Pytorch提取模型特征向量

# -*- coding: utf-8 -*-"""dj"""import torchimport torch.nn as nnimport osfrom torchvision import models, transformsfrom torch.autograd import Variable import numpy as npfrom PIL import Image import torchvision.models as modelsimport pretrainedmodelsimport pandas as pdclass FCViewer(nn.Module): def forward(self, x): return x.view(x.size(0), -1)class M(nn.Module): def __init__(self, backbone1, drop, pretrained=True): super(M,self).__init__() if pretrained: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained='imagenet') else: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained=None) self.img_encoder = list(img_model.children())[:-2] self.img_encoder.append(nn.AdaptiveAvgPool2d(1)) self.img_encoder = nn.Sequential(*self.img_encoder) if drop > 0: self.img_fc = nn.Sequential(FCViewer()) else: self.img_fc = nn.Sequential( FCViewer()) def forward(self, x_img): x_img = self.img_encoder(x_img) x_img = self.img_fc(x_img) return x_img model1=M('resnet18',0,pretrained=True)features_dir = '/home/cc/Desktop/features' transform1 = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor()]) file_path='/home/cc/Desktop/picture'names = os.listdir(file_path)print(names)for name in names: pic=file_path+'/'+name img = Image.open(pic) img1 = transform1(img) x = Variable(torch.unsqueeze(img1, dim=0).float(), requires_grad=False) y = model1(x) y = y.data.numpy() y = y.tolist() #print(y) test=pd.DataFrame(data=y) #print(test) test.to_csv("/home/cc/Desktop/features/3.csv",mode='a+',index=None,header=None)

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import torchimport torch.nn.functional as Fimport torch.nn as nnimport torch.optim as optimimport torchvisionimport torchvision.transforms as transformsimport argparseclass ResidualBlock(nn.Module): def __init__(self, inchannel, outchannel, stride=1): super(ResidualBlock, self).__init__() self.left = nn.Sequential( nn.Conv2d(inchannel, outchannel, kernel_size=3, stride=stride, padding=1, bias=False), nn.BatchNorm2d(outchannel), nn.ReLU(inplace=True), nn.Conv2d(outchannel, outchannel, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(outchannel) ) self.shortcut = nn.Sequential() if stride != 1 or inchannel != outchannel: self.shortcut = nn.Sequential( nn.Conv2d(inchannel, outchannel, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(outchannel) ) def forward(self, x): out = self.left(x) out += self.shortcut(x) out = F.relu(out) return outclass ResNet(nn.Module): def __init__(self, ResidualBlock, num_classes=10): super(ResNet, self).__init__() self.inchannel = 64 self.conv1 = nn.Sequential( nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False), nn.BatchNorm2d(64), nn.ReLU(), ) self.layer1 = self.make_layer(ResidualBlock, 64, 2, stride=1) self.layer2 = self.make_layer(ResidualBlock, 128, 2, stride=2) self.layer3 = self.make_layer(ResidualBlock, 256, 2, stride=2) self.layer4 = self.make_layer(ResidualBlock, 512, 2, stride=2) self.fc = nn.Linear(512, num_classes) def make_layer(self, block, channels, num_blocks, stride): strides = [stride] + [1] * (num_blocks - 1) #strides=[1,1] layers = [] for stride in strides: layers.append(block(self.inchannel, channels, stride)) self.inchannel = channels return nn.Sequential(*layers) def forward(self, x): out = self.conv1(x) out = self.layer1(out) out = self.layer2(out) out = self.layer3(out) out = self.layer4(out) out = F.avg_pool2d(out, 4) out = out.view(out.size(0), -1) out = self.fc(out) return outdef ResNet18(): return ResNet(ResidualBlock)import osfrom torchvision import models, transformsfrom torch.autograd import Variable import numpy as npfrom PIL import Image import torchvision.models as modelsimport pretrainedmodelsimport pandas as pdclass FCViewer(nn.Module): def forward(self, x): return x.view(x.size(0), -1)class M(nn.Module): def __init__(self, backbone1, drop, pretrained=True): super(M,self).__init__() if pretrained: img_model = pretrainedmodels.__dict__[backbone1](num_classes=1000, pretrained='imagenet') else: img_model = ResNet18() we='/home/cc/Desktop/dj/model1/incption--7' # 模型定义-ResNet #net = ResNet18().to(device) img_model.load_state_dict(torch.load(we))#diaoyong self.img_encoder = list(img_model.children())[:-2] self.img_encoder.append(nn.AdaptiveAvgPool2d(1)) self.img_encoder = nn.Sequential(*self.img_encoder) if drop > 0: self.img_fc = nn.Sequential(FCViewer()) else: self.img_fc = nn.Sequential( FCViewer()) def forward(self, x_img): x_img = self.img_encoder(x_img) x_img = self.img_fc(x_img) return x_img model1=M('resnet18',0,pretrained=None)features_dir = '/home/cc/Desktop/features' transform1 = transforms.Compose([ transforms.Resize(56), transforms.CenterCrop(32), transforms.ToTensor()]) file_path='/home/cc/Desktop/picture'names = os.listdir(file_path)print(names)for name in names: pic=file_path+'/'+name img = Image.open(pic) img1 = transform1(img) x = Variable(torch.unsqueeze(img1, dim=0).float(), requires_grad=False) y = model1(x) y = y.data.numpy() y = y.tolist() #print(y) test=pd.DataFrame(data=y) #print(test) test.to_csv("/home/cc/Desktop/features/3.csv",mode='a+',index=None,header=None)

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