diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..8adc0ed51136a83355366e1c55c8110373b4246d
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,32 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..c04da465614c22642a91b3094cdc607e5f0df3a8
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,162 @@
+## creative commons
+
+# Attribution-NonCommercial 4.0 International
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+
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..12c30d70536ad6ae21c3e829f1f1d731dd1520b2
--- /dev/null
+++ b/README.md
@@ -0,0 +1,13 @@
+---
+title: Stable Diffusion Mat Outpainting Primer
+emoji: 🐢
+colorFrom: red
+colorTo: purple
+sdk: gradio
+sdk_version: 3.4
+app_file: app.py
+pinned: false
+license: cc-by-nc-4.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
diff --git a/app.py b/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..19c20eaf97fea5bfd660c3c9e8e8a54e1420c3a0
--- /dev/null
+++ b/app.py
@@ -0,0 +1,327 @@
+# %%
+
+# 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 networks.mat import Generator
+import gradio as gr
+import gradio.components as gc
+import base64
+import glob
+import os
+import random
+import re
+from http import HTTPStatus
+from io import BytesIO
+from typing import Dict, List, NamedTuple, Optional, Tuple
+
+import click
+import cv2
+import numpy as np
+import PIL.Image
+import torch
+import torch.nn.functional as F
+from PIL import Image, ImageDraw, ImageOps
+from pydantic import BaseModel
+
+import dnnlib
+import legacy
+
+
+pyspng = None
+
+
+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]
+
+
+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)
+
+
+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())
+
+
+class Inpainter:
+ def __init__(self,
+ network_pkl,
+ resolution=512,
+ truncation_psi=1,
+ noise_mode='const',
+ sdevice='cpu'
+ ):
+ self.resolution = resolution
+ self.truncation_psi = truncation_psi
+ self.noise_mode = noise_mode
+ print(f'Loading networks from: {network_pkl}')
+ self.device = torch.device(sdevice)
+ with dnnlib.util.open_url(network_pkl) as f:
+ G_saved = (
+ legacy.load_network_pkl(f)
+ ['G_ema']
+ .to(self.device)
+ .eval()
+ .requires_grad_(False)) # type: ignore
+ net_res = 512 if resolution > 512 else resolution
+ self.G = (
+ Generator(
+ z_dim=512,
+ c_dim=0,
+ w_dim=512,
+ img_resolution=net_res,
+ img_channels=3
+ )
+ .to(self.device)
+ .eval()
+ .requires_grad_(False)
+ )
+ copy_params_and_buffers(G_saved, self.G, require_all=True)
+
+ def generate_images2(
+ self,
+ dpath: List[PIL.Image.Image],
+ mpath: List[Optional[PIL.Image.Image]],
+ seed: int = 42,
+ ):
+ """
+ Generate images using pretrained network pickle.
+ """
+ resolution = self.resolution
+ truncation_psi = self.truncation_psi
+ noise_mode = self.noise_mode
+ # seed = 240 # pick up a random number
+
+ def seed_all(seed):
+ random.seed(seed)
+ np.random.seed(seed)
+ torch.manual_seed(seed)
+ torch.cuda.manual_seed(seed)
+ if seed is not None:
+ seed_all(seed)
+
+ # no Labels.
+ label = torch.zeros([1, self.G.c_dim], device=self.device)
+
+ def read_image(image):
+ image = np.array(image)
+ if image.ndim == 2:
+ image = image[:, :, np.newaxis] # HW => HWC
+ image = np.repeat(image, 3, axis=2)
+ image = image.transpose(2, 0, 1) # HWC => CHW
+ image = image[:3]
+ return image
+ if resolution != 512:
+ noise_mode = 'random'
+ results = []
+ with torch.no_grad():
+ for i, (ipath, m) in enumerate(zip(dpath, mpath)):
+ if seed is None:
+ seed_all(i)
+
+ image = read_image(ipath)
+ image = (torch.from_numpy(image).float().to(
+ self. device) / 127.5 - 1).unsqueeze(0)
+
+ mask = np.array(m).astype(np.float32) / 255.0
+ mask = torch.from_numpy(mask).float().to(
+ self. device).unsqueeze(0).unsqueeze(0)
+
+ z = torch.from_numpy(np.random.randn(
+ 1, self.G.z_dim)).to(self.device)
+ output = self.G(image, mask, z, label,
+ truncation_psi=truncation_psi, noise_mode=noise_mode)
+ output = (output.permute(0, 2, 3, 1) * 127.5 +
+ 127.5).round().clamp(0, 255).to(torch.uint8)
+ output = output[0].cpu().numpy()
+ results.append(PIL.Image.fromarray(output, 'RGB'))
+
+ return results
+
+
+# if __name__ == "__main__":
+# generate_images() # pylint: disable=no-value-for-parameter
+
+# ----------------------------------------------------------------------------
+def mask_to_alpha(img, mask):
+ img = img.copy()
+ img.putalpha(mask)
+ return img
+
+
+def blend(src, target, mask):
+ mask = np.expand_dims(mask, axis=-1)
+ result = (1-mask) * src + mask * target
+ return Image.fromarray(result.astype(np.uint8))
+
+
+def pad(img, size=(128, 128), tosize=(512, 512), border=1):
+ if isinstance(size, float):
+ size = (int(img.size[0] * size), int(img.size[1] * size))
+ # remove border
+ w, h = tosize
+
+ new_img = Image.new('RGBA', (w, h))
+
+ rimg = img.resize(size, resample=Image.Resampling.NEAREST)
+ rimg = ImageOps.crop(rimg, border=border)
+ tw, th = size
+ tw, th = tw - border*2, th - border*2
+ tc = ((w-tw)//2, (h-th)//2)
+
+ new_img.paste(rimg, tc)
+ mask = Image.new('L', (w, h))
+ white = Image.new('L', (tw, th), 255)
+ mask.paste(white, tc)
+
+ if 'A' in rimg.getbands():
+ mask.paste(img.getchannel('A'), tc)
+ return new_img, mask
+
+
+def b64_to_img(b64):
+ return Image.open(BytesIO(base64.b64decode(b64)))
+
+
+def img_to_b64(img):
+ with BytesIO() as f:
+ img.save(f, format='PNG')
+ return base64.b64encode(f.getvalue()).decode('utf-8')
+
+
+class Predictor:
+ def __init__(self):
+ """Load the model into memory to make running multiple predictions efficient"""
+ self.models = {
+ "places2": Inpainter(
+ network_pkl='models/Places_512_FullData.pkl',
+ resolution=512,
+ truncation_psi=1.,
+ noise_mode='const',
+ ),
+ "places2+laion300k": Inpainter(
+ network_pkl='models/Places_512_FullData+LAION300k.pkl',
+ resolution=512,
+ truncation_psi=1.,
+ noise_mode='const',
+ ),
+ }
+
+ # The arguments and types the model takes as input
+
+ def predict(
+ self,
+ img: Image.Image,
+ tosize=(512, 512),
+ border=5,
+ seed=42,
+ size=0.5,
+ model='places2',
+ ) -> Image:
+ i, m = pad(
+ img,
+ size=size, # (328, 328),
+ tosize=tosize,
+ border=border
+ )
+ """Run a single prediction on the model"""
+ imgs = self.models[model].generate_images2(
+ dpath=[i.resize((512, 512), resample=Image.Resampling.NEAREST)],
+ mpath=[m.resize((512, 512), resample=Image.Resampling.NEAREST)],
+ seed=seed,
+ )
+ img_op_raw = imgs[0].convert('RGBA')
+ img_op_raw = img_op_raw.resize(
+ tosize, resample=Image.Resampling.NEAREST)
+ inpainted = img_op_raw.copy()
+
+ # paste original image to remove inpainting/scaling artifacts
+ inpainted = blend(
+ i,
+ inpainted,
+ 1-(np.array(m) / 255)
+ )
+ minpainted = mask_to_alpha(inpainted, m)
+ return minpainted, inpainted, ImageOps.invert(m)
+
+
+predictor = Predictor()
+
+# %%
+
+
+def _outpaint(img, tosize, border, seed, size, model):
+ img_op = predictor.predict(
+ img,
+ border=border,
+ seed=seed,
+ tosize=(tosize, tosize),
+ size=float(size),
+ model=model,
+ )
+ return img_op
+# %%
+
+
+searchimage = gc.Image(shape=(224, 224), label="image", type='pil')
+to_size = gc.Slider(1, 1920, 512, step=1, label='output size')
+border = gc.Slider(
+ 1, 50, 0, step=1, label='border to crop from the image before outpainting')
+seed = gc.Slider(1, 65536, 10, step=1, label='seed')
+size = gc.Slider(0, 1, .5, step=0.01,
+ label='scale of the image before outpainting')
+
+out = gc.Image(label="primed image with alpha channel", type='pil')
+outwithoutalpha = gc.Image(
+ label="primed image without alpha channel", type='pil')
+mask = gc.Image(label="outpainting mask", type='pil')
+
+model = gc.Dropdown(
+ choices=['places2', 'places2+laion300k'],
+ value='places2',
+ label='model',
+)
+
+
+maturl = 'https://github.com/fenglinglwb/MAT'
+gr.Interface(
+ _outpaint,
+ [searchimage, to_size, border, seed, size, model],
+ [out, outwithoutalpha, mask],
+ title=f"MAT Primer for Stable Diffusion\n\nbased on MAT: Mask-Aware Transformer for Large Hole Image Inpainting\n\n{maturl}",
+ description=f"""
+ create an primer for use in stable diffusion outpainting
+ example with strength 0.5
+ 
+ """,
+ analytics_enabled=False,
+ allow_flagging='never',
+
+
+).launch()
diff --git a/dataset_tool.py b/dataset_tool.py
new file mode 100644
index 0000000000000000000000000000000000000000..c59e6292891c3896722965020af7c60056729f2d
--- /dev/null
+++ b/dataset_tool.py
@@ -0,0 +1,444 @@
+# 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
diff --git a/datasets/dataset_256.py b/datasets/dataset_256.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e6aff7630077aba6d26780deb0fea5370ea90c8
--- /dev/null
+++ b/datasets/dataset_256.py
@@ -0,0 +1,286 @@
+# 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 cv2
+import os
+import numpy as np
+import zipfile
+import PIL.Image
+import json
+import torch
+import dnnlib
+import random
+
+try:
+ import pyspng
+except ImportError:
+ pyspng = None
+
+from datasets.mask_generator_256 import RandomMask
+
+#----------------------------------------------------------------------------
+
+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 ImageFolderMaskDataset(Dataset):
+ def __init__(self,
+ path, # Path to directory or zip.
+ resolution = None, # Ensure specific resolution, None = highest available.
+ hole_range=[0,1],
+ **super_kwargs, # Additional arguments for the Dataset base class.
+ ):
+ self._path = path
+ self._zipfile = None
+ self._hole_range = hole_range
+
+ 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
+
+ # for grayscale image
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+
+ # restricted to 256x256
+ res = 256
+ H, W, C = image.shape
+ if H < res or W < res:
+ top = 0
+ bottom = max(0, res - H)
+ left = 0
+ right = max(0, res - W)
+ image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_REFLECT)
+ H, W, C = image.shape
+ h = random.randint(0, H - res)
+ w = random.randint(0, W - res)
+ image = image[h:h+res, w:w+res, :]
+
+ image = np.ascontiguousarray(image.transpose(2, 0, 1)) # HWC => CHW
+
+ return image
+
+ def _load_raw_labels(self):
+ fname = 'labels.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
+
+ 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]
+ mask = RandomMask(image.shape[-1], hole_range=self._hole_range) # hole as 0, reserved as 1
+ return image.copy(), mask, self.get_label(idx)
+
+
+if __name__ == '__main__':
+ res = 256
+ dpath = '/data/liwenbo/datasets/Places365/standard/val_256'
+ D = ImageFolderMaskDataset(path=dpath)
+ print(D.__len__())
+ for i in range(D.__len__()):
+ print(i)
+ a, b, c = D.__getitem__(i)
+ if a.shape != (3, 256, 256):
+ print(i, a.shape)
diff --git a/datasets/dataset_256_val.py b/datasets/dataset_256_val.py
new file mode 100644
index 0000000000000000000000000000000000000000..26b619986ce380a88da88ff5792cb11166cf7e6d
--- /dev/null
+++ b/datasets/dataset_256_val.py
@@ -0,0 +1,282 @@
+# 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 cv2
+import json
+import torch
+import dnnlib
+import glob
+
+try:
+ import pyspng
+except ImportError:
+ pyspng = None
+
+from datasets.mask_generator_256 import RandomMask
+
+#----------------------------------------------------------------------------
+
+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 ImageFolderMaskDataset(Dataset):
+ def __init__(self,
+ path, # Path to directory or zip.
+ resolution = None, # Ensure specific resolution, None = highest available.
+ hole_range=[0,1],
+ **super_kwargs, # Additional arguments for the Dataset base class.
+ ):
+ self._path = path
+ self._zipfile = None
+ self._hole_range = hole_range
+
+ 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')
+ self._load_mask()
+ super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
+
+ def _load_mask(self, mpath='/data/liwenbo/datasets/Places365/standard/masks_val_256_eval'):
+ self.masks = sorted(glob.glob(mpath + '/*.png'))
+
+ @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
+
+ # for grayscale image
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+
+ # restricted to 256x256
+ res = 256
+ H, W, C = image.shape
+ if H < res or W < res:
+ top = 0
+ bottom = max(0, res - H)
+ left = 0
+ right = max(0, res - W)
+ image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_REFLECT)
+ H, W, C = image.shape
+ h = (H - res) // 2
+ w = (W - res) // 2
+ image = image[h:h+res, w:w+res, :]
+
+ image = np.ascontiguousarray(image.transpose(2, 0, 1)) # HWC => CHW
+ return image
+
+ def _load_raw_labels(self):
+ fname = 'labels.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
+
+ def __getitem__(self, idx):
+ image = self._load_raw_image(self._raw_idx[idx])
+
+ # for grayscale image
+ if image.shape[0] == 1:
+ image = np.repeat(image, 3, axis=0)
+
+ 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]
+ # mask = RandomMask(image.shape[-1], hole_range=self._hole_range) # hole as 0, reserved as 1
+ mask = cv2.imread(self.masks[idx], cv2.IMREAD_GRAYSCALE).astype(np.float32)[np.newaxis, :, :] / 255.0
+ return image.copy(), mask, self.get_label(idx)
diff --git a/datasets/dataset_512.py b/datasets/dataset_512.py
new file mode 100644
index 0000000000000000000000000000000000000000..27fc1ce862f1b00e427670d393d70bec56d063da
--- /dev/null
+++ b/datasets/dataset_512.py
@@ -0,0 +1,286 @@
+# 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 cv2
+import os
+import numpy as np
+import zipfile
+import PIL.Image
+import json
+import torch
+import dnnlib
+import random
+
+try:
+ import pyspng
+except ImportError:
+ pyspng = None
+
+from datasets.mask_generator_512 import RandomMask
+
+#----------------------------------------------------------------------------
+
+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 ImageFolderMaskDataset(Dataset):
+ def __init__(self,
+ path, # Path to directory or zip.
+ resolution = None, # Ensure specific resolution, None = highest available.
+ hole_range=[0,1],
+ **super_kwargs, # Additional arguments for the Dataset base class.
+ ):
+ self._path = path
+ self._zipfile = None
+ self._hole_range = hole_range
+
+ 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
+
+ # for grayscale image
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+
+ # restricted to 512x512
+ res = 512
+ H, W, C = image.shape
+ if H < res or W < res:
+ top = 0
+ bottom = max(0, res - H)
+ left = 0
+ right = max(0, res - W)
+ image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_REFLECT)
+ H, W, C = image.shape
+ h = random.randint(0, H - res)
+ w = random.randint(0, W - res)
+ image = image[h:h+res, w:w+res, :]
+
+ image = np.ascontiguousarray(image.transpose(2, 0, 1)) # HWC => CHW
+
+ return image
+
+ def _load_raw_labels(self):
+ fname = 'labels.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
+
+ 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]
+ mask = RandomMask(image.shape[-1], hole_range=self._hole_range) # hole as 0, reserved as 1
+ return image.copy(), mask, self.get_label(idx)
+
+
+if __name__ == '__main__':
+ res = 512
+ dpath = '/data/liwenbo/datasets/Places365/standard/val_large'
+ D = ImageFolderMaskDataset(path=dpath)
+ print(D.__len__())
+ for i in range(D.__len__()):
+ print(i)
+ a, b, c = D.__getitem__(i)
+ if a.shape != (3, 512, 512):
+ print(i, a.shape)
diff --git a/datasets/dataset_512_val.py b/datasets/dataset_512_val.py
new file mode 100644
index 0000000000000000000000000000000000000000..ef802863464db76ae79f7e06bdb9722b3525f0cf
--- /dev/null
+++ b/datasets/dataset_512_val.py
@@ -0,0 +1,282 @@
+# 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 cv2
+import json
+import torch
+import dnnlib
+import glob
+
+try:
+ import pyspng
+except ImportError:
+ pyspng = None
+
+from datasets.mask_generator_512 import RandomMask
+
+#----------------------------------------------------------------------------
+
+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 ImageFolderMaskDataset(Dataset):
+ def __init__(self,
+ path, # Path to directory or zip.
+ resolution = None, # Ensure specific resolution, None = highest available.
+ hole_range=[0,1],
+ **super_kwargs, # Additional arguments for the Dataset base class.
