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Fix santa model positional args #6

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Fix santa model positional args #6

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6819862
Fix to SANTAModel.translate() takes 2 positional arguments but 3 were…
erickillian Aug 17, 2025
83fc730
Changes epoch eval freq back to original value of 5
erickillian Aug 17, 2025
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32 changes: 21 additions & 11 deletions models/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -73,22 +73,32 @@ def save_image_numpy(image_numpy, image_path, aspect_ratio=1.0):
@torch.no_grad()
def eval_loader(model, test_loader_a, test_loader_b, output_directory, opt):
fake_dir = os.path.join(output_directory, 'fake')
real_dir = os.path.join(output_directory, 'real')

if not os.path.exists(fake_dir):
os.mkdir(fake_dir)
if not os.path.exists(real_dir):
os.mkdir(real_dir)
if opt.direction == 'AtoB':
test_loader = test_loader_a
real_dir = os.path.join(opt.dataroot, 'testB')
acc_loader = test_loader_b
real_loader = test_loader_b
else:
test_loader = test_loader_b
real_dir = os.path.join(opt.dataroot, 'testA')
acc_loader = test_loader_a

for it, (data, acc_data) in enumerate(zip(test_loader,acc_loader)):
fake = model.translate(data['A'].cuda(), acc_data['A'].cuda())
path_fake = os.path.join(fake_dir, os.path.basename(data['A_paths'][0]).replace('jpg', 'png'))
im = tensor2im(fake)
save_image_numpy(im, path_fake)
real_loader = test_loader_a

for it, (test_data, real_data) in enumerate(zip(test_loader,real_loader)):
fake = model.translate(test_data['A'].cuda())
real = model.translate(real_data['A'].cuda())

path_fake = os.path.join(fake_dir, os.path.basename(test_data['A_paths'][0]).replace('jpg', 'png'))
path_real = os.path.join(real_dir, os.path.basename(real_data['A_paths'][0]).replace('jpg', 'png'))

# print('Saving %s and %s' % (path_fake, path_real))
im_fake = tensor2im(fake)
im_real = tensor2im(real)

save_image_numpy(im_fake, path_fake)
save_image_numpy(im_real, path_real)
eval_dict = eval_method(real_dir, fake_dir)
return eval_dict

Expand All @@ -102,7 +112,7 @@ def eval_method(real_path, fake_path):
print(real_path)
print(fake_path)
eval_dict = {}
eval_args = {'fid': True, 'kid': True, 'kid_subset_size': 50, 'kid_subsets': 10, 'verbose': False, 'cuda': True}
eval_args = {'fid': True, 'kid': True, 'kid_subset_size': 20, 'kid_subsets': 10, 'verbose': False, 'cuda': True}
metric_dict_AB = torch_fidelity.calculate_metrics(input1=real_path, input2=fake_path, **eval_args)
eval_dict['FID'] = metric_dict_AB['frechet_inception_distance']
eval_dict['KID'] = metric_dict_AB['kernel_inception_distance_mean']*100.
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