remove old files

.gitattributes

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-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text

LICENSE.txt

@@ -1,97 +0,0 @@
-Copyright (c) 2021, NVIDIA Corporation. All rights reserved.
-
-
-NVIDIA Source Code License for StyleGAN2 with Adaptive Discriminator Augmentation (ADA)
-
-
-=======================================================================
-
-1. Definitions
-
-"Licensor" means any person or entity that distributes its Work.
-
-"Software" means the original work of authorship made available under
-this License.
-
-"Work" means the Software and any additions to or derivative works of
-the Software that are made available under this License.
-
-The terms "reproduce," "reproduction," "derivative works," and
-"distribution" have the meaning as provided under U.S. copyright law;
-provided, however, that for the purposes of this License, derivative
-works shall not include works that remain separable from, or merely
-link (or bind by name) to the interfaces of, the Work.
-
-Works, including the Software, are "made available" under this License
-by including in or with the Work either (a) a copyright notice
-referencing the applicability of this License to the Work, or (b) a
-copy of this License.
-
-2. License Grants
-
-    2.1 Copyright Grant. Subject to the terms and conditions of this
-    License, each Licensor grants to you a perpetual, worldwide,
-    non-exclusive, royalty-free, copyright license to reproduce,
-    prepare derivative works of, publicly display, publicly perform,
-    sublicense and distribute its Work and any resulting derivative
-    works in any form.
-
-3. Limitations
-
-    3.1 Redistribution. You may reproduce or distribute the Work only
-    if (a) you do so under this License, (b) you include a complete
-    copy of this License with your distribution, and (c) you retain
-    without modification any copyright, patent, trademark, or
-    attribution notices that are present in the Work.
-
-    3.2 Derivative Works. You may specify that additional or different
-    terms apply to the use, reproduction, and distribution of your
-    derivative works of the Work ("Your Terms") only if (a) Your Terms
-    provide that the use limitation in Section 3.3 applies to your
-    derivative works, and (b) you identify the specific derivative
-    works that are subject to Your Terms. Notwithstanding Your Terms,
-    this License (including the redistribution requirements in Section
-    3.1) will continue to apply to the Work itself.
-
-    3.3 Use Limitation. The Work and any derivative works thereof only
-    may be used or intended for use non-commercially. Notwithstanding
-    the foregoing, NVIDIA and its affiliates may use the Work and any
-    derivative works commercially. As used herein, "non-commercially"
-    means for research or evaluation purposes only.
-
-    3.4 Patent Claims. If you bring or threaten to bring a patent claim
-    against any Licensor (including any claim, cross-claim or
-    counterclaim in a lawsuit) to enforce any patents that you allege
-    are infringed by any Work, then your rights under this License from
-    such Licensor (including the grant in Section 2.1) will terminate
-    immediately.
-
-    3.5 Trademarks. This License does not grant any rights to use any
-    Licensor’s or its affiliates’ names, logos, or trademarks, except
-    as necessary to reproduce the notices described in this License.
-
-    3.6 Termination. If you violate any term of this License, then your
-    rights under this License (including the grant in Section 2.1) will
-    terminate immediately.
-
-4. Disclaimer of Warranty.
-
-THE WORK IS PROVIDED "AS IS" WITHOUT WARRANTIES OR CONDITIONS OF ANY
-KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WARRANTIES OR CONDITIONS OF
-MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, TITLE OR
-NON-INFRINGEMENT. YOU BEAR THE RISK OF UNDERTAKING ANY ACTIVITIES UNDER
-THIS LICENSE.
-
-5. Limitation of Liability.
-
-EXCEPT AS PROHIBITED BY APPLICABLE LAW, IN NO EVENT AND UNDER NO LEGAL
-THEORY, WHETHER IN TORT (INCLUDING NEGLIGENCE), CONTRACT, OR OTHERWISE
-SHALL ANY LICENSOR BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY DIRECT,
-INDIRECT, SPECIAL, INCIDENTAL, OR CONSEQUENTIAL DAMAGES ARISING OUT OF
-OR RELATED TO THIS LICENSE, THE USE OR INABILITY TO USE THE WORK
-(INCLUDING BUT NOT LIMITED TO LOSS OF GOODWILL, BUSINESS INTERRUPTION,
-LOST PROFITS OR DATA, COMPUTER FAILURE OR MALFUNCTION, OR ANY OTHER
-COMMERCIAL DAMAGES OR LOSSES), EVEN IF THE LICENSOR HAS BEEN ADVISED OF
-THE POSSIBILITY OF SUCH DAMAGES.
-
-=======================================================================

README.md

@@ -1,37 +0,0 @@
----
-title: StyleGAN_CLIP
-emoji: 🌍
-colorFrom: yellow
-colorTo: yellow
-sdk: gradio
-app_file: app.py
-pinned: false
----
-
-# Configuration
-
-`title`: _string_  
-Display title for the Space
-
-`emoji`: _string_  
-Space emoji (emoji-only character allowed)
-
-`colorFrom`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`colorTo`: _string_  
-Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
-
-`sdk`: _string_  
-Can be either `gradio` or `streamlit`
-
-`sdk_version` : _string_  
-Only applicable for `streamlit` SDK.  
-See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
-
-`app_file`: _string_  
-Path to your main application file (which contains either `gradio` or `streamlit` Python code).  
-Path is relative to the root of the repository.
-
-`pinned`: _boolean_  
-Whether the Space stays on top of your list.

app.py

@@ -1,203 +0,0 @@
-import os
-os.system("pip install --upgrade torch==1.9.1+cu111 torchvision==0.10.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html")
-os.system("git clone https://github.com/NVlabs/stylegan3")
-os.system("git clone https://github.com/openai/CLIP")
-os.system("pip install -e ./CLIP")
-os.system("pip install einops ninja scipy numpy Pillow tqdm")
-
-import sys
-sys.path.append('./CLIP')
-sys.path.append('./stylegan3')
-
-import io
-import os, time
-import pickle
-import shutil
-import numpy as np
-from PIL import Image
-import torch
-import torch.nn.functional as F
-import requests
-import torchvision.transforms as transforms
-import torchvision.transforms.functional as TF
-import clip
-from tqdm.notebook import tqdm
-from torchvision.transforms import Compose, Resize, ToTensor, Normalize
-from einops import rearrange
-
-device = torch.device('cuda:0')
-
-
-def fetch(url_or_path):
-    if str(url_or_path).startswith('http://') or str(url_or_path).startswith('https://'):
-        r = requests.get(url_or_path)
-        r.raise_for_status()
-        fd = io.BytesIO()
-        fd.write(r.content)
-        fd.seek(0)
-        return fd
-    return open(url_or_path, 'rb')
-
-def fetch_model(url_or_path):
-    basename = os.path.basename(url_or_path)
-    if os.path.exists(basename):
-        return basename
-    else:
-        os.system("wget -c '{url_or_path}'")
-        return basename
-
-def norm1(prompt):
-    "Normalize to the unit sphere."
-    return prompt / prompt.square().sum(dim=-1,keepdim=True).sqrt()
-
-def spherical_dist_loss(x, y):
-    x = F.normalize(x, dim=-1)
-    y = F.normalize(y, dim=-1)
-    return (x - y).norm(dim=-1).div(2).arcsin().pow(2).mul(2)
-
-class MakeCutouts(torch.nn.Module):
-    def __init__(self, cut_size, cutn, cut_pow=1.):
-        super().__init__()
-        self.cut_size = cut_size
-        self.cutn = cutn
-        self.cut_pow = cut_pow
-
-    def forward(self, input):
-        sideY, sideX = input.shape[2:4]
-        max_size = min(sideX, sideY)
-        min_size = min(sideX, sideY, self.cut_size)
-        cutouts = []
-        for _ in range(self.cutn):
-            size = int(torch.rand([])**self.cut_pow * (max_size - min_size) + min_size)
-            offsetx = torch.randint(0, sideX - size + 1, ())
-            offsety = torch.randint(0, sideY - size + 1, ())
-            cutout = input[:, :, offsety:offsety + size, offsetx:offsetx + size]
-            cutouts.append(F.adaptive_avg_pool2d(cutout, self.cut_size))
-        return torch.cat(cutouts)
-
-make_cutouts = MakeCutouts(224, 32, 0.5)
-
-def embed_image(image):
-  n = image.shape[0]
-  cutouts = make_cutouts(image)
-  embeds = clip_model.embed_cutout(cutouts)
-  embeds = rearrange(embeds, '(cc n) c -> cc n c', n=n)
-  return embeds
-
-def embed_url(url):
-  image = Image.open(fetch(url)).convert('RGB')
-  return embed_image(TF.to_tensor(image).to(device).unsqueeze(0)).mean(0).squeeze(0)
-
-class CLIP(object):
-  def __init__(self):
-    clip_model = "ViT-B/32"
-    self.model, _ = clip.load(clip_model)
-    self.model = self.model.requires_grad_(False)
-    self.normalize = transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073],
-                                          std=[0.26862954, 0.26130258, 0.27577711])
-
-  @torch.no_grad()
-  def embed_text(self, prompt):
-      "Normalized clip text embedding."
-      return norm1(self.model.encode_text(clip.tokenize(prompt).to(device)).float())
-
-  def embed_cutout(self, image):
-      "Normalized clip image embedding."
-      return norm1(self.model.encode_image(self.normalize(image)))
-  
-clip_model = CLIP()
-
-# Load stylegan model
-
-base_url = "https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/"
-model_name = "stylegan3-t-ffhqu-1024x1024.pkl"
-#model_name = "stylegan3-r-metfacesu-1024x1024.pkl"
-#model_name = "stylegan3-t-afhqv2-512x512.pkl"
-network_url = base_url + model_name
-
-os.system("wget -c https://api.ngc.nvidia.com/v2/models/nvidia/research/stylegan3/versions/1/files/stylegan3-t-ffhqu-1024x1024.pkl")
-
-with open('stylegan3-t-ffhqu-1024x1024.pkl', 'rb') as fp:
-  G = pickle.load(fp)['G_ema'].to(device)
-
-zs = torch.randn([10000, G.mapping.z_dim], device=device)
-w_stds = G.mapping(zs, None).std(0)
-    
-
-
-    
-def inference(text):
-  target = clip_model.embed_text(text)
-
-  steps = 600
-  seed = 2
-
-  tf = Compose([
-      Resize(224),
-      lambda x: torch.clamp((x+1)/2,min=0,max=1),
-      ])
-
-  torch.manual_seed(seed)
-  timestring = time.strftime('%Y%m%d%H%M%S')
-
-  with torch.no_grad():
-    qs = []
-    losses = []
-    for _ in range(8):
-      q = (G.mapping(torch.randn([4,G.mapping.z_dim], device=device), None, truncation_psi=0.7) - G.mapping.w_avg) / w_stds
-      images = G.synthesis(q * w_stds + G.mapping.w_avg)
-      embeds = embed_image(images.add(1).div(2))
-      loss = spherical_dist_loss(embeds, target).mean(0)
-      i = torch.argmin(loss)
-      qs.append(q[i])
-      losses.append(loss[i])
-    qs = torch.stack(qs)
-    losses = torch.stack(losses)
-    print(losses)
-    print(losses.shape, qs.shape)
-    i = torch.argmin(losses)
-    q = qs[i].unsqueeze(0)
-
-  q.requires_grad_()
-
-  q_ema = q
-  opt = torch.optim.AdamW([q], lr=0.03, betas=(0.0,0.999))
-  loop = tqdm(range(steps))
-  for i in loop:
-    opt.zero_grad()
-    w = q * w_stds
-    image = G.synthesis(w + G.mapping.w_avg, noise_mode='const')
-    embed = embed_image(image.add(1).div(2))
-    loss = spherical_dist_loss(embed, target).mean()
-    loss.backward()
-    opt.step()
-    loop.set_postfix(loss=loss.item(), q_magnitude=q.std().item())
-
-    q_ema = q_ema * 0.9 + q * 0.1
-    image = G.synthesis(q_ema * w_stds + G.mapping.w_avg, noise_mode='const')
-
-    if i % 10 == 0:
-      display(TF.to_pil_image(tf(image)[0]))
-    pil_image = TF.to_pil_image(image[0].add(1).div(2).clamp(0,1))
-    #os.makedirs(f'samples/{timestring}', exist_ok=True)
-    #pil_image.save(f'samples/{timestring}/{i:04}.jpg')
-
-  return pil_image
-  
-  
-
-title = "StyleGAN+CLIP_with_Latent_Bootstraping"
-description = "Gradio demo for StyleGAN+CLIP_with_Latent_Bootstraping. To use it, simply add your text, or click one of the examples to load them. Read more at the links below."
-article = "<p style='text-align: center'>colab by https://twitter.com/EricHallahan <a href='https://colab.research.google.com/drive/1br7GP_D6XCgulxPTAFhwGaV-ijFe084X' target='_blank'>Colab</a></p>"
-
-examples = [['elon musk']]
-gr.Interface(
-    inference, 
-    "text", 
-    gr.outputs.Image(type="pil", label="Output"),
-    title=title,
-    description=description,
-    article=article,
-    enable_queue=True,
-    examples=examples
-    ).launch(debug=True)

calc_metrics.py

@@ -1,190 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Calculate quality metrics for previous training run or pretrained network pickle."""
-
-import os
-import click
-import json
-import tempfile
-import copy
-import torch
-import dnnlib
-
-import legacy
-from metrics import metric_main
-from metrics import metric_utils
-from torch_utils import training_stats
-from torch_utils import custom_ops
-from torch_utils import misc
-
-#----------------------------------------------------------------------------
-
-def subprocess_fn(rank, args, temp_dir):
-    dnnlib.util.Logger(should_flush=True)
-
-    # Init torch.distributed.
-    if args.num_gpus > 1:
-        init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
-        if os.name == 'nt':
-            init_method = 'file:///' + init_file.replace('\\', '/')
-            torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus)
-        else:
-            init_method = f'file://{init_file}'
-            torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus)
-
-    # Init torch_utils.
-    sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None
-    training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
-    if rank != 0 or not args.verbose:
-        custom_ops.verbosity = 'none'
-
-    # Print network summary.
-    device = torch.device('cuda', rank)
-    torch.backends.cudnn.benchmark = True
-    torch.backends.cuda.matmul.allow_tf32 = False
-    torch.backends.cudnn.allow_tf32 = False
-    G = copy.deepcopy(args.G).eval().requires_grad_(False).to(device)
-    if rank == 0 and args.verbose:
-        z = torch.empty([1, G.z_dim], device=device)
-        c = torch.empty([1, G.c_dim], device=device)
-        misc.print_module_summary(G, [z, c])
-
-    # Calculate each metric.
-    for metric in args.metrics:
-        if rank == 0 and args.verbose:
-            print(f'Calculating {metric}...')
-        progress = metric_utils.ProgressMonitor(verbose=args.verbose)
-        result_dict = metric_main.calc_metric(metric=metric, G=G, dataset_kwargs=args.dataset_kwargs,
-            num_gpus=args.num_gpus, rank=rank, device=device, progress=progress)
-        if rank == 0:
-            metric_main.report_metric(result_dict, run_dir=args.run_dir, snapshot_pkl=args.network_pkl)
-        if rank == 0 and args.verbose:
-            print()
-
-    # Done.
-    if rank == 0 and args.verbose:
-        print('Exiting...')
-
-#----------------------------------------------------------------------------
-
-class CommaSeparatedList(click.ParamType):
-    name = 'list'
-
-    def convert(self, value, param, ctx):
-        _ = param, ctx
-        if value is None or value.lower() == 'none' or value == '':
-            return []
-        return value.split(',')
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.pass_context
-@click.option('network_pkl', '--network', help='Network pickle filename or URL', metavar='PATH', required=True)
-@click.option('--metrics', help='Comma-separated list or "none"', type=CommaSeparatedList(), default='fid50k_full', show_default=True)
-@click.option('--data', help='Dataset to evaluate metrics against (directory or zip) [default: same as training data]', metavar='PATH')
-@click.option('--mirror', help='Whether the dataset was augmented with x-flips during training [default: look up]', type=bool, metavar='BOOL')
-@click.option('--gpus', help='Number of GPUs to use', type=int, default=1, metavar='INT', show_default=True)
-@click.option('--verbose', help='Print optional information', type=bool, default=True, metavar='BOOL', show_default=True)
-
-def calc_metrics(ctx, network_pkl, metrics, data, mirror, gpus, verbose):
-    """Calculate quality metrics for previous training run or pretrained network pickle.
-
-    Examples:
-
-    \b
-    # Previous training run: look up options automatically, save result to JSONL file.
-    python calc_metrics.py --metrics=pr50k3_full \\
-        --network=~/training-runs/00000-ffhq10k-res64-auto1/network-snapshot-000000.pkl
-
-    \b
-    # Pre-trained network pickle: specify dataset explicitly, print result to stdout.
-    python calc_metrics.py --metrics=fid50k_full --data=~/datasets/ffhq.zip --mirror=1 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl
-
-    Available metrics:
-
-    \b
-      ADA paper:
-        fid50k_full  Frechet inception distance against the full dataset.
-        kid50k_full  Kernel inception distance against the full dataset.
-        pr50k3_full  Precision and recall againt the full dataset.
-        is50k        Inception score for CIFAR-10.
-
-    \b
-      StyleGAN and StyleGAN2 papers:
-        fid50k       Frechet inception distance against 50k real images.
-        kid50k       Kernel inception distance against 50k real images.
-        pr50k3       Precision and recall against 50k real images.
-        ppl2_wend    Perceptual path length in W at path endpoints against full image.
-        ppl_zfull    Perceptual path length in Z for full paths against cropped image.
-        ppl_wfull    Perceptual path length in W for full paths against cropped image.
-        ppl_zend     Perceptual path length in Z at path endpoints against cropped image.
-        ppl_wend     Perceptual path length in W at path endpoints against cropped image.
-    """
-    dnnlib.util.Logger(should_flush=True)
-
-    # Validate arguments.
-    args = dnnlib.EasyDict(metrics=metrics, num_gpus=gpus, network_pkl=network_pkl, verbose=verbose)
-    if not all(metric_main.is_valid_metric(metric) for metric in args.metrics):
-        ctx.fail('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
-    if not args.num_gpus >= 1:
-        ctx.fail('--gpus must be at least 1')
-
-    # Load network.
-    if not dnnlib.util.is_url(network_pkl, allow_file_urls=True) and not os.path.isfile(network_pkl):
-        ctx.fail('--network must point to a file or URL')
-    if args.verbose:
-        print(f'Loading network from "{network_pkl}"...')
-    with dnnlib.util.open_url(network_pkl, verbose=args.verbose) as f:
-        network_dict = legacy.load_network_pkl(f)
-        args.G = network_dict['G_ema'] # subclass of torch.nn.Module
-
-    # Initialize dataset options.
-    if data is not None:
-        args.dataset_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data)
-    elif network_dict['training_set_kwargs'] is not None:
-        args.dataset_kwargs = dnnlib.EasyDict(network_dict['training_set_kwargs'])
-    else:
-        ctx.fail('Could not look up dataset options; please specify --data')
-
-    # Finalize dataset options.
-    args.dataset_kwargs.resolution = args.G.img_resolution
-    args.dataset_kwargs.use_labels = (args.G.c_dim != 0)
-    if mirror is not None:
-        args.dataset_kwargs.xflip = mirror
-
-    # Print dataset options.
-    if args.verbose:
-        print('Dataset options:')
-        print(json.dumps(args.dataset_kwargs, indent=2))
-
-    # Locate run dir.
-    args.run_dir = None
-    if os.path.isfile(network_pkl):
-        pkl_dir = os.path.dirname(network_pkl)
-        if os.path.isfile(os.path.join(pkl_dir, 'training_options.json')):
-            args.run_dir = pkl_dir
-
-    # Launch processes.
-    if args.verbose:
-        print('Launching processes...')
-    torch.multiprocessing.set_start_method('spawn')
-    with tempfile.TemporaryDirectory() as temp_dir:
-        if args.num_gpus == 1:
-            subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
-        else:
-            torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus)
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    calc_metrics() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------

dataset_tool.py

@@ -1,444 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import functools
-import io
-import json
-import os
-import pickle
-import sys
-import tarfile
-import gzip
-import zipfile
-from pathlib import Path
-from typing import Callable, Optional, Tuple, Union
-
-import click
-import numpy as np
-import PIL.Image
-from tqdm import tqdm
-
-#----------------------------------------------------------------------------
-
-def error(msg):
-    print('Error: ' + msg)
-    sys.exit(1)
-
-#----------------------------------------------------------------------------
-
-def maybe_min(a: int, b: Optional[int]) -> int:
-    if b is not None:
-        return min(a, b)
-    return a
-
-#----------------------------------------------------------------------------
-
-def file_ext(name: Union[str, Path]) -> str:
-    return str(name).split('.')[-1]
-
-#----------------------------------------------------------------------------
-
-def is_image_ext(fname: Union[str, Path]) -> bool:
-    ext = file_ext(fname).lower()
-    return f'.{ext}' in PIL.Image.EXTENSION # type: ignore
-
-#----------------------------------------------------------------------------
-
-def open_image_folder(source_dir, *, max_images: Optional[int]):
-    input_images = [str(f) for f in sorted(Path(source_dir).rglob('*')) if is_image_ext(f) and os.path.isfile(f)]
-
-    # Load labels.
-    labels = {}
-    meta_fname = os.path.join(source_dir, 'dataset.json')
-    if os.path.isfile(meta_fname):
-        with open(meta_fname, 'r') as file:
-            labels = json.load(file)['labels']
-            if labels is not None:
-                labels = { x[0]: x[1] for x in labels }
-            else:
-                labels = {}
-
-    max_idx = maybe_min(len(input_images), max_images)
-
-    def iterate_images():
-        for idx, fname in enumerate(input_images):
-            arch_fname = os.path.relpath(fname, source_dir)
-            arch_fname = arch_fname.replace('\\', '/')
-            img = np.array(PIL.Image.open(fname))
-            yield dict(img=img, label=labels.get(arch_fname))
-            if idx >= max_idx-1:
-                break
-    return max_idx, iterate_images()
-
-#----------------------------------------------------------------------------
-
-def open_image_zip(source, *, max_images: Optional[int]):
-    with zipfile.ZipFile(source, mode='r') as z:
-        input_images = [str(f) for f in sorted(z.namelist()) if is_image_ext(f)]
-
-        # Load labels.
-        labels = {}
-        if 'dataset.json' in z.namelist():
-            with z.open('dataset.json', 'r') as file:
-                labels = json.load(file)['labels']
-                if labels is not None:
-                    labels = { x[0]: x[1] for x in labels }
-                else:
-                    labels = {}
-
-    max_idx = maybe_min(len(input_images), max_images)
-
-    def iterate_images():
-        with zipfile.ZipFile(source, mode='r') as z:
-            for idx, fname in enumerate(input_images):
-                with z.open(fname, 'r') as file:
-                    img = PIL.Image.open(file) # type: ignore
-                    img = np.array(img)
-                yield dict(img=img, label=labels.get(fname))
-                if idx >= max_idx-1:
-                    break
-    return max_idx, iterate_images()
-
-#----------------------------------------------------------------------------
-
-def open_lmdb(lmdb_dir: str, *, max_images: Optional[int]):
-    import cv2  # pip install opencv-python
-    import lmdb  # pip install lmdb # pylint: disable=import-error
-
-    with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn:
-        max_idx = maybe_min(txn.stat()['entries'], max_images)
-
-    def iterate_images():
-        with lmdb.open(lmdb_dir, readonly=True, lock=False).begin(write=False) as txn:
-            for idx, (_key, value) in enumerate(txn.cursor()):
-                try:
-                    try:
-                        img = cv2.imdecode(np.frombuffer(value, dtype=np.uint8), 1)
-                        if img is None:
-                            raise IOError('cv2.imdecode failed')
-                        img = img[:, :, ::-1] # BGR => RGB
-                    except IOError:
-                        img = np.array(PIL.Image.open(io.BytesIO(value)))
-                    yield dict(img=img, label=None)
-                    if idx >= max_idx-1:
-                        break
-                except:
-                    print(sys.exc_info()[1])
-
-    return max_idx, iterate_images()
-
-#----------------------------------------------------------------------------
-
-def open_cifar10(tarball: str, *, max_images: Optional[int]):
-    images = []
-    labels = []
-
-    with tarfile.open(tarball, 'r:gz') as tar:
-        for batch in range(1, 6):
-            member = tar.getmember(f'cifar-10-batches-py/data_batch_{batch}')
-            with tar.extractfile(member) as file:
-                data = pickle.load(file, encoding='latin1')
-            images.append(data['data'].reshape(-1, 3, 32, 32))
-            labels.append(data['labels'])
-
-    images = np.concatenate(images)
-    labels = np.concatenate(labels)
-    images = images.transpose([0, 2, 3, 1]) # NCHW -> NHWC
-    assert images.shape == (50000, 32, 32, 3) and images.dtype == np.uint8
-    assert labels.shape == (50000,) and labels.dtype in [np.int32, np.int64]
-    assert np.min(images) == 0 and np.max(images) == 255
-    assert np.min(labels) == 0 and np.max(labels) == 9
-
-    max_idx = maybe_min(len(images), max_images)
-
-    def iterate_images():
-        for idx, img in enumerate(images):
-            yield dict(img=img, label=int(labels[idx]))
-            if idx >= max_idx-1:
-                break
-
-    return max_idx, iterate_images()
-
-#----------------------------------------------------------------------------
-
-def open_mnist(images_gz: str, *, max_images: Optional[int]):
-    labels_gz = images_gz.replace('-images-idx3-ubyte.gz', '-labels-idx1-ubyte.gz')
-    assert labels_gz != images_gz
-    images = []
-    labels = []
-
-    with gzip.open(images_gz, 'rb') as f:
-        images = np.frombuffer(f.read(), np.uint8, offset=16)
-    with gzip.open(labels_gz, 'rb') as f:
-        labels = np.frombuffer(f.read(), np.uint8, offset=8)
-
-    images = images.reshape(-1, 28, 28)
-    images = np.pad(images, [(0,0), (2,2), (2,2)], 'constant', constant_values=0)
-    assert images.shape == (60000, 32, 32) and images.dtype == np.uint8
-    assert labels.shape == (60000,) and labels.dtype == np.uint8
-    assert np.min(images) == 0 and np.max(images) == 255
-    assert np.min(labels) == 0 and np.max(labels) == 9
-
-    max_idx = maybe_min(len(images), max_images)
-
-    def iterate_images():
-        for idx, img in enumerate(images):
-            yield dict(img=img, label=int(labels[idx]))
-            if idx >= max_idx-1:
-                break
-
-    return max_idx, iterate_images()
-
-#----------------------------------------------------------------------------
-
-def make_transform(
-    transform: Optional[str],
-    output_width: Optional[int],
-    output_height: Optional[int],
-    resize_filter: str
-) -> Callable[[np.ndarray], Optional[np.ndarray]]:
-    resample = { 'box': PIL.Image.BOX, 'lanczos': PIL.Image.LANCZOS }[resize_filter]
-    def scale(width, height, img):
-        w = img.shape[1]
-        h = img.shape[0]
-        if width == w and height == h:
-            return img
-        img = PIL.Image.fromarray(img)
-        ww = width if width is not None else w
-        hh = height if height is not None else h
-        img = img.resize((ww, hh), resample)
-        return np.array(img)
-
-    def center_crop(width, height, img):
-        crop = np.min(img.shape[:2])
-        img = img[(img.shape[0] - crop) // 2 : (img.shape[0] + crop) // 2, (img.shape[1] - crop) // 2 : (img.shape[1] + crop) // 2]
-        img = PIL.Image.fromarray(img, 'RGB')
-        img = img.resize((width, height), resample)
-        return np.array(img)
-
-    def center_crop_wide(width, height, img):
-        ch = int(np.round(width * img.shape[0] / img.shape[1]))
-        if img.shape[1] < width or ch < height:
-            return None
-
-        img = img[(img.shape[0] - ch) // 2 : (img.shape[0] + ch) // 2]
-        img = PIL.Image.fromarray(img, 'RGB')
-        img = img.resize((width, height), resample)
-        img = np.array(img)
-
-        canvas = np.zeros([width, width, 3], dtype=np.uint8)
-        canvas[(width - height) // 2 : (width + height) // 2, :] = img
-        return canvas
-
-    if transform is None:
-        return functools.partial(scale, output_width, output_height)
-    if transform == 'center-crop':
-        if (output_width is None) or (output_height is None):
-            error ('must specify --width and --height when using ' + transform + 'transform')
-        return functools.partial(center_crop, output_width, output_height)
-    if transform == 'center-crop-wide':
-        if (output_width is None) or (output_height is None):
-            error ('must specify --width and --height when using ' + transform + ' transform')
-        return functools.partial(center_crop_wide, output_width, output_height)
-    assert False, 'unknown transform'
-
-#----------------------------------------------------------------------------
-
-def open_dataset(source, *, max_images: Optional[int]):
-    if os.path.isdir(source):
-        if source.rstrip('/').endswith('_lmdb'):
-            return open_lmdb(source, max_images=max_images)
-        else:
-            return open_image_folder(source, max_images=max_images)
-    elif os.path.isfile(source):
-        if os.path.basename(source) == 'cifar-10-python.tar.gz':
-            return open_cifar10(source, max_images=max_images)
-        elif os.path.basename(source) == 'train-images-idx3-ubyte.gz':
-            return open_mnist(source, max_images=max_images)
-        elif file_ext(source) == 'zip':
-            return open_image_zip(source, max_images=max_images)
-        else:
-            assert False, 'unknown archive type'
-    else:
-        error(f'Missing input file or directory: {source}')
-
-#----------------------------------------------------------------------------
-
-def open_dest(dest: str) -> Tuple[str, Callable[[str, Union[bytes, str]], None], Callable[[], None]]:
-    dest_ext = file_ext(dest)
-
-    if dest_ext == 'zip':
-        if os.path.dirname(dest) != '':
-            os.makedirs(os.path.dirname(dest), exist_ok=True)
-        zf = zipfile.ZipFile(file=dest, mode='w', compression=zipfile.ZIP_STORED)
-        def zip_write_bytes(fname: str, data: Union[bytes, str]):
-            zf.writestr(fname, data)
-        return '', zip_write_bytes, zf.close
-    else:
-        # If the output folder already exists, check that is is
-        # empty.
-        #
-        # Note: creating the output directory is not strictly
-        # necessary as folder_write_bytes() also mkdirs, but it's better
-        # to give an error message earlier in case the dest folder
-        # somehow cannot be created.
-        if os.path.isdir(dest) and len(os.listdir(dest)) != 0:
-            error('--dest folder must be empty')
-        os.makedirs(dest, exist_ok=True)
-
-        def folder_write_bytes(fname: str, data: Union[bytes, str]):
-            os.makedirs(os.path.dirname(fname), exist_ok=True)
-            with open(fname, 'wb') as fout:
-                if isinstance(data, str):
-                    data = data.encode('utf8')
-                fout.write(data)
-        return dest, folder_write_bytes, lambda: None
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.pass_context
-@click.option('--source', help='Directory or archive name for input dataset', required=True, metavar='PATH')
-@click.option('--dest', help='Output directory or archive name for output dataset', required=True, metavar='PATH')
-@click.option('--max-images', help='Output only up to `max-images` images', type=int, default=None)
-@click.option('--resize-filter', help='Filter to use when resizing images for output resolution', type=click.Choice(['box', 'lanczos']), default='lanczos', show_default=True)
-@click.option('--transform', help='Input crop/resize mode', type=click.Choice(['center-crop', 'center-crop-wide']))
-@click.option('--width', help='Output width', type=int)
-@click.option('--height', help='Output height', type=int)
-def convert_dataset(
-    ctx: click.Context,
-    source: str,
-    dest: str,
-    max_images: Optional[int],
-    transform: Optional[str],
-    resize_filter: str,
-    width: Optional[int],
-    height: Optional[int]
-):
-    """Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch.
-
-    The input dataset format is guessed from the --source argument:
-
-    \b
-    --source *_lmdb/                    Load LSUN dataset
-    --source cifar-10-python.tar.gz     Load CIFAR-10 dataset
-    --source train-images-idx3-ubyte.gz Load MNIST dataset
-    --source path/                      Recursively load all images from path/
-    --source dataset.zip                Recursively load all images from dataset.zip
-
-    Specifying the output format and path:
-
-    \b
-    --dest /path/to/dir                 Save output files under /path/to/dir
-    --dest /path/to/dataset.zip         Save output files into /path/to/dataset.zip
-
-    The output dataset format can be either an image folder or an uncompressed zip archive.
-    Zip archives makes it easier to move datasets around file servers and clusters, and may
-    offer better training performance on network file systems.
-
-    Images within the dataset archive will be stored as uncompressed PNG.
-    Uncompresed PNGs can be efficiently decoded in the training loop.
-
-    Class labels are stored in a file called 'dataset.json' that is stored at the
-    dataset root folder.  This file has the following structure:
-
-    \b
-    {
-        "labels": [
-            ["00000/img00000000.png",6],
-            ["00000/img00000001.png",9],
-            ... repeated for every image in the datase
-            ["00049/img00049999.png",1]
-        ]
-    }
-
-    If the 'dataset.json' file cannot be found, the dataset is interpreted as
-    not containing class labels.
-
-    Image scale/crop and resolution requirements:
-
-    Output images must be square-shaped and they must all have the same power-of-two
-    dimensions.
-
-    To scale arbitrary input image size to a specific width and height, use the
-    --width and --height options.  Output resolution will be either the original
-    input resolution (if --width/--height was not specified) or the one specified with
-    --width/height.
-
-    Use the --transform=center-crop or --transform=center-crop-wide options to apply a
-    center crop transform on the input image.  These options should be used with the
-    --width and --height options.  For example:
-
-    \b
-    python dataset_tool.py --source LSUN/raw/cat_lmdb --dest /tmp/lsun_cat \\
-        --transform=center-crop-wide --width 512 --height=384
-    """
-
-    PIL.Image.init() # type: ignore
-
-    if dest == '':
-        ctx.fail('--dest output filename or directory must not be an empty string')
-
-    num_files, input_iter = open_dataset(source, max_images=max_images)
-    archive_root_dir, save_bytes, close_dest = open_dest(dest)
-
-    transform_image = make_transform(transform, width, height, resize_filter)
-
-    dataset_attrs = None
-
-    labels = []
-    for idx, image in tqdm(enumerate(input_iter), total=num_files):
-        idx_str = f'{idx:08d}'
-        archive_fname = f'{idx_str[:5]}/img{idx_str}.png'
-
-        # Apply crop and resize.
-        img = transform_image(image['img'])
-
-        # Transform may drop images.
-        if img is None:
-            continue
-
-        # Error check to require uniform image attributes across
-        # the whole dataset.
-        channels = img.shape[2] if img.ndim == 3 else 1
-        cur_image_attrs = {
-            'width': img.shape[1],
-            'height': img.shape[0],
-            'channels': channels
-        }
-        if dataset_attrs is None:
-            dataset_attrs = cur_image_attrs
-            width = dataset_attrs['width']
-            height = dataset_attrs['height']
-            if width != height:
-                error(f'Image dimensions after scale and crop are required to be square.  Got {width}x{height}')
-            if dataset_attrs['channels'] not in [1, 3]:
-                error('Input images must be stored as RGB or grayscale')
-            if width != 2 ** int(np.floor(np.log2(width))):
-                error('Image width/height after scale and crop are required to be power-of-two')
-        elif dataset_attrs != cur_image_attrs:
-            err = [f'  dataset {k}/cur image {k}: {dataset_attrs[k]}/{cur_image_attrs[k]}' for k in dataset_attrs.keys()]
-            error(f'Image {archive_fname} attributes must be equal across all images of the dataset.  Got:\n' + '\n'.join(err))
-
-        # Save the image as an uncompressed PNG.
-        img = PIL.Image.fromarray(img, { 1: 'L', 3: 'RGB' }[channels])
-        image_bits = io.BytesIO()
-        img.save(image_bits, format='png', compress_level=0, optimize=False)
-        save_bytes(os.path.join(archive_root_dir, archive_fname), image_bits.getbuffer())
-        labels.append([archive_fname, image['label']] if image['label'] is not None else None)
-
-    metadata = {
-        'labels': labels if all(x is not None for x in labels) else None
-    }
-    save_bytes(os.path.join(archive_root_dir, 'dataset.json'), json.dumps(metadata))
-    close_dest()
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    convert_dataset() # pylint: disable=no-value-for-parameter

dnnlib/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-from .util import EasyDict, make_cache_dir_path

