238 lines
9.9 KiB
Python
238 lines
9.9 KiB
Python
import re
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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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__all__ = ['DenseNet', 'densenet121', 'densenet161', 'densenet169', 'densenet201',
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'dilated_densenet121', 'dilated_densenet161', 'dilated_densenet169', 'dilated_densenet201']
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model_urls = {
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'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth',
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'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth',
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'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth',
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'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth',
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}
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class _DenseLayer(nn.Sequential):
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def __init__(self, num_input_features, growth_rate, bn_size, drop_rate, dilation=1, norm_layer=nn.BatchNorm2d):
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super(_DenseLayer, self).__init__()
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self.add_module('norm1', norm_layer(num_input_features)),
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self.add_module('relu1', nn.ReLU(True)),
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self.add_module('conv1', nn.Conv2d(num_input_features, bn_size * growth_rate, 1, 1, bias=False)),
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self.add_module('norm2', norm_layer(bn_size * growth_rate)),
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self.add_module('relu2', nn.ReLU(True)),
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self.add_module('conv2', nn.Conv2d(bn_size * growth_rate, growth_rate, 3, 1, dilation, dilation, 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,
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growth_rate, drop_rate, dilation=1, norm_layer=nn.BatchNorm2d):
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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,
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growth_rate, bn_size, drop_rate, dilation, norm_layer)
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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, norm_layer=nn.BatchNorm2d):
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super(_Transition, self).__init__()
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self.add_module('norm', norm_layer(num_input_features))
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self.add_module('relu', nn.ReLU(True))
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self.add_module('conv', nn.Conv2d(num_input_features, num_output_features, 1, 1, bias=False))
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self.add_module('pool', nn.AvgPool2d(2, 2))
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# Net
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class DenseNet(nn.Module): #这是一个全新的构建模型的方法，<先构造模块后两步传递数据：features和classifier>；另一种常见的是，<边构造边传递数据>
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def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16), num_init_features=64,
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bn_size=4, drop_rate=0, num_classes=1000, norm_layer=nn.BatchNorm2d, **kwargs):
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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, 7, 2, 3, bias=False)),
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('norm0', norm_layer(num_init_features)),
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('relu0', nn.ReLU(True)),
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('pool0', nn.MaxPool2d(3, 2, 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_features, bn_size, growth_rate, drop_rate, norm_layer=norm_layer)
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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(num_features, num_features // 2, norm_layer=norm_layer)
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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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self.num_features = num_features
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# Final batch norm
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self.features.add_module('norm5', norm_layer(num_features))
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# Linear layer
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self.classifier = nn.Linear(num_features, num_classes)
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# Official init from torch repo.
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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nn.init.kaiming_normal_(m.weight)
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elif isinstance(m, nn.BatchNorm2d):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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elif isinstance(m, nn.Linear):
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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features = self.features(x)
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print('11',features.shape)
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out = F.relu(features, True)
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out = F.adaptive_avg_pool2d(out, (1, 1)).view(features.size(0), -1)
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out = self.classifier(out)
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return out
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class DilatedDenseNet(DenseNet):
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def __init__(self, growth_rate=12, block_config=(6, 12, 24, 16), num_init_features=64,
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bn_size=4, drop_rate=0, num_classes=1000, dilate_scale=8, norm_layer=nn.BatchNorm2d, **kwargs):
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super(DilatedDenseNet, self).__init__(growth_rate, block_config, num_init_features,
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bn_size, drop_rate, num_classes, norm_layer)
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assert (dilate_scale == 8 or dilate_scale == 16), "dilate_scale can only set as 8 or 16"
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from functools import partial
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if dilate_scale == 8: # output_stride
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self.features.denseblock3.apply(partial(self._conv_dilate, dilate=2))#利用partial函数给
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self.features.denseblock4.apply(partial(self._conv_dilate, dilate=4))
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del self.features.transition2.pool
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del self.features.transition3.pool
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elif dilate_scale == 16:
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self.features.denseblock4.apply(partial(self._conv_dilate, dilate=2))
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del self.features.transition3.pool
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def _conv_dilate(self, m, dilate):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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if m.kernel_size == (3, 3):
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m.padding = (dilate, dilate)
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m.dilation = (dilate, dilate)
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# Specification
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densenet_spec = {121: (64, 32, [6, 12, 24, 16]),
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161: (96, 48, [6, 12, 36, 24]),
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169: (64, 32, [6, 12, 32, 32]),
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201: (64, 32, [6, 12, 48, 32])}
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# Constructor
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def get_densenet(num_layers, pretrained=False, **kwargs):
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r"""Densenet-BC model from the
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`"Densely Connected Convolutional Networks" <https://arxiv.org/pdf/1608.06993.pdf>`_ paper.
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Parameters
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----------
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num_layers : int
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Number of layers for the variant of densenet. Options are 121, 161, 169, 201.
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pretrained : bool or str
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Boolean value controls whether to load the default pretrained weights for model.
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String value represents the hashtag for a certain version of pretrained weights.
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root : str, default $TORCH_HOME/models
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Location for keeping the model parameters.
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"""
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num_init_features, growth_rate, block_config = densenet_spec[num_layers]
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model = DenseNet(growth_rate, block_config, num_init_features, **kwargs)
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if pretrained:
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# '.'s are no longer allowed in module names, but pervious _DenseLayer
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# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
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# They are also in the checkpoints in model_urls. This pattern is used
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# to find such keys.
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pattern = re.compile(
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r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
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state_dict = model_zoo.load_url(model_urls['densenet%d' % num_layers])
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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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model.load_state_dict(state_dict) #初始化（加载权重）
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return model
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def get_dilated_densenet(num_layers, dilate_scale, pretrained=False, **kwargs):
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num_init_features, growth_rate, block_config = densenet_spec[num_layers]
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model = DilatedDenseNet(growth_rate, block_config, num_init_features, dilate_scale=dilate_scale)
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if pretrained:
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# '.'s are no longer allowed in module names, but pervious _DenseLayer
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# has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'.
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# They are also in the checkpoints in model_urls. This pattern is used
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# to find such keys.
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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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state_dict = model_zoo.load_url(model_urls['densenet%d' % num_layers])
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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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model.load_state_dict(state_dict)
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return model
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def densenet121(**kwargs):
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return get_densenet(121, **kwargs)
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def densenet161(**kwargs):
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return get_densenet(161, **kwargs)
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def densenet169(**kwargs):
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return get_densenet(169, **kwargs)
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def densenet201(**kwargs):
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return get_densenet(201, **kwargs)
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def dilated_densenet121(dilate_scale, **kwargs):
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return get_dilated_densenet(121, dilate_scale, **kwargs)
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def dilated_densenet161(dilate_scale, **kwargs):
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return get_dilated_densenet(161, dilate_scale, **kwargs)
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def dilated_densenet169(dilate_scale, **kwargs):
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return get_dilated_densenet(169, dilate_scale, **kwargs)
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def dilated_densenet201(dilate_scale, **kwargs):
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return get_dilated_densenet(201, dilate_scale, **kwargs)
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if __name__ == '__main__':
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img = torch.randn(2, 3, 512, 512).cuda()
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model = dilated_densenet121(8).cuda()
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outputs = model(img)
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print(outputs.shape)
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from torchsummary import summary
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summary(model, (3, 224, 224)) # 打印表格，按顺序输出每层的输出形状和参数
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for name, parameters in model.named_parameters():
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print(name, ':', parameters.size())
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