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203 lines
8.1 KiB
203 lines
8.1 KiB
"""Pytorch Densenet implementation tweaks
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This file is a copy of https://github.com/pytorch/vision 'densenet.py' (BSD-3-Clause) with
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fixed kwargs passthrough and addition of dynamic global avg/max pool.
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"""
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.utils.model_zoo as model_zoo
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from collections import OrderedDict
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from .adaptive_avgmax_pool import *
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import re
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__all__ = ['DenseNet', 'densenet121', 'densenet169', 'densenet201', 'densenet161']
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model_urls = {
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'densenet121': 'https://download.pytorch.org/models/densenet121-241335ed.pth',
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'densenet169': 'https://download.pytorch.org/models/densenet169-6f0f7f60.pth',
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'densenet201': 'https://download.pytorch.org/models/densenet201-4c113574.pth',
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'densenet161': 'https://download.pytorch.org/models/densenet161-17b70270.pth',
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}
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def _filter_pretrained(state_dict):
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pattern = re.compile(
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r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
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for key in list(state_dict.keys()):
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res = pattern.match(key)
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if res:
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new_key = res.group(1) + res.group(2)
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state_dict[new_key] = state_dict[key]
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del state_dict[key]
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return state_dict
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def densenet121(pretrained=False, **kwargs):
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r"""Densenet-121 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16), **kwargs)
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if pretrained:
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state_dict = model_zoo.load_url(model_urls['densenet121'])
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model.load_state_dict(_filter_pretrained(state_dict))
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return model
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def densenet169(pretrained=False, **kwargs):
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r"""Densenet-169 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32), **kwargs)
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if pretrained:
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state_dict = model_zoo.load_url(model_urls['densenet169'])
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model.load_state_dict(_filter_pretrained(state_dict))
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return model
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def densenet201(pretrained=False, **kwargs):
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r"""Densenet-201 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32), **kwargs)
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if pretrained:
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state_dict = model_zoo.load_url(model_urls['densenet201'])
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model.load_state_dict(_filter_pretrained(state_dict))
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return model
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def densenet161(pretrained=False, **kwargs):
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r"""Densenet-201 model from
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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"""
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print(kwargs)
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model = DenseNet(num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24), **kwargs)
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if pretrained:
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state_dict = model_zoo.load_url(model_urls['densenet161'])
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model.load_state_dict(_filter_pretrained(state_dict))
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return model
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class _DenseLayer(nn.Sequential):
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def __init__(self, num_input_features, growth_rate, bn_size, drop_rate):
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super(_DenseLayer, self).__init__()
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self.add_module('norm1', nn.BatchNorm2d(num_input_features)),
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self.add_module('relu1', nn.ReLU(inplace=True)),
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self.add_module('conv1', nn.Conv2d(num_input_features, bn_size *
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growth_rate, kernel_size=1, stride=1, bias=False)),
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self.add_module('norm2', nn.BatchNorm2d(bn_size * growth_rate)),
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self.add_module('relu2', nn.ReLU(inplace=True)),
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self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate,
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kernel_size=3, stride=1, padding=1, bias=False)),
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self.drop_rate = drop_rate
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def forward(self, x):
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new_features = super(_DenseLayer, self).forward(x)
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if self.drop_rate > 0:
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new_features = F.dropout(new_features, p=self.drop_rate, training=self.training)
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return torch.cat([x, new_features], 1)
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class _DenseBlock(nn.Sequential):
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def __init__(self, num_layers, num_input_features, bn_size, growth_rate, drop_rate):
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super(_DenseBlock, self).__init__()
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for i in range(num_layers):
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layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate)
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self.add_module('denselayer%d' % (i + 1), layer)
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class _Transition(nn.Sequential):
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def __init__(self, num_input_features, num_output_features):
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super(_Transition, self).__init__()
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self.add_module('norm', nn.BatchNorm2d(num_input_features))
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self.add_module('relu', nn.ReLU(inplace=True))
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self.add_module('conv', nn.Conv2d(num_input_features, num_output_features,
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kernel_size=1, stride=1, bias=False))
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self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2))
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class DenseNet(nn.Module):
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r"""Densenet-BC model class, based on
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`
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Args:
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growth_rate (int) - how many filters to add each layer (`k` in paper)
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block_config (list of 4 ints) - how many layers in each pooling block
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num_init_features (int) - the number of filters to learn in the first convolution layer
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bn_size (int) - multiplicative factor for number of bottle neck layers
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(i.e. bn_size * k features in the bottleneck layer)
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drop_rate (float) - dropout rate after each dense layer
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num_classes (int) - number of classification classes
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"""
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def __init__(self, growth_rate=32, block_config=(6, 12, 24, 16),
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num_init_features=64, bn_size=4, drop_rate=0, num_classes=1000, global_pool='avg'):
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self.global_pool = global_pool
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self.num_classes = num_classes
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super(DenseNet, self).__init__()
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# First convolution
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self.features = nn.Sequential(OrderedDict([
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('conv0', nn.Conv2d(3, num_init_features, kernel_size=7, stride=2, padding=3, bias=False)),
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('norm0', nn.BatchNorm2d(num_init_features)),
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('relu0', nn.ReLU(inplace=True)),
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('pool0', nn.MaxPool2d(kernel_size=3, stride=2, padding=1)),
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]))
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# Each denseblock
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num_features = num_init_features
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for i, num_layers in enumerate(block_config):
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block = _DenseBlock(num_layers=num_layers, num_input_features=num_features,
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bn_size=bn_size, growth_rate=growth_rate, drop_rate=drop_rate)
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self.features.add_module('denseblock%d' % (i + 1), block)
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num_features = num_features + num_layers * growth_rate
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if i != len(block_config) - 1:
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trans = _Transition(
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num_input_features=num_features, num_output_features=num_features // 2)
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self.features.add_module('transition%d' % (i + 1), trans)
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num_features = num_features // 2
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# Final batch norm
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self.features.add_module('norm5', nn.BatchNorm2d(num_features))
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# Linear layer
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self.classifier = torch.nn.Linear(num_features, num_classes)
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self.num_features = num_features
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def get_classifier(self):
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return self.classifier
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def reset_classifier(self, num_classes, global_pool='avg'):
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self.global_pool = global_pool
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self.num_classes = num_classes
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del self.classifier
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if num_classes:
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self.classifier = torch.nn.Linear(self.num_features, num_classes)
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else:
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self.classifier = None
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def forward_features(self, x, pool=True):
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x = self.features(x)
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x = F.relu(x, inplace=True)
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if pool:
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x = adaptive_avgmax_pool2d(x, self.global_pool)
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x = x.view(x.size(0), -1)
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return x
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def forward(self, x):
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return self.classifier(self.forward_features(x, pool=True))
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