dnnlib/util.py

@@ -1,477 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Miscellaneous utility classes and functions."""
-
-import ctypes
-import fnmatch
-import importlib
-import inspect
-import numpy as np
-import os
-import shutil
-import sys
-import types
-import io
-import pickle
-import re
-import requests
-import html
-import hashlib
-import glob
-import tempfile
-import urllib
-import urllib.request
-import uuid
-
-from distutils.util import strtobool
-from typing import Any, List, Tuple, Union
-
-
-# Util classes
-# ------------------------------------------------------------------------------------------
-
-
-class EasyDict(dict):
-    """Convenience class that behaves like a dict but allows access with the attribute syntax."""
-
-    def __getattr__(self, name: str) -> Any:
-        try:
-            return self[name]
-        except KeyError:
-            raise AttributeError(name)
-
-    def __setattr__(self, name: str, value: Any) -> None:
-        self[name] = value
-
-    def __delattr__(self, name: str) -> None:
-        del self[name]
-
-
-class Logger(object):
-    """Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file."""
-
-    def __init__(self, file_name: str = None, file_mode: str = "w", should_flush: bool = True):
-        self.file = None
-
-        if file_name is not None:
-            self.file = open(file_name, file_mode)
-
-        self.should_flush = should_flush
-        self.stdout = sys.stdout
-        self.stderr = sys.stderr
-
-        sys.stdout = self
-        sys.stderr = self
-
-    def __enter__(self) -> "Logger":
-        return self
-
-    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
-        self.close()
-
-    def write(self, text: Union[str, bytes]) -> None:
-        """Write text to stdout (and a file) and optionally flush."""
-        if isinstance(text, bytes):
-            text = text.decode()
-        if len(text) == 0: # workaround for a bug in VSCode debugger: sys.stdout.write(''); sys.stdout.flush() => crash
-            return
-
-        if self.file is not None:
-            self.file.write(text)
-
-        self.stdout.write(text)
-
-        if self.should_flush:
-            self.flush()
-
-    def flush(self) -> None:
-        """Flush written text to both stdout and a file, if open."""
-        if self.file is not None:
-            self.file.flush()
-
-        self.stdout.flush()
-
-    def close(self) -> None:
-        """Flush, close possible files, and remove stdout/stderr mirroring."""
-        self.flush()
-
-        # if using multiple loggers, prevent closing in wrong order
-        if sys.stdout is self:
-            sys.stdout = self.stdout
-        if sys.stderr is self:
-            sys.stderr = self.stderr
-
-        if self.file is not None:
-            self.file.close()
-            self.file = None
-
-
-# Cache directories
-# ------------------------------------------------------------------------------------------
-
-_dnnlib_cache_dir = None
-
-def set_cache_dir(path: str) -> None:
-    global _dnnlib_cache_dir
-    _dnnlib_cache_dir = path
-
-def make_cache_dir_path(*paths: str) -> str:
-    if _dnnlib_cache_dir is not None:
-        return os.path.join(_dnnlib_cache_dir, *paths)
-    if 'DNNLIB_CACHE_DIR' in os.environ:
-        return os.path.join(os.environ['DNNLIB_CACHE_DIR'], *paths)
-    if 'HOME' in os.environ:
-        return os.path.join(os.environ['HOME'], '.cache', 'dnnlib', *paths)
-    if 'USERPROFILE' in os.environ:
-        return os.path.join(os.environ['USERPROFILE'], '.cache', 'dnnlib', *paths)
-    return os.path.join(tempfile.gettempdir(), '.cache', 'dnnlib', *paths)
-
-# Small util functions
-# ------------------------------------------------------------------------------------------
-
-
-def format_time(seconds: Union[int, float]) -> str:
-    """Convert the seconds to human readable string with days, hours, minutes and seconds."""
-    s = int(np.rint(seconds))
-
-    if s < 60:
-        return "{0}s".format(s)
-    elif s < 60 * 60:
-        return "{0}m {1:02}s".format(s // 60, s % 60)
-    elif s < 24 * 60 * 60:
-        return "{0}h {1:02}m {2:02}s".format(s // (60 * 60), (s // 60) % 60, s % 60)
-    else:
-        return "{0}d {1:02}h {2:02}m".format(s // (24 * 60 * 60), (s // (60 * 60)) % 24, (s // 60) % 60)
-
-
-def ask_yes_no(question: str) -> bool:
-    """Ask the user the question until the user inputs a valid answer."""
-    while True:
-        try:
-            print("{0} [y/n]".format(question))
-            return strtobool(input().lower())
-        except ValueError:
-            pass
-
-
-def tuple_product(t: Tuple) -> Any:
-    """Calculate the product of the tuple elements."""
-    result = 1
-
-    for v in t:
-        result *= v
-
-    return result
-
-
-_str_to_ctype = {
-    "uint8": ctypes.c_ubyte,
-    "uint16": ctypes.c_uint16,
-    "uint32": ctypes.c_uint32,
-    "uint64": ctypes.c_uint64,
-    "int8": ctypes.c_byte,
-    "int16": ctypes.c_int16,
-    "int32": ctypes.c_int32,
-    "int64": ctypes.c_int64,
-    "float32": ctypes.c_float,
-    "float64": ctypes.c_double
-}
-
-
-def get_dtype_and_ctype(type_obj: Any) -> Tuple[np.dtype, Any]:
-    """Given a type name string (or an object having a __name__ attribute), return matching Numpy and ctypes types that have the same size in bytes."""
-    type_str = None
-
-    if isinstance(type_obj, str):
-        type_str = type_obj
-    elif hasattr(type_obj, "__name__"):
-        type_str = type_obj.__name__
-    elif hasattr(type_obj, "name"):
-        type_str = type_obj.name
-    else:
-        raise RuntimeError("Cannot infer type name from input")
-
-    assert type_str in _str_to_ctype.keys()
-
-    my_dtype = np.dtype(type_str)
-    my_ctype = _str_to_ctype[type_str]
-
-    assert my_dtype.itemsize == ctypes.sizeof(my_ctype)
-
-    return my_dtype, my_ctype
-
-
-def is_pickleable(obj: Any) -> bool:
-    try:
-        with io.BytesIO() as stream:
-            pickle.dump(obj, stream)
-        return True
-    except:
-        return False
-
-
-# Functionality to import modules/objects by name, and call functions by name
-# ------------------------------------------------------------------------------------------
-
-def get_module_from_obj_name(obj_name: str) -> Tuple[types.ModuleType, str]:
-    """Searches for the underlying module behind the name to some python object.
-    Returns the module and the object name (original name with module part removed)."""
-
-    # allow convenience shorthands, substitute them by full names
-    obj_name = re.sub("^np.", "numpy.", obj_name)
-    obj_name = re.sub("^tf.", "tensorflow.", obj_name)
-
-    # list alternatives for (module_name, local_obj_name)
-    parts = obj_name.split(".")
-    name_pairs = [(".".join(parts[:i]), ".".join(parts[i:])) for i in range(len(parts), 0, -1)]
-
-    # try each alternative in turn
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-            return module, local_obj_name
-        except:
-            pass
-
-    # maybe some of the modules themselves contain errors?
-    for module_name, _local_obj_name in name_pairs:
-        try:
-            importlib.import_module(module_name) # may raise ImportError
-        except ImportError:
-            if not str(sys.exc_info()[1]).startswith("No module named '" + module_name + "'"):
-                raise
-
-    # maybe the requested attribute is missing?
-    for module_name, local_obj_name in name_pairs:
-        try:
-            module = importlib.import_module(module_name) # may raise ImportError
-            get_obj_from_module(module, local_obj_name) # may raise AttributeError
-        except ImportError:
-            pass
-
-    # we are out of luck, but we have no idea why
-    raise ImportError(obj_name)
-
-
-def get_obj_from_module(module: types.ModuleType, obj_name: str) -> Any:
-    """Traverses the object name and returns the last (rightmost) python object."""
-    if obj_name == '':
-        return module
-    obj = module
-    for part in obj_name.split("."):
-        obj = getattr(obj, part)
-    return obj
-
-
-def get_obj_by_name(name: str) -> Any:
-    """Finds the python object with the given name."""
-    module, obj_name = get_module_from_obj_name(name)
-    return get_obj_from_module(module, obj_name)
-
-
-def call_func_by_name(*args, func_name: str = None, **kwargs) -> Any:
-    """Finds the python object with the given name and calls it as a function."""
-    assert func_name is not None
-    func_obj = get_obj_by_name(func_name)
-    assert callable(func_obj)
-    return func_obj(*args, **kwargs)
-
-
-def construct_class_by_name(*args, class_name: str = None, **kwargs) -> Any:
-    """Finds the python class with the given name and constructs it with the given arguments."""
-    return call_func_by_name(*args, func_name=class_name, **kwargs)
-
-
-def get_module_dir_by_obj_name(obj_name: str) -> str:
-    """Get the directory path of the module containing the given object name."""
-    module, _ = get_module_from_obj_name(obj_name)
-    return os.path.dirname(inspect.getfile(module))
-
-
-def is_top_level_function(obj: Any) -> bool:
-    """Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'."""
-    return callable(obj) and obj.__name__ in sys.modules[obj.__module__].__dict__
-
-
-def get_top_level_function_name(obj: Any) -> str:
-    """Return the fully-qualified name of a top-level function."""
-    assert is_top_level_function(obj)
-    module = obj.__module__
-    if module == '__main__':
-        module = os.path.splitext(os.path.basename(sys.modules[module].__file__))[0]
-    return module + "." + obj.__name__
-
-
-# File system helpers
-# ------------------------------------------------------------------------------------------
-
-def list_dir_recursively_with_ignore(dir_path: str, ignores: List[str] = None, add_base_to_relative: bool = False) -> List[Tuple[str, str]]:
-    """List all files recursively in a given directory while ignoring given file and directory names.
-    Returns list of tuples containing both absolute and relative paths."""
-    assert os.path.isdir(dir_path)
-    base_name = os.path.basename(os.path.normpath(dir_path))
-
-    if ignores is None:
-        ignores = []
-
-    result = []
-
-    for root, dirs, files in os.walk(dir_path, topdown=True):
-        for ignore_ in ignores:
-            dirs_to_remove = [d for d in dirs if fnmatch.fnmatch(d, ignore_)]
-
-            # dirs need to be edited in-place
-            for d in dirs_to_remove:
-                dirs.remove(d)
-
-            files = [f for f in files if not fnmatch.fnmatch(f, ignore_)]
-
-        absolute_paths = [os.path.join(root, f) for f in files]
-        relative_paths = [os.path.relpath(p, dir_path) for p in absolute_paths]
-
-        if add_base_to_relative:
-            relative_paths = [os.path.join(base_name, p) for p in relative_paths]
-
-        assert len(absolute_paths) == len(relative_paths)
-        result += zip(absolute_paths, relative_paths)
-
-    return result
-
-
-def copy_files_and_create_dirs(files: List[Tuple[str, str]]) -> None:
-    """Takes in a list of tuples of (src, dst) paths and copies files.
-    Will create all necessary directories."""
-    for file in files:
-        target_dir_name = os.path.dirname(file[1])
-
-        # will create all intermediate-level directories
-        if not os.path.exists(target_dir_name):
-            os.makedirs(target_dir_name)
-
-        shutil.copyfile(file[0], file[1])
-
-
-# URL helpers
-# ------------------------------------------------------------------------------------------
-
-def is_url(obj: Any, allow_file_urls: bool = False) -> bool:
-    """Determine whether the given object is a valid URL string."""
-    if not isinstance(obj, str) or not "://" in obj:
-        return False
-    if allow_file_urls and obj.startswith('file://'):
-        return True
-    try:
-        res = requests.compat.urlparse(obj)
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-        res = requests.compat.urlparse(requests.compat.urljoin(obj, "/"))
-        if not res.scheme or not res.netloc or not "." in res.netloc:
-            return False
-    except:
-        return False
-    return True
-
-
-def open_url(url: str, cache_dir: str = None, num_attempts: int = 10, verbose: bool = True, return_filename: bool = False, cache: bool = True) -> Any:
-    """Download the given URL and return a binary-mode file object to access the data."""
-    assert num_attempts >= 1
-    assert not (return_filename and (not cache))
-
-    # Doesn't look like an URL scheme so interpret it as a local filename.
-    if not re.match('^[a-z]+://', url):
-        return url if return_filename else open(url, "rb")
-
-    # Handle file URLs.  This code handles unusual file:// patterns that
-    # arise on Windows:
-    #
-    # file:///c:/foo.txt
-    #
-    # which would translate to a local '/c:/foo.txt' filename that's
-    # invalid.  Drop the forward slash for such pathnames.
-    #
-    # If you touch this code path, you should test it on both Linux and
-    # Windows.
-    #
-    # Some internet resources suggest using urllib.request.url2pathname() but
-    # but that converts forward slashes to backslashes and this causes
-    # its own set of problems.
-    if url.startswith('file://'):
-        filename = urllib.parse.urlparse(url).path
-        if re.match(r'^/[a-zA-Z]:', filename):
-            filename = filename[1:]
-        return filename if return_filename else open(filename, "rb")
-
-    assert is_url(url)
-
-    # Lookup from cache.
-    if cache_dir is None:
-        cache_dir = make_cache_dir_path('downloads')
-
-    url_md5 = hashlib.md5(url.encode("utf-8")).hexdigest()
-    if cache:
-        cache_files = glob.glob(os.path.join(cache_dir, url_md5 + "_*"))
-        if len(cache_files) == 1:
-            filename = cache_files[0]
-            return filename if return_filename else open(filename, "rb")
-
-    # Download.
-    url_name = None
-    url_data = None
-    with requests.Session() as session:
-        if verbose:
-            print("Downloading %s ..." % url, end="", flush=True)
-        for attempts_left in reversed(range(num_attempts)):
-            try:
-                with session.get(url) as res:
-                    res.raise_for_status()
-                    if len(res.content) == 0:
-                        raise IOError("No data received")
-
-                    if len(res.content) < 8192:
-                        content_str = res.content.decode("utf-8")
-                        if "download_warning" in res.headers.get("Set-Cookie", ""):
-                            links = [html.unescape(link) for link in content_str.split('"') if "export=download" in link]
-                            if len(links) == 1:
-                                url = requests.compat.urljoin(url, links[0])
-                                raise IOError("Google Drive virus checker nag")
-                        if "Google Drive - Quota exceeded" in content_str:
-                            raise IOError("Google Drive download quota exceeded -- please try again later")
-
-                    match = re.search(r'filename="([^"]*)"', res.headers.get("Content-Disposition", ""))
-                    url_name = match[1] if match else url
-                    url_data = res.content
-                    if verbose:
-                        print(" done")
-                    break
-            except KeyboardInterrupt:
-                raise
-            except:
-                if not attempts_left:
-                    if verbose:
-                        print(" failed")
-                    raise
-                if verbose:
-                    print(".", end="", flush=True)
-
-    # Save to cache.
-    if cache:
-        safe_name = re.sub(r"[^0-9a-zA-Z-._]", "_", url_name)
-        cache_file = os.path.join(cache_dir, url_md5 + "_" + safe_name)
-        temp_file = os.path.join(cache_dir, "tmp_" + uuid.uuid4().hex + "_" + url_md5 + "_" + safe_name)
-        os.makedirs(cache_dir, exist_ok=True)
-        with open(temp_file, "wb") as f:
-            f.write(url_data)
-        os.replace(temp_file, cache_file) # atomic
-        if return_filename:
-            return cache_file
-
-    # Return data as file object.
-    assert not return_filename
-    return io.BytesIO(url_data)

generate.py

@@ -1,129 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generate images using pretrained network pickle."""
-
-import os
-import re
-from typing import List, Optional
-
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-
-import legacy
-
-#----------------------------------------------------------------------------
-
-def num_range(s: str) -> List[int]:
-    '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
-
-    range_re = re.compile(r'^(\d+)-(\d+)$')
-    m = range_re.match(s)
-    if m:
-        return list(range(int(m.group(1)), int(m.group(2))+1))
-    vals = s.split(',')
-    return [int(x) for x in vals]
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.pass_context
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--seeds', type=num_range, help='List of random seeds')
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)')
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--projected-w', help='Projection result file', type=str, metavar='FILE')
-@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR')
-def generate_images(
-    ctx: click.Context,
-    network_pkl: str,
-    seeds: Optional[List[int]],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str,
-    class_idx: Optional[int],
-    projected_w: Optional[str]
-):
-    """Generate images using pretrained network pickle.
-
-    Examples:
-
-    \b
-    # Generate curated MetFaces images without truncation (Fig.10 left)
-    python generate.py --outdir=out --trunc=1 --seeds=85,265,297,849 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-
-    \b
-    # Generate uncurated MetFaces images with truncation (Fig.12 upper left)
-    python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-
-    \b
-    # Generate class conditional CIFAR-10 images (Fig.17 left, Car)
-    python generate.py --outdir=out --seeds=0-35 --class=1 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/cifar10.pkl
-
-    \b
-    # Render an image from projected W
-    python generate.py --outdir=out --projected_w=projected_w.npz \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-    """
-
-    print('Loading networks from "%s"...' % network_pkl)
-    device = torch.device('cuda')
-    with dnnlib.util.open_url(network_pkl) as f:
-        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
-
-    os.makedirs(outdir, exist_ok=True)
-
-    # Synthesize the result of a W projection.
-    if projected_w is not None:
-        if seeds is not None:
-            print ('warn: --seeds is ignored when using --projected-w')
-        print(f'Generating images from projected W "{projected_w}"')
-        ws = np.load(projected_w)['w']
-        ws = torch.tensor(ws, device=device) # pylint: disable=not-callable
-        assert ws.shape[1:] == (G.num_ws, G.w_dim)
-        for idx, w in enumerate(ws):
-            img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode)
-            img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-            img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/proj{idx:02d}.png')
-        return
-
-    if seeds is None:
-        ctx.fail('--seeds option is required when not using --projected-w')
-
-    # Labels.
-    label = torch.zeros([1, G.c_dim], device=device)
-    if G.c_dim != 0:
-        if class_idx is None:
-            ctx.fail('Must specify class label with --class when using a conditional network')
-        label[:, class_idx] = 1
-    else:
-        if class_idx is not None:
-            print ('warn: --class=lbl ignored when running on an unconditional network')
-
-    # Generate images.
-    for seed_idx, seed in enumerate(seeds):
-        print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds)))
-        z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device)
-        img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode)
-        img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-        PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png')
-
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    generate_images() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------

legacy.py

@@ -1,320 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import click
-import pickle
-import re
-import copy
-import numpy as np
-import torch
-import dnnlib
-from torch_utils import misc
-
-#----------------------------------------------------------------------------
-
-def load_network_pkl(f, force_fp16=False):
-    data = _LegacyUnpickler(f).load()
-
-    # Legacy TensorFlow pickle => convert.
-    if isinstance(data, tuple) and len(data) == 3 and all(isinstance(net, _TFNetworkStub) for net in data):
-        tf_G, tf_D, tf_Gs = data
-        G = convert_tf_generator(tf_G)
-        D = convert_tf_discriminator(tf_D)
-        G_ema = convert_tf_generator(tf_Gs)
-        data = dict(G=G, D=D, G_ema=G_ema)
-
-    # Add missing fields.
-    if 'training_set_kwargs' not in data:
-        data['training_set_kwargs'] = None
-    if 'augment_pipe' not in data:
-        data['augment_pipe'] = None
-
-    # Validate contents.
-    assert isinstance(data['G'], torch.nn.Module)
-    assert isinstance(data['D'], torch.nn.Module)
-    assert isinstance(data['G_ema'], torch.nn.Module)
-    assert isinstance(data['training_set_kwargs'], (dict, type(None)))
-    assert isinstance(data['augment_pipe'], (torch.nn.Module, type(None)))
-
-    # Force FP16.
-    if force_fp16:
-        for key in ['G', 'D', 'G_ema']:
-            old = data[key]
-            kwargs = copy.deepcopy(old.init_kwargs)
-            if key.startswith('G'):
-                kwargs.synthesis_kwargs = dnnlib.EasyDict(kwargs.get('synthesis_kwargs', {}))
-                kwargs.synthesis_kwargs.num_fp16_res = 4
-                kwargs.synthesis_kwargs.conv_clamp = 256
-            if key.startswith('D'):
-                kwargs.num_fp16_res = 4
-                kwargs.conv_clamp = 256
-            if kwargs != old.init_kwargs:
-                new = type(old)(**kwargs).eval().requires_grad_(False)
-                misc.copy_params_and_buffers(old, new, require_all=True)
-                data[key] = new
-    return data
-
-#----------------------------------------------------------------------------
-
-class _TFNetworkStub(dnnlib.EasyDict):
-    pass
-
-class _LegacyUnpickler(pickle.Unpickler):
-    def find_class(self, module, name):
-        if module == 'dnnlib.tflib.network' and name == 'Network':
-            return _TFNetworkStub
-        return super().find_class(module, name)
-
-#----------------------------------------------------------------------------
-
-def _collect_tf_params(tf_net):
-    # pylint: disable=protected-access
-    tf_params = dict()
-    def recurse(prefix, tf_net):
-        for name, value in tf_net.variables:
-            tf_params[prefix + name] = value
-        for name, comp in tf_net.components.items():
-            recurse(prefix + name + '/', comp)
-    recurse('', tf_net)
-    return tf_params
-
-#----------------------------------------------------------------------------
-
-def _populate_module_params(module, *patterns):
-    for name, tensor in misc.named_params_and_buffers(module):
-        found = False
-        value = None
-        for pattern, value_fn in zip(patterns[0::2], patterns[1::2]):
-            match = re.fullmatch(pattern, name)
-            if match:
-                found = True
-                if value_fn is not None:
-                    value = value_fn(*match.groups())
-                break
-        try:
-            assert found
-            if value is not None:
-                tensor.copy_(torch.from_numpy(np.array(value)))
-        except:
-            print(name, list(tensor.shape))
-            raise
-
-#----------------------------------------------------------------------------
-
-def convert_tf_generator(tf_G):
-    if tf_G.version < 4:
-        raise ValueError('TensorFlow pickle version too low')
-
-    # Collect kwargs.
-    tf_kwargs = tf_G.static_kwargs
-    known_kwargs = set()
-    def kwarg(tf_name, default=None, none=None):
-        known_kwargs.add(tf_name)
-        val = tf_kwargs.get(tf_name, default)
-        return val if val is not None else none
-
-    # Convert kwargs.
-    kwargs = dnnlib.EasyDict(
-        z_dim                   = kwarg('latent_size',          512),
-        c_dim                   = kwarg('label_size',           0),
-        w_dim                   = kwarg('dlatent_size',         512),
-        img_resolution          = kwarg('resolution',           1024),
-        img_channels            = kwarg('num_channels',         3),
-        mapping_kwargs = dnnlib.EasyDict(
-            num_layers          = kwarg('mapping_layers',       8),
-            embed_features      = kwarg('label_fmaps',          None),
-            layer_features      = kwarg('mapping_fmaps',        None),
-            activation          = kwarg('mapping_nonlinearity', 'lrelu'),
-            lr_multiplier       = kwarg('mapping_lrmul',        0.01),
-            w_avg_beta          = kwarg('w_avg_beta',           0.995,  none=1),
-        ),
-        synthesis_kwargs = dnnlib.EasyDict(
-            channel_base        = kwarg('fmap_base',            16384) * 2,
-            channel_max         = kwarg('fmap_max',             512),
-            num_fp16_res        = kwarg('num_fp16_res',         0),
-            conv_clamp          = kwarg('conv_clamp',           None),
-            architecture        = kwarg('architecture',         'skip'),
-            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
-            use_noise           = kwarg('use_noise',            True),
-            activation          = kwarg('nonlinearity',         'lrelu'),
-        ),
-    )
-
-    # Check for unknown kwargs.
-    kwarg('truncation_psi')
-    kwarg('truncation_cutoff')
-    kwarg('style_mixing_prob')
-    kwarg('structure')
-    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
-    if len(unknown_kwargs) > 0:
-        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
-
-    # Collect params.
-    tf_params = _collect_tf_params(tf_G)
-    for name, value in list(tf_params.items()):
-        match = re.fullmatch(r'ToRGB_lod(\d+)/(.*)', name)
-        if match:
-            r = kwargs.img_resolution // (2 ** int(match.group(1)))
-            tf_params[f'{r}x{r}/ToRGB/{match.group(2)}'] = value
-            kwargs.synthesis.kwargs.architecture = 'orig'
-    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
-
-    # Convert params.
-    from training import networks
-    G = networks.Generator(**kwargs).eval().requires_grad_(False)
-    # pylint: disable=unnecessary-lambda
-    _populate_module_params(G,
-        r'mapping\.w_avg',                                  lambda:     tf_params[f'dlatent_avg'],
-        r'mapping\.embed\.weight',                          lambda:     tf_params[f'mapping/LabelEmbed/weight'].transpose(),
-        r'mapping\.embed\.bias',                            lambda:     tf_params[f'mapping/LabelEmbed/bias'],
-        r'mapping\.fc(\d+)\.weight',                        lambda i:   tf_params[f'mapping/Dense{i}/weight'].transpose(),
-        r'mapping\.fc(\d+)\.bias',                          lambda i:   tf_params[f'mapping/Dense{i}/bias'],
-        r'synthesis\.b4\.const',                            lambda:     tf_params[f'synthesis/4x4/Const/const'][0],
-        r'synthesis\.b4\.conv1\.weight',                    lambda:     tf_params[f'synthesis/4x4/Conv/weight'].transpose(3, 2, 0, 1),
-        r'synthesis\.b4\.conv1\.bias',                      lambda:     tf_params[f'synthesis/4x4/Conv/bias'],
-        r'synthesis\.b4\.conv1\.noise_const',               lambda:     tf_params[f'synthesis/noise0'][0, 0],
-        r'synthesis\.b4\.conv1\.noise_strength',            lambda:     tf_params[f'synthesis/4x4/Conv/noise_strength'],
-        r'synthesis\.b4\.conv1\.affine\.weight',            lambda:     tf_params[f'synthesis/4x4/Conv/mod_weight'].transpose(),
-        r'synthesis\.b4\.conv1\.affine\.bias',              lambda:     tf_params[f'synthesis/4x4/Conv/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.conv0\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
-        r'synthesis\.b(\d+)\.conv0\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/bias'],
-        r'synthesis\.b(\d+)\.conv0\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-5}'][0, 0],
-        r'synthesis\.b(\d+)\.conv0\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/noise_strength'],
-        r'synthesis\.b(\d+)\.conv0\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_weight'].transpose(),
-        r'synthesis\.b(\d+)\.conv0\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv0_up/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.conv1\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/weight'].transpose(3, 2, 0, 1),
-        r'synthesis\.b(\d+)\.conv1\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/bias'],
-        r'synthesis\.b(\d+)\.conv1\.noise_const',           lambda r:   tf_params[f'synthesis/noise{int(np.log2(int(r)))*2-4}'][0, 0],
-        r'synthesis\.b(\d+)\.conv1\.noise_strength',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/noise_strength'],
-        r'synthesis\.b(\d+)\.conv1\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_weight'].transpose(),
-        r'synthesis\.b(\d+)\.conv1\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/Conv1/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.torgb\.weight',                lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/weight'].transpose(3, 2, 0, 1),
-        r'synthesis\.b(\d+)\.torgb\.bias',                  lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/bias'],
-        r'synthesis\.b(\d+)\.torgb\.affine\.weight',        lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_weight'].transpose(),
-        r'synthesis\.b(\d+)\.torgb\.affine\.bias',          lambda r:   tf_params[f'synthesis/{r}x{r}/ToRGB/mod_bias'] + 1,
-        r'synthesis\.b(\d+)\.skip\.weight',                 lambda r:   tf_params[f'synthesis/{r}x{r}/Skip/weight'][::-1, ::-1].transpose(3, 2, 0, 1),
-        r'.*\.resample_filter',                             None,
-    )
-    return G
-
-#----------------------------------------------------------------------------
-
-def convert_tf_discriminator(tf_D):
-    if tf_D.version < 4:
-        raise ValueError('TensorFlow pickle version too low')
-
-    # Collect kwargs.
-    tf_kwargs = tf_D.static_kwargs
-    known_kwargs = set()
-    def kwarg(tf_name, default=None):
-        known_kwargs.add(tf_name)
-        return tf_kwargs.get(tf_name, default)
-
-    # Convert kwargs.
-    kwargs = dnnlib.EasyDict(
-        c_dim                   = kwarg('label_size',           0),
-        img_resolution          = kwarg('resolution',           1024),
-        img_channels            = kwarg('num_channels',         3),
-        architecture            = kwarg('architecture',         'resnet'),
-        channel_base            = kwarg('fmap_base',            16384) * 2,
-        channel_max             = kwarg('fmap_max',             512),
-        num_fp16_res            = kwarg('num_fp16_res',         0),
-        conv_clamp              = kwarg('conv_clamp',           None),
-        cmap_dim                = kwarg('mapping_fmaps',        None),
-        block_kwargs = dnnlib.EasyDict(
-            activation          = kwarg('nonlinearity',         'lrelu'),
-            resample_filter     = kwarg('resample_kernel',      [1,3,3,1]),
-            freeze_layers       = kwarg('freeze_layers',        0),
-        ),
-        mapping_kwargs = dnnlib.EasyDict(
-            num_layers          = kwarg('mapping_layers',       0),
-            embed_features      = kwarg('mapping_fmaps',        None),
-            layer_features      = kwarg('mapping_fmaps',        None),
-            activation          = kwarg('nonlinearity',         'lrelu'),
-            lr_multiplier       = kwarg('mapping_lrmul',        0.1),
-        ),
-        epilogue_kwargs = dnnlib.EasyDict(
-            mbstd_group_size    = kwarg('mbstd_group_size',     None),
-            mbstd_num_channels  = kwarg('mbstd_num_features',   1),
-            activation          = kwarg('nonlinearity',         'lrelu'),
-        ),
-    )
-
-    # Check for unknown kwargs.
-    kwarg('structure')
-    unknown_kwargs = list(set(tf_kwargs.keys()) - known_kwargs)
-    if len(unknown_kwargs) > 0:
-        raise ValueError('Unknown TensorFlow kwarg', unknown_kwargs[0])
-
-    # Collect params.
-    tf_params = _collect_tf_params(tf_D)
-    for name, value in list(tf_params.items()):
-        match = re.fullmatch(r'FromRGB_lod(\d+)/(.*)', name)
-        if match:
-            r = kwargs.img_resolution // (2 ** int(match.group(1)))
-            tf_params[f'{r}x{r}/FromRGB/{match.group(2)}'] = value
-            kwargs.architecture = 'orig'
-    #for name, value in tf_params.items(): print(f'{name:<50s}{list(value.shape)}')
-
-    # Convert params.
-    from training import networks
-    D = networks.Discriminator(**kwargs).eval().requires_grad_(False)
-    # pylint: disable=unnecessary-lambda
-    _populate_module_params(D,
-        r'b(\d+)\.fromrgb\.weight',     lambda r:       tf_params[f'{r}x{r}/FromRGB/weight'].transpose(3, 2, 0, 1),
-        r'b(\d+)\.fromrgb\.bias',       lambda r:       tf_params[f'{r}x{r}/FromRGB/bias'],
-        r'b(\d+)\.conv(\d+)\.weight',   lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/weight'].transpose(3, 2, 0, 1),
-        r'b(\d+)\.conv(\d+)\.bias',     lambda r, i:    tf_params[f'{r}x{r}/Conv{i}{["","_down"][int(i)]}/bias'],
-        r'b(\d+)\.skip\.weight',        lambda r:       tf_params[f'{r}x{r}/Skip/weight'].transpose(3, 2, 0, 1),
-        r'mapping\.embed\.weight',      lambda:         tf_params[f'LabelEmbed/weight'].transpose(),
-        r'mapping\.embed\.bias',        lambda:         tf_params[f'LabelEmbed/bias'],
-        r'mapping\.fc(\d+)\.weight',    lambda i:       tf_params[f'Mapping{i}/weight'].transpose(),
-        r'mapping\.fc(\d+)\.bias',      lambda i:       tf_params[f'Mapping{i}/bias'],
-        r'b4\.conv\.weight',            lambda:         tf_params[f'4x4/Conv/weight'].transpose(3, 2, 0, 1),
-        r'b4\.conv\.bias',              lambda:         tf_params[f'4x4/Conv/bias'],
-        r'b4\.fc\.weight',              lambda:         tf_params[f'4x4/Dense0/weight'].transpose(),
-        r'b4\.fc\.bias',                lambda:         tf_params[f'4x4/Dense0/bias'],
-        r'b4\.out\.weight',             lambda:         tf_params[f'Output/weight'].transpose(),
-        r'b4\.out\.bias',               lambda:         tf_params[f'Output/bias'],
-        r'.*\.resample_filter',         None,
-    )
-    return D
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.option('--source', help='Input pickle', required=True, metavar='PATH')
-@click.option('--dest', help='Output pickle', required=True, metavar='PATH')
-@click.option('--force-fp16', help='Force the networks to use FP16', type=bool, default=False, metavar='BOOL', show_default=True)
-def convert_network_pickle(source, dest, force_fp16):
-    """Convert legacy network pickle into the native PyTorch format.
-
-    The tool is able to load the main network configurations exported using the TensorFlow version of StyleGAN2 or StyleGAN2-ADA.
-    It does not support e.g. StyleGAN2-ADA comparison methods, StyleGAN2 configs A-D, or StyleGAN1 networks.
-
-    Example:
-
-    \b
-    python legacy.py \\
-        --source=https://nvlabs-fi-cdn.nvidia.com/stylegan2/networks/stylegan2-cat-config-f.pkl \\
-        --dest=stylegan2-cat-config-f.pkl
-    """
-    print(f'Loading "{source}"...')
-    with dnnlib.util.open_url(source) as f:
-        data = load_network_pkl(f, force_fp16=force_fp16)
-    print(f'Saving "{dest}"...')
-    with open(dest, 'wb') as f:
-        pickle.dump(data, f)
-    print('Done.')
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    convert_network_pickle() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------

metrics/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty

metrics/frechet_inception_distance.py

@@ -1,41 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Frechet Inception Distance (FID) from the paper
-"GANs trained by a two time-scale update rule converge to a local Nash
-equilibrium". Matches the original implementation by Heusel et al. at
-https://github.com/bioinf-jku/TTUR/blob/master/fid.py"""
-
-import numpy as np
-import scipy.linalg
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_fid(opts, max_real, num_gen):
-    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-    detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer.
-
-    mu_real, sigma_real = metric_utils.compute_feature_stats_for_dataset(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=0, capture_mean_cov=True, max_items=max_real).get_mean_cov()
-
-    mu_gen, sigma_gen = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=1, capture_mean_cov=True, max_items=num_gen).get_mean_cov()
-
-    if opts.rank != 0:
-        return float('nan')
-
-    m = np.square(mu_gen - mu_real).sum()
-    s, _ = scipy.linalg.sqrtm(np.dot(sigma_gen, sigma_real), disp=False) # pylint: disable=no-member
-    fid = np.real(m + np.trace(sigma_gen + sigma_real - s * 2))
-    return float(fid)
-
-#----------------------------------------------------------------------------

metrics/inception_score.py

@@ -1,38 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Inception Score (IS) from the paper "Improved techniques for training
-GANs". Matches the original implementation by Salimans et al. at
-https://github.com/openai/improved-gan/blob/master/inception_score/model.py"""
-
-import numpy as np
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_is(opts, num_gen, num_splits):
-    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-    detector_kwargs = dict(no_output_bias=True) # Match the original implementation by not applying bias in the softmax layer.
-
-    gen_probs = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        capture_all=True, max_items=num_gen).get_all()
-
-    if opts.rank != 0:
-        return float('nan'), float('nan')
-
-    scores = []
-    for i in range(num_splits):
-        part = gen_probs[i * num_gen // num_splits : (i + 1) * num_gen // num_splits]
-        kl = part * (np.log(part) - np.log(np.mean(part, axis=0, keepdims=True)))
-        kl = np.mean(np.sum(kl, axis=1))
-        scores.append(np.exp(kl))
-    return float(np.mean(scores)), float(np.std(scores))
-
-#----------------------------------------------------------------------------

metrics/kernel_inception_distance.py

@@ -1,46 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Kernel Inception Distance (KID) from the paper "Demystifying MMD
-GANs". Matches the original implementation by Binkowski et al. at
-https://github.com/mbinkowski/MMD-GAN/blob/master/gan/compute_scores.py"""
-
-import numpy as np
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_kid(opts, max_real, num_gen, num_subsets, max_subset_size):
-    # Direct TorchScript translation of http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/inception-2015-12-05.pt'
-    detector_kwargs = dict(return_features=True) # Return raw features before the softmax layer.
-
-    real_features = metric_utils.compute_feature_stats_for_dataset(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all()
-
-    gen_features = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all()
-
-    if opts.rank != 0:
-        return float('nan')
-
-    n = real_features.shape[1]
-    m = min(min(real_features.shape[0], gen_features.shape[0]), max_subset_size)
-    t = 0
-    for _subset_idx in range(num_subsets):
-        x = gen_features[np.random.choice(gen_features.shape[0], m, replace=False)]
-        y = real_features[np.random.choice(real_features.shape[0], m, replace=False)]
-        a = (x @ x.T / n + 1) ** 3 + (y @ y.T / n + 1) ** 3
-        b = (x @ y.T / n + 1) ** 3
-        t += (a.sum() - np.diag(a).sum()) / (m - 1) - b.sum() * 2 / m
-    kid = t / num_subsets / m
-    return float(kid)
-
-#----------------------------------------------------------------------------

metrics/metric_main.py

@@ -1,152 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import time
-import json
-import torch
-import dnnlib
-
-from . import metric_utils
-from . import frechet_inception_distance
-from . import kernel_inception_distance
-from . import precision_recall
-from . import perceptual_path_length
-from . import inception_score
-
-#----------------------------------------------------------------------------
-
-_metric_dict = dict() # name => fn
-
-def register_metric(fn):
-    assert callable(fn)
-    _metric_dict[fn.__name__] = fn
-    return fn
-
-def is_valid_metric(metric):
-    return metric in _metric_dict
-
-def list_valid_metrics():
-    return list(_metric_dict.keys())
-
-#----------------------------------------------------------------------------
-
-def calc_metric(metric, **kwargs): # See metric_utils.MetricOptions for the full list of arguments.
-    assert is_valid_metric(metric)
-    opts = metric_utils.MetricOptions(**kwargs)
-
-    # Calculate.
-    start_time = time.time()
-    results = _metric_dict[metric](opts)
-    total_time = time.time() - start_time
-
-    # Broadcast results.
-    for key, value in list(results.items()):
-        if opts.num_gpus > 1:
-            value = torch.as_tensor(value, dtype=torch.float64, device=opts.device)
-            torch.distributed.broadcast(tensor=value, src=0)
-            value = float(value.cpu())
-        results[key] = value
-
-    # Decorate with metadata.
-    return dnnlib.EasyDict(
-        results         = dnnlib.EasyDict(results),
-        metric          = metric,
-        total_time      = total_time,
-        total_time_str  = dnnlib.util.format_time(total_time),
-        num_gpus        = opts.num_gpus,
-    )
-
-#----------------------------------------------------------------------------
-
-def report_metric(result_dict, run_dir=None, snapshot_pkl=None):
-    metric = result_dict['metric']
-    assert is_valid_metric(metric)
-    if run_dir is not None and snapshot_pkl is not None:
-        snapshot_pkl = os.path.relpath(snapshot_pkl, run_dir)
-
-    jsonl_line = json.dumps(dict(result_dict, snapshot_pkl=snapshot_pkl, timestamp=time.time()))
-    print(jsonl_line)
-    if run_dir is not None and os.path.isdir(run_dir):
-        with open(os.path.join(run_dir, f'metric-{metric}.jsonl'), 'at') as f:
-            f.write(jsonl_line + '\n')
-
-#----------------------------------------------------------------------------
-# Primary metrics.
-
-@register_metric
-def fid50k_full(opts):
-    opts.dataset_kwargs.update(max_size=None, xflip=False)
-    fid = frechet_inception_distance.compute_fid(opts, max_real=None, num_gen=50000)
-    return dict(fid50k_full=fid)
-
-@register_metric
-def kid50k_full(opts):
-    opts.dataset_kwargs.update(max_size=None, xflip=False)
-    kid = kernel_inception_distance.compute_kid(opts, max_real=1000000, num_gen=50000, num_subsets=100, max_subset_size=1000)
-    return dict(kid50k_full=kid)
-
-@register_metric
-def pr50k3_full(opts):
-    opts.dataset_kwargs.update(max_size=None, xflip=False)
-    precision, recall = precision_recall.compute_pr(opts, max_real=200000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000)
-    return dict(pr50k3_full_precision=precision, pr50k3_full_recall=recall)
-
-@register_metric
-def ppl2_wend(opts):
-    ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='end', crop=False, batch_size=2)
-    return dict(ppl2_wend=ppl)
-
-@register_metric
-def is50k(opts):
-    opts.dataset_kwargs.update(max_size=None, xflip=False)
-    mean, std = inception_score.compute_is(opts, num_gen=50000, num_splits=10)
-    return dict(is50k_mean=mean, is50k_std=std)
-
-#----------------------------------------------------------------------------
-# Legacy metrics.
-
-@register_metric
-def fid50k(opts):
-    opts.dataset_kwargs.update(max_size=None)
-    fid = frechet_inception_distance.compute_fid(opts, max_real=50000, num_gen=50000)
-    return dict(fid50k=fid)
-
-@register_metric
-def kid50k(opts):
-    opts.dataset_kwargs.update(max_size=None)
-    kid = kernel_inception_distance.compute_kid(opts, max_real=50000, num_gen=50000, num_subsets=100, max_subset_size=1000)
-    return dict(kid50k=kid)
-
-@register_metric
-def pr50k3(opts):
-    opts.dataset_kwargs.update(max_size=None)
-    precision, recall = precision_recall.compute_pr(opts, max_real=50000, num_gen=50000, nhood_size=3, row_batch_size=10000, col_batch_size=10000)
-    return dict(pr50k3_precision=precision, pr50k3_recall=recall)
-
-@register_metric
-def ppl_zfull(opts):
-    ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='z', sampling='full', crop=True, batch_size=2)
-    return dict(ppl_zfull=ppl)
-
-@register_metric
-def ppl_wfull(opts):
-    ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='full', crop=True, batch_size=2)
-    return dict(ppl_wfull=ppl)
-
-@register_metric
-def ppl_zend(opts):
-    ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='z', sampling='end', crop=True, batch_size=2)
-    return dict(ppl_zend=ppl)
-
-@register_metric
-def ppl_wend(opts):
-    ppl = perceptual_path_length.compute_ppl(opts, num_samples=50000, epsilon=1e-4, space='w', sampling='end', crop=True, batch_size=2)
-    return dict(ppl_wend=ppl)
-
-#----------------------------------------------------------------------------

