs_input_grad[1]: 40 | db = _unbroadcast(dout * a, b.shape) 41 | 42 | if ctx.needs_input_grad[2]: 43 | dc = _unbroadcast(dout, c_shape) 44 | 45 | return da, db, dc 46 | 47 | #---------------------------------------------------------------------------- 48 | 49 | def _unbroadcast(x, shape): 50 | extra_dims = x.ndim - len(shape) 51 | assert extra_dims >= 0 52 | dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)] 53 | if len(dim): 54 | x = x.sum(dim=dim, keepdim=True) 55 | if extra_dims: 56 | x = x.reshape(-1, *x.shape[extra_dims+1:]) 57 | assert x.shape == shape 58 | return x 59 | 60 | #---------------------------------------------------------------------------- 61 | -------------------------------------------------------------------------------- /torch_utils/ops/grid_sample_gradfix.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | # 3 | # NVIDIA CORPORATION and its licensors retain all intellectual property 4 | # and proprietary rights in and to this software, related documentation 5 | # and any modifications thereto. Any use, reproduction, disclosure or 6 | # distribution of this software and related documentation without an express 7 | # license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | """Custom replacement for `torch.nn.functional.grid_sample` that 10 | supports arbitrarily high order gradients between the input and output. 11 | Only works on 2D images and assumes 12 | `mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`.""" 13 | 14 | import torch 15 | 16 | # pylint: disable=redefined-builtin 17 | # pylint: disable=arguments-differ 18 | # pylint: disable=protected-access 19 | 20 | #---------------------------------------------------------------------------- 21 | 22 | enabled = False # Enable the custom op by setting this to true. 23 | 24 | #---------------------------------------------------------------------------- 25 | 26 | def grid_sample(input, grid): 27 | if _should_use_custom_op(): 28 | return _GridSample2dForward.apply(input, grid) 29 | return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) 30 | 31 | #---------------------------------------------------------------------------- 32 | 33 | def _should_use_custom_op(): 34 | return enabled 35 | 36 | #---------------------------------------------------------------------------- 37 | 38 | class _GridSample2dForward(torch.autograd.Function): 39 | @staticmethod 40 | def forward(ctx, input, grid): 41 | assert input.ndim == 4 42 | assert grid.ndim == 4 43 | output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False) 44 | ctx.save_for_backward(input, grid) 45 | return output 46 | 47 | @staticmethod 48 | def backward(ctx, grad_output): 49 | input, grid = ctx.saved_tensors 50 | grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid) 51 | return grad_input, grad_grid 52 | 53 | #---------------------------------------------------------------------------- 54 | 55 | class _GridSample2dBackward(torch.autograd.Function): 56 | @staticmethod 57 | def forward(ctx, grad_output, input, grid): 58 | op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward') 59 | grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False) 60 | ctx.save_for_backward(grid) 61 | return grad_input, grad_grid 62 | 63 | @staticmethod 64 | def backward(ctx, grad2_grad_input, grad2_grad_grid): 65 | _ = grad2_grad_grid # unused 66 | grid, = ctx.saved_tensors 67 | grad2_grad_output = None 68 | grad2_input = None 69 | grad2_grid = None 70 | 71 | if ctx.needs_input_grad[0]: 72 | grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid) 73 | 74 | assert not ctx.needs_input_grad[2] 75 | return grad2_grad_output, grad2_input, grad2_grid 76 | 77 | #---------------------------------------------------------------------------- 78 | -------------------------------------------------------------------------------- /torch_utils/ops/upfirdn2d.cpp: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include 10 | #include 11 | #include 12 | #include "upfirdn2d.h" 13 | 14 | //------------------------------------------------------------------------ 15 | 16 | static torch::Tensor upfirdn2d(torch::Tensor x, torch::Tensor f, int upx, int upy, int downx, int downy, int padx0, int padx1, int pady0, int pady1, bool flip, float gain) 17 | { 18 | // Validate arguments. 