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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)感谢各位的阅读！关于“Pytorch如何提取模型特征向量保存至csv”这篇文章就分享到这里了，希望以上内容可以对大家有一定的帮助，让大家可以学到更多知识，如果觉得文章不错，可以把它分享出去让更多的人看到吧！