#!/usr/bin/env python3 # encoding: utf-8 import re 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_AG'] model_urls = { 'densenet121': 'https://download.pytorch.org/models/densenet121-a639ec97.pth', 'densenet169': 'https://download.pytorch.org/models/densenet169-b2777c0a.pth', 'densenet201': 'https://download.pytorch.org/models/densenet201-c1103571.pth', 'densenet161': 'https://download.pytorch.org/models/densenet161-8d451a50.pth', } def Densenet121_AG(pretrained=False, **kwargs): r"""Densenet-121 model from `"Densely Connected Convolutional Networks" `_ 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: # '.'s are no longer allowed in module names, but pervious _DenseLayer # has keys 'norm.1', 'relu.1', 'conv.1', 'norm.2', 'relu.2', 'conv.2'. # They are also in the checkpoints in model_urls. This pattern is used # to find such keys. pattern = re.compile( r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$') state_dict = model_zoo.load_url(model_urls['densenet121']) for key in list(state_dict.keys()): res = pattern.match(key) if res: new_key = res.group(1) + res.group(2) state_dict[new_key] = state_dict[key] del state_dict[key] model.load_state_dict(state_dict) return model class _DenseLayer(nn.Sequential): def __init__(self, num_input_features, growth_rate, bn_size, drop_rate): 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 = 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): super(_DenseBlock, self).__init__() for i in range(num_layers): layer = _DenseLayer(num_input_features + i * growth_rate, growth_rate, bn_size, drop_rate) self.add_module('denselayer%d' % (i + 1), layer) 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, bias=False)) self.add_module('pool', nn.AvgPool2d(kernel_size=2, stride=2)) class DenseNet(nn.Module): r"""Densenet-BC model class, based on `"Densely Connected Convolutional Networks" `_ 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([ ('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)), ])) # 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) 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) 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) self.Sigmoid = nn.Sigmoid() # 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_after_pooling = F.avg_pool2d(out, kernel_size=7, stride=1).view(features.size(0), -1) out = self.classifier(out_after_pooling) out = self.Sigmoid(out) return out, features, out_after_pooling class Fusion_Branch(nn.Module): def __init__(self, input_size, output_size): super(Fusion_Branch, self).__init__() self.fc = nn.Linear(input_size, output_size) self.Sigmoid = nn.Sigmoid() def forward(self, global_pool, local_pool): #fusion = torch.cat((global_pool.unsqueeze(2), local_pool.unsqueeze(2)), 2).cuda() #fusion = fusion.max(2)[0]#.squeeze(2).cuda() #print(fusion.shape) fusion = torch.cat((global_pool,local_pool), 1).cuda() fusion_var = torch.autograd.Variable(fusion) x = self.fc(fusion_var) x = self.Sigmoid(x) return x ''' class DenseNet121(nn.Module): """Model modified. The architecture of our model is the same as standard DenseNet121 except the classifier layer which has an additional sigmoid function. """ def __init__(self, out_size): super(DenseNet121, self).__init__() self.densenet121 = torchvision.models.densenet121(pretrained=True) num_ftrs = self.densenet121.classifier.in_features self.densenet121.classifier = nn.Sequential( nn.Linear(num_ftrs, out_size), nn.Sigmoid() ) def forward(self, x): x = self.densenet121(x) return x ''' #model = AG_CNN_densenet121(pretrained = True) #model.cuda() #input = torch.rand([1,3,224,224]).cuda() #output = model(input)