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呐喊教程。
声明：本文内容来源于网络，版权归原作者所有，内容由互联网用户自发贡献自行上传，本网站不拥有所有权，未作人工编辑处理，也不承担相关法律责任。如果您发现有涉嫌版权的内容，欢迎发送邮件至：notice#nhooo.com（发邮件时，请将#更换为@）进行举报，并提供相关证据，一经查实，本站将立刻删除涉嫌侵权内容。