metrics/metric_utils.py

@@ -1,275 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import time
-import hashlib
-import pickle
-import copy
-import uuid
-import numpy as np
-import torch
-import dnnlib
-
-#----------------------------------------------------------------------------
-
-class MetricOptions:
-    def __init__(self, G=None, G_kwargs={}, dataset_kwargs={}, num_gpus=1, rank=0, device=None, progress=None, cache=True):
-        assert 0 <= rank < num_gpus
-        self.G              = G
-        self.G_kwargs       = dnnlib.EasyDict(G_kwargs)
-        self.dataset_kwargs = dnnlib.EasyDict(dataset_kwargs)
-        self.num_gpus       = num_gpus
-        self.rank           = rank
-        self.device         = device if device is not None else torch.device('cuda', rank)
-        self.progress       = progress.sub() if progress is not None and rank == 0 else ProgressMonitor()
-        self.cache          = cache
-
-#----------------------------------------------------------------------------
-
-_feature_detector_cache = dict()
-
-def get_feature_detector_name(url):
-    return os.path.splitext(url.split('/')[-1])[0]
-
-def get_feature_detector(url, device=torch.device('cpu'), num_gpus=1, rank=0, verbose=False):
-    assert 0 <= rank < num_gpus
-    key = (url, device)
-    if key not in _feature_detector_cache:
-        is_leader = (rank == 0)
-        if not is_leader and num_gpus > 1:
-            torch.distributed.barrier() # leader goes first
-        with dnnlib.util.open_url(url, verbose=(verbose and is_leader)) as f:
-            _feature_detector_cache[key] = torch.jit.load(f).eval().to(device)
-        if is_leader and num_gpus > 1:
-            torch.distributed.barrier() # others follow
-    return _feature_detector_cache[key]
-
-#----------------------------------------------------------------------------
-
-class FeatureStats:
-    def __init__(self, capture_all=False, capture_mean_cov=False, max_items=None):
-        self.capture_all = capture_all
-        self.capture_mean_cov = capture_mean_cov
-        self.max_items = max_items
-        self.num_items = 0
-        self.num_features = None
-        self.all_features = None
-        self.raw_mean = None
-        self.raw_cov = None
-
-    def set_num_features(self, num_features):
-        if self.num_features is not None:
-            assert num_features == self.num_features
-        else:
-            self.num_features = num_features
-            self.all_features = []
-            self.raw_mean = np.zeros([num_features], dtype=np.float64)
-            self.raw_cov = np.zeros([num_features, num_features], dtype=np.float64)
-
-    def is_full(self):
-        return (self.max_items is not None) and (self.num_items >= self.max_items)
-
-    def append(self, x):
-        x = np.asarray(x, dtype=np.float32)
-        assert x.ndim == 2
-        if (self.max_items is not None) and (self.num_items + x.shape[0] > self.max_items):
-            if self.num_items >= self.max_items:
-                return
-            x = x[:self.max_items - self.num_items]
-
-        self.set_num_features(x.shape[1])
-        self.num_items += x.shape[0]
-        if self.capture_all:
-            self.all_features.append(x)
-        if self.capture_mean_cov:
-            x64 = x.astype(np.float64)
-            self.raw_mean += x64.sum(axis=0)
-            self.raw_cov += x64.T @ x64
-
-    def append_torch(self, x, num_gpus=1, rank=0):
-        assert isinstance(x, torch.Tensor) and x.ndim == 2
-        assert 0 <= rank < num_gpus
-        if num_gpus > 1:
-            ys = []
-            for src in range(num_gpus):
-                y = x.clone()
-                torch.distributed.broadcast(y, src=src)
-                ys.append(y)
-            x = torch.stack(ys, dim=1).flatten(0, 1) # interleave samples
-        self.append(x.cpu().numpy())
-
-    def get_all(self):
-        assert self.capture_all
-        return np.concatenate(self.all_features, axis=0)
-
-    def get_all_torch(self):
-        return torch.from_numpy(self.get_all())
-
-    def get_mean_cov(self):
-        assert self.capture_mean_cov
-        mean = self.raw_mean / self.num_items
-        cov = self.raw_cov / self.num_items
-        cov = cov - np.outer(mean, mean)
-        return mean, cov
-
-    def save(self, pkl_file):
-        with open(pkl_file, 'wb') as f:
-            pickle.dump(self.__dict__, f)
-
-    @staticmethod
-    def load(pkl_file):
-        with open(pkl_file, 'rb') as f:
-            s = dnnlib.EasyDict(pickle.load(f))
-        obj = FeatureStats(capture_all=s.capture_all, max_items=s.max_items)
-        obj.__dict__.update(s)
-        return obj
-
-#----------------------------------------------------------------------------
-
-class ProgressMonitor:
-    def __init__(self, tag=None, num_items=None, flush_interval=1000, verbose=False, progress_fn=None, pfn_lo=0, pfn_hi=1000, pfn_total=1000):
-        self.tag = tag
-        self.num_items = num_items
-        self.verbose = verbose
-        self.flush_interval = flush_interval
-        self.progress_fn = progress_fn
-        self.pfn_lo = pfn_lo
-        self.pfn_hi = pfn_hi
-        self.pfn_total = pfn_total
-        self.start_time = time.time()
-        self.batch_time = self.start_time
-        self.batch_items = 0
-        if self.progress_fn is not None:
-            self.progress_fn(self.pfn_lo, self.pfn_total)
-
-    def update(self, cur_items):
-        assert (self.num_items is None) or (cur_items <= self.num_items)
-        if (cur_items < self.batch_items + self.flush_interval) and (self.num_items is None or cur_items < self.num_items):
-            return
-        cur_time = time.time()
-        total_time = cur_time - self.start_time
-        time_per_item = (cur_time - self.batch_time) / max(cur_items - self.batch_items, 1)
-        if (self.verbose) and (self.tag is not None):
-            print(f'{self.tag:<19s} items {cur_items:<7d} time {dnnlib.util.format_time(total_time):<12s} ms/item {time_per_item*1e3:.2f}')
-        self.batch_time = cur_time
-        self.batch_items = cur_items
-
-        if (self.progress_fn is not None) and (self.num_items is not None):
-            self.progress_fn(self.pfn_lo + (self.pfn_hi - self.pfn_lo) * (cur_items / self.num_items), self.pfn_total)
-
-    def sub(self, tag=None, num_items=None, flush_interval=1000, rel_lo=0, rel_hi=1):
-        return ProgressMonitor(
-            tag             = tag,
-            num_items       = num_items,
-            flush_interval  = flush_interval,
-            verbose         = self.verbose,
-            progress_fn     = self.progress_fn,
-            pfn_lo          = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_lo,
-            pfn_hi          = self.pfn_lo + (self.pfn_hi - self.pfn_lo) * rel_hi,
-            pfn_total       = self.pfn_total,
-        )
-
-#----------------------------------------------------------------------------
-
-def compute_feature_stats_for_dataset(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, data_loader_kwargs=None, max_items=None, **stats_kwargs):
-    dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
-    if data_loader_kwargs is None:
-        data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)
-
-    # Try to lookup from cache.
-    cache_file = None
-    if opts.cache:
-        # Choose cache file name.
-        args = dict(dataset_kwargs=opts.dataset_kwargs, detector_url=detector_url, detector_kwargs=detector_kwargs, stats_kwargs=stats_kwargs)
-        md5 = hashlib.md5(repr(sorted(args.items())).encode('utf-8'))
-        cache_tag = f'{dataset.name}-{get_feature_detector_name(detector_url)}-{md5.hexdigest()}'
-        cache_file = dnnlib.make_cache_dir_path('gan-metrics', cache_tag + '.pkl')
-
-        # Check if the file exists (all processes must agree).
-        flag = os.path.isfile(cache_file) if opts.rank == 0 else False
-        if opts.num_gpus > 1:
-            flag = torch.as_tensor(flag, dtype=torch.float32, device=opts.device)
-            torch.distributed.broadcast(tensor=flag, src=0)
-            flag = (float(flag.cpu()) != 0)
-
-        # Load.
-        if flag:
-            return FeatureStats.load(cache_file)
-
-    # Initialize.
-    num_items = len(dataset)
-    if max_items is not None:
-        num_items = min(num_items, max_items)
-    stats = FeatureStats(max_items=num_items, **stats_kwargs)
-    progress = opts.progress.sub(tag='dataset features', num_items=num_items, rel_lo=rel_lo, rel_hi=rel_hi)
-    detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)
-
-    # Main loop.
-    item_subset = [(i * opts.num_gpus + opts.rank) % num_items for i in range((num_items - 1) // opts.num_gpus + 1)]
-    for images, _labels in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset, batch_size=batch_size, **data_loader_kwargs):
-        if images.shape[1] == 1:
-            images = images.repeat([1, 3, 1, 1])
-        features = detector(images.to(opts.device), **detector_kwargs)
-        stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
-        progress.update(stats.num_items)
-
-    # Save to cache.
-    if cache_file is not None and opts.rank == 0:
-        os.makedirs(os.path.dirname(cache_file), exist_ok=True)
-        temp_file = cache_file + '.' + uuid.uuid4().hex
-        stats.save(temp_file)
-        os.replace(temp_file, cache_file) # atomic
-    return stats
-
-#----------------------------------------------------------------------------
-
-def compute_feature_stats_for_generator(opts, detector_url, detector_kwargs, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, jit=False, **stats_kwargs):
-    if batch_gen is None:
-        batch_gen = min(batch_size, 4)
-    assert batch_size % batch_gen == 0
-
-    # Setup generator and load labels.
-    G = copy.deepcopy(opts.G).eval().requires_grad_(False).to(opts.device)
-    dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
-
-    # Image generation func.
-    def run_generator(z, c):
-        img = G(z=z, c=c, **opts.G_kwargs)
-        img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-        return img
-
-    # JIT.
-    if jit:
-        z = torch.zeros([batch_gen, G.z_dim], device=opts.device)
-        c = torch.zeros([batch_gen, G.c_dim], device=opts.device)
-        run_generator = torch.jit.trace(run_generator, [z, c], check_trace=False)
-
-    # Initialize.
-    stats = FeatureStats(**stats_kwargs)
-    assert stats.max_items is not None
-    progress = opts.progress.sub(tag='generator features', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi)
-    detector = get_feature_detector(url=detector_url, device=opts.device, num_gpus=opts.num_gpus, rank=opts.rank, verbose=progress.verbose)
-
-    # Main loop.
-    while not stats.is_full():
-        images = []
-        for _i in range(batch_size // batch_gen):
-            z = torch.randn([batch_gen, G.z_dim], device=opts.device)
-            c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_gen)]
-            c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
-            images.append(run_generator(z, c))
-        images = torch.cat(images)
-        if images.shape[1] == 1:
-            images = images.repeat([1, 3, 1, 1])
-        features = detector(images, **detector_kwargs)
-        stats.append_torch(features, num_gpus=opts.num_gpus, rank=opts.rank)
-        progress.update(stats.num_items)
-    return stats
-
-#----------------------------------------------------------------------------

metrics/perceptual_path_length.py

@@ -1,131 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Perceptual Path Length (PPL) from the paper "A Style-Based Generator
-Architecture for Generative Adversarial Networks". Matches the original
-implementation by Karras et al. at
-https://github.com/NVlabs/stylegan/blob/master/metrics/perceptual_path_length.py"""
-
-import copy
-import numpy as np
-import torch
-import dnnlib
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-# Spherical interpolation of a batch of vectors.
-def slerp(a, b, t):
-    a = a / a.norm(dim=-1, keepdim=True)
-    b = b / b.norm(dim=-1, keepdim=True)
-    d = (a * b).sum(dim=-1, keepdim=True)
-    p = t * torch.acos(d)
-    c = b - d * a
-    c = c / c.norm(dim=-1, keepdim=True)
-    d = a * torch.cos(p) + c * torch.sin(p)
-    d = d / d.norm(dim=-1, keepdim=True)
-    return d
-
-#----------------------------------------------------------------------------
-
-class PPLSampler(torch.nn.Module):
-    def __init__(self, G, G_kwargs, epsilon, space, sampling, crop, vgg16):
-        assert space in ['z', 'w']
-        assert sampling in ['full', 'end']
-        super().__init__()
-        self.G = copy.deepcopy(G)
-        self.G_kwargs = G_kwargs
-        self.epsilon = epsilon
-        self.space = space
-        self.sampling = sampling
-        self.crop = crop
-        self.vgg16 = copy.deepcopy(vgg16)
-
-    def forward(self, c):
-        # Generate random latents and interpolation t-values.
-        t = torch.rand([c.shape[0]], device=c.device) * (1 if self.sampling == 'full' else 0)
-        z0, z1 = torch.randn([c.shape[0] * 2, self.G.z_dim], device=c.device).chunk(2)
-
-        # Interpolate in W or Z.
-        if self.space == 'w':
-            w0, w1 = self.G.mapping(z=torch.cat([z0,z1]), c=torch.cat([c,c])).chunk(2)
-            wt0 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2))
-            wt1 = w0.lerp(w1, t.unsqueeze(1).unsqueeze(2) + self.epsilon)
-        else: # space == 'z'
-            zt0 = slerp(z0, z1, t.unsqueeze(1))
-            zt1 = slerp(z0, z1, t.unsqueeze(1) + self.epsilon)
-            wt0, wt1 = self.G.mapping(z=torch.cat([zt0,zt1]), c=torch.cat([c,c])).chunk(2)
-
-        # Randomize noise buffers.
-        for name, buf in self.G.named_buffers():
-            if name.endswith('.noise_const'):
-                buf.copy_(torch.randn_like(buf))
-
-        # Generate images.
-        img = self.G.synthesis(ws=torch.cat([wt0,wt1]), noise_mode='const', force_fp32=True, **self.G_kwargs)
-
-        # Center crop.
-        if self.crop:
-            assert img.shape[2] == img.shape[3]
-            c = img.shape[2] // 8
-            img = img[:, :, c*3 : c*7, c*2 : c*6]
-
-        # Downsample to 256x256.
-        factor = self.G.img_resolution // 256
-        if factor > 1:
-            img = img.reshape([-1, img.shape[1], img.shape[2] // factor, factor, img.shape[3] // factor, factor]).mean([3, 5])
-
-        # Scale dynamic range from [-1,1] to [0,255].
-        img = (img + 1) * (255 / 2)
-        if self.G.img_channels == 1:
-            img = img.repeat([1, 3, 1, 1])
-
-        # Evaluate differential LPIPS.
-        lpips_t0, lpips_t1 = self.vgg16(img, resize_images=False, return_lpips=True).chunk(2)
-        dist = (lpips_t0 - lpips_t1).square().sum(1) / self.epsilon ** 2
-        return dist
-
-#----------------------------------------------------------------------------
-
-def compute_ppl(opts, num_samples, epsilon, space, sampling, crop, batch_size, jit=False):
-    dataset = dnnlib.util.construct_class_by_name(**opts.dataset_kwargs)
-    vgg16_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    vgg16 = metric_utils.get_feature_detector(vgg16_url, num_gpus=opts.num_gpus, rank=opts.rank, verbose=opts.progress.verbose)
-
-    # Setup sampler.
-    sampler = PPLSampler(G=opts.G, G_kwargs=opts.G_kwargs, epsilon=epsilon, space=space, sampling=sampling, crop=crop, vgg16=vgg16)
-    sampler.eval().requires_grad_(False).to(opts.device)
-    if jit:
-        c = torch.zeros([batch_size, opts.G.c_dim], device=opts.device)
-        sampler = torch.jit.trace(sampler, [c], check_trace=False)
-
-    # Sampling loop.
-    dist = []
-    progress = opts.progress.sub(tag='ppl sampling', num_items=num_samples)
-    for batch_start in range(0, num_samples, batch_size * opts.num_gpus):
-        progress.update(batch_start)
-        c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(batch_size)]
-        c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
-        x = sampler(c)
-        for src in range(opts.num_gpus):
-            y = x.clone()
-            if opts.num_gpus > 1:
-                torch.distributed.broadcast(y, src=src)
-            dist.append(y)
-    progress.update(num_samples)
-
-    # Compute PPL.
-    if opts.rank != 0:
-        return float('nan')
-    dist = torch.cat(dist)[:num_samples].cpu().numpy()
-    lo = np.percentile(dist, 1, interpolation='lower')
-    hi = np.percentile(dist, 99, interpolation='higher')
-    ppl = np.extract(np.logical_and(dist >= lo, dist <= hi), dist).mean()
-    return float(ppl)
-
-#----------------------------------------------------------------------------

metrics/precision_recall.py

@@ -1,62 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Precision/Recall (PR) from the paper "Improved Precision and Recall
-Metric for Assessing Generative Models". Matches the original implementation
-by Kynkaanniemi et al. at
-https://github.com/kynkaat/improved-precision-and-recall-metric/blob/master/precision_recall.py"""
-
-import torch
-from . import metric_utils
-
-#----------------------------------------------------------------------------
-
-def compute_distances(row_features, col_features, num_gpus, rank, col_batch_size):
-    assert 0 <= rank < num_gpus
-    num_cols = col_features.shape[0]
-    num_batches = ((num_cols - 1) // col_batch_size // num_gpus + 1) * num_gpus
-    col_batches = torch.nn.functional.pad(col_features, [0, 0, 0, -num_cols % num_batches]).chunk(num_batches)
-    dist_batches = []
-    for col_batch in col_batches[rank :: num_gpus]:
-        dist_batch = torch.cdist(row_features.unsqueeze(0), col_batch.unsqueeze(0))[0]
-        for src in range(num_gpus):
-            dist_broadcast = dist_batch.clone()
-            if num_gpus > 1:
-                torch.distributed.broadcast(dist_broadcast, src=src)
-            dist_batches.append(dist_broadcast.cpu() if rank == 0 else None)
-    return torch.cat(dist_batches, dim=1)[:, :num_cols] if rank == 0 else None
-
-#----------------------------------------------------------------------------
-
-def compute_pr(opts, max_real, num_gen, nhood_size, row_batch_size, col_batch_size):
-    detector_url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    detector_kwargs = dict(return_features=True)
-
-    real_features = metric_utils.compute_feature_stats_for_dataset(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=0, capture_all=True, max_items=max_real).get_all_torch().to(torch.float16).to(opts.device)
-
-    gen_features = metric_utils.compute_feature_stats_for_generator(
-        opts=opts, detector_url=detector_url, detector_kwargs=detector_kwargs,
-        rel_lo=0, rel_hi=1, capture_all=True, max_items=num_gen).get_all_torch().to(torch.float16).to(opts.device)
-
-    results = dict()
-    for name, manifold, probes in [('precision', real_features, gen_features), ('recall', gen_features, real_features)]:
-        kth = []
-        for manifold_batch in manifold.split(row_batch_size):
-            dist = compute_distances(row_features=manifold_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
-            kth.append(dist.to(torch.float32).kthvalue(nhood_size + 1).values.to(torch.float16) if opts.rank == 0 else None)
-        kth = torch.cat(kth) if opts.rank == 0 else None
-        pred = []
-        for probes_batch in probes.split(row_batch_size):
-            dist = compute_distances(row_features=probes_batch, col_features=manifold, num_gpus=opts.num_gpus, rank=opts.rank, col_batch_size=col_batch_size)
-            pred.append((dist <= kth).any(dim=1) if opts.rank == 0 else None)
-        results[name] = float(torch.cat(pred).to(torch.float32).mean() if opts.rank == 0 else 'nan')
-    return results['precision'], results['recall']
-
-#----------------------------------------------------------------------------

packages.txt

@@ -1 +0,0 @@
-libtinfo5

projector.py

@@ -1,212 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Project given image to the latent space of pretrained network pickle."""
-
-import copy
-import os
-from time import perf_counter
-
-import click
-import imageio
-import numpy as np
-import PIL.Image
-import torch
-import torch.nn.functional as F
-
-import dnnlib
-import legacy
-
-def project(
-    G,
-    target: torch.Tensor, # [C,H,W] and dynamic range [0,255], W & H must match G output resolution
-    *,
-    num_steps                  = 1000,
-    w_avg_samples              = 10000,
-    initial_learning_rate      = 0.1,
-    initial_noise_factor       = 0.05,
-    lr_rampdown_length         = 0.25,
-    lr_rampup_length           = 0.05,
-    noise_ramp_length          = 0.75,
-    regularize_noise_weight    = 1e5,
-    verbose                    = False,
-    device: torch.device
-):
-    assert target.shape == (G.img_channels, G.img_resolution, G.img_resolution)
-
-    def logprint(*args):
-        if verbose:
-            print(*args)
-
-    G = copy.deepcopy(G).eval().requires_grad_(False).to(device) # type: ignore
-
-    # Compute w stats.
-    logprint(f'Computing W midpoint and stddev using {w_avg_samples} samples...')
-    z_samples = np.random.RandomState(123).randn(w_avg_samples, G.z_dim)
-    w_samples = G.mapping(torch.from_numpy(z_samples).to(device), None)  # [N, L, C]
-    w_samples = w_samples[:, :1, :].cpu().numpy().astype(np.float32)       # [N, 1, C]
-    w_avg = np.mean(w_samples, axis=0, keepdims=True)      # [1, 1, C]
-    w_std = (np.sum((w_samples - w_avg) ** 2) / w_avg_samples) ** 0.5
-
-    # Setup noise inputs.
-    noise_bufs = { name: buf for (name, buf) in G.synthesis.named_buffers() if 'noise_const' in name }
-
-    # Load VGG16 feature detector.
-    url = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metrics/vgg16.pt'
-    with dnnlib.util.open_url(url) as f:
-        vgg16 = torch.jit.load(f).eval().to(device)
-
-    # Features for target image.
-    target_images = target.unsqueeze(0).to(device).to(torch.float32)
-    if target_images.shape[2] > 256:
-        target_images = F.interpolate(target_images, size=(256, 256), mode='area')
-    target_features = vgg16(target_images, resize_images=False, return_lpips=True)
-
-    w_opt = torch.tensor(w_avg, dtype=torch.float32, device=device, requires_grad=True) # pylint: disable=not-callable
-    w_out = torch.zeros([num_steps] + list(w_opt.shape[1:]), dtype=torch.float32, device=device)
-    optimizer = torch.optim.Adam([w_opt] + list(noise_bufs.values()), betas=(0.9, 0.999), lr=initial_learning_rate)
-
-    # Init noise.
-    for buf in noise_bufs.values():
-        buf[:] = torch.randn_like(buf)
-        buf.requires_grad = True
-
-    for step in range(num_steps):
-        # Learning rate schedule.
-        t = step / num_steps
-        w_noise_scale = w_std * initial_noise_factor * max(0.0, 1.0 - t / noise_ramp_length) ** 2
-        lr_ramp = min(1.0, (1.0 - t) / lr_rampdown_length)
-        lr_ramp = 0.5 - 0.5 * np.cos(lr_ramp * np.pi)
-        lr_ramp = lr_ramp * min(1.0, t / lr_rampup_length)
-        lr = initial_learning_rate * lr_ramp
-        for param_group in optimizer.param_groups:
-            param_group['lr'] = lr
-
-        # Synth images from opt_w.
-        w_noise = torch.randn_like(w_opt) * w_noise_scale
-        ws = (w_opt + w_noise).repeat([1, G.mapping.num_ws, 1])
-        synth_images = G.synthesis(ws, noise_mode='const')
-
-        # Downsample image to 256x256 if it's larger than that. VGG was built for 224x224 images.
-        synth_images = (synth_images + 1) * (255/2)
-        if synth_images.shape[2] > 256:
-            synth_images = F.interpolate(synth_images, size=(256, 256), mode='area')
-
-        # Features for synth images.
-        synth_features = vgg16(synth_images, resize_images=False, return_lpips=True)
-        dist = (target_features - synth_features).square().sum()
-
-        # Noise regularization.
-        reg_loss = 0.0
-        for v in noise_bufs.values():
-            noise = v[None,None,:,:] # must be [1,1,H,W] for F.avg_pool2d()
-            while True:
-                reg_loss += (noise*torch.roll(noise, shifts=1, dims=3)).mean()**2
-                reg_loss += (noise*torch.roll(noise, shifts=1, dims=2)).mean()**2
-                if noise.shape[2] <= 8:
-                    break
-                noise = F.avg_pool2d(noise, kernel_size=2)
-        loss = dist + reg_loss * regularize_noise_weight
-
-        # Step
-        optimizer.zero_grad(set_to_none=True)
-        loss.backward()
-        optimizer.step()
-        logprint(f'step {step+1:>4d}/{num_steps}: dist {dist:<4.2f} loss {float(loss):<5.2f}')
-
-        # Save projected W for each optimization step.
-        w_out[step] = w_opt.detach()[0]
-
-        # Normalize noise.
-        with torch.no_grad():
-            for buf in noise_bufs.values():
-                buf -= buf.mean()
-                buf *= buf.square().mean().rsqrt()
-
-    return w_out.repeat([1, G.mapping.num_ws, 1])
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--target', 'target_fname', help='Target image file to project to', required=True, metavar='FILE')
-@click.option('--num-steps',              help='Number of optimization steps', type=int, default=1000, show_default=True)
-@click.option('--seed',                   help='Random seed', type=int, default=303, show_default=True)
-@click.option('--save-video',             help='Save an mp4 video of optimization progress', type=bool, default=True, show_default=True)
-@click.option('--outdir',                 help='Where to save the output images', required=True, metavar='DIR')
-def run_projection(
-    network_pkl: str,
-    target_fname: str,
-    outdir: str,
-    save_video: bool,
-    seed: int,
-    num_steps: int
-):
-    """Project given image to the latent space of pretrained network pickle.
-
-    Examples:
-
-    \b
-    python projector.py --outdir=out --target=~/mytargetimg.png \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl
-    """
-    np.random.seed(seed)
-    torch.manual_seed(seed)
-
-    # Load networks.
-    print('Loading networks from "%s"...' % network_pkl)
-    device = torch.device('cuda')
-    with dnnlib.util.open_url(network_pkl) as fp:
-        G = legacy.load_network_pkl(fp)['G_ema'].requires_grad_(False).to(device) # type: ignore
-
-    # Load target image.
-    target_pil = PIL.Image.open(target_fname).convert('RGB')
-    w, h = target_pil.size
-    s = min(w, h)
-    target_pil = target_pil.crop(((w - s) // 2, (h - s) // 2, (w + s) // 2, (h + s) // 2))
-    target_pil = target_pil.resize((G.img_resolution, G.img_resolution), PIL.Image.LANCZOS)
-    target_uint8 = np.array(target_pil, dtype=np.uint8)
-
-    # Optimize projection.
-    start_time = perf_counter()
-    projected_w_steps = project(
-        G,
-        target=torch.tensor(target_uint8.transpose([2, 0, 1]), device=device), # pylint: disable=not-callable
-        num_steps=num_steps,
-        device=device,
-        verbose=True
-    )
-    print (f'Elapsed: {(perf_counter()-start_time):.1f} s')
-
-    # Render debug output: optional video and projected image and W vector.
-    os.makedirs(outdir, exist_ok=True)
-    if save_video:
-        video = imageio.get_writer(f'{outdir}/proj.mp4', mode='I', fps=10, codec='libx264', bitrate='16M')
-        print (f'Saving optimization progress video "{outdir}/proj.mp4"')
-        for projected_w in projected_w_steps:
-            synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode='const')
-            synth_image = (synth_image + 1) * (255/2)
-            synth_image = synth_image.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
-            video.append_data(np.concatenate([target_uint8, synth_image], axis=1))
-        video.close()
-
-    # Save final projected frame and W vector.
-    target_pil.save(f'{outdir}/target.png')
-    projected_w = projected_w_steps[-1]
-    synth_image = G.synthesis(projected_w.unsqueeze(0), noise_mode='const')
-    synth_image = (synth_image + 1) * (255/2)
-    synth_image = synth_image.permute(0, 2, 3, 1).clamp(0, 255).to(torch.uint8)[0].cpu().numpy()
-    PIL.Image.fromarray(synth_image, 'RGB').save(f'{outdir}/proj.png')
-    np.savez(f'{outdir}/projected_w.npz', w=projected_w.unsqueeze(0).cpu().numpy())
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    run_projection() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------

style_mixing.py

@@ -1,118 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Generate style mixing image matrix using pretrained network pickle."""
-
-import os
-import re
-from typing import List
-
-import click
-import dnnlib
-import numpy as np
-import PIL.Image
-import torch
-
-import legacy
-
-#----------------------------------------------------------------------------
-
-def num_range(s: str) -> List[int]:
-    '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.'''
-
-    range_re = re.compile(r'^(\d+)-(\d+)$')
-    m = range_re.match(s)
-    if m:
-        return list(range(int(m.group(1)), int(m.group(2))+1))
-    vals = s.split(',')
-    return [int(x) for x in vals]
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.option('--network', 'network_pkl', help='Network pickle filename', required=True)
-@click.option('--rows', 'row_seeds', type=num_range, help='Random seeds to use for image rows', required=True)
-@click.option('--cols', 'col_seeds', type=num_range, help='Random seeds to use for image columns', required=True)
-@click.option('--styles', 'col_styles', type=num_range, help='Style layer range', default='0-6', show_default=True)
-@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True)
-@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True)
-@click.option('--outdir', type=str, required=True)
-def generate_style_mix(
-    network_pkl: str,
-    row_seeds: List[int],
-    col_seeds: List[int],
-    col_styles: List[int],
-    truncation_psi: float,
-    noise_mode: str,
-    outdir: str
-):
-    """Generate images using pretrained network pickle.
-
-    Examples:
-
-    \b
-    python style_mixing.py --outdir=out --rows=85,100,75,458,1500 --cols=55,821,1789,293 \\
-        --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl
-    """
-    print('Loading networks from "%s"...' % network_pkl)
-    device = torch.device('cuda')
-    with dnnlib.util.open_url(network_pkl) as f:
-        G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore
-
-    os.makedirs(outdir, exist_ok=True)
-
-    print('Generating W vectors...')
-    all_seeds = list(set(row_seeds + col_seeds))
-    all_z = np.stack([np.random.RandomState(seed).randn(G.z_dim) for seed in all_seeds])
-    all_w = G.mapping(torch.from_numpy(all_z).to(device), None)
-    w_avg = G.mapping.w_avg
-    all_w = w_avg + (all_w - w_avg) * truncation_psi
-    w_dict = {seed: w for seed, w in zip(all_seeds, list(all_w))}
-
-    print('Generating images...')
-    all_images = G.synthesis(all_w, noise_mode=noise_mode)
-    all_images = (all_images.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8).cpu().numpy()
-    image_dict = {(seed, seed): image for seed, image in zip(all_seeds, list(all_images))}
-
-    print('Generating style-mixed images...')
-    for row_seed in row_seeds:
-        for col_seed in col_seeds:
-            w = w_dict[row_seed].clone()
-            w[col_styles] = w_dict[col_seed][col_styles]
-            image = G.synthesis(w[np.newaxis], noise_mode=noise_mode)
-            image = (image.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8)
-            image_dict[(row_seed, col_seed)] = image[0].cpu().numpy()
-
-    print('Saving images...')
-    os.makedirs(outdir, exist_ok=True)
-    for (row_seed, col_seed), image in image_dict.items():
-        PIL.Image.fromarray(image, 'RGB').save(f'{outdir}/{row_seed}-{col_seed}.png')
-
-    print('Saving image grid...')
-    W = G.img_resolution
-    H = G.img_resolution
-    canvas = PIL.Image.new('RGB', (W * (len(col_seeds) + 1), H * (len(row_seeds) + 1)), 'black')
-    for row_idx, row_seed in enumerate([0] + row_seeds):
-        for col_idx, col_seed in enumerate([0] + col_seeds):
-            if row_idx == 0 and col_idx == 0:
-                continue
-            key = (row_seed, col_seed)
-            if row_idx == 0:
-                key = (col_seed, col_seed)
-            if col_idx == 0:
-                key = (row_seed, row_seed)
-            canvas.paste(PIL.Image.fromarray(image_dict[key], 'RGB'), (W * col_idx, H * row_idx))
-    canvas.save(f'{outdir}/grid.png')
-
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    generate_style_mix() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------

torch_utils/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty

torch_utils/custom_ops.py

@@ -1,126 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import glob
-import torch
-import torch.utils.cpp_extension
-import importlib
-import hashlib
-import shutil
-from pathlib import Path
-
-from torch.utils.file_baton import FileBaton
-
-#----------------------------------------------------------------------------
-# Global options.
-
-verbosity = 'brief' # Verbosity level: 'none', 'brief', 'full'
-
-#----------------------------------------------------------------------------
-# Internal helper funcs.
-
-def _find_compiler_bindir():
-    patterns = [
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Professional/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/BuildTools/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio/*/Community/VC/Tools/MSVC/*/bin/Hostx64/x64',
-        'C:/Program Files (x86)/Microsoft Visual Studio */vc/bin',
-    ]
-    for pattern in patterns:
-        matches = sorted(glob.glob(pattern))
-        if len(matches):
-            return matches[-1]
-    return None
-
-#----------------------------------------------------------------------------
-# Main entry point for compiling and loading C++/CUDA plugins.
-
-_cached_plugins = dict()
-
-def get_plugin(module_name, sources, **build_kwargs):
-    assert verbosity in ['none', 'brief', 'full']
-
-    # Already cached?
-    if module_name in _cached_plugins:
-        return _cached_plugins[module_name]
-
-    # Print status.
-    if verbosity == 'full':
-        print(f'Setting up PyTorch plugin "{module_name}"...')
-    elif verbosity == 'brief':
-        print(f'Setting up PyTorch plugin "{module_name}"... ', end='', flush=True)
-
-    try: # pylint: disable=too-many-nested-blocks
-        # Make sure we can find the necessary compiler binaries.
-        if os.name == 'nt' and os.system("where cl.exe >nul 2>nul") != 0:
-            compiler_bindir = _find_compiler_bindir()
-            if compiler_bindir is None:
-                raise RuntimeError(f'Could not find MSVC/GCC/CLANG installation on this computer. Check _find_compiler_bindir() in "{__file__}".')
-            os.environ['PATH'] += ';' + compiler_bindir
-
-        # Compile and load.
-        verbose_build = (verbosity == 'full')
-
-        # Incremental build md5sum trickery.  Copies all the input source files
-        # into a cached build directory under a combined md5 digest of the input
-        # source files.  Copying is done only if the combined digest has changed.
-        # This keeps input file timestamps and filenames the same as in previous
-        # extension builds, allowing for fast incremental rebuilds.
-        #
-        # This optimization is done only in case all the source files reside in
-        # a single directory (just for simplicity) and if the TORCH_EXTENSIONS_DIR
-        # environment variable is set (we take this as a signal that the user
-        # actually cares about this.)
-        source_dirs_set = set(os.path.dirname(source) for source in sources)
-        if len(source_dirs_set) == 1 and ('TORCH_EXTENSIONS_DIR' in os.environ):
-            all_source_files = sorted(list(x for x in Path(list(source_dirs_set)[0]).iterdir() if x.is_file()))
-
-            # Compute a combined hash digest for all source files in the same
-            # custom op directory (usually .cu, .cpp, .py and .h files).
-            hash_md5 = hashlib.md5()
-            for src in all_source_files:
-                with open(src, 'rb') as f:
-                    hash_md5.update(f.read())
-            build_dir = torch.utils.cpp_extension._get_build_directory(module_name, verbose=verbose_build) # pylint: disable=protected-access
-            digest_build_dir = os.path.join(build_dir, hash_md5.hexdigest())
-
-            if not os.path.isdir(digest_build_dir):
-                os.makedirs(digest_build_dir, exist_ok=True)
-                baton = FileBaton(os.path.join(digest_build_dir, 'lock'))
-                if baton.try_acquire():
-                    try:
-                        for src in all_source_files:
-                            shutil.copyfile(src, os.path.join(digest_build_dir, os.path.basename(src)))
-                    finally:
-                        baton.release()
-                else:
-                    # Someone else is copying source files under the digest dir,
-                    # wait until done and continue.
-                    baton.wait()
-            digest_sources = [os.path.join(digest_build_dir, os.path.basename(x)) for x in sources]
-            torch.utils.cpp_extension.load(name=module_name, build_directory=build_dir,
-                verbose=verbose_build, sources=digest_sources, **build_kwargs)
-        else:
-            torch.utils.cpp_extension.load(name=module_name, verbose=verbose_build, sources=sources, **build_kwargs)
-        module = importlib.import_module(module_name)
-
-    except:
-        if verbosity == 'brief':
-            print('Failed!')
-        raise
-
-    # Print status and add to cache.
-    if verbosity == 'full':
-        print(f'Done setting up PyTorch plugin "{module_name}".')
-    elif verbosity == 'brief':
-        print('Done.')
-    _cached_plugins[module_name] = module
-    return module
-
-#----------------------------------------------------------------------------

torch_utils/misc.py

@@ -1,262 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import re
-import contextlib
-import numpy as np
-import torch
-import warnings
-import dnnlib
-
-#----------------------------------------------------------------------------
-# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
-# same constant is used multiple times.
-
-_constant_cache = dict()
-
-def constant(value, shape=None, dtype=None, device=None, memory_format=None):
-    value = np.asarray(value)
-    if shape is not None:
-        shape = tuple(shape)
-    if dtype is None:
-        dtype = torch.get_default_dtype()
-    if device is None:
-        device = torch.device('cpu')
-    if memory_format is None:
-        memory_format = torch.contiguous_format
-
-    key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
-    tensor = _constant_cache.get(key, None)
-    if tensor is None:
-        tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
-        if shape is not None:
-            tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
-        tensor = tensor.contiguous(memory_format=memory_format)
-        _constant_cache[key] = tensor
-    return tensor
-
-#----------------------------------------------------------------------------
-# Replace NaN/Inf with specified numerical values.
-
-try:
-    nan_to_num = torch.nan_to_num # 1.8.0a0
-except AttributeError:
-    def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
-        assert isinstance(input, torch.Tensor)
-        if posinf is None:
-            posinf = torch.finfo(input.dtype).max
-        if neginf is None:
-            neginf = torch.finfo(input.dtype).min
-        assert nan == 0
-        return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
-
-#----------------------------------------------------------------------------
-# Symbolic assert.
-
-try:
-    symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
-except AttributeError:
-    symbolic_assert = torch.Assert # 1.7.0
-
-#----------------------------------------------------------------------------
-# Context manager to suppress known warnings in torch.jit.trace().
-
-class suppress_tracer_warnings(warnings.catch_warnings):
-    def __enter__(self):
-        super().__enter__()
-        warnings.simplefilter('ignore', category=torch.jit.TracerWarning)
-        return self
-
-#----------------------------------------------------------------------------
-# Assert that the shape of a tensor matches the given list of integers.
-# None indicates that the size of a dimension is allowed to vary.
-# Performs symbolic assertion when used in torch.jit.trace().
-
-def assert_shape(tensor, ref_shape):
-    if tensor.ndim != len(ref_shape):
-        raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
-    for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
-        if ref_size is None:
-            pass
-        elif isinstance(ref_size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
-        elif isinstance(size, torch.Tensor):
-            with suppress_tracer_warnings(): # as_tensor results are registered as constants
-                symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
-        elif size != ref_size:
-            raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
-
-#----------------------------------------------------------------------------
-# Function decorator that calls torch.autograd.profiler.record_function().
-
-def profiled_function(fn):
-    def decorator(*args, **kwargs):
-        with torch.autograd.profiler.record_function(fn.__name__):
-            return fn(*args, **kwargs)
-    decorator.__name__ = fn.__name__
-    return decorator
-
-#----------------------------------------------------------------------------
-# Sampler for torch.utils.data.DataLoader that loops over the dataset
-# indefinitely, shuffling items as it goes.
-
-class InfiniteSampler(torch.utils.data.Sampler):
-    def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
-        assert len(dataset) > 0
-        assert num_replicas > 0
-        assert 0 <= rank < num_replicas
-        assert 0 <= window_size <= 1
-        super().__init__(dataset)
-        self.dataset = dataset
-        self.rank = rank
-        self.num_replicas = num_replicas
-        self.shuffle = shuffle
-        self.seed = seed
-        self.window_size = window_size
-
-    def __iter__(self):
-        order = np.arange(len(self.dataset))
-        rnd = None
-        window = 0
-        if self.shuffle:
-            rnd = np.random.RandomState(self.seed)
-            rnd.shuffle(order)
-            window = int(np.rint(order.size * self.window_size))
-
-        idx = 0
-        while True:
-            i = idx % order.size
-            if idx % self.num_replicas == self.rank:
-                yield order[i]
-            if window >= 2:
-                j = (i - rnd.randint(window)) % order.size
-                order[i], order[j] = order[j], order[i]
-            idx += 1
-
-#----------------------------------------------------------------------------
-# Utilities for operating with torch.nn.Module parameters and buffers.
-
-def params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.parameters()) + list(module.buffers())
-
-def named_params_and_buffers(module):
-    assert isinstance(module, torch.nn.Module)
-    return list(module.named_parameters()) + list(module.named_buffers())
-
-def copy_params_and_buffers(src_module, dst_module, require_all=False):
-    assert isinstance(src_module, torch.nn.Module)
-    assert isinstance(dst_module, torch.nn.Module)
-    src_tensors = {name: tensor for name, tensor in named_params_and_buffers(src_module)}
-    for name, tensor in named_params_and_buffers(dst_module):
-        assert (name in src_tensors) or (not require_all)
-        if name in src_tensors:
-            tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
-
-#----------------------------------------------------------------------------
-# Context manager for easily enabling/disabling DistributedDataParallel
-# synchronization.
-
-@contextlib.contextmanager
-def ddp_sync(module, sync):
-    assert isinstance(module, torch.nn.Module)
-    if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
-        yield
-    else:
-        with module.no_sync():
-            yield
-
-#----------------------------------------------------------------------------
-# Check DistributedDataParallel consistency across processes.
-
-def check_ddp_consistency(module, ignore_regex=None):
-    assert isinstance(module, torch.nn.Module)
-    for name, tensor in named_params_and_buffers(module):
-        fullname = type(module).__name__ + '.' + name
-        if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
-            continue
-        tensor = tensor.detach()
-        other = tensor.clone()
-        torch.distributed.broadcast(tensor=other, src=0)
-        assert (nan_to_num(tensor) == nan_to_num(other)).all(), fullname
-
-#----------------------------------------------------------------------------
-# Print summary table of module hierarchy.
-
-def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
-    assert isinstance(module, torch.nn.Module)
-    assert not isinstance(module, torch.jit.ScriptModule)
-    assert isinstance(inputs, (tuple, list))
-
-    # Register hooks.
-    entries = []
-    nesting = [0]
-    def pre_hook(_mod, _inputs):
-        nesting[0] += 1
-    def post_hook(mod, _inputs, outputs):
-        nesting[0] -= 1
-        if nesting[0] <= max_nesting:
-            outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
-            outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
-            entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
-    hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
-    hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
-
-    # Run module.
-    outputs = module(*inputs)
-    for hook in hooks:
-        hook.remove()
-
-    # Identify unique outputs, parameters, and buffers.
-    tensors_seen = set()
-    for e in entries:
-        e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
-        e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
-        e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
-        tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
-
-    # Filter out redundant entries.
-    if skip_redundant:
-        entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
-
-    # Construct table.
-    rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
-    rows += [['---'] * len(rows[0])]
-    param_total = 0
-    buffer_total = 0
-    submodule_names = {mod: name for name, mod in module.named_modules()}
-    for e in entries:
-        name = '<top-level>' if e.mod is module else submodule_names[e.mod]
-        param_size = sum(t.numel() for t in e.unique_params)
-        buffer_size = sum(t.numel() for t in e.unique_buffers)
-        output_shapes = [str(list(e.outputs[0].shape)) for t in e.outputs]
-        output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
-        rows += [[
-            name + (':0' if len(e.outputs) >= 2 else ''),
-            str(param_size) if param_size else '-',
-            str(buffer_size) if buffer_size else '-',
-            (output_shapes + ['-'])[0],
-            (output_dtypes + ['-'])[0],
-        ]]
-        for idx in range(1, len(e.outputs)):
-            rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
-        param_total += param_size
-        buffer_total += buffer_size
-    rows += [['---'] * len(rows[0])]
-    rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
-
-    # Print table.
-    widths = [max(len(cell) for cell in column) for column in zip(*rows)]
-    print()
-    for row in rows:
-        print('  '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
-    print()
-    return outputs
-
-#----------------------------------------------------------------------------

