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Update for TorchVision 0.13 #114

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69 changes: 61 additions & 8 deletions lpips/pretrained_networks.py
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Original file line number Diff line number Diff line change
@@ -1,11 +1,20 @@
from collections import namedtuple
from packaging import version
import torch
import torchvision
from torchvision import models as tv

class squeezenet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(squeezenet, self).__init__()
pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
pretrained_features = tv.squeezenet1_1(weights=tv.SqueezeNet1_1_Weights.IMAGENET1K_V1).features
else:
pretrained_features = tv.squeezenet1_1(weights=None).features
else: #torchvision.__version__ < 0.13
pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features

self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
Expand Down Expand Up @@ -56,7 +65,14 @@ def forward(self, X):
class alexnet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(alexnet, self).__init__()
alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
alexnet_pretrained_features = tv.alexnet(weights=tv.AlexNet_Weights.IMAGENET1K_V1).features
else:
alexnet_pretrained_features = tv.alexnet(weights=None).features
else: # torchvision.__version__ < 0.13
alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features

self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
Expand Down Expand Up @@ -96,7 +112,14 @@ def forward(self, X):
class vgg16(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(vgg16, self).__init__()
vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
vgg_pretrained_features = tv.vgg16(weights=tv.VGG16_Weights.IMAGENET1K_V1).features
else:
vgg_pretrained_features = tv.vgg16(weights=None).features
else: # torchvision.__version__ < 0.13
vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features

self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
Expand Down Expand Up @@ -139,15 +162,45 @@ class resnet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True, num=18):
super(resnet, self).__init__()
if(num==18):
self.net = tv.resnet18(pretrained=pretrained)
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
self.net = tv.resnet18(weights=tv.ResNet18_Weights.IMAGENET1K_V1)
else:
self.net = tv.resnet18(weights=None)
else: # torchvision.__version__ < 0.13
self.net = tv.resnet18(pretrained=pretrained)
elif(num==34):
self.net = tv.resnet34(pretrained=pretrained)
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
self.net = tv.resnet34(weights=tv.ResNet34_Weights.IMAGENET1K_V1)
else:
self.net = tv.resnet34(weights=None)
else: # torchvision.__version__ < 0.13
self.net = tv.resnet34(pretrained=pretrained)
elif(num==50):
self.net = tv.resnet50(pretrained=pretrained)
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
self.net = tv.resnet50(weights=tv.ResNet50_Weights.IMAGENET1K_V1)
else:
self.net = tv.resnet50(weights=None)
else: # torchvision.__version__ < 0.13
self.net = tv.resnet50(pretrained=pretrained)
elif(num==101):
self.net = tv.resnet101(pretrained=pretrained)
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
self.net = tv.resnet101(weights=tv.ResNet101_Weights.IMAGENET1K_V1)
else:
self.net = tv.resnet101(weights=None)
else: # torchvision.__version__ < 0.13
self.net = tv.resnet101(pretrained=pretrained)
elif(num==152):
self.net = tv.resnet152(pretrained=pretrained)
if version.parse(torchvision.__version__) >= version.parse('0.13'):
if pretrained:
self.net = tv.resnet152(weights=tv.ResNet152_Weights.IMAGENET1K_V1)
else:
self.net = tv.resnet152(weights=None)
else: # torchvision.__version__ < 0.13
self.net = tv.resnet152(pretrained=pretrained)
self.N_slices = 5

self.conv1 = self.net.conv1
Expand Down