Pytorch提取模型特征向量保存至csv的例子
更新时间:2020年01月03日 08:47:34 作者:朴素.无恙
今天小编就为大家分享一篇Pytorch提取模型特征向量保存至csv的例子,具有很好的参考价值,希望对大家有所帮助。一起跟随小编过来看看吧
Pytorch提取模型特征向量
# -*- coding: utf-8 -*- """ dj """ import torch import torch.nn as nn import os from torchvision import models, transforms from torch.autograd import Variable import numpy as np from PIL import Image import torchvision.models as models import pretrainedmodels import pandas as pd class 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 torch import torch.nn.functional as F import torch.nn as nn import torch.optim as optim import torchvision import torchvision.transforms as transforms import argparse class 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 out class 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 out def ResNet18(): return ResNet(ResidualBlock) import os from torchvision import models, transforms from torch.autograd import Variable import numpy as np from PIL import Image import torchvision.models as models import pretrainedmodels import pandas as pd class 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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