19 | TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device"); 20 | TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x"); 21 | TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32"); 22 | TORCH_CHECK(x.numel() <= INT_MAX, "x is too large"); 23 | TORCH_CHECK(f.numel() <= INT_MAX, "f is too large"); 24 | TORCH_CHECK(x.numel() > 0, "x has zero size"); 25 | TORCH_CHECK(f.numel() > 0, "f has zero size"); 26 | TORCH_CHECK(x.dim() == 4, "x must be rank 4"); 27 | TORCH_CHECK(f.dim() == 2, "f must be rank 2"); 28 | TORCH_CHECK((x.size(0)-1)*x.stride(0) + (x.size(1)-1)*x.stride(1) + (x.size(2)-1)*x.stride(2) + (x.size(3)-1)*x.stride(3) <= INT_MAX, "x memory footprint is too large"); 29 | TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1"); 30 | TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1"); 31 | TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1"); 32 | 33 | // Create output tensor. 34 | const at::cuda::OptionalCUDAGuard device_guard(device_of(x)); 35 | int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx; 36 | int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy; 37 | TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1"); 38 | torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format()); 39 | TORCH_CHECK(y.numel() <= INT_MAX, "output is too large"); 40 | TORCH_CHECK((y.size(0)-1)*y.stride(0) + (y.size(1)-1)*y.stride(1) + (y.size(2)-1)*y.stride(2) + (y.size(3)-1)*y.stride(3) <= INT_MAX, "output memory footprint is too large"); 41 | 42 | // Initialize CUDA kernel parameters. 43 | upfirdn2d_kernel_params p; 44 | p.x = x.data_ptr(); 45 | p.f = f.data_ptr(); 46 | p.y = y.data_ptr(); 47 | p.up = make_int2(upx, upy); 48 | p.down = make_int2(downx, downy); 49 | p.pad0 = make_int2(padx0, pady0); 50 | p.flip = (flip) ? 1 : 0; 51 | p.gain = gain; 52 | p.inSize = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0)); 53 | p.inStride = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0)); 54 | p.filterSize = make_int2((int)f.size(1), (int)f.size(0)); 55 | p.filterStride = make_int2((int)f.stride(1), (int)f.stride(0)); 56 | p.outSize = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0)); 57 | p.outStride = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0)); 58 | p.sizeMajor = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z; 59 | p.sizeMinor = (p.inStride.z == 1) ? p.inSize.z : 1; 60 | 61 | // Choose CUDA kernel. 62 | upfirdn2d_kernel_spec spec; 63 | AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&] 64 | { 65 | spec = choose_upfirdn2d_kernel(p); 66 | }); 67 | 68 | // Set looping options. 69 | p.loopMajor = (p.sizeMajor - 1) / 16384 + 1; 70 | p.loopMinor = spec.loopMinor; 71 | p.loopX = spec.loopX; 72 | p.launchMinor = (p.sizeMinor - 1) / p.loopMinor + 1; 73 | p.launchMajor = (p.sizeMajor - 1) / p.loopMajor + 1; 74 | 75 | // Compute grid size. 76 | dim3 blockSize, gridSize; 77 | if (spec.tileOutW < 0) // large 78 | { 79 | blockSize = dim3(4, 32, 1); 80 | gridSize = dim3( 81 | ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor, 82 | (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1, 83 | p.launchMajor); 84 | } 85 | else // small 86 | { 87 | blockSize = dim3(256, 1, 1); 88 | gridSize = dim3( 89 | ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor, 90 | (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1, 91 | p.launchMajor); 92 | } 93 | 94 | // Launch CUDA kernel. 