+ ):
+ self._path = path
+ self._zipfile = None
+ self._hole_range = hole_range
+
+ 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')
+ self._load_mask()
+ super().__init__(name=name, raw_shape=raw_shape, **super_kwargs)
+
+ def _load_mask(self, mpath='/data/liwenbo/datasets/Places365/standard/masks_val_512_eval'):
+ self.masks = sorted(glob.glob(mpath + '/*.png'))
+
+ @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
+
+ # for grayscale image
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+
+ # restricted to 512x512
+ res = 512
+ H, W, C = image.shape
+ if H < res or W < res:
+ top = 0
+ bottom = max(0, res - H)
+ left = 0
+ right = max(0, res - W)
+ image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_REFLECT)
+ H, W, C = image.shape
+ h = (H - res) // 2
+ w = (W - res) // 2
+ image = image[h:h+res, w:w+res, :]
+
+ image = np.ascontiguousarray(image.transpose(2, 0, 1)) # HWC => CHW
+ return image
+
+ def _load_raw_labels(self):
+ fname = 'labels.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
+
+ def __getitem__(self, idx):
+ image = self._load_raw_image(self._raw_idx[idx])
+
+ # for grayscale image
+ if image.shape[0] == 1:
+ image = np.repeat(image, 3, axis=0)
+
+ 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]
+ # mask = RandomMask(image.shape[-1], hole_range=self._hole_range) # hole as 0, reserved as 1
+ mask = cv2.imread(self.masks[idx], cv2.IMREAD_GRAYSCALE).astype(np.float32)[np.newaxis, :, :] / 255.0
+ return image.copy(), mask, self.get_label(idx)
diff --git a/datasets/mask_generator_256.py b/datasets/mask_generator_256.py
new file mode 100644
index 0000000000000000000000000000000000000000..766e7403071d4349893a5ea2288c6c2e356e7a25
--- /dev/null
+++ b/datasets/mask_generator_256.py
@@ -0,0 +1,93 @@
+import numpy as np
+from PIL import Image, ImageDraw
+import math
+import random
+
+
+def RandomBrush(
+ max_tries,
+ s,
+ min_num_vertex = 4,
+ max_num_vertex = 18,
+ mean_angle = 2*math.pi / 5,
+ angle_range = 2*math.pi / 15,
+ min_width = 12,
+ max_width = 48):
+ H, W = s, s
+ average_radius = math.sqrt(H*H+W*W) / 8
+ mask = Image.new('L', (W, H), 0)
+ for _ in range(np.random.randint(max_tries)):
+ num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
+ angle_min = mean_angle - np.random.uniform(0, angle_range)
+ angle_max = mean_angle + np.random.uniform(0, angle_range)
+ angles = []
+ vertex = []
+ for i in range(num_vertex):
+ if i % 2 == 0:
+ angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
+ else:
+ angles.append(np.random.uniform(angle_min, angle_max))
+
+ h, w = mask.size
+ vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
+ for i in range(num_vertex):
+ r = np.clip(
+ np.random.normal(loc=average_radius, scale=average_radius//2),
+ 0, 2*average_radius)
+ new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
+ new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
+ vertex.append((int(new_x), int(new_y)))
+
+ draw = ImageDraw.Draw(mask)
+ width = int(np.random.uniform(min_width, max_width))
+ draw.line(vertex, fill=1, width=width)
+ for v in vertex:
+ draw.ellipse((v[0] - width//2,
+ v[1] - width//2,
+ v[0] + width//2,
+ v[1] + width//2),
+ fill=1)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_LEFT_RIGHT)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_TOP_BOTTOM)
+ mask = np.asarray(mask, np.uint8)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 0)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 1)
+ return mask
+
+def RandomMask(s, hole_range=[0,1]):
+ coef = min(hole_range[0] + hole_range[1], 1.0)
+ while True:
+ mask = np.ones((s, s), np.uint8)
+ def Fill(max_size):
+ w, h = np.random.randint(max_size), np.random.randint(max_size)
+ ww, hh = w // 2, h // 2
+ x, y = np.random.randint(-ww, s - w + ww), np.random.randint(-hh, s - h + hh)
+ mask[max(y, 0): min(y + h, s), max(x, 0): min(x + w, s)] = 0
+ def MultiFill(max_tries, max_size):
+ for _ in range(np.random.randint(max_tries)):
+ Fill(max_size)
+ MultiFill(int(4 * coef), s // 2)
+ MultiFill(int(2 * coef), s)
+ mask = np.logical_and(mask, 1 - RandomBrush(int(8 * coef), s)) # hole denoted as 0, reserved as 1
+ hole_ratio = 1 - np.mean(mask)
+ if hole_range is not None and (hole_ratio <= hole_range[0] or hole_ratio >= hole_range[1]):
+ continue
+ return mask[np.newaxis, ...].astype(np.float32)
+
+def BatchRandomMask(batch_size, s, hole_range=[0, 1]):
+ return np.stack([RandomMask(s, hole_range=hole_range) for _ in range(batch_size)], axis=0)
+
+
+if __name__ == '__main__':
+ # res = 512
+ res = 256
+ cnt = 2000
+ tot = 0
+ for i in range(cnt):
+ mask = RandomMask(s=res)
+ tot += mask.mean()
+ print(tot / cnt)
diff --git a/datasets/mask_generator_256_small.py b/datasets/mask_generator_256_small.py
new file mode 100644
index 0000000000000000000000000000000000000000..288eba3ac3b249fc22b2503aad7651ce368c68a4
--- /dev/null
+++ b/datasets/mask_generator_256_small.py
@@ -0,0 +1,93 @@
+import numpy as np
+from PIL import Image, ImageDraw
+import math
+import random
+
+
+def RandomBrush(
+ max_tries,
+ s,
+ min_num_vertex = 4,
+ max_num_vertex = 18,
+ mean_angle = 2*math.pi / 5,
+ angle_range = 2*math.pi / 15,
+ min_width = 12,
+ max_width = 48):
+ H, W = s, s
+ average_radius = math.sqrt(H*H+W*W) / 8
+ mask = Image.new('L', (W, H), 0)
+ for _ in range(np.random.randint(max_tries)):
+ num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
+ angle_min = mean_angle - np.random.uniform(0, angle_range)
+ angle_max = mean_angle + np.random.uniform(0, angle_range)
+ angles = []
+ vertex = []
+ for i in range(num_vertex):
+ if i % 2 == 0:
+ angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
+ else:
+ angles.append(np.random.uniform(angle_min, angle_max))
+
+ h, w = mask.size
+ vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
+ for i in range(num_vertex):
+ r = np.clip(
+ np.random.normal(loc=average_radius, scale=average_radius//2),
+ 0, 2*average_radius)
+ new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
+ new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
+ vertex.append((int(new_x), int(new_y)))
+
+ draw = ImageDraw.Draw(mask)
+ width = int(np.random.uniform(min_width, max_width))
+ draw.line(vertex, fill=1, width=width)
+ for v in vertex:
+ draw.ellipse((v[0] - width//2,
+ v[1] - width//2,
+ v[0] + width//2,
+ v[1] + width//2),
+ fill=1)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_LEFT_RIGHT)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_TOP_BOTTOM)
+ mask = np.asarray(mask, np.uint8)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 0)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 1)
+ return mask
+
+def RandomMask(s, hole_range=[0,1]):
+ coef = min(hole_range[0] + hole_range[1], 1.0)
+ while True:
+ mask = np.ones((s, s), np.uint8)
+ def Fill(max_size):
+ w, h = np.random.randint(max_size), np.random.randint(max_size)
+ ww, hh = w // 2, h // 2
+ x, y = np.random.randint(-ww, s - w + ww), np.random.randint(-hh, s - h + hh)
+ mask[max(y, 0): min(y + h, s), max(x, 0): min(x + w, s)] = 0
+ def MultiFill(max_tries, max_size):
+ for _ in range(np.random.randint(max_tries)):
+ Fill(max_size)
+ MultiFill(int(2 * coef), s // 2)
+ MultiFill(int(2 * coef), s)
+ mask = np.logical_and(mask, 1 - RandomBrush(int(3 * coef), s)) # hole denoted as 0, reserved as 1
+ hole_ratio = 1 - np.mean(mask)
+ if hole_range is not None and (hole_ratio <= hole_range[0] or hole_ratio >= hole_range[1]):
+ continue
+ return mask[np.newaxis, ...].astype(np.float32)
+
+def BatchRandomMask(batch_size, s, hole_range=[0, 1]):
+ return np.stack([RandomMask(s, hole_range=hole_range) for _ in range(batch_size)], axis=0)
+
+
+if __name__ == '__main__':
+ # res = 512
+ res = 256
+ cnt = 2000
+ tot = 0
+ for i in range(cnt):
+ mask = RandomMask(s=res)
+ tot += mask.mean()
+ print(tot / cnt)
diff --git a/datasets/mask_generator_512.py b/datasets/mask_generator_512.py
new file mode 100644
index 0000000000000000000000000000000000000000..d61f93e4c2ce6fc7478171b7788a6fecbceb3ace
--- /dev/null
+++ b/datasets/mask_generator_512.py
@@ -0,0 +1,93 @@
+import numpy as np
+from PIL import Image, ImageDraw
+import math
+import random
+
+
+def RandomBrush(
+ max_tries,
+ s,
+ min_num_vertex = 4,
+ max_num_vertex = 18,
+ mean_angle = 2*math.pi / 5,
+ angle_range = 2*math.pi / 15,
+ min_width = 12,
+ max_width = 48):
+ H, W = s, s
+ average_radius = math.sqrt(H*H+W*W) / 8
+ mask = Image.new('L', (W, H), 0)
+ for _ in range(np.random.randint(max_tries)):
+ num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
+ angle_min = mean_angle - np.random.uniform(0, angle_range)
+ angle_max = mean_angle + np.random.uniform(0, angle_range)
+ angles = []
+ vertex = []
+ for i in range(num_vertex):
+ if i % 2 == 0:
+ angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
+ else:
+ angles.append(np.random.uniform(angle_min, angle_max))
+
+ h, w = mask.size
+ vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
+ for i in range(num_vertex):
+ r = np.clip(
+ np.random.normal(loc=average_radius, scale=average_radius//2),
+ 0, 2*average_radius)
+ new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
+ new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
+ vertex.append((int(new_x), int(new_y)))
+
+ draw = ImageDraw.Draw(mask)
+ width = int(np.random.uniform(min_width, max_width))
+ draw.line(vertex, fill=1, width=width)
+ for v in vertex:
+ draw.ellipse((v[0] - width//2,
+ v[1] - width//2,
+ v[0] + width//2,
+ v[1] + width//2),
+ fill=1)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_LEFT_RIGHT)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_TOP_BOTTOM)
+ mask = np.asarray(mask, np.uint8)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 0)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 1)
+ return mask
+
+def RandomMask(s, hole_range=[0,1]):
+ coef = min(hole_range[0] + hole_range[1], 1.0)
+ while True:
+ mask = np.ones((s, s), np.uint8)
+ def Fill(max_size):
+ w, h = np.random.randint(max_size), np.random.randint(max_size)
+ ww, hh = w // 2, h // 2
+ x, y = np.random.randint(-ww, s - w + ww), np.random.randint(-hh, s - h + hh)
+ mask[max(y, 0): min(y + h, s), max(x, 0): min(x + w, s)] = 0
+ def MultiFill(max_tries, max_size):
+ for _ in range(np.random.randint(max_tries)):
+ Fill(max_size)
+ MultiFill(int(5 * coef), s // 2)
+ MultiFill(int(3 * coef), s)
+ mask = np.logical_and(mask, 1 - RandomBrush(int(9 * coef), s)) # hole denoted as 0, reserved as 1
+ hole_ratio = 1 - np.mean(mask)
+ if hole_range is not None and (hole_ratio <= hole_range[0] or hole_ratio >= hole_range[1]):
+ continue
+ return mask[np.newaxis, ...].astype(np.float32)
+
+def BatchRandomMask(batch_size, s, hole_range=[0, 1]):
+ return np.stack([RandomMask(s, hole_range=hole_range) for _ in range(batch_size)], axis=0)
+
+
+if __name__ == '__main__':
+ res = 512
+ # res = 256
+ cnt = 2000
+ tot = 0
+ for i in range(cnt):
+ mask = RandomMask(s=res)
+ tot += mask.mean()
+ print(tot / cnt)
diff --git a/datasets/mask_generator_512_small.py b/datasets/mask_generator_512_small.py
new file mode 100644
index 0000000000000000000000000000000000000000..40ae3259b99bafe6e4a756766d2727eed657e233
--- /dev/null
+++ b/datasets/mask_generator_512_small.py
@@ -0,0 +1,93 @@
+import numpy as np
+from PIL import Image, ImageDraw
+import math
+import random
+
+
+def RandomBrush(
+ max_tries,
+ s,
+ min_num_vertex = 4,
+ max_num_vertex = 18,
+ mean_angle = 2*math.pi / 5,
+ angle_range = 2*math.pi / 15,
+ min_width = 12,
+ max_width = 48):
+ H, W = s, s
+ average_radius = math.sqrt(H*H+W*W) / 8
+ mask = Image.new('L', (W, H), 0)
+ for _ in range(np.random.randint(max_tries)):
+ num_vertex = np.random.randint(min_num_vertex, max_num_vertex)
+ angle_min = mean_angle - np.random.uniform(0, angle_range)
+ angle_max = mean_angle + np.random.uniform(0, angle_range)
+ angles = []
+ vertex = []
+ for i in range(num_vertex):
+ if i % 2 == 0:
+ angles.append(2*math.pi - np.random.uniform(angle_min, angle_max))
+ else:
+ angles.append(np.random.uniform(angle_min, angle_max))
+
+ h, w = mask.size
+ vertex.append((int(np.random.randint(0, w)), int(np.random.randint(0, h))))
+ for i in range(num_vertex):
+ r = np.clip(
+ np.random.normal(loc=average_radius, scale=average_radius//2),
+ 0, 2*average_radius)
+ new_x = np.clip(vertex[-1][0] + r * math.cos(angles[i]), 0, w)
+ new_y = np.clip(vertex[-1][1] + r * math.sin(angles[i]), 0, h)
+ vertex.append((int(new_x), int(new_y)))
+
+ draw = ImageDraw.Draw(mask)
+ width = int(np.random.uniform(min_width, max_width))
+ draw.line(vertex, fill=1, width=width)
+ for v in vertex:
+ draw.ellipse((v[0] - width//2,
+ v[1] - width//2,
+ v[0] + width//2,
+ v[1] + width//2),
+ fill=1)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_LEFT_RIGHT)
+ if np.random.random() > 0.5:
+ mask.transpose(Image.FLIP_TOP_BOTTOM)
+ mask = np.asarray(mask, np.uint8)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 0)
+ if np.random.random() > 0.5:
+ mask = np.flip(mask, 1)
+ return mask
+
+def RandomMask(s, hole_range=[0,1]):
+ coef = min(hole_range[0] + hole_range[1], 1.0)
+ while True:
+ mask = np.ones((s, s), np.uint8)
+ def Fill(max_size):
+ w, h = np.random.randint(max_size), np.random.randint(max_size)
+ ww, hh = w // 2, h // 2
+ x, y = np.random.randint(-ww, s - w + ww), np.random.randint(-hh, s - h + hh)
+ mask[max(y, 0): min(y + h, s), max(x, 0): min(x + w, s)] = 0
+ def MultiFill(max_tries, max_size):
+ for _ in range(np.random.randint(max_tries)):
+ Fill(max_size)
+ MultiFill(int(3 * coef), s // 2)
+ MultiFill(int(2 * coef), s)
+ mask = np.logical_and(mask, 1 - RandomBrush(int(4 * coef), s)) # hole denoted as 0, reserved as 1
+ hole_ratio = 1 - np.mean(mask)
+ if hole_range is not None and (hole_ratio <= hole_range[0] or hole_ratio >= hole_range[1]):
+ continue
+ return mask[np.newaxis, ...].astype(np.float32)
+
+def BatchRandomMask(batch_size, s, hole_range=[0, 1]):
+ return np.stack([RandomMask(s, hole_range=hole_range) for _ in range(batch_size)], axis=0)
+
+
+if __name__ == '__main__':
+ res = 512
+ # res = 256
+ cnt = 2000
+ tot = 0
+ for i in range(cnt):
+ mask = RandomMask(s=res)
+ tot += mask.mean()
+ print(tot / cnt)
diff --git a/dnnlib/__init__.py b/dnnlib/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..2f08cf36f11f9b0fd94c1b7caeadf69b98375b04
--- /dev/null
+++ b/dnnlib/__init__.py
@@ -0,0 +1,9 @@
+# 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
diff --git a/dnnlib/util.py b/dnnlib/util.py
new file mode 100644
index 0000000000000000000000000000000000000000..76725336d01e75e1c68daa88be47f4fde0bbc63b
--- /dev/null
+++ b/dnnlib/util.py
@@ -0,0 +1,477 @@
+# 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)
diff --git a/evaluatoin/cal_fid_pids_uids.py b/evaluatoin/cal_fid_pids_uids.py
new file mode 100644
index 0000000000000000000000000000000000000000..ba57c3fcd47ac6aa6c292588de1a0a1696bea655
--- /dev/null
+++ b/evaluatoin/cal_fid_pids_uids.py
@@ -0,0 +1,193 @@
+import cv2
+import os
+import sys
+sys.path.insert(0, '../')
+import numpy as np
+import math
+import glob
+import pyspng
+import PIL.Image
+import torch
+import dnnlib
+import scipy.linalg
+import sklearn.svm
+
+
+_feature_detector_cache = dict()
+
+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]
+
+
+def read_image(image_path):
+ with open(image_path, 'rb') as f:
+ if pyspng is not None and image_path.endswith('.png'):
+ image = pyspng.load(f.read())
+ else:
+ image = np.array(PIL.Image.open(f))
+ if image.ndim == 2:
+ image = image[:, :, np.newaxis] # HW => HWC
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+ image = image.transpose(2, 0, 1) # HWC => CHW
+ image = torch.from_numpy(image).unsqueeze(0).to(torch.uint8)
+
+ return image
+
+
+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
+
+
+def calculate_metrics(folder1, folder2):
+ l1 = sorted(glob.glob(folder1 + '/*.png') + glob.glob(folder1 + '/*.jpg'))
+ l2 = sorted(glob.glob(folder2 + '/*.png') + glob.glob(folder2 + '/*.jpg'))
+ assert(len(l1) == len(l2))
+ print('length:', len(l1))
+
+ # l1 = l1[:3]; l2 = l2[:3];
+
+ # build detector
+ 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.
