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import torch.nn as nn
from collections import namedtuple
from torchvision.models import vgg16, VGG16_Weights
class VGG16(nn.Module):
def __init__(self, requires_grad=False):
super(VGG16, self).__init__()
weights = VGG16_Weights.DEFAULT
vgg_pretrained_features = vgg16(weights=weights).features
self.slice1 = nn.Sequential()
self.slice2 = nn.Sequential()
self.slice3 = nn.Sequential()
self.slice4 = nn.Sequential()
for x in range(4):
self.slice1.add_module(str(x), vgg_pretrained_features[x])
for x in range(4, 9):
self.slice2.add_module(str(x), vgg_pretrained_features[x])
for x in range(9, 16):
self.slice3.add_module(str(x), vgg_pretrained_features[x])
for x in range(16, 23):
self.slice4.add_module(str(x), vgg_pretrained_features[x])
if not requires_grad:
for param in self.parameters():
param.requires_grad = False
def forward(self, X):
h = self.slice1(X)
h_relu1_2 = h
h = self.slice2(h)
h_relu2_2 = h
h = self.slice3(h)
h_relu3_3 = h
h = self.slice4(h)
h_relu4_3 = h
vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3'])
out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3)
return out
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