def __init__(self, num_channels, num_block, res_scale=1.0):
super().__init__()
for nb in range(num_block):
setattr(self, "b{}".format(nb + 1),
ResidualBlockNoBN(num_channels, res_scale))
setattr(self, "c{}".format(nb + 1),
nn.Conv2d(num_channels * (nb + 2), num_channels, 1, 1, 0))
self.num_block = num_block
def __init__(self, in_channels, mid_channels, out_channels):
super(EAM, self).__init__()
self.merge = MergeRun(in_channels, mid_channels)
self.block1 = ResidualBlockNoBN(mid_channels)
self.block2 = EResidualBlockNoBN(mid_channels, out_channels)
self.ca = ChannelAttention(out_channels)
# The residual block in the paper contains a relu after addition.
self.relu = nn.ReLU(inplace=True)
def __init__(self, num_in_ch=3, num_feat=64, hr_in=False):
super(PredeblurModule, self).__init__()
self.hr_in = hr_in
self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
if self.hr_in:
# downsample x4 by stride conv
self.stride_conv_hr1 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.stride_conv_hr2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
# generate feature pyramid
self.stride_conv_l2 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.stride_conv_l3 = nn.Conv2d(num_feat, num_feat, 3, 2, 1)
self.resblock_l3 = ResidualBlockNoBN(num_feat=num_feat)
self.resblock_l2_1 = ResidualBlockNoBN(num_feat=num_feat)
self.resblock_l2_2 = ResidualBlockNoBN(num_feat=num_feat)
self.resblock_l1 = nn.ModuleList(
[ResidualBlockNoBN(num_feat=num_feat) for i in range(5)])
self.upsample = nn.Upsample(
scale_factor=2, mode='bilinear', align_corners=False)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
