File size: 11,866 Bytes
d643072 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 |
import json
import os
import pathlib
from argparse import ArgumentDefaultsHelpFormatter, ArgumentParser
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
from pytorch_fid.inception import InceptionV3
from scipy import linalg
from torch.nn.functional import adaptive_avg_pool2d
from tools.metrics.utils import tracker
try:
from tqdm import tqdm
except ImportError:
# If tqdm is not available, provide a mock version of it
def tqdm(x):
return x
IMAGE_EXTENSIONS = {"bmp", "jpg", "jpeg", "pgm", "png", "ppm", "tif", "tiff", "webp"}
class ImagePathDataset(torch.utils.data.Dataset):
def __init__(self, files, transforms=None):
self.files = files
self.transforms = transforms
def __len__(self):
return len(self.files)
def __getitem__(self, i):
path = self.files[i]
try:
img = Image.open(path)
assert img.mode == "RGB"
if self.transforms is not None:
img = self.transforms(img)
except Exception as e:
raise FileNotFoundError(path, "\n", e)
return img
def get_activations(files, model, batch_size=50, dims=2048, device="cpu", num_workers=1):
model.eval()
if batch_size > len(files):
print("Warning: batch size is bigger than the data size. " "Setting batch size to data size")
batch_size = len(files)
transform = T.Compose(
[
T.Resize(args.img_size), # Image.BICUBIC
T.CenterCrop(args.img_size),
T.ToTensor(),
]
)
dataset = ImagePathDataset(files, transforms=transform)
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, shuffle=False, drop_last=False, num_workers=num_workers
)
pred_arr = np.empty((len(files), dims))
start_idx = 0
for batch in tqdm(dataloader, desc=f"FID: {args.exp_name}", position=args.gpu_id, leave=True):
batch = batch.to(device)
with torch.no_grad():
pred = model(batch)[0]
# If model output is not scalar, apply global spatial average pooling.
# This happens if you choose a dimensionality not equal 2048.
if pred.size(2) != 1 or pred.size(3) != 1:
pred = adaptive_avg_pool2d(pred, output_size=(1, 1))
pred = pred.squeeze(3).squeeze(2).cpu().numpy()
pred_arr[start_idx : start_idx + pred.shape[0]] = pred
start_idx = start_idx + pred.shape[0]
return pred_arr
def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
mu1 = np.atleast_1d(mu1)
mu2 = np.atleast_1d(mu2)
sigma1 = np.atleast_2d(sigma1)
sigma2 = np.atleast_2d(sigma2)
assert mu1.shape == mu2.shape, "Training and test mean vectors have different lengths"
assert sigma1.shape == sigma2.shape, "Training and test covariances have different dimensions"
diff = mu1 - mu2
# Product might be almost singular
covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False)
if not np.isfinite(covmean).all():
msg = ("fid calculation produces singular product; " "adding %s to diagonal of cov estimates") % eps
print(msg)
offset = np.eye(sigma1.shape[0]) * eps
covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset))
# Numerical error might give slight imaginary component
if np.iscomplexobj(covmean):
if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3):
m = np.max(np.abs(covmean.imag))
raise ValueError(f"Imaginary component {m}")
covmean = covmean.real
tr_covmean = np.trace(covmean)
return diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
def calculate_activation_statistics(files, model, batch_size=50, dims=2048, device="cpu", num_workers=1):
act = get_activations(files, model, batch_size, dims, device, num_workers)
mu = np.mean(act, axis=0)
sigma = np.cov(act, rowvar=False)
return mu, sigma
def compute_statistics_of_path(path, model, batch_size, dims, device, num_workers=1, flag="ref"):
if path.endswith(".npz"):
print("loaded from npz files")
with np.load(path) as f:
m, s = f["mu"][:], f["sigma"][:]
elif path.endswith(".json"):
with open(path) as file:
data_dict = json.load(file)
all_lines = list(data_dict.keys())[:sample_nums]
files = []
if isinstance(all_lines, list):
for k in all_lines:
v = data_dict[k]
if "PG-eval-data" in args.img_path:
img_path = os.path.join(args.img_path, v["category"], f"{k}.jpg")
else:
img_path = os.path.join(args.img_path, args.exp_name, f"{k}.jpg")
files.append(img_path)
elif isinstance(all_lines, dict):
assert sample_nums >= 30_000, ValueError(f"{sample_nums} is not supported for json files")
for k, v in all_lines.items():
if "PG-eval-data" in args.img_path:
img_path = os.path.join(args.img_path, v["category"], f"{k}.jpg")
else:
img_path = os.path.join(args.img_path, args.exp_name, f"{k}.jpg")
files.append(img_path)
files = sorted(files)
m, s = calculate_activation_statistics(files, model, batch_size, dims, device, num_workers)
else:
path = pathlib.Path(path)
files = sorted([file for ext in IMAGE_EXTENSIONS for file in path.glob(f"*.{ext}")])
m, s = calculate_activation_statistics(files, model, batch_size, dims, device, num_workers)
return m, s
def calculate_fid_given_paths(paths, batch_size, device, dims, num_workers=1):
"""Calculates the FID of two paths"""
