import argparse
import math
import numpy as np
import torch
from torch.utils.data import DataLoader
from basicsr.data import build_dataset
from basicsr.metrics.fid import calculate_fid, extract_inception_features, load_patched_inception_v3
[docs]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, 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)
if __name__ == '__main__':
calculate_fid_folder()