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)
def calculate_metrics(nets, args, step, mode, eval_loader):
print('Calculating evaluation metrics...')
assert mode in ['latent', 'reference']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
domains = os.listdir(args.style_dir)
domains.sort()
num_domains = len(domains)
print('Number of domains: %d' % num_domains)
#generate_new = True
#num_files = sum([len(files) for r, d, files in os.walk(args.eval_dir)])
#print("num_files", num_files, len(eval_loader), (1 + args.num_outs_per_domain)*len(eval_loader)*args.batch_size)
#if num_files != (1 + args.num_outs_per_domain)*len(eval_loader):
#shutil.rmtree(args.eval_dir, ignore_errors=True)
#os.makedirs(args.eval_dir)
generate_new = True
tcl_dict = {}
# prepare
for d in range(1, num_domains):
src_domain = "style0"
trg_domain = "style" + str(d)
t1 = '%s2%s' % (src_domain, trg_domain)
t2 = '%s2%s' % (trg_domain, src_domain)
tcl_dict[t1] = []
tcl_dict[t2] = []
if generate_new:
create_task_folders(args, t1)
create_task_folders(args, t2)
# generate
for i, x_src_all in enumerate(tqdm(eval_loader, total=len(eval_loader))):
x_real, x_real2, y_org, x_ref, y_trg, mask, flow = x_src_all
x_real = x_real.to(device)
x_real2 = x_real2.to(device)
y_org = y_org.to(device)
x_ref = x_ref.to(device)
y_trg = y_trg.to(device)
mask = mask.to(device)
flow = flow.to(device)
N = x_real.size(0)
masks = nets.fan.get_heatmap(x_real) if args.w_hpf > 0 else None
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:
s_trg = nets.style_encoder(x_ref, y_trg)
x_fake = nets.generator(x_real, s_trg, masks=masks)
x_fake2 = nets.generator(x_real2, s_trg, masks=masks)
x_warp = warp(x_fake, flow)
tcl_err = ((mask*(x_fake2 - x_warp))**2).mean(dim=(1, 2, 3))**0.5
for k in range(N):
src_domain = "style" + str(y_org[k].cpu().numpy())
trg_domain = "style" + str(y_trg[k].cpu().numpy())
if src_domain == trg_domain:
continue
task = '%s2%s' % (src_domain, trg_domain)
tcl_dict[task].append(tcl_err[k].cpu().numpy())
path_ref = os.path.join(args.eval_dir, task + "/ref")
path_fake = os.path.join(args.eval_dir, task + "/fake")
#if not os.path.exists(path_ref):
# os.makedirs(path_ref)
#if not os.path.exists(path_fake):
# os.makedirs(path_fake)
if generate_new:
filename = os.path.join(path_ref, '%.4i_%.2i.png' % (i*args.val_batch_size+(k+1), j+1))
utils.save_image(x_ref[k], ncol=1, filename=filename)
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)
#filename = os.path.join(args.eval_dir, task + "/tcl_losses.txt")
#with open(filename, "a") as text_file:
# text_file.write(str(tcl_err[k].cpu().numpy()) + "\n")
# evaluate
print("computing fid, lpips and tcl")
tasks = [dir for dir in os.listdir(args.eval_dir) if os.path.isdir(os.path.join(args.eval_dir, dir))]
tasks.sort()
# fid and lpips
fid_values = OrderedDict()
lpips_dict = OrderedDict()
tcl_values = OrderedDict()
for task in tasks:
print(task)
path_ref = os.path.join(args.eval_dir, task + "/ref")
path_fake = os.path.join(args.eval_dir, task + "/fake")
#path_tcl = os.path.join(args.eval_dir, task + "/tcl_losses.txt")
fake_group = load_images(path_fake)
#with open(path_tcl, "r") as text_file:
# tcl_data = text_file.read()
#tcl_data = tcl_data.split("\n")[:-1]
#tcl_data = [float(td) for td in tcl_data]
tcl_data = tcl_dict[task]
print("TCL", len(tcl_data))
tcl_mean = np.array(tcl_data).mean()
print(tcl_mean)
tcl_values['TCL_%s/%s' % (mode, task)] = float(tcl_mean)
lpips_values = []
fake_chunks = chunks(fake_group, args.num_outs_per_domain)
for cidx in range(len(fake_chunks)):
lpips_value = calculate_lpips_given_images(fake_chunks[cidx])
lpips_values.append(lpips_value)
print("LPIPS")
# 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
print("FID")
fid_value = calculate_fid_given_paths(paths=[path_ref, path_fake], img_size=args.img_size, batch_size=args.val_batch_size)
fid_values['FID_%s/%s' % (mode, task)] = fid_value
# 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 the average FID for all tasks
fid_mean = 0
for _, value in fid_values.items():
fid_mean += value / len(fid_values)
fid_values['FID_%s/mean' % mode] = fid_mean
# report FID values
filename = os.path.join(args.eval_dir, 'FID_%.5i_%s.json' % (step, mode))
utils.save_json(fid_values, filename)
# calculate the average TCL for all tasks
tcl_mean = 0
for _, value in tcl_values.items():
print(value, len(tcl_values))
tcl_mean += value / len(tcl_values)
print(tcl_mean)
tcl_values['TCL_%s/mean' % mode] = float(tcl_mean)
# report TCL values
filename = os.path.join(args.eval_dir, 'TCL_%.5i_%s.json' % (step, mode))
utils.save_json(tcl_values, filename)
