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,