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'

# 模型定義-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)

以上這篇Pytorch提取模型特征向量保存至csv的例子就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支持python博客。