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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().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
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