+ device = torch.device('cuda:0')
+ detector = get_feature_detector(url=detector_url, device=device, num_gpus=1, rank=0, verbose=False)
+ detector.eval()
+
+ stat1 = FeatureStats(capture_all=True, capture_mean_cov=True, max_items=len(l1))
+ stat2 = FeatureStats(capture_all=True, capture_mean_cov=True, max_items=len(l1))
+
+ with torch.no_grad():
+ for i, (fpath1, fpath2) in enumerate(zip(l1, l2)):
+ print(i)
+ _, name1 = os.path.split(fpath1)
+ _, name2 = os.path.split(fpath2)
+ name1 = name1.split('.')[0]
+ name2 = name2.split('.')[0]
+ assert name1 == name2, 'Illegal mapping: %s, %s' % (name1, name2)
+
+ img1 = read_image(fpath1).to(device)
+ img2 = read_image(fpath2).to(device)
+ assert img1.shape == img2.shape, 'Illegal shape'
+ fea1 = detector(img1, **detector_kwargs)
+ stat1.append_torch(fea1, num_gpus=1, rank=0)
+ fea2 = detector(img2, **detector_kwargs)
+ stat2.append_torch(fea2, num_gpus=1, rank=0)
+
+ # calculate fid
+ mu1, sigma1 = stat1.get_mean_cov()
+ mu2, sigma2 = stat2.get_mean_cov()
+ m = np.square(mu1 - mu2).sum()
+ s, _ = scipy.linalg.sqrtm(np.dot(sigma1, sigma2), disp=False) # pylint: disable=no-member
+ fid = np.real(m + np.trace(sigma1 + sigma2 - s * 2))
+
+ # calculate pids and uids
+ fake_activations = stat1.get_all()
+ real_activations = stat2.get_all()
+ svm = sklearn.svm.LinearSVC(dual=False)
+ svm_inputs = np.concatenate([real_activations, fake_activations])
+ svm_targets = np.array([1] * real_activations.shape[0] + [0] * fake_activations.shape[0])
+ print('SVM fitting ...')
+ svm.fit(svm_inputs, svm_targets)
+ uids = 1 - svm.score(svm_inputs, svm_targets)
+ real_outputs = svm.decision_function(real_activations)
+ fake_outputs = svm.decision_function(fake_activations)
+ pids = np.mean(fake_outputs > real_outputs)
+
+ return fid, pids, uids
+
+
+if __name__ == '__main__':
+ folder1 = 'path to the inpainted result'
+ folder2 = 'path to the gt'
+
+ fid, pids, uids = calculate_metrics(folder1, folder2)
+ print('fid: %.4f, pids: %.4f, uids: %.4f' % (fid, pids, uids))
+ with open('fid_pids_uids.txt', 'w') as f:
+ f.write('fid: %.4f, pids: %.4f, uids: %.4f' % (fid, pids, uids))
+
diff --git a/evaluatoin/cal_lpips.py b/evaluatoin/cal_lpips.py
new file mode 100644
index 0000000000000000000000000000000000000000..a66d53b6de3ed0af6441d633990aa0d16c49b7e4
--- /dev/null
+++ b/evaluatoin/cal_lpips.py
@@ -0,0 +1,71 @@
+import cv2
+import os
+import sys
+import numpy as np
+import math
+import glob
+import pyspng
+import PIL.Image
+
+import torch
+import lpips
+
+
+def read_image(image_path):
+ with open(image_path, 'rb') as f:
+ if pyspng is not None and image_path.endswith('.png'):
+ image = pyspng.load(f.read())
+ else:
+ image = np.array(PIL.Image.open(f))
+ if image.ndim == 2:
+ image = image[:, :, np.newaxis] # HW => HWC
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+ image = image.transpose(2, 0, 1) # HWC => CHW
+ image = torch.from_numpy(image).float().unsqueeze(0)
+ image = image / 127.5 - 1
+
+ return image
+
+
+def calculate_metrics(folder1, folder2):
+ l1 = sorted(glob.glob(folder1 + '/*.png') + glob.glob(folder1 + '/*.jpg'))
+ l2 = sorted(glob.glob(folder2 + '/*.png') + glob.glob(folder2 + '/*.jpg'))
+ assert(len(l1) == len(l2))
+ print('length:', len(l1))
+
+ # l1 = l1[:3]; l2 = l2[:3];
+
+ device = torch.device('cuda:0')
+ loss_fn = lpips.LPIPS(net='alex').to(device)
+ loss_fn.eval()
+ # loss_fn = lpips.LPIPS(net='vgg').to(device)
+
+ lpips_l = []
+ with torch.no_grad():
+ for i, (fpath1, fpath2) in enumerate(zip(l1, l2)):
+ print(i)
+ _, name1 = os.path.split(fpath1)
+ _, name2 = os.path.split(fpath2)
+ name1 = name1.split('.')[0]
+ name2 = name2.split('.')[0]
+ assert name1 == name2, 'Illegal mapping: %s, %s' % (name1, name2)
+
+ img1 = read_image(fpath1).to(device)
+ img2 = read_image(fpath2).to(device)
+ assert img1.shape == img2.shape, 'Illegal shape'
+ lpips_l.append(loss_fn(img1, img2).mean().cpu().numpy())
+
+ res = sum(lpips_l) / len(lpips_l)
+
+ return res
+
+
+if __name__ == '__main__':
+ folder1 = 'path to the inpainted result'
+ folder2 = 'path to the gt'
+
+ res = calculate_metrics(folder1, folder2)
+ print('lpips: %.4f' % res)
+ with open('lpips.txt', 'w') as f:
+ f.write('lpips: %.4f' % res)
diff --git a/evaluatoin/cal_psnr_ssim_l1.py b/evaluatoin/cal_psnr_ssim_l1.py
new file mode 100644
index 0000000000000000000000000000000000000000..2dbd401e9ccd87b06a1549aaf9656f6b64756d79
--- /dev/null
+++ b/evaluatoin/cal_psnr_ssim_l1.py
@@ -0,0 +1,107 @@
+import cv2
+import os
+import sys
+import numpy as np
+import math
+import glob
+import pyspng
+import PIL.Image
+
+
+def calculate_psnr(img1, img2):
+ # img1 and img2 have range [0, 255]
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ mse = np.mean((img1 - img2) ** 2)
+ if mse == 0:
+ return float('inf')
+
+ return 20 * math.log10(255.0 / math.sqrt(mse))
+
+
+def calculate_ssim(img1, img2):
+ C1 = (0.01 * 255) ** 2
+ C2 = (0.03 * 255) ** 2
+
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ kernel = cv2.getGaussianKernel(11, 1.5)
+ window = np.outer(kernel, kernel.transpose())
+
+ mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
+ mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+ mu1_sq = mu1 ** 2
+ mu2_sq = mu2 ** 2
+ mu1_mu2 = mu1 * mu2
+ sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
+ sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
+ sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+ ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+ return ssim_map.mean()
+
+
+def calculate_l1(img1, img2):
+ img1 = img1.astype(np.float64) / 255.0
+ img2 = img2.astype(np.float64) / 255.0
+ l1 = np.mean(np.abs(img1 - img2))
+
+ return l1
+
+
+def read_image(image_path):
+ with open(image_path, 'rb') as f:
+ if pyspng is not None and image_path.endswith('.png'):
+ image = pyspng.load(f.read())
+ else:
+ image = np.array(PIL.Image.open(f))
+ if image.ndim == 2:
+ image = image[:, :, np.newaxis] # HW => HWC
+ if image.shape[2] == 1:
+ image = np.repeat(image, 3, axis=2)
+ # image = image.transpose(2, 0, 1) # HWC => CHW
+
+ return image
+
+
+def calculate_metrics(folder1, folder2):
+ l1 = sorted(glob.glob(folder1 + '/*.png') + glob.glob(folder1 + '/*.jpg'))
+ l2 = sorted(glob.glob(folder2 + '/*.png') + glob.glob(folder2 + '/*.jpg'))
+ assert(len(l1) == len(l2))
+ print('length:', len(l1))
+
+ # l1 = l1[:3]; l2 = l2[:3];
+
+ psnr_l, ssim_l, dl1_l = [], [], []
+ for i, (fpath1, fpath2) in enumerate(zip(l1, l2)):
+ print(i)
+ _, name1 = os.path.split(fpath1)
+ _, name2 = os.path.split(fpath2)
+ name1 = name1.split('.')[0]
+ name2 = name2.split('.')[0]
+ assert name1 == name2, 'Illegal mapping: %s, %s' % (name1, name2)
+
+ img1 = read_image(fpath1).astype(np.float64)
+ img2 = read_image(fpath2).astype(np.float64)
+ assert img1.shape == img2.shape, 'Illegal shape'
+ psnr_l.append(calculate_psnr(img1, img2))
+ ssim_l.append(calculate_ssim(img1, img2))
+ dl1_l.append(calculate_l1(img1, img2))
+
+ psnr = sum(psnr_l) / len(psnr_l)
+ ssim = sum(ssim_l) / len(ssim_l)
+ dl1 = sum(dl1_l) / len(dl1_l)
+
+ return psnr, ssim, dl1
+
+
+if __name__ == '__main__':
+ folder1 = 'path to the inpainted result'
+ folder2 = 'path to the gt'
+
+ psnr, ssim, dl1 = calculate_metrics(folder1, folder2)
+ print('psnr: %.4f, ssim: %.4f, l1: %.4f' % (psnr, ssim, dl1))
+ with open('psnr_ssim_l1.txt', 'w') as f:
+ f.write('psnr: %.4f, ssim: %.4f, l1: %.4f' % (psnr, ssim, dl1))
+
diff --git a/legacy.py b/legacy.py
new file mode 100644
index 0000000000000000000000000000000000000000..a22d32c80f313f6dead3ba2887caab5bb8cf7e23
--- /dev/null
+++ b/legacy.py
@@ -0,0 +1,323 @@
+# 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 == 'torch.storage' and name == '_load_from_bytes':
+ import io
+ return lambda b: torch.load(io.BytesIO(b), map_location='cpu')
+ 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
+
+#----------------------------------------------------------------------------
diff --git a/losses/loss.py b/losses/loss.py
new file mode 100644
index 0000000000000000000000000000000000000000..8812f883045afedfcfbf6cc37be39959af96fcb4
--- /dev/null
+++ b/losses/loss.py
@@ -0,0 +1,170 @@
+# 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
+from losses.pcp import PerceptualLoss
+
+#----------------------------------------------------------------------------
+
+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 TwoStageLoss(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, truncation_psi=1, pcp_ratio=1.0):
+ 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)
+ self.truncation_psi = truncation_psi
+ self.pcp = PerceptualLoss(layer_weights=dict(conv4_4=1/4, conv5_4=1/2)).to(device)
+ self.pcp_ratio = pcp_ratio
+
+ def run_G(self, img_in, mask_in, z, c, sync):
+ with misc.ddp_sync(self.G_mapping, sync):
+ ws = self.G_mapping(z, c, truncation_psi=self.truncation_psi)
+ 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, truncation_psi=self.truncation_psi, skip_w_avg_update=True)[:, cutoff:]
+ with misc.ddp_sync(self.G_synthesis, sync):
+ img, img_stg1 = self.G_synthesis(img_in, mask_in, ws, return_stg1=True)
+ return img, ws, img_stg1
+
+ def run_D(self, img, mask, img_stg1, c, sync):
+ # if self.augment_pipe is not None:
+ # # img = self.augment_pipe(img)
+ # # !!!!! have to remove the color transform
+ # tmp_img = torch.cat([img, mask], dim=1)
+ # tmp_img = self.augment_pipe(tmp_img)
+ # img, mask = torch.split(tmp_img, [3, 1])
+ with misc.ddp_sync(self.D, sync):
+ logits, logits_stg1 = self.D(img, mask, img_stg1, c)
+ return logits, logits_stg1
+
+ def accumulate_gradients(self, phase, real_img, mask, 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, gen_img_stg1 = self.run_G(real_img, mask, gen_z, gen_c, sync=(sync and not do_Gpl)) # May get synced by Gpl.
+ gen_logits, gen_logits_stg1 = self.run_D(gen_img, mask, gen_img_stg1, gen_c, sync=False)
+ training_stats.report('Loss/scores/fake', gen_logits)
+ training_stats.report('Loss/signs/fake', gen_logits.sign())
+ training_stats.report('Loss/scores/fake_s1', gen_logits_stg1)
+ training_stats.report('Loss/signs/fake_s1', gen_logits_stg1.sign())
+ loss_Gmain = torch.nn.functional.softplus(-gen_logits) # -log(sigmoid(gen_logits))
+ training_stats.report('Loss/G/loss', loss_Gmain)
+ loss_Gmain_stg1 = torch.nn.functional.softplus(-gen_logits_stg1)
+ training_stats.report('Loss/G/loss_s1', loss_Gmain_stg1)
+ # just for showing
+ l1_loss = torch.mean(torch.abs(gen_img - real_img))
+ training_stats.report('Loss/G/l1_loss', l1_loss)
+ pcp_loss, _ = self.pcp(gen_img, real_img)
+ training_stats.report('Loss/G/pcp_loss', pcp_loss)
+ with torch.autograd.profiler.record_function('Gmain_backward'):
+ loss_Gmain_all = loss_Gmain + loss_Gmain_stg1 + pcp_loss * self.pcp_ratio
+ loss_Gmain_all.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(real_img[:batch_size], mask[:batch_size], 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
+ loss_Dgen_stg1 = 0
+ if do_Dmain:
+ with torch.autograd.profiler.record_function('Dgen_forward'):
+ gen_img, _gen_ws, gen_img_stg1 = self.run_G(real_img, mask, gen_z, gen_c, sync=False)
+ gen_logits, gen_logits_stg1 = self.run_D(gen_img, mask, gen_img_stg1, 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))
+ training_stats.report('Loss/scores/fake_s1', gen_logits_stg1)
+ training_stats.report('Loss/signs/fake_s1', gen_logits_stg1.sign())
+ loss_Dgen_stg1 = torch.nn.functional.softplus(gen_logits_stg1) # -log(1 - sigmoid(gen_logits))
+ with torch.autograd.profiler.record_function('Dgen_backward'):
+ loss_Dgen_all = loss_Dgen + loss_Dgen_stg1
+ loss_Dgen_all.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)
+ mask_tmp = mask.detach().requires_grad_(do_Dr1)
+ real_img_tmp_stg1 = real_img.detach().requires_grad_(do_Dr1)
+ real_logits, real_logits_stg1 = self.run_D(real_img_tmp, mask_tmp, real_img_tmp_stg1, real_c, sync=sync)
+ training_stats.report('Loss/scores/real', real_logits)
+ training_stats.report('Loss/signs/real', real_logits.sign())
+ training_stats.report('Loss/scores/real_s1', real_logits_stg1)
+ training_stats.report('Loss/signs/real_s1', real_logits_stg1.sign())
+
+ loss_Dreal = 0
+ loss_Dreal_stg1 = 0
+ if do_Dmain:
+ loss_Dreal = torch.nn.functional.softplus(-real_logits) # -log(sigmoid(real_logits))
+ loss_Dreal_stg1 = torch.nn.functional.softplus(-real_logits_stg1) # -log(sigmoid(real_logits))
+ training_stats.report('Loss/D/loss', loss_Dgen + loss_Dreal)
+ training_stats.report('Loss/D/loss_s1', loss_Dgen_stg1 + loss_Dreal_stg1)
+
+ loss_Dr1 = 0
+ loss_Dr1_stg1 = 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_grads_stg1 = torch.autograd.grad(outputs=[real_logits_stg1.sum()], inputs=[real_img_tmp_stg1], 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)
+
+ r1_penalty_stg1 = r1_grads_stg1.square().sum([1, 2, 3])
+ loss_Dr1_stg1 = r1_penalty_stg1 * (self.r1_gamma / 2)
+ training_stats.report('Loss/r1_penalty_s1', r1_penalty_stg1)
+ training_stats.report('Loss/D/reg_s1', loss_Dr1_stg1)
+
+ with torch.autograd.profiler.record_function(name + '_backward'):
+ ((real_logits + real_logits_stg1) * 0 + loss_Dreal + loss_Dreal_stg1 + loss_Dr1 + loss_Dr1_stg1).mean().mul(gain).backward()
+
+#----------------------------------------------------------------------------
diff --git a/losses/pcp.py b/losses/pcp.py
new file mode 100644
index 0000000000000000000000000000000000000000..3e4ebc2559478f0e98ad1b5f7e8b0da7ccbe9d15
--- /dev/null
+++ b/losses/pcp.py
@@ -0,0 +1,126 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from losses.vggNet import VGGFeatureExtractor
+import numpy as np
+
+
+class PerceptualLoss(nn.Module):
+ """Perceptual loss with commonly used style loss.
+
+ Args:
+ layer_weights (dict): The weight for each layer of vgg feature.
+ Here is an example: {'conv5_4': 1.}, which means the conv5_4
+ feature layer (before relu5_4) will be extracted with weight
+ 1.0 in calculting losses.
+ vgg_type (str): The type of vgg network used as feature extractor.
+ Default: 'vgg19'.
+ use_input_norm (bool): If True, normalize the input image in vgg.
+ Default: True.
+ perceptual_weight (float): If `perceptual_weight > 0`, the perceptual
+ loss will be calculated and the loss will multiplied by the
+ weight. Default: 1.0.
+ style_weight (float): If `style_weight > 0`, the style loss will be
+ calculated and the loss will multiplied by the weight.
+ Default: 0.
+ norm_img (bool): If True, the image will be normed to [0, 1]. Note that
+ this is different from the `use_input_norm` which norm the input in
+ in forward function of vgg according to the statistics of dataset.
+ Importantly, the input image must be in range [-1, 1].
+ Default: False.
+ criterion (str): Criterion used for perceptual loss. Default: 'l1'.
+ """
+
+ def __init__(self,
+ layer_weights,
+ vgg_type='vgg19',
+ use_input_norm=True,
+ use_pcp_loss=True,
+ use_style_loss=False,
+ norm_img=True,
+ criterion='l1'):
+ super(PerceptualLoss, self).__init__()
+ self.norm_img = norm_img
+ self.use_pcp_loss = use_pcp_loss
+ self.use_style_loss = use_style_loss
+ self.layer_weights = layer_weights
+ self.vgg = VGGFeatureExtractor(
+ layer_name_list=list(layer_weights.keys()),
+ vgg_type=vgg_type,
+ use_input_norm=use_input_norm)
+
+ self.criterion_type = criterion
+ if self.criterion_type == 'l1':
+ self.criterion = torch.nn.L1Loss()
+ elif self.criterion_type == 'l2':
+ self.criterion = torch.nn.L2loss()
+ elif self.criterion_type == 'fro':
+ self.criterion = None
+ else:
+ raise NotImplementedError('%s criterion has not been supported.' % self.criterion_type)
+
+ def forward(self, x, gt):
+ """Forward function.
+
+ Args:
+ x (Tensor): Input tensor with shape (n, c, h, w).
+ gt (Tensor): Ground-truth tensor with shape (n, c, h, w).
+
+ Returns:
+ Tensor: Forward results.
