File size: 994 Bytes
9b9b1dc |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 |
import torch
import torch.nn as nn
from model.MIRNet.MultiScaleResidualBlock import MultiScaleResidualBlock
class ResidualRecurrentGroup(nn.Module):
"""
Group of multi-scale residual blocks followed by a convolutional layer. The output is what is added to the input image for restoration.
"""
def __init__(
self, num_features, number_msrb_blocks, height, width, stride, bias=False
):
super().__init__()
blocks = [
MultiScaleResidualBlock(num_features, height, width, stride, bias)
for _ in range(number_msrb_blocks)
]
blocks.append(
nn.Conv2d(
num_features,
num_features,
kernel_size=3,
padding=1,
stride=1,
bias=bias,
)
)
self.blocks = nn.Sequential(*blocks)
def forward(self, x):
output = self.blocks(x)
return x + output # restored image, HxWxC
|