def check_dc_discriminator():
"""Checks the output and number of parameters of the DCDiscriminator class.
"""
state = torch.load('checker_files/dc_discriminator.pt')
D = DCDiscriminator(conv_dim=32)
D.load_state_dict(state['state_dict'])
images = state['input']
dc_discriminator_expected = state['output']
output = D(images)
output_np = output.data.cpu().numpy()
if np.allclose(output_np, dc_discriminator_expected):
print('DCDiscriminator output: EQUAL')
else:
print('DCDiscriminator output: NOT EQUAL')
num_params = count_parameters(D)
expected_params = 167872
print('DCDiscriminator #params = {}, expected #params = {}, {}'.format(
num_params, expected_params,
'EQUAL' if num_params == expected_params else 'NOT EQUAL'))
print('-' * 80)
def load_checkpoint(opts):
"""Loads the generator and discriminator models from checkpoints.
"""
G_XtoY_path = os.path.join(opts.load, 'G_XtoY.pkl')
G_YtoX_path = os.path.join(opts.load, 'G_YtoX.pkl')
D_X_path = os.path.join(opts.load, 'D_X.pkl')
D_Y_path = os.path.join(opts.load, 'D_Y.pkl')
G_XtoY = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
G_YtoX = CycleGenerator(conv_dim=opts.g_conv_dim,
init_zero_weights=opts.init_zero_weights)
D_X = DCDiscriminator(conv_dim=opts.d_conv_dim)
D_Y = DCDiscriminator(conv_dim=opts.d_conv_dim)
G_XtoY.load_state_dict(
torch.load(G_XtoY_path, map_location=lambda storage, loc: storage))
G_YtoX.load_state_dict(
torch.load(G_YtoX_path, map_location=lambda storage, loc: storage))
D_X.load_state_dict(
torch.load(D_X_path, map_location=lambda storage, loc: storage))
D_Y.load_state_dict(
torch.load(D_Y_path, map_location=lambda storage, loc: storage))
if torch.cuda.is_available():
G_XtoY.cuda()
G_YtoX.cuda()
D_X.cuda()
D_Y.cuda()
print('Models moved to GPU.')
return G_XtoY, G_YtoX, D_X, D_Y
def check_dc_discriminator():
"""Checks the output and number of parameters of the DCDiscriminator class.
"""
state = torch.load('/home/love_you/Documents/Study/deep_learning/a4-code/a4-code-v2-updated/checker_files/dc_discriminator.pt')
# for key, value in state.items():
# print(key)
D = DCDiscriminator(conv_dim=32)
# summary(D, input_size=(3, 32, 32))
D.load_state_dict(state['state_dict'])
images = state['input']
dc_discriminator_expected = state['output']
output = D(images)
output_np = output.data.cpu().numpy()
if np.allclose(output_np, dc_discriminator_expected):
print('DCDiscriminator output: EQUAL')
else:
print("output_np: ", output_np.shape)
print("dc_discriminator_expected: ", dc_discriminator_expected.shape)
print('DCDiscriminator output: NOT EQUAL')
num_params = count_parameters(D)
expected_params = 167872
print('DCDiscriminator #params = {}, expected #params = {}, {}'.format(
num_params, expected_params, 'EQUAL' if num_params == expected_params else 'NOT EQUAL'))
print('-' * 80)