+ """
+
+ if self.norm_img:
+ x = (x + 1.) * 0.5
+ gt = (gt + 1.) * 0.5
+
+ # extract vgg features
+ x_features = self.vgg(x)
+ gt_features = self.vgg(gt.detach())
+
+ # calculate perceptual loss
+ if self.use_pcp_loss:
+ percep_loss = 0
+ for k in x_features.keys():
+ if self.criterion_type == 'fro':
+ percep_loss += torch.norm(
+ x_features[k] - gt_features[k],
+ p='fro') * self.layer_weights[k]
+ else:
+ percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k]
+ else:
+ percep_loss = None
+
+ # calculate style loss
+ if self.use_style_loss:
+ style_loss = 0
+ for k in x_features.keys():
+ if self.criterion_type == 'fro':
+ style_loss += torch.norm(
+ self._gram_mat(x_features[k]) -
+ self._gram_mat(gt_features[k]),
+ p='fro') * self.layer_weights[k]
+ else:
+ style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat(gt_features[k])) \
+ * self.layer_weights[k]
+ else:
+ style_loss = None
+
+ return percep_loss, style_loss
+
+ def _gram_mat(self, x):
+ """Calculate Gram matrix.
+
+ Args:
+ x (torch.Tensor): Tensor with shape of (n, c, h, w).
+
+ Returns:
+ torch.Tensor: Gram matrix.
+ """
+ n, c, h, w = x.size()
+ features = x.view(n, c, w * h)
+ features_t = features.transpose(1, 2)
+ gram = features.bmm(features_t) / (c * h * w)
+ return gram
+
diff --git a/losses/vggNet.py b/losses/vggNet.py
new file mode 100644
index 0000000000000000000000000000000000000000..0c28e3ce8b50e6a1089e8c85630feeb2cb7e02a6
--- /dev/null
+++ b/losses/vggNet.py
@@ -0,0 +1,178 @@
+from collections import OrderedDict
+
+import torch
+import torch.nn as nn
+from torch.nn import DataParallel
+from torch.nn.parallel import DistributedDataParallel
+from torchvision.models import vgg as vgg
+
+
+NAMES = {
+ 'vgg11': [
+ 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2',
+ 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1',
+ 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1',
+ 'conv5_2', 'relu5_2', 'pool5'
+ ],
+ 'vgg13': [
+ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
+ 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
+ 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2',
+ 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5'
+ ],
+ 'vgg16': [
+ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
+ 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
+ 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1',
+ 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4',
+ 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3',
+ 'pool5'
+ ],
+ 'vgg19': [
+ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1',
+ 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1',
+ 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4',
+ 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
+ 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1',
+ 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4',
+ 'pool5'
+ ]
+}
+
+
+# MODEL_PATH = {
+# 'vgg19': 'losses/pretrained/vgg19-dcbb9e9d.pth'
+# }
+
+
+def load_model(model, model_path, strict=True, cpu=False):
+ if isinstance(model, DataParallel) or isinstance(model, DistributedDataParallel):
+ model = model.module
+ if cpu:
+ loaded_model = torch.load(model_path, map_location='cpu')
+ else:
+ loaded_model = torch.load(model_path)
+ model.load_state_dict(loaded_model, strict=strict)
+
+
+def insert_bn(names):
+ """Insert bn layer after each conv.
+
+ Args:
+ names (list): The list of layer names.
+
+ Returns:
+ list: The list of layer names with bn layers.
+ """
+ names_bn = []
+ for name in names:
+ names_bn.append(name)
+ if 'conv' in name:
+ position = name.replace('conv', '')
+ names_bn.append('bn' + position)
+ return names_bn
+
+
+class VGGFeatureExtractor(nn.Module):
+ """VGG network for feature extraction.
+
+ In this implementation, we allow users to choose whether use normalization
+ in the input feature and the type of vgg network. Note that the pretrained
+ path must fit the vgg type.
+
+ Args:
+ layer_name_list (list[str]): Forward function returns the corresponding
+ features according to the layer_name_list.
+ Example: {'relu1_1', 'relu2_1', 'relu3_1'}.
+ vgg_type (str): Set the type of vgg network. Default: 'vgg19'.
+ use_input_norm (bool): If True, normalize the input image. Importantly,
+ the input feature must in the range [0, 1]. Default: True.
+ requires_grad (bool): If true, the parameters of VGG network will be
+ optimized. Default: False.
+ remove_pooling (bool): If true, the max pooling operations in VGG net
+ will be removed. Default: False.
+ pooling_stride (int): The stride of max pooling operation. Default: 2.
+ """
+
+ def __init__(self,
+ layer_name_list,
+ vgg_type='vgg19',
+ use_input_norm=True,
+ requires_grad=False,
+ remove_pooling=False,
+ pooling_stride=2):
+ super(VGGFeatureExtractor, self).__init__()
+
+ self.layer_name_list = layer_name_list
+ self.use_input_norm = use_input_norm
+
+ self.names = NAMES[vgg_type.replace('_bn', '')]
+ if 'bn' in vgg_type:
+ self.names = insert_bn(self.names)
+
+ # only borrow layers that will be used to avoid unused params
+ max_idx = 0
+ for v in layer_name_list:
+ idx = self.names.index(v)
+ if idx > max_idx:
+ max_idx = idx
+
+ features = getattr(vgg, vgg_type)(pretrained=True).features[:max_idx + 1]
+ # vgg_model = getattr(vgg, vgg_type)(pretrained=False)
+ # load_model(vgg_model, MODEL_PATH[vgg_type], strict=True)
+ # features = vgg_model.features[:max_idx + 1]
+
+ modified_net = OrderedDict()
+ for k, v in zip(self.names, features):
+ if 'pool' in k:
+ # if remove_pooling is true, pooling operation will be removed
+ if remove_pooling:
+ continue
+ else:
+ # in some cases, we may want to change the default stride
+ modified_net[k] = nn.MaxPool2d(
+ kernel_size=2, stride=pooling_stride)
+ else:
+ modified_net[k] = v
+
+ self.vgg_net = nn.Sequential(modified_net)
+
+ if not requires_grad:
+ self.vgg_net.eval()
+ for param in self.parameters():
+ param.requires_grad = False
+ else:
+ self.vgg_net.train()
+ for param in self.parameters():
+ param.requires_grad = True
+
+ if self.use_input_norm:
+ # the mean is for image with range [0, 1]
+ self.register_buffer(
+ 'mean',
+ torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1))
+ # the std is for image with range [0, 1]
+ self.register_buffer(
+ 'std',
+ torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1))
+
+ def forward(self, x):
+ """Forward function.
+
+ Args:
+ x (Tensor): Input tensor with shape (n, c, h, w).
+
+ Returns:
+ Tensor: Forward results.
+ """
+
+ if self.use_input_norm:
+ x = (x - self.mean) / self.std
+
+ output = {}
+ for key, layer in self.vgg_net._modules.items():
+ x = layer(x)
+ if key in self.layer_name_list:
+ output[key] = x.clone()
+
+ return output
diff --git a/metrics/__init__.py b/metrics/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1e1a5ba99e56a56ecaa14f7d4fa41777789c0cf
--- /dev/null
+++ b/metrics/__init__.py
@@ -0,0 +1,9 @@
+# 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
diff --git a/metrics/frechet_inception_distance.py b/metrics/frechet_inception_distance.py
new file mode 100644
index 0000000000000000000000000000000000000000..1d38ec731b33e6f4f20cd4601e58d7e5ce2eaaa3
--- /dev/null
+++ b/metrics/frechet_inception_distance.py
@@ -0,0 +1,41 @@
+# 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)
+
+#----------------------------------------------------------------------------
diff --git a/metrics/inception_discriminative_score.py b/metrics/inception_discriminative_score.py
new file mode 100644
index 0000000000000000000000000000000000000000..38e867f05c429d7ce97c0ec737492f4342a20b81
--- /dev/null
+++ b/metrics/inception_discriminative_score.py
@@ -0,0 +1,37 @@
+
+import numpy as np
+import scipy.linalg
+from . import metric_utils
+import sklearn.svm
+
+#----------------------------------------------------------------------------
+
+def compute_ids(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.
+
+ real_activations = 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()
+
+ fake_activations = 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')
+
+ svm = sklearn.svm.LinearSVC(dual=False)
+ svm_inputs = np.concatenate([real_activations, fake_activations])
+ svm_targets = np.array([1] * real_activations.shape[0] + [0] * fake_activations.shape[0])
+ print('Fitting ...')
+ svm.fit(svm_inputs, svm_targets)
+ u_ids = 1 - svm.score(svm_inputs, svm_targets)
+ real_outputs = svm.decision_function(real_activations)
+ fake_outputs = svm.decision_function(fake_activations)
+ p_ids = np.mean(fake_outputs > real_outputs)
+
+ return float(u_ids), float(p_ids)
+
+#----------------------------------------------------------------------------
diff --git a/metrics/inception_score.py b/metrics/inception_score.py
new file mode 100644
index 0000000000000000000000000000000000000000..3822c1435901a47e8c192b52cd3ed1ce5de67acd
--- /dev/null
+++ b/metrics/inception_score.py
@@ -0,0 +1,38 @@
+# 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))
+
+#----------------------------------------------------------------------------
diff --git a/metrics/kernel_inception_distance.py b/metrics/kernel_inception_distance.py
new file mode 100644
index 0000000000000000000000000000000000000000..3ac978925b5cf810463ef8e8a6f0dcd3f9078e6d
--- /dev/null
+++ b/metrics/kernel_inception_distance.py
@@ -0,0 +1,46 @@
+# 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)
+
+#----------------------------------------------------------------------------
diff --git a/metrics/metric_main.py b/metrics/metric_main.py
new file mode 100644
index 0000000000000000000000000000000000000000..089917a96fc23d31d1b18b5be9051b30070da572
--- /dev/null
+++ b/metrics/metric_main.py
@@ -0,0 +1,184 @@
+# 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
+from . import psnr_ssim_l1
+from . import inception_discriminative_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 fid2993_full(opts):
+ opts.dataset_kwargs.update(max_size=None, xflip=False)
+ fid = frechet_inception_distance.compute_fid(opts, max_real=2993, num_gen=2993)
+ return dict(fid2993_full=fid)
+
+@register_metric
+def fid36k5_full(opts):
+ opts.dataset_kwargs.update(max_size=None, xflip=False)
+ fid = frechet_inception_distance.compute_fid(opts, max_real=36500, num_gen=36500)
+ return dict(fid36k5_full=fid)
+
+@register_metric
+def fid_places(opts):
+ opts.dataset_kwargs.update(max_size=None, xflip=False)
+ fid = frechet_inception_distance.compute_fid(opts, max_real=36500, num_gen=36500)
+ return dict(fid36k5_full=fid)
+
+@register_metric
+def ids_places(opts):
+ opts.dataset_kwargs.update(max_size=None, xflip=False)
+ u_ids, p_ids = inception_discriminative_score.compute_ids(opts, max_real=36500, num_gen=36500)
+ return dict(u_ids=u_ids, p_ids=p_ids)
+
+@register_metric
+def psnr36k5_full(opts):
+ opts.dataset_kwargs.update(max_size=None, xflip=False)
+ psnr, ssim, l1 = psnr_ssim_l1.compute_psnr(opts, max_real=36500)
+ return dict(psnr=psnr, ssim=ssim, l1=l1)
+
+@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)
+
+#----------------------------------------------------------------------------
diff --git a/metrics/metric_utils.py b/metrics/metric_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..1a64bbf488880aef5580a2c6b6dfdf447d9fd9a5
--- /dev/null
+++ b/metrics/metric_utils.py
@@ -0,0 +1,434 @@
+# 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
+import math
+import cv2
+
+#----------------------------------------------------------------------------
+
+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):
+ # adaptation to inpainting
+ for images, masks, _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, data_loader_kwargs=None, **stats_kwargs):
+ if data_loader_kwargs is None:
+ data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)
+
+ 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(img_in, mask_in, z, c):
+ img = G(img_in, mask_in, z, c, **opts.G_kwargs)
+ # img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+ img = ((img + 1.0) * 127.5).clamp(0, 255).round().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.
+ item_subset = [(i * opts.num_gpus + opts.rank) % stats.max_items for i in range((stats.max_items - 1) // opts.num_gpus + 1)]
+ for imgs_batch, masks_batch, labels_batch in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset,
+ batch_size=batch_size,
+ **data_loader_kwargs):
+ images = []
+ imgs_gen = (imgs_batch.to(opts.device).to(torch.float32) / 127.5 - 1).split(batch_gen)
+ masks_gen = masks_batch.to(opts.device).to(torch.float32).split(batch_gen)
+ for img_in, mask_in in zip(imgs_gen, masks_gen):
+ z = torch.randn([img_in.shape[0], G.z_dim], device=opts.device)
+ c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(img_in.shape[0])]
+ c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
+ images.append(run_generator(img_in, mask_in, 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
+
+#----------------------------------------------------------------------------
+
+def compute_image_stats_for_generator(opts, rel_lo=0, rel_hi=1, batch_size=64, batch_gen=None, jit=False, data_loader_kwargs=None, **stats_kwargs):
+ if data_loader_kwargs is None:
+ data_loader_kwargs = dict(pin_memory=True, num_workers=3, prefetch_factor=2)
+
+ 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(img_in, mask_in, z, c):
+ img = G(img_in, mask_in, z, c, **opts.G_kwargs)
+ # img = (img * 127.5 + 128).clamp(0, 255).to(torch.uint8)
+ img = ((img + 1.0) * 127.5).clamp(0, 255).round().to(torch.uint8)
+ return img
+
+ # Initialize.
+ stats = FeatureStats(**stats_kwargs)
+ assert stats.max_items is not None
+ progress = opts.progress.sub(tag='generator images', num_items=stats.max_items, rel_lo=rel_lo, rel_hi=rel_hi)
+
+ # Main loop.
+ item_subset = [(i * opts.num_gpus + opts.rank) % stats.max_items for i in range((stats.max_items - 1) // opts.num_gpus + 1)]
+ for imgs_batch, masks_batch, labels_batch in torch.utils.data.DataLoader(dataset=dataset, sampler=item_subset,
+ batch_size=batch_size,
+ **data_loader_kwargs):
+ images = []
+ imgs_gen = (imgs_batch.to(opts.device).to(torch.float32) / 127.5 - 1).split(batch_gen)
+ masks_gen = masks_batch.to(opts.device).to(torch.float32).split(batch_gen)
+ for img_in, mask_in in zip(imgs_gen, masks_gen):
+ z = torch.randn([img_in.shape[0], G.z_dim], device=opts.device)
+ c = [dataset.get_label(np.random.randint(len(dataset))) for _i in range(img_in.shape[0])]
+ c = torch.from_numpy(np.stack(c)).pin_memory().to(opts.device)
+ images.append(run_generator(img_in, mask_in, z, c))
+ images = torch.cat(images)
+ if images.shape[1] == 1:
+ images = images.repeat([1, 3, 1, 1])
+
+ assert imgs_batch.shape == images.shape
+ metrics = []
+ for i in range(imgs_batch.shape[0]):
+ img_real = np.transpose(imgs_batch[i].cpu().numpy(), [1, 2, 0])
+ img_gen = np.transpose(images[i].cpu().numpy(), [1, 2, 0])
+ psnr = calculate_psnr(img_gen, img_real)
+ ssim = calculate_ssim(img_gen, img_real)
+ l1 = calculate_l1(img_gen, img_real)
+ metrics.append([psnr, ssim, l1])
+ metrics = torch.from_numpy(np.array(metrics)).to(torch.float32).to(opts.device)
+
+ stats.append_torch(metrics, num_gpus=opts.num_gpus, rank=opts.rank)
+ progress.update(stats.num_items)
+ return stats
+
+
+def calculate_psnr(img1, img2):
+ # img1 and img2 have range [0, 255]
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ mse = np.mean((img1 - img2) ** 2)
+ if mse == 0:
+ return float('inf')
+
+ return 20 * math.log10(255.0 / math.sqrt(mse))
+
+
+def calculate_ssim(img1, img2):
+ C1 = (0.01 * 255) ** 2
+ C2 = (0.03 * 255) ** 2
+
+ img1 = img1.astype(np.float64)
+ img2 = img2.astype(np.float64)
+ kernel = cv2.getGaussianKernel(11, 1.5)
+ window = np.outer(kernel, kernel.transpose())
+
+ mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5]
+ mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
+ mu1_sq = mu1 ** 2
+ mu2_sq = mu2 ** 2
+ mu1_mu2 = mu1 * mu2
+ sigma1_sq = cv2.filter2D(img1 ** 2, -1, window)[5:-5, 5:-5] - mu1_sq
+ sigma2_sq = cv2.filter2D(img2 ** 2, -1, window)[5:-5, 5:-5] - mu2_sq
+ sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
+
+ ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2))
+
+ return ssim_map.mean()
+
+
+def calculate_l1(img1, img2):
+ img1 = img1.astype(np.float64) / 255.0
+ img2 = img2.astype(np.float64) / 255.0
+ l1 = np.mean(np.abs(img1 - img2))
+
+ return l1
+
+
+# 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
+#
+# #----------------------------------------------------------------------------
diff --git a/metrics/perceptual_path_length.py b/metrics/perceptual_path_length.py
new file mode 100644
index 0000000000000000000000000000000000000000..d070f45a04efed7e9492fddb85078be306753282
--- /dev/null
+++ b/metrics/perceptual_path_length.py
@@ -0,0 +1,131 @@
+# 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)
+
+#----------------------------------------------------------------------------
diff --git a/metrics/precision_recall.py b/metrics/precision_recall.py
new file mode 100644
index 0000000000000000000000000000000000000000..8200b7ef51963ae218e3b871de270a826bf10459
--- /dev/null
+++ b/metrics/precision_recall.py
@@ -0,0 +1,62 @@
+# 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']
+
+#----------------------------------------------------------------------------
diff --git a/metrics/psnr_ssim_l1.py b/metrics/psnr_ssim_l1.py
new file mode 100644
index 0000000000000000000000000000000000000000..68fa061b9924d025bc5491fb2d02a61b2daf085f
--- /dev/null
+++ b/metrics/psnr_ssim_l1.py
@@ -0,0 +1,19 @@
+import numpy as np
+import scipy.linalg
+from . import metric_utils
+import math
+import cv2
+
+
+def compute_psnr(opts, max_real):
+ # stats: numpy, [N, 3]
+ stats = metric_utils.compute_image_stats_for_generator(opts=opts, capture_all=True, max_items=max_real).get_all()
+
+ if opts.rank != 0:
+ return float('nan'), float('nan'), float('nan')
+
+ print('Number of samples: %d' % stats.shape[0])
+ avg_psnr = stats[:, 0].sum() / stats.shape[0]
+ avg_ssim = stats[:, 1].sum() / stats.shape[0]
+ avg_l1 = stats[:, 2].sum() / stats.shape[0]
+ return avg_psnr, avg_ssim, avg_l1
\ No newline at end of file
diff --git a/models/Places_512_FullData+LAION300k.pkl b/models/Places_512_FullData+LAION300k.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..896ac585bc1e863313f36c5888b73b8dd3c30e71
--- /dev/null
+++ b/models/Places_512_FullData+LAION300k.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0230b8b39287e4a1ec4c53a7c724188cf0fe6dab2610bf79cdff3756b8517291
+size 661315824
diff --git a/models/Places_512_FullData.pkl b/models/Places_512_FullData.pkl
new file mode 100644
index 0000000000000000000000000000000000000000..fc08c1e6099ce5ebca9b9eca0986e6a451f62e7a
--- /dev/null
+++ b/models/Places_512_FullData.pkl
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d960c4e6b3266b6b9fa74ee4458a9482160d54c06d7738696bc9a9e2b34c66dc
+size 661420475
diff --git a/networks/basic_module.py b/networks/basic_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..12d71999f66cf9891950e0979fe697a392e7fc21
--- /dev/null
+++ b/networks/basic_module.py
@@ -0,0 +1,583 @@
+import sys
+sys.path.insert(0, '../')
+from collections import OrderedDict
+import numpy as np
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+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
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def normalize_2nd_moment(x, dim=1, eps=1e-8):
+ return x * (x.square().mean(dim=dim, keepdim=True) + eps).rsqrt()
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class FullyConnectedLayer(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.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.activation = activation
+
+ self.weight_gain = lr_multiplier / np.sqrt(in_features)
+ self.bias_gain = lr_multiplier
+
+ def forward(self, x):
+ w = self.weight * self.weight_gain
+ b = self.bias
+ if b is not None and self.bias_gain != 1:
+ b = b * self.bias_gain
+
+ if self.activation == 'linear' and b is not None:
+ # out = torch.addmm(b.unsqueeze(0), x, w.t())
+ x = x.matmul(w.t())
+ out = x + b.reshape([-1 if i == x.ndim-1 else 1 for i in range(x.ndim)])
+ else:
+ x = x.matmul(w.t())
+ out = bias_act.bias_act(x, b, act=self.activation, dim=x.ndim-1)
+ return out
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class Conv2dLayer(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.