torch_utils/ops/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty

torch_utils/ops/bias_act.cpp

@@ -1,99 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-
-static bool has_same_layout(torch::Tensor x, torch::Tensor y)
-{
-    if (x.dim() != y.dim())
-        return false;
-    for (int64_t i = 0; i < x.dim(); i++)
-    {
-        if (x.size(i) != y.size(i))
-            return false;
-        if (x.size(i) >= 2 && x.stride(i) != y.stride(i))
-            return false;
-    }
-    return true;
-}
-
-//------------------------------------------------------------------------
-
-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)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(b.numel() == 0 || (b.dtype() == x.dtype() && b.device() == x.device()), "b must have the same dtype and device as x");
-    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");
-    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");
-    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");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(b.dim() == 1, "b must have rank 1");
-    TORCH_CHECK(b.numel() == 0 || (dim >= 0 && dim < x.dim()), "dim is out of bounds");
-    TORCH_CHECK(b.numel() == 0 || b.numel() == x.size(dim), "b has wrong number of elements");
-    TORCH_CHECK(grad >= 0, "grad must be non-negative");
-
-    // Validate layout.
-    TORCH_CHECK(x.is_non_overlapping_and_dense(), "x must be non-overlapping and dense");
-    TORCH_CHECK(b.is_contiguous(), "b must be contiguous");
-    TORCH_CHECK(xref.numel() == 0 || has_same_layout(xref, x), "xref must have the same layout as x");
-    TORCH_CHECK(yref.numel() == 0 || has_same_layout(yref, x), "yref must have the same layout as x");
-    TORCH_CHECK(dy.numel() == 0 || has_same_layout(dy, x), "dy must have the same layout as x");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    torch::Tensor y = torch::empty_like(x);
-    TORCH_CHECK(has_same_layout(y, x), "y must have the same layout as x");
-
-    // Initialize CUDA kernel parameters.
-    bias_act_kernel_params p;
-    p.x     = x.data_ptr();
-    p.b     = (b.numel()) ? b.data_ptr() : NULL;
-    p.xref  = (xref.numel()) ? xref.data_ptr() : NULL;
-    p.yref  = (yref.numel()) ? yref.data_ptr() : NULL;
-    p.dy    = (dy.numel()) ? dy.data_ptr() : NULL;
-    p.y     = y.data_ptr();
-    p.grad  = grad;
-    p.act   = act;
-    p.alpha = alpha;
-    p.gain  = gain;
-    p.clamp = clamp;
-    p.sizeX = (int)x.numel();
-    p.sizeB = (int)b.numel();
-    p.stepB = (b.numel()) ? (int)x.stride(dim) : 1;
-
-    // Choose CUDA kernel.
-    void* kernel;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        kernel = choose_bias_act_kernel<scalar_t>(p);
-    });
-    TORCH_CHECK(kernel, "no CUDA kernel found for the specified activation func");
-
-    // Launch CUDA kernel.
-    p.loopX = 4;
-    int blockSize = 4 * 32;
-    int gridSize = (p.sizeX - 1) / (p.loopX * blockSize) + 1;
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("bias_act", &bias_act);
-}
-
-//------------------------------------------------------------------------

torch_utils/ops/bias_act.cu

@@ -1,173 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <c10/util/Half.h>
-#include "bias_act.h"
-
-//------------------------------------------------------------------------
-// Helpers.
-
-template <class T> struct InternalType;
-template <> struct InternalType<double>     { typedef double scalar_t; };
-template <> struct InternalType<float>      { typedef float  scalar_t; };
-template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
-
-//------------------------------------------------------------------------
-// CUDA kernel.
-
-template <class T, int A>
-__global__ void bias_act_kernel(bias_act_kernel_params p)
-{
-    typedef typename InternalType<T>::scalar_t scalar_t;
-    int G                 = p.grad;
-    scalar_t alpha        = (scalar_t)p.alpha;
-    scalar_t gain         = (scalar_t)p.gain;
-    scalar_t clamp        = (scalar_t)p.clamp;
-    scalar_t one          = (scalar_t)1;
-    scalar_t two          = (scalar_t)2;
-    scalar_t expRange     = (scalar_t)80;
-    scalar_t halfExpRange = (scalar_t)40;
-    scalar_t seluScale    = (scalar_t)1.0507009873554804934193349852946;
-    scalar_t seluAlpha    = (scalar_t)1.6732632423543772848170429916717;
-
-    // Loop over elements.
-    int xi = blockIdx.x * p.loopX * blockDim.x + threadIdx.x;
-    for (int loopIdx = 0; loopIdx < p.loopX && xi < p.sizeX; loopIdx++, xi += blockDim.x)
-    {
-        // Load.
-        scalar_t x = (scalar_t)((const T*)p.x)[xi];
-        scalar_t b = (p.b) ? (scalar_t)((const T*)p.b)[(xi / p.stepB) % p.sizeB] : 0;
-        scalar_t xref = (p.xref) ? (scalar_t)((const T*)p.xref)[xi] : 0;
-        scalar_t yref = (p.yref) ? (scalar_t)((const T*)p.yref)[xi] : 0;
-        scalar_t dy = (p.dy) ? (scalar_t)((const T*)p.dy)[xi] : one;
-        scalar_t yy = (gain != 0) ? yref / gain : 0;
-        scalar_t y = 0;
-
-        // Apply bias.
-        ((G == 0) ? x : xref) += b;
-
-        // linear
-        if (A == 1)
-        {
-            if (G == 0) y = x;
-            if (G == 1) y = x;
-        }
-
-        // relu
-        if (A == 2)
-        {
-            if (G == 0) y = (x > 0) ? x : 0;
-            if (G == 1) y = (yy > 0) ? x : 0;
-        }
-
-        // lrelu
-        if (A == 3)
-        {
-            if (G == 0) y = (x > 0) ? x : x * alpha;
-            if (G == 1) y = (yy > 0) ? x : x * alpha;
-        }
-
-        // tanh
-        if (A == 4)
-        {
-            if (G == 0) { scalar_t c = exp(x); scalar_t d = one / c; y = (x < -expRange) ? -one : (x > expRange) ? one : (c - d) / (c + d); }
-            if (G == 1) y = x * (one - yy * yy);
-            if (G == 2) y = x * (one - yy * yy) * (-two * yy);
-        }
-
-        // sigmoid
-        if (A == 5)
-        {
-            if (G == 0) y = (x < -expRange) ? 0 : one / (exp(-x) + one);
-            if (G == 1) y = x * yy * (one - yy);
-            if (G == 2) y = x * yy * (one - yy) * (one - two * yy);
-        }
-
-        // elu
-        if (A == 6)
-        {
-            if (G == 0) y = (x >= 0) ? x : exp(x) - one;
-            if (G == 1) y = (yy >= 0) ? x : x * (yy + one);
-            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + one);
-        }
-
-        // selu
-        if (A == 7)
-        {
-            if (G == 0) y = (x >= 0) ? seluScale * x : (seluScale * seluAlpha) * (exp(x) - one);
-            if (G == 1) y = (yy >= 0) ? x * seluScale : x * (yy + seluScale * seluAlpha);
-            if (G == 2) y = (yy >= 0) ? 0 : x * (yy + seluScale * seluAlpha);
-        }
-
-        // softplus
-        if (A == 8)
-        {
-            if (G == 0) y = (x > expRange) ? x : log(exp(x) + one);
-            if (G == 1) y = x * (one - exp(-yy));
-            if (G == 2) { scalar_t c = exp(-yy); y = x * c * (one - c); }
-        }
-
-        // swish
-        if (A == 9)
-        {
-            if (G == 0)
-                y = (x < -expRange) ? 0 : x / (exp(-x) + one);
-            else
-            {
-                scalar_t c = exp(xref);
-                scalar_t d = c + one;
-                if (G == 1)
-                    y = (xref > halfExpRange) ? x : x * c * (xref + d) / (d * d);
-                else
-                    y = (xref > halfExpRange) ? 0 : x * c * (xref * (two - d) + two * d) / (d * d * d);
-                yref = (xref < -expRange) ? 0 : xref / (exp(-xref) + one) * gain;
-            }
-        }
-
-        // Apply gain.
-        y *= gain * dy;
-
-        // Clamp.
-        if (clamp >= 0)
-        {
-            if (G == 0)
-                y = (y > -clamp & y < clamp) ? y : (y >= 0) ? clamp : -clamp;
-            else
-                y = (yref > -clamp & yref < clamp) ? y : 0;
-        }
-
-        // Store.
-        ((T*)p.y)[xi] = (T)y;
-    }
-}
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p)
-{
-    if (p.act == 1) return (void*)bias_act_kernel<T, 1>;
-    if (p.act == 2) return (void*)bias_act_kernel<T, 2>;
-    if (p.act == 3) return (void*)bias_act_kernel<T, 3>;
-    if (p.act == 4) return (void*)bias_act_kernel<T, 4>;
-    if (p.act == 5) return (void*)bias_act_kernel<T, 5>;
-    if (p.act == 6) return (void*)bias_act_kernel<T, 6>;
-    if (p.act == 7) return (void*)bias_act_kernel<T, 7>;
-    if (p.act == 8) return (void*)bias_act_kernel<T, 8>;
-    if (p.act == 9) return (void*)bias_act_kernel<T, 9>;
-    return NULL;
-}
-
-//------------------------------------------------------------------------
-// Template specializations.
-
-template void* choose_bias_act_kernel<double>       (const bias_act_kernel_params& p);
-template void* choose_bias_act_kernel<float>        (const bias_act_kernel_params& p);
-template void* choose_bias_act_kernel<c10::Half>    (const bias_act_kernel_params& p);
-
-//------------------------------------------------------------------------

torch_utils/ops/bias_act.h

@@ -1,38 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct bias_act_kernel_params
-{
-    const void* x;      // [sizeX]
-    const void* b;      // [sizeB] or NULL
-    const void* xref;   // [sizeX] or NULL
-    const void* yref;   // [sizeX] or NULL
-    const void* dy;     // [sizeX] or NULL
-    void*       y;      // [sizeX]
-
-    int         grad;
-    int         act;
-    float       alpha;
-    float       gain;
-    float       clamp;
-
-    int         sizeX;
-    int         sizeB;
-    int         stepB;
-    int         loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> void* choose_bias_act_kernel(const bias_act_kernel_params& p);
-
-//------------------------------------------------------------------------

torch_utils/ops/bias_act.py

@@ -1,212 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient bias and activation."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import dnnlib
-import traceback
-
-from .. import custom_ops
-from .. import misc
-
-#----------------------------------------------------------------------------
-
-activation_funcs = {
-    'linear':   dnnlib.EasyDict(func=lambda x, **_:         x,                                          def_alpha=0,    def_gain=1,             cuda_idx=1, ref='',  has_2nd_grad=False),
-    'relu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.relu(x),                def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=2, ref='y', has_2nd_grad=False),
-    'lrelu':    dnnlib.EasyDict(func=lambda x, alpha, **_:  torch.nn.functional.leaky_relu(x, alpha),   def_alpha=0.2,  def_gain=np.sqrt(2),    cuda_idx=3, ref='y', has_2nd_grad=False),
-    'tanh':     dnnlib.EasyDict(func=lambda x, **_:         torch.tanh(x),                              def_alpha=0,    def_gain=1,             cuda_idx=4, ref='y', has_2nd_grad=True),
-    'sigmoid':  dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x),                           def_alpha=0,    def_gain=1,             cuda_idx=5, ref='y', has_2nd_grad=True),
-    'elu':      dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.elu(x),                 def_alpha=0,    def_gain=1,             cuda_idx=6, ref='y', has_2nd_grad=True),
-    'selu':     dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.selu(x),                def_alpha=0,    def_gain=1,             cuda_idx=7, ref='y', has_2nd_grad=True),
-    'softplus': dnnlib.EasyDict(func=lambda x, **_:         torch.nn.functional.softplus(x),            def_alpha=0,    def_gain=1,             cuda_idx=8, ref='y', has_2nd_grad=True),
-    'swish':    dnnlib.EasyDict(func=lambda x, **_:         torch.sigmoid(x) * x,                       def_alpha=0,    def_gain=np.sqrt(2),    cuda_idx=9, ref='x', has_2nd_grad=True),
-}
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-_null_tensor = torch.empty([0])
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        _inited = True
-        sources = ['bias_act.cpp', 'bias_act.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('bias_act_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for bias_act. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-#----------------------------------------------------------------------------
-
-def bias_act(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None, impl='cuda'):
-    r"""Fused bias and activation function.
-
-    Adds bias `b` to activation tensor `x`, evaluates activation function `act`,
-    and scales the result by `gain`. Each of the steps is optional. In most cases,
-    the fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports first and second order gradients,
-    but not third order gradients.
-
-    Args:
-        x:      Input activation tensor. Can be of any shape.
-        b:      Bias vector, or `None` to disable. Must be a 1D tensor of the same type
-                as `x`. The shape must be known, and it must match the dimension of `x`
-                corresponding to `dim`.
-        dim:    The dimension in `x` corresponding to the elements of `b`.
-                The value of `dim` is ignored if `b` is not specified.
-        act:    Name of the activation function to evaluate, or `"linear"` to disable.
-                Can be e.g. `"relu"`, `"lrelu"`, `"tanh"`, `"sigmoid"`, `"swish"`, etc.
-                See `activation_funcs` for a full list. `None` is not allowed.
-        alpha:  Shape parameter for the activation function, or `None` to use the default.
-        gain:   Scaling factor for the output tensor, or `None` to use default.
-                See `activation_funcs` for the default scaling of each activation function.
-                If unsure, consider specifying 1.
-        clamp:  Clamp the output values to `[-clamp, +clamp]`, or `None` to disable
-                the clamping (default).
-        impl:   Name of the implementation to use. Can be `"ref"` or `"cuda"` (default).
-
-    Returns:
-        Tensor of the same shape and datatype as `x`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _bias_act_cuda(dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp).apply(x, b)
-    return _bias_act_ref(x=x, b=b, dim=dim, act=act, alpha=alpha, gain=gain, clamp=clamp)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _bias_act_ref(x, b=None, dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Slow reference implementation of `bias_act()` using standard TensorFlow ops.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Add bias.
-    if b is not None:
-        assert isinstance(b, torch.Tensor) and b.ndim == 1
-        assert 0 <= dim < x.ndim
-        assert b.shape[0] == x.shape[dim]
-        x = x + b.reshape([-1 if i == dim else 1 for i in range(x.ndim)])
-
-    # Evaluate activation function.
-    alpha = float(alpha)
-    x = spec.func(x, alpha=alpha)
-
-    # Scale by gain.
-    gain = float(gain)
-    if gain != 1:
-        x = x * gain
-
-    # Clamp.
-    if clamp >= 0:
-        x = x.clamp(-clamp, clamp) # pylint: disable=invalid-unary-operand-type
-    return x
-
-#----------------------------------------------------------------------------
-
-_bias_act_cuda_cache = dict()
-
-def _bias_act_cuda(dim=1, act='linear', alpha=None, gain=None, clamp=None):
-    """Fast CUDA implementation of `bias_act()` using custom ops.
-    """
-    # Parse arguments.
-    assert clamp is None or clamp >= 0
-    spec = activation_funcs[act]
-    alpha = float(alpha if alpha is not None else spec.def_alpha)
-    gain = float(gain if gain is not None else spec.def_gain)
-    clamp = float(clamp if clamp is not None else -1)
-
-    # Lookup from cache.
-    key = (dim, act, alpha, gain, clamp)
-    if key in _bias_act_cuda_cache:
-        return _bias_act_cuda_cache[key]
-
-    # Forward op.
-    class BiasActCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, b): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if x.ndim > 2 and x.stride()[1] == 1 else torch.contiguous_format
-            x = x.contiguous(memory_format=ctx.memory_format)
-            b = b.contiguous() if b is not None else _null_tensor
-            y = x
-            if act != 'linear' or gain != 1 or clamp >= 0 or b is not _null_tensor:
-                y = _plugin.bias_act(x, b, _null_tensor, _null_tensor, _null_tensor, 0, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                x if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                b if 'x' in spec.ref or spec.has_2nd_grad else _null_tensor,
-                y if 'y' in spec.ref else _null_tensor)
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            dy = dy.contiguous(memory_format=ctx.memory_format)
-            x, b, y = ctx.saved_tensors
-            dx = None
-            db = None
-
-            if ctx.needs_input_grad[0] or ctx.needs_input_grad[1]:
-                dx = dy
-                if act != 'linear' or gain != 1 or clamp >= 0:
-                    dx = BiasActCudaGrad.apply(dy, x, b, y)
-
-            if ctx.needs_input_grad[1]:
-                db = dx.sum([i for i in range(dx.ndim) if i != dim])
-
-            return dx, db
-
-    # Backward op.
-    class BiasActCudaGrad(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, dy, x, b, y): # pylint: disable=arguments-differ
-            ctx.memory_format = torch.channels_last if dy.ndim > 2 and dy.stride()[1] == 1 else torch.contiguous_format
-            dx = _plugin.bias_act(dy, b, x, y, _null_tensor, 1, dim, spec.cuda_idx, alpha, gain, clamp)
-            ctx.save_for_backward(
-                dy if spec.has_2nd_grad else _null_tensor,
-                x, b, y)
-            return dx
-
-        @staticmethod
-        def backward(ctx, d_dx): # pylint: disable=arguments-differ
-            d_dx = d_dx.contiguous(memory_format=ctx.memory_format)
-            dy, x, b, y = ctx.saved_tensors
-            d_dy = None
-            d_x = None
-            d_b = None
-            d_y = None
-
-            if ctx.needs_input_grad[0]:
-                d_dy = BiasActCudaGrad.apply(d_dx, x, b, y)
-
-            if spec.has_2nd_grad and (ctx.needs_input_grad[1] or ctx.needs_input_grad[2]):
-                d_x = _plugin.bias_act(d_dx, b, x, y, dy, 2, dim, spec.cuda_idx, alpha, gain, clamp)
-
-            if spec.has_2nd_grad and ctx.needs_input_grad[2]:
-                d_b = d_x.sum([i for i in range(d_x.ndim) if i != dim])
-
-            return d_dy, d_x, d_b, d_y
-
-    # Add to cache.
-    _bias_act_cuda_cache[key] = BiasActCuda
-    return BiasActCuda
-
-#----------------------------------------------------------------------------

torch_utils/ops/conv2d_gradfix.py

@@ -1,170 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.conv2d` that supports
-arbitrarily high order gradients with zero performance penalty."""
-
-import warnings
-import contextlib
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False                     # Enable the custom op by setting this to true.
-weight_gradients_disabled = False   # Forcefully disable computation of gradients with respect to the weights.
-
-@contextlib.contextmanager
-def no_weight_gradients():
-    global weight_gradients_disabled
-    old = weight_gradients_disabled
-    weight_gradients_disabled = True
-    yield
-    weight_gradients_disabled = old
-
-#----------------------------------------------------------------------------
-
-def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=False, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=0, dilation=dilation, groups=groups).apply(input, weight, bias)
-    return torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, dilation=dilation, groups=groups)
-
-def conv_transpose2d(input, weight, bias=None, stride=1, padding=0, output_padding=0, groups=1, dilation=1):
-    if _should_use_custom_op(input):
-        return _conv2d_gradfix(transpose=True, weight_shape=weight.shape, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation).apply(input, weight, bias)
-    return torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, stride=stride, padding=padding, output_padding=output_padding, groups=groups, dilation=dilation)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op(input):
-    assert isinstance(input, torch.Tensor)
-    if (not enabled) or (not torch.backends.cudnn.enabled):
-        return False
-    if input.device.type != 'cuda':
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'conv2d_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.conv2d().')
-    return False
-
-def _tuple_of_ints(xs, ndim):
-    xs = tuple(xs) if isinstance(xs, (tuple, list)) else (xs,) * ndim
-    assert len(xs) == ndim
-    assert all(isinstance(x, int) for x in xs)
-    return xs
-
-#----------------------------------------------------------------------------
-
-_conv2d_gradfix_cache = dict()
-
-def _conv2d_gradfix(transpose, weight_shape, stride, padding, output_padding, dilation, groups):
-    # Parse arguments.
-    ndim = 2
-    weight_shape = tuple(weight_shape)
-    stride = _tuple_of_ints(stride, ndim)
-    padding = _tuple_of_ints(padding, ndim)
-    output_padding = _tuple_of_ints(output_padding, ndim)
-    dilation = _tuple_of_ints(dilation, ndim)
-
-    # Lookup from cache.
-    key = (transpose, weight_shape, stride, padding, output_padding, dilation, groups)
-    if key in _conv2d_gradfix_cache:
-        return _conv2d_gradfix_cache[key]
-
-    # Validate arguments.
-    assert groups >= 1
-    assert len(weight_shape) == ndim + 2
-    assert all(stride[i] >= 1 for i in range(ndim))
-    assert all(padding[i] >= 0 for i in range(ndim))
-    assert all(dilation[i] >= 0 for i in range(ndim))
-    if not transpose:
-        assert all(output_padding[i] == 0 for i in range(ndim))
-    else: # transpose
-        assert all(0 <= output_padding[i] < max(stride[i], dilation[i]) for i in range(ndim))
-
-    # Helpers.
-    common_kwargs = dict(stride=stride, padding=padding, dilation=dilation, groups=groups)
-    def calc_output_padding(input_shape, output_shape):
-        if transpose:
-            return [0, 0]
-        return [
-            input_shape[i + 2]
-            - (output_shape[i + 2] - 1) * stride[i]
-            - (1 - 2 * padding[i])
-            - dilation[i] * (weight_shape[i + 2] - 1)
-            for i in range(ndim)
-        ]
-
-    # Forward & backward.
-    class Conv2d(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, input, weight, bias):
-            assert weight.shape == weight_shape
-            if not transpose:
-                output = torch.nn.functional.conv2d(input=input, weight=weight, bias=bias, **common_kwargs)
-            else: # transpose
-                output = torch.nn.functional.conv_transpose2d(input=input, weight=weight, bias=bias, output_padding=output_padding, **common_kwargs)
-            ctx.save_for_backward(input, weight)
-            return output
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            input, weight = ctx.saved_tensors
-            grad_input = None
-            grad_weight = None
-            grad_bias = None
-
-            if ctx.needs_input_grad[0]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, weight, None)
-                assert grad_input.shape == input.shape
-
-            if ctx.needs_input_grad[1] and not weight_gradients_disabled:
-                grad_weight = Conv2dGradWeight.apply(grad_output, input)
-                assert grad_weight.shape == weight_shape
-
-            if ctx.needs_input_grad[2]:
-                grad_bias = grad_output.sum([0, 2, 3])
-
-            return grad_input, grad_weight, grad_bias
-
-    # Gradient with respect to the weights.
-    class Conv2dGradWeight(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, grad_output, input):
-            op = torch._C._jit_get_operation('aten::cudnn_convolution_backward_weight' if not transpose else 'aten::cudnn_convolution_transpose_backward_weight')
-            flags = [torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.allow_tf32]
-            grad_weight = op(weight_shape, grad_output, input, padding, stride, dilation, groups, *flags)
-            assert grad_weight.shape == weight_shape
-            ctx.save_for_backward(grad_output, input)
-            return grad_weight
-
-        @staticmethod
-        def backward(ctx, grad2_grad_weight):
-            grad_output, input = ctx.saved_tensors
-            grad2_grad_output = None
-            grad2_input = None
-
-            if ctx.needs_input_grad[0]:
-                grad2_grad_output = Conv2d.apply(input, grad2_grad_weight, None)
-                assert grad2_grad_output.shape == grad_output.shape
-
-            if ctx.needs_input_grad[1]:
-                p = calc_output_padding(input_shape=input.shape, output_shape=grad_output.shape)
-                grad2_input = _conv2d_gradfix(transpose=(not transpose), weight_shape=weight_shape, output_padding=p, **common_kwargs).apply(grad_output, grad2_grad_weight, None)
-                assert grad2_input.shape == input.shape
-
-            return grad2_grad_output, grad2_input
-
-    _conv2d_gradfix_cache[key] = Conv2d
-    return Conv2d
-
-#----------------------------------------------------------------------------

torch_utils/ops/conv2d_resample.py

@@ -1,156 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""2D convolution with optional up/downsampling."""
-
-import torch
-
-from .. import misc
-from . import conv2d_gradfix
-from . import upfirdn2d
-from .upfirdn2d import _parse_padding
-from .upfirdn2d import _get_filter_size
-
-#----------------------------------------------------------------------------
-
-def _get_weight_shape(w):
-    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-        shape = [int(sz) for sz in w.shape]
-    misc.assert_shape(w, shape)
-    return shape
-
-#----------------------------------------------------------------------------
-
-def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True):
-    """Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.
-    """
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-
-    # Flip weight if requested.
-    if not flip_weight: # conv2d() actually performs correlation (flip_weight=True) not convolution (flip_weight=False).
-        w = w.flip([2, 3])
-
-    # Workaround performance pitfall in cuDNN 8.0.5, triggered when using
-    # 1x1 kernel + memory_format=channels_last + less than 64 channels.
-    if kw == 1 and kh == 1 and stride == 1 and padding in [0, [0, 0], (0, 0)] and not transpose:
-        if x.stride()[1] == 1 and min(out_channels, in_channels_per_group) < 64:
-            if out_channels <= 4 and groups == 1:
-                in_shape = x.shape
-                x = w.squeeze(3).squeeze(2) @ x.reshape([in_shape[0], in_channels_per_group, -1])
-                x = x.reshape([in_shape[0], out_channels, in_shape[2], in_shape[3]])
-            else:
-                x = x.to(memory_format=torch.contiguous_format)
-                w = w.to(memory_format=torch.contiguous_format)
-                x = conv2d_gradfix.conv2d(x, w, groups=groups)
-            return x.to(memory_format=torch.channels_last)
-
-    # Otherwise => execute using conv2d_gradfix.
-    op = conv2d_gradfix.conv_transpose2d if transpose else conv2d_gradfix.conv2d
-    return op(x, w, stride=stride, padding=padding, groups=groups)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def conv2d_resample(x, w, f=None, up=1, down=1, padding=0, groups=1, flip_weight=True, flip_filter=False):
-    r"""2D convolution with optional up/downsampling.
-
-    Padding is performed only once at the beginning, not between the operations.
-
-    Args:
-        x:              Input tensor of shape
-                        `[batch_size, in_channels, in_height, in_width]`.
-        w:              Weight tensor of shape
-                        `[out_channels, in_channels//groups, kernel_height, kernel_width]`.
-        f:              Low-pass filter for up/downsampling. Must be prepared beforehand by
-                        calling upfirdn2d.setup_filter(). None = identity (default).
-        up:             Integer upsampling factor (default: 1).
-        down:           Integer downsampling factor (default: 1).
-        padding:        Padding with respect to the upsampled image. Can be a single number
-                        or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                        (default: 0).
-        groups:         Split input channels into N groups (default: 1).
-        flip_weight:    False = convolution, True = correlation (default: True).
-        flip_filter:    False = convolution, True = correlation (default: False).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and (x.ndim == 4)
-    assert isinstance(w, torch.Tensor) and (w.ndim == 4) and (w.dtype == x.dtype)
-    assert f is None or (isinstance(f, torch.Tensor) and f.ndim in [1, 2] and f.dtype == torch.float32)
-    assert isinstance(up, int) and (up >= 1)
-    assert isinstance(down, int) and (down >= 1)
-    assert isinstance(groups, int) and (groups >= 1)
-    out_channels, in_channels_per_group, kh, kw = _get_weight_shape(w)
-    fw, fh = _get_filter_size(f)
-    px0, px1, py0, py1 = _parse_padding(padding)
-
-    # Adjust padding to account for up/downsampling.
-    if up > 1:
-        px0 += (fw + up - 1) // 2
-        px1 += (fw - up) // 2
-        py0 += (fh + up - 1) // 2
-        py1 += (fh - up) // 2
-    if down > 1:
-        px0 += (fw - down + 1) // 2
-        px1 += (fw - down) // 2
-        py0 += (fh - down + 1) // 2
-        py1 += (fh - down) // 2
-
-    # Fast path: 1x1 convolution with downsampling only => downsample first, then convolve.
-    if kw == 1 and kh == 1 and (down > 1 and up == 1):
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: 1x1 convolution with upsampling only => convolve first, then upsample.
-    if kw == 1 and kh == 1 and (up > 1 and down == 1):
-        x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-        x = upfirdn2d.upfirdn2d(x=x, f=f, up=up, padding=[px0,px1,py0,py1], gain=up**2, flip_filter=flip_filter)
-        return x
-
-    # Fast path: downsampling only => use strided convolution.
-    if down > 1 and up == 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0,px1,py0,py1], flip_filter=flip_filter)
-        x = _conv2d_wrapper(x=x, w=w, stride=down, groups=groups, flip_weight=flip_weight)
-        return x
-
-    # Fast path: upsampling with optional downsampling => use transpose strided convolution.
-    if up > 1:
-        if groups == 1:
-            w = w.transpose(0, 1)
-        else:
-            w = w.reshape(groups, out_channels // groups, in_channels_per_group, kh, kw)
-            w = w.transpose(1, 2)
-            w = w.reshape(groups * in_channels_per_group, out_channels // groups, kh, kw)
-        px0 -= kw - 1
-        px1 -= kw - up
-        py0 -= kh - 1
-        py1 -= kh - up
-        pxt = max(min(-px0, -px1), 0)
-        pyt = max(min(-py0, -py1), 0)
-        x = _conv2d_wrapper(x=x, w=w, stride=up, padding=[pyt,pxt], groups=groups, transpose=True, flip_weight=(not flip_weight))
-        x = upfirdn2d.upfirdn2d(x=x, f=f, padding=[px0+pxt,px1+pxt,py0+pyt,py1+pyt], gain=up**2, flip_filter=flip_filter)
-        if down > 1:
-            x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-        return x
-
-    # Fast path: no up/downsampling, padding supported by the underlying implementation => use plain conv2d.
-    if up == 1 and down == 1:
-        if px0 == px1 and py0 == py1 and px0 >= 0 and py0 >= 0:
-            return _conv2d_wrapper(x=x, w=w, padding=[py0,px0], groups=groups, flip_weight=flip_weight)
-
-    # Fallback: Generic reference implementation.
-    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)
-    x = _conv2d_wrapper(x=x, w=w, groups=groups, flip_weight=flip_weight)
-    if down > 1:
-        x = upfirdn2d.upfirdn2d(x=x, f=f, down=down, flip_filter=flip_filter)
-    return x
-
-#----------------------------------------------------------------------------

torch_utils/ops/fma.py

@@ -1,60 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Fused multiply-add, with slightly faster gradients than `torch.addcmul()`."""
-
-import torch
-
-#----------------------------------------------------------------------------
-
-def fma(a, b, c): # => a * b + c
-    return _FusedMultiplyAdd.apply(a, b, c)
-
-#----------------------------------------------------------------------------
-
-class _FusedMultiplyAdd(torch.autograd.Function): # a * b + c
-    @staticmethod
-    def forward(ctx, a, b, c): # pylint: disable=arguments-differ
-        out = torch.addcmul(c, a, b)
-        ctx.save_for_backward(a, b)
-        ctx.c_shape = c.shape
-        return out
-
-    @staticmethod
-    def backward(ctx, dout): # pylint: disable=arguments-differ
-        a, b = ctx.saved_tensors
-        c_shape = ctx.c_shape
-        da = None
-        db = None
-        dc = None
-
-        if ctx.needs_input_grad[0]:
-            da = _unbroadcast(dout * b, a.shape)
-
-        if ctx.needs_input_grad[1]:
-            db = _unbroadcast(dout * a, b.shape)
-
-        if ctx.needs_input_grad[2]:
-            dc = _unbroadcast(dout, c_shape)
-
-        return da, db, dc
-
-#----------------------------------------------------------------------------
-
-def _unbroadcast(x, shape):
-    extra_dims = x.ndim - len(shape)
-    assert extra_dims >= 0
-    dim = [i for i in range(x.ndim) if x.shape[i] > 1 and (i < extra_dims or shape[i - extra_dims] == 1)]
-    if len(dim):
-        x = x.sum(dim=dim, keepdim=True)
-    if extra_dims:
-        x = x.reshape(-1, *x.shape[extra_dims+1:])
-    assert x.shape == shape
-    return x
-
-#----------------------------------------------------------------------------

torch_utils/ops/grid_sample_gradfix.py

@@ -1,83 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom replacement for `torch.nn.functional.grid_sample` that
-supports arbitrarily high order gradients between the input and output.
-Only works on 2D images and assumes
-`mode='bilinear'`, `padding_mode='zeros'`, `align_corners=False`."""
-
-import warnings
-import torch
-
-# pylint: disable=redefined-builtin
-# pylint: disable=arguments-differ
-# pylint: disable=protected-access
-
-#----------------------------------------------------------------------------
-
-enabled = False  # Enable the custom op by setting this to true.
-
-#----------------------------------------------------------------------------
-
-def grid_sample(input, grid):
-    if _should_use_custom_op():
-        return _GridSample2dForward.apply(input, grid)
-    return torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-
-#----------------------------------------------------------------------------
-
-def _should_use_custom_op():
-    if not enabled:
-        return False
-    if any(torch.__version__.startswith(x) for x in ['1.7.', '1.8.', '1.9']):
-        return True
-    warnings.warn(f'grid_sample_gradfix not supported on PyTorch {torch.__version__}. Falling back to torch.nn.functional.grid_sample().')
-    return False
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dForward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, input, grid):
-        assert input.ndim == 4
-        assert grid.ndim == 4
-        output = torch.nn.functional.grid_sample(input=input, grid=grid, mode='bilinear', padding_mode='zeros', align_corners=False)
-        ctx.save_for_backward(input, grid)
-        return output
-
-    @staticmethod
-    def backward(ctx, grad_output):
-        input, grid = ctx.saved_tensors
-        grad_input, grad_grid = _GridSample2dBackward.apply(grad_output, input, grid)
-        return grad_input, grad_grid
-
-#----------------------------------------------------------------------------
-
-class _GridSample2dBackward(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, grad_output, input, grid):
-        op = torch._C._jit_get_operation('aten::grid_sampler_2d_backward')
-        grad_input, grad_grid = op(grad_output, input, grid, 0, 0, False)
-        ctx.save_for_backward(grid)
-        return grad_input, grad_grid
-
-    @staticmethod
-    def backward(ctx, grad2_grad_input, grad2_grad_grid):
-        _ = grad2_grad_grid # unused
-        grid, = ctx.saved_tensors
-        grad2_grad_output = None
-        grad2_input = None
-        grad2_grid = None
-
-        if ctx.needs_input_grad[0]:
-            grad2_grad_output = _GridSample2dForward.apply(grad2_grad_input, grid)
-
-        assert not ctx.needs_input_grad[2]
-        return grad2_grad_output, grad2_input, grad2_grid
-
-#----------------------------------------------------------------------------

torch_utils/ops/upfirdn2d.cpp

@@ -1,103 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <torch/extension.h>
-#include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAGuard.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-
-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)
-{
-    // Validate arguments.
-    TORCH_CHECK(x.is_cuda(), "x must reside on CUDA device");
-    TORCH_CHECK(f.device() == x.device(), "f must reside on the same device as x");
-    TORCH_CHECK(f.dtype() == torch::kFloat, "f must be float32");
-    TORCH_CHECK(x.numel() <= INT_MAX, "x is too large");
-    TORCH_CHECK(f.numel() <= INT_MAX, "f is too large");
-    TORCH_CHECK(x.dim() == 4, "x must be rank 4");
-    TORCH_CHECK(f.dim() == 2, "f must be rank 2");
-    TORCH_CHECK(f.size(0) >= 1 && f.size(1) >= 1, "f must be at least 1x1");
-    TORCH_CHECK(upx >= 1 && upy >= 1, "upsampling factor must be at least 1");
-    TORCH_CHECK(downx >= 1 && downy >= 1, "downsampling factor must be at least 1");
-
-    // Create output tensor.
-    const at::cuda::OptionalCUDAGuard device_guard(device_of(x));
-    int outW = ((int)x.size(3) * upx + padx0 + padx1 - (int)f.size(1) + downx) / downx;
-    int outH = ((int)x.size(2) * upy + pady0 + pady1 - (int)f.size(0) + downy) / downy;
-    TORCH_CHECK(outW >= 1 && outH >= 1, "output must be at least 1x1");
-    torch::Tensor y = torch::empty({x.size(0), x.size(1), outH, outW}, x.options(), x.suggest_memory_format());
-    TORCH_CHECK(y.numel() <= INT_MAX, "output is too large");
-
-    // Initialize CUDA kernel parameters.
-    upfirdn2d_kernel_params p;
-    p.x             = x.data_ptr();
-    p.f             = f.data_ptr<float>();
-    p.y             = y.data_ptr();
-    p.up            = make_int2(upx, upy);
-    p.down          = make_int2(downx, downy);
-    p.pad0          = make_int2(padx0, pady0);
-    p.flip          = (flip) ? 1 : 0;
-    p.gain          = gain;
-    p.inSize        = make_int4((int)x.size(3), (int)x.size(2), (int)x.size(1), (int)x.size(0));
-    p.inStride      = make_int4((int)x.stride(3), (int)x.stride(2), (int)x.stride(1), (int)x.stride(0));
-    p.filterSize    = make_int2((int)f.size(1), (int)f.size(0));
-    p.filterStride  = make_int2((int)f.stride(1), (int)f.stride(0));
-    p.outSize       = make_int4((int)y.size(3), (int)y.size(2), (int)y.size(1), (int)y.size(0));
-    p.outStride     = make_int4((int)y.stride(3), (int)y.stride(2), (int)y.stride(1), (int)y.stride(0));
-    p.sizeMajor     = (p.inStride.z == 1) ? p.inSize.w : p.inSize.w * p.inSize.z;
-    p.sizeMinor     = (p.inStride.z == 1) ? p.inSize.z : 1;
-
-    // Choose CUDA kernel.
-    upfirdn2d_kernel_spec spec;
-    AT_DISPATCH_FLOATING_TYPES_AND_HALF(x.scalar_type(), "upfirdn2d_cuda", [&]
-    {
-        spec = choose_upfirdn2d_kernel<scalar_t>(p);
-    });
-
-    // Set looping options.
-    p.loopMajor     = (p.sizeMajor - 1) / 16384 + 1;
-    p.loopMinor     = spec.loopMinor;
-    p.loopX         = spec.loopX;
-    p.launchMinor   = (p.sizeMinor - 1) / p.loopMinor + 1;
-    p.launchMajor   = (p.sizeMajor - 1) / p.loopMajor + 1;
-
-    // Compute grid size.
-    dim3 blockSize, gridSize;
-    if (spec.tileOutW < 0) // large
-    {
-        blockSize = dim3(4, 32, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / blockSize.x + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (blockSize.y * p.loopX) + 1,
-            p.launchMajor);
-    }
-    else // small
-    {
-        blockSize = dim3(256, 1, 1);
-        gridSize = dim3(
-            ((p.outSize.y - 1) / spec.tileOutH + 1) * p.launchMinor,
-            (p.outSize.x - 1) / (spec.tileOutW * p.loopX) + 1,
-            p.launchMajor);
-    }
-
-    // Launch CUDA kernel.
-    void* args[] = {&p};
-    AT_CUDA_CHECK(cudaLaunchKernel(spec.kernel, gridSize, blockSize, args, 0, at::cuda::getCurrentCUDAStream()));
-    return y;
-}
-
-//------------------------------------------------------------------------
-
-PYBIND11_MODULE(TORCH_EXTENSION_NAME, m)
-{
-    m.def("upfirdn2d", &upfirdn2d);
-}
-
-//------------------------------------------------------------------------

