47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
import torch
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from torch import nn
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from lambda_networks import LambdaLayer
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from torch.nn import GroupNorm
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from models.archs.RRDBNet_arch import ResidualDenseBlock
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from models.archs.arch_util import ConvGnLelu
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class LambdaRRDB(nn.Module):
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"""Residual in Residual Dense Block.
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Used in RRDB-Net in ESRGAN.
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Args:
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mid_channels (int): Channel number of intermediate features.
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growth_channels (int): Channels for each growth.
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"""
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def __init__(self, mid_channels, growth_channels=32, reduce_to=None):
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super(LambdaRRDB, self).__init__()
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if reduce_to is None:
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reduce_to = mid_channels
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self.lam1 = LambdaLayer(dim=mid_channels, dim_out=mid_channels, r=23, dim_k=16, heads=4, dim_u=4)
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self.gn1 = GroupNorm(num_groups=8, num_channels=mid_channels)
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self.lam2 = LambdaLayer(dim=mid_channels, dim_out=mid_channels, r=23, dim_k=16, heads=4, dim_u=4)
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self.gn2 = GroupNorm(num_groups=8, num_channels=mid_channels)
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self.lam3 = LambdaLayer(dim=mid_channels, dim_out=reduce_to, r=23, dim_k=16, heads=4, dim_u=4)
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self.gn3 = GroupNorm(num_groups=8, num_channels=mid_channels)
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self.conv = ConvGnLelu(reduce_to, reduce_to, kernel_size=1, bias=True, norm=False, activation=False, weight_init_factor=.1)
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def forward(self, x):
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"""Forward function.
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Args:
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x (Tensor): Input tensor with shape (n, c, h, w).
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Returns:
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Tensor: Forward results.
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"""
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out = self.lam1(x)
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out = self.gn1(out)
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out = self.lam2(out)
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out = self.gn1(out)
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out = self.lam3(out)
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out = self.gn3(out)
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return self.conv(out) * .2 + x |