for p in paths:
if not os.path.exists(p):
raise RuntimeError("Invalid path: %s" % p)
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx]).to(device)
m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, flag="ref")
m2, s2 = compute_statistics_of_path(paths[1], model, batch_size, dims, device, num_workers, flag="gen")
fid_value = calculate_frechet_distance(m1, s1, m2, s2)
return fid_value
def save_fid_stats(paths, batch_size, device, dims, num_workers=1):
"""Calculates the FID of two paths"""
if not os.path.exists(paths[0]):
raise RuntimeError("Invalid path: %s" % paths[0])
if os.path.exists(paths[1]):
raise RuntimeError("Existing output file: %s" % paths[1])
block_idx = InceptionV3.BLOCK_INDEX_BY_DIM[dims]
model = InceptionV3([block_idx]).to(device)
print(f"Saving statistics for {paths[0]}")
m1, s1 = compute_statistics_of_path(paths[0], model, batch_size, dims, device, num_workers, flag="ref")
np.savez_compressed(paths[1], mu=m1, sigma=s1)
def main():
txt_path = args.txt_path if args.txt_path is not None else args.img_path
save_txt_path = os.path.join(txt_path, f"{args.exp_name}_sample{sample_nums}.txt")
if os.path.exists(save_txt_path):
with open(save_txt_path) as f:
fid_value = f.readlines()[0].strip()
print(f"FID {fid_value}: {args.exp_name}")
return {args.exp_name: float(fid_value)}
if args.device is None:
device = torch.device("cuda" if (torch.cuda.is_available()) else "cpu")
else:
device = torch.device(args.device)
if args.num_workers is None:
try:
num_cpus = len(os.sched_getaffinity(0))
except AttributeError:
num_cpus = os.cpu_count()
num_workers = min(num_cpus, 8) if num_cpus is not None else 0
else:
num_workers = args.num_workers
if args.save_stats:
save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers)
return
fid_value = calculate_fid_given_paths(args.path, args.batch_size, device, args.dims, num_workers)
print(f"FID {fid_value}: {args.exp_name}")
with open(save_txt_path, "w") as file:
file.write(str(fid_value))
return {args.exp_name: fid_value}
def parse_args():
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument("--batch-size", type=int, default=50, help="Batch size to use")
parser.add_argument(
"--num-workers", type=int, help="Number of processes to use for data loading. Defaults to `min(8, num_cpus)`"
)
parser.add_argument("--img_size", type=int, default=512)
parser.add_argument("--device", type=str, default="cuda", help="Device to use. Like cuda, cuda:0 or cpu")
parser.add_argument("--img_path", type=str, default=None)
parser.add_argument("--exp_name", type=str, default="Sana")
parser.add_argument("--txt_path", type=str, default=None)
parser.add_argument("--sample_nums", type=int, default=30_000)
parser.add_argument(
"--dims",
type=int,
default=2048,
choices=list(InceptionV3.BLOCK_INDEX_BY_DIM),
help="Dimensionality of Inception features to use. By default, uses pool3 features",
)
parser.add_argument(
"--save-stats",
action="store_true",
help="Generate an npz archive from a directory of samples. The first path is used as input and the second as output.",
)
parser.add_argument("--stat", action="store_true")
parser.add_argument(
"--path", type=str, nargs=2, default=["", ""], help="Paths to the generated images or to .npz statistic files"
)
# online logging setting
parser.add_argument("--log_metric", type=str, default="metric")
parser.add_argument("--gpu_id", type=int, default=0)
parser.add_argument("--log_fid", action="store_true")
parser.add_argument("--suffix_label", type=str, default="", help="used for fid online log")
parser.add_argument("--tracker_pattern", type=str, default="epoch_step", help="used for fid online log")
parser.add_argument(
"--report_to",
type=str,
default=None,
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
parser.add_argument(
"--tracker_project_name",
type=str,
default="t2i-evit-baseline",
help=(
"The `project_name` argument passed to Accelerator.init_trackers for"
" more information see https://huggingface.co/docs/accelerate/v0.17.0/en/package_reference/accelerator#accelerate.Accelerator"
),
)
parser.add_argument(
"--name",
type=str,
default="baseline",
help=("Wandb Project Name"),
)
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
sample_nums = args.sample_nums
if args.stat:
if args.device is None:
device = torch.device("cuda" if (torch.cuda.is_available()) else "cpu")
else:
device = torch.device(args.device)
if args.num_workers is None:
try:
num_cpus = len(os.sched_getaffinity(0))
except AttributeError:
num_cpus = os.cpu_count()
num_workers = min(num_cpus, 8) if num_cpus is not None else 0
else:
num_workers = args.num_workers
save_fid_stats(args.path, args.batch_size, device, args.dims, num_workers)
else:
print(args.path, args.exp_name)
args.exp_name = os.path.basename(args.exp_name) or os.path.dirname(args.exp_name)
fid_result = main()
if args.log_fid:
tracker(args, fid_result, args.suffix_label, pattern=args.tracker_pattern, metric="FID")
|