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segmentor_v1.py
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segmentor_v1.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.model_zoo as model_zoo
from collections import OrderedDict
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super(_DenseLayer, self).__init__()
self.add_module('norm1', nn.BatchNorm3d(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module('conv1', nn.Conv3d(num_input_features, bn_size *
growth_rate, kernel_size=1, stride=1, bias=False)),
self.add_module('norm2', nn.BatchNorm3d(bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module('conv2', nn.Conv3d(bn_size * growth_rate, growth_rate,
kernel_size=3, stride=1, padding=1, bias=False)),
self.drop_rate = drop_rate
def forward(self, x):
new_features = super(_DenseLayer, self).forward(x)
if self.drop_rate > 0:
new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
return torch.cat([x, new_features], 1)
class _DenseBlock(nn.Sequential):
def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
self.add_module('denselayer%d' % (i + 1), layer)
class _Transition(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_Transition, self).__init__()
self.add_module('norm', nn.BatchNorm3d(num_input_features))
self.add_module('relu', nn.ReLU(inplace=True))
self.add_module('conv', nn.Conv3d(num_input_features, num_output_features,
kernel_size=1, stride=1, bias=False))
self.add_module('pool_norm', nn.BatchNorm3d(num_output_features))
self.add_module('pool_relu', nn.ReLU(inplace=True))
self.add_module('pool', nn.Conv3d(num_output_features, num_output_features, kernel_size=2, stride=2))
class DenseNet(nn.Module):
r"""Densenet-BC model class, based on
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
growth_rate (int) - how many filters to add each layer (`k` in paper)
block_config (list of 4 ints) - how many layers in each pooling block
num_init_features (int) - the number of filters to learn in the first convolution layer
bn_size (int) - multiplicative factor for number of bottle neck layers
(i.e. bn_size * k features in the bottleneck layer)
drop_rate (float) - dropout rate after each dense layer
num_classes (int) - number of classification classes
"""
def __init__(self, growth_rate=16, block_config=(6, 12, 24, 16),
num_init_features=32, bn_size=4, drop_rate=0, num_classes=9):
super(DenseNet, self).__init__()
# First three convolutions
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv3d(2, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)),
('norm0', nn.BatchNorm3d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('conv1', nn.Conv3d(num_init_features, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)),
('norm1', nn.BatchNorm3d(num_init_features)),
('relu1', nn.ReLU(inplace=True)),
('conv2', nn.Conv3d(num_init_features, num_init_features, kernel_size=3, stride=1, padding=1, bias=False)),
]))
self.features_bn = nn.Sequential(OrderedDict([
('norm2', nn.BatchNorm3d(num_init_features)),
('relu2', nn.ReLU(inplace=True)),
]))
self.conv_pool_first = nn.Conv3d(num_init_features, num_init_features, kernel_size=2, stride=2, padding=0,
bias=False)
# Each denseblock
num_features = num_init_features
self.dense_blocks = nn.ModuleList([])
self.transit_blocks = nn.ModuleList([])
self.upsampling_blocks = nn.ModuleList([])
for i, num_layers in enumerate(block_config):
block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
self.dense_blocks.append(block)
num_features = num_features + num_layers * growth_rate
up_block = nn.ConvTranspose3d(num_features, num_classes, kernel_size=2 ** (i + 1) + 2,
stride=2 ** (i + 1),
padding=1, groups=num_classes, bias=False)
self.upsampling_blocks.append(up_block)
if i != len(block_config) - 1:
trans = _Transition(num_input_features=num_features, num_output_features=num_features // 2)
self.transit_blocks.append(trans)
#self.features.add_module('transition%d' % (i + 1), trans)
num_features = num_features // 2
# Final batch norm
#self.features.add_module('norm5', nn.BatchNorm3d(num_features))
# Linear layer
#self.classifier = nn.Linear(num_features, num_classes)
#self.bn4 = nn.BatchNorm3d(num_features)
# ----------------------- classifier -----------------------
self.bn_class = nn.BatchNorm3d(num_classes * 4 +num_init_features)
self.conv_class = nn.Conv3d(num_classes * 4+num_init_features , num_classes, kernel_size=1, padding=0)
# ----------------------------------------------------------
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal(m.weight)
#nn.Conv3d.bias.data.fill_(-0.1)
elif isinstance(m, nn.BatchNorm3d):
nn.init.constant(m.weight, 1)
nn.init.constant(m.bias, 0)
def forward(self, x):
first_three_features = self.features(x)
first_three_features_bn = self.features_bn(first_three_features)
out = self.conv_pool_first(first_three_features_bn)
out = self.dense_blocks[0](out)
up_block1 = self.upsampling_blocks[0](out)
out = self.transit_blocks[0](out)
out = self.dense_blocks[1](out)
up_block2 = self.upsampling_blocks[1](out)
out = self.transit_blocks[1](out)
out = self.dense_blocks[2](out)
up_block3 = self.upsampling_blocks[2](out)
out = self.transit_blocks[2](out)
out = self.dense_blocks[3](out)
up_block4 = self.upsampling_blocks[3](out)
#
out = torch.cat([up_block1, up_block2, up_block3, up_block4, first_three_features], 1)
# ----------------------- classifier -----------------------
out = self.conv_class(F.relu(self.bn_class(out)))
# ----------------------------------------------------------
return out