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| from collections import namedtuple | |
| import torch | |
| from torchvision import models as tv | |
| from IPython import embed | |
| 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 | |
| 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() | |
| self.slice6 = torch.nn.Sequential() | |
| self.slice7 = torch.nn.Sequential() | |
| self.N_slices = 7 | |
| for x in range(2): | |
| self.slice1.add_module(str(x), pretrained_features[x]) | |
| for x in range(2,5): | |
| self.slice2.add_module(str(x), pretrained_features[x]) | |
| for x in range(5, 8): | |
| self.slice3.add_module(str(x), pretrained_features[x]) | |
| for x in range(8, 10): | |
| self.slice4.add_module(str(x), pretrained_features[x]) | |
| for x in range(10, 11): | |
| self.slice5.add_module(str(x), pretrained_features[x]) | |
| for x in range(11, 12): | |
| self.slice6.add_module(str(x), pretrained_features[x]) | |
| for x in range(12, 13): | |
| self.slice7.add_module(str(x), 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 = h | |
| h = self.slice2(h) | |
| h_relu2 = h | |
| h = self.slice3(h) | |
| h_relu3 = h | |
| h = self.slice4(h) | |
| h_relu4 = h | |
| h = self.slice5(h) | |
| h_relu5 = h | |
| h = self.slice6(h) | |
| h_relu6 = h | |
| h = self.slice7(h) | |
| h_relu7 = h | |
| vgg_outputs = namedtuple("SqueezeOutputs", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7']) | |
| out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7) | |
| return out | |
| 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 | |
| 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() | |
| self.N_slices = 5 | |
| for x in range(2): | |
| self.slice1.add_module(str(x), alexnet_pretrained_features[x]) | |
| for x in range(2, 5): | |
| self.slice2.add_module(str(x), alexnet_pretrained_features[x]) | |
| for x in range(5, 8): | |
| self.slice3.add_module(str(x), alexnet_pretrained_features[x]) | |
| for x in range(8, 10): | |
| self.slice4.add_module(str(x), alexnet_pretrained_features[x]) | |
| for x in range(10, 12): | |
| self.slice5.add_module(str(x), alexnet_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 = h | |
| h = self.slice2(h) | |
| h_relu2 = h | |
| h = self.slice3(h) | |
| h_relu3 = h | |
| h = self.slice4(h) | |
| h_relu4 = h | |
| h = self.slice5(h) | |
| h_relu5 = h | |
| alexnet_outputs = namedtuple("AlexnetOutputs", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5']) | |
| out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5) | |
| return out | |
| 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 | |
| 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() | |
| self.N_slices = 5 | |
| 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]) | |
| for x in range(23, 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): | |
| 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 | |
| h = self.slice5(h) | |
| h_relu5_3 = h | |
| vgg_outputs = namedtuple("VggOutputs", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3']) | |
| out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3) | |
| return out | |
| 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) | |
| elif(num==34): | |
| self.net = tv.resnet34(pretrained=pretrained) | |
| elif(num==50): | |
| self.net = tv.resnet50(pretrained=pretrained) | |
| elif(num==101): | |
| self.net = tv.resnet101(pretrained=pretrained) | |
| elif(num==152): | |
| self.net = tv.resnet152(pretrained=pretrained) | |
| self.N_slices = 5 | |
| self.conv1 = self.net.conv1 | |
| self.bn1 = self.net.bn1 | |
| self.relu = self.net.relu | |
| self.maxpool = self.net.maxpool | |
| self.layer1 = self.net.layer1 | |
| self.layer2 = self.net.layer2 | |
| self.layer3 = self.net.layer3 | |
| self.layer4 = self.net.layer4 | |
| def forward(self, X): | |
| h = self.conv1(X) | |
| h = self.bn1(h) | |
| h = self.relu(h) | |
| h_relu1 = h | |
| h = self.maxpool(h) | |
| h = self.layer1(h) | |
| h_conv2 = h | |
| h = self.layer2(h) | |
| h_conv3 = h | |
| h = self.layer3(h) | |
| h_conv4 = h | |
| h = self.layer4(h) | |
| h_conv5 = h | |
| outputs = namedtuple("Outputs", ['relu1','conv2','conv3','conv4','conv5']) | |
| out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5) | |
| return out | |