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| from collections import namedtuple | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.init as init | |
| from torchvision import models | |
| def init_weights(modules): | |
| for m in modules: | |
| if isinstance(m, nn.Conv2d): | |
| init.xavier_uniform_(m.weight.data) | |
| if m.bias is not None: | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.BatchNorm2d): | |
| m.weight.data.fill_(1) | |
| m.bias.data.zero_() | |
| elif isinstance(m, nn.Linear): | |
| m.weight.data.normal_(0, 0.01) | |
| m.bias.data.zero_() | |
| class vgg16_bn(torch.nn.Module): | |
| def __init__(self, pretrained=True, freeze=True): | |
| super(vgg16_bn, self).__init__() | |
| vgg_pretrained_features = models.vgg16_bn(pretrained=pretrained).features | |
| self.slice1 = torch.nn.Sequential() | |
| self.slice2 = torch.nn.Sequential() | |
| self.slice3 = torch.nn.Sequential() | |
| self.slice4 = torch.nn.Sequential() | |
| self.slice5 = torch.nn.Sequential() | |
| for x in range(12): # conv2_2 | |
| self.slice1.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(12, 19): # conv3_3 | |
| self.slice2.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(19, 29): # conv4_3 | |
| self.slice3.add_module(str(x), vgg_pretrained_features[x]) | |
| for x in range(29, 39): # conv5_3 | |
| self.slice4.add_module(str(x), vgg_pretrained_features[x]) | |
| # fc6, fc7 without atrous conv | |
| self.slice5 = torch.nn.Sequential( | |
| nn.MaxPool2d(kernel_size=3, stride=1, padding=1), | |
| nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6), | |
| nn.Conv2d(1024, 1024, kernel_size=1) | |
| ) | |
| if not pretrained: | |
| init_weights(self.slice1.modules()) | |
| init_weights(self.slice2.modules()) | |
| init_weights(self.slice3.modules()) | |
| init_weights(self.slice4.modules()) | |
| init_weights(self.slice5.modules()) # no pretrained model for fc6 and fc7 | |
| if freeze: | |
| for param in self.slice1.parameters(): # only first conv | |
| param.requires_grad= False | |
| def forward(self, X): | |
| h = self.slice1(X) | |
| h_relu2_2 = h | |
| h = self.slice2(h) | |
| h_relu3_2 = h | |
| h = self.slice3(h) | |
| h_relu4_3 = h | |
| h = self.slice4(h) | |
| h_relu5_3 = h | |
| h = self.slice5(h) | |
| h_fc7 = h | |
| vgg_outputs = namedtuple("VggOutputs", ['fc7', 'relu5_3', 'relu4_3', 'relu3_2', 'relu2_2']) | |
| out = vgg_outputs(h_fc7, h_relu5_3, h_relu4_3, h_relu3_2, h_relu2_2) | |
| return out | |