def __init__(self,
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=16,
upscale=4):
super(MSRResNet, self).__init__()
self.upscale = upscale
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
self.body = arch_util.make_layer(
arch_util.ResidualBlockNoBN, num_block, num_feat=num_feat)
# upsampling
if self.upscale in [2, 3]:
self.upconv1 = nn.Conv2d(num_feat,
num_feat * self.upscale * self.upscale, 3,
1, 1)
self.pixel_shuffle = nn.PixelShuffle(self.upscale)
elif self.upscale == 4:
self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(2)
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
arch_util.default_init_weights(
[self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1)
if self.upscale == 4:
arch_util.default_init_weights(self.upconv2, 0.1)
def __init__(self, num_feat=64, num_grow_ch=32):
super(ResidualDenseBlock, self).__init__()
self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1,
1)
self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1,
1)
self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
default_init_weights(
[self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
def __init__(self,
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=16,
upscale=4):
super(MSRResNet_WT_Pixel, self).__init__()
self.upscale = upscale
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
self.body = arch_util.make_layer(
arch_util.ResidualBlockNoBN, num_block, num_feat=num_feat)
# upsampling
if self.upscale in [2, 3]:
self.upconv1 = nn.Conv2d(num_feat,
num_feat * self.upscale * self.upscale, 3,
1, 1)
self.pixel_shuffle = nn.PixelShuffle(self.upscale)
elif self.upscale == 4:
self.upconv1 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.upconv2 = nn.Conv2d(num_feat, num_feat * 4, 3, 1, 1)
self.pixel_shuffle = nn.PixelShuffle(2)
self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
arch_util.default_init_weights(
[self.conv_first, self.upconv1, self.conv_hr, self.conv_last], 0.1)
if self.upscale == 4:
arch_util.default_init_weights(self.upconv2, 0.1)
# WT filter
inv_filters = arch_util.create_inv_filters()
self.register_buffer('inv_filters', inv_filters)
# Normalization buffers
self.register_buffer(
'shift',
torch.Tensor([3.0]))
self.register_buffer(
'scale',
torch.Tensor([10.0]))
def __init__(
self,
num_in_ch=3,
num_out_ch=3,
num_feat=64,
num_block=16,
):
super().__init__()
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
self.body = arch_util.make_layer(arch_util.ResidualBlockNoBN,
num_block,
num_feat=num_feat)
self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
# activation function
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
# initialization
arch_util.default_init_weights([self.conv_first, self.conv_last], 0.1)
def __init__(self, num_feat=64, num_grow_ch=32):
super(LowrankResidualDenseBlock, self).__init__()
#self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
self.conv1_u = nn.Conv2d(num_feat, int(num_grow_ch / 4), 3, 1, 1)
self.conv1_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1)
#self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
self.conv2_u = nn.Conv2d(num_feat + num_grow_ch, int(num_grow_ch / 4),
3, 1, 1)
self.conv2_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1)
#self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1,
# 1)
self.conv3_u = nn.Conv2d(num_feat + 2 * num_grow_ch,
int(num_grow_ch / 4), 3, 1, 1)
self.conv3_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1)
#self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1,
# 1)
self.conv4_u = nn.Conv2d(num_feat + 3 * num_grow_ch,
int(num_grow_ch / 4), 3, 1, 1)
self.conv4_v = nn.Conv2d(int(num_grow_ch / 4), num_grow_ch, 1, 1)
#self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
self.conv5_u = nn.Conv2d(num_feat + 4 * num_grow_ch, int(num_feat / 4),
3, 1, 1)
self.conv5_v = nn.Conv2d(int(num_feat / 4), num_feat, 1, 1)
self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
# initialization
#default_init_weights(
# [self.conv1, self.conv2, self.conv3, self.conv4, self.conv5], 0.1)
default_init_weights([
self.conv1_u, self.conv1_v, self.conv2_u, self.conv2_v,
self.conv3_u, self.conv3_v, self.conv4_u, self.conv4_v,
self.conv5_u, self.conv5_v
], 0.1)