torch_utils/ops/upfirdn2d.cu

@@ -1,350 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <c10/util/Half.h>
-#include "upfirdn2d.h"
-
-//------------------------------------------------------------------------
-// Helpers.
-
-template <class T> struct InternalType;
-template <> struct InternalType<double>     { typedef double scalar_t; };
-template <> struct InternalType<float>      { typedef float  scalar_t; };
-template <> struct InternalType<c10::Half>  { typedef float  scalar_t; };
-
-static __device__ __forceinline__ int floor_div(int a, int b)
-{
-    int t = 1 - a / b;
-    return (a + t * b) / b - t;
-}
-
-//------------------------------------------------------------------------
-// Generic CUDA implementation for large filters.
-
-template <class T> static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
-{
-    typedef typename InternalType<T>::scalar_t scalar_t;
-
-    // Calculate thread index.
-    int minorBase = blockIdx.x * blockDim.x + threadIdx.x;
-    int outY = minorBase / p.launchMinor;
-    minorBase -= outY * p.launchMinor;
-    int outXBase = blockIdx.y * p.loopX * blockDim.y + threadIdx.y;
-    int majorBase = blockIdx.z * p.loopMajor;
-    if (outXBase >= p.outSize.x | outY >= p.outSize.y | majorBase >= p.sizeMajor)
-        return;
-
-    // Setup Y receptive field.
-    int midY = outY * p.down.y + p.up.y - 1 - p.pad0.y;
-    int inY = min(max(floor_div(midY, p.up.y), 0), p.inSize.y);
-    int h = min(max(floor_div(midY + p.filterSize.y, p.up.y), 0), p.inSize.y) - inY;
-    int filterY = midY + p.filterSize.y - (inY + 1) * p.up.y;
-    if (p.flip)
-        filterY = p.filterSize.y - 1 - filterY;
-
-    // Loop over major, minor, and X.
-    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
-    for (int minorIdx = 0, minor = minorBase; minorIdx < p.loopMinor & minor < p.sizeMinor; minorIdx++, minor += p.launchMinor)
-    {
-        int nc = major * p.sizeMinor + minor;
-        int n = nc / p.inSize.z;
-        int c = nc - n * p.inSize.z;
-        for (int loopX = 0, outX = outXBase; loopX < p.loopX & outX < p.outSize.x; loopX++, outX += blockDim.y)
-        {
-            // Setup X receptive field.
-            int midX = outX * p.down.x + p.up.x - 1 - p.pad0.x;
-            int inX = min(max(floor_div(midX, p.up.x), 0), p.inSize.x);
-            int w = min(max(floor_div(midX + p.filterSize.x, p.up.x), 0), p.inSize.x) - inX;
-            int filterX = midX + p.filterSize.x - (inX + 1) * p.up.x;
-            if (p.flip)
-                filterX = p.filterSize.x - 1 - filterX;
-
-            // Initialize pointers.
-            const T* xp = &((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
-            const float* fp = &p.f[filterX * p.filterStride.x + filterY * p.filterStride.y];
-            int filterStepX = ((p.flip) ? p.up.x : -p.up.x) * p.filterStride.x;
-            int filterStepY = ((p.flip) ? p.up.y : -p.up.y) * p.filterStride.y;
-
-            // Inner loop.
-            scalar_t v = 0;
-            for (int y = 0; y < h; y++)
-            {
-                for (int x = 0; x < w; x++)
-                {
-                    v += (scalar_t)(*xp) * (scalar_t)(*fp);
-                    xp += p.inStride.x;
-                    fp += filterStepX;
-                }
-                xp += p.inStride.y - w * p.inStride.x;
-                fp += filterStepY - w * filterStepX;
-            }
-
-            // Store result.
-            v *= p.gain;
-            ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
-        }
-    }
-}
-
-//------------------------------------------------------------------------
-// Specialized CUDA implementation for small filters.
-
-template <class T, int upx, int upy, int downx, int downy, int filterW, int filterH, int tileOutW, int tileOutH, int loopMinor>
-static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
-{
-    typedef typename InternalType<T>::scalar_t scalar_t;
-    const int tileInW = ((tileOutW - 1) * downx + filterW - 1) / upx + 1;
-    const int tileInH = ((tileOutH - 1) * downy + filterH - 1) / upy + 1;
-    __shared__ volatile scalar_t sf[filterH][filterW];
-    __shared__ volatile scalar_t sx[tileInH][tileInW][loopMinor];
-
-    // Calculate tile index.
-    int minorBase = blockIdx.x;
-    int tileOutY = minorBase / p.launchMinor;
-    minorBase -= tileOutY * p.launchMinor;
-    minorBase *= loopMinor;
-    tileOutY *= tileOutH;
-    int tileOutXBase = blockIdx.y * p.loopX * tileOutW;
-    int majorBase = blockIdx.z * p.loopMajor;
-    if (tileOutXBase >= p.outSize.x | tileOutY >= p.outSize.y | majorBase >= p.sizeMajor)
-        return;
-
-    // Load filter (flipped).
-    for (int tapIdx = threadIdx.x; tapIdx < filterH * filterW; tapIdx += blockDim.x)
-    {
-        int fy = tapIdx / filterW;
-        int fx = tapIdx - fy * filterW;
-        scalar_t v = 0;
-        if (fx < p.filterSize.x & fy < p.filterSize.y)
-        {
-            int ffx = (p.flip) ? fx : p.filterSize.x - 1 - fx;
-            int ffy = (p.flip) ? fy : p.filterSize.y - 1 - fy;
-            v = (scalar_t)p.f[ffx * p.filterStride.x + ffy * p.filterStride.y];
-        }
-        sf[fy][fx] = v;
-    }
-
-    // Loop over major and X.
-    for (int majorIdx = 0, major = majorBase; majorIdx < p.loopMajor & major < p.sizeMajor; majorIdx++, major++)
-    {
-        int baseNC = major * p.sizeMinor + minorBase;
-        int n = baseNC / p.inSize.z;
-        int baseC = baseNC - n * p.inSize.z;
-        for (int loopX = 0, tileOutX = tileOutXBase; loopX < p.loopX & tileOutX < p.outSize.x; loopX++, tileOutX += tileOutW)
-        {
-            // Load input pixels.
-            int tileMidX = tileOutX * downx + upx - 1 - p.pad0.x;
-            int tileMidY = tileOutY * downy + upy - 1 - p.pad0.y;
-            int tileInX = floor_div(tileMidX, upx);
-            int tileInY = floor_div(tileMidY, upy);
-            __syncthreads();
-            for (int inIdx = threadIdx.x; inIdx < tileInH * tileInW * loopMinor; inIdx += blockDim.x)
-            {
-                int relC = inIdx;
-                int relInX = relC / loopMinor;
-                int relInY = relInX / tileInW;
-                relC -= relInX * loopMinor;
-                relInX -= relInY * tileInW;
-                int c = baseC + relC;
-                int inX = tileInX + relInX;
-                int inY = tileInY + relInY;
-                scalar_t v = 0;
-                if (inX >= 0 & inY >= 0 & inX < p.inSize.x & inY < p.inSize.y & c < p.inSize.z)
-                    v = (scalar_t)((const T*)p.x)[inX * p.inStride.x + inY * p.inStride.y + c * p.inStride.z + n * p.inStride.w];
-                sx[relInY][relInX][relC] = v;
-            }
-
-            // Loop over output pixels.
-            __syncthreads();
-            for (int outIdx = threadIdx.x; outIdx < tileOutH * tileOutW * loopMinor; outIdx += blockDim.x)
-            {
-                int relC = outIdx;
-                int relOutX = relC / loopMinor;
-                int relOutY = relOutX / tileOutW;
-                relC -= relOutX * loopMinor;
-                relOutX -= relOutY * tileOutW;
-                int c = baseC + relC;
-                int outX = tileOutX + relOutX;
-                int outY = tileOutY + relOutY;
-
-                // Setup receptive field.
-                int midX = tileMidX + relOutX * downx;
-                int midY = tileMidY + relOutY * downy;
-                int inX = floor_div(midX, upx);
-                int inY = floor_div(midY, upy);
-                int relInX = inX - tileInX;
-                int relInY = inY - tileInY;
-                int filterX = (inX + 1) * upx - midX - 1; // flipped
-                int filterY = (inY + 1) * upy - midY - 1; // flipped
-
-                // Inner loop.
-                if (outX < p.outSize.x & outY < p.outSize.y & c < p.outSize.z)
-                {
-                    scalar_t v = 0;
-                    #pragma unroll
-                    for (int y = 0; y < filterH / upy; y++)
-                        #pragma unroll
-                        for (int x = 0; x < filterW / upx; x++)
-                            v += sx[relInY + y][relInX + x][relC] * sf[filterY + y * upy][filterX + x * upx];
-                    v *= p.gain;
-                    ((T*)p.y)[outX * p.outStride.x + outY * p.outStride.y + c * p.outStride.z + n * p.outStride.w] = (T)v;
-                }
-            }
-        }
-    }
-}
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p)
-{
-    int s = p.inStride.z, fx = p.filterSize.x, fy = p.filterSize.y;
-
-    upfirdn2d_kernel_spec spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,1, 4}; // contiguous
-    if (s == 1)           spec = {(void*)upfirdn2d_kernel_large<T>, -1,-1,4, 1}; // channels_last
-
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,  64,16,1>, 64,16,1, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 6,6,  64,16,1>, 64,16,1, 1};
-        if (fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 5,5,  64,16,1>, 64,16,1, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  64,16,1>, 64,16,1, 1};
-        if (fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 3,3,  64,16,1>, 64,16,1, 1};
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 20,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 12,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,20, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,12, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 7  && fy <= 7 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 7,7,  16,16,8>,  16,16,8,  1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 5  && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 3  && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 4,4,  16,16,8>,  16,16,8,  1};
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 20,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 12,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,20, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,12, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
-    }
-    if (s != 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 64,16,1>, 64,16,1, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 64,16,1>, 64,16,1, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 64,16,1>, 64,16,1, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 64,16,1>, 64,16,1, 1};
-    }
-    if (s == 1 && p.up.x == 2 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 8,8, 16,16,8>, 16,16,8, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 6,6, 16,16,8>, 16,16,8, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 4,4, 16,16,8>, 16,16,8, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,2, 1,1, 2,2, 16,16,8>, 16,16,8, 1};
-    }
-    if (s != 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 20,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 12,1, 128,8,1>, 128,8,1, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,8,1>, 128,8,1, 1};
-    }
-    if (s == 1 && p.up.x == 2 && p.up.y == 1 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 24,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 20,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 16,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 12,1, 128,1,16>, 128,1,16, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 2,1, 1,1, 8,1,  128,1,16>, 128,1,16, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,20, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,12, 32,32,1>, 32,32,1, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  32,32,1>, 32,32,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 2 && p.down.x == 1 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,24, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,20, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,16, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,12, 1,128,16>, 1,128,16, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,2, 1,1, 1,8,  1,128,16>, 1,128,16, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // contiguous
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,  32,8,1>, 32,8,1, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,  32,8,1>, 32,8,1, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,  32,8,1>, 32,8,1, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,  32,8,1>, 32,8,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 2) // channels_last
-    {
-        if (fx <= 8  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 8,8,  8,8,8>, 8,8,8, 1};
-        if (fx <= 6  && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 6,6,  8,8,8>, 8,8,8, 1};
-        if (fx <= 4  && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 4,4,  8,8,8>, 8,8,8, 1};
-        if (fx <= 2  && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,2, 2,2,  8,8,8>, 8,8,8, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // contiguous
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 20,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 12,1, 64,8,1>, 64,8,1, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,8,1>, 64,8,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 2 && p.down.y == 1) // channels_last
-    {
-        if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 24,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 20,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 16,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 12,1, 64,1,8>, 64,1,8, 1};
-        if (fx <= 8  && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 2,1, 8,1,  64,1,8>, 64,1,8, 1};
-    }
-    if (s != 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // contiguous
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,20, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,12, 32,16,1>, 32,16,1, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  32,16,1>, 32,16,1, 1};
-    }
-    if (s == 1 && p.up.x == 1 && p.up.y == 1 && p.down.x == 1 && p.down.y == 2) // channels_last
-    {
-        if (fx <= 1  && fy <= 24) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,24, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 20) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,20, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 16) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,16, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 12) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,12, 1,64,8>, 1,64,8, 1};
-        if (fx <= 1  && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small<T, 1,1, 1,2, 1,8,  1,64,8>, 1,64,8, 1};
-    }
-    return spec;
-}
-
-//------------------------------------------------------------------------
-// Template specializations.
-
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<double>   (const upfirdn2d_kernel_params& p);
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<float>    (const upfirdn2d_kernel_params& p);
-template upfirdn2d_kernel_spec choose_upfirdn2d_kernel<c10::Half>(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------

torch_utils/ops/upfirdn2d.h

@@ -1,59 +0,0 @@
-// Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-//
-// NVIDIA CORPORATION and its licensors retain all intellectual property
-// and proprietary rights in and to this software, related documentation
-// and any modifications thereto.  Any use, reproduction, disclosure or
-// distribution of this software and related documentation without an express
-// license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-#include <cuda_runtime.h>
-
-//------------------------------------------------------------------------
-// CUDA kernel parameters.
-
-struct upfirdn2d_kernel_params
-{
-    const void*     x;
-    const float*    f;
-    void*           y;
-
-    int2            up;
-    int2            down;
-    int2            pad0;
-    int             flip;
-    float           gain;
-
-    int4            inSize;         // [width, height, channel, batch]
-    int4            inStride;
-    int2            filterSize;     // [width, height]
-    int2            filterStride;
-    int4            outSize;        // [width, height, channel, batch]
-    int4            outStride;
-    int             sizeMinor;
-    int             sizeMajor;
-
-    int             loopMinor;
-    int             loopMajor;
-    int             loopX;
-    int             launchMinor;
-    int             launchMajor;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel specialization.
-
-struct upfirdn2d_kernel_spec
-{
-    void*   kernel;
-    int     tileOutW;
-    int     tileOutH;
-    int     loopMinor;
-    int     loopX;
-};
-
-//------------------------------------------------------------------------
-// CUDA kernel selection.
-
-template <class T> upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
-
-//------------------------------------------------------------------------

torch_utils/ops/upfirdn2d.py

@@ -1,384 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Custom PyTorch ops for efficient resampling of 2D images."""
-
-import os
-import warnings
-import numpy as np
-import torch
-import traceback
-
-from .. import custom_ops
-from .. import misc
-from . import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-
-_inited = False
-_plugin = None
-
-def _init():
-    global _inited, _plugin
-    if not _inited:
-        sources = ['upfirdn2d.cpp', 'upfirdn2d.cu']
-        sources = [os.path.join(os.path.dirname(__file__), s) for s in sources]
-        try:
-            _plugin = custom_ops.get_plugin('upfirdn2d_plugin', sources=sources, extra_cuda_cflags=['--use_fast_math'])
-        except:
-            warnings.warn('Failed to build CUDA kernels for upfirdn2d. Falling back to slow reference implementation. Details:\n\n' + traceback.format_exc())
-    return _plugin is not None
-
-def _parse_scaling(scaling):
-    if isinstance(scaling, int):
-        scaling = [scaling, scaling]
-    assert isinstance(scaling, (list, tuple))
-    assert all(isinstance(x, int) for x in scaling)
-    sx, sy = scaling
-    assert sx >= 1 and sy >= 1
-    return sx, sy
-
-def _parse_padding(padding):
-    if isinstance(padding, int):
-        padding = [padding, padding]
-    assert isinstance(padding, (list, tuple))
-    assert all(isinstance(x, int) for x in padding)
-    if len(padding) == 2:
-        padx, pady = padding
-        padding = [padx, padx, pady, pady]
-    padx0, padx1, pady0, pady1 = padding
-    return padx0, padx1, pady0, pady1
-
-def _get_filter_size(f):
-    if f is None:
-        return 1, 1
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    fw = f.shape[-1]
-    fh = f.shape[0]
-    with misc.suppress_tracer_warnings():
-        fw = int(fw)
-        fh = int(fh)
-    misc.assert_shape(f, [fh, fw][:f.ndim])
-    assert fw >= 1 and fh >= 1
-    return fw, fh
-
-#----------------------------------------------------------------------------
-
-def setup_filter(f, device=torch.device('cpu'), normalize=True, flip_filter=False, gain=1, separable=None):
-    r"""Convenience function to setup 2D FIR filter for `upfirdn2d()`.
-
-    Args:
-        f:           Torch tensor, numpy array, or python list of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable),
-                     `[]` (impulse), or
-                     `None` (identity).
-        device:      Result device (default: cpu).
-        normalize:   Normalize the filter so that it retains the magnitude
-                     for constant input signal (DC)? (default: True).
-        flip_filter: Flip the filter? (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        separable:   Return a separable filter? (default: select automatically).
-
-    Returns:
-        Float32 tensor of the shape
-        `[filter_height, filter_width]` (non-separable) or
-        `[filter_taps]` (separable).
-    """
-    # Validate.
-    if f is None:
-        f = 1
-    f = torch.as_tensor(f, dtype=torch.float32)
-    assert f.ndim in [0, 1, 2]
-    assert f.numel() > 0
-    if f.ndim == 0:
-        f = f[np.newaxis]
-
-    # Separable?
-    if separable is None:
-        separable = (f.ndim == 1 and f.numel() >= 8)
-    if f.ndim == 1 and not separable:
-        f = f.ger(f)
-    assert f.ndim == (1 if separable else 2)
-
-    # Apply normalize, flip, gain, and device.
-    if normalize:
-        f /= f.sum()
-    if flip_filter:
-        f = f.flip(list(range(f.ndim)))
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(device=device)
-    return f
-
-#----------------------------------------------------------------------------
-
-def upfirdn2d(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Pad, upsample, filter, and downsample a batch of 2D images.
-
-    Performs the following sequence of operations for each channel:
-
-    1. Upsample the image by inserting N-1 zeros after each pixel (`up`).
-
-    2. Pad the image with the specified number of zeros on each side (`padding`).
-       Negative padding corresponds to cropping the image.
-
-    3. Convolve the image with the specified 2D FIR filter (`f`), shrinking it
-       so that the footprint of all output pixels lies within the input image.
-
-    4. Downsample the image by keeping every Nth pixel (`down`).
-
-    This sequence of operations bears close resemblance to scipy.signal.upfirdn().
-    The fused op is considerably more efficient than performing the same calculation
-    using standard PyTorch ops. It supports gradients of arbitrary order.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the upsampled image. Can be a single number
-                     or a list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    assert isinstance(x, torch.Tensor)
-    assert impl in ['ref', 'cuda']
-    if impl == 'cuda' and x.device.type == 'cuda' and _init():
-        return _upfirdn2d_cuda(up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain).apply(x, f)
-    return _upfirdn2d_ref(x, f, up=up, down=down, padding=padding, flip_filter=flip_filter, gain=gain)
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def _upfirdn2d_ref(x, f, up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.
-    """
-    # Validate arguments.
-    assert isinstance(x, torch.Tensor) and x.ndim == 4
-    if f is None:
-        f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-    assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-    assert f.dtype == torch.float32 and not f.requires_grad
-    batch_size, num_channels, in_height, in_width = x.shape
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Upsample by inserting zeros.
-    x = x.reshape([batch_size, num_channels, in_height, 1, in_width, 1])
-    x = torch.nn.functional.pad(x, [0, upx - 1, 0, 0, 0, upy - 1])
-    x = x.reshape([batch_size, num_channels, in_height * upy, in_width * upx])
-
-    # Pad or crop.
-    x = torch.nn.functional.pad(x, [max(padx0, 0), max(padx1, 0), max(pady0, 0), max(pady1, 0)])
-    x = x[:, :, max(-pady0, 0) : x.shape[2] - max(-pady1, 0), max(-padx0, 0) : x.shape[3] - max(-padx1, 0)]
-
-    # Setup filter.
-    f = f * (gain ** (f.ndim / 2))
-    f = f.to(x.dtype)
-    if not flip_filter:
-        f = f.flip(list(range(f.ndim)))
-
-    # Convolve with the filter.
-    f = f[np.newaxis, np.newaxis].repeat([num_channels, 1] + [1] * f.ndim)
-    if f.ndim == 4:
-        x = conv2d_gradfix.conv2d(input=x, weight=f, groups=num_channels)
-    else:
-        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(2), groups=num_channels)
-        x = conv2d_gradfix.conv2d(input=x, weight=f.unsqueeze(3), groups=num_channels)
-
-    # Downsample by throwing away pixels.
-    x = x[:, :, ::downy, ::downx]
-    return x
-
-#----------------------------------------------------------------------------
-
-_upfirdn2d_cuda_cache = dict()
-
-def _upfirdn2d_cuda(up=1, down=1, padding=0, flip_filter=False, gain=1):
-    """Fast CUDA implementation of `upfirdn2d()` using custom ops.
-    """
-    # Parse arguments.
-    upx, upy = _parse_scaling(up)
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-
-    # Lookup from cache.
-    key = (upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-    if key in _upfirdn2d_cuda_cache:
-        return _upfirdn2d_cuda_cache[key]
-
-    # Forward op.
-    class Upfirdn2dCuda(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x, f): # pylint: disable=arguments-differ
-            assert isinstance(x, torch.Tensor) and x.ndim == 4
-            if f is None:
-                f = torch.ones([1, 1], dtype=torch.float32, device=x.device)
-            assert isinstance(f, torch.Tensor) and f.ndim in [1, 2]
-            y = x
-            if f.ndim == 2:
-                y = _plugin.upfirdn2d(y, f, upx, upy, downx, downy, padx0, padx1, pady0, pady1, flip_filter, gain)
-            else:
-                y = _plugin.upfirdn2d(y, f.unsqueeze(0), upx, 1, downx, 1, padx0, padx1, 0, 0, flip_filter, np.sqrt(gain))
-                y = _plugin.upfirdn2d(y, f.unsqueeze(1), 1, upy, 1, downy, 0, 0, pady0, pady1, flip_filter, np.sqrt(gain))
-            ctx.save_for_backward(f)
-            ctx.x_shape = x.shape
-            return y
-
-        @staticmethod
-        def backward(ctx, dy): # pylint: disable=arguments-differ
-            f, = ctx.saved_tensors
-            _, _, ih, iw = ctx.x_shape
-            _, _, oh, ow = dy.shape
-            fw, fh = _get_filter_size(f)
-            p = [
-                fw - padx0 - 1,
-                iw * upx - ow * downx + padx0 - upx + 1,
-                fh - pady0 - 1,
-                ih * upy - oh * downy + pady0 - upy + 1,
-            ]
-            dx = None
-            df = None
-
-            if ctx.needs_input_grad[0]:
-                dx = _upfirdn2d_cuda(up=down, down=up, padding=p, flip_filter=(not flip_filter), gain=gain).apply(dy, f)
-
-            assert not ctx.needs_input_grad[1]
-            return dx, df
-
-    # Add to cache.
-    _upfirdn2d_cuda_cache[key] = Upfirdn2dCuda
-    return Upfirdn2dCuda
-
-#----------------------------------------------------------------------------
-
-def filter2d(x, f, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Filter a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape matches the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + fw // 2,
-        padx1 + (fw - 1) // 2,
-        pady0 + fh // 2,
-        pady1 + (fh - 1) // 2,
-    ]
-    return upfirdn2d(x, f, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def upsample2d(x, f, up=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Upsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a multiple of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        up:          Integer upsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the output. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    upx, upy = _parse_scaling(up)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw + upx - 1) // 2,
-        padx1 + (fw - upx) // 2,
-        pady0 + (fh + upy - 1) // 2,
-        pady1 + (fh - upy) // 2,
-    ]
-    return upfirdn2d(x, f, up=up, padding=p, flip_filter=flip_filter, gain=gain*upx*upy, impl=impl)
-
-#----------------------------------------------------------------------------
-
-def downsample2d(x, f, down=2, padding=0, flip_filter=False, gain=1, impl='cuda'):
-    r"""Downsample a batch of 2D images using the given 2D FIR filter.
-
-    By default, the result is padded so that its shape is a fraction of the input.
-    User-specified padding is applied on top of that, with negative values
-    indicating cropping. Pixels outside the image are assumed to be zero.
-
-    Args:
-        x:           Float32/float64/float16 input tensor of the shape
-                     `[batch_size, num_channels, in_height, in_width]`.
-        f:           Float32 FIR filter of the shape
-                     `[filter_height, filter_width]` (non-separable),
-                     `[filter_taps]` (separable), or
-                     `None` (identity).
-        down:        Integer downsampling factor. Can be a single int or a list/tuple
-                     `[x, y]` (default: 1).
-        padding:     Padding with respect to the input. Can be a single number or a
-                     list/tuple `[x, y]` or `[x_before, x_after, y_before, y_after]`
-                     (default: 0).
-        flip_filter: False = convolution, True = correlation (default: False).
-        gain:        Overall scaling factor for signal magnitude (default: 1).
-        impl:        Implementation to use. Can be `'ref'` or `'cuda'` (default: `'cuda'`).
-
-    Returns:
-        Tensor of the shape `[batch_size, num_channels, out_height, out_width]`.
-    """
-    downx, downy = _parse_scaling(down)
-    padx0, padx1, pady0, pady1 = _parse_padding(padding)
-    fw, fh = _get_filter_size(f)
-    p = [
-        padx0 + (fw - downx + 1) // 2,
-        padx1 + (fw - downx) // 2,
-        pady0 + (fh - downy + 1) // 2,
-        pady1 + (fh - downy) // 2,
-    ]
-    return upfirdn2d(x, f, down=down, padding=p, flip_filter=flip_filter, gain=gain, impl=impl)
-
-#----------------------------------------------------------------------------

torch_utils/persistence.py

@@ -1,251 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for pickling Python code alongside other data.
-
-The pickled code is automatically imported into a separate Python module
-during unpickling. This way, any previously exported pickles will remain
-usable even if the original code is no longer available, or if the current
-version of the code is not consistent with what was originally pickled."""
-
-import sys
-import pickle
-import io
-import inspect
-import copy
-import uuid
-import types
-import dnnlib
-
-#----------------------------------------------------------------------------
-
-_version            = 6         # internal version number
-_decorators         = set()     # {decorator_class, ...}
-_import_hooks       = []        # [hook_function, ...]
-_module_to_src_dict = dict()    # {module: src, ...}
-_src_to_module_dict = dict()    # {src: module, ...}
-
-#----------------------------------------------------------------------------
-
-def persistent_class(orig_class):
-    r"""Class decorator that extends a given class to save its source code
-    when pickled.
-
-    Example:
-
-        from torch_utils import persistence
-
-        @persistence.persistent_class
-        class MyNetwork(torch.nn.Module):
-            def __init__(self, num_inputs, num_outputs):
-                super().__init__()
-                self.fc = MyLayer(num_inputs, num_outputs)
-                ...
-
-        @persistence.persistent_class
-        class MyLayer(torch.nn.Module):
-            ...
-
-    When pickled, any instance of `MyNetwork` and `MyLayer` will save its
-    source code alongside other internal state (e.g., parameters, buffers,
-    and submodules). This way, any previously exported pickle will remain
-    usable even if the class definitions have been modified or are no
-    longer available.
-
-    The decorator saves the source code of the entire Python module
-    containing the decorated class. It does *not* save the source code of
-    any imported modules. Thus, the imported modules must be available
-    during unpickling, also including `torch_utils.persistence` itself.
-
-    It is ok to call functions defined in the same module from the
-    decorated class. However, if the decorated class depends on other
-    classes defined in the same module, they must be decorated as well.
-    This is illustrated in the above example in the case of `MyLayer`.
-
-    It is also possible to employ the decorator just-in-time before
-    calling the constructor. For example:
-
-        cls = MyLayer
-        if want_to_make_it_persistent:
-            cls = persistence.persistent_class(cls)
-        layer = cls(num_inputs, num_outputs)
-
-    As an additional feature, the decorator also keeps track of the
-    arguments that were used to construct each instance of the decorated
-    class. The arguments can be queried via `obj.init_args` and
-    `obj.init_kwargs`, and they are automatically pickled alongside other
-    object state. A typical use case is to first unpickle a previous
-    instance of a persistent class, and then upgrade it to use the latest
-    version of the source code:
-
-        with open('old_pickle.pkl', 'rb') as f:
-            old_net = pickle.load(f)
-        new_net = MyNetwork(*old_obj.init_args, **old_obj.init_kwargs)
-        misc.copy_params_and_buffers(old_net, new_net, require_all=True)
-    """
-    assert isinstance(orig_class, type)
-    if is_persistent(orig_class):
-        return orig_class
-
-    assert orig_class.__module__ in sys.modules
-    orig_module = sys.modules[orig_class.__module__]
-    orig_module_src = _module_to_src(orig_module)
-
-    class Decorator(orig_class):
-        _orig_module_src = orig_module_src
-        _orig_class_name = orig_class.__name__
-
-        def __init__(self, *args, **kwargs):
-            super().__init__(*args, **kwargs)
-            self._init_args = copy.deepcopy(args)
-            self._init_kwargs = copy.deepcopy(kwargs)
-            assert orig_class.__name__ in orig_module.__dict__
-            _check_pickleable(self.__reduce__())
-
-        @property
-        def init_args(self):
-            return copy.deepcopy(self._init_args)
-
-        @property
-        def init_kwargs(self):
-            return dnnlib.EasyDict(copy.deepcopy(self._init_kwargs))
-
-        def __reduce__(self):
-            fields = list(super().__reduce__())
-            fields += [None] * max(3 - len(fields), 0)
-            if fields[0] is not _reconstruct_persistent_obj:
-                meta = dict(type='class', version=_version, module_src=self._orig_module_src, class_name=self._orig_class_name, state=fields[2])
-                fields[0] = _reconstruct_persistent_obj # reconstruct func
-                fields[1] = (meta,) # reconstruct args
-                fields[2] = None # state dict
-            return tuple(fields)
-
-    Decorator.__name__ = orig_class.__name__
-    _decorators.add(Decorator)
-    return Decorator
-
-#----------------------------------------------------------------------------
-
-def is_persistent(obj):
-    r"""Test whether the given object or class is persistent, i.e.,
-    whether it will save its source code when pickled.
-    """
-    try:
-        if obj in _decorators:
-            return True
-    except TypeError:
-        pass
-    return type(obj) in _decorators # pylint: disable=unidiomatic-typecheck
-
-#----------------------------------------------------------------------------
-
-def import_hook(hook):
-    r"""Register an import hook that is called whenever a persistent object
-    is being unpickled. A typical use case is to patch the pickled source
-    code to avoid errors and inconsistencies when the API of some imported
-    module has changed.
-
-    The hook should have the following signature:
-
-        hook(meta) -> modified meta
-
-    `meta` is an instance of `dnnlib.EasyDict` with the following fields:
-
-        type:       Type of the persistent object, e.g. `'class'`.
-        version:    Internal version number of `torch_utils.persistence`.
-        module_src  Original source code of the Python module.
-        class_name: Class name in the original Python module.
-        state:      Internal state of the object.
-
-    Example:
-
-        @persistence.import_hook
-        def wreck_my_network(meta):
-            if meta.class_name == 'MyNetwork':
-                print('MyNetwork is being imported. I will wreck it!')
-                meta.module_src = meta.module_src.replace("True", "False")
-            return meta
-    """
-    assert callable(hook)
-    _import_hooks.append(hook)
-
-#----------------------------------------------------------------------------
-
-def _reconstruct_persistent_obj(meta):
-    r"""Hook that is called internally by the `pickle` module to unpickle
-    a persistent object.
-    """
-    meta = dnnlib.EasyDict(meta)
-    meta.state = dnnlib.EasyDict(meta.state)
-    for hook in _import_hooks:
-        meta = hook(meta)
-        assert meta is not None
-
-    assert meta.version == _version
-    module = _src_to_module(meta.module_src)
-
-    assert meta.type == 'class'
-    orig_class = module.__dict__[meta.class_name]
-    decorator_class = persistent_class(orig_class)
-    obj = decorator_class.__new__(decorator_class)
-
-    setstate = getattr(obj, '__setstate__', None)
-    if callable(setstate):
-        setstate(meta.state) # pylint: disable=not-callable
-    else:
-        obj.__dict__.update(meta.state)
-    return obj
-
-#----------------------------------------------------------------------------
-
-def _module_to_src(module):
-    r"""Query the source code of a given Python module.
-    """
-    src = _module_to_src_dict.get(module, None)
-    if src is None:
-        src = inspect.getsource(module)
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-    return src
-
-def _src_to_module(src):
-    r"""Get or create a Python module for the given source code.
-    """
-    module = _src_to_module_dict.get(src, None)
-    if module is None:
-        module_name = "_imported_module_" + uuid.uuid4().hex
-        module = types.ModuleType(module_name)
-        sys.modules[module_name] = module
-        _module_to_src_dict[module] = src
-        _src_to_module_dict[src] = module
-        exec(src, module.__dict__) # pylint: disable=exec-used
-    return module
-
-#----------------------------------------------------------------------------
-
-def _check_pickleable(obj):
-    r"""Check that the given object is pickleable, raising an exception if
-    it is not. This function is expected to be considerably more efficient
-    than actually pickling the object.
-    """
-    def recurse(obj):
-        if isinstance(obj, (list, tuple, set)):
-            return [recurse(x) for x in obj]
-        if isinstance(obj, dict):
-            return [[recurse(x), recurse(y)] for x, y in obj.items()]
-        if isinstance(obj, (str, int, float, bool, bytes, bytearray)):
-            return None # Python primitive types are pickleable.
-        if f'{type(obj).__module__}.{type(obj).__name__}' in ['numpy.ndarray', 'torch.Tensor']:
-            return None # NumPy arrays and PyTorch tensors are pickleable.
-        if is_persistent(obj):
-            return None # Persistent objects are pickleable, by virtue of the constructor check.
-        return obj
-    with io.BytesIO() as f:
-        pickle.dump(recurse(obj), f)
-
-#----------------------------------------------------------------------------

torch_utils/training_stats.py

@@ -1,268 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Facilities for reporting and collecting training statistics across
-multiple processes and devices. The interface is designed to minimize
-synchronization overhead as well as the amount of boilerplate in user
-code."""
-
-import re
-import numpy as np
-import torch
-import dnnlib
-
-from . import misc
-
-#----------------------------------------------------------------------------
-
-_num_moments    = 3             # [num_scalars, sum_of_scalars, sum_of_squares]
-_reduce_dtype   = torch.float32 # Data type to use for initial per-tensor reduction.
-_counter_dtype  = torch.float64 # Data type to use for the internal counters.
-_rank           = 0             # Rank of the current process.
-_sync_device    = None          # Device to use for multiprocess communication. None = single-process.
-_sync_called    = False         # Has _sync() been called yet?
-_counters       = dict()        # Running counters on each device, updated by report(): name => device => torch.Tensor
-_cumulative     = dict()        # Cumulative counters on the CPU, updated by _sync(): name => torch.Tensor
-
-#----------------------------------------------------------------------------
-
-def init_multiprocessing(rank, sync_device):
-    r"""Initializes `torch_utils.training_stats` for collecting statistics
-    across multiple processes.
-
-    This function must be called after
-    `torch.distributed.init_process_group()` and before `Collector.update()`.
-    The call is not necessary if multi-process collection is not needed.
-
-    Args:
-        rank:           Rank of the current process.
-        sync_device:    PyTorch device to use for inter-process
-                        communication, or None to disable multi-process
-                        collection. Typically `torch.device('cuda', rank)`.
-    """
-    global _rank, _sync_device
-    assert not _sync_called
-    _rank = rank
-    _sync_device = sync_device
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def report(name, value):
-    r"""Broadcasts the given set of scalars to all interested instances of
-    `Collector`, across device and process boundaries.
-
-    This function is expected to be extremely cheap and can be safely
-    called from anywhere in the training loop, loss function, or inside a
-    `torch.nn.Module`.
-
-    Warning: The current implementation expects the set of unique names to
-    be consistent across processes. Please make sure that `report()` is
-    called at least once for each unique name by each process, and in the
-    same order. If a given process has no scalars to broadcast, it can do
-    `report(name, [])` (empty list).
-
-    Args:
-        name:   Arbitrary string specifying the name of the statistic.
-                Averages are accumulated separately for each unique name.
-        value:  Arbitrary set of scalars. Can be a list, tuple,
-                NumPy array, PyTorch tensor, or Python scalar.
-
-    Returns:
-        The same `value` that was passed in.
-    """
-    if name not in _counters:
-        _counters[name] = dict()
-
-    elems = torch.as_tensor(value)
-    if elems.numel() == 0:
-        return value
-
-    elems = elems.detach().flatten().to(_reduce_dtype)
-    moments = torch.stack([
-        torch.ones_like(elems).sum(),
-        elems.sum(),
-        elems.square().sum(),
-    ])
-    assert moments.ndim == 1 and moments.shape[0] == _num_moments
-    moments = moments.to(_counter_dtype)
-
-    device = moments.device
-    if device not in _counters[name]:
-        _counters[name][device] = torch.zeros_like(moments)
-    _counters[name][device].add_(moments)
-    return value
-
-#----------------------------------------------------------------------------
-
-def report0(name, value):
-    r"""Broadcasts the given set of scalars by the first process (`rank = 0`),
-    but ignores any scalars provided by the other processes.
-    See `report()` for further details.
-    """
-    report(name, value if _rank == 0 else [])
-    return value
-
-#----------------------------------------------------------------------------
-
-class Collector:
-    r"""Collects the scalars broadcasted by `report()` and `report0()` and
-    computes their long-term averages (mean and standard deviation) over
-    user-defined periods of time.
-
-    The averages are first collected into internal counters that are not
-    directly visible to the user. They are then copied to the user-visible
-    state as a result of calling `update()` and can then be queried using
-    `mean()`, `std()`, `as_dict()`, etc. Calling `update()` also resets the
-    internal counters for the next round, so that the user-visible state
-    effectively reflects averages collected between the last two calls to
-    `update()`.
-
-    Args:
-        regex:          Regular expression defining which statistics to
-                        collect. The default is to collect everything.
-        keep_previous:  Whether to retain the previous averages if no
-                        scalars were collected on a given round
-                        (default: True).
-    """
-    def __init__(self, regex='.*', keep_previous=True):
-        self._regex = re.compile(regex)
-        self._keep_previous = keep_previous
-        self._cumulative = dict()
-        self._moments = dict()
-        self.update()
-        self._moments.clear()
-
-    def names(self):
-        r"""Returns the names of all statistics broadcasted so far that
-        match the regular expression specified at construction time.
-        """
-        return [name for name in _counters if self._regex.fullmatch(name)]
-
-    def update(self):
-        r"""Copies current values of the internal counters to the
-        user-visible state and resets them for the next round.
-
-        If `keep_previous=True` was specified at construction time, the
-        operation is skipped for statistics that have received no scalars
-        since the last update, retaining their previous averages.
-
-        This method performs a number of GPU-to-CPU transfers and one
-        `torch.distributed.all_reduce()`. It is intended to be called
-        periodically in the main training loop, typically once every
-        N training steps.
-        """
-        if not self._keep_previous:
-            self._moments.clear()
-        for name, cumulative in _sync(self.names()):
-            if name not in self._cumulative:
-                self._cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-            delta = cumulative - self._cumulative[name]
-            self._cumulative[name].copy_(cumulative)
-            if float(delta[0]) != 0:
-                self._moments[name] = delta
-
-    def _get_delta(self, name):
-        r"""Returns the raw moments that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        assert self._regex.fullmatch(name)
-        if name not in self._moments:
-            self._moments[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        return self._moments[name]
-
-    def num(self, name):
-        r"""Returns the number of scalars that were accumulated for the given
-        statistic between the last two calls to `update()`, or zero if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        return int(delta[0])
-
-    def mean(self, name):
-        r"""Returns the mean of the scalars that were accumulated for the
-        given statistic between the last two calls to `update()`, or NaN if
-        no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0:
-            return float('nan')
-        return float(delta[1] / delta[0])
-
-    def std(self, name):
-        r"""Returns the standard deviation of the scalars that were
-        accumulated for the given statistic between the last two calls to
-        `update()`, or NaN if no scalars were collected.
-        """
-        delta = self._get_delta(name)
-        if int(delta[0]) == 0 or not np.isfinite(float(delta[1])):
-            return float('nan')
-        if int(delta[0]) == 1:
-            return float(0)
-        mean = float(delta[1] / delta[0])
-        raw_var = float(delta[2] / delta[0])
-        return np.sqrt(max(raw_var - np.square(mean), 0))
-
-    def as_dict(self):
-        r"""Returns the averages accumulated between the last two calls to
-        `update()` as an `dnnlib.EasyDict`. The contents are as follows:
-
-            dnnlib.EasyDict(
-                NAME = dnnlib.EasyDict(num=FLOAT, mean=FLOAT, std=FLOAT),
-                ...
-            )
-        """
-        stats = dnnlib.EasyDict()
-        for name in self.names():
-            stats[name] = dnnlib.EasyDict(num=self.num(name), mean=self.mean(name), std=self.std(name))
-        return stats
-
-    def __getitem__(self, name):
-        r"""Convenience getter.
-        `collector[name]` is a synonym for `collector.mean(name)`.
-        """
-        return self.mean(name)
-
-#----------------------------------------------------------------------------
-
-def _sync(names):
-    r"""Synchronize the global cumulative counters across devices and
-    processes. Called internally by `Collector.update()`.
-    """
-    if len(names) == 0:
-        return []
-    global _sync_called
-    _sync_called = True
-
-    # Collect deltas within current rank.
-    deltas = []
-    device = _sync_device if _sync_device is not None else torch.device('cpu')
-    for name in names:
-        delta = torch.zeros([_num_moments], dtype=_counter_dtype, device=device)
-        for counter in _counters[name].values():
-            delta.add_(counter.to(device))
-            counter.copy_(torch.zeros_like(counter))
-        deltas.append(delta)
-    deltas = torch.stack(deltas)
-
-    # Sum deltas across ranks.
-    if _sync_device is not None:
-        torch.distributed.all_reduce(deltas)
-
-    # Update cumulative values.
-    deltas = deltas.cpu()
-    for idx, name in enumerate(names):
-        if name not in _cumulative:
-            _cumulative[name] = torch.zeros([_num_moments], dtype=_counter_dtype)
-        _cumulative[name].add_(deltas[idx])
-
-    # Return name-value pairs.
-    return [(name, _cumulative[name]) for name in names]
-
-#----------------------------------------------------------------------------

