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densenet.py
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densenet.py
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# Learned from torchvision
import torch
import torch.nn as nn
import torch.nn.functional as F
from collections import OrderedDict
class _Transition(nn.Sequential):
def __init__(self,num_input_features,num_output_features):
super(_Transition,self).__init__()
self.add_module('norm',nn.BatchNorm2d(num_input_features))
self.add_module('relu',nn.ReLU(inplace=True))
self.add_module('conv',nn.Conv2d(num_input_features,num_output_features,kernel_size=1,stride=1,padding=1,bias=False))
self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
class _DenseLayer(nn.Module):
def __init__(self,num_input_features,growth_rate,bn_size, drop_rate,memory_efficient=False):
super(_DenseLayer,self).__init__()
self.add_module('norm1',nn.BatchNorm2d(num_input_features)),
self.add_module('relu1', nn.ReLU(inplace=True)),
self.add_module('conv1', nn.Conv2d(num_input_features,bn_size * growth_rate, kernel_size=1,stride=1,bias=False)),
self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
self.add_module('relu2', nn.ReLU(inplace=True)),
self.add_module('conv2', nn.Conv2d(bn_size* growth_rate,growth_rate,kernel_size=3,stride=1,padding=1,bias=False)),
self.drop_rate = float(drop_rate)
self.memory_efficient = memory_efficient
def bn_function(self,input):
"bottleneck function"
# type: (List[Tensor]) -- >Tensor
concat_feature = torch.cat(input,1)
bottleneck_output = self.conv1(self.relu1(self.norm1(concat_feature)))
return bottleneck_output
def forward(self,input_image):
if isinstance(input_image, Tensor):
prev_feature = [input_image]
else:
prev_feature = input_image
bn_output = self.bn_function(prev_feature)
new_feature = self.conv2(self.relu2(self.norm2(bn_output)))
if self.drop_rate > 0:
new_feature = F.dropout(new_feature, p = self.drop_rate, training=self.training)
return new_feature
class _DenseBlock(nn.ModuleDict):
_version = 2
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=growth_rate,
bn_size = bn_size,
drop_rate=drop_rate,)
self.add_module('denselayer%d' % (i + 1), layer)
def forward(self,init_features):
features = [init_features]
for name,layer in self.items():
new_features = layer(features)
features.append(new_features)
return torch.cat(features,1)
class DenseNet(nn.Module):
def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, memory_efficient=False):
super(DenseNet, self).__init__()
# Convolution and pooling part from table-1
self.features = nn.Sequential(OrderedDict([
('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2,
padding=3, bias=False)),
('norm0', nn.BatchNorm2d(num_init_features)),
('relu0', nn.ReLU(inplace=True)),
('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
]))
# Add multiple denseblocks based on config
# for densenet-121 config: [6,12,24,16]
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:
# add transition layer between denseblocks to
# downsample
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.BatchNorm2d(num_features))
# Linear layer
self.classifier = nn.Linear(num_features, num_classes)
# Official init from torch repo.
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight)
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.constant_(m.bias, 0)
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.adaptive_avg_pool2d(out, (1, 1))
out = torch.flatten(out, 1)
out = self.classifier(out)
return out
def _densenet(arch, growth_rate, block_config, num_init_features, pretrained, progress,
**kwargs):
model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
return model
def densenet121(pretrained=False, progress=True, **kwargs):
return _densenet('densenet121', 32, (6, 12, 24, 16), 64, pretrained, progress,
**kwargs)
if __name__ == "__main__":
x = torch.randn((2,3,224,224))
print(densenet121(x))