def calculate_fid_given_paths(paths, img_size=256, batch_size=50):
print('Calculating FID given paths %s and %s...' % (paths[0], paths[1]))
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
inception = InceptionV3().eval().to(device)
loaders = [get_eval_loader(path, img_size, batch_size) for path in paths]
mu, cov = [], []
for loader in loaders:
actvs = []
for x in tqdm(loader, total=len(loader)):
actv = inception(x.to(device))
actvs.append(actv)
actvs = torch.cat(actvs, dim=0).cpu().detach().numpy()
mu.append(np.mean(actvs, axis=0))
cov.append(np.cov(actvs, rowvar=False))
fid_value = frechet_distance(mu[0], cov[0], mu[1], cov[1])
return fid_value
def calculate_fid_given_paths(paths, img_size=256, batch_size=50):
print('Calculating FID given paths %s and %s...' % (paths[0], paths[1]))
device = porch.device('cuda' if porch.cuda.is_available() else 'cpu')
inception = InceptionV3("./metrics/inception_v3_pretrained.pdparams")
inception.eval()
loaders = [get_eval_loader(path, img_size, batch_size) for path in paths]
mu, cov = [], []
for loader in loaders:
actvs = []
for x in tqdm(loader, total=len(loader)):
x = porch.varbase_to_tensor(x[0])
actv = inception(x)
actvs.append(actv)
actvs = porch.cat(actvs, dim=0).numpy()
mu.append(np.mean(actvs, axis=0))
cov.append(np.cov(actvs, rowvar=False))
fid_value = frechet_distance(mu[0], cov[0], mu[1], cov[1])
return fid_value.astype(float)
def calculate_metrics(nets, args, step, mode):
print('Calculating evaluation metrics...')
assert mode in ['latent', 'reference']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
domains = os.listdir(args.val_img_dir)
domains.sort()
num_domains = len(domains)
print('Number of domains: %d' % num_domains)
lpips_dict = OrderedDict()
for trg_idx, trg_domain in enumerate(domains):
src_domains = [x for x in domains if x != trg_domain]
if mode == 'reference':
path_ref = os.path.join(args.val_img_dir, trg_domain)
loader_ref = get_eval_loader(root=path_ref,
img_size=args.img_size,
batch_size=args.val_batch_size,
imagenet_normalize=False,
drop_last=True)
for src_idx, src_domain in enumerate(src_domains):
path_src = os.path.join(args.val_img_dir, src_domain)
loader_src = get_eval_loader(root=path_src,
img_size=args.img_size,
batch_size=args.val_batch_size,
imagenet_normalize=False)
task = '%s2%s' % (src_domain, trg_domain)
path_fake = os.path.join(args.eval_dir, task)
shutil.rmtree(path_fake, ignore_errors=True)
os.makedirs(path_fake)
lpips_values = []
iter_ref = iter(loader_ref)
print('Generating images and calculating LPIPS for %s...' % task)
for i, x_src in enumerate(tqdm(loader_src, total=len(loader_src))):
N = x_src.size(0)
x_src = x_src.to(device)
y_trg = torch.tensor([trg_idx] * N).to(device)
masks = nets.fan.get_heatmap(x_src) if args.w_hpf > 0 else None
# generate 10 outputs from the same input
group_of_images = []
for j in range(args.num_outs_per_domain):
if mode == 'latent':
z_trg = torch.randn(N, args.latent_dim).to(device)
s_trg = nets.mapping_network(z_trg, y_trg)
else:
try:
x_ref = next(iter_ref).to(device)
except (NameError, StopIteration):
iter_ref = iter(loader_ref)
x_ref = next(iter_ref).to(device)
if x_ref.size(0) > N:
x_ref = x_ref[:N]
s_trg = nets.style_encoder(x_ref, y_trg)
x_fake = nets.generator(x_src, s_trg, masks=masks)
group_of_images.append(x_fake)
# save generated images to calculate FID later
for k in range(N):
filename = os.path.join(
path_fake, '%.4i_%.2i.png' %
(i * args.val_batch_size + (k + 1), j + 1))
utils.save_image(x_fake[k], ncol=1, filename=filename)
lpips_value = calculate_lpips_given_images(group_of_images)
lpips_values.append(lpips_value)
# calculate LPIPS for each task (e.g. cat2dog, dog2cat)
lpips_mean = np.array(lpips_values).mean()
lpips_dict['LPIPS_%s/%s' % (mode, task)] = lpips_mean
# delete dataloaders
del loader_src
if mode == 'reference':
del loader_ref
del iter_ref
# calculate the average LPIPS for all tasks
lpips_mean = 0
for _, value in lpips_dict.items():
lpips_mean += value / len(lpips_dict)
lpips_dict['LPIPS_%s/mean' % mode] = lpips_mean
# report LPIPS values
filename = os.path.join(args.eval_dir, 'LPIPS_%.5i_%s.json' % (step, mode))
utils.save_json(lpips_dict, filename)
# calculate and report fid values
calculate_fid_for_all_tasks(args, domains, step=step, mode=mode)
