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FC-DenseNet Implementation in PyTorch
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import torch | |
from torch import nn | |
__all__ = ['FCDenseNet', 'fcdensenet_tiny', 'fcdensenet56_nodrop', | |
'fcdensenet56', 'fcdensenet67', 'fcdensenet103', | |
'fcdensenet103_nodrop'] | |
class DenseBlock(nn.Module): | |
def __init__(self, nIn, growth_rate, depth, drop_rate=0, only_new=False, | |
bottle_neck=False): | |
super(DenseBlock, self).__init__() | |
self.only_new = only_new | |
self.depth = depth | |
self.growth_rate = growth_rate | |
self.layers = nn.ModuleList([self.get_transform( | |
nIn + i * growth_rate, growth_rate, bottle_neck, | |
drop_rate) for i in range(depth)]) | |
def forward(self, x): | |
if self.only_new: | |
outputs = [] | |
for i in range(self.depth): | |
tx = self.layers[i](x) | |
x = torch.cat((x, tx), 1) | |
outputs.append(tx) | |
return torch.cat(outputs, 1) | |
else: | |
for i in range(self.depth): | |
x = torch.cat((x, self.layers[i](x)), 1) | |
return x | |
def get_transform(self, nIn, nOut, bottle_neck=None, drop_rate=0): | |
if not bottle_neck or nIn <= nOut * bottle_neck: | |
return nn.Sequential( | |
nn.BatchNorm2d(nIn), | |
nn.ReLU(True), | |
nn.Conv2d(nIn, nOut, 3, stride=1, padding=1, bias=True), | |
nn.Dropout(drop_rate), | |
) | |
else: | |
nBottle = nOut * bottle_neck | |
return nn.Sequential( | |
nn.BatchNorm2d(nIn), | |
nn.ReLU(True), | |
nn.Conv2d(nIn, nBottle, 1, stride=1, padding=0, bias=True), | |
nn.BatchNorm2d(nBottle), | |
nn.ReLU(True), | |
nn.Conv2d(nBottle, nOut, 3, stride=1, padding=1, bias=True), | |
nn.Dropout(drop_rate), | |
) | |
class FCDenseNet(nn.Module): | |
def __init__(self, depths, growth_rates, n_scales=5, n_channel_start=48, | |
n_classes=12, drop_rate=0, bottle_neck=False): | |
super(FCDenseNet, self).__init__() | |
self.n_scales = n_scales | |
self.n_classes = n_classes | |
self.n_channel_start = n_channel_start | |
self.depths = [depths] * \ | |
(2 * n_scales + 1) if type(depths) == int else depths | |
self.growth_rates = [growth_rates] * (2 * n_scales + 1) if \ | |
type(growth_rates) == int else growth_rates | |
self.drop_rate = drop_rate | |
assert len(self.depths) == len(self.growth_rates) == 2 * n_scales + 1 | |
self.conv_first = nn.Conv2d( | |
3, n_channel_start, 3, stride=1, padding=1, bias=True) | |
self.dense_blocks = nn.ModuleList([]) | |
self.transition_downs = nn.ModuleList([]) | |
self.transition_ups = nn.ModuleList([]) | |
nskip = [] | |
nIn = self.n_channel_start | |
for i in range(n_scales): | |
self.dense_blocks.append( | |
DenseBlock(nIn, self.growth_rates[i], self.depths[i], | |
drop_rate=drop_rate, bottle_neck=bottle_neck)) | |
nIn += self.growth_rates[i] * self.depths[i] | |
nskip.append(nIn) | |
self.transition_downs.append(self.get_TD(nIn, drop_rate)) | |
self.dense_blocks.append( | |
DenseBlock(nIn, self.growth_rates[n_scales], self.depths[n_scales], | |
only_new=True, drop_rate=drop_rate, | |
bottle_neck=bottle_neck)) | |
nIn = self.growth_rates[n_scales] * self.depths[n_scales] | |
for i in range(n_scales-1): | |
self.transition_ups.append(nn.ConvTranspose2d( | |
nIn, nIn, 3, stride=2, padding=1, bias=True)) | |
nIn += nskip.pop() | |
self.dense_blocks.append( | |
DenseBlock(nIn, self.growth_rates[n_scales + 1 + i], | |
self.depths[n_scales + 1 + i], | |
only_new=True, drop_rate=drop_rate, | |
bottle_neck=bottle_neck)) | |
nIn = self.growth_rates[n_scales + 1 + i] * \ | |
self.depths[n_scales + 1 + i] | |
# last dense block | |
self.transition_ups.append(nn.ConvTranspose2d( | |
nIn, nIn, 3, stride=2, padding=1, bias=True)) | |
nIn += nskip.pop() | |
self.dense_blocks.append( | |
DenseBlock(nIn, self.growth_rates[2 * n_scales], | |
self.depths[2 * n_scales], drop_rate=drop_rate, | |
bottle_neck=bottle_neck)) | |
nIn += self.growth_rates[2 * n_scales] * \ | |
self.depths[2 * n_scales] | |
self.conv_last = nn.Conv2d(nIn, n_classes, 1, bias=True) | |
self.logsoftmax = nn.LogSoftmax() | |
def forward(self, x): | |
x = self.conv_first(x) | |
skip_connects = [] | |
# down sample | |
for i in range(self.n_scales): | |
x = self.dense_blocks[i](x) | |
skip_connects.append(x) | |
x = self.transition_downs[i](x) | |
# bottle neck | |
x = self.dense_blocks[self.n_scales](x) | |
# up sample | |
for i in range(self.n_scales): | |
skip = skip_connects.pop() | |
TU = self.transition_ups[i] | |
# adjust padding | |
TU.padding = (((x.size(2) - 1) * TU.stride[0] - skip.size(2) | |
+ TU.kernel_size[0] + 1) // 2, | |
((x.size(3) - 1) * TU.stride[1] - skip.size(3) | |
+ TU.kernel_size[1] + 1) // 2) | |
x = TU(x, output_size=skip.size()) | |
x = torch.cat((skip, x), 1) | |
x = self.dense_blocks[self.n_scales + 1 + i](x) | |
x = self.conv_last(x) | |
return self.logsoftmax(x) | |
def get_TD(self, nIn, drop_rate): | |
layers = [nn.BatchNorm2d(nIn), nn.ReLU( | |
True), nn.Conv2d(nIn, nIn, 1, bias=True)] | |
if drop_rate > 0: | |
layers.append(nn.Dropout(drop_rate)) | |
layers.append(nn.MaxPool2d(2)) | |
return nn.Sequential(*layers) | |
def fcdensenet_tiny(drop_rate=0): | |
return FCDenseNet(2, 6, drop_rate=drop_rate) | |
def fcdensenet56_nodrop(): | |
return FCDenseNet(4, 12, drop_rate=0) | |
def fcdensenet56(drop_rate=0.2): | |
return FCDenseNet(4, 12, drop_rate=drop_rate) | |
def fcdensenet67(drop_rate=0.2): | |
return FCDenseNet(5, 16, drop_rate=drop_rate) | |
def fcdensenet103(drop_rate=0.2): | |
return FCDenseNet([4, 5, 7, 10, 12, 15, 12, 10, 7, 5, 4], 16, | |
drop_rate=drop_rate) | |
def fcdensenet103_nodrop(drop_rate=0): | |
return FCDenseNet([4, 5, 7, 10, 12, 15, 12, 10, 7, 5, 4], 16, | |
drop_rate=drop_rate) |
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