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Net.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
import math
__all__ = ['DenseNet']
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 DenseNet(nn.Module):
"""Densenet-BC model class
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
n_classes (int) - number of classification classes
"""
def __init__(self,
growth_rate,
block_config,
bn_size=4,
drop_rate=0,
n_classes=10,
in_channels=3):
super(DenseNet, self).__init__()
num_init_features=64 if in_channels==3 else 32
# First convolution
self.features = nn.Sequential(
OrderedDict([
('conv0',
nn.Conv3d(
in_channels,
num_init_features,
kernel_size=7,
stride=(1, 2, 2),
padding=(3, 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:
trans = _Transition(
num_input_features=num_features,
num_output_features=num_features // 2)
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))
for m in self.modules():
if isinstance(m, nn.Conv3d):
m.weight = nn.init.kaiming_normal_(m.weight, mode='fan_out')
elif isinstance(m, nn.BatchNorm3d):
m.weight.data.fill_(1)
m.bias.data.zero_()
# Linear layer
# self.classifier = nn.Linear(num_features, n_classes)
self.classifier = nn.Sequential(
nn.Linear( num_features, 20),
nn.Dropout(),
nn.Linear( 20, n_classes),
)
print(self.classifier)
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
# last_duration = int(math.ceil(self.sample_duration / 16))
# last_size = int(math.floor(self.sample_size / 32))
# out = F.avg_pool3d(
# out, kernel_size=(last_duration, last_size, last_size)).view(
# features.size(0), -1)
out = F.adaptive_avg_pool3d(out, (1, 1, 1)).view(features.size(0), -1)
out = self.classifier(out)
return out
def cal_features(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool3d(out, (1, 1, 1)).view(features.size(0), -1)
return out
if __name__ == '__main__':
def densenet121_3d(**kwargs):
model = DenseNet(
growth_rate=32,
block_config=(6, 12, 24, 16),
**kwargs)
return model
def densenet169_3d(**kwargs):
model = DenseNet(
growth_rate=32,
block_config=(6, 12, 32, 32),
**kwargs)
return model
def densenet201_3d(**kwargs):
model = DenseNet(
growth_rate=32,
block_config=(6, 12, 48, 32),
**kwargs)
return model
a = 64
img_size=(a, a)
model = densenet201_3d(n_classes=2, in_channels=1)
# x = torch.randn(3, 1, 30, img_size[0], img_size[1])
# (BatchSize, channels, depth, h, w)
# y = model.cal_features(x)
torch.save(model.state_dict(), 'm.pth')