target_frames.permute(0, 2, 1, 3, 4).reshape(-1, C, H, W), epoch) writer.add_images( 'Image/Fake_frame', fake_ending.permute(0, 2, 1, 3, 4).reshape(-1, C, H, W), epoch) writer.add_video('Video/Input_video_fake', fake_video, epoch, fps=2) writer.add_video('Video/Input_video_real', real_video, epoch, fps=2) train_folder = './data/train/' test_folder = './data/test/' if __name__ == '__main__': writer = SummaryWriter() # Training dataset train_dataset = data.VideoFolderDataset(train_folder, cache=os.path.join( train_folder, 'train.db')) train_video_dataset = data.VideoDataset(train_dataset, 11) train_loader = DataLoader(train_video_dataset, batch_size=10, drop_last=True, num_workers=6, shuffle=True) test_dataset = data.VideoFolderDataset(test_folder, cache=os.path.join( test_folder, 'test.db')) test_video_dataset = data.VideoDataset(test_dataset, 11) test_loader = DataLoader(test_video_dataset, batch_size=6, drop_last=True,
transforms.Normalize((0.5, 0.5, .5), (0.5, 0.5, 0.5)), ]) video_transforms = functools.partial(video_transform, image_transform=image_transforms) video_length = int(args['--video_length']) image_batch = int(args['--image_batch']) video_batch = int(args['--video_batch']) dim_z_content = int(args['--dim_z_content']) dim_z_motion = int(args['--dim_z_motion']) dim_z_category = int(args['--dim_z_category']) # dataset = data.VideoFolderDataset(args['<dataset>'], cache=os.path.join(args['<dataset>'], 'local.db')) dataset = data.VideoFolderDataset(args['<dataset>'], cache=None) image_dataset = data.ImageDataset(dataset, image_transforms) image_loader = DataLoader(image_dataset, batch_size=image_batch, drop_last=True, num_workers=2, shuffle=True) video_dataset = data.VideoDataset(dataset, 16, 2, video_transforms) video_loader = DataLoader(video_dataset, batch_size=video_batch, drop_last=True, num_workers=2, shuffle=True) generator = models.VideoGenerator(n_channels, dim_z_content,
transforms.Normalize((0.5, 0.5, .5), (0.5, 0.5, 0.5)), ]) video_transforms = functools.partial(video_transform, image_transform=image_transforms) video_length = int(args.video_length) image_batch = int(args.image_batch) video_batch = int(args.video_batch) dim_z_content = int(args.dim_z_content) dim_z_motion = int(args.dim_z_motion) dim_z_category = int(args.dim_z_category) dataset = data.VideoFolderDataset(args.dataset, cache=os.path.join( args.dataset, 'local.db')) image_dataset = data.ImageDataset(dataset, image_transforms) image_loader = DataLoader(image_dataset, batch_size=image_batch, drop_last=True, num_workers=2, shuffle=True) video_dataset = data.VideoDataset(dataset, 16, 2, video_transforms) video_loader = DataLoader(video_dataset, batch_size=video_batch, drop_last=True, num_workers=2, shuffle=True)
# video_transform is the name of function be partial # image_transform : is the one of two parameters of video_transform # The second parameter can be taken in the "partial" funtion after that video_transforms = functools.partial(video_transform, image_transform=image_transforms) video_length = int(args['--video_length']) # Fixed number of frames in one video image_batch = int(args['--image_batch']) # batchsize of images video_batch = int(args['--video_batch']) # batchsize of videos dim_z_content = int(args['--dim_z_content']) # number of element in vector z_content dim_z_motion = int(args['--dim_z_motion']) #number of element in vector z_motion dim_z_category = int(args['--dim_z_category']) # number of element in vector z_category dim_z_view = int(args['--dim_z_view']) # Dataset of all dataset = data.VideoFolderDataset(args['<dataset>'], cache=os.path.join(args['<dataset>'], 'local.db')) # Object to get images from dataset VideoFolderDataset above image_dataset = data.ImageDataset(dataset, image_transforms) # Dataloader to load images image_loader = DataLoader(image_dataset, batch_size=image_batch, drop_last=True, num_workers=2, shuffle=True) # Object to get videos from dataset VideoFolderDataset above video_dataset = data.VideoDataset(dataset, video_length=16, every_nth=2, transform=video_transforms) # Dataloader to load videos video_loader = DataLoader(video_dataset, batch_size=video_batch, drop_last=True, num_workers=2, shuffle=True) # Create object of VideoGenerator generator = models.VideoGenerator(n_channels, dim_z_content=dim_z_content, dim_z_view=dim_z_view, dim_z_motion=dim_z_motion, dim_z_category=dim_z_category, video_length=video_length)
video_transforms = functools.partial(video_transform, image_transform=image_transforms) #다음 하이퍼파라미터들은 최적화 하면서 다시 수정해야됨! #특히 z_content, z_motion 조절 필요, z_category는 우리 task에 맡게 수정 video_length = 16 image_batch = 32 video_batch = 32 dim_z_content = 30 dim_z_motion = 10 dim_z_category = 4 data_path = '../data/actions' log_path = '../logs' dataset = data.VideoFolderDataset(data_path) image_dataset = data.ImageDataset(dataset, image_transforms) image_loader = DataLoader(image_dataset, batch_size=image_batch, drop_last=True, num_workers=2, shuffle=True) video_dataset = data.VideoDataset(dataset, 16, 2, video_transforms) video_loader = DataLoader(video_dataset, batch_size=video_batch, drop_last=True, num_workers=2, shuffle=True) generator = models.VideoGenerator(n_channels, dim_z_content,