DDR爱好者之家 Design By 杰米
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)
jiazaixunlianhaodemoxing
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)
以上这篇Pytorch提取模型特征向量保存至csv的例子就是小编分享给大家的全部内容了,希望能给大家一个参考,也希望大家多多支持。
DDR爱好者之家 Design By 杰米
广告合作:本站广告合作请联系QQ:858582 申请时备注:广告合作(否则不回)
免责声明:本站资源来自互联网收集,仅供用于学习和交流,请遵循相关法律法规,本站一切资源不代表本站立场,如有侵权、后门、不妥请联系本站删除!
免责声明:本站资源来自互联网收集,仅供用于学习和交流,请遵循相关法律法规,本站一切资源不代表本站立场,如有侵权、后门、不妥请联系本站删除!
DDR爱好者之家 Design By 杰米
暂无评论...
更新日志
2025年02月19日
2025年02月19日
- 小骆驼-《草原狼2(蓝光CD)》[原抓WAV+CUE]
- 群星《欢迎来到我身边 电影原声专辑》[320K/MP3][105.02MB]
- 群星《欢迎来到我身边 电影原声专辑》[FLAC/分轨][480.9MB]
- 雷婷《梦里蓝天HQⅡ》 2023头版限量编号低速原抓[WAV+CUE][463M]
- 群星《2024好听新歌42》AI调整音效【WAV分轨】
- 王思雨-《思念陪着鸿雁飞》WAV
- 王思雨《喜马拉雅HQ》头版限量编号[WAV+CUE]
- 李健《无时无刻》[WAV+CUE][590M]
- 陈奕迅《酝酿》[WAV分轨][502M]
- 卓依婷《化蝶》2CD[WAV+CUE][1.1G]
- 群星《吉他王(黑胶CD)》[WAV+CUE]
- 齐秦《穿乐(穿越)》[WAV+CUE]
- 发烧珍品《数位CD音响测试-动向效果(九)》【WAV+CUE】
- 邝美云《邝美云精装歌集》[DSF][1.6G]
- 吕方《爱一回伤一回》[WAV+CUE][454M]