update densenet
KaiyangZhou
parent
d076e41e78
commit
35235fe4bb
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@ -3,6 +3,7 @@ from __future__ import absolute_import
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from .resnet import *
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from .resnetmid import *
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from .senet import *
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from .densenet import *
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from .mudeep import *
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from .hacnn import *
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from .mobilenetv2 import *
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@ -30,8 +31,9 @@ __model_factory = {
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'se_resnet101': se_resnet101,
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'se_resnext50_32x4d': se_resnext50_32x4d,
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'se_resnext101_32x4d': se_resnext101_32x4d,
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'densenet121': densenet121,
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'densenet121_fc512': densenet121_fc512,
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#'resnext101': ResNeXt101_32x4d,
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#'densenet121': DenseNet121,
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#'squeezenet': SqueezeNet, # https://github.com/pytorch/vision/blob/master/torchvision/models/squeezenet.py
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'mobilenetv2': MobileNetV2,
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'shufflenet': ShuffleNet,
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@ -1,35 +1,232 @@
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from __future__ import absolute_import
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from __future__ import division
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import re
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import torch
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from torch import nn
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from torch.nn import functional as F
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import torchvision
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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__ = ['densenet121', 'densenet121_fc512']
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__all__ = ['DenseNet121']
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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 DenseNet121(nn.Module):
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def __init__(self, num_classes, loss={'xent'}, **kwargs):
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super(DenseNet121, self).__init__()
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self.loss = loss
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densenet121 = torchvision.models.densenet121(pretrained=True)
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self.base = densenet121.features
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self.classifier = nn.Linear(1024, num_classes)
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self.feat_dim = 1024
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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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x = self.base(x)
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x = F.avg_pool2d(x, x.size()[2:])
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f = x.view(x.size(0), -1)
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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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def __init__(self, num_classes, loss, growth_rate=32, block_config=(6, 12, 24, 16),
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num_init_features=64, bn_size=4, drop_rate=0, fc_dims=None, dropout_p=None, **kwargs):
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super(DenseNet, self).__init__()
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self.loss = loss
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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(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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self.global_avgpool = nn.AdaptiveAvgPool2d(1)
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self.feature_dim = num_features
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self.fc = self._construct_fc_layer(fc_dims, num_features, dropout_p)
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# Linear layer
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self.classifier = nn.Linear(self.feature_dim, num_classes)
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self._init_params()
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def _construct_fc_layer(self, fc_dims, input_dim, dropout_p=None):
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"""
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Construct fully connected layer
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- fc_dims (list or tuple): dimensions of fc layers, if None,
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no fc layers are constructed
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- input_dim (int): input dimension
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- dropout_p (float): dropout probability, if None, dropout is unused
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"""
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if fc_dims is None:
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self.feature_dim = input_dim
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return None
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assert isinstance(fc_dims, (list, tuple)), "fc_dims must be either list or tuple, but got {}".format(type(fc_dims))
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layers = []
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for dim in fc_dims:
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layers.append(nn.Linear(input_dim, dim))
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layers.append(nn.BatchNorm1d(dim))
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layers.append(nn.ReLU(inplace=True))
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if dropout_p is not None:
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layers.append(nn.Dropout(p=dropout_p))
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input_dim = dim
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self.feature_dim = fc_dims[-1]
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return nn.Sequential(*layers)
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def _init_params(self):
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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, mode='fan_out', nonlinearity='relu')
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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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.BatchNorm1d):
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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.normal_(m.weight, 0, 0.01)
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if m.bias is not None:
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nn.init.constant_(m.bias, 0)
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def forward(self, x):
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f = self.features(x)
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f = F.relu(f, inplace=True)
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v = self.global_avgpool(f)
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v = v.view(v.size(0), -1)
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if self.fc is not None:
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v = self.fc(v)
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if not self.training:
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return f
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y = self.classifier(f)
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return v
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y = self.classifier(v)
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if self.loss == {'xent'}:
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return y
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elif self.loss == {'xent', 'htri'}:
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return y, f
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return y, v
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else:
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raise KeyError("Unsupported loss: {}".format(self.loss))
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raise KeyError("Unsupported loss: {}".format(self.loss))
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def init_pretrained_weights(model, model_url):
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"""
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Initialize model with pretrained weights.
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Layers that don't match with pretrained layers in name or size are kept unchanged.
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"""
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pretrain_dict = model_zoo.load_url(model_url, map_location=None)
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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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for key in list(pretrain_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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pretrain_dict[new_key] = pretrain_dict[key]
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del pretrain_dict[key]
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model_dict = model.state_dict()
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pretrain_dict = {k: v for k, v in pretrain_dict.items() if k in model_dict and model_dict[k].size() == v.size()}
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model_dict.update(pretrain_dict)
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model.load_state_dict(model_dict)
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print("Initialized model with pretrained weights from {}".format(model_url))
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"""
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Dense network configurations:
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--
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densenet121: num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)
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densenet169: num_init_features=64, growth_rate=32, block_config=(6, 12, 32, 32)
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densenet201: num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32)
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densenet161: num_init_features=96, growth_rate=48, block_config=(6, 12, 36, 24)
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"""
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def densenet121(num_classes, loss, pretrained=False, **kwargs):
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model = DenseNet(
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num_classes=num_classes,
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loss=loss,
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num_init_features=64,
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growth_rate=32,
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block_config=(6, 12, 24, 16),
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fc_dims=None,
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['densenet121'])
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return model
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def densenet121_fc512(num_classes, loss, pretrained=False, **kwargs):
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model = DenseNet(
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num_classes=num_classes,
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loss=loss,
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num_init_features=64,
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growth_rate=32,
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block_config=(6, 12, 24, 16),
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fc_dims=[512],
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dropout_p=None,
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**kwargs
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)
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if pretrained:
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init_pretrained_weights(model, model_urls['densenet121'])
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return model
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