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dense3d3.py
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dense3d3.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Mar 27 17:51:01 2021
@author: Ding
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
__all__ = ['DenseNet', 'T3D121', 'T3D161','T3D169'] # with DropOut
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
super(_DenseLayer, self).__init__()
self.add_module('norm_1', nn.BatchNorm3d(num_input_features)),
self.add_module('relu_1', nn.ReLU(inplace=True)),
self.add_module('conv_1', nn.Conv3d(num_input_features, bn_size *
growth_rate, kernel_size=1, stride=1, bias=False)),
self.add_module('norm_2', nn.BatchNorm3d(bn_size * growth_rate)),
self.add_module('relu_2', nn.ReLU(inplace=True)),
self.add_module('conv_2', 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', nn.AvgPool3d(kernel_size=2, stride=2))
class _TTL(nn.Sequential):
def __init__(self, num_input_features, num_output_features):
super(_TTL, self).__init__()
self.b1 = nn.Sequential(OrderedDict([
('norm', nn.BatchNorm3d(num_input_features)),
('relu', nn.ReLU(inplace=True)),
('conv', nn.Conv3d(num_input_features, 128,
kernel_size=1, stride=1, bias=False))
]))
self.b2 = nn.Sequential(OrderedDict([
('norm', nn.BatchNorm3d(num_input_features)),
('relu', nn.ReLU(inplace=True)),
('conv', nn.Conv3d(num_input_features, 128,
kernel_size=(3,3,3), stride=1,padding=(0,1,1), bias=False))
]))
self.b3 = nn.Sequential(OrderedDict([
('norm', nn.BatchNorm3d(num_input_features)),
('relu', nn.ReLU(inplace=True)),
('conv', nn.Conv3d(num_input_features, 128,
kernel_size=(4,3,3), stride=1,padding=(0,1,1), bias=False))
]))
def forward(self, x):
y1 = self.b1(x)
y2 = self.b2(x)
y3 = self.b3(x)
return torch.cat([y1,y2,y3], 2)
class DenseNet3D(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=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=1):
super(DenseNet3D, self).__init__()
# First convolution
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv3d(1, num_init_features, kernel_size=(3, 7, 7), stride=2, padding=(1, 3, 3), bias=False)),
('norm0', nn.BatchNorm3d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool3d(kernel_size=3, stride=2, padding=1)),
]))
# Each denseblock
num_features = num_init_features
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.features.add_module('denseblock%d' % (i + 1), block)
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
ttl = _TTL(num_input_features=num_features, num_output_features=num_features)
self.features.add_module('ttl%d' % (i + 1), ttl)
# num_features = 128*3
trans = _Transition(num_input_features=num_features, num_output_features=num_features )
self.features.add_module('transition%d' % (i + 1), trans)
num_features = num_features
# Final batch norm
self.features.add_module('norm5', nn.BatchNorm3d(num_features))
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.avg_pool3d(out, kernel_size=(1,7,7)).view(features.size(0), -1)
out = self.classifier(out)
return out
def T3D121_3D():
model = DenseNet3D(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16))
return model
def T3D121_3D_DropOut():
model = DenseNet3D(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), drop_rate=0.2)
return model
def T3D169_3D():
model = DenseNet3D(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32))
return model
def T3D161_3D():
model = DenseNet3D(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24))
return model
def T3D161_3D_DropOut():
model = DenseNet3D(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), drop_rate=0.2)
return model
def T3D201_3D():
model = DenseNet3D(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32))
return model
def T3D121(**kwargs):
"""Constructs a DenseNet-121_DropOut model.
"""
model = DenseNet3D(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), drop_rate=0.2, **kwargs)
return model
def densenet161(**kwargs):
"""Constructs a DenseNet-161_DropOut model.
"""
model = DenseNet3D(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), drop_rate=0.2, **kwargs)
return model