# ELEKTRONN3 - Neural Network Toolkit
#
# Copyright (c) 2017 - now
# Max Planck Institute of Neurobiology, Munich, Germany
"""
adopted from https://github.com/bfortuner/pytorch_tiramisu/blob/master/models/
LICENSE https://github.com/bfortuner/pytorch_tiramisu/blob/master/LICENSE
"""
import torch
import torch.nn as nn
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class FCDenseNet(nn.Module):
def __init__(self, in_channels=3, down_blocks=(5,5,5,5,5),
up_blocks=(5,5,5,5,5), bottleneck_layers=5,
growth_rate=16, out_chans_first_conv=48, n_classes=12):
super().__init__()
self.down_blocks = down_blocks
self.up_blocks = up_blocks
cur_channels_count = 0
skip_connection_channel_counts = []
## First Convolution ##
self.add_module('firstconv', nn.Conv2d(in_channels=in_channels,
out_channels=out_chans_first_conv, kernel_size=3,
stride=1, padding=1, bias=True))
cur_channels_count = out_chans_first_conv
#####################
# Downsampling path #
#####################
self.denseBlocksDown = nn.ModuleList([])
self.transDownBlocks = nn.ModuleList([])
for i in range(len(down_blocks)):
self.denseBlocksDown.append(
DenseBlock(cur_channels_count, growth_rate, down_blocks[i]))
cur_channels_count += (growth_rate*down_blocks[i])
skip_connection_channel_counts.insert(0,cur_channels_count)
self.transDownBlocks.append(TransitionDown(cur_channels_count))
#####################
# Bottleneck #
#####################
self.add_module('bottleneck',Bottleneck(cur_channels_count,
growth_rate, bottleneck_layers))
prev_block_channels = growth_rate*bottleneck_layers
cur_channels_count += prev_block_channels
#######################
# Upsampling path #
#######################
self.transUpBlocks = nn.ModuleList([])
self.denseBlocksUp = nn.ModuleList([])
for i in range(len(up_blocks)-1):
self.transUpBlocks.append(TransitionUp(prev_block_channels, prev_block_channels))
cur_channels_count = prev_block_channels + skip_connection_channel_counts[i]
self.denseBlocksUp.append(DenseBlock(
cur_channels_count, growth_rate, up_blocks[i],
upsample=True))
prev_block_channels = growth_rate*up_blocks[i]
cur_channels_count += prev_block_channels
## Final DenseBlock ##
self.transUpBlocks.append(TransitionUp(
prev_block_channels, prev_block_channels))
cur_channels_count = prev_block_channels + skip_connection_channel_counts[-1]
self.denseBlocksUp.append(DenseBlock(
cur_channels_count, growth_rate, up_blocks[-1],
upsample=False))
cur_channels_count += growth_rate*up_blocks[-1]
## Softmax ##
self.finalConv = nn.Conv2d(in_channels=cur_channels_count,
out_channels=n_classes, kernel_size=1, stride=1,
padding=0, bias=True)
self.softmax = nn.LogSoftmax(dim=1)
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def forward(self, x):
out = self.firstconv(x)
skip_connections = []
for i in range(len(self.down_blocks)):
out = self.denseBlocksDown[i](out)
skip_connections.append(out)
out = self.transDownBlocks[i](out)
out = self.bottleneck(out)
for i in range(len(self.up_blocks)):
skip = skip_connections.pop()
out = self.transUpBlocks[i](out, skip)
out = self.denseBlocksUp[i](out)
out = self.finalConv(out)
out = self.softmax(out)
return out
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def FCDenseNet57(n_classes):
return FCDenseNet(
in_channels=3, down_blocks=(4, 4, 4, 4, 4),
up_blocks=(4, 4, 4, 4, 4), bottleneck_layers=4,
growth_rate=12, out_chans_first_conv=48, n_classes=n_classes)
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def FCDenseNet67(n_classes):
return FCDenseNet(
in_channels=3, down_blocks=(5, 5, 5, 5, 5),
up_blocks=(5, 5, 5, 5, 5), bottleneck_layers=5,
growth_rate=16, out_chans_first_conv=48, n_classes=n_classes)
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def FCDenseNet103(n_classes):
return FCDenseNet(
in_channels=3, down_blocks=(4,5,7,10,12),
up_blocks=(12,10,7,5,4), bottleneck_layers=15,
growth_rate=16, out_chans_first_conv=48, n_classes=n_classes)
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class DenseLayer(nn.Sequential):
def __init__(self, in_channels, growth_rate):
super().__init__()
self.add_module('norm', nn.BatchNorm2d(in_channels))
self.add_module('relu', nn.ReLU(True))
self.add_module('conv', nn.Conv2d(in_channels, growth_rate, kernel_size=3,
stride=1, padding=1, bias=True))
self.add_module('drop', nn.Dropout2d(0.2))
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def forward(self, x):
return super().forward(x)
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class DenseBlock(nn.Module):
def __init__(self, in_channels, growth_rate, n_layers, upsample=False):
super().__init__()
self.upsample = upsample
self.layers = nn.ModuleList([DenseLayer(
in_channels + i*growth_rate, growth_rate)
for i in range(n_layers)])
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def forward(self, x):
if self.upsample:
new_features = []
#we pass all previous activations into each dense layer normally
#But we only store each dense layer's output in the new_features array
for layer in self.layers:
out = layer(x)
x = torch.cat([x, out], 1)
new_features.append(out)
return torch.cat(new_features,1)
else:
for layer in self.layers:
out = layer(x)
x = torch.cat([x, out], 1) # 1 = channel axis
return x
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class TransitionDown(nn.Sequential):
def __init__(self, in_channels):
super().__init__()
self.add_module('norm', nn.BatchNorm2d(num_features=in_channels))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module('conv', nn.Conv2d(in_channels, in_channels,
kernel_size=1, stride=1,
padding=0, bias=True))
self.add_module('drop', nn.Dropout2d(0.2))
self.add_module('maxpool', nn.MaxPool2d(2))
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def forward(self, x):
return super().forward(x)
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class TransitionUp(nn.Module):
def __init__(self, in_channels, out_channels):
super().__init__()
self.convTrans = nn.ConvTranspose2d(
in_channels=in_channels, out_channels=out_channels,
kernel_size=3, stride=2, padding=0, bias=True)
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def forward(self, x, skip):
out = self.convTrans(x)
out = center_crop(out, skip.size(2), skip.size(3))
out = torch.cat([out, skip], 1)
return out
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class Bottleneck(nn.Sequential):
def __init__(self, in_channels, growth_rate, n_layers):
super().__init__()
self.add_module('bottleneck', DenseBlock(
in_channels, growth_rate, n_layers, upsample=True))
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def forward(self, x):
return super().forward(x)
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def center_crop(layer, max_height, max_width):
_, _, h, w = layer.size()
xy1 = (w - max_width) // 2
xy2 = (h - max_height) // 2
return layer[:, :, xy2:(xy2 + max_height), xy1:(xy1 + max_width)]