train.py

@@ -1,540 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-"""Train a GAN using the techniques described in the paper
-"Training Generative Adversarial Networks with Limited Data"."""
-
-import os
-import click
-import re
-import json
-import tempfile
-import torch
-import dnnlib
-
-from training import training_loop
-from metrics import metric_main
-from torch_utils import training_stats
-from torch_utils import custom_ops
-
-#----------------------------------------------------------------------------
-
-class UserError(Exception):
-    pass
-
-#----------------------------------------------------------------------------
-
-def setup_training_loop_kwargs(
-    # General options (not included in desc).
-    gpus       = None, # Number of GPUs: <int>, default = 1 gpu
-    snap       = None, # Snapshot interval: <int>, default = 50 ticks
-    metrics    = None, # List of metric names: [], ['fid50k_full'] (default), ...
-    seed       = None, # Random seed: <int>, default = 0
-
-    # Dataset.
-    data       = None, # Training dataset (required): <path>
-    cond       = None, # Train conditional model based on dataset labels: <bool>, default = False
-    subset     = None, # Train with only N images: <int>, default = all
-    mirror     = None, # Augment dataset with x-flips: <bool>, default = False
-
-    # Base config.
-    cfg        = None, # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar'
-    gamma      = None, # Override R1 gamma: <float>
-    kimg       = None, # Override training duration: <int>
-    batch      = None, # Override batch size: <int>
-
-    # Discriminator augmentation.
-    aug        = None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed'
-    p          = None, # Specify p for 'fixed' (required): <float>
-    target     = None, # Override ADA target for 'ada': <float>, default = depends on aug
-    augpipe    = None, # Augmentation pipeline: 'blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc' (default), ..., 'bgcfnc'
-
-    # Transfer learning.
-    resume     = None, # Load previous network: 'noresume' (default), 'ffhq256', 'ffhq512', 'ffhq1024', 'celebahq256', 'lsundog256', <file>, <url>
-    freezed    = None, # Freeze-D: <int>, default = 0 discriminator layers
-
-    # Performance options (not included in desc).
-    fp32       = None, # Disable mixed-precision training: <bool>, default = False
-    nhwc       = None, # Use NHWC memory format with FP16: <bool>, default = False
-    allow_tf32 = None, # Allow PyTorch to use TF32 for matmul and convolutions: <bool>, default = False
-    nobench    = None, # Disable cuDNN benchmarking: <bool>, default = False
-    workers    = None, # Override number of DataLoader workers: <int>, default = 3
-):
-    args = dnnlib.EasyDict()
-
-    # ------------------------------------------
-    # General options: gpus, snap, metrics, seed
-    # ------------------------------------------
-
-    if gpus is None:
-        gpus = 1
-    assert isinstance(gpus, int)
-    if not (gpus >= 1 and gpus & (gpus - 1) == 0):
-        raise UserError('--gpus must be a power of two')
-    args.num_gpus = gpus
-
-    if snap is None:
-        snap = 50
-    assert isinstance(snap, int)
-    if snap < 1:
-        raise UserError('--snap must be at least 1')
-    args.image_snapshot_ticks = snap
-    args.network_snapshot_ticks = snap
-
-    if metrics is None:
-        metrics = ['fid50k_full']
-    assert isinstance(metrics, list)
-    if not all(metric_main.is_valid_metric(metric) for metric in metrics):
-        raise UserError('\n'.join(['--metrics can only contain the following values:'] + metric_main.list_valid_metrics()))
-    args.metrics = metrics
-
-    if seed is None:
-        seed = 0
-    assert isinstance(seed, int)
-    args.random_seed = seed
-
-    # -----------------------------------
-    # Dataset: data, cond, subset, mirror
-    # -----------------------------------
-
-    assert data is not None
-    assert isinstance(data, str)
-    args.training_set_kwargs = dnnlib.EasyDict(class_name='training.dataset.ImageFolderDataset', path=data, use_labels=True, max_size=None, xflip=False)
-    args.data_loader_kwargs = dnnlib.EasyDict(pin_memory=True, num_workers=3, prefetch_factor=2)
-    try:
-        training_set = dnnlib.util.construct_class_by_name(**args.training_set_kwargs) # subclass of training.dataset.Dataset
-        args.training_set_kwargs.resolution = training_set.resolution # be explicit about resolution
-        args.training_set_kwargs.use_labels = training_set.has_labels # be explicit about labels
-        args.training_set_kwargs.max_size = len(training_set) # be explicit about dataset size
-        desc = training_set.name
-        del training_set # conserve memory
-    except IOError as err:
-        raise UserError(f'--data: {err}')
-
-    if cond is None:
-        cond = False
-    assert isinstance(cond, bool)
-    if cond:
-        if not args.training_set_kwargs.use_labels:
-            raise UserError('--cond=True requires labels specified in dataset.json')
-        desc += '-cond'
-    else:
-        args.training_set_kwargs.use_labels = False
-
-    if subset is not None:
-        assert isinstance(subset, int)
-        if not 1 <= subset <= args.training_set_kwargs.max_size:
-            raise UserError(f'--subset must be between 1 and {args.training_set_kwargs.max_size}')
-        desc += f'-subset{subset}'
-        if subset < args.training_set_kwargs.max_size:
-            args.training_set_kwargs.max_size = subset
-            args.training_set_kwargs.random_seed = args.random_seed
-
-    if mirror is None:
-        mirror = False
-    assert isinstance(mirror, bool)
-    if mirror:
-        desc += '-mirror'
-        args.training_set_kwargs.xflip = True
-
-    # ------------------------------------
-    # Base config: cfg, gamma, kimg, batch
-    # ------------------------------------
-
-    if cfg is None:
-        cfg = 'auto'
-    assert isinstance(cfg, str)
-    desc += f'-{cfg}'
-
-    cfg_specs = {
-        'auto':      dict(ref_gpus=-1, kimg=25000,  mb=-1, mbstd=-1, fmaps=-1,  lrate=-1,     gamma=-1,   ema=-1,  ramp=0.05, map=2), # Populated dynamically based on resolution and GPU count.
-        'stylegan2': dict(ref_gpus=8,  kimg=25000,  mb=32, mbstd=4,  fmaps=1,   lrate=0.002,  gamma=10,   ema=10,  ramp=None, map=8), # Uses mixed-precision, unlike the original StyleGAN2.
-        'paper256':  dict(ref_gpus=8,  kimg=25000,  mb=64, mbstd=8,  fmaps=0.5, lrate=0.0025, gamma=1,    ema=20,  ramp=None, map=8),
-        'paper512':  dict(ref_gpus=8,  kimg=25000,  mb=64, mbstd=8,  fmaps=1,   lrate=0.0025, gamma=0.5,  ema=20,  ramp=None, map=8),
-        'paper1024': dict(ref_gpus=8,  kimg=25000,  mb=32, mbstd=4,  fmaps=1,   lrate=0.002,  gamma=2,    ema=10,  ramp=None, map=8),
-        'cifar':     dict(ref_gpus=2,  kimg=100000, mb=64, mbstd=32, fmaps=1,   lrate=0.0025, gamma=0.01, ema=500, ramp=0.05, map=2),
-    }
-
-    assert cfg in cfg_specs
-    spec = dnnlib.EasyDict(cfg_specs[cfg])
-    if cfg == 'auto':
-        desc += f'{gpus:d}'
-        spec.ref_gpus = gpus
-        res = args.training_set_kwargs.resolution
-        spec.mb = max(min(gpus * min(4096 // res, 32), 64), gpus) # keep gpu memory consumption at bay
-        spec.mbstd = min(spec.mb // gpus, 4) # other hyperparams behave more predictably if mbstd group size remains fixed
-        spec.fmaps = 1 if res >= 512 else 0.5
-        spec.lrate = 0.002 if res >= 1024 else 0.0025
-        spec.gamma = 0.0002 * (res ** 2) / spec.mb # heuristic formula
-        spec.ema = spec.mb * 10 / 32
-
-    args.G_kwargs = dnnlib.EasyDict(class_name='training.networks.Generator', z_dim=512, w_dim=512, mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict())
-    args.D_kwargs = dnnlib.EasyDict(class_name='training.networks.Discriminator', block_kwargs=dnnlib.EasyDict(), mapping_kwargs=dnnlib.EasyDict(), epilogue_kwargs=dnnlib.EasyDict())
-    args.G_kwargs.synthesis_kwargs.channel_base = args.D_kwargs.channel_base = int(spec.fmaps * 32768)
-    args.G_kwargs.synthesis_kwargs.channel_max = args.D_kwargs.channel_max = 512
-    args.G_kwargs.mapping_kwargs.num_layers = spec.map
-    args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 4 # enable mixed-precision training
-    args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = 256 # clamp activations to avoid float16 overflow
-    args.D_kwargs.epilogue_kwargs.mbstd_group_size = spec.mbstd
-
-    args.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8)
-    args.D_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0,0.99], eps=1e-8)
-    args.loss_kwargs = dnnlib.EasyDict(class_name='training.loss.StyleGAN2Loss', r1_gamma=spec.gamma)
-
-    args.total_kimg = spec.kimg
-    args.batch_size = spec.mb
-    args.batch_gpu = spec.mb // spec.ref_gpus
-    args.ema_kimg = spec.ema
-    args.ema_rampup = spec.ramp
-
-    if cfg == 'cifar':
-        args.loss_kwargs.pl_weight = 0 # disable path length regularization
-        args.loss_kwargs.style_mixing_prob = 0 # disable style mixing
-        args.D_kwargs.architecture = 'orig' # disable residual skip connections
-
-    if gamma is not None:
-        assert isinstance(gamma, float)
-        if not gamma >= 0:
-            raise UserError('--gamma must be non-negative')
-        desc += f'-gamma{gamma:g}'
-        args.loss_kwargs.r1_gamma = gamma
-
-    if kimg is not None:
-        assert isinstance(kimg, int)
-        if not kimg >= 1:
-            raise UserError('--kimg must be at least 1')
-        desc += f'-kimg{kimg:d}'
-        args.total_kimg = kimg
-
-    if batch is not None:
-        assert isinstance(batch, int)
-        if not (batch >= 1 and batch % gpus == 0):
-            raise UserError('--batch must be at least 1 and divisible by --gpus')
-        desc += f'-batch{batch}'
-        args.batch_size = batch
-        args.batch_gpu = batch // gpus
-
-    # ---------------------------------------------------
-    # Discriminator augmentation: aug, p, target, augpipe
-    # ---------------------------------------------------
-
-    if aug is None:
-        aug = 'ada'
-    else:
-        assert isinstance(aug, str)
-        desc += f'-{aug}'
-
-    if aug == 'ada':
-        args.ada_target = 0.6
-
-    elif aug == 'noaug':
-        pass
-
-    elif aug == 'fixed':
-        if p is None:
-            raise UserError(f'--aug={aug} requires specifying --p')
-
-    else:
-        raise UserError(f'--aug={aug} not supported')
-
-    if p is not None:
-        assert isinstance(p, float)
-        if aug != 'fixed':
-            raise UserError('--p can only be specified with --aug=fixed')
-        if not 0 <= p <= 1:
-            raise UserError('--p must be between 0 and 1')
-        desc += f'-p{p:g}'
-        args.augment_p = p
-
-    if target is not None:
-        assert isinstance(target, float)
-        if aug != 'ada':
-            raise UserError('--target can only be specified with --aug=ada')
-        if not 0 <= target <= 1:
-            raise UserError('--target must be between 0 and 1')
-        desc += f'-target{target:g}'
-        args.ada_target = target
-
-    assert augpipe is None or isinstance(augpipe, str)
-    if augpipe is None:
-        augpipe = 'bgc'
-    else:
-        if aug == 'noaug':
-            raise UserError('--augpipe cannot be specified with --aug=noaug')
-        desc += f'-{augpipe}'
-
-    augpipe_specs = {
-        'blit':   dict(xflip=1, rotate90=1, xint=1),
-        'geom':   dict(scale=1, rotate=1, aniso=1, xfrac=1),
-        'color':  dict(brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
-        'filter': dict(imgfilter=1),
-        'noise':  dict(noise=1),
-        'cutout': dict(cutout=1),
-        'bg':     dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1),
-        'bgc':    dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1),
-        'bgcf':   dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1),
-        'bgcfn':  dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1),
-        'bgcfnc': dict(xflip=1, rotate90=1, xint=1, scale=1, rotate=1, aniso=1, xfrac=1, brightness=1, contrast=1, lumaflip=1, hue=1, saturation=1, imgfilter=1, noise=1, cutout=1),
-    }
-
-    assert augpipe in augpipe_specs
-    if aug != 'noaug':
-        args.augment_kwargs = dnnlib.EasyDict(class_name='training.augment.AugmentPipe', **augpipe_specs[augpipe])
-
-    # ----------------------------------
-    # Transfer learning: resume, freezed
-    # ----------------------------------
-
-    resume_specs = {
-        'ffhq256':     'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res256-mirror-paper256-noaug.pkl',
-        'ffhq512':     'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res512-mirror-stylegan2-noaug.pkl',
-        'ffhq1024':    'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/ffhq-res1024-mirror-stylegan2-noaug.pkl',
-        'celebahq256': 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/celebahq-res256-mirror-paper256-kimg100000-ada-target0.5.pkl',
-        'lsundog256':  'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/transfer-learning-source-nets/lsundog-res256-paper256-kimg100000-noaug.pkl',
-    }
-
-    assert resume is None or isinstance(resume, str)
-    if resume is None:
-        resume = 'noresume'
-    elif resume == 'noresume':
-        desc += '-noresume'
-    elif resume in resume_specs:
-        desc += f'-resume{resume}'
-        args.resume_pkl = resume_specs[resume] # predefined url
-    else:
-        desc += '-resumecustom'
-        args.resume_pkl = resume # custom path or url
-
-    if resume != 'noresume':
-        args.ada_kimg = 100 # make ADA react faster at the beginning
-        args.ema_rampup = None # disable EMA rampup
-
-    if freezed is not None:
-        assert isinstance(freezed, int)
-        if not freezed >= 0:
-            raise UserError('--freezed must be non-negative')
-        desc += f'-freezed{freezed:d}'
-        args.D_kwargs.block_kwargs.freeze_layers = freezed
-
-    # -------------------------------------------------
-    # Performance options: fp32, nhwc, nobench, workers
-    # -------------------------------------------------
-
-    if fp32 is None:
-        fp32 = False
-    assert isinstance(fp32, bool)
-    if fp32:
-        args.G_kwargs.synthesis_kwargs.num_fp16_res = args.D_kwargs.num_fp16_res = 0
-        args.G_kwargs.synthesis_kwargs.conv_clamp = args.D_kwargs.conv_clamp = None
-
-    if nhwc is None:
-        nhwc = False
-    assert isinstance(nhwc, bool)
-    if nhwc:
-        args.G_kwargs.synthesis_kwargs.fp16_channels_last = args.D_kwargs.block_kwargs.fp16_channels_last = True
-
-    if nobench is None:
-        nobench = False
-    assert isinstance(nobench, bool)
-    if nobench:
-        args.cudnn_benchmark = False
-
-    if allow_tf32 is None:
-        allow_tf32 = False
-    assert isinstance(allow_tf32, bool)
-    if allow_tf32:
-        args.allow_tf32 = True
-
-    if workers is not None:
-        assert isinstance(workers, int)
-        if not workers >= 1:
-            raise UserError('--workers must be at least 1')
-        args.data_loader_kwargs.num_workers = workers
-
-    return desc, args
-
-#----------------------------------------------------------------------------
-
-def subprocess_fn(rank, args, temp_dir):
-    dnnlib.util.Logger(file_name=os.path.join(args.run_dir, 'log.txt'), file_mode='a', should_flush=True)
-
-    # Init torch.distributed.
-    if args.num_gpus > 1:
-        init_file = os.path.abspath(os.path.join(temp_dir, '.torch_distributed_init'))
-        if os.name == 'nt':
-            init_method = 'file:///' + init_file.replace('\\', '/')
-            torch.distributed.init_process_group(backend='gloo', init_method=init_method, rank=rank, world_size=args.num_gpus)
-        else:
-            init_method = f'file://{init_file}'
-            torch.distributed.init_process_group(backend='nccl', init_method=init_method, rank=rank, world_size=args.num_gpus)
-
-    # Init torch_utils.
-    sync_device = torch.device('cuda', rank) if args.num_gpus > 1 else None
-    training_stats.init_multiprocessing(rank=rank, sync_device=sync_device)
-    if rank != 0:
-        custom_ops.verbosity = 'none'
-
-    # Execute training loop.
-    training_loop.training_loop(rank=rank, **args)
-
-#----------------------------------------------------------------------------
-
-class CommaSeparatedList(click.ParamType):
-    name = 'list'
-
-    def convert(self, value, param, ctx):
-        _ = param, ctx
-        if value is None or value.lower() == 'none' or value == '':
-            return []
-        return value.split(',')
-
-#----------------------------------------------------------------------------
-
-@click.command()
-@click.pass_context
-
-# General options.
-@click.option('--outdir', help='Where to save the results', required=True, metavar='DIR')
-@click.option('--gpus', help='Number of GPUs to use [default: 1]', type=int, metavar='INT')
-@click.option('--snap', help='Snapshot interval [default: 50 ticks]', type=int, metavar='INT')
-@click.option('--metrics', help='Comma-separated list or "none" [default: fid50k_full]', type=CommaSeparatedList())
-@click.option('--seed', help='Random seed [default: 0]', type=int, metavar='INT')
-@click.option('-n', '--dry-run', help='Print training options and exit', is_flag=True)
-
-# Dataset.
-@click.option('--data', help='Training data (directory or zip)', metavar='PATH', required=True)
-@click.option('--cond', help='Train conditional model based on dataset labels [default: false]', type=bool, metavar='BOOL')
-@click.option('--subset', help='Train with only N images [default: all]', type=int, metavar='INT')
-@click.option('--mirror', help='Enable dataset x-flips [default: false]', type=bool, metavar='BOOL')
-
-# Base config.
-@click.option('--cfg', help='Base config [default: auto]', type=click.Choice(['auto', 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar']))
-@click.option('--gamma', help='Override R1 gamma', type=float)
-@click.option('--kimg', help='Override training duration', type=int, metavar='INT')
-@click.option('--batch', help='Override batch size', type=int, metavar='INT')
-
-# Discriminator augmentation.
-@click.option('--aug', help='Augmentation mode [default: ada]', type=click.Choice(['noaug', 'ada', 'fixed']))
-@click.option('--p', help='Augmentation probability for --aug=fixed', type=float)
-@click.option('--target', help='ADA target value for --aug=ada', type=float)
-@click.option('--augpipe', help='Augmentation pipeline [default: bgc]', type=click.Choice(['blit', 'geom', 'color', 'filter', 'noise', 'cutout', 'bg', 'bgc', 'bgcf', 'bgcfn', 'bgcfnc']))
-
-# Transfer learning.
-@click.option('--resume', help='Resume training [default: noresume]', metavar='PKL')
-@click.option('--freezed', help='Freeze-D [default: 0 layers]', type=int, metavar='INT')
-
-# Performance options.
-@click.option('--fp32', help='Disable mixed-precision training', type=bool, metavar='BOOL')
-@click.option('--nhwc', help='Use NHWC memory format with FP16', type=bool, metavar='BOOL')
-@click.option('--nobench', help='Disable cuDNN benchmarking', type=bool, metavar='BOOL')
-@click.option('--allow-tf32', help='Allow PyTorch to use TF32 internally', type=bool, metavar='BOOL')
-@click.option('--workers', help='Override number of DataLoader workers', type=int, metavar='INT')
-
-def main(ctx, outdir, dry_run, **config_kwargs):
-    """Train a GAN using the techniques described in the paper
-    "Training Generative Adversarial Networks with Limited Data".
-
-    Examples:
-
-    \b
-    # Train with custom dataset using 1 GPU.
-    python train.py --outdir=~/training-runs --data=~/mydataset.zip --gpus=1
-
-    \b
-    # Train class-conditional CIFAR-10 using 2 GPUs.
-    python train.py --outdir=~/training-runs --data=~/datasets/cifar10.zip \\
-        --gpus=2 --cfg=cifar --cond=1
-
-    \b
-    # Transfer learn MetFaces from FFHQ using 4 GPUs.
-    python train.py --outdir=~/training-runs --data=~/datasets/metfaces.zip \\
-        --gpus=4 --cfg=paper1024 --mirror=1 --resume=ffhq1024 --snap=10
-
-    \b
-    # Reproduce original StyleGAN2 config F.
-    python train.py --outdir=~/training-runs --data=~/datasets/ffhq.zip \\
-        --gpus=8 --cfg=stylegan2 --mirror=1 --aug=noaug
-
-    \b
-    Base configs (--cfg):
-      auto       Automatically select reasonable defaults based on resolution
-                 and GPU count. Good starting point for new datasets.
-      stylegan2  Reproduce results for StyleGAN2 config F at 1024x1024.
-      paper256   Reproduce results for FFHQ and LSUN Cat at 256x256.
-      paper512   Reproduce results for BreCaHAD and AFHQ at 512x512.
-      paper1024  Reproduce results for MetFaces at 1024x1024.
-      cifar      Reproduce results for CIFAR-10 at 32x32.
-
-    \b
-    Transfer learning source networks (--resume):
-      ffhq256        FFHQ trained at 256x256 resolution.
-      ffhq512        FFHQ trained at 512x512 resolution.
-      ffhq1024       FFHQ trained at 1024x1024 resolution.
-      celebahq256    CelebA-HQ trained at 256x256 resolution.
-      lsundog256     LSUN Dog trained at 256x256 resolution.
-      <PATH or URL>  Custom network pickle.
-    """
-    dnnlib.util.Logger(should_flush=True)
-
-    # Setup training options.
-    try:
-        run_desc, args = setup_training_loop_kwargs(**config_kwargs)
-    except UserError as err:
-        ctx.fail(err)
-
-    # Pick output directory.
-    prev_run_dirs = []
-    if os.path.isdir(outdir):
-        prev_run_dirs = [x for x in os.listdir(outdir) if os.path.isdir(os.path.join(outdir, x))]
-    prev_run_ids = [re.match(r'^\d+', x) for x in prev_run_dirs]
-    prev_run_ids = [int(x.group()) for x in prev_run_ids if x is not None]
-    cur_run_id = max(prev_run_ids, default=-1) + 1
-    args.run_dir = os.path.join(outdir, f'{cur_run_id:05d}-{run_desc}')
-    assert not os.path.exists(args.run_dir)
-
-    # Print options.
-    print()
-    print('Training options:')
-    print(json.dumps(args, indent=2))
-    print()
-    print(f'Output directory:   {args.run_dir}')
-    print(f'Training data:      {args.training_set_kwargs.path}')
-    print(f'Training duration:  {args.total_kimg} kimg')
-    print(f'Number of GPUs:     {args.num_gpus}')
-    print(f'Number of images:   {args.training_set_kwargs.max_size}')
-    print(f'Image resolution:   {args.training_set_kwargs.resolution}')
-    print(f'Conditional model:  {args.training_set_kwargs.use_labels}')
-    print(f'Dataset x-flips:    {args.training_set_kwargs.xflip}')
-    print()
-
-    # Dry run?
-    if dry_run:
-        print('Dry run; exiting.')
-        return
-
-    # Create output directory.
-    print('Creating output directory...')
-    os.makedirs(args.run_dir)
-    with open(os.path.join(args.run_dir, 'training_options.json'), 'wt') as f:
-        json.dump(args, f, indent=2)
-
-    # Launch processes.
-    print('Launching processes...')
-    torch.multiprocessing.set_start_method('spawn')
-    with tempfile.TemporaryDirectory() as temp_dir:
-        if args.num_gpus == 1:
-            subprocess_fn(rank=0, args=args, temp_dir=temp_dir)
-        else:
-            torch.multiprocessing.spawn(fn=subprocess_fn, args=(args, temp_dir), nprocs=args.num_gpus)
-
-#----------------------------------------------------------------------------
-
-if __name__ == "__main__":
-    main() # pylint: disable=no-value-for-parameter
-
-#----------------------------------------------------------------------------

