File size: 3,217 Bytes
347b44e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | from collections import namedtuple
import torch
from torchvision import models
class Vgg16(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
self.slice4 = torch.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
class Vgg19(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg19, self).__init__()
# vgg_pretrained_features = models.vgg19(pretrained=True).features
self.vgg_pretrained_features = models.vgg19(pretrained=True).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(2):
# self.slice1.add_module(str(x), vgg_pretrained_features[x])
# for x in range(2, 7):
# self.slice2.add_module(str(x), vgg_pretrained_features[x])
# for x in range(7, 12):
# self.slice3.add_module(str(x), vgg_pretrained_features[x])
# for x in range(12, 21):
# self.slice4.add_module(str(x), vgg_pretrained_features[x])
# for x in range(21, 30):
# self.slice5.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, indices=None):
if indices is None:
indices = [2, 7, 12, 21, 30]
out = []
# indices = sorted(indices)
for i in range(indices[-1]):
X = self.vgg_pretrained_features[i](X)
if (i + 1) in indices:
out.append(X)
return out
# h_relu1 = self.slice1(X)
# h_relu2 = self.slice2(h_relu1)
# h_relu3 = self.slice3(h_relu2)
# h_relu4 = self.slice4(h_relu3)
# h_relu5 = self.slice5(h_relu4)
# out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
# return out
if __name__ == '__main__':
vgg = Vgg19()
import ipdb
ipdb.set_trace() |