def __init__(self, num_res=30, channel=128):
super().__init__()
self.model = nn.Sequential(
ConvolutionBlock(in_channels=3, out_channels=channel),
*tuple([ResBlock(in_channels=channel) for _ in range(num_res)]),
ConvolutionBlock(in_channels=channel),
PixelShuffleUpscale(channels=channel),
PixelShuffleUpscale(channels=channel),
ConvolutionBlock(in_channels=channel, out_channels=3)
)
def __init__(self, num_se=30, channel=128, bias=True):
super().__init__()
self.model = nn.Sequential(
ConvolutionBlock(in_channels=3, out_channels=channel),
*tuple([
ResBlock(in_channels=channel),
ChannelAttentionBlock(channel=channel)
] for _ in range(num_se)),
ConvolutionBlock(in_channels=channel, out_channels=channel),
PixelShuffleUpscale(channels=channel),
PixelShuffleUpscale(channels=channel),
ConvolutionBlock(in_channels=channel, out_channels=3))
def __init__(self, block_sequence=None, num_channel=256,
rgb_mean=(0.4488, 0.4371, 0.4040),
rgb_std=(1.0, 1.0, 1.0), rgb_range=255):
super().__init__()
if block_sequence is None:
block_sequence = [15, 10, 5]
self.init = nn.Sequential(
MeanShift(rgb_range, rgb_mean, rgb_std, sign=-1),
ConvolutionBlock(in_channels=3, out_channels=num_channel)
)
self.skip_upscale = nn.Sequential(
TransposeUpscale(channels=num_channel),
ConvolutionBlock(in_channels=num_channel, out_channels=3)
)
model_body = []
for seq in block_sequence:
model_body.extend([ResBlock(in_channels=num_channel) for _ in range(seq)])
model_body.append(PixelShuffleUpscale(channels=num_channel))
model_body.append(ConvolutionBlock(in_channels=num_channel, out_channels=3))
self.blocks = nn.Sequential(*tuple(model_body))
self.model_end = nn.Sequential(
ConvolutionBlock(in_channels=num_channel, out_channels=3),
MeanShift(rgb_range, rgb_mean, rgb_std, sign=1)
)
def __init__(self, num_groups=None, num_channel=128):
super().__init__()
if num_groups is None:
num_groups = [6, 3]
self.model_0 = nn.Sequential(
MeanShift(sign=-1),
ConvolutionBlock(in_channels=3, out_channels=num_channel)
)
self.model_1 = nn.Sequential(*tuple([BasicGroup(num_channel) for _ in range(num_groups[0])]))
self.upscale_1 = PixelShuffleUpscale(channels=num_channel)
self.model_2 = nn.Sequential(*tuple([BasicGroup(num_channel) for _ in range(num_groups[1])]))
self.upscale_2 = PixelShuffleUpscale(channels=num_channel)
self.model_3 = nn.Sequential(
ConvolutionBlock(in_channels=num_channel, out_channels=3),
MeanShift(sign=1)
)
def __init__(self, scale, num_blocks=32, num_channel=256):
super().__init__()
self.model_0 = nn.Sequential(
MeanShift(sign=-1),
ConvolutionBlock(in_channels=3, out_channels=num_channel)
)
self.model_1 = nn.Sequential(*tuple([ResBlock(num_channel) for _ in range(num_blocks)]))
self.model_2 = nn.Sequential(
*tuple([PixelShuffleUpscale(channels=num_channel) for _ in range(int(log2(scale)))]),
ConvolutionBlock(in_channels=num_channel, out_channels=3),
MeanShift(sign=1)
)
def __init__(self, scale=4, num_groups=12, num_channel=128, rep_pad=False):
super().__init__()
self.model_0 = nn.Sequential(
MeanShift(sign=-1),
ConvolutionBlock(in_channels=3, out_channels=num_channel, rep_pad=rep_pad)
)
self.model_1 = nn.Sequential(*tuple([BasicGroup(num_channel, rep_pad=rep_pad) for _ in range(num_groups)]))
upscaler = [PixelShuffleUpscale(channels=num_channel,
basic_block=DepthSeparableConvBlock, rep_pad=rep_pad) for _ in range(int(log2(scale)))]
self.model_2 = nn.Sequential(
*tuple(upscaler),
ConvolutionBlock(in_channels=num_channel, out_channels=3, rep_pad=rep_pad),
MeanShift(sign=1)
)
def __init__(self, scale=4, num_groups=20, num_channel=64):
super().__init__()
self.model_0 = nn.Sequential(
MeanShift(sign=-1),
ConvolutionBlock(in_channels=3, out_channels=num_channel))
self.model_1 = nn.Sequential(
*tuple([BasicGroup(num_channel) for _ in range(num_groups)]))
upscaler = [
PixelShuffleUpscale(channels=num_channel)
for _ in range(int(log2(scale)))
]
self.model_2 = nn.Sequential(
*tuple(upscaler),
ConvolutionBlock(in_channels=num_channel, out_channels=3),
MeanShift(sign=1))