70 lines
2.7 KiB
Python
70 lines
2.7 KiB
Python
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from collections import namedtuple
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import torch
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from torchvision import models
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from torchvision.models.vgg import model_urls
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from torchutil import *
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import os
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weights_folder = os.path.join(os.path.dirname(__file__))
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class vgg16_bn(torch.nn.Module):
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def __init__(self, pretrained=True, freeze=False):
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super(vgg16_bn, self).__init__()
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model_urls['vgg16_bn'] = model_urls['vgg16_bn'].replace('https://', 'http://')
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# vgg_pretrained_features = models.vgg16_bn(pretrained=pretrained).features
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vgg_pretrained_features = models.vgg16_bn(pretrained=False)
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if pretrained:
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print('./pretrain/vgg16_bn-6c64b313.pth')
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vgg_pretrained_features.load_state_dict(
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copyStateDict(torch.load('./pretrain/vgg16_bn-6c64b313.pth')))
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vgg_pretrained_features = vgg_pretrained_features.features
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self.slice1 = torch.nn.Sequential()
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self.slice2 = torch.nn.Sequential()
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self.slice3 = torch.nn.Sequential()
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self.slice4 = torch.nn.Sequential()
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self.slice5 = torch.nn.Sequential()
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for x in range(12): # conv2_2
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self.slice1.add_module(str(x), vgg_pretrained_features[x])
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for x in range(12, 19): # conv3_3
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self.slice2.add_module(str(x), vgg_pretrained_features[x])
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for x in range(19, 29): # conv4_3
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self.slice3.add_module(str(x), vgg_pretrained_features[x])
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for x in range(29, 39): # conv5_3
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self.slice4.add_module(str(x), vgg_pretrained_features[x])
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# fc6, fc7 without atrous conv
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self.slice5 = torch.nn.Sequential(
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nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
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nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6),
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nn.Conv2d(1024, 1024, kernel_size=1)
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)
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if not pretrained:
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init_weights(self.slice1.modules())
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init_weights(self.slice2.modules())
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init_weights(self.slice3.modules())
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init_weights(self.slice4.modules())
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init_weights(self.slice5.modules()) # no pretrained model for fc6 and fc7
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if freeze:
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for param in self.slice1.parameters(): # only first conv
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param.requires_grad = False
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def forward(self, X):
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h = self.slice1(X)
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h_relu2_2 = h
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h = self.slice2(h)
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h_relu3_2 = h
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h = self.slice3(h)
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h_relu4_3 = h
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h = self.slice4(h)
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h_relu5_3 = h
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h = self.slice5(h)
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h_fc7 = h
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vgg_outputs = namedtuple("VggOutputs", ['fc7', 'relu5_3', 'relu4_3', 'relu3_2', 'relu2_2'])
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out = vgg_outputs(h_fc7, h_relu5_3, h_relu4_3, h_relu3_2, h_relu2_2)
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return out
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