+ trainable = True, # Update the weights of this layer during training?
+ ):
+ super().__init__()
+ self.activation = activation
+ self.up = up
+ self.down = down
+ self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+ self.conv_clamp = conv_clamp
+ 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
+
+ weight = torch.randn([out_channels, in_channels, kernel_size, kernel_size])
+ 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
+ x = conv2d_resample.conv2d_resample(x=x, w=w, f=self.resample_filter, up=self.up, down=self.down,
+ padding=self.padding)
+
+ act_gain = self.act_gain * gain
+ act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+ out = bias_act.bias_act(x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp)
+ return out
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class ModulatedConv2d(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.
+ style_dim, # dimension of the style code
+ demodulate=True, # perfrom demodulation
+ 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.
+ ):
+ super().__init__()
+ self.demodulate = demodulate
+
+ self.weight = torch.nn.Parameter(torch.randn([1, out_channels, in_channels, kernel_size, kernel_size]))
+ self.out_channels = out_channels
+ self.kernel_size = kernel_size
+ self.weight_gain = 1 / np.sqrt(in_channels * (kernel_size ** 2))
+ self.padding = self.kernel_size // 2
+ self.up = up
+ self.down = down
+ self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+ self.conv_clamp = conv_clamp
+
+ self.affine = FullyConnectedLayer(style_dim, in_channels, bias_init=1)
+
+ def forward(self, x, style):
+ batch, in_channels, height, width = x.shape
+ style = self.affine(style).view(batch, 1, in_channels, 1, 1)
+ weight = self.weight * self.weight_gain * style
+
+ if self.demodulate:
+ decoefs = (weight.pow(2).sum(dim=[2, 3, 4]) + 1e-8).rsqrt()
+ weight = weight * decoefs.view(batch, self.out_channels, 1, 1, 1)
+
+ weight = weight.view(batch * self.out_channels, in_channels, self.kernel_size, self.kernel_size)
+ x = x.view(1, batch * in_channels, height, width)
+ x = conv2d_resample.conv2d_resample(x=x, w=weight, f=self.resample_filter, up=self.up, down=self.down,
+ padding=self.padding, groups=batch)
+ out = x.view(batch, self.out_channels, *x.shape[2:])
+
+ return out
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class StyleConv(torch.nn.Module):
+ def __init__(self,
+ in_channels, # Number of input channels.
+ out_channels, # Number of output channels.
+ style_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.
+ demodulate = True, # perform demodulation
+ ):
+ super().__init__()
+
+ self.conv = ModulatedConv2d(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ style_dim=style_dim,
+ demodulate=demodulate,
+ up=up,
+ resample_filter=resample_filter,
+ conv_clamp=conv_clamp)
+
+ self.use_noise = use_noise
+ self.resolution = resolution
+ 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]))
+ self.activation = activation
+ self.act_gain = bias_act.activation_funcs[activation].def_gain
+ self.conv_clamp = conv_clamp
+
+ def forward(self, x, style, noise_mode='random', gain=1):
+ x = self.conv(x, style)
+
+ assert noise_mode in ['random', 'const', 'none']
+
+ if self.use_noise:
+ if noise_mode == 'random':
+ xh, xw = x.size()[-2:]
+ noise = torch.randn([x.shape[0], 1, xh, xw], device=x.device) \
+ * self.noise_strength
+ if noise_mode == 'const':
+ noise = self.noise_const * self.noise_strength
+ x = x + noise
+
+ act_gain = self.act_gain * gain
+ act_clamp = self.conv_clamp * gain if self.conv_clamp is not None else None
+ out = bias_act.bias_act(x, self.bias, act=self.activation, gain=act_gain, clamp=act_clamp)
+
+ return out
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class ToRGB(torch.nn.Module):
+ def __init__(self,
+ in_channels,
+ out_channels,
+ style_dim,
+ kernel_size=1,
+ resample_filter=[1,3,3,1],
+ conv_clamp=None,
+ demodulate=False):
+ super().__init__()
+
+ self.conv = ModulatedConv2d(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ style_dim=style_dim,
+ demodulate=demodulate,
+ resample_filter=resample_filter,
+ conv_clamp=conv_clamp)
+ self.bias = torch.nn.Parameter(torch.zeros([out_channels]))
+ self.register_buffer('resample_filter', upfirdn2d.setup_filter(resample_filter))
+ self.conv_clamp = conv_clamp
+
+ def forward(self, x, style, skip=None):
+ x = self.conv(x, style)
+ out = bias_act.bias_act(x, self.bias, clamp=self.conv_clamp)
+
+ if skip is not None:
+ if skip.shape != out.shape:
+ skip = upfirdn2d.upsample2d(skip, self.resample_filter)
+ out = out + skip
+
+ return out
+
+#----------------------------------------------------------------------------
+
+@misc.profiled_function
+def get_style_code(a, b):
+ return torch.cat([a, b], dim=1)
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class DecBlockFirst(nn.Module):
+ def __init__(self, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels):
+ super().__init__()
+ self.fc = FullyConnectedLayer(in_features=in_channels*2,
+ out_features=in_channels*4**2,
+ activation=activation)
+ self.conv = StyleConv(in_channels=in_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=4,
+ kernel_size=3,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.toRGB = ToRGB(in_channels=out_channels,
+ out_channels=img_channels,
+ style_dim=style_dim,
+ kernel_size=1,
+ demodulate=False,
+ )
+
+ def forward(self, x, ws, gs, E_features, noise_mode='random'):
+ x = self.fc(x).view(x.shape[0], -1, 4, 4)
+ x = x + E_features[2]
+ style = get_style_code(ws[:, 0], gs)
+ x = self.conv(x, style, noise_mode=noise_mode)
+ style = get_style_code(ws[:, 1], gs)
+ img = self.toRGB(x, style, skip=None)
+
+ return x, img
+
+
+@persistence.persistent_class
+class DecBlockFirstV2(nn.Module):
+ def __init__(self, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels):
+ super().__init__()
+ self.conv0 = Conv2dLayer(in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=3,
+ activation=activation,
+ )
+ self.conv1 = StyleConv(in_channels=in_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=4,
+ kernel_size=3,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.toRGB = ToRGB(in_channels=out_channels,
+ out_channels=img_channels,
+ style_dim=style_dim,
+ kernel_size=1,
+ demodulate=False,
+ )
+
+ def forward(self, x, ws, gs, E_features, noise_mode='random'):
+ # x = self.fc(x).view(x.shape[0], -1, 4, 4)
+ x = self.conv0(x)
+ x = x + E_features[2]
+ style = get_style_code(ws[:, 0], gs)
+ x = self.conv1(x, style, noise_mode=noise_mode)
+ style = get_style_code(ws[:, 1], gs)
+ img = self.toRGB(x, style, skip=None)
+
+ return x, img
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class DecBlock(nn.Module):
+ def __init__(self, res, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels): # res = 2, ..., resolution_log2
+ super().__init__()
+ self.res = res
+
+ self.conv0 = StyleConv(in_channels=in_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ up=2,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.conv1 = StyleConv(in_channels=out_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.toRGB = ToRGB(in_channels=out_channels,
+ out_channels=img_channels,
+ style_dim=style_dim,
+ kernel_size=1,
+ demodulate=False,
+ )
+
+ def forward(self, x, img, ws, gs, E_features, noise_mode='random'):
+ style = get_style_code(ws[:, self.res * 2 - 5], gs)
+ x = self.conv0(x, style, noise_mode=noise_mode)
+ x = x + E_features[self.res]
+ style = get_style_code(ws[:, self.res * 2 - 4], gs)
+ x = self.conv1(x, style, noise_mode=noise_mode)
+ style = get_style_code(ws[:, self.res * 2 - 3], gs)
+ img = self.toRGB(x, style, skip=img)
+
+ return x, img
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class MappingNet(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:
+ x = normalize_2nd_moment(z.to(torch.float32))
+ if self.c_dim > 0:
+ 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 DisFromRGB(nn.Module):
+ def __init__(self, in_channels, out_channels, activation): # res = 2, ..., resolution_log2
+ super().__init__()
+ self.conv = Conv2dLayer(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ activation=activation,
+ )
+
+ def forward(self, x):
+ return self.conv(x)
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class DisBlock(nn.Module):
+ def __init__(self, in_channels, out_channels, activation): # res = 2, ..., resolution_log2
+ super().__init__()
+ self.conv0 = Conv2dLayer(in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=3,
+ activation=activation,
+ )
+ self.conv1 = Conv2dLayer(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ down=2,
+ activation=activation,
+ )
+ self.skip = Conv2dLayer(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ down=2,
+ bias=False,
+ )
+
+ def forward(self, x):
+ skip = self.skip(x, gain=np.sqrt(0.5))
+ x = self.conv0(x)
+ x = self.conv1(x, gain=np.sqrt(0.5))
+ out = skip + x
+
+ return out
+
+#----------------------------------------------------------------------------
+
+@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 Discriminator(torch.nn.Module):
+ def __init__(self,
+ c_dim, # Conditioning label (C) dimensionality.
+ img_resolution, # Input resolution.
+ img_channels, # Number of input color channels.
+ channel_base = 32768, # Overall multiplier for the number of channels.
+ channel_max = 512, # Maximum number of channels in any layer.
+ channel_decay = 1,
+ cmap_dim = None, # Dimensionality of mapped conditioning label, None = default.
+ activation = 'lrelu',
+ 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.
+ ):
+ super().__init__()
+ self.c_dim = c_dim
+ self.img_resolution = img_resolution
+ self.img_channels = img_channels
+
+ resolution_log2 = int(np.log2(img_resolution))
+ assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
+ self.resolution_log2 = resolution_log2
+
+ def nf(stage):
+ return np.clip(int(channel_base / 2 ** (stage * channel_decay)), 1, channel_max)
+
+ if cmap_dim == None:
+ cmap_dim = nf(2)
+ if c_dim == 0:
+ cmap_dim = 0
+ self.cmap_dim = cmap_dim
+
+ if c_dim > 0:
+ self.mapping = MappingNet(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None)
+
+ Dis = [DisFromRGB(img_channels+1, nf(resolution_log2), activation)]
+ for res in range(resolution_log2, 2, -1):
+ Dis.append(DisBlock(nf(res), nf(res-1), activation))
+
+ if mbstd_num_channels > 0:
+ Dis.append(MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels))
+ Dis.append(Conv2dLayer(nf(2) + mbstd_num_channels, nf(2), kernel_size=3, activation=activation))
+ self.Dis = nn.Sequential(*Dis)
+
+ self.fc0 = FullyConnectedLayer(nf(2)*4**2, nf(2), activation=activation)
+ self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
+
+ def forward(self, images_in, masks_in, c):
+ x = torch.cat([masks_in - 0.5, images_in], dim=1)
+ x = self.Dis(x)
+ x = self.fc1(self.fc0(x.flatten(start_dim=1)))
+
+ if self.c_dim > 0:
+ cmap = self.mapping(None, c)
+
+ if self.cmap_dim > 0:
+ x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+ return x
diff --git a/networks/mat.py b/networks/mat.py
new file mode 100644
index 0000000000000000000000000000000000000000..c640dc45ed5df64ae0eaa5d1f277618ff3791d6b
--- /dev/null
+++ b/networks/mat.py
@@ -0,0 +1,996 @@
+import numpy as np
+import math
+import sys
+sys.path.insert(0, '../')
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from torch_utils import misc
+from torch_utils import persistence
+from networks.basic_module import FullyConnectedLayer, Conv2dLayer, MappingNet, MinibatchStdLayer, DisFromRGB, DisBlock, StyleConv, ToRGB, get_style_code
+
+
+@misc.profiled_function
+def nf(stage, channel_base=32768, channel_decay=1.0, channel_max=512):
+ NF = {512: 64, 256: 128, 128: 256, 64: 512, 32: 512, 16: 512, 8: 512, 4: 512}
+ return NF[2 ** stage]
+
+
+@persistence.persistent_class
+class Mlp(nn.Module):
+ def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
+ super().__init__()
+ out_features = out_features or in_features
+ hidden_features = hidden_features or in_features
+ self.fc1 = FullyConnectedLayer(in_features=in_features, out_features=hidden_features, activation='lrelu')
+ self.fc2 = FullyConnectedLayer(in_features=hidden_features, out_features=out_features)
+
+ def forward(self, x):
+ x = self.fc1(x)
+ x = self.fc2(x)
+ return x
+
+
+@misc.profiled_function
+def window_partition(x, window_size):
+ """
+ Args:
+ x: (B, H, W, C)
+ window_size (int): window size
+ Returns:
+ windows: (num_windows*B, window_size, window_size, C)
+ """
+ B, H, W, C = x.shape
+ x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+ windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+ return windows
+
+
+@misc.profiled_function
+def window_reverse(windows, window_size, H, W):
+ """
+ Args:
+ windows: (num_windows*B, window_size, window_size, C)
+ window_size (int): Window size
+ H (int): Height of image
+ W (int): Width of image
+ Returns:
+ x: (B, H, W, C)
+ """
+ B = int(windows.shape[0] / (H * W / window_size / window_size))
+ x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+ x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+ return x
+
+
+@persistence.persistent_class
+class Conv2dLayerPartial(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.
+ trainable = True, # Update the weights of this layer during training?
+ ):
+ super().__init__()
+ self.conv = Conv2dLayer(in_channels, out_channels, kernel_size, bias, activation, up, down, resample_filter,
+ conv_clamp, trainable)
+
+ self.weight_maskUpdater = torch.ones(1, 1, kernel_size, kernel_size)
+ self.slide_winsize = kernel_size ** 2
+ self.stride = down
+ self.padding = kernel_size // 2 if kernel_size % 2 == 1 else 0
+
+ def forward(self, x, mask=None):
+ if mask is not None:
+ with torch.no_grad():
+ if self.weight_maskUpdater.type() != x.type():
+ self.weight_maskUpdater = self.weight_maskUpdater.to(x)
+ update_mask = F.conv2d(mask, self.weight_maskUpdater, bias=None, stride=self.stride, padding=self.padding)
+ mask_ratio = self.slide_winsize / (update_mask + 1e-8)
+ update_mask = torch.clamp(update_mask, 0, 1) # 0 or 1
+ mask_ratio = torch.mul(mask_ratio, update_mask)
+ x = self.conv(x)
+ x = torch.mul(x, mask_ratio)
+ return x, update_mask
+ else:
+ x = self.conv(x)
+ return x, None
+
+
+@persistence.persistent_class
+class WindowAttention(nn.Module):
+ r""" Window based multi-head self attention (W-MSA) module with relative position bias.
+ It supports both of shifted and non-shifted window.
+ Args:
+ dim (int): Number of input channels.
+ window_size (tuple[int]): The height and width of the window.
+ num_heads (int): Number of attention heads.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+ attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+ proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+ """
+
+ def __init__(self, dim, window_size, num_heads, down_ratio=1, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.):
+
+ super().__init__()
+ self.dim = dim
+ self.window_size = window_size # Wh, Ww
+ self.num_heads = num_heads
+ head_dim = dim // num_heads
+ self.scale = qk_scale or head_dim ** -0.5
+
+ self.q = FullyConnectedLayer(in_features=dim, out_features=dim)
+ self.k = FullyConnectedLayer(in_features=dim, out_features=dim)
+ self.v = FullyConnectedLayer(in_features=dim, out_features=dim)
+ self.proj = FullyConnectedLayer(in_features=dim, out_features=dim)
+
+ self.softmax = nn.Softmax(dim=-1)
+
+ def forward(self, x, mask_windows=None, mask=None):
+ """
+ Args:
+ x: input features with shape of (num_windows*B, N, C)
+ mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+ """
+ B_, N, C = x.shape
+ norm_x = F.normalize(x, p=2.0, dim=-1)
+ q = self.q(norm_x).reshape(B_, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+ k = self.k(norm_x).view(B_, -1, self.num_heads, C // self.num_heads).permute(0, 2, 3, 1)
+ v = self.v(x).view(B_, -1, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
+
+ attn = (q @ k) * self.scale
+
+ if mask is not None:
+ nW = mask.shape[0]
+ attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+ attn = attn.view(-1, self.num_heads, N, N)
+
+ if mask_windows is not None:
+ attn_mask_windows = mask_windows.squeeze(-1).unsqueeze(1).unsqueeze(1)
+ attn = attn + attn_mask_windows.masked_fill(attn_mask_windows == 0, float(-100.0)).masked_fill(
+ attn_mask_windows == 1, float(0.0))
+ with torch.no_grad():
+ mask_windows = torch.clamp(torch.sum(mask_windows, dim=1, keepdim=True), 0, 1).repeat(1, N, 1)
+
+ attn = self.softmax(attn)
+
+ x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+ x = self.proj(x)
+ return x, mask_windows
+
+
+@persistence.persistent_class
+class SwinTransformerBlock(nn.Module):
+ r""" Swin Transformer Block.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resulotion.
