def _compute_fid(self):
r"""We will compute FID for the regular model using the eval mode.
For the moving average model, we will use the eval mode.
"""
self.net_G.eval()
net_G_for_evaluation = \
functools.partial(self.net_G, random_style=True)
regular_fid_path = self._get_save_path('regular_fid', 'npy')
preprocess = \
functools.partial(self._start_of_iteration, current_iteration=0)
regular_fid_value = compute_fid(regular_fid_path,
self.val_data_loader,
net_G_for_evaluation,
preprocess=preprocess)
print('Epoch {:05}, Iteration {:09}, Regular FID {}'.format(
self.current_epoch, self.current_iteration, regular_fid_value))
if self.cfg.trainer.model_average:
avg_net_G_for_evaluation = \
functools.partial(self.net_G.module.averaged_model,
random_style=True)
fid_path = self._get_save_path('average_fid', 'npy')
fid_value = compute_fid(fid_path,
self.val_data_loader,
avg_net_G_for_evaluation,
preprocess=preprocess)
print('Epoch {:05}, Iteration {:09}, FID {}'.format(
self.current_epoch, self.current_iteration, fid_value))
self.net_G.float()
return regular_fid_value, fid_value
else:
self.net_G.float()
return regular_fid_value
def _compute_fid(self):
r"""Compute FID. We will compute a FID value per test class. That is
if you have 30 test classes, we will compute 30 different FID values.
We will then report the mean of the FID values as the final
performance number as described in the FUNIT paper.
"""
self.net_G.eval()
if self.cfg.trainer.model_average:
net_G_for_evaluation = self.net_G.module.averaged_model
else:
net_G_for_evaluation = self.net_G
all_fid_values = []
num_test_classes = self.val_data_loader.dataset.num_style_classes
for class_idx in range(num_test_classes):
fid_path = self._get_save_path(os.path.join('fid', str(class_idx)),
'npy')
self.val_data_loader.dataset.set_sample_class_idx(class_idx)
fid_value = compute_fid(fid_path, self.val_data_loader,
net_G_for_evaluation, 'images_style',
'images_trans')
all_fid_values.append(fid_value)
if is_master():
mean_fid = np.mean(all_fid_values)
print('Epoch {:05}, Iteration {:09}, Mean FID {}'.format(
self.current_epoch, self.current_iteration, mean_fid))
return mean_fid
else:
return None
def _compute_fid(self):
r"""Compute FID for both domains.
"""
self.net_G.eval()
if self.cfg.trainer.model_average:
net_G_for_evaluation = self.net_G.module.averaged_model
else:
net_G_for_evaluation = self.net_G
fid_a_path = self._get_save_path('fid_a', 'npy')
fid_b_path = self._get_save_path('fid_b', 'npy')
fid_value_a = compute_fid(fid_a_path, self.val_data_loader,
net_G_for_evaluation, 'images_a', 'images_ba')
fid_value_b = compute_fid(fid_b_path, self.val_data_loader,
net_G_for_evaluation, 'images_b', 'images_ab')
print('Epoch {:05}, Iteration {:09}, FID a {}, FID b {}'.format(
self.current_epoch, self.current_iteration,
fid_value_a, fid_value_b))
return fid_value_a, fid_value_b