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import torch.nn as nn
class PixelShuffleDecoder(nn.Module):
"""Pixel shuffle decoder."""
def __init__(self, input_feat_dim=128, num_upsample=2, output_channel=2):
super(PixelShuffleDecoder, self).__init__()
# Get channel parameters
self.channel_conf = self.get_channel_conf(num_upsample)
# Define the pixel shuffle
self.pixshuffle = nn.PixelShuffle(2)
# Process the feature
self.conv_block_lst = []
# The input block
self.conv_block_lst.append(
nn.Sequential(
nn.Conv2d(
input_feat_dim,
self.channel_conf[0],
kernel_size=3,
stride=1,
padding=1,
),
nn.BatchNorm2d(self.channel_conf[0]),
nn.ReLU(inplace=True),
)
)
# Intermediate block
for channel in self.channel_conf[1:-1]:
self.conv_block_lst.append(
nn.Sequential(
nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1),
nn.BatchNorm2d(channel),
nn.ReLU(inplace=True),
)
)
# Output block
self.conv_block_lst.append(
nn.Conv2d(
self.channel_conf[-1],
output_channel,
kernel_size=1,
stride=1,
padding=0,
)
)
self.conv_block_lst = nn.ModuleList(self.conv_block_lst)
# Get num of channels based on number of upsampling.
def get_channel_conf(self, num_upsample):
if num_upsample == 2:
return [256, 64, 16]
elif num_upsample == 3:
return [256, 64, 16, 4]
def forward(self, input_features):
# Iterate til output block
out = input_features
for block in self.conv_block_lst[:-1]:
out = block(out)
out = self.pixshuffle(out)
# Output layer
out = self.conv_block_lst[-1](out)
return out
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