+ num_heads (int): Number of attention heads.
+ window_size (int): Window size.
+ shift_size (int): Shift size for SW-MSA.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float, optional): Stochastic depth rate. Default: 0.0
+ act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ """
+
+ def __init__(self, dim, input_resolution, num_heads, down_ratio=1, window_size=7, shift_size=0,
+ mlp_ratio=4., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0., drop_path=0.,
+ act_layer=nn.GELU, norm_layer=nn.LayerNorm):
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.num_heads = num_heads
+ self.window_size = window_size
+ self.shift_size = shift_size
+ self.mlp_ratio = mlp_ratio
+ if min(self.input_resolution) <= self.window_size:
+ # if window size is larger than input resolution, we don't partition windows
+ self.shift_size = 0
+ self.window_size = min(self.input_resolution)
+ assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+ if self.shift_size > 0:
+ down_ratio = 1
+ self.attn = WindowAttention(dim, window_size=to_2tuple(self.window_size), num_heads=num_heads,
+ down_ratio=down_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop,
+ proj_drop=drop)
+
+ self.fuse = FullyConnectedLayer(in_features=dim * 2, out_features=dim, activation='lrelu')
+
+ mlp_hidden_dim = int(dim * mlp_ratio)
+ self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)
+
+ if self.shift_size > 0:
+ attn_mask = self.calculate_mask(self.input_resolution)
+ else:
+ attn_mask = None
+
+ self.register_buffer("attn_mask", attn_mask)
+
+ def calculate_mask(self, x_size):
+ # calculate attention mask for SW-MSA
+ H, W = x_size
+ img_mask = torch.zeros((1, H, W, 1)) # 1 H W 1
+ h_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ w_slices = (slice(0, -self.window_size),
+ slice(-self.window_size, -self.shift_size),
+ slice(-self.shift_size, None))
+ cnt = 0
+ for h in h_slices:
+ for w in w_slices:
+ img_mask[:, h, w, :] = cnt
+ cnt += 1
+
+ mask_windows = window_partition(img_mask, self.window_size) # nW, window_size, window_size, 1
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+ attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+ attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(attn_mask == 0, float(0.0))
+
+ return attn_mask
+
+ def forward(self, x, x_size, mask=None):
+ # H, W = self.input_resolution
+ H, W = x_size
+ B, L, C = x.shape
+ assert L == H * W, "input feature has wrong size"
+
+ shortcut = x
+ x = x.view(B, H, W, C)
+ if mask is not None:
+ mask = mask.view(B, H, W, 1)
+
+ # cyclic shift
+ if self.shift_size > 0:
+ shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ if mask is not None:
+ shifted_mask = torch.roll(mask, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+ else:
+ shifted_x = x
+ if mask is not None:
+ shifted_mask = mask
+
+ # partition windows
+ x_windows = window_partition(shifted_x, self.window_size) # nW*B, window_size, window_size, C
+ x_windows = x_windows.view(-1, self.window_size * self.window_size, C) # nW*B, window_size*window_size, C
+ if mask is not None:
+ mask_windows = window_partition(shifted_mask, self.window_size)
+ mask_windows = mask_windows.view(-1, self.window_size * self.window_size, 1)
+ else:
+ mask_windows = None
+
+ # W-MSA/SW-MSA (to be compatible for testing on images whose shapes are the multiple of window size
+ if self.input_resolution == x_size:
+ attn_windows, mask_windows = self.attn(x_windows, mask_windows, mask=self.attn_mask) # nW*B, window_size*window_size, C
+ else:
+ attn_windows, mask_windows = self.attn(x_windows, mask_windows, mask=self.calculate_mask(x_size).to(x.device)) # nW*B, window_size*window_size, C
+
+ # merge windows
+ attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+ shifted_x = window_reverse(attn_windows, self.window_size, H, W) # B H' W' C
+ if mask is not None:
+ mask_windows = mask_windows.view(-1, self.window_size, self.window_size, 1)
+ shifted_mask = window_reverse(mask_windows, self.window_size, H, W)
+
+ # reverse cyclic shift
+ if self.shift_size > 0:
+ x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ if mask is not None:
+ mask = torch.roll(shifted_mask, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+ else:
+ x = shifted_x
+ if mask is not None:
+ mask = shifted_mask
+ x = x.view(B, H * W, C)
+ if mask is not None:
+ mask = mask.view(B, H * W, 1)
+
+ # FFN
+ x = self.fuse(torch.cat([shortcut, x], dim=-1))
+ x = self.mlp(x)
+
+ return x, mask
+
+
+@persistence.persistent_class
+class PatchMerging(nn.Module):
+ def __init__(self, in_channels, out_channels, down=2):
+ super().__init__()
+ self.conv = Conv2dLayerPartial(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ activation='lrelu',
+ down=down,
+ )
+ self.down = down
+
+ def forward(self, x, x_size, mask=None):
+ x = token2feature(x, x_size)
+ if mask is not None:
+ mask = token2feature(mask, x_size)
+ x, mask = self.conv(x, mask)
+ if self.down != 1:
+ ratio = 1 / self.down
+ x_size = (int(x_size[0] * ratio), int(x_size[1] * ratio))
+ x = feature2token(x)
+ if mask is not None:
+ mask = feature2token(mask)
+ return x, x_size, mask
+
+
+@persistence.persistent_class
+class PatchUpsampling(nn.Module):
+ def __init__(self, in_channels, out_channels, up=2):
+ super().__init__()
+ self.conv = Conv2dLayerPartial(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ activation='lrelu',
+ up=up,
+ )
+ self.up = up
+
+ def forward(self, x, x_size, mask=None):
+ x = token2feature(x, x_size)
+ if mask is not None:
+ mask = token2feature(mask, x_size)
+ x, mask = self.conv(x, mask)
+ if self.up != 1:
+ x_size = (int(x_size[0] * self.up), int(x_size[1] * self.up))
+ x = feature2token(x)
+ if mask is not None:
+ mask = feature2token(mask)
+ return x, x_size, mask
+
+
+
+@persistence.persistent_class
+class BasicLayer(nn.Module):
+ """ A basic Swin Transformer layer for one stage.
+ Args:
+ dim (int): Number of input channels.
+ input_resolution (tuple[int]): Input resolution.
+ depth (int): Number of blocks.
+ num_heads (int): Number of attention heads.
+ window_size (int): Local window size.
+ mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+ qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+ qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+ drop (float, optional): Dropout rate. Default: 0.0
+ attn_drop (float, optional): Attention dropout rate. Default: 0.0
+ drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+ norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+ downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+ use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+ """
+
+ def __init__(self, dim, input_resolution, depth, num_heads, window_size, down_ratio=1,
+ mlp_ratio=2., qkv_bias=True, qk_scale=None, drop=0., attn_drop=0.,
+ drop_path=0., norm_layer=nn.LayerNorm, downsample=None, use_checkpoint=False):
+
+ super().__init__()
+ self.dim = dim
+ self.input_resolution = input_resolution
+ self.depth = depth
+ self.use_checkpoint = use_checkpoint
+
+ # patch merging layer
+ if downsample is not None:
+ # self.downsample = downsample(input_resolution, dim=dim, norm_layer=norm_layer)
+ self.downsample = downsample
+ else:
+ self.downsample = None
+
+ # build blocks
+ self.blocks = nn.ModuleList([
+ SwinTransformerBlock(dim=dim, input_resolution=input_resolution,
+ num_heads=num_heads, down_ratio=down_ratio, window_size=window_size,
+ shift_size=0 if (i % 2 == 0) else window_size // 2,
+ mlp_ratio=mlp_ratio,
+ qkv_bias=qkv_bias, qk_scale=qk_scale,
+ drop=drop, attn_drop=attn_drop,
+ drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+ norm_layer=norm_layer)
+ for i in range(depth)])
+
+ self.conv = Conv2dLayerPartial(in_channels=dim, out_channels=dim, kernel_size=3, activation='lrelu')
+
+ def forward(self, x, x_size, mask=None):
+ if self.downsample is not None:
+ x, x_size, mask = self.downsample(x, x_size, mask)
+ identity = x
+ for blk in self.blocks:
+ if self.use_checkpoint:
+ x, mask = checkpoint.checkpoint(blk, x, x_size, mask)
+ else:
+ x, mask = blk(x, x_size, mask)
+ if mask is not None:
+ mask = token2feature(mask, x_size)
+ x, mask = self.conv(token2feature(x, x_size), mask)
+ x = feature2token(x) + identity
+ if mask is not None:
+ mask = feature2token(mask)
+ return x, x_size, mask
+
+
+@persistence.persistent_class
+class ToToken(nn.Module):
+ def __init__(self, in_channels=3, dim=128, kernel_size=5, stride=1):
+ super().__init__()
+
+ self.proj = Conv2dLayerPartial(in_channels=in_channels, out_channels=dim, kernel_size=kernel_size, activation='lrelu')
+
+ def forward(self, x, mask):
+ x, mask = self.proj(x, mask)
+
+ return x, mask
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class EncFromRGB(nn.Module):
+ def __init__(self, in_channels, out_channels, activation): # res = 2, ..., resolution_log2
+ super().__init__()
+ self.conv0 = Conv2dLayer(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=1,
+ activation=activation,
+ )
+ self.conv1 = Conv2dLayer(in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ activation=activation,
+ )
+
+ def forward(self, x):
+ x = self.conv0(x)
+ x = self.conv1(x)
+
+ return x
+
+@persistence.persistent_class
+class ConvBlockDown(nn.Module):
+ def __init__(self, in_channels, out_channels, activation): # res = 2, ..., resolution_log
+ super().__init__()
+
+ self.conv0 = Conv2dLayer(in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ activation=activation,
+ down=2,
+ )
+ self.conv1 = Conv2dLayer(in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ activation=activation,
+ )
+
+ def forward(self, x):
+ x = self.conv0(x)
+ x = self.conv1(x)
+
+ return x
+
+
+def token2feature(x, x_size):
+ B, N, C = x.shape
+ h, w = x_size
+ x = x.permute(0, 2, 1).reshape(B, C, h, w)
+ return x
+
+
+def feature2token(x):
+ B, C, H, W = x.shape
+ x = x.view(B, C, -1).transpose(1, 2)
+ return x
+
+
+@persistence.persistent_class
+class Encoder(nn.Module):
+ def __init__(self, res_log2, img_channels, activation, patch_size=5, channels=16, drop_path_rate=0.1):
+ super().__init__()
+
+ self.resolution = []
+
+ for idx, i in enumerate(range(res_log2, 3, -1)): # from input size to 16x16
+ res = 2 ** i
+ self.resolution.append(res)
+ if i == res_log2:
+ block = EncFromRGB(img_channels * 2 + 1, nf(i), activation)
+ else:
+ block = ConvBlockDown(nf(i+1), nf(i), activation)
+ setattr(self, 'EncConv_Block_%dx%d' % (res, res), block)
+
+ def forward(self, x):
+ out = {}
+ for res in self.resolution:
+ res_log2 = int(np.log2(res))
+ x = getattr(self, 'EncConv_Block_%dx%d' % (res, res))(x)
+ out[res_log2] = x
+
+ return out
+
+
+@persistence.persistent_class
+class ToStyle(nn.Module):
+ def __init__(self, in_channels, out_channels, activation, drop_rate):
+ super().__init__()
+ self.conv = nn.Sequential(
+ Conv2dLayer(in_channels=in_channels, out_channels=in_channels, kernel_size=3, activation=activation, down=2),
+ Conv2dLayer(in_channels=in_channels, out_channels=in_channels, kernel_size=3, activation=activation, down=2),
+ Conv2dLayer(in_channels=in_channels, out_channels=in_channels, kernel_size=3, activation=activation, down=2),
+ )
+
+ self.pool = nn.AdaptiveAvgPool2d(1)
+ self.fc = FullyConnectedLayer(in_features=in_channels,
+ out_features=out_channels,
+ activation=activation)
+ # self.dropout = nn.Dropout(drop_rate)
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.pool(x)
+ x = self.fc(x.flatten(start_dim=1))
+ # x = self.dropout(x)
+
+ return x
+
+
+@persistence.persistent_class
+class DecBlockFirstV2(nn.Module):
+ def __init__(self, res, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels):
+ super().__init__()
+ self.res = res
+
+ self.conv0 = Conv2dLayer(in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=3,
+ activation=activation,
+ )
+ self.conv1 = StyleConv(in_channels=in_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.toRGB = ToRGB(in_channels=out_channels,
+ out_channels=img_channels,
+ style_dim=style_dim,
+ kernel_size=1,
+ demodulate=False,
+ )
+
+ def forward(self, x, ws, gs, E_features, noise_mode='random'):
+ # x = self.fc(x).view(x.shape[0], -1, 4, 4)
+ x = self.conv0(x)
+ x = x + E_features[self.res]
+ style = get_style_code(ws[:, 0], gs)
+ x = self.conv1(x, style, noise_mode=noise_mode)
+ style = get_style_code(ws[:, 1], gs)
+ img = self.toRGB(x, style, skip=None)
+
+ return x, img
+
+#----------------------------------------------------------------------------
+
+@persistence.persistent_class
+class DecBlock(nn.Module):
+ def __init__(self, res, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels): # res = 4, ..., resolution_log2
+ super().__init__()
+ self.res = res
+
+ self.conv0 = StyleConv(in_channels=in_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ up=2,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.conv1 = StyleConv(in_channels=out_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.toRGB = ToRGB(in_channels=out_channels,
+ out_channels=img_channels,
+ style_dim=style_dim,
+ kernel_size=1,
+ demodulate=False,
+ )
+
+ def forward(self, x, img, ws, gs, E_features, noise_mode='random'):
+ style = get_style_code(ws[:, self.res * 2 - 9], gs)
+ x = self.conv0(x, style, noise_mode=noise_mode)
+ x = x + E_features[self.res]
+ style = get_style_code(ws[:, self.res * 2 - 8], gs)
+ x = self.conv1(x, style, noise_mode=noise_mode)
+ style = get_style_code(ws[:, self.res * 2 - 7], gs)
+ img = self.toRGB(x, style, skip=img)
+
+ return x, img
+
+
+@persistence.persistent_class
+class Decoder(nn.Module):
+ def __init__(self, res_log2, activation, style_dim, use_noise, demodulate, img_channels):
+ super().__init__()
+ self.Dec_16x16 = DecBlockFirstV2(4, nf(4), nf(4), activation, style_dim, use_noise, demodulate, img_channels)
+ for res in range(5, res_log2 + 1):
+ setattr(self, 'Dec_%dx%d' % (2 ** res, 2 ** res),
+ DecBlock(res, nf(res - 1), nf(res), activation, style_dim, use_noise, demodulate, img_channels))
+ self.res_log2 = res_log2
+
+ def forward(self, x, ws, gs, E_features, noise_mode='random'):
+ x, img = self.Dec_16x16(x, ws, gs, E_features, noise_mode=noise_mode)
+ for res in range(5, self.res_log2 + 1):
+ block = getattr(self, 'Dec_%dx%d' % (2 ** res, 2 ** res))
+ x, img = block(x, img, ws, gs, E_features, noise_mode=noise_mode)
+
+ return img
+
+
+@persistence.persistent_class
+class DecStyleBlock(nn.Module):
+ def __init__(self, res, in_channels, out_channels, activation, style_dim, use_noise, demodulate, img_channels):
+ super().__init__()
+ self.res = res
+
+ self.conv0 = StyleConv(in_channels=in_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ up=2,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.conv1 = StyleConv(in_channels=out_channels,
+ out_channels=out_channels,
+ style_dim=style_dim,
+ resolution=2**res,
+ kernel_size=3,
+ use_noise=use_noise,
+ activation=activation,
+ demodulate=demodulate,
+ )
+ self.toRGB = ToRGB(in_channels=out_channels,
+ out_channels=img_channels,
+ style_dim=style_dim,
+ kernel_size=1,
+ demodulate=False,
+ )
+
+ def forward(self, x, img, style, skip, noise_mode='random'):
+ x = self.conv0(x, style, noise_mode=noise_mode)
+ x = x + skip
+ x = self.conv1(x, style, noise_mode=noise_mode)
+ img = self.toRGB(x, style, skip=img)
+
+ return x, img
+
+
+@persistence.persistent_class
+class FirstStage(nn.Module):
+ def __init__(self, img_channels, img_resolution=256, dim=180, w_dim=512, use_noise=False, demodulate=True, activation='lrelu'):
+ super().__init__()
+ res = 64
+
+ self.conv_first = Conv2dLayerPartial(in_channels=img_channels+1, out_channels=dim, kernel_size=3, activation=activation)
+ self.enc_conv = nn.ModuleList()
+ down_time = int(np.log2(img_resolution // res))
+ for i in range(down_time): # from input size to 64
+ self.enc_conv.append(
+ Conv2dLayerPartial(in_channels=dim, out_channels=dim, kernel_size=3, down=2, activation=activation)
+ )
+
+ # from 64 -> 16 -> 64
+ depths = [2, 3, 4, 3, 2]
+ ratios = [1, 1/2, 1/2, 2, 2]
+ num_heads = 6
+ window_sizes = [8, 16, 16, 16, 8]
+ drop_path_rate = 0.1
+ dpr = [x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))]
+
+ self.tran = nn.ModuleList()
+ for i, depth in enumerate(depths):
+ res = int(res * ratios[i])
+ if ratios[i] < 1:
+ merge = PatchMerging(dim, dim, down=int(1/ratios[i]))
+ elif ratios[i] > 1:
+ merge = PatchUpsampling(dim, dim, up=ratios[i])
+ else:
+ merge = None
+ self.tran.append(
+ BasicLayer(dim=dim, input_resolution=[res, res], depth=depth, num_heads=num_heads,
+ window_size=window_sizes[i], drop_path=dpr[sum(depths[:i]):sum(depths[:i + 1])],
+ downsample=merge)
+ )
+
+ # global style
+ down_conv = []
+ for i in range(int(np.log2(16))):
+ down_conv.append(Conv2dLayer(in_channels=dim, out_channels=dim, kernel_size=3, down=2, activation=activation))
+ down_conv.append(nn.AdaptiveAvgPool2d((1, 1)))
+ self.down_conv = nn.Sequential(*down_conv)
+ self.to_style = FullyConnectedLayer(in_features=dim, out_features=dim*2, activation=activation)
+ self.ws_style = FullyConnectedLayer(in_features=w_dim, out_features=dim, activation=activation)
+ self.to_square = FullyConnectedLayer(in_features=dim, out_features=16*16, activation=activation)
+
+ style_dim = dim * 3
+ self.dec_conv = nn.ModuleList()
+ for i in range(down_time): # from 64 to input size
+ res = res * 2
+ self.dec_conv.append(DecStyleBlock(res, dim, dim, activation, style_dim, use_noise, demodulate, img_channels))
+
+ def forward(self, images_in, masks_in, ws, noise_mode='random'):
+ x = torch.cat([masks_in - 0.5, images_in * masks_in], dim=1)
+
+ skips = []
+ x, mask = self.conv_first(x, masks_in) # input size
+ skips.append(x)
+ for i, block in enumerate(self.enc_conv): # input size to 64
+ x, mask = block(x, mask)
+ if i != len(self.enc_conv) - 1:
+ skips.append(x)
+
+ x_size = x.size()[-2:]
+ x = feature2token(x)
+ mask = feature2token(mask)
+ mid = len(self.tran) // 2
+ for i, block in enumerate(self.tran): # 64 to 16
+ if i < mid:
+ x, x_size, mask = block(x, x_size, mask)
+ skips.append(x)
+ elif i > mid:
+ x, x_size, mask = block(x, x_size, None)
+ x = x + skips[mid - i]
+ else:
+ x, x_size, mask = block(x, x_size, None)
+
+ mul_map = torch.ones_like(x) * 0.5
+ mul_map = F.dropout(mul_map, training=True)
+ ws = self.ws_style(ws[:, -1])
+ add_n = self.to_square(ws).unsqueeze(1)
+ add_n = F.interpolate(add_n, size=x.size(1), mode='linear', align_corners=False).squeeze(1).unsqueeze(-1)
+ x = x * mul_map + add_n * (1 - mul_map)
+ gs = self.to_style(self.down_conv(token2feature(x, x_size)).flatten(start_dim=1))
+ style = torch.cat([gs, ws], dim=1)
+
+ x = token2feature(x, x_size).contiguous()
+ img = None
+ for i, block in enumerate(self.dec_conv):
+ x, img = block(x, img, style, skips[len(self.dec_conv)-i-1], noise_mode=noise_mode)
+
+ # ensemble
+ img = img * (1 - masks_in) + images_in * masks_in
+
+ return img
+
+
+@persistence.persistent_class
+class SynthesisNet(nn.Module):
+ def __init__(self,
+ w_dim, # Intermediate latent (W) dimensionality.
