# generator model = FullNetwork(vp_value_count=VP_VALUE_COUNT, stdev=STDEV, output_shape=(BATCH_SIZE, CHANNELS, FRAMES, HEIGHT, WIDTH), use_est_vp=False) model = model.to(device) if device == 'cuda': net = torch.nn.DataParallel(model) cudnn.benchmark = True criterion = nn.MSELoss() optimizer = optim.Adam(model.parameters(), lr=LR) # data trainset = PanopticDataset(root_dir=data_root_dir, data_file=train_split, resize_height=HEIGHT, resize_width=WIDTH, clip_len=FRAMES, skip_len=SKIP_LEN, random_all=RANDOM_ALL, close_views=CLOSE_VIEWS, close_cams_file=close_cams_file, precrop=PRECROP) trainloader = torch.utils.data.DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2) testset = PanopticDataset(root_dir=data_root_dir, data_file=test_split, resize_height=HEIGHT, resize_width=WIDTH, clip_len=FRAMES, skip_len=SKIP_LEN, random_all=RANDOM_ALL, close_views=CLOSE_VIEWS, close_cams_file=close_cams_file, precrop=PRECROP) testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2) else: print('This network has only been set up to train on the NTU and panoptic datasets.') print_params()
model = FullNetwork(vp_value_count=3, output_shape=(BATCH_SIZE, CHANNELS, FRAMES, HEIGHT, WIDTH)) model.load_state_dict(torch.load(weights_path)) model = model.to(device) if device == 'cuda': net = torch.nn.DataParallel(model) cudnn.benchmark = True criterion = nn.MSELoss() testset = PanopticDataset(root_dir=data_root_dir, data_file=test_split, resize_height=HEIGHT, resize_width=WIDTH, clip_len=FRAMES, skip_len=SKIP_LEN, random_all=RANDOM_ALL, precrop=PRECROP) testloader = torch.utils.data.DataLoader(testset, batch_size=BATCH_SIZE, shuffle=False, num_workers=2) else: print( 'This network has only been set up to run on the NTU and panoptic datasets.' ) print_params() print(model)