def calculate_stylegan2_fid():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser()
parser.add_argument('ckpt', type=str, help='Path to the stylegan2 checkpoint.')
parser.add_argument('fid_stats', type=str, help='Path to the dataset fid statistics.')
parser.add_argument('--size', type=int, default=256)
parser.add_argument('--channel_multiplier', type=int, default=2)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--num_sample', type=int, default=50000)
parser.add_argument('--truncation', type=float, default=1)
parser.add_argument('--truncation_mean', type=int, default=4096)
args = parser.parse_args()
# create stylegan2 model
generator = StyleGAN2Generator(
out_size=args.size,
num_style_feat=512,
num_mlp=8,
channel_multiplier=args.channel_multiplier,
resample_kernel=(1, 3, 3, 1))
generator.load_state_dict(torch.load(args.ckpt)['params_ema'])
generator = nn.DataParallel(generator).eval().to(device)
if args.truncation < 1:
with torch.no_grad():
truncation_latent = generator.mean_latent(args.truncation_mean)
else:
truncation_latent = None
# inception model
inception = load_patched_inception_v3(device)
total_batch = math.ceil(args.num_sample / args.batch_size)
def sample_generator(total_batch):
for _ in range(total_batch):
with torch.no_grad():
latent = torch.randn(args.batch_size, 512, device=device)
samples, _ = generator([latent], truncation=args.truncation, truncation_latent=truncation_latent)
yield samples
features = extract_inception_features(sample_generator(total_batch), inception, total_batch, device)
features = features.numpy()
total_len = features.shape[0]
features = features[:args.num_sample]
print(f'Extracted {total_len} features, use the first {features.shape[0]} features to calculate stats.')
sample_mean = np.mean(features, 0)
sample_cov = np.cov(features, rowvar=False)
# load the dataset stats
stats = torch.load(args.fid_stats)
real_mean = stats['mean']
real_cov = stats['cov']
# calculate FID metric
fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov)
print('fid:', fid)
def calculate_stats_from_dataset():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser()
parser.add_argument('--num_sample', type=int, default=50000)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--size', type=int, default=512)
parser.add_argument('--dataroot', type=str, default='datasets/ffhq')
args = parser.parse_args()
# inception model
inception = load_patched_inception_v3(device)
# create dataset
opt = {}
opt['name'] = 'FFHQ'
opt['type'] = 'FFHQDataset'
opt['dataroot_gt'] = f'datasets/ffhq/ffhq_{args.size}.lmdb'
opt['io_backend'] = dict(type='lmdb')
opt['use_hflip'] = False
opt['mean'] = [0.5, 0.5, 0.5]
opt['std'] = [0.5, 0.5, 0.5]
dataset = build_dataset(opt)
# create dataloader
data_loader = DataLoader(
dataset=dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=4,
sampler=None,
drop_last=False)
total_batch = math.ceil(args.num_sample / args.batch_size)
def data_generator(data_loader, total_batch):
for idx, data in enumerate(data_loader):
if idx >= total_batch:
break
else:
yield data['gt']
features = extract_inception_features(
data_generator(data_loader, total_batch), inception, total_batch,
device)
features = features.numpy()
total_len = features.shape[0]
features = features[:args.num_sample]
print(f'Extracted {total_len} features, '
f'use the first {features.shape[0]} features to calculate stats.')
mean = np.mean(features, 0)
cov = np.cov(features, rowvar=False)
save_path = f'inception_{opt["name"]}_{args.size}.pth'
torch.save(
dict(name=opt['name'], size=args.size, mean=mean, cov=cov),
save_path,
_use_new_zipfile_serialization=False)
def calculate_fid_folder():
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser = argparse.ArgumentParser()
parser.add_argument('folder', type=str, help='Path to the folder.')
parser.add_argument('--fid_stats',
type=str,
help='Path to the dataset fid statistics.')
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--num_sample', type=int, default=50000)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--backend',
type=str,
default='disk',
help='io backend for dataset. Option: disk, lmdb')
args = parser.parse_args()
# inception model
inception = load_patched_inception_v3(device)
# create dataset
opt = {}
opt['name'] = 'SingleImageDataset'
opt['type'] = 'SingleImageDataset'
opt['dataroot_lq'] = args.folder
opt['io_backend'] = dict(type=args.backend)
opt['mean'] = [0.5, 0.5, 0.5]
opt['std'] = [0.5, 0.5, 0.5]
dataset = build_dataset(opt)
# create dataloader
data_loader = DataLoader(dataset=dataset,
batch_size=args.batch_size,
shuffle=False,
num_workers=args.num_workers,
sampler=None,
drop_last=False)
args.num_sample = min(args.num_sample, len(dataset))
total_batch = math.ceil(args.num_sample / args.batch_size)
def data_generator(data_loader, total_batch):
for idx, data in enumerate(data_loader):
if idx >= total_batch:
break
else:
yield data['lq']
features = extract_inception_features(
data_generator(data_loader, total_batch), inception, total_batch,
device)
features = features.numpy()
total_len = features.shape[0]
features = features[:args.num_sample]
print(f'Extracted {total_len} features, '
f'use the first {features.shape[0]} features to calculate stats.')
sample_mean = np.mean(features, 0)
sample_cov = np.cov(features, rowvar=False)
# load the dataset stats
stats = torch.load(args.fid_stats)
real_mean = stats['mean']
real_cov = stats['cov']
# calculate FID metric
fid = calculate_fid(sample_mean, sample_cov, real_mean, real_cov)
print('fid:', fid)