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densenet.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
__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
model_locs = {
'densenet121': '/media/inkers/InkersDisk1/vikram/pytorch_model_weights/densenet121_lyr_names_fixed.pth',
'densenet169': '/media/inkers/InkersDisk1/vikram/pytorch_model_weights/densenet169_lyr_names_fixed.pth',
'densenet201': '/media/inkers/InkersDisk1/vikram/pytorch_model_weights/densenet201_lyr_names_fixed.pth',
'densenet161': '/media/inkers/InkersDisk1/vikram/pytorch_model_weights/densenet161_lyr_names_fixed.pth',
}
def densenet121(pretrained=False, **kwargs):
r"""Densenet-121 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16),
**kwargs)
if pretrained:
# model.load_state_dict(model_zoo.load_url(model_urls['densenet121']))
model.load_state_dict(torch.load(model_locs['densenet121']))
return model
def densenet169(pretrained=False, **kwargs):
r"""Densenet-169 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32),
**kwargs)
if pretrained:
#model.load_state_dict(model_zoo.load_url(model_urls['densenet169']))
model.load_state_dict(torch.load(model_locs['densenet169']))
return model
def densenet201(pretrained=False, **kwargs):
r"""Densenet-201 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32),
**kwargs)
if pretrained:
#model.load_state_dict(model_zoo.load_url(model_urls['densenet201']))
model.load_state_dict(torch.load(model_locs['densenet201']))
return model
def densenet161(pretrained=False, **kwargs):
r"""Densenet-161 model from
`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24),
**kwargs)
if pretrained:
#model.load_state_dict(model_zoo.load_url(model_urls['densenet161']))
model.load_state_dict(torch.load(model_locs['densenet161']))
return model
class _DenseLayer(nn.Sequential):
def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, block_id):
super(_DenseLayer, self).__init__()
self.add_module(f'norm.1_block_{block_id}', nn.BatchNorm2d(num_input_features)),
self.add_module(f'relu.1_block_{block_id}', nn.ReLU(inplace=True)),
self.add_module(f'conv.1_block_{block_id}', nn.Conv2d(num_input_features, bn_size *
growth_rate, kernel_size=1, stride=1, bias=False)),
self.add_module(f'norm.2_block_{block_id}', nn.BatchNorm2d(bn_size * growth_rate)),
self.add_module(f'relu.2_block_{block_id}', nn.ReLU(inplace=True)),
self.add_module(f'conv.2_block_{block_id}', nn.Conv2d(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, block_id):
super(_DenseBlock, self).__init__()
for i in range(num_layers):
layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate, block_id)
self.add_module('denselayer%d' % (i + 1), layer)
class _Transition(nn.Sequential):
def __init__(self, num_input_features, num_output_features, block_id):
super(_Transition, self).__init__()
self.add_module(f'norm_block_{block_id}', nn.BatchNorm2d(num_input_features))
self.add_module(f'relu_block_{block_id}', nn.ReLU(inplace=True))
self.add_module(f'conv_block_{block_id}', nn.Conv2d(num_input_features, num_output_features,
kernel_size=1, stride=1, bias=False))
self.add_module(f'pool_block_{block_id}', nn.AvgPool2d(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=32, block_config=(6, 12, 24, 16),
num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000):
super(DenseNet, self).__init__()
# First convolution
self.features = nn.Sequential(OrderedDict([
(f'conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
(f'norm0', nn.BatchNorm2d(num_init_features)),
(f'relu0', nn.ReLU(inplace=True)),
(f'pool0', nn.MaxPool2d(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, block_id=i+1)
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,
block_id=i+1)
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)
def forward(self, x):
features = self.features(x)
out = F.relu(features, inplace=True)
out = F.avg_pool2d(out, kernel_size=7, stride=1).view(features.size(0), -1)
out = self.classifier(out)
return out