def __init__(self,
in_channels=3,
n_sequence=3,
out_channels=3,
n_resblock=3,
n_feat=32,
load_flow_net=False,
load_recons_net=False,
flow_pretrain_fn='',
recons_pretrain_fn='',
is_mask_filter=False,
device='cuda',
lbd=4,
hbd=16):
super(VBDE_QM, self).__init__()
print("Creating VBDE_QM Net")
self.n_sequence = n_sequence
self.device = device
self.is_mask_filter = is_mask_filter
self.lbd = lbd
self.hbd = hbd
assert n_sequence == 3, "Only support args.n_sequence=3; but get args.n_sequence={}".format(
n_sequence)
extra_channels = 1
print('Concat quantization mask')
self.flow_net = flow_pwc.Flow_PWC(load_pretrain=load_flow_net,
pretrain_fn=flow_pretrain_fn,
device=device)
self.recons_net = recons_video.RECONS_VIDEO(
in_channels=in_channels,
n_sequence=3,
out_channels=out_channels,
n_resblock=n_resblock,
n_feat=n_feat,
extra_channels=extra_channels)
if load_recons_net:
self.recons_net.load_state_dict(torch.load(recons_pretrain_fn))
print('Loading reconstruction pretrain model from {}'.format(
recons_pretrain_fn))
def __init__(self,
in_channels=3,
n_sequence=3,
out_channels=3,
n_resblock=3,
n_feat=32,
load_flow_net=False,
load_recons_net=False,
flow_pretrain_fn='',
recons_pretrain_fn='',
is_mask_filter=False,
device='cuda'):
super(CDVD_TSP, self).__init__()
print("Creating CDVD-TSP Net")
self.n_sequence = n_sequence
self.device = device
assert n_sequence == 5, "Only support args.n_sequence=5; but get args.n_sequence={}".format(
n_sequence)
self.is_mask_filter = is_mask_filter
print('Is meanfilter image when process mask:',
'True' if is_mask_filter else 'False')
extra_channels = 1
print('Select mask mode: concat, num_mask={}'.format(extra_channels))
self.flow_net = flow_pwc.Flow_PWC(load_pretrain=load_flow_net,
pretrain_fn=flow_pretrain_fn,
device=device)
self.recons_net = recons_video.RECONS_VIDEO(
in_channels=in_channels,
n_sequence=3,
out_channels=out_channels,
n_resblock=n_resblock,
n_feat=n_feat,
extra_channels=extra_channels)
if load_recons_net:
self.recons_net.load_state_dict(torch.load(recons_pretrain_fn))
print('Loading reconstruction pretrain model from {}'.format(
recons_pretrain_fn))
def __init__(self,
in_channels=3,
n_sequence=3,
out_channels=3,
n_resblock=3,
n_feat=32,
load_flow_net=False,
load_recons_net=False,
flow_pretrain_fn='',
recons_pretrain_fn='',
is_mask_filter=False,
device='cuda'):
super(VBDE, self).__init__()
print("Creating VBDE Net")
self.n_sequence = n_sequence
self.device = device
assert n_sequence == 3, "Only support args.n_sequence=3; but get args.n_sequence={}".format(
n_sequence)
extra_channels = 0
print('Select mask mode: concat, num_mask={}'.format(extra_channels))
self.space2depth = SpaceToDepth(2)
self.depth2space = nn.PixelShuffle(2)
self.flow_net = flow_pwc.Flow_PWC(load_pretrain=load_flow_net,
pretrain_fn=flow_pretrain_fn,
device=device)
self.recons_net = recons_video.RECONS_VIDEO(
in_channels=in_channels,
n_sequence=3,
out_channels=out_channels,
n_resblock=n_resblock,
n_feat=n_feat,
extra_channels=extra_channels)
if load_recons_net:
self.recons_net.load_state_dict(torch.load(recons_pretrain_fn))
print('Loading reconstruction pretrain model from {}'.format(
recons_pretrain_fn))
def __init__(self, in_channels=3, n_sequence=3, out_channels=3, n_resblock=3, n_feat=32,
load_flow_net=False, load_recons_net=False, flow_pretrain_fn='', recons_pretrain_fn='',
is_mask_filter=False, device='cuda', lbd=4, hbd=16):
super(VBDE_STEPMASK, self).__init__()
print("Creating VBDE_STEPMASK Net")
self.n_sequence = n_sequence
self.device = device
self.is_mask_filter = is_mask_filter
self.lbd = lbd
self.hbd = hbd
self.quantization_step = 2**(self.hbd - self.lbd)/(2**self.hbd - 1)
# initialize quantization step filter
top_kernel = torch.FloatTensor([[0, -1, 0], [0, 1, 0], [0, 0, 0]]).unsqueeze(0).unsqueeze(0)
down_kernel = torch.FloatTensor([[0, 0, 0], [0, 1, 0], [0, -1, 0]]).unsqueeze(0).unsqueeze(0)
left_kernel = torch.FloatTensor([[0, 0, 0], [-1, 1, 0], [0, 0, 0]]).unsqueeze(0).unsqueeze(0)
right_kernel = torch.FloatTensor([[0, 0, 0], [0, 1, -1], [0, 0, 0]]).unsqueeze(0).unsqueeze(0)
self.top_filter = nn.Parameter(data=top_kernel, requires_grad=False)
self.down_filter = nn.Parameter(data=down_kernel, requires_grad=False)
self.left_filter = nn.Parameter(data=left_kernel, requires_grad=False)
self.right_filter = nn.Parameter(data=right_kernel, requires_grad=False)
assert n_sequence == 3, "Only support args.n_sequence=3; but get args.n_sequence={}".format(n_sequence)
extra_channels = 1
print('Concat quantization step mask')
self.flow_net = flow_pwc.Flow_PWC(load_pretrain=load_flow_net, pretrain_fn=flow_pretrain_fn, device=device)
self.recons_net = recons_video.RECONS_VIDEO(in_channels=in_channels, n_sequence=3, out_channels=out_channels,
n_resblock=n_resblock, n_feat=n_feat,
extra_channels=extra_channels)
if load_recons_net:
self.recons_net.load_state_dict(torch.load(recons_pretrain_fn))
print('Loading reconstruction pretrain model from {}'.format(recons_pretrain_fn))