+ img_resolution, # Output image resolution.
+ img_channels = 3, # Number of color channels.
+ channel_base = 32768, # Overall multiplier for the number of channels.
+ channel_decay = 1.0,
+ channel_max = 512, # Maximum number of channels in any layer.
+ activation = 'lrelu', # Activation function: 'relu', 'lrelu', etc.
+ drop_rate = 0.5,
+ use_noise = True,
+ demodulate = True,
+ ):
+ super().__init__()
+ resolution_log2 = int(np.log2(img_resolution))
+ assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
+
+ self.num_layers = resolution_log2 * 2 - 3 * 2
+ self.img_resolution = img_resolution
+ self.resolution_log2 = resolution_log2
+
+ # first stage
+ self.first_stage = FirstStage(img_channels, img_resolution=img_resolution, w_dim=w_dim, use_noise=False, demodulate=demodulate)
+
+ # second stage
+ self.enc = Encoder(resolution_log2, img_channels, activation, patch_size=5, channels=16)
+ self.to_square = FullyConnectedLayer(in_features=w_dim, out_features=16*16, activation=activation)
+ self.to_style = ToStyle(in_channels=nf(4), out_channels=nf(2) * 2, activation=activation, drop_rate=drop_rate)
+ style_dim = w_dim + nf(2) * 2
+ self.dec = Decoder(resolution_log2, activation, style_dim, use_noise, demodulate, img_channels)
+
+ def forward(self, images_in, masks_in, ws, noise_mode='random', return_stg1=False):
+ out_stg1 = self.first_stage(images_in, masks_in, ws, noise_mode=noise_mode)
+
+ # encoder
+ x = images_in * masks_in + out_stg1 * (1 - masks_in)
+ x = torch.cat([masks_in - 0.5, x, images_in * masks_in], dim=1)
+ E_features = self.enc(x)
+
+ fea_16 = E_features[4]
+ mul_map = torch.ones_like(fea_16) * 0.5
+ mul_map = F.dropout(mul_map, training=True)
+ add_n = self.to_square(ws[:, 0]).view(-1, 16, 16).unsqueeze(1)
+ add_n = F.interpolate(add_n, size=fea_16.size()[-2:], mode='bilinear', align_corners=False)
+ fea_16 = fea_16 * mul_map + add_n * (1 - mul_map)
+ E_features[4] = fea_16
+
+ # style
+ gs = self.to_style(fea_16)
+
+ # decoder
+ img = self.dec(fea_16, ws, gs, E_features, noise_mode=noise_mode)
+
+ # ensemble
+ img = img * (1 - masks_in) + images_in * masks_in
+
+ if not return_stg1:
+ return img
+ else:
+ return img, out_stg1
+
+
+@persistence.persistent_class
+class Generator(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.
+ img_resolution, # resolution of generated image
+ img_channels, # Number of input color channels.
+ synthesis_kwargs = {}, # Arguments for SynthesisNetwork.
+ mapping_kwargs = {}, # Arguments for MappingNetwork.
+ ):
+ 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 = SynthesisNet(w_dim=w_dim,
+ img_resolution=img_resolution,
+ img_channels=img_channels,
+ **synthesis_kwargs)
+ self.mapping = MappingNet(z_dim=z_dim,
+ c_dim=c_dim,
+ w_dim=w_dim,
+ num_ws=self.synthesis.num_layers,
+ **mapping_kwargs)
+
+ def forward(self, images_in, masks_in, z, c, truncation_psi=1, truncation_cutoff=None, skip_w_avg_update=False,
+ noise_mode='random', return_stg1=False):
+ ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff,
+ skip_w_avg_update=skip_w_avg_update)
+
+ if not return_stg1:
+ img = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode)
+ return img
+ else:
+ img, out_stg1 = self.synthesis(images_in, masks_in, ws, noise_mode=noise_mode, return_stg1=True)
+ return img, out_stg1
+
+
+@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.
+ channel_base = 32768, # Overall multiplier for the number of channels.
+ channel_max = 512, # Maximum number of channels in any layer.
+ channel_decay = 1,
+ cmap_dim = None, # Dimensionality of mapped conditioning label, None = default.
+ activation = 'lrelu',
+ 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.
+ ):
+ super().__init__()
+ self.c_dim = c_dim
+ self.img_resolution = img_resolution
+ self.img_channels = img_channels
+
+ resolution_log2 = int(np.log2(img_resolution))
+ assert img_resolution == 2 ** resolution_log2 and img_resolution >= 4
+ self.resolution_log2 = resolution_log2
+
+ if cmap_dim == None:
+ cmap_dim = nf(2)
+ if c_dim == 0:
+ cmap_dim = 0
+ self.cmap_dim = cmap_dim
+
+ if c_dim > 0:
+ self.mapping = MappingNet(z_dim=0, c_dim=c_dim, w_dim=cmap_dim, num_ws=None, w_avg_beta=None)
+
+ Dis = [DisFromRGB(img_channels+1, nf(resolution_log2), activation)]
+ for res in range(resolution_log2, 2, -1):
+ Dis.append(DisBlock(nf(res), nf(res-1), activation))
+
+ if mbstd_num_channels > 0:
+ Dis.append(MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels))
+ Dis.append(Conv2dLayer(nf(2) + mbstd_num_channels, nf(2), kernel_size=3, activation=activation))
+ self.Dis = nn.Sequential(*Dis)
+
+ self.fc0 = FullyConnectedLayer(nf(2)*4**2, nf(2), activation=activation)
+ self.fc1 = FullyConnectedLayer(nf(2), 1 if cmap_dim == 0 else cmap_dim)
+
+ # for 64x64
+ Dis_stg1 = [DisFromRGB(img_channels+1, nf(resolution_log2) // 2, activation)]
+ for res in range(resolution_log2, 2, -1):
+ Dis_stg1.append(DisBlock(nf(res) // 2, nf(res - 1) // 2, activation))
+
+ if mbstd_num_channels > 0:
+ Dis_stg1.append(MinibatchStdLayer(group_size=mbstd_group_size, num_channels=mbstd_num_channels))
+ Dis_stg1.append(Conv2dLayer(nf(2) // 2 + mbstd_num_channels, nf(2) // 2, kernel_size=3, activation=activation))
+ self.Dis_stg1 = nn.Sequential(*Dis_stg1)
+
+ self.fc0_stg1 = FullyConnectedLayer(nf(2) // 2 * 4 ** 2, nf(2) // 2, activation=activation)
+ self.fc1_stg1 = FullyConnectedLayer(nf(2) // 2, 1 if cmap_dim == 0 else cmap_dim)
+
+ def forward(self, images_in, masks_in, images_stg1, c):
+ x = self.Dis(torch.cat([masks_in - 0.5, images_in], dim=1))
+ x = self.fc1(self.fc0(x.flatten(start_dim=1)))
+
+ x_stg1 = self.Dis_stg1(torch.cat([masks_in - 0.5, images_stg1], dim=1))
+ x_stg1 = self.fc1_stg1(self.fc0_stg1(x_stg1.flatten(start_dim=1)))
+
+ if self.c_dim > 0:
+ cmap = self.mapping(None, c)
+
+ if self.cmap_dim > 0:
+ x = (x * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+ x_stg1 = (x_stg1 * cmap).sum(dim=1, keepdim=True) * (1 / np.sqrt(self.cmap_dim))
+
+ return x, x_stg1
+
+
+if __name__ == '__main__':
+ device = torch.device('cuda:0')
+ batch = 1
+ res = 512
+ G = Generator(z_dim=512, c_dim=0, w_dim=512, img_resolution=512, img_channels=3).to(device)
+ D = Discriminator(c_dim=0, img_resolution=res, img_channels=3).to(device)
+ img = torch.randn(batch, 3, res, res).to(device)
+ mask = torch.randn(batch, 1, res, res).to(device)
+ z = torch.randn(batch, 512).to(device)
+ G.eval()
+
+ # def count(block):
+ # return sum(p.numel() for p in block.parameters()) / 10 ** 6
+ # print('Generator', count(G))
+ # print('discriminator', count(D))
+
+ with torch.no_grad():
+ img, img_stg1 = G(img, mask, z, None, return_stg1=True)
+ print('output of G:', img.shape, img_stg1.shape)
+ score, score_stg1 = D(img, mask, img_stg1, None)
+ print('output of D:', score.shape, score_stg1.shape)
diff --git a/op.gif b/op.gif
new file mode 100644
index 0000000000000000000000000000000000000000..d42a0b3f73a40a21f52fc2c192673907f91c4474
--- /dev/null
+++ b/op.gif
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2f046c9635d86f7856a4038925b1ecafcccd8113401da4f6883ef4d97a708430
+size 6566324
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000000000000000000000000000000000000..de5cd7c006ac55ecab90b558c123f37d162e4267
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,16 @@
+easydict
+future
+matplotlib
+numpy
+opencv-python
+scikit-image
+scipy
+click
+requests
+tqdm
+pyspng
+ninja
+imageio-ffmpeg==0.4.3
+timm
+psutil
+scikit-learn
diff --git a/test_sets/CelebA-HQ/images/test1.png b/test_sets/CelebA-HQ/images/test1.png
new file mode 100644
index 0000000000000000000000000000000000000000..a086ca5541d93b3b024892e7bbd9f78defdf0e6b
Binary files /dev/null and b/test_sets/CelebA-HQ/images/test1.png differ
diff --git a/test_sets/CelebA-HQ/images/test2.png b/test_sets/CelebA-HQ/images/test2.png
new file mode 100644
index 0000000000000000000000000000000000000000..e114e438881d97008947c9b6ca3d614a508772b0
Binary files /dev/null and b/test_sets/CelebA-HQ/images/test2.png differ
diff --git a/test_sets/CelebA-HQ/masks/mask1.png b/test_sets/CelebA-HQ/masks/mask1.png
new file mode 100644
index 0000000000000000000000000000000000000000..84f4914d68cd0f81879e7f0cc11c60ae38d7e906
Binary files /dev/null and b/test_sets/CelebA-HQ/masks/mask1.png differ
diff --git a/test_sets/CelebA-HQ/masks/mask2.png b/test_sets/CelebA-HQ/masks/mask2.png
new file mode 100644
index 0000000000000000000000000000000000000000..c97e584d676b06e10c058f587a103bf4043d4c12
Binary files /dev/null and b/test_sets/CelebA-HQ/masks/mask2.png differ
diff --git a/test_sets/Places/images/test1.jpg b/test_sets/Places/images/test1.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..06a43a92d76c54242cd08bbc3e35c53c704deb34
Binary files /dev/null and b/test_sets/Places/images/test1.jpg differ
diff --git a/test_sets/Places/images/test2.jpg b/test_sets/Places/images/test2.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..94f3ecc4b9e3635713dca4ce6e30a3f12cc15a01
Binary files /dev/null and b/test_sets/Places/images/test2.jpg differ
diff --git a/test_sets/Places/masks/mask1.png b/test_sets/Places/masks/mask1.png
new file mode 100644
index 0000000000000000000000000000000000000000..9f66d1488fe6d90b97293204eaaff179d552f908
Binary files /dev/null and b/test_sets/Places/masks/mask1.png differ
diff --git a/test_sets/Places/masks/mask2.png b/test_sets/Places/masks/mask2.png
new file mode 100644
index 0000000000000000000000000000000000000000..c97e584d676b06e10c058f587a103bf4043d4c12
Binary files /dev/null and b/test_sets/Places/masks/mask2.png differ
diff --git a/torch_utils/__init__.py b/torch_utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ece0ea08fe2e939cc260a1dafc0ab5b391b773d9
--- /dev/null
+++ b/torch_utils/__init__.py
@@ -0,0 +1,9 @@
+# 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
diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py
new file mode 100644
index 0000000000000000000000000000000000000000..4cc4e43fc6f6ce79f2bd68a44ba87990b9b8564e
--- /dev/null
+++ b/torch_utils/custom_ops.py
@@ -0,0 +1,126 @@
+# 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/misc.py b/torch_utils/misc.py
new file mode 100644
index 0000000000000000000000000000000000000000..d447829a091d94e56b2984e801de74b4c9ec5d19
--- /dev/null
+++ b/torch_utils/misc.py
@@ -0,0 +1,268 @@
+# 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
+ flag = False
+ if ignore_regex is not None:
+ for regex in ignore_regex:
+ if re.fullmatch(regex, fullname):
+ flag = True
+ break
+ if flag:
+ 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 = '' 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/__init__.py b/torch_utils/ops/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ece0ea08fe2e939cc260a1dafc0ab5b391b773d9
--- /dev/null
+++ b/torch_utils/ops/__init__.py
@@ -0,0 +1,9 @@
+# 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
diff --git a/torch_utils/ops/bias_act.cpp b/torch_utils/ops/bias_act.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..5d2425d8054991a8e8b6f7a940fd0ff7fa0bb330
--- /dev/null
+++ b/torch_utils/ops/bias_act.cpp
@@ -0,0 +1,99 @@
+// 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
+#include
+#include
+#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(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);
+}
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/bias_act.cu b/torch_utils/ops/bias_act.cu
new file mode 100644
index 0000000000000000000000000000000000000000..dd8fc4756d7d94727f94af738665b68d9c518880
--- /dev/null
+++ b/torch_utils/ops/bias_act.cu
@@ -0,0 +1,173 @@
+// 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
+#include "bias_act.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template struct InternalType;
+template <> struct InternalType { typedef double scalar_t; };
+template <> struct InternalType { typedef float scalar_t; };
+template <> struct InternalType { typedef float scalar_t; };
+
+//------------------------------------------------------------------------
+// CUDA kernel.