95 | void* args[] = {&p}; 96 | AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream())); 97 | return y; 98 | } 99 | 100 | //------------------------------------------------------------------------ 101 | 102 | PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) 103 | { 104 | m.def("upfirdn2d", &upfirdn2d); 105 | } 106 | 107 | //------------------------------------------------------------------------ 108 | -------------------------------------------------------------------------------- /torch_utils/ops/upfirdn2d.h: -------------------------------------------------------------------------------- 1 | // Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved. 2 | // 3 | // NVIDIA CORPORATION and its licensors retain all intellectual property 4 | // and proprietary rights in and to this software, related documentation 5 | // and any modifications thereto. Any use, reproduction, disclosure or 6 | // distribution of this software and related documentation without an express 7 | // license agreement from NVIDIA CORPORATION is strictly prohibited. 8 | 9 | #include 10 | 11 | //------------------------------------------------------------------------ 12 | // CUDA kernel parameters. 13 | 14 | struct upfirdn2d_kernel_params 15 | { 16 | const void* x; 17 | const float* f; 18 | void* y; 19 | 20 | int2 up; 21 | int2 down; 22 | int2 pad0; 23 | int flip; 24 | float gain; 25 | 26 | int4 inSize; // [width, height, channel, batch] 27 | int4 inStride; 28 | int2 filterSize; // [width, height] 29 | int2 filterStride; 30 | int4 outSize; // [width, height, channel, batch] 31 | int4 outStride; 32 | int sizeMinor; 33 | int sizeMajor; 34 | 35 | int loopMinor; 36 | int loopMajor; 37 | int loopX; 38 | int launchMinor; 39 | int launchMajor; 40 | }; 41 | 42 | //------------------------------------------------------------------------ 43 | // CUDA kernel specialization. 44 | 45 | struct upfirdn2d_kernel_spec 46 | { 47 | void* kernel; 48 | int tileOutW; 49 | int tileOutH; 50 | int loopMinor; 51 | int loopX; 52 | }; 53 | 54 | //------------------------------------------------------------------------ 55 | // CUDA kernel selection. 56 | 57 | template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p); 58 | 59 | //------------------------------------------------------------------------ 60 | -------------------------------------------------------------------------------- /train_boundary.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Trains semantic boundary from latent space. 3 | 4 | Basically, this file takes a collection of `latent code - attribute score` 5 | pairs, and find the separation boundary by treating it as a bi-classification 6 | problem and training a linear SVM classifier. The well-trained decision boundary 7 | of the SVM classifier will be saved as the boundary corresponding to a 8 | particular semantic from the latent space. The normal direction of the boundary 9 | can be used to manipulate the correpsonding attribute of the synthesis. 10 | """ 11 | 12 | import os.path 13 | import argparse 14 | import numpy as np 15 | 16 | from utils.logger import setup_logger 17 | from utils.manipulator import train_boundary 18 | 19 | def parse_args(): 20 | """Parses arguments.""" 21 | parser = argparse.ArgumentParser( 22 | description='Train semantic boundary with given latent codes and ' 23 | 'attribute scores.') 24 | parser.add_argument('-o', '--output_dir', type=str, required=True, 25 | help='Directory to save the output results. (required)') 26 | parser.add_argument('-c', '--latent_codes_path', type=str, required=True, 27 | help='Path to the input latent codes. (required)') 28 | parser.add_argument('-s', '--scores_path', type=str, required=True, 29 | help='Path to the input attribute scores. (required)') 30 | parser.add_argument('-n', '--chosen_num_or_ratio', type=float, default=0.02, 31 | help='How many samples to choose for training. ' 32 | '(default: 0.2)') 33 | parser.add_argument('-r', '--split_ratio', type=float, default=0.7, 34 | help='Ratio with which to split training and validation ' 35 | 'sets. (default: 0.7)') 36 | parser.add_argument('-V', '--invalid_value', type=float, default=None, 37 | help='Sample whose attribute score is equal to this ' 38 | 'field will be ignored. (default: None)') 39 | 40 | return parser.parse_args() 41 | 42 | 43 | def main(): 44 | """Main function.""" 