def check_cycle_generator():
"""Checks the output and number of parameters of the CycleGenerator class.
"""
state = torch.load('checker_files/cycle_generator.pt')
G_XtoY = CycleGenerator(conv_dim=32, init_zero_weights=False)
G_XtoY.load_state_dict(state['state_dict'])
images = state['input']
cycle_generator_expected = state['output']
output = G_XtoY(images)
output_np = output.data.cpu().numpy()
if np.allclose(output_np, cycle_generator_expected):
print('CycleGenerator output: EQUAL')
else:
print('CycleGenerator output: NOT EQUAL')
num_params = count_parameters(G_XtoY)
expected_params = 105856
print('CycleGenerator #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
print(opt)
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
###### Definition of variables ######
# Networks
netG_A2B = CycleGenerator(opt.A_nc, opt.B_nc)
netG_B2A = CycleGenerator(opt.B_nc, opt.A_nc)
if opt.cuda:
netG_A2B.cuda()
netG_B2A.cuda()
# Load state dicts
netG_A2B.load_state_dict(torch.load(opt.generator_A2B))
netG_B2A.load_state_dict(torch.load(opt.generator_B2A))
# Set model's test mode
netG_A2B.eval()
netG_B2A.eval()
# Inputs & targets memory allocation
Tensor = torch.cuda.FloatTensor if opt.cuda else torch.Tensor
input_A = Tensor(opt.batch_size, opt.A_nc, opt.size, opt.size)
input_B = Tensor(opt.batch_size, opt.B_nc, opt.size, opt.size)
# Dataset loader
data_loader = datasetloader.DatasetLoader(opt)
dataset = data_loader.load_data()
dataset_size = len(dataset)