+
+template
+__global__ void bias_act_kernel(bias_act_kernel_params p)
+{
+ typedef typename InternalType::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 void* choose_bias_act_kernel(const bias_act_kernel_params& p)
+{
+ if (p.act == 1) return (void*)bias_act_kernel;
+ if (p.act == 2) return (void*)bias_act_kernel;
+ if (p.act == 3) return (void*)bias_act_kernel;
+ if (p.act == 4) return (void*)bias_act_kernel;
+ if (p.act == 5) return (void*)bias_act_kernel;
+ if (p.act == 6) return (void*)bias_act_kernel;
+ if (p.act == 7) return (void*)bias_act_kernel;
+ if (p.act == 8) return (void*)bias_act_kernel;
+ if (p.act == 9) return (void*)bias_act_kernel;
+ return NULL;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template void* choose_bias_act_kernel (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel (const bias_act_kernel_params& p);
+template void* choose_bias_act_kernel (const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/bias_act.h b/torch_utils/ops/bias_act.h
new file mode 100644
index 0000000000000000000000000000000000000000..a32187e1fb7e3bae509d4eceaf900866866875a4
--- /dev/null
+++ b/torch_utils/ops/bias_act.h
@@ -0,0 +1,38 @@
+// 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 void* choose_bias_act_kernel(const bias_act_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/bias_act.py b/torch_utils/ops/bias_act.py
new file mode 100644
index 0000000000000000000000000000000000000000..4bcb409a89ccf6c6f6ecfca5962683df2d280b1f
--- /dev/null
+++ b/torch_utils/ops/bias_act.py
@@ -0,0 +1,212 @@
+# 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/conv2d_gradfix.py b/torch_utils/ops/conv2d_gradfix.py
new file mode 100644
index 0000000000000000000000000000000000000000..e95e10d0b1d0315a63a76446fd4c5c293c8bbc6d
--- /dev/null
+++ b/torch_utils/ops/conv2d_gradfix.py
@@ -0,0 +1,170 @@
+# 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/conv2d_resample.py b/torch_utils/ops/conv2d_resample.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd4750744c83354bab78704d4ef51ad1070fcc4a
--- /dev/null
+++ b/torch_utils/ops/conv2d_resample.py
@@ -0,0 +1,156 @@
+# 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/fma.py b/torch_utils/ops/fma.py
new file mode 100644
index 0000000000000000000000000000000000000000..2eeac58a626c49231e04122b93e321ada954c5d3
--- /dev/null
+++ b/torch_utils/ops/fma.py
@@ -0,0 +1,60 @@
+# 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/grid_sample_gradfix.py b/torch_utils/ops/grid_sample_gradfix.py
new file mode 100644
index 0000000000000000000000000000000000000000..ca6b3413ea72a734703c34382c023b84523601fd
--- /dev/null
+++ b/torch_utils/ops/grid_sample_gradfix.py
@@ -0,0 +1,83 @@
+# 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
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.cpp b/torch_utils/ops/upfirdn2d.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..2d7177fc60040751d20e9a8da0301fa3ab64968a
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.cpp
@@ -0,0 +1,103 @@
+// 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
+#include
+#include
+#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();
+ 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(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);
+}
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.cu b/torch_utils/ops/upfirdn2d.cu
new file mode 100644
index 0000000000000000000000000000000000000000..ebdd9879f4bb16fc57a23cbc81f9de8ef54e4916
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.cu
@@ -0,0 +1,350 @@
+// 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
+#include "upfirdn2d.h"
+
+//------------------------------------------------------------------------
+// Helpers.
+
+template struct InternalType;
+template <> struct InternalType { typedef double scalar_t; };
+template <> struct InternalType { typedef float scalar_t; };
+template <> struct InternalType { 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 static __global__ void upfirdn2d_kernel_large(upfirdn2d_kernel_params p)
+{
+ typedef typename InternalType::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
+static __global__ void upfirdn2d_kernel_small(upfirdn2d_kernel_params p)
+{
+ typedef typename InternalType::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 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, -1,-1,1, 4}; // contiguous
+ if (s == 1) spec = {(void*)upfirdn2d_kernel_large, -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, 64,16,1, 1};
+ if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1};
+ if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1};
+ if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1};
+ if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1};
+ if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 16,16,8, 1};
+ if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1};
+ if (fx <= 5 && fy <= 5 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1};
+ if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1};
+ if (fx <= 3 && fy <= 3 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1};
+ if (fx <= 24 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 1 && fy <= 24) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 64,16,1, 1};
+ if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1};
+ if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 64,16,1, 1};
+ if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 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, 16,16,8, 1};
+ if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1};
+ if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 16,16,8, 1};
+ if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 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, 128,8,1, 1};
+ if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,8,1, 1};
+ if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 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, 128,1,16, 1};
+ if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 128,1,16, 1};
+ if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 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, 32,32,1, 1};
+ if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,32,1, 1};
+ if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 1,128,16, 1};
+ if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,128,16, 1};
+ if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 32,8,1, 1};
+ if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1};
+ if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 32,8,1, 1};
+ if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 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, 8,8,8, 1};
+ if (fx <= 6 && fy <= 6 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1};
+ if (fx <= 4 && fy <= 4 ) spec = {(void*)upfirdn2d_kernel_small, 8,8,8, 1};
+ if (fx <= 2 && fy <= 2 ) spec = {(void*)upfirdn2d_kernel_small, 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, 64,8,1, 1};
+ if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1};
+ if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1};
+ if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,8,1, 1};
+ if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 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, 64,1,8, 1};
+ if (fx <= 20 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1};
+ if (fx <= 16 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1};
+ if (fx <= 12 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 64,1,8, 1};
+ if (fx <= 8 && fy <= 1 ) spec = {(void*)upfirdn2d_kernel_small, 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, 32,16,1, 1};
+ if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1};
+ if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1};
+ if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 32,16,1, 1};
+ if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 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, 1,64,8, 1};
+ if (fx <= 1 && fy <= 20) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1};
+ if (fx <= 1 && fy <= 16) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1};
+ if (fx <= 1 && fy <= 12) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1};
+ if (fx <= 1 && fy <= 8 ) spec = {(void*)upfirdn2d_kernel_small, 1,64,8, 1};
+ }
+ return spec;
+}
+
+//------------------------------------------------------------------------
+// Template specializations.
+
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel (const upfirdn2d_kernel_params& p);
+template upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.h b/torch_utils/ops/upfirdn2d.h
new file mode 100644
index 0000000000000000000000000000000000000000..c9e2032bcac9d2abde7a75eea4d812da348afadd
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.h
@@ -0,0 +1,59 @@
+// 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 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 upfirdn2d_kernel_spec choose_upfirdn2d_kernel(const upfirdn2d_kernel_params& p);
+
+//------------------------------------------------------------------------
diff --git a/torch_utils/ops/upfirdn2d.py b/torch_utils/ops/upfirdn2d.py
new file mode 100644
index 0000000000000000000000000000000000000000..ceeac2b9834e33b7c601c28bf27f32aa91c69256
--- /dev/null
+++ b/torch_utils/ops/upfirdn2d.py
@@ -0,0 +1,384 @@
+# 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)
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/persistence.py b/torch_utils/persistence.py
new file mode 100644
index 0000000000000000000000000000000000000000..0186cfd97bca0fcb397a7b73643520c1d1105a02
--- /dev/null
+++ b/torch_utils/persistence.py
@@ -0,0 +1,251 @@
+# 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)
+
+#----------------------------------------------------------------------------
diff --git a/torch_utils/training_stats.py b/torch_utils/training_stats.py
new file mode 100644
index 0000000000000000000000000000000000000000..26f467f9eaa074ee13de1cf2625cd7da44880847
--- /dev/null
+++ b/torch_utils/training_stats.py
@@ -0,0 +1,268 @@
+# 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]
+
+#----------------------------------------------------------------------------
diff --git a/train.py b/train.py
new file mode 100644
index 0000000000000000000000000000000000000000..da02b4e4f6d86456f0ff21c62545fe5933b60c28
--- /dev/null
+++ b/train.py
@@ -0,0 +1,650 @@
+# 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 training import training_loop_simmim as training_loop
+# from training import training_loop_woMap as 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: , default = 1 gpu
+ snap = None, # Snapshot interval: , default = 50 ticks
+ metrics = None, # List of metric names: [], ['fid50k_full'] (default), ...
+ seed = None, # Random seed: , default = 0
+
+ # Dataset.
+ data = None, # Training dataset (required):
+ data_val = None, # Validation dataset: , default = None. If none, data_val = data
+ dataloader = None, # Dataloader, string
+ cond = None, # Train conditional model based on dataset labels: , default = False
+ subset = None, # Train with only N images: , default = all
+ mirror = None, # Augment dataset with x-flips: , default = False
+
+ # Base config.
+ cfg = None, # Base config: 'auto' (default), 'stylegan2', 'paper256', 'paper512', 'paper1024', 'cifar'
+ generator = None, # Path of the generator class
+ wdim = None,
+ zdim = None,
+ discriminator = None, # Path of the discriminator class
+ loss = None,
+ gamma = None, # Override R1 gamma:
+ pr = None,
+ pl = None, # Train with path length regularization: , default = True
+ kimg = None, # Override training duration:
+ batch = None, # Override batch size:
+ truncation = None, # truncation for training:
+ style_mix = None, # style mixing probability for training:
+ ema = None, # Half-life of the exponential moving average (EMA) of generator weights:
+ lr = None, # learning rate
+ lrt = None, # learning rate of transformer:
+
+ # Discriminator augmentation.
+ aug = None, # Augmentation mode: 'ada' (default), 'noaug', 'fixed'
+ p = None, # Specify p for 'fixed' (required):
+ target = None, # Override ADA target for 'ada': , 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', ,
+ freezed = None, # Freeze-D: , default = 0 discriminator layers
+
+ # Performance options (not included in desc).
+ fp32 = None, # Disable mixed-precision training: , default = False
+ nhwc = None, # Use NHWC memory format with FP16: , default = False
+ allow_tf32 = None, # Allow PyTorch to use TF32 for matmul and convolutions: , default = False
+ nobench = None, # Disable cuDNN benchmarking: , default = False
+ workers = None, # Override number of DataLoader workers: , 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)
+ if data_val is None:
+ data_val = data
+ if dataloader is None:
+ dataloader = 'datasets.dataset_512.ImageFolderMaskDataset'
+
+ args.training_set_kwargs = dnnlib.EasyDict(class_name=dataloader, path=data,
+ use_labels=True, max_size=None, xflip=False)
+ args.val_set_kwargs = dnnlib.EasyDict(class_name=dataloader, path=data_val,
+ 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 part
+ 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
+ # validation part
+ val_set = dnnlib.util.construct_class_by_name(**args.val_set_kwargs)
+ args.val_set_kwargs.resolution = val_set.resolution
+ args.val_set_kwargs.use_labels = val_set.has_labels
+ args.val_set_kwargs.max_size = len(val_set)
+
+ del training_set, val_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 or not args.val_set_kwargs.use_labels:
+ raise UserError('--cond=True requires labels specified in labels.json')
+ desc += '-cond'
+ else:
+ args.training_set_kwargs.use_labels = False
+ args.val_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.
+ 'places256': dict(ref_gpus=8, kimg=50000, mb=64, mbstd=8, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8),
+ 'places512': dict(ref_gpus=8, kimg=50000, mb=64, mbstd=8, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8),
+ 'celeba512': dict(ref_gpus=8, kimg=25000, mb=64, mbstd=8, fmaps=1, lrate=0.002, gamma=10, ema=10, ramp=None, map=8),
+ }
+
+ 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
+
+ if generator is None:
+ generator = 'networks.mat.Generator'
+ else:
+ desc += '-' + generator.split('.')[1]
+ if discriminator is None:
+ discriminator = 'networks.mat.Discriminator'
+ if wdim is None:
+ wdim = 512
+ if zdim is None:
+ zdim = 512
+ args.G_kwargs = dnnlib.EasyDict(class_name=generator, z_dim=zdim, w_dim=wdim, mapping_kwargs=dnnlib.EasyDict(), synthesis_kwargs=dnnlib.EasyDict())
+ args.D_kwargs = dnnlib.EasyDict(class_name=discriminator)
+ 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.D_kwargs.mbstd_group_size = spec.mbstd
+
+ if lr is not None:
+ assert isinstance(lr, float)
+ spec.lrate = lr
+ desc += f'-lr{lr:g}'
+ if lrt is not None:
+ assert isinstance(lrt, float)
+ spec.lrt = lrt
+ desc += f'-lrt{lrt:g}'
+
+ if lrt is None:
+ args.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, betas=[0, 0.99], eps=1e-8)
+ else:
+ args.G_opt_kwargs = dnnlib.EasyDict(class_name='torch.optim.Adam', lr=spec.lrate, lrt=spec.lrt, 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)
+
+ if loss is None:
+ loss = 'losses.loss.TwoStageLoss'
+ else:
+ desc += '-' + loss.split('.')[-1]
+ args.loss_kwargs = dnnlib.EasyDict(class_name=loss, 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 pr is not None:
+ assert isinstance(pr, float)
+ desc += f'-pr{pr:g}'
+ args.loss_kwargs.pcp_ratio = pr
+
+ if pl is None:
+ pl = True
+ assert isinstance(pl, bool)
+ if pl is False:
+ desc += f'-nopl'
+ args.loss_kwargs.pl_weight = 0 # disable path length regularization
+
+ 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
+
+ if truncation is not None:
+ assert isinstance(truncation, float)
+ desc += '-tc' + str(truncation)
+ args.loss_kwargs.truncation_psi = truncation
+
+ if style_mix is not None:
+ assert isinstance(style_mix, float)
+ desc += '-sm' + str(style_mix)
+ args.loss_kwargs.style_mixing_prob = style_mix
+
+ if ema is not None:
+ assert isinstance(ema, int)
+ desc += '-ema' + str(ema)
+ args.ema_kimg = ema
+
+ # ---------------------------------------------------
+ # 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
+ desc += '-fp32'
+
+ 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('--data_val', help='Validation data (directory or zip)', metavar='PATH')
+@click.option('--dataloader', help='dataloader', type=str, metavar='STRING')
+@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', 'inp512', 'paper1024', 'cifar', 'places256', 'places512', 'celeba512']))
+@click.option('--generator', help='the path of generator', type=str, metavar='STRING')
+@click.option('--wdim', help='dimension of w', type=int, metavar='INT')
+@click.option('--zdim', help='dimension of noise input', type=int, metavar='INT')
+@click.option('--discriminator', help='the path of discriminator', type=str, metavar='STRING')
+@click.option('--loss', help='the path of loss', type=str, metavar='STRING')
+@click.option('--gamma', help='Override R1 gamma', type=float)
+@click.option('--pr', help='Override ratio of pcp loss', type=float)
+@click.option('--pl', help='Enable path length regularization [default: true]', type=bool, metavar='BOOL')
+@click.option('--kimg', help='Override training duration', type=int, metavar='INT')
+@click.option('--batch', help='Override batch size', type=int, metavar='INT')
+@click.option('--truncation', help='truncation for training', type=float)
+@click.option('--style_mix', help='style mixing probability for training', type=float)
+@click.option('--ema', help='Half-life of the exponential moving average (EMA) of generator weights', type=int, metavar='INT')
+@click.option('--lr', help='learning rate', type=float)
+@click.option('--lrt', help='learning rate', type=float)
+
+# 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.
+ Custom network pickle.
+ """
+ print('Start')
+ 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()
+ print('Validation options:')
+ print(f'Validation data: {args.val_set_kwargs.path}')
+ print(f'Number of images: {args.val_set_kwargs.max_size}')
+ print(f'Image resolution: {args.val_set_kwargs.resolution}')
+ print(f'Conditional model: {args.val_set_kwargs.use_labels}')
+ print(f'Dataset x-flips: {args.val_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
+
+#----------------------------------------------------------------------------
diff --git a/training/__init__.py b/training/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e1e1a5ba99e56a56ecaa14f7d4fa41777789c0cf
--- /dev/null
+++ b/training/__init__.py
@@ -0,0 +1,9 @@
+# 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
diff --git a/training/augment.py b/training/augment.py
new file mode 100644
index 0000000000000000000000000000000000000000..896fbb138ade02503e59e3dc9e2c38d645ed9749
--- /dev/null
+++ b/training/augment.py
@@ -0,0 +1,432 @@
+# 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:
+ pass
+ # 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
+
+#----------------------------------------------------------------------------
diff --git a/training/training_loop.py b/training/training_loop.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd06243fbf260ab7ff470b4c8c5782f5709334b4
--- /dev/null
+++ b/training/training_loop.py
@@ -0,0 +1,464 @@
+# 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, masks, labels = zip(*[training_set[i] for i in grid_indices])
+ return (gw, gh), np.stack(images), np.stack(masks), 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.
+ val_set_kwargs = {},
+ 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
+ val_set = dnnlib.util.construct_class_by_name(**val_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)
+ # adaptation to inpainting config
+ # G
+ img_in = torch.empty([batch_gpu, training_set.num_channels, training_set.resolution, training_set.resolution], device=device)
+ mask_in = torch.empty([batch_gpu, 1, training_set.resolution, training_set.resolution], device=device)
+ img = misc.print_module_summary(G, [img_in, mask_in, z, c])
+ # D
+ img_stg1 = torch.empty([batch_gpu, 3, training_set.resolution, training_set.resolution], device=device)
+ misc.print_module_summary(D, [img, mask_in, img_stg1, 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]
+ if 'lrt' in opt_kwargs:
+ filter_list = ['tran', 'Tran']
+ base_params = []
+ tran_params = []
+ for pname, param in module.named_parameters():
+ flag = False
+ for fname in filter_list:
+ if fname in pname:
+ flag = True
+ if flag:
+ tran_params.append(param)
+ else:
+ base_params.append(param)
+ optim_params = [{'params': base_params}, {'params': tran_params, 'lr': opt_kwargs.lrt * mb_ratio}]
+ optim_kwargs = dnnlib.EasyDict()
+ for key, val in opt_kwargs.items():
+ if 'lrt' != key:
+ optim_kwargs[key] = val
+ else:
+ optim_params = module.parameters()
+ optim_kwargs = opt_kwargs
+ opt = dnnlib.util.construct_class_by_name(optim_params, **optim_kwargs)
+ 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
+ grid_img = None
+ grid_mask = None
+ if rank == 0:
+ print('Exporting sample images...')
+ grid_size, images, masks, labels = setup_snapshot_image_grid(training_set=val_set)
+ save_image_grid(images, os.path.join(run_dir, 'reals.png'), drange=[0, 255], grid_size=grid_size)
+ # adaptation to inpainting config
+ save_image_grid(masks, os.path.join(run_dir, 'masks.png'), drange=[0, 1], 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)
+ # adaptation to inpainting config
+ grid_img = (torch.from_numpy(images).to(device) / 127.5 - 1).split(batch_gpu) # [-1, 1]
+ grid_mask = torch.from_numpy(masks).to(device).split(batch_gpu) # {0, 1}
+ images = torch.cat([G_ema(img_in, mask_in, z, c, noise_mode='const').cpu() \
+ for img_in, mask_in, z, c in zip(grid_img, grid_mask, 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_mask, phase_real_c = next(training_set_iterator)
+ phase_real_img = (phase_real_img.to(device).to(torch.float32) / 127.5 - 1).split(batch_gpu)
+ # adaptation to inpainting config
+ phase_mask = phase_mask.to(device).to(torch.float32).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, mask, real_c, gen_z, gen_c) in enumerate(zip(phase_real_img, phase_mask, 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, mask=mask, 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(img_in, mask_in, z, c, noise_mode='const').cpu() \
+ for img_in, mask_in, z, c in zip(grid_img, grid_mask, 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), val_set_kwargs=dict(val_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', r'.*\.relative_position_index', r'.*\.avg_weight', r'.*\.attn_mask', r'.*\.resample_filter'])
+ 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=val_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...')
+
+#----------------------------------------------------------------------------