mirror of https://github.com/XingangPan/DragGAN
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182 lines
6.4 KiB
Python
182 lines
6.4 KiB
Python
from collections import namedtuple
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import torch
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from torchvision import models as tv
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from IPython import embed
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class squeezenet(torch.nn.Module):
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def __init__(self, requires_grad=False, pretrained=True):
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super(squeezenet, self).__init__()
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pretrained_features = tv.squeezenet1_1(pretrained=pretrained).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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self.slice6 = torch.nn.Sequential()
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self.slice7 = torch.nn.Sequential()
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self.N_slices = 7
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for x in range(2):
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self.slice1.add_module(str(x), pretrained_features[x])
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for x in range(2,5):
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self.slice2.add_module(str(x), pretrained_features[x])
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for x in range(5, 8):
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self.slice3.add_module(str(x), pretrained_features[x])
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for x in range(8, 10):
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self.slice4.add_module(str(x), pretrained_features[x])
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for x in range(10, 11):
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self.slice5.add_module(str(x), pretrained_features[x])
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for x in range(11, 12):
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self.slice6.add_module(str(x), pretrained_features[x])
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for x in range(12, 13):
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self.slice7.add_module(str(x), pretrained_features[x])
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if not requires_grad:
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for param in self.parameters():
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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_relu1 = h
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h = self.slice2(h)
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h_relu2 = h
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h = self.slice3(h)
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h_relu3 = h
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h = self.slice4(h)
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h_relu4 = h
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h = self.slice5(h)
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h_relu5 = h
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h = self.slice6(h)
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h_relu6 = h
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h = self.slice7(h)
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h_relu7 = h
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vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])
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out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)
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return out
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class alexnet(torch.nn.Module):
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def __init__(self, requires_grad=False, pretrained=True):
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super(alexnet, self).__init__()
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alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).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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self.N_slices = 5
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for x in range(2):
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self.slice1.add_module(str(x), alexnet_pretrained_features[x])
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for x in range(2, 5):
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self.slice2.add_module(str(x), alexnet_pretrained_features[x])
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for x in range(5, 8):
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self.slice3.add_module(str(x), alexnet_pretrained_features[x])
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for x in range(8, 10):
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self.slice4.add_module(str(x), alexnet_pretrained_features[x])
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for x in range(10, 12):
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self.slice5.add_module(str(x), alexnet_pretrained_features[x])
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if not requires_grad:
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for param in self.parameters():
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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_relu1 = h
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h = self.slice2(h)
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h_relu2 = h
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h = self.slice3(h)
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h_relu3 = h
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h = self.slice4(h)
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h_relu4 = h
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h = self.slice5(h)
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h_relu5 = h
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alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])
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out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)
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return out
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class vgg16(torch.nn.Module):
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def __init__(self, requires_grad=False, pretrained=True):
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super(vgg16, self).__init__()
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vgg_pretrained_features = tv.vgg16(pretrained=pretrained).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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self.N_slices = 5
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for x in range(4):
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self.slice1.add_module(str(x), vgg_pretrained_features[x])
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for x in range(4, 9):
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self.slice2.add_module(str(x), vgg_pretrained_features[x])
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for x in range(9, 16):
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self.slice3.add_module(str(x), vgg_pretrained_features[x])
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for x in range(16, 23):
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self.slice4.add_module(str(x), vgg_pretrained_features[x])
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for x in range(23, 30):
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self.slice5.add_module(str(x), vgg_pretrained_features[x])
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if not requires_grad:
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for param in self.parameters():
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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_relu1_2 = h
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h = self.slice2(h)
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h_relu2_2 = h
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h = self.slice3(h)
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h_relu3_3 = h
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h = self.slice4(h)
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h_relu4_3 = h
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h = self.slice5(h)
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h_relu5_3 = h
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vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])
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out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)
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return out
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class resnet(torch.nn.Module):
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def __init__(self, requires_grad=False, pretrained=True, num=18):
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super(resnet, self).__init__()
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if(num==18):
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self.net = tv.resnet18(pretrained=pretrained)
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elif(num==34):
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self.net = tv.resnet34(pretrained=pretrained)
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elif(num==50):
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self.net = tv.resnet50(pretrained=pretrained)
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elif(num==101):
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self.net = tv.resnet101(pretrained=pretrained)
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elif(num==152):
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self.net = tv.resnet152(pretrained=pretrained)
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self.N_slices = 5
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self.conv1 = self.net.conv1
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self.bn1 = self.net.bn1
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self.relu = self.net.relu
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self.maxpool = self.net.maxpool
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self.layer1 = self.net.layer1
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self.layer2 = self.net.layer2
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self.layer3 = self.net.layer3
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self.layer4 = self.net.layer4
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def forward(self, X):
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h = self.conv1(X)
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h = self.bn1(h)
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h = self.relu(h)
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h_relu1 = h
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h = self.maxpool(h)
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h = self.layer1(h)
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h_conv2 = h
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h = self.layer2(h)
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h_conv3 = h
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h = self.layer3(h)
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h_conv4 = h
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h = self.layer4(h)
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h_conv5 = h
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outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5'])
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out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)
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return out
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