training/__init__.py

@@ -1,9 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-# empty

training/augment.py

@@ -1,431 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import numpy as np
-import scipy.signal
-import torch
-from torch_utils import persistence
-from torch_utils import misc
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import grid_sample_gradfix
-from torch_utils.ops import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-# Coefficients of various wavelet decomposition low-pass filters.
-
-wavelets = {
-    'haar': [0.7071067811865476, 0.7071067811865476],
-    'db1':  [0.7071067811865476, 0.7071067811865476],
-    'db2':  [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
-    'db3':  [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
-    'db4':  [-0.010597401784997278, 0.032883011666982945, 0.030841381835986965, -0.18703481171888114, -0.02798376941698385, 0.6308807679295904, 0.7148465705525415, 0.23037781330885523],
-    'db5':  [0.003335725285001549, -0.012580751999015526, -0.006241490213011705, 0.07757149384006515, -0.03224486958502952, -0.24229488706619015, 0.13842814590110342, 0.7243085284385744, 0.6038292697974729, 0.160102397974125],
-    'db6':  [-0.00107730108499558, 0.004777257511010651, 0.0005538422009938016, -0.031582039318031156, 0.02752286553001629, 0.09750160558707936, -0.12976686756709563, -0.22626469396516913, 0.3152503517092432, 0.7511339080215775, 0.4946238903983854, 0.11154074335008017],
-    'db7':  [0.0003537138000010399, -0.0018016407039998328, 0.00042957797300470274, 0.012550998556013784, -0.01657454163101562, -0.03802993693503463, 0.0806126091510659, 0.07130921926705004, -0.22403618499416572, -0.14390600392910627, 0.4697822874053586, 0.7291320908465551, 0.39653931948230575, 0.07785205408506236],
-    'db8':  [-0.00011747678400228192, 0.0006754494059985568, -0.0003917403729959771, -0.00487035299301066, 0.008746094047015655, 0.013981027917015516, -0.04408825393106472, -0.01736930100202211, 0.128747426620186, 0.00047248457399797254, -0.2840155429624281, -0.015829105256023893, 0.5853546836548691, 0.6756307362980128, 0.3128715909144659, 0.05441584224308161],
-    'sym2': [-0.12940952255092145, 0.22414386804185735, 0.836516303737469, 0.48296291314469025],
-    'sym3': [0.035226291882100656, -0.08544127388224149, -0.13501102001039084, 0.4598775021193313, 0.8068915093133388, 0.3326705529509569],
-    'sym4': [-0.07576571478927333, -0.02963552764599851, 0.49761866763201545, 0.8037387518059161, 0.29785779560527736, -0.09921954357684722, -0.012603967262037833, 0.0322231006040427],
-    'sym5': [0.027333068345077982, 0.029519490925774643, -0.039134249302383094, 0.1993975339773936, 0.7234076904024206, 0.6339789634582119, 0.01660210576452232, -0.17532808990845047, -0.021101834024758855, 0.019538882735286728],
-    'sym6': [0.015404109327027373, 0.0034907120842174702, -0.11799011114819057, -0.048311742585633, 0.4910559419267466, 0.787641141030194, 0.3379294217276218, -0.07263752278646252, -0.021060292512300564, 0.04472490177066578, 0.0017677118642428036, -0.007800708325034148],
-    'sym7': [0.002681814568257878, -0.0010473848886829163, -0.01263630340325193, 0.03051551316596357, 0.0678926935013727, -0.049552834937127255, 0.017441255086855827, 0.5361019170917628, 0.767764317003164, 0.2886296317515146, -0.14004724044296152, -0.10780823770381774, 0.004010244871533663, 0.010268176708511255],
-    'sym8': [-0.0033824159510061256, -0.0005421323317911481, 0.03169508781149298, 0.007607487324917605, -0.1432942383508097, -0.061273359067658524, 0.4813596512583722, 0.7771857517005235, 0.3644418948353314, -0.05194583810770904, -0.027219029917056003, 0.049137179673607506, 0.003808752013890615, -0.01495225833704823, -0.0003029205147213668, 0.0018899503327594609],
-}
-
-#----------------------------------------------------------------------------
-# Helpers for constructing transformation matrices.
-
-def matrix(*rows, device=None):
-    assert all(len(row) == len(rows[0]) for row in rows)
-    elems = [x for row in rows for x in row]
-    ref = [x for x in elems if isinstance(x, torch.Tensor)]
-    if len(ref) == 0:
-        return misc.constant(np.asarray(rows), device=device)
-    assert device is None or device == ref[0].device
-    elems = [x if isinstance(x, torch.Tensor) else misc.constant(x, shape=ref[0].shape, device=ref[0].device) for x in elems]
-    return torch.stack(elems, dim=-1).reshape(ref[0].shape + (len(rows), -1))
-
-def translate2d(tx, ty, **kwargs):
-    return matrix(
-        [1, 0, tx],
-        [0, 1, ty],
-        [0, 0, 1],
-        **kwargs)
-
-def translate3d(tx, ty, tz, **kwargs):
-    return matrix(
-        [1, 0, 0, tx],
-        [0, 1, 0, ty],
-        [0, 0, 1, tz],
-        [0, 0, 0, 1],
-        **kwargs)
-
-def scale2d(sx, sy, **kwargs):
-    return matrix(
-        [sx, 0,  0],
-        [0,  sy, 0],
-        [0,  0,  1],
-        **kwargs)
-
-def scale3d(sx, sy, sz, **kwargs):
-    return matrix(
-        [sx, 0,  0,  0],
-        [0,  sy, 0,  0],
-        [0,  0,  sz, 0],
-        [0,  0,  0,  1],
-        **kwargs)
-
-def rotate2d(theta, **kwargs):
-    return matrix(
-        [torch.cos(theta), torch.sin(-theta), 0],
-        [torch.sin(theta), torch.cos(theta),  0],
-        [0,                0,                 1],
-        **kwargs)
-
-def rotate3d(v, theta, **kwargs):
-    vx = v[..., 0]; vy = v[..., 1]; vz = v[..., 2]
-    s = torch.sin(theta); c = torch.cos(theta); cc = 1 - c
-    return matrix(
-        [vx*vx*cc+c,    vx*vy*cc-vz*s, vx*vz*cc+vy*s, 0],
-        [vy*vx*cc+vz*s, vy*vy*cc+c,    vy*vz*cc-vx*s, 0],
-        [vz*vx*cc-vy*s, vz*vy*cc+vx*s, vz*vz*cc+c,    0],
-        [0,             0,             0,             1],
-        **kwargs)
-
-def translate2d_inv(tx, ty, **kwargs):
-    return translate2d(-tx, -ty, **kwargs)
-
-def scale2d_inv(sx, sy, **kwargs):
-    return scale2d(1 / sx, 1 / sy, **kwargs)
-
-def rotate2d_inv(theta, **kwargs):
-    return rotate2d(-theta, **kwargs)
-
-#----------------------------------------------------------------------------
-# Versatile image augmentation pipeline from the paper
-# "Training Generative Adversarial Networks with Limited Data".
-#
-# All augmentations are disabled by default; individual augmentations can
-# be enabled by setting their probability multipliers to 1.
-
-@persistence.persistent_class
-class AugmentPipe(torch.nn.Module):
-    def __init__(self,
-        xflip=0, rotate90=0, xint=0, xint_max=0.125,
-        scale=0, rotate=0, aniso=0, xfrac=0, scale_std=0.2, rotate_max=1, aniso_std=0.2, xfrac_std=0.125,
-        brightness=0, contrast=0, lumaflip=0, hue=0, saturation=0, brightness_std=0.2, contrast_std=0.5, hue_max=1, saturation_std=1,
-        imgfilter=0, imgfilter_bands=[1,1,1,1], imgfilter_std=1,
-        noise=0, cutout=0, noise_std=0.1, cutout_size=0.5,
-    ):
-        super().__init__()
-        self.register_buffer('p', torch.ones([]))       # Overall multiplier for augmentation probability.
-
-        # Pixel blitting.
-        self.xflip            = float(xflip)            # Probability multiplier for x-flip.
-        self.rotate90         = float(rotate90)         # Probability multiplier for 90 degree rotations.
-        self.xint             = float(xint)             # Probability multiplier for integer translation.
-        self.xint_max         = float(xint_max)         # Range of integer translation, relative to image dimensions.
-
-        # General geometric transformations.
-        self.scale            = float(scale)            # Probability multiplier for isotropic scaling.
-        self.rotate           = float(rotate)           # Probability multiplier for arbitrary rotation.
-        self.aniso            = float(aniso)            # Probability multiplier for anisotropic scaling.
-        self.xfrac            = float(xfrac)            # Probability multiplier for fractional translation.
-        self.scale_std        = float(scale_std)        # Log2 standard deviation of isotropic scaling.
-        self.rotate_max       = float(rotate_max)       # Range of arbitrary rotation, 1 = full circle.
-        self.aniso_std        = float(aniso_std)        # Log2 standard deviation of anisotropic scaling.
-        self.xfrac_std        = float(xfrac_std)        # Standard deviation of frational translation, relative to image dimensions.
-
-        # Color transformations.
-        self.brightness       = float(brightness)       # Probability multiplier for brightness.
-        self.contrast         = float(contrast)         # Probability multiplier for contrast.
-        self.lumaflip         = float(lumaflip)         # Probability multiplier for luma flip.
-        self.hue              = float(hue)              # Probability multiplier for hue rotation.
-        self.saturation       = float(saturation)       # Probability multiplier for saturation.
-        self.brightness_std   = float(brightness_std)   # Standard deviation of brightness.
-        self.contrast_std     = float(contrast_std)     # Log2 standard deviation of contrast.
-        self.hue_max          = float(hue_max)          # Range of hue rotation, 1 = full circle.
-        self.saturation_std   = float(saturation_std)   # Log2 standard deviation of saturation.
-
-        # Image-space filtering.
-        self.imgfilter        = float(imgfilter)        # Probability multiplier for image-space filtering.
-        self.imgfilter_bands  = list(imgfilter_bands)   # Probability multipliers for individual frequency bands.
-        self.imgfilter_std    = float(imgfilter_std)    # Log2 standard deviation of image-space filter amplification.
-
-        # Image-space corruptions.
-        self.noise            = float(noise)            # Probability multiplier for additive RGB noise.
-        self.cutout           = float(cutout)           # Probability multiplier for cutout.
-        self.noise_std        = float(noise_std)        # Standard deviation of additive RGB noise.
-        self.cutout_size      = float(cutout_size)      # Size of the cutout rectangle, relative to image dimensions.
-
-        # Setup orthogonal lowpass filter for geometric augmentations.
-        self.register_buffer('Hz_geom', upfirdn2d.setup_filter(wavelets['sym6']))
-
-        # Construct filter bank for image-space filtering.
-        Hz_lo = np.asarray(wavelets['sym2'])            # H(z)
-        Hz_hi = Hz_lo * ((-1) ** np.arange(Hz_lo.size)) # H(-z)
-        Hz_lo2 = np.convolve(Hz_lo, Hz_lo[::-1]) / 2    # H(z) * H(z^-1) / 2
-        Hz_hi2 = np.convolve(Hz_hi, Hz_hi[::-1]) / 2    # H(-z) * H(-z^-1) / 2
-        Hz_fbank = np.eye(4, 1)                         # Bandpass(H(z), b_i)
-        for i in range(1, Hz_fbank.shape[0]):
-            Hz_fbank = np.dstack([Hz_fbank, np.zeros_like(Hz_fbank)]).reshape(Hz_fbank.shape[0], -1)[:, :-1]
-            Hz_fbank = scipy.signal.convolve(Hz_fbank, [Hz_lo2])
-            Hz_fbank[i, (Hz_fbank.shape[1] - Hz_hi2.size) // 2 : (Hz_fbank.shape[1] + Hz_hi2.size) // 2] += Hz_hi2
-        self.register_buffer('Hz_fbank', torch.as_tensor(Hz_fbank, dtype=torch.float32))
-
-    def forward(self, images, debug_percentile=None):
-        assert isinstance(images, torch.Tensor) and images.ndim == 4
-        batch_size, num_channels, height, width = images.shape
-        device = images.device
-        if debug_percentile is not None:
-            debug_percentile = torch.as_tensor(debug_percentile, dtype=torch.float32, device=device)
-
-        # -------------------------------------
-        # Select parameters for pixel blitting.
-        # -------------------------------------
-
-        # Initialize inverse homogeneous 2D transform: G_inv @ pixel_out ==> pixel_in
-        I_3 = torch.eye(3, device=device)
-        G_inv = I_3
-
-        # Apply x-flip with probability (xflip * strength).
-        if self.xflip > 0:
-            i = torch.floor(torch.rand([batch_size], device=device) * 2)
-            i = torch.where(torch.rand([batch_size], device=device) < self.xflip * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 2))
-            G_inv = G_inv @ scale2d_inv(1 - 2 * i, 1)
-
-        # Apply 90 degree rotations with probability (rotate90 * strength).
-        if self.rotate90 > 0:
-            i = torch.floor(torch.rand([batch_size], device=device) * 4)
-            i = torch.where(torch.rand([batch_size], device=device) < self.rotate90 * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 4))
-            G_inv = G_inv @ rotate2d_inv(-np.pi / 2 * i)
-
-        # Apply integer translation with probability (xint * strength).
-        if self.xint > 0:
-            t = (torch.rand([batch_size, 2], device=device) * 2 - 1) * self.xint_max
-            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xint * self.p, t, torch.zeros_like(t))
-            if debug_percentile is not None:
-                t = torch.full_like(t, (debug_percentile * 2 - 1) * self.xint_max)
-            G_inv = G_inv @ translate2d_inv(torch.round(t[:,0] * width), torch.round(t[:,1] * height))
-
-        # --------------------------------------------------------
-        # Select parameters for general geometric transformations.
-        # --------------------------------------------------------
-
-        # Apply isotropic scaling with probability (scale * strength).
-        if self.scale > 0:
-            s = torch.exp2(torch.randn([batch_size], device=device) * self.scale_std)
-            s = torch.where(torch.rand([batch_size], device=device) < self.scale * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.scale_std))
-            G_inv = G_inv @ scale2d_inv(s, s)
-
-        # Apply pre-rotation with probability p_rot.
-        p_rot = 1 - torch.sqrt((1 - self.rotate * self.p).clamp(0, 1)) # P(pre OR post) = p
-        if self.rotate > 0:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
-            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.rotate_max)
-            G_inv = G_inv @ rotate2d_inv(-theta) # Before anisotropic scaling.
-
-        # Apply anisotropic scaling with probability (aniso * strength).
-        if self.aniso > 0:
-            s = torch.exp2(torch.randn([batch_size], device=device) * self.aniso_std)
-            s = torch.where(torch.rand([batch_size], device=device) < self.aniso * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.aniso_std))
-            G_inv = G_inv @ scale2d_inv(s, 1 / s)
-
-        # Apply post-rotation with probability p_rot.
-        if self.rotate > 0:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.rotate_max
-            theta = torch.where(torch.rand([batch_size], device=device) < p_rot, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.zeros_like(theta)
-            G_inv = G_inv @ rotate2d_inv(-theta) # After anisotropic scaling.
-
-        # Apply fractional translation with probability (xfrac * strength).
-        if self.xfrac > 0:
-            t = torch.randn([batch_size, 2], device=device) * self.xfrac_std
-            t = torch.where(torch.rand([batch_size, 1], device=device) < self.xfrac * self.p, t, torch.zeros_like(t))
-            if debug_percentile is not None:
-                t = torch.full_like(t, torch.erfinv(debug_percentile * 2 - 1) * self.xfrac_std)
-            G_inv = G_inv @ translate2d_inv(t[:,0] * width, t[:,1] * height)
-
-        # ----------------------------------
-        # Execute geometric transformations.
-        # ----------------------------------
-
-        # Execute if the transform is not identity.
-        if G_inv is not I_3:
-
-            # Calculate padding.
-            cx = (width - 1) / 2
-            cy = (height - 1) / 2
-            cp = matrix([-cx, -cy, 1], [cx, -cy, 1], [cx, cy, 1], [-cx, cy, 1], device=device) # [idx, xyz]
-            cp = G_inv @ cp.t() # [batch, xyz, idx]
-            Hz_pad = self.Hz_geom.shape[0] // 4
-            margin = cp[:, :2, :].permute(1, 0, 2).flatten(1) # [xy, batch * idx]
-            margin = torch.cat([-margin, margin]).max(dim=1).values # [x0, y0, x1, y1]
-            margin = margin + misc.constant([Hz_pad * 2 - cx, Hz_pad * 2 - cy] * 2, device=device)
-            margin = margin.max(misc.constant([0, 0] * 2, device=device))
-            margin = margin.min(misc.constant([width-1, height-1] * 2, device=device))
-            mx0, my0, mx1, my1 = margin.ceil().to(torch.int32)
-
-            # Pad image and adjust origin.
-            images = torch.nn.functional.pad(input=images, pad=[mx0,mx1,my0,my1], mode='reflect')
-            G_inv = translate2d((mx0 - mx1) / 2, (my0 - my1) / 2) @ G_inv
-
-            # Upsample.
-            images = upfirdn2d.upsample2d(x=images, f=self.Hz_geom, up=2)
-            G_inv = scale2d(2, 2, device=device) @ G_inv @ scale2d_inv(2, 2, device=device)
-            G_inv = translate2d(-0.5, -0.5, device=device) @ G_inv @ translate2d_inv(-0.5, -0.5, device=device)
-
-            # Execute transformation.
-            shape = [batch_size, num_channels, (height + Hz_pad * 2) * 2, (width + Hz_pad * 2) * 2]
-            G_inv = scale2d(2 / images.shape[3], 2 / images.shape[2], device=device) @ G_inv @ scale2d_inv(2 / shape[3], 2 / shape[2], device=device)
-            grid = torch.nn.functional.affine_grid(theta=G_inv[:,:2,:], size=shape, align_corners=False)
-            images = grid_sample_gradfix.grid_sample(images, grid)
-
-            # Downsample and crop.
-            images = upfirdn2d.downsample2d(x=images, f=self.Hz_geom, down=2, padding=-Hz_pad*2, flip_filter=True)
-
-        # --------------------------------------------
-        # Select parameters for color transformations.
-        # --------------------------------------------
-
-        # Initialize homogeneous 3D transformation matrix: C @ color_in ==> color_out
-        I_4 = torch.eye(4, device=device)
-        C = I_4
-
-        # Apply brightness with probability (brightness * strength).
-        if self.brightness > 0:
-            b = torch.randn([batch_size], device=device) * self.brightness_std
-            b = torch.where(torch.rand([batch_size], device=device) < self.brightness * self.p, b, torch.zeros_like(b))
-            if debug_percentile is not None:
-                b = torch.full_like(b, torch.erfinv(debug_percentile * 2 - 1) * self.brightness_std)
-            C = translate3d(b, b, b) @ C
-
-        # Apply contrast with probability (contrast * strength).
-        if self.contrast > 0:
-            c = torch.exp2(torch.randn([batch_size], device=device) * self.contrast_std)
-            c = torch.where(torch.rand([batch_size], device=device) < self.contrast * self.p, c, torch.ones_like(c))
-            if debug_percentile is not None:
-                c = torch.full_like(c, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.contrast_std))
-            C = scale3d(c, c, c) @ C
-
-        # Apply luma flip with probability (lumaflip * strength).
-        v = misc.constant(np.asarray([1, 1, 1, 0]) / np.sqrt(3), device=device) # Luma axis.
-        if self.lumaflip > 0:
-            i = torch.floor(torch.rand([batch_size, 1, 1], device=device) * 2)
-            i = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.lumaflip * self.p, i, torch.zeros_like(i))
-            if debug_percentile is not None:
-                i = torch.full_like(i, torch.floor(debug_percentile * 2))
-            C = (I_4 - 2 * v.ger(v) * i) @ C # Householder reflection.
-
-        # Apply hue rotation with probability (hue * strength).
-        if self.hue > 0 and num_channels > 1:
-            theta = (torch.rand([batch_size], device=device) * 2 - 1) * np.pi * self.hue_max
-            theta = torch.where(torch.rand([batch_size], device=device) < self.hue * self.p, theta, torch.zeros_like(theta))
-            if debug_percentile is not None:
-                theta = torch.full_like(theta, (debug_percentile * 2 - 1) * np.pi * self.hue_max)
-            C = rotate3d(v, theta) @ C # Rotate around v.
-
-        # Apply saturation with probability (saturation * strength).
-        if self.saturation > 0 and num_channels > 1:
-            s = torch.exp2(torch.randn([batch_size, 1, 1], device=device) * self.saturation_std)
-            s = torch.where(torch.rand([batch_size, 1, 1], device=device) < self.saturation * self.p, s, torch.ones_like(s))
-            if debug_percentile is not None:
-                s = torch.full_like(s, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.saturation_std))
-            C = (v.ger(v) + (I_4 - v.ger(v)) * s) @ C
-
-        # ------------------------------
-        # Execute color transformations.
-        # ------------------------------
-
-        # Execute if the transform is not identity.
-        if C is not I_4:
-            images = images.reshape([batch_size, num_channels, height * width])
-            if num_channels == 3:
-                images = C[:, :3, :3] @ images + C[:, :3, 3:]
-            elif num_channels == 1:
-                C = C[:, :3, :].mean(dim=1, keepdims=True)
-                images = images * C[:, :, :3].sum(dim=2, keepdims=True) + C[:, :, 3:]
-            else:
-                raise ValueError('Image must be RGB (3 channels) or L (1 channel)')
-            images = images.reshape([batch_size, num_channels, height, width])
-
-        # ----------------------
-        # Image-space filtering.
-        # ----------------------
-
-        if self.imgfilter > 0:
-            num_bands = self.Hz_fbank.shape[0]
-            assert len(self.imgfilter_bands) == num_bands
-            expected_power = misc.constant(np.array([10, 1, 1, 1]) / 13, device=device) # Expected power spectrum (1/f).
-
-            # Apply amplification for each band with probability (imgfilter * strength * band_strength).
-            g = torch.ones([batch_size, num_bands], device=device) # Global gain vector (identity).
-            for i, band_strength in enumerate(self.imgfilter_bands):
-                t_i = torch.exp2(torch.randn([batch_size], device=device) * self.imgfilter_std)
-                t_i = torch.where(torch.rand([batch_size], device=device) < self.imgfilter * self.p * band_strength, t_i, torch.ones_like(t_i))
-                if debug_percentile is not None:
-                    t_i = torch.full_like(t_i, torch.exp2(torch.erfinv(debug_percentile * 2 - 1) * self.imgfilter_std)) if band_strength > 0 else torch.ones_like(t_i)
-                t = torch.ones([batch_size, num_bands], device=device)                  # Temporary gain vector.
-                t[:, i] = t_i                                                           # Replace i'th element.
-                t = t / (expected_power * t.square()).sum(dim=-1, keepdims=True).sqrt() # Normalize power.
-                g = g * t                                                               # Accumulate into global gain.
-
-            # Construct combined amplification filter.
-            Hz_prime = g @ self.Hz_fbank                                    # [batch, tap]
-            Hz_prime = Hz_prime.unsqueeze(1).repeat([1, num_channels, 1])   # [batch, channels, tap]
-            Hz_prime = Hz_prime.reshape([batch_size * num_channels, 1, -1]) # [batch * channels, 1, tap]
-
-            # Apply filter.
-            p = self.Hz_fbank.shape[1] // 2
-            images = images.reshape([1, batch_size * num_channels, height, width])
-            images = torch.nn.functional.pad(input=images, pad=[p,p,p,p], mode='reflect')
-            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(2), groups=batch_size*num_channels)
-            images = conv2d_gradfix.conv2d(input=images, weight=Hz_prime.unsqueeze(3), groups=batch_size*num_channels)
-            images = images.reshape([batch_size, num_channels, height, width])
-
-        # ------------------------
-        # Image-space corruptions.
-        # ------------------------
-
-        # Apply additive RGB noise with probability (noise * strength).
-        if self.noise > 0:
-            sigma = torch.randn([batch_size, 1, 1, 1], device=device).abs() * self.noise_std
-            sigma = torch.where(torch.rand([batch_size, 1, 1, 1], device=device) < self.noise * self.p, sigma, torch.zeros_like(sigma))
-            if debug_percentile is not None:
-                sigma = torch.full_like(sigma, torch.erfinv(debug_percentile) * self.noise_std)
-            images = images + torch.randn([batch_size, num_channels, height, width], device=device) * sigma
-
-        # Apply cutout with probability (cutout * strength).
-        if self.cutout > 0:
-            size = torch.full([batch_size, 2, 1, 1, 1], self.cutout_size, device=device)
-            size = torch.where(torch.rand([batch_size, 1, 1, 1, 1], device=device) < self.cutout * self.p, size, torch.zeros_like(size))
-            center = torch.rand([batch_size, 2, 1, 1, 1], device=device)
-            if debug_percentile is not None:
-                size = torch.full_like(size, self.cutout_size)
-                center = torch.full_like(center, debug_percentile)
-            coord_x = torch.arange(width, device=device).reshape([1, 1, 1, -1])
-            coord_y = torch.arange(height, device=device).reshape([1, 1, -1, 1])
-            mask_x = (((coord_x + 0.5) / width - center[:, 0]).abs() >= size[:, 0] / 2)
-            mask_y = (((coord_y + 0.5) / height - center[:, 1]).abs() >= size[:, 1] / 2)
-            mask = torch.logical_or(mask_x, mask_y).to(torch.float32)
-            images = images * mask
-
-        return images
-
-#----------------------------------------------------------------------------

training/dataset.py

@@ -1,236 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import numpy as np
-import zipfile
-import PIL.Image
-import json
-import torch
-import dnnlib
-
-try:
-    import pyspng
-except ImportError:
-    pyspng = None
-
-#----------------------------------------------------------------------------
-
-class Dataset(torch.utils.data.Dataset):
-    def __init__(self,
-        name,                   # Name of the dataset.
-        raw_shape,              # Shape of the raw image data (NCHW).
-        max_size    = None,     # Artificially limit the size of the dataset. None = no limit. Applied before xflip.
-        use_labels  = False,    # Enable conditioning labels? False = label dimension is zero.
-        xflip       = False,    # Artificially double the size of the dataset via x-flips. Applied after max_size.
-        random_seed = 0,        # Random seed to use when applying max_size.
-    ):
-        self._name = name
-        self._raw_shape = list(raw_shape)
-        self._use_labels = use_labels
-        self._raw_labels = None
-        self._label_shape = None
-
-        # Apply max_size.
-        self._raw_idx = np.arange(self._raw_shape[0], dtype=np.int64)
-        if (max_size is not None) and (self._raw_idx.size > max_size):
-            np.random.RandomState(random_seed).shuffle(self._raw_idx)
-            self._raw_idx = np.sort(self._raw_idx[:max_size])
-
-        # Apply xflip.
-        self._xflip = np.zeros(self._raw_idx.size, dtype=np.uint8)
-        if xflip:
-            self._raw_idx = np.tile(self._raw_idx, 2)
-            self._xflip = np.concatenate([self._xflip, np.ones_like(self._xflip)])
-
-    def _get_raw_labels(self):
-        if self._raw_labels is None:
-            self._raw_labels = self._load_raw_labels() if self._use_labels else None
-            if self._raw_labels is None:
-                self._raw_labels = np.zeros([self._raw_shape[0], 0], dtype=np.float32)
-            assert isinstance(self._raw_labels, np.ndarray)
-            assert self._raw_labels.shape[0] == self._raw_shape[0]
-            assert self._raw_labels.dtype in [np.float32, np.int64]
-            if self._raw_labels.dtype == np.int64:
-                assert self._raw_labels.ndim == 1
-                assert np.all(self._raw_labels >= 0)
-        return self._raw_labels
-
-    def close(self): # to be overridden by subclass
-        pass
-
-    def _load_raw_image(self, raw_idx): # to be overridden by subclass
-        raise NotImplementedError
-
-    def _load_raw_labels(self): # to be overridden by subclass
-        raise NotImplementedError
-
-    def __getstate__(self):
-        return dict(self.__dict__, _raw_labels=None)
-
-    def __del__(self):
-        try:
-            self.close()
-        except:
-            pass
-
-    def __len__(self):
-        return self._raw_idx.size
-
-    def __getitem__(self, idx):
-        image = self._load_raw_image(self._raw_idx[idx])
-        assert isinstance(image, np.ndarray)
-        assert list(image.shape) == self.image_shape
-        assert image.dtype == np.uint8
-        if self._xflip[idx]:
-            assert image.ndim == 3 # CHW
-            image = image[:, :, ::-1]
-        return image.copy(), self.get_label(idx)
-
-    def get_label(self, idx):
-        label = self._get_raw_labels()[self._raw_idx[idx]]
-        if label.dtype == np.int64:
-            onehot = np.zeros(self.label_shape, dtype=np.float32)
-            onehot[label] = 1
-            label = onehot
-        return label.copy()
-
-    def get_details(self, idx):
-        d = dnnlib.EasyDict()
-        d.raw_idx = int(self._raw_idx[idx])
-        d.xflip = (int(self._xflip[idx]) != 0)
-        d.raw_label = self._get_raw_labels()[d.raw_idx].copy()
-        return d
-
-    @property
-    def name(self):
-        return self._name
-
-    @property
-    def image_shape(self):
-        return list(self._raw_shape[1:])
-
-    @property
-    def num_channels(self):
-        assert len(self.image_shape) == 3 # CHW
-        return self.image_shape[0]
-
-    @property
-    def resolution(self):
-        assert len(self.image_shape) == 3 # CHW
-        assert self.image_shape[1] == self.image_shape[2]
-        return self.image_shape[1]
-
-    @property
-    def label_shape(self):
-        if self._label_shape is None:
-            raw_labels = self._get_raw_labels()
-            if raw_labels.dtype == np.int64:
-                self._label_shape = [int(np.max(raw_labels)) + 1]
-            else:
-                self._label_shape = raw_labels.shape[1:]
-        return list(self._label_shape)
-
-    @property
-    def label_dim(self):
-        assert len(self.label_shape) == 1
-        return self.label_shape[0]
-
-    @property
-    def has_labels(self):
-        return any(x != 0 for x in self.label_shape)
-
-    @property
-    def has_onehot_labels(self):
-        return self._get_raw_labels().dtype == np.int64
-
-#----------------------------------------------------------------------------
-
-class ImageFolderDataset(Dataset):
-    def __init__(self,
-        path,                   # Path to directory or zip.
-        resolution      = None, # Ensure specific resolution, None = highest available.
-        **super_kwargs,         # Additional arguments for the Dataset base class.
-    ):
-        self._path = path
-        self._zipfile = None
-
-        if os.path.isdir(self._path):
-            self._type = 'dir'
-            self._all_fnames = {os.path.relpath(os.path.join(root, fname), start=self._path) for root, _dirs, files in os.walk(self._path) for fname in files}
-        elif self._file_ext(self._path) == '.zip':
-            self._type = 'zip'
-            self._all_fnames = set(self._get_zipfile().namelist())
-        else:
-            raise IOError('Path must point to a directory or zip')
-
-        PIL.Image.init()
-        self._image_fnames = sorted(fname for fname in self._all_fnames if self._file_ext(fname) in PIL.Image.EXTENSION)
-        if len(self._image_fnames) == 0:
-            raise IOError('No image files found in the specified path')
-
-        name = os.path.splitext(os.path.basename(self._path))[0]
-        raw_shape = [len(self._image_fnames)] + list(self._load_raw_image(0).shape)
-        if resolution is not None and (raw_shape[2] != resolution or raw_shape[3] != resolution):
-            raise IOError('Image files do not match the specified resolution')
-        super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
-
-    @staticmethod
-    def _file_ext(fname):
-        return os.path.splitext(fname)[1].lower()
-
-    def _get_zipfile(self):
-        assert self._type == 'zip'
-        if self._zipfile is None:
-            self._zipfile = zipfile.ZipFile(self._path)
-        return self._zipfile
-
-    def _open_file(self, fname):
-        if self._type == 'dir':
-            return open(os.path.join(self._path, fname), 'rb')
-        if self._type == 'zip':
-            return self._get_zipfile().open(fname, 'r')
-        return None
-
-    def close(self):
-        try:
-            if self._zipfile is not None:
-                self._zipfile.close()
-        finally:
-            self._zipfile = None
-
-    def __getstate__(self):
-        return dict(super().__getstate__(), _zipfile=None)
-
-    def _load_raw_image(self, raw_idx):
-        fname = self._image_fnames[raw_idx]
-        with self._open_file(fname) as f:
-            if pyspng is not None and self._file_ext(fname) == '.png':
-                image = pyspng.load(f.read())
-            else:
-                image = np.array(PIL.Image.open(f))
-        if image.ndim == 2:
-            image = image[:, :, np.newaxis] # HW => HWC
-        image = image.transpose(2, 0, 1) # HWC => CHW
-        return image
-
-    def _load_raw_labels(self):
-        fname = 'dataset.json'
-        if fname not in self._all_fnames:
-            return None
-        with self._open_file(fname) as f:
-            labels = json.load(f)['labels']
-        if labels is None:
-            return None
-        labels = dict(labels)
-        labels = [labels[fname.replace('\\', '/')] for fname in self._image_fnames]
-        labels = np.array(labels)
-        labels = labels.astype({1: np.int64, 2: np.float32}[labels.ndim])
-        return labels
-
-#----------------------------------------------------------------------------

training/loss.py

@@ -1,133 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import numpy as np
-import torch
-from torch_utils import training_stats
-from torch_utils import misc
-from torch_utils.ops import conv2d_gradfix
-
-#----------------------------------------------------------------------------
-
-class Loss:
-    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain): # to be overridden by subclass
-        raise NotImplementedError()
-
-#----------------------------------------------------------------------------
-
-class StyleGAN2Loss(Loss):
-    def __init__(self, device, G_mapping, G_synthesis, D, augment_pipe=None, style_mixing_prob=0.9, r1_gamma=10, pl_batch_shrink=2, pl_decay=0.01, pl_weight=2):
-        super().__init__()
-        self.device = device
-        self.G_mapping = G_mapping
-        self.G_synthesis = G_synthesis
-        self.D = D
-        self.augment_pipe = augment_pipe
-        self.style_mixing_prob = style_mixing_prob
-        self.r1_gamma = r1_gamma
-        self.pl_batch_shrink = pl_batch_shrink
-        self.pl_decay = pl_decay
-        self.pl_weight = pl_weight
-        self.pl_mean = torch.zeros([], device=device)
-
-    def run_G(self, z, c, sync):
-        with misc.ddp_sync(self.G_mapping, sync):
-            ws = self.G_mapping(z, c)
-            if self.style_mixing_prob > 0:
-                with torch.autograd.profiler.record_function('style_mixing'):
-                    cutoff = torch.empty([], dtype=torch.int64, device=ws.device).random_(1, ws.shape[1])
-                    cutoff = torch.where(torch.rand([], device=ws.device) < self.style_mixing_prob, cutoff, torch.full_like(cutoff, ws.shape[1]))
-                    ws[:, cutoff:] = self.G_mapping(torch.randn_like(z), c, skip_w_avg_update=True)[:, cutoff:]
-        with misc.ddp_sync(self.G_synthesis, sync):
-            img = self.G_synthesis(ws)
-        return img, ws
-
-    def run_D(self, img, c, sync):
-        if self.augment_pipe is not None:
-            img = self.augment_pipe(img)
-        with misc.ddp_sync(self.D, sync):
-            logits = self.D(img, c)
-        return logits
-
-    def accumulate_gradients(self, phase, real_img, real_c, gen_z, gen_c, sync, gain):
-        assert phase in ['Gmain', 'Greg', 'Gboth', 'Dmain', 'Dreg', 'Dboth']
-        do_Gmain = (phase in ['Gmain', 'Gboth'])
-        do_Dmain = (phase in ['Dmain', 'Dboth'])
-        do_Gpl   = (phase in ['Greg', 'Gboth']) and (self.pl_weight != 0)
-        do_Dr1   = (phase in ['Dreg', 'Dboth']) and (self.r1_gamma != 0)
-
-        # Gmain: Maximize logits for generated images.
-        if do_Gmain:
-            with torch.autograd.profiler.record_function('Gmain_forward'):
-                gen_img, _gen_ws = self.run_G(gen_z, gen_c, sync=(sync and not do_Gpl)) # May get synced by Gpl.
-                gen_logits = self.run_D(gen_img, gen_c, sync=False)
-                training_stats.report('Loss/scores/fake', gen_logits)
-                training_stats.report('Loss/signs/fake', gen_logits.sign())
-                loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits))
-                training_stats.report('Loss/G/loss', loss_Gmain)
-            with torch.autograd.profiler.record_function('Gmain_backward'):
-                loss_Gmain.mean().mul(gain).backward()
-
-        # Gpl: Apply path length regularization.
-        if do_Gpl:
-            with torch.autograd.profiler.record_function('Gpl_forward'):
-                batch_size = gen_z.shape[0] // self.pl_batch_shrink
-                gen_img, gen_ws = self.run_G(gen_z[:batch_size], gen_c[:batch_size], sync=sync)
-                pl_noise = torch.randn_like(gen_img) / np.sqrt(gen_img.shape[2] * gen_img.shape[3])
-                with torch.autograd.profiler.record_function('pl_grads'), conv2d_gradfix.no_weight_gradients():
-                    pl_grads = torch.autograd.grad(outputs=[(gen_img * pl_noise).sum()], inputs=[gen_ws], create_graph=True, only_inputs=True)[0]
-                pl_lengths = pl_grads.square().sum(2).mean(1).sqrt()
-                pl_mean = self.pl_mean.lerp(pl_lengths.mean(), self.pl_decay)
-                self.pl_mean.copy_(pl_mean.detach())
-                pl_penalty = (pl_lengths - pl_mean).square()
-                training_stats.report('Loss/pl_penalty', pl_penalty)
-                loss_Gpl = pl_penalty * self.pl_weight
-                training_stats.report('Loss/G/reg', loss_Gpl)
-            with torch.autograd.profiler.record_function('Gpl_backward'):
-                (gen_img[:, 0, 0, 0] * 0 + loss_Gpl).mean().mul(gain).backward()
-
-        # Dmain: Minimize logits for generated images.
-        loss_Dgen = 0
-        if do_Dmain:
-            with torch.autograd.profiler.record_function('Dgen_forward'):
-                gen_img, _gen_ws = self.run_G(gen_z, gen_c, sync=False)
-                gen_logits = self.run_D(gen_img, gen_c, sync=False) # Gets synced by loss_Dreal.
-                training_stats.report('Loss/scores/fake', gen_logits)
-                training_stats.report('Loss/signs/fake', gen_logits.sign())
-                loss_Dgen = torch.nn.functional.softplus(gen_logits) # -log(1 - sigmoid(gen_logits))
-            with torch.autograd.profiler.record_function('Dgen_backward'):
-                loss_Dgen.mean().mul(gain).backward()
-
-        # Dmain: Maximize logits for real images.
-        # Dr1: Apply R1 regularization.
-        if do_Dmain or do_Dr1:
-            name = 'Dreal_Dr1' if do_Dmain and do_Dr1 else 'Dreal' if do_Dmain else 'Dr1'
-            with torch.autograd.profiler.record_function(name + '_forward'):
-                real_img_tmp = real_img.detach().requires_grad_(do_Dr1)
-                real_logits = self.run_D(real_img_tmp, real_c, sync=sync)
-                training_stats.report('Loss/scores/real', real_logits)
-                training_stats.report('Loss/signs/real', real_logits.sign())
-
-                loss_Dreal = 0
-                if do_Dmain:
-                    loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits))
-                    training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal)
-
-                loss_Dr1 = 0
-                if do_Dr1:
-                    with torch.autograd.profiler.record_function('r1_grads'), conv2d_gradfix.no_weight_gradients():
-                        r1_grads = torch.autograd.grad(outputs=[real_logits.sum()], inputs=[real_img_tmp], create_graph=True, only_inputs=True)[0]
-                    r1_penalty = r1_grads.square().sum([1,2,3])
-                    loss_Dr1 = r1_penalty * (self.r1_gamma / 2)
-                    training_stats.report('Loss/r1_penalty', r1_penalty)
-                    training_stats.report('Loss/D/reg', loss_Dr1)
-
-            with torch.autograd.profiler.record_function(name + '_backward'):
-                (real_logits * 0 + loss_Dreal + loss_Dr1).mean().mul(gain).backward()
-
-#----------------------------------------------------------------------------