45 | args = parse_args() 46 | logger = setup_logger(args.output_dir, logger_name='train_boundary') 47 | 48 | logger.info('Loading latent codes.') 49 | if not os.path.isfile(args.latent_codes_path): 50 | raise ValueError(f'Latent codes `{args.latent_codes_path}` does not exist!') 51 | latent_codes = np.load(args.latent_codes_path) 52 | 53 | logger.info('Loading attribute scores.') 54 | if not os.path.isfile(args.scores_path): 55 | raise ValueError(f'Attribute scores `{args.scores_path}` does not exist!') 56 | scores = np.load(args.scores_path) 57 | 58 | boundary = train_boundary(latent_codes=latent_codes, 59 | scores=scores, 60 | chosen_num_or_ratio=args.chosen_num_or_ratio, 61 | split_ratio=args.split_ratio, 62 | invalid_value=args.invalid_value, 63 | logger=logger) 64 | np.save(os.path.join(args.output_dir, 'boundary.npy'), boundary) 65 | 66 | 67 | if __name__ == '__main__': 68 | main() 69 | -------------------------------------------------------------------------------- /utils/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/younesbelkada/interfacegan/9d8df611af795490abddfa6ddd0452c6ef309a41/utils/__init__.py -------------------------------------------------------------------------------- /utils/logger.py: -------------------------------------------------------------------------------- 1 | # python3.7 2 | """Utility functions for logging.""" 3 | 4 | import os 5 | import sys 6 | import logging 7 | 8 | __all__ = ['setup_logger'] 9 | 10 | 11 | def setup_logger(work_dir=None, logfile_name='log.txt', logger_name='logger'): 12 | """Sets up logger from target work directory. 13 | 14 | The function will sets up a logger with `DEBUG` log level. Two handlers will 15 | be added to the logger automatically. One is the `sys.stdout` stream, with 16 | `INFO` log level, which will print improtant messages on the screen. The other 17 | is used to save all messages to file `$WORK_DIR/$LOGFILE_NAME`. Messages will 18 | be added time stamp and log level before logged. 19 | 20 | NOTE: If `work_dir` or `logfile_name` is empty, the file stream will be 21 | skipped. 22 | 23 | Args: 24 | work_dir: The work directory. All intermediate files will be saved here. 25 | (default: None) 26 | logfile_name: Name of the file to save log message. (default: `log.txt`) 27 | logger_name: Unique name for the logger. (default: `logger`) 28 | 29 | Returns: 30 | A `logging.Logger` object. 31 | 32 | Raises: 33 | SystemExit: If the work directory has already existed, of the logger with 34 | specified name `logger_name` has already existed. 35 | """ 36 | 37 | logger = logging.getLogger(logger_name) 38 | # if logger.hasHandlers(): # Already existed 39 | # raise SystemExit(f'Logger name `{logger_name}` has already been set up!\n' 40 | # f'Please use another name, or otherwise the messages ' 41 | # f'may be mixed between these two loggers.') 42 | 43 | logger.setLevel(logging.DEBUG) 44 | formatter = logging.Formatter("[%(asctime)s][%(levelname)s] %(message)s") 45 | 46 | # Print log message with `INFO` level or above onto the screen. 47 | sh = logging.StreamHandler(stream=sys.stdout) 48 | sh.setLevel(logging.INFO) 49 | sh.setFormatter(formatter) 50 | logger.addHandler(sh) 51 | 52 | if not work_dir or not logfile_name: 53 | return logger 54 | 55 | if os.path.exists(work_dir): 56 | logger.warning('WARNING: Directory already exists, overwriting images') 57 | #raise SystemExit(f'Work directory `{work_dir}` has already existed!\n' 58 | # f'Please specify another one.') 59 | else: 60 | os.makedirs(work_dir) 61 | # Save log message with all levels in log file. 62 | fh = logging.FileHandler(os.path.join(work_dir, logfile_name)) 63 | fh.setLevel(logging.DEBUG) 64 | fh.setFormatter(formatter) 65 | logger.addHandler(fh) 66 | 67 | return logger 68 | --------------------------------------------------------------------------------