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 create_model(opts): """Builds the generators and discriminators. """ G = DCGenerator(noise_size=opts.noise_size, conv_dim=opts.conv_dim) D = DCDiscriminator(conv_dim=opts.conv_dim) print_models(G, D) if torch.cuda.is_available(): G.cuda() D.cuda() print('Models moved to GPU.') return G, D
def create_model(opts): """Builds the generators and discriminators. """ 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) if torch.cuda.is_available(): G_XtoY.cuda() G_YtoX.cuda() D_X.cuda() D_Y.cuda() return G_XtoY, G_YtoX, D_X, D_Y
def create_model(opts): """Builds the generators and discriminators. """ G_XtoY = CycleGenerator(conv_dim=opts.g_conv_dim, init_zero_weights=opts.init_zero_weights, batch_norm=not opts.disable_bn) G_YtoX = CycleGenerator(conv_dim=opts.g_conv_dim, init_zero_weights=opts.init_zero_weights, batch_norm=not opts.disable_bn) D_X = DCDiscriminator(conv_dim=opts.d_conv_dim) D_Y = DCDiscriminator(conv_dim=opts.d_conv_dim) print_models(G_XtoY, G_YtoX, D_X, D_Y) 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