training/networks.py

@@ -1,729 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import numpy as np
-import torch
-from torch_utils import misc
-from torch_utils import persistence
-from torch_utils.ops import conv2d_resample
-from torch_utils.ops import upfirdn2d
-from torch_utils.ops import bias_act
-from torch_utils.ops import fma
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def normalize_2nd_moment(x, dim=1, eps=1e-8):
-    return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
-
-#----------------------------------------------------------------------------
-
-@misc.profiled_function
-def modulated_conv2d(
-    x,                          # Input tensor of shape [batch_size, in_channels, in_height, in_width].
-    weight,                     # Weight tensor of shape [out_channels, in_channels, kernel_height, kernel_width].
-    styles,                     # Modulation coefficients of shape [batch_size, in_channels].
-    noise           = None,     # Optional noise tensor to add to the output activations.
-    up              = 1,        # Integer upsampling factor.
-    down            = 1,        # Integer downsampling factor.
-    padding         = 0,        # Padding with respect to the upsampled image.
-    resample_filter = None,     # Low-pass filter to apply when resampling activations. Must be prepared beforehand by calling upfirdn2d.setup_filter().
-    demodulate      = True,     # Apply weight demodulation?
-    flip_weight     = True,     # False = convolution, True = correlation (matches torch.nn.functional.conv2d).
-    fused_modconv   = True,     # Perform modulation, convolution, and demodulation as a single fused operation?
-):
-    batch_size = x.shape[0]
-    out_channels, in_channels, kh, kw = weight.shape
-    misc.assert_shape(weight, [out_channels, in_channels, kh, kw]) # [OIkk]
-    misc.assert_shape(x, [batch_size, in_channels, None, None]) # [NIHW]
-    misc.assert_shape(styles, [batch_size, in_channels]) # [NI]
-
-    # Pre-normalize inputs to avoid FP16 overflow.
-    if x.dtype == torch.float16 and demodulate:
-        weight = weight * (1 / np.sqrt(in_channels * kh * kw) / weight.norm(float('inf'), dim=[1,2,3], keepdim=True)) # max_Ikk
-        styles = styles / styles.norm(float('inf'), dim=1, keepdim=True) # max_I
-
-    # Calculate per-sample weights and demodulation coefficients.
-    w = None
-    dcoefs = None
-    if demodulate or fused_modconv:
-        w = weight.unsqueeze(0) # [NOIkk]
-        w = w * styles.reshape(batch_size, 1, -1, 1, 1) # [NOIkk]
-    if demodulate:
-        dcoefs = (w.square().sum(dim=[2,3,4]) + 1e-8).rsqrt() # [NO]
-    if demodulate and fused_modconv:
-        w = w * dcoefs.reshape(batch_size, -1, 1, 1, 1) # [NOIkk]
-
-    # Execute by scaling the activations before and after the convolution.
-    if not fused_modconv:
-        x = x * styles.to(x.dtype).reshape(batch_size, -1, 1, 1)
-        x = conv2d_resample.conv2d_resample(x=x, w=weight.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, flip_weight=flip_weight)
-        if demodulate and noise is not None:
-            x = fma.fma(x, dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1), noise.to(x.dtype))
-        elif demodulate:
-            x = x * dcoefs.to(x.dtype).reshape(batch_size, -1, 1, 1)
-        elif noise is not None:
-            x = x.add_(noise.to(x.dtype))
-        return x
-
-    # Execute as one fused op using grouped convolution.
-    with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-        batch_size = int(batch_size)
-    misc.assert_shape(x, [batch_size, in_channels, None, None])
-    x = x.reshape(1, -1, *x.shape[2:])
-    w = w.reshape(-1, in_channels, kh, kw)
-    x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=resample_filter, up=up, down=down, padding=padding, groups=batch_size, flip_weight=flip_weight)
-    x = x.reshape(batch_size, -1, *x.shape[2:])
-    if noise is not None:
-        x = x.add_(noise)
-    return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class FullyConnectedLayer(torch.nn.Module):
-    def __init__(self,
-        in_features,                # Number of input features.
-        out_features,               # Number of output features.
-        bias            = True,     # Apply additive bias before the activation function?
-        activation      = 'linear', # Activation function: 'relu', 'lrelu', etc.
-        lr_multiplier   = 1,        # Learning rate multiplier.
-        bias_init       = 0,        # Initial value for the additive bias.
-    ):
-        super().__init__()
-        self.activation = activation
-        self.weight = torch.nn.Parameter(torch.randn([out_features, in_features]) / lr_multiplier)
-        self.bias = torch.nn.Parameter(torch.full([out_features], np.float32(bias_init))) if bias else None
-        self.weight_gain = lr_multiplier / np.sqrt(in_features)
-        self.bias_gain = lr_multiplier
-
-    def forward(self, x):
-        w = self.weight.to(x.dtype) * self.weight_gain
-        b = self.bias
-        if b is not None:
-            b = b.to(x.dtype)
-            if self.bias_gain != 1:
-                b = b * self.bias_gain
-
-        if self.activation == 'linear' and b is not None:
-            x = torch.addmm(b.unsqueeze(0), x, w.t())
-        else:
-            x = x.matmul(w.t())
-            x = bias_act.bias_act(x, b, act=self.activation)
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class Conv2dLayer(torch.nn.Module):
-    def __init__(self,
-        in_channels,                    # Number of input channels.
-        out_channels,                   # Number of output channels.
-        kernel_size,                    # Width and height of the convolution kernel.
-        bias            = True,         # Apply additive bias before the activation function?
-        activation      = 'linear',     # Activation function: 'relu', 'lrelu', etc.
-        up              = 1,            # Integer upsampling factor.
-        down            = 1,            # Integer downsampling factor.
-        resample_filter = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
-        conv_clamp      = None,         # Clamp the output to +-X, None = disable clamping.
-        channels_last   = False,        # Expect the input to have memory_format=channels_last?
-        trainable       = True,         # Update the weights of this layer during training?
-    ):
-        super().__init__()
-        self.activation = activation
-        self.up = up
-        self.down = down
-        self.conv_clamp = conv_clamp
-        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.padding = kernel_size // 2
-        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-        self.act_gain = bias_act.activation_funcs[activation].def_gain
-
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format)
-        bias = torch.zeros([out_channels]) if bias else None
-        if trainable:
-            self.weight = torch.nn.Parameter(weight)
-            self.bias = torch.nn.Parameter(bias) if bias is not None else None
-        else:
-            self.register_buffer('weight', weight)
-            if bias is not None:
-                self.register_buffer('bias', bias)
-            else:
-                self.bias = None
-
-    def forward(self, x, gain=1):
-        w = self.weight * self.weight_gain
-        b = self.bias.to(x.dtype) if self.bias is not None else None
-        flip_weight = (self.up == 1) # slightly faster
-        x = conv2d_resample.conv2d_resample(x=x, w=w.to(x.dtype), f=self.resample_filter, up=self.up, down=self.down, padding=self.padding, flip_weight=flip_weight)
-
-        act_gain = self.act_gain * gain
-        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
-        x = bias_act.bias_act(x, b, act=self.activation, gain=act_gain, clamp=act_clamp)
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class MappingNetwork(torch.nn.Module):
-    def __init__(self,
-        z_dim,                      # Input latent (Z) dimensionality, 0 = no latent.
-        c_dim,                      # Conditioning label (C) dimensionality, 0 = no label.
-        w_dim,                      # Intermediate latent (W) dimensionality.
-        num_ws,                     # Number of intermediate latents to output, None = do not broadcast.
-        num_layers      = 8,        # Number of mapping layers.
-        embed_features  = None,     # Label embedding dimensionality, None = same as w_dim.
-        layer_features  = None,     # Number of intermediate features in the mapping layers, None = same as w_dim.
-        activation      = 'lrelu',  # Activation function: 'relu', 'lrelu', etc.
-        lr_multiplier   = 0.01,     # Learning rate multiplier for the mapping layers.
-        w_avg_beta      = 0.995,    # Decay for tracking the moving average of W during training, None = do not track.
-    ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.num_ws = num_ws
-        self.num_layers = num_layers
-        self.w_avg_beta = w_avg_beta
-
-        if embed_features is None:
-            embed_features = w_dim
-        if c_dim == 0:
-            embed_features = 0
-        if layer_features is None:
-            layer_features = w_dim
-        features_list = [z_dim + embed_features] + [layer_features] * (num_layers - 1) + [w_dim]
-
-        if c_dim > 0:
-            self.embed = FullyConnectedLayer(c_dim, embed_features)
-        for idx in range(num_layers):
-            in_features = features_list[idx]
-            out_features = features_list[idx + 1]
-            layer = FullyConnectedLayer(in_features, out_features, activation=activation, lr_multiplier=lr_multiplier)
-            setattr(self, f'fc{idx}', layer)
-
-        if num_ws is not None and w_avg_beta is not None:
-            self.register_buffer('w_avg', torch.zeros([w_dim]))
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False):
-        # Embed, normalize, and concat inputs.
-        x = None
-        with torch.autograd.profiler.record_function('input'):
-            if self.z_dim > 0:
-                misc.assert_shape(z, [None, self.z_dim])
-                x = normalize_2nd_moment(z.to(torch.float32))
-            if self.c_dim > 0:
-                misc.assert_shape(c, [None, self.c_dim])
-                y = normalize_2nd_moment(self.embed(c.to(torch.float32)))
-                x = torch.cat([x, y], dim=1) if x is not None else y
-
-        # Main layers.
-        for idx in range(self.num_layers):
-            layer = getattr(self, f'fc{idx}')
-            x = layer(x)
-
-        # Update moving average of W.
-        if self.w_avg_beta is not None and self.training and not skip_w_avg_update:
-            with torch.autograd.profiler.record_function('update_w_avg'):
-                self.w_avg.copy_(x.detach().mean(dim=0).lerp(self.w_avg, self.w_avg_beta))
-
-        # Broadcast.
-        if self.num_ws is not None:
-            with torch.autograd.profiler.record_function('broadcast'):
-                x = x.unsqueeze(1).repeat([1, self.num_ws, 1])
-
-        # Apply truncation.
-        if truncation_psi != 1:
-            with torch.autograd.profiler.record_function('truncate'):
-                assert self.w_avg_beta is not None
-                if self.num_ws is None or truncation_cutoff is None:
-                    x = self.w_avg.lerp(x, truncation_psi)
-                else:
-                    x[:, :truncation_cutoff] = self.w_avg.lerp(x[:, :truncation_cutoff], truncation_psi)
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisLayer(torch.nn.Module):
-    def __init__(self,
-        in_channels,                    # Number of input channels.
-        out_channels,                   # Number of output channels.
-        w_dim,                          # Intermediate latent (W) dimensionality.
-        resolution,                     # Resolution of this layer.
-        kernel_size     = 3,            # Convolution kernel size.
-        up              = 1,            # Integer upsampling factor.
-        use_noise       = True,         # Enable noise input?
-        activation      = 'lrelu',      # Activation function: 'relu', 'lrelu', etc.
-        resample_filter = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
-        conv_clamp      = None,         # Clamp the output of convolution layers to +-X, None = disable clamping.
-        channels_last   = False,        # Use channels_last format for the weights?
-    ):
-        super().__init__()
-        self.resolution = resolution
-        self.up = up
-        self.use_noise = use_noise
-        self.activation = activation
-        self.conv_clamp = conv_clamp
-        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.padding = kernel_size // 2
-        self.act_gain = bias_act.activation_funcs[activation].def_gain
-
-        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
-        if use_noise:
-            self.register_buffer('noise_const', torch.randn([resolution, resolution]))
-            self.noise_strength = torch.nn.Parameter(torch.zeros([]))
-        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-
-    def forward(self, x, w, noise_mode='random', fused_modconv=True, gain=1):
-        assert noise_mode in ['random', 'const', 'none']
-        in_resolution = self.resolution // self.up
-        misc.assert_shape(x, [None, self.weight.shape[1], in_resolution, in_resolution])
-        styles = self.affine(w)
-
-        noise = None
-        if self.use_noise and noise_mode == 'random':
-            noise = torch.randn([x.shape[0], 1, self.resolution, self.resolution], device=x.device) * self.noise_strength
-        if self.use_noise and noise_mode == 'const':
-            noise = self.noise_const * self.noise_strength
-
-        flip_weight = (self.up == 1) # slightly faster
-        x = modulated_conv2d(x=x, weight=self.weight, styles=styles, noise=noise, up=self.up,
-            padding=self.padding, resample_filter=self.resample_filter, flip_weight=flip_weight, fused_modconv=fused_modconv)
-
-        act_gain = self.act_gain * gain
-        act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
-        x = bias_act.bias_act(x, self.bias.to(x.dtype), act=self.activation, gain=act_gain, clamp=act_clamp)
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class ToRGBLayer(torch.nn.Module):
-    def __init__(self, in_channels, out_channels, w_dim, kernel_size=1, conv_clamp=None, channels_last=False):
-        super().__init__()
-        self.conv_clamp = conv_clamp
-        self.affine = FullyConnectedLayer(w_dim, in_channels, bias_init=1)
-        memory_format = torch.channels_last if channels_last else torch.contiguous_format
-        self.weight = torch.nn.Parameter(torch.randn([out_channels, in_channels, kernel_size, kernel_size]).to(memory_format=memory_format))
-        self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
-        self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
-
-    def forward(self, x, w, fused_modconv=True):
-        styles = self.affine(w) * self.weight_gain
-        x = modulated_conv2d(x=x, weight=self.weight, styles=styles, demodulate=False, fused_modconv=fused_modconv)
-        x = bias_act.bias_act(x, self.bias.to(x.dtype), clamp=self.conv_clamp)
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisBlock(torch.nn.Module):
-    def __init__(self,
-        in_channels,                        # Number of input channels, 0 = first block.
-        out_channels,                       # Number of output channels.
-        w_dim,                              # Intermediate latent (W) dimensionality.
-        resolution,                         # Resolution of this block.
-        img_channels,                       # Number of output color channels.
-        is_last,                            # Is this the last block?
-        architecture        = 'skip',       # Architecture: 'orig', 'skip', 'resnet'.
-        resample_filter     = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
-        conv_clamp          = None,         # Clamp the output of convolution layers to +-X, None = disable clamping.
-        use_fp16            = False,        # Use FP16 for this block?
-        fp16_channels_last  = False,        # Use channels-last memory format with FP16?
-        **layer_kwargs,                     # Arguments for SynthesisLayer.
-    ):
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.w_dim = w_dim
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.is_last = is_last
-        self.architecture = architecture
-        self.use_fp16 = use_fp16
-        self.channels_last = (use_fp16 and fp16_channels_last)
-        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
-        self.num_conv = 0
-        self.num_torgb = 0
-
-        if in_channels == 0:
-            self.const = torch.nn.Parameter(torch.randn([out_channels, resolution, resolution]))
-
-        if in_channels != 0:
-            self.conv0 = SynthesisLayer(in_channels, out_channels, w_dim=w_dim, resolution=resolution, up=2,
-                resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
-            self.num_conv += 1
-
-        self.conv1 = SynthesisLayer(out_channels, out_channels, w_dim=w_dim, resolution=resolution,
-            conv_clamp=conv_clamp, channels_last=self.channels_last, **layer_kwargs)
-        self.num_conv += 1
-
-        if is_last or architecture == 'skip':
-            self.torgb = ToRGBLayer(out_channels, img_channels, w_dim=w_dim,
-                conv_clamp=conv_clamp, channels_last=self.channels_last)
-            self.num_torgb += 1
-
-        if in_channels != 0 and architecture == 'resnet':
-            self.skip = Conv2dLayer(in_channels, out_channels, kernel_size=1, bias=False, up=2,
-                resample_filter=resample_filter, channels_last=self.channels_last)
-
-    def forward(self, x, img, ws, force_fp32=False, fused_modconv=None, **layer_kwargs):
-        misc.assert_shape(ws, [None, self.num_conv + self.num_torgb, self.w_dim])
-        w_iter = iter(ws.unbind(dim=1))
-        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
-        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-        if fused_modconv is None:
-            with misc.suppress_tracer_warnings(): # this value will be treated as a constant
-                fused_modconv = (not self.training) and (dtype == torch.float32 or int(x.shape[0]) == 1)
-
-        # Input.
-        if self.in_channels == 0:
-            x = self.const.to(dtype=dtype, memory_format=memory_format)
-            x = x.unsqueeze(0).repeat([ws.shape[0], 1, 1, 1])
-        else:
-            misc.assert_shape(x, [None, self.in_channels, self.resolution // 2, self.resolution // 2])
-            x = x.to(dtype=dtype, memory_format=memory_format)
-
-        # Main layers.
-        if self.in_channels == 0:
-            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
-        elif self.architecture == 'resnet':
-            y = self.skip(x, gain=np.sqrt(0.5))
-            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
-            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, gain=np.sqrt(0.5), **layer_kwargs)
-            x = y.add_(x)
-        else:
-            x = self.conv0(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
-            x = self.conv1(x, next(w_iter), fused_modconv=fused_modconv, **layer_kwargs)
-
-        # ToRGB.
-        if img is not None:
-            misc.assert_shape(img, [None, self.img_channels, self.resolution // 2, self.resolution // 2])
-            img = upfirdn2d.upsample2d(img, self.resample_filter)
-        if self.is_last or self.architecture == 'skip':
-            y = self.torgb(x, next(w_iter), fused_modconv=fused_modconv)
-            y = y.to(dtype=torch.float32, memory_format=torch.contiguous_format)
-            img = img.add_(y) if img is not None else y
-
-        assert x.dtype == dtype
-        assert img is None or img.dtype == torch.float32
-        return x, img
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class SynthesisNetwork(torch.nn.Module):
-    def __init__(self,
-        w_dim,                      # Intermediate latent (W) dimensionality.
-        img_resolution,             # Output image resolution.
-        img_channels,               # Number of color channels.
-        channel_base    = 32768,    # Overall multiplier for the number of channels.
-        channel_max     = 512,      # Maximum number of channels in any layer.
-        num_fp16_res    = 0,        # Use FP16 for the N highest resolutions.
-        **block_kwargs,             # Arguments for SynthesisBlock.
-    ):
-        assert img_resolution >= 4 and img_resolution & (img_resolution - 1) == 0
-        super().__init__()
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_resolution_log2 = int(np.log2(img_resolution))
-        self.img_channels = img_channels
-        self.block_resolutions = [2 ** i for i in range(2, self.img_resolution_log2 + 1)]
-        channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions}
-        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
-        self.num_ws = 0
-        for res in self.block_resolutions:
-            in_channels = channels_dict[res // 2] if res > 4 else 0
-            out_channels = channels_dict[res]
-            use_fp16 = (res >= fp16_resolution)
-            is_last = (res == self.img_resolution)
-            block = SynthesisBlock(in_channels, out_channels, w_dim=w_dim, resolution=res,
-                img_channels=img_channels, is_last=is_last, use_fp16=use_fp16, **block_kwargs)
-            self.num_ws += block.num_conv
-            if is_last:
-                self.num_ws += block.num_torgb
-            setattr(self, f'b{res}', block)
-
-    def forward(self, ws, **block_kwargs):
-        block_ws = []
-        with torch.autograd.profiler.record_function('split_ws'):
-            misc.assert_shape(ws, [None, self.num_ws, self.w_dim])
-            ws = ws.to(torch.float32)
-            w_idx = 0
-            for res in self.block_resolutions:
-                block = getattr(self, f'b{res}')
-                block_ws.append(ws.narrow(1, w_idx, block.num_conv + block.num_torgb))
-                w_idx += block.num_conv
-
-        x = img = None
-        for res, cur_ws in zip(self.block_resolutions, block_ws):
-            block = getattr(self, f'b{res}')
-            x, img = block(x, img, cur_ws, **block_kwargs)
-        return img
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class Generator(torch.nn.Module):
-    def __init__(self,
-        z_dim,                      # Input latent (Z) dimensionality.
-        c_dim,                      # Conditioning label (C) dimensionality.
-        w_dim,                      # Intermediate latent (W) dimensionality.
-        img_resolution,             # Output resolution.
-        img_channels,               # Number of output color channels.
-        mapping_kwargs      = {},   # Arguments for MappingNetwork.
-        synthesis_kwargs    = {},   # Arguments for SynthesisNetwork.
-    ):
-        super().__init__()
-        self.z_dim = z_dim
-        self.c_dim = c_dim
-        self.w_dim = w_dim
-        self.img_resolution = img_resolution
-        self.img_channels = img_channels
-        self.synthesis = SynthesisNetwork(w_dim=w_dim, img_resolution=img_resolution, img_channels=img_channels, **synthesis_kwargs)
-        self.num_ws = self.synthesis.num_ws
-        self.mapping = MappingNetwork(z_dim=z_dim, c_dim=c_dim, w_dim=w_dim, num_ws=self.num_ws, **mapping_kwargs)
-
-    def forward(self, z, c, truncation_psi=1, truncation_cutoff=None, **synthesis_kwargs):
-        ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff)
-        img = self.synthesis(ws, **synthesis_kwargs)
-        return img
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class DiscriminatorBlock(torch.nn.Module):
-    def __init__(self,
-        in_channels,                        # Number of input channels, 0 = first block.
-        tmp_channels,                       # Number of intermediate channels.
-        out_channels,                       # Number of output channels.
-        resolution,                         # Resolution of this block.
-        img_channels,                       # Number of input color channels.
-        first_layer_idx,                    # Index of the first layer.
-        architecture        = 'resnet',     # Architecture: 'orig', 'skip', 'resnet'.
-        activation          = 'lrelu',      # Activation function: 'relu', 'lrelu', etc.
-        resample_filter     = [1,3,3,1],    # Low-pass filter to apply when resampling activations.
-        conv_clamp          = None,         # Clamp the output of convolution layers to +-X, None = disable clamping.
-        use_fp16            = False,        # Use FP16 for this block?
-        fp16_channels_last  = False,        # Use channels-last memory format with FP16?
-        freeze_layers       = 0,            # Freeze-D: Number of layers to freeze.
-    ):
-        assert in_channels in [0, tmp_channels]
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.first_layer_idx = first_layer_idx
-        self.architecture = architecture
-        self.use_fp16 = use_fp16
-        self.channels_last = (use_fp16 and fp16_channels_last)
-        self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
-
-        self.num_layers = 0
-        def trainable_gen():
-            while True:
-                layer_idx = self.first_layer_idx + self.num_layers
-                trainable = (layer_idx >= freeze_layers)
-                self.num_layers += 1
-                yield trainable
-        trainable_iter = trainable_gen()
-
-        if in_channels == 0 or architecture == 'skip':
-            self.fromrgb = Conv2dLayer(img_channels, tmp_channels, kernel_size=1, activation=activation,
-                trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        self.conv0 = Conv2dLayer(tmp_channels, tmp_channels, kernel_size=3, activation=activation,
-            trainable=next(trainable_iter), conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        self.conv1 = Conv2dLayer(tmp_channels, out_channels, kernel_size=3, activation=activation, down=2,
-            trainable=next(trainable_iter), resample_filter=resample_filter, conv_clamp=conv_clamp, channels_last=self.channels_last)
-
-        if architecture == 'resnet':
-            self.skip = Conv2dLayer(tmp_channels, out_channels, kernel_size=1, bias=False, down=2,
-                trainable=next(trainable_iter), resample_filter=resample_filter, channels_last=self.channels_last)
-
-    def forward(self, x, img, force_fp32=False):
-        dtype = torch.float16 if self.use_fp16 and not force_fp32 else torch.float32
-        memory_format = torch.channels_last if self.channels_last and not force_fp32 else torch.contiguous_format
-
-        # Input.
-        if x is not None:
-            misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution])
-            x = x.to(dtype=dtype, memory_format=memory_format)
-
-        # FromRGB.
-        if self.in_channels == 0 or self.architecture == 'skip':
-            misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
-            img = img.to(dtype=dtype, memory_format=memory_format)
-            y = self.fromrgb(img)
-            x = x + y if x is not None else y
-            img = upfirdn2d.downsample2d(img, self.resample_filter) if self.architecture == 'skip' else None
-
-        # Main layers.
-        if self.architecture == 'resnet':
-            y = self.skip(x, gain=np.sqrt(0.5))
-            x = self.conv0(x)
-            x = self.conv1(x, gain=np.sqrt(0.5))
-            x = y.add_(x)
-        else:
-            x = self.conv0(x)
-            x = self.conv1(x)
-
-        assert x.dtype == dtype
-        return x, img
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class MinibatchStdLayer(torch.nn.Module):
-    def __init__(self, group_size, num_channels=1):
-        super().__init__()
-        self.group_size = group_size
-        self.num_channels = num_channels
-
-    def forward(self, x):
-        N, C, H, W = x.shape
-        with misc.suppress_tracer_warnings(): # as_tensor results are registered as constants
-            G = torch.min(torch.as_tensor(self.group_size), torch.as_tensor(N)) if self.group_size is not None else N
-        F = self.num_channels
-        c = C // F
-
-        y = x.reshape(G, -1, F, c, H, W)    # [GnFcHW] Split minibatch N into n groups of size G, and channels C into F groups of size c.
-        y = y - y.mean(dim=0)               # [GnFcHW] Subtract mean over group.
-        y = y.square().mean(dim=0)          # [nFcHW]  Calc variance over group.
-        y = (y + 1e-8).sqrt()               # [nFcHW]  Calc stddev over group.
-        y = y.mean(dim=[2,3,4])             # [nF]     Take average over channels and pixels.
-        y = y.reshape(-1, F, 1, 1)          # [nF11]   Add missing dimensions.
-        y = y.repeat(G, 1, H, W)            # [NFHW]   Replicate over group and pixels.
-        x = torch.cat([x, y], dim=1)        # [NCHW]   Append to input as new channels.
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class DiscriminatorEpilogue(torch.nn.Module):
-    def __init__(self,
-        in_channels,                    # Number of input channels.
-        cmap_dim,                       # Dimensionality of mapped conditioning label, 0 = no label.
-        resolution,                     # Resolution of this block.
-        img_channels,                   # Number of input color channels.
-        architecture        = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
-        mbstd_group_size    = 4,        # Group size for the minibatch standard deviation layer, None = entire minibatch.
-        mbstd_num_channels  = 1,        # Number of features for the minibatch standard deviation layer, 0 = disable.
-        activation          = 'lrelu',  # Activation function: 'relu', 'lrelu', etc.
-        conv_clamp          = None,     # Clamp the output of convolution layers to +-X, None = disable clamping.
-    ):
-        assert architecture in ['orig', 'skip', 'resnet']
-        super().__init__()
-        self.in_channels = in_channels
-        self.cmap_dim = cmap_dim
-        self.resolution = resolution
-        self.img_channels = img_channels
-        self.architecture = architecture
-
-        if architecture == 'skip':
-            self.fromrgb = Conv2dLayer(img_channels, in_channels, kernel_size=1, activation=activation)
-        self.mbstd = MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels) if mbstd_num_channels > 0 else None
-        self.conv = Conv2dLayer(in_channels + mbstd_num_channels, in_channels, kernel_size=3, activation=activation, conv_clamp=conv_clamp)
-        self.fc = FullyConnectedLayer(in_channels * (resolution ** 2), in_channels, activation=activation)
-        self.out = FullyConnectedLayer(in_channels, 1 if cmap_dim == 0 else cmap_dim)
-
-    def forward(self, x, img, cmap, force_fp32=False):
-        misc.assert_shape(x, [None, self.in_channels, self.resolution, self.resolution]) # [NCHW]
-        _ = force_fp32 # unused
-        dtype = torch.float32
-        memory_format = torch.contiguous_format
-
-        # FromRGB.
-        x = x.to(dtype=dtype, memory_format=memory_format)
-        if self.architecture == 'skip':
-            misc.assert_shape(img, [None, self.img_channels, self.resolution, self.resolution])
-            img = img.to(dtype=dtype, memory_format=memory_format)
-            x = x + self.fromrgb(img)
-
-        # Main layers.
-        if self.mbstd is not None:
-            x = self.mbstd(x)
-        x = self.conv(x)
-        x = self.fc(x.flatten(1))
-        x = self.out(x)
-
-        # Conditioning.
-        if self.cmap_dim > 0:
-            misc.assert_shape(cmap, [None, self.cmap_dim])
-            x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
-
-        assert x.dtype == dtype
-        return x
-
-#----------------------------------------------------------------------------
-
-@persistence.persistent_class
-class Discriminator(torch.nn.Module):
-    def __init__(self,
-        c_dim,                          # Conditioning label (C) dimensionality.
-        img_resolution,                 # Input resolution.
-        img_channels,                   # Number of input color channels.
-        architecture        = 'resnet', # Architecture: 'orig', 'skip', 'resnet'.
-        channel_base        = 32768,    # Overall multiplier for the number of channels.
-        channel_max         = 512,      # Maximum number of channels in any layer.
-        num_fp16_res        = 0,        # Use FP16 for the N highest resolutions.
-        conv_clamp          = None,     # Clamp the output of convolution layers to +-X, None = disable clamping.
-        cmap_dim            = None,     # Dimensionality of mapped conditioning label, None = default.
-        block_kwargs        = {},       # Arguments for DiscriminatorBlock.
-        mapping_kwargs      = {},       # Arguments for MappingNetwork.
-        epilogue_kwargs     = {},       # Arguments for DiscriminatorEpilogue.
-    ):
-        super().__init__()
-        self.c_dim = c_dim
-        self.img_resolution = img_resolution
-        self.img_resolution_log2 = int(np.log2(img_resolution))
-        self.img_channels = img_channels
-        self.block_resolutions = [2 ** i for i in range(self.img_resolution_log2, 2, -1)]
-        channels_dict = {res: min(channel_base // res, channel_max) for res in self.block_resolutions + [4]}
-        fp16_resolution = max(2 ** (self.img_resolution_log2 + 1 - num_fp16_res), 8)
-
-        if cmap_dim is None:
-            cmap_dim = channels_dict[4]
-        if c_dim == 0:
-            cmap_dim = 0
-
-        common_kwargs = dict(img_channels=img_channels, architecture=architecture, conv_clamp=conv_clamp)
-        cur_layer_idx = 0
-        for res in self.block_resolutions:
-            in_channels = channels_dict[res] if res < img_resolution else 0
-            tmp_channels = channels_dict[res]
-            out_channels = channels_dict[res // 2]
-            use_fp16 = (res >= fp16_resolution)
-            block = DiscriminatorBlock(in_channels, tmp_channels, out_channels, resolution=res,
-                first_layer_idx=cur_layer_idx, use_fp16=use_fp16, **block_kwargs, **common_kwargs)
-            setattr(self, f'b{res}', block)
-            cur_layer_idx += block.num_layers
-        if c_dim > 0:
-            self.mapping = MappingNetwork(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None, **mapping_kwargs)
-        self.b4 = DiscriminatorEpilogue(channels_dict[4], cmap_dim=cmap_dim, resolution=4, **epilogue_kwargs, **common_kwargs)
-
-    def forward(self, img, c, **block_kwargs):
-        x = None
-        for res in self.block_resolutions:
-            block = getattr(self, f'b{res}')
-            x, img = block(x, img, **block_kwargs)
-
-        cmap = None
-        if self.c_dim > 0:
-            cmap = self.mapping(None, c)
-        x = self.b4(x, img, cmap)
-        return x
-
-#----------------------------------------------------------------------------

training/training_loop.py

@@ -1,421 +0,0 @@
-# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
-#
-# NVIDIA CORPORATION and its licensors retain all intellectual property
-# and proprietary rights in and to this software, related documentation
-# and any modifications thereto.  Any use, reproduction, disclosure or
-# distribution of this software and related documentation without an express
-# license agreement from NVIDIA CORPORATION is strictly prohibited.
-
-import os
-import time
-import copy
-import json
-import pickle
-import psutil
-import PIL.Image
-import numpy as np
-import torch
-import dnnlib
-from torch_utils import misc
-from torch_utils import training_stats
-from torch_utils.ops import conv2d_gradfix
-from torch_utils.ops import grid_sample_gradfix
-
-import legacy
-from metrics import metric_main
-
-#----------------------------------------------------------------------------
-
-def setup_snapshot_image_grid(training_set, random_seed=0):
-    rnd = np.random.RandomState(random_seed)
-    gw = np.clip(7680 // training_set.image_shape[2], 7, 32)
-    gh = np.clip(4320 // training_set.image_shape[1], 4, 32)
-
-    # No labels => show random subset of training samples.
-    if not training_set.has_labels:
-        all_indices = list(range(len(training_set)))
-        rnd.shuffle(all_indices)
-        grid_indices = [all_indices[i % len(all_indices)] for i in range(gw * gh)]
-
-    else:
-        # Group training samples by label.
-        label_groups = dict() # label => [idx, ...]
-        for idx in range(len(training_set)):
-            label = tuple(training_set.get_details(idx).raw_label.flat[::-1])
-            if label not in label_groups:
-                label_groups[label] = []
-            label_groups[label].append(idx)
-
-        # Reorder.
-        label_order = sorted(label_groups.keys())
-        for label in label_order:
-            rnd.shuffle(label_groups[label])
-
-        # Organize into grid.
-        grid_indices = []
-        for y in range(gh):
-            label = label_order[y % len(label_order)]
-            indices = label_groups[label]
-            grid_indices += [indices[x % len(indices)] for x in range(gw)]
-            label_groups[label] = [indices[(i + gw) % len(indices)] for i in range(len(indices))]
-
-    # Load data.
-    images, labels = zip(*[training_set[i] for i in grid_indices])
-    return (gw, gh), np.stack(images), np.stack(labels)
-
-#----------------------------------------------------------------------------
-
-def save_image_grid(img, fname, drange, grid_size):
-    lo, hi = drange
-    img = np.asarray(img, dtype=np.float32)
-    img = (img - lo) * (255 / (hi - lo))
-    img = np.rint(img).clip(0, 255).astype(np.uint8)
-
-    gw, gh = grid_size
-    _N, C, H, W = img.shape
-    img = img.reshape(gh, gw, C, H, W)
-    img = img.transpose(0, 3, 1, 4, 2)
-    img = img.reshape(gh * H, gw * W, C)
-
-    assert C in [1, 3]
-    if C == 1:
-        PIL.Image.fromarray(img[:, :, 0], 'L').save(fname)
-    if C == 3:
-        PIL.Image.fromarray(img, 'RGB').save(fname)
-
-#----------------------------------------------------------------------------
-
-def training_loop(
-    run_dir                 = '.',      # Output directory.
-    training_set_kwargs     = {},       # Options for training set.
-    data_loader_kwargs      = {},       # Options for torch.utils.data.DataLoader.
-    G_kwargs                = {},       # Options for generator network.
-    D_kwargs                = {},       # Options for discriminator network.
-    G_opt_kwargs            = {},       # Options for generator optimizer.
-    D_opt_kwargs            = {},       # Options for discriminator optimizer.
-    augment_kwargs          = None,     # Options for augmentation pipeline. None = disable.
-    loss_kwargs             = {},       # Options for loss function.
-    metrics                 = [],       # Metrics to evaluate during training.
-    random_seed             = 0,        # Global random seed.
-    num_gpus                = 1,        # Number of GPUs participating in the training.
-    rank                    = 0,        # Rank of the current process in [0, num_gpus[.
-    batch_size              = 4,        # Total batch size for one training iteration. Can be larger than batch_gpu * num_gpus.
-    batch_gpu               = 4,        # Number of samples processed at a time by one GPU.
-    ema_kimg                = 10,       # Half-life of the exponential moving average (EMA) of generator weights.
-    ema_rampup              = None,     # EMA ramp-up coefficient.
-    G_reg_interval          = 4,        # How often to perform regularization for G? None = disable lazy regularization.
-    D_reg_interval          = 16,       # How often to perform regularization for D? None = disable lazy regularization.
-    augment_p               = 0,        # Initial value of augmentation probability.
-    ada_target              = None,     # ADA target value. None = fixed p.
-    ada_interval            = 4,        # How often to perform ADA adjustment?
-    ada_kimg                = 500,      # ADA adjustment speed, measured in how many kimg it takes for p to increase/decrease by one unit.
-    total_kimg              = 25000,    # Total length of the training, measured in thousands of real images.
-    kimg_per_tick           = 4,        # Progress snapshot interval.
-    image_snapshot_ticks    = 50,       # How often to save image snapshots? None = disable.
-    network_snapshot_ticks  = 50,       # How often to save network snapshots? None = disable.
-    resume_pkl              = None,     # Network pickle to resume training from.
-    cudnn_benchmark         = True,     # Enable torch.backends.cudnn.benchmark?
-    allow_tf32              = False,    # Enable torch.backends.cuda.matmul.allow_tf32 and torch.backends.cudnn.allow_tf32?
-    abort_fn                = None,     # Callback function for determining whether to abort training. Must return consistent results across ranks.
-    progress_fn             = None,     # Callback function for updating training progress. Called for all ranks.
-):
-    # Initialize.
-    start_time = time.time()
-    device = torch.device('cuda', rank)
-    np.random.seed(random_seed * num_gpus + rank)
-    torch.manual_seed(random_seed * num_gpus + rank)
-    torch.backends.cudnn.benchmark = cudnn_benchmark    # Improves training speed.
-    torch.backends.cuda.matmul.allow_tf32 = allow_tf32  # Allow PyTorch to internally use tf32 for matmul
-    torch.backends.cudnn.allow_tf32 = allow_tf32        # Allow PyTorch to internally use tf32 for convolutions
-    conv2d_gradfix.enabled = True                       # Improves training speed.
-    grid_sample_gradfix.enabled = True                  # Avoids errors with the augmentation pipe.
-
-    # Load training set.
-    if rank == 0:
-        print('Loading training set...')
-    training_set = dnnlib.util.construct_class_by_name(**training_set_kwargs) # subclass of training.dataset.Dataset
-    training_set_sampler = misc.InfiniteSampler(dataset=training_set, rank=rank, num_replicas=num_gpus, seed=random_seed)
-    training_set_iterator = iter(torch.utils.data.DataLoader(dataset=training_set, sampler=training_set_sampler, batch_size=batch_size//num_gpus, **data_loader_kwargs))
-    if rank == 0:
-        print()
-        print('Num images: ', len(training_set))
-        print('Image shape:', training_set.image_shape)
-        print('Label shape:', training_set.label_shape)
-        print()
-
-    # Construct networks.
-    if rank == 0:
-        print('Constructing networks...')
-    common_kwargs = dict(c_dim=training_set.label_dim, img_resolution=training_set.resolution, img_channels=training_set.num_channels)
-    G = dnnlib.util.construct_class_by_name(**G_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
-    D = dnnlib.util.construct_class_by_name(**D_kwargs, **common_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
-    G_ema = copy.deepcopy(G).eval()
-
-    # Resume from existing pickle.
-    if (resume_pkl is not None) and (rank == 0):
-        print(f'Resuming from "{resume_pkl}"')
-        with dnnlib.util.open_url(resume_pkl) as f:
-            resume_data = legacy.load_network_pkl(f)
-        for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
-            misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
-
-    # Print network summary tables.
-    if rank == 0:
-        z = torch.empty([batch_gpu, G.z_dim], device=device)
-        c = torch.empty([batch_gpu, G.c_dim], device=device)
-        img = misc.print_module_summary(G, [z, c])
-        misc.print_module_summary(D, [img, c])
-
-    # Setup augmentation.
-    if rank == 0:
-        print('Setting up augmentation...')
-    augment_pipe = None
-    ada_stats = None
-    if (augment_kwargs is not None) and (augment_p > 0 or ada_target is not None):
-        augment_pipe = dnnlib.util.construct_class_by_name(**augment_kwargs).train().requires_grad_(False).to(device) # subclass of torch.nn.Module
-        augment_pipe.p.copy_(torch.as_tensor(augment_p))
-        if ada_target is not None:
-            ada_stats = training_stats.Collector(regex='Loss/signs/real')
-
-    # Distribute across GPUs.
-    if rank == 0:
-        print(f'Distributing across {num_gpus} GPUs...')
-    ddp_modules = dict()
-    for name, module in [('G_mapping', G.mapping), ('G_synthesis', G.synthesis), ('D', D), (None, G_ema), ('augment_pipe', augment_pipe)]:
-        if (num_gpus > 1) and (module is not None) and len(list(module.parameters())) != 0:
-            module.requires_grad_(True)
-            module = torch.nn.parallel.DistributedDataParallel(module, device_ids=[device], broadcast_buffers=False)
-            module.requires_grad_(False)
-        if name is not None:
-            ddp_modules[name] = module
-
-    # Setup training phases.
-    if rank == 0:
-        print('Setting up training phases...')
-    loss = dnnlib.util.construct_class_by_name(device=device, **ddp_modules, **loss_kwargs) # subclass of training.loss.Loss
-    phases = []
-    for name, module, opt_kwargs, reg_interval in [('G', G, G_opt_kwargs, G_reg_interval), ('D', D, D_opt_kwargs, D_reg_interval)]:
-        if reg_interval is None:
-            opt = dnnlib.util.construct_class_by_name(params=module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
-            phases += [dnnlib.EasyDict(name=name+'both', module=module, opt=opt, interval=1)]
-        else: # Lazy regularization.
-            mb_ratio = reg_interval / (reg_interval + 1)
-            opt_kwargs = dnnlib.EasyDict(opt_kwargs)
-            opt_kwargs.lr = opt_kwargs.lr * mb_ratio
-            opt_kwargs.betas = [beta ** mb_ratio for beta in opt_kwargs.betas]
-            opt = dnnlib.util.construct_class_by_name(module.parameters(), **opt_kwargs) # subclass of torch.optim.Optimizer
-            phases += [dnnlib.EasyDict(name=name+'main', module=module, opt=opt, interval=1)]
-            phases += [dnnlib.EasyDict(name=name+'reg', module=module, opt=opt, interval=reg_interval)]
-    for phase in phases:
-        phase.start_event = None
-        phase.end_event = None
-        if rank == 0:
-            phase.start_event = torch.cuda.Event(enable_timing=True)
-            phase.end_event = torch.cuda.Event(enable_timing=True)
-
-    # Export sample images.
-    grid_size = None
-    grid_z = None
-    grid_c = None
-    if rank == 0:
-        print('Exporting sample images...')
-        grid_size, images, labels = setup_snapshot_image_grid(training_set=training_set)
-        save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0,255], grid_size=grid_size)
-        grid_z = torch.randn([labels.shape[0], G.z_dim], device=device).split(batch_gpu)
-        grid_c = torch.from_numpy(labels).to(device).split(batch_gpu)
-        images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
-        save_image_grid(images, os.path.join(run_dir, 'fakes_init.png'), drange=[-1,1], grid_size=grid_size)
-
-    # Initialize logs.
-    if rank == 0:
-        print('Initializing logs...')
-    stats_collector = training_stats.Collector(regex='.*')
-    stats_metrics = dict()
-    stats_jsonl = None
-    stats_tfevents = None
-    if rank == 0:
-        stats_jsonl = open(os.path.join(run_dir, 'stats.jsonl'), 'wt')
-        try:
-            import torch.utils.tensorboard as tensorboard
-            stats_tfevents = tensorboard.SummaryWriter(run_dir)
-        except ImportError as err:
-            print('Skipping tfevents export:', err)
-
-    # Train.
-    if rank == 0:
-        print(f'Training for {total_kimg} kimg...')
-        print()
-    cur_nimg = 0
-    cur_tick = 0
-    tick_start_nimg = cur_nimg
-    tick_start_time = time.time()
-    maintenance_time = tick_start_time - start_time
-    batch_idx = 0
-    if progress_fn is not None:
-        progress_fn(0, total_kimg)
-    while True:
-
-        # Fetch training data.
-        with torch.autograd.profiler.record_function('data_fetch'):
-            phase_real_img, phase_real_c = next(training_set_iterator)
-            phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu)
-            phase_real_c = phase_real_c.to(device).split(batch_gpu)
-            all_gen_z = torch.randn([len(phases) * batch_size, G.z_dim], device=device)
-            all_gen_z = [phase_gen_z.split(batch_gpu) for phase_gen_z in all_gen_z.split(batch_size)]
-            all_gen_c = [training_set.get_label(np.random.randint(len(training_set))) for _ in range(len(phases) * batch_size)]
-            all_gen_c = torch.from_numpy(np.stack(all_gen_c)).pin_memory().to(device)
-            all_gen_c = [phase_gen_c.split(batch_gpu) for phase_gen_c in all_gen_c.split(batch_size)]
-
-        # Execute training phases.
-        for phase, phase_gen_z, phase_gen_c in zip(phases, all_gen_z, all_gen_c):
-            if batch_idx % phase.interval != 0:
-                continue
-
-            # Initialize gradient accumulation.
-            if phase.start_event is not None:
-                phase.start_event.record(torch.cuda.current_stream(device))
-            phase.opt.zero_grad(set_to_none=True)
-            phase.module.requires_grad_(True)
-
-            # Accumulate gradients over multiple rounds.
-            for round_idx, (real_img, real_c, gen_z, gen_c) in enumerate(zip(phase_real_img, phase_real_c, phase_gen_z, phase_gen_c)):
-                sync = (round_idx == batch_size // (batch_gpu * num_gpus) - 1)
-                gain = phase.interval
-                loss.accumulate_gradients(phase=phase.name, real_img=real_img, real_c=real_c, gen_z=gen_z, gen_c=gen_c, sync=sync, gain=gain)
-
-            # Update weights.
-            phase.module.requires_grad_(False)
-            with torch.autograd.profiler.record_function(phase.name + '_opt'):
-                for param in phase.module.parameters():
-                    if param.grad is not None:
-                        misc.nan_to_num(param.grad, nan=0, posinf=1e5, neginf=-1e5, out=param.grad)
-                phase.opt.step()
-            if phase.end_event is not None:
-                phase.end_event.record(torch.cuda.current_stream(device))
-
-        # Update G_ema.
-        with torch.autograd.profiler.record_function('Gema'):
-            ema_nimg = ema_kimg * 1000
-            if ema_rampup is not None:
-                ema_nimg = min(ema_nimg, cur_nimg * ema_rampup)
-            ema_beta = 0.5 ** (batch_size / max(ema_nimg, 1e-8))
-            for p_ema, p in zip(G_ema.parameters(), G.parameters()):
-                p_ema.copy_(p.lerp(p_ema, ema_beta))
-            for b_ema, b in zip(G_ema.buffers(), G.buffers()):
-                b_ema.copy_(b)
-
-        # Update state.
-        cur_nimg += batch_size
-        batch_idx += 1
-
-        # Execute ADA heuristic.
-        if (ada_stats is not None) and (batch_idx % ada_interval == 0):
-            ada_stats.update()
-            adjust = np.sign(ada_stats['Loss/signs/real'] - ada_target) * (batch_size * ada_interval) / (ada_kimg * 1000)
-            augment_pipe.p.copy_((augment_pipe.p + adjust).max(misc.constant(0, device=device)))
-
-        # Perform maintenance tasks once per tick.
-        done = (cur_nimg >= total_kimg * 1000)
-        if (not done) and (cur_tick != 0) and (cur_nimg < tick_start_nimg + kimg_per_tick * 1000):
-            continue
-
-        # Print status line, accumulating the same information in stats_collector.
-        tick_end_time = time.time()
-        fields = []
-        fields += [f"tick {training_stats.report0('Progress/tick', cur_tick):<5d}"]
-        fields += [f"kimg {training_stats.report0('Progress/kimg', cur_nimg / 1e3):<8.1f}"]
-        fields += [f"time {dnnlib.util.format_time(training_stats.report0('Timing/total_sec', tick_end_time - start_time)):<12s}"]
-        fields += [f"sec/tick {training_stats.report0('Timing/sec_per_tick', tick_end_time - tick_start_time):<7.1f}"]
-        fields += [f"sec/kimg {training_stats.report0('Timing/sec_per_kimg', (tick_end_time - tick_start_time) / (cur_nimg - tick_start_nimg) * 1e3):<7.2f}"]
-        fields += [f"maintenance {training_stats.report0('Timing/maintenance_sec', maintenance_time):<6.1f}"]
-        fields += [f"cpumem {training_stats.report0('Resources/cpu_mem_gb', psutil.Process(os.getpid()).memory_info().rss / 2**30):<6.2f}"]
-        fields += [f"gpumem {training_stats.report0('Resources/peak_gpu_mem_gb', torch.cuda.max_memory_allocated(device) / 2**30):<6.2f}"]
-        torch.cuda.reset_peak_memory_stats()
-        fields += [f"augment {training_stats.report0('Progress/augment', float(augment_pipe.p.cpu()) if augment_pipe is not None else 0):.3f}"]
-        training_stats.report0('Timing/total_hours', (tick_end_time - start_time) / (60 * 60))
-        training_stats.report0('Timing/total_days', (tick_end_time - start_time) / (24 * 60 * 60))
-        if rank == 0:
-            print(' '.join(fields))
-
-        # Check for abort.
-        if (not done) and (abort_fn is not None) and abort_fn():
-            done = True
-            if rank == 0:
-                print()
-                print('Aborting...')
-
-        # Save image snapshot.
-        if (rank == 0) and (image_snapshot_ticks is not None) and (done or cur_tick % image_snapshot_ticks == 0):
-            images = torch.cat([G_ema(z=z, c=c, noise_mode='const').cpu() for z, c in zip(grid_z, grid_c)]).numpy()
-            save_image_grid(images, os.path.join(run_dir, f'fakes{cur_nimg//1000:06d}.png'), drange=[-1,1], grid_size=grid_size)
-
-        # Save network snapshot.
-        snapshot_pkl = None
-        snapshot_data = None
-        if (network_snapshot_ticks is not None) and (done or cur_tick % network_snapshot_ticks == 0):
-            snapshot_data = dict(training_set_kwargs=dict(training_set_kwargs))
-            for name, module in [('G', G), ('D', D), ('G_ema', G_ema), ('augment_pipe', augment_pipe)]:
-                if module is not None:
-                    if num_gpus > 1:
-                        misc.check_ddp_consistency(module, ignore_regex=r'.*\.w_avg')
-                    module = copy.deepcopy(module).eval().requires_grad_(False).cpu()
-                snapshot_data[name] = module
-                del module # conserve memory
-            snapshot_pkl = os.path.join(run_dir, f'network-snapshot-{cur_nimg//1000:06d}.pkl')
-            if rank == 0:
-                with open(snapshot_pkl, 'wb') as f:
-                    pickle.dump(snapshot_data, f)
-
-        # Evaluate metrics.
-        if (snapshot_data is not None) and (len(metrics) > 0):
-            if rank == 0:
-                print('Evaluating metrics...')
-            for metric in metrics:
-                result_dict = metric_main.calc_metric(metric=metric, G=snapshot_data['G_ema'],
-                    dataset_kwargs=training_set_kwargs, num_gpus=num_gpus, rank=rank, device=device)
-                if rank == 0:
-                    metric_main.report_metric(result_dict, run_dir=run_dir, snapshot_pkl=snapshot_pkl)
-                stats_metrics.update(result_dict.results)
-        del snapshot_data # conserve memory
-
-        # Collect statistics.
-        for phase in phases:
-            value = []
-            if (phase.start_event is not None) and (phase.end_event is not None):
-                phase.end_event.synchronize()
-                value = phase.start_event.elapsed_time(phase.end_event)
-            training_stats.report0('Timing/' + phase.name, value)
-        stats_collector.update()
-        stats_dict = stats_collector.as_dict()
-
-        # Update logs.
-        timestamp = time.time()
-        if stats_jsonl is not None:
-            fields = dict(stats_dict, timestamp=timestamp)
-            stats_jsonl.write(json.dumps(fields) + '\n')
-            stats_jsonl.flush()
-        if stats_tfevents is not None:
-            global_step = int(cur_nimg / 1e3)
-            walltime = timestamp - start_time
-            for name, value in stats_dict.items():
-                stats_tfevents.add_scalar(name, value.mean, global_step=global_step, walltime=walltime)
-            for name, value in stats_metrics.items():
-                stats_tfevents.add_scalar(f'Metrics/{name}', value, global_step=global_step, walltime=walltime)
-            stats_tfevents.flush()
-        if progress_fn is not None:
-            progress_fn(cur_nimg // 1000, total_kimg)
-
-        # Update state.
-        cur_tick += 1
-        tick_start_nimg = cur_nimg
-        tick_start_time = time.time()
-        maintenance_time = tick_start_time - tick_end_time
-        if done:
-            break
-
-    # Done.
-    if rank == 0:
-        print()
-        print('Exiting...')
-
-#----------------------------------------------------------------------------