Pytorch提取模型特征向量保存至csv的例子

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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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