迁移学习 VGGNet、VGG16、ResNet50

按照下载好的数据集运行时,会出现如下错误：

RuntimeError: Found 0 files in subfolders of: ./DogsVSCats/valid
Supported extensions are: .jpg,.jpeg,.png,.ppm,.bmp,.pgm,.tif,.tiff,.webp

此时需要按照image/image/picture的排列方式，新建文件夹。

data_dir = "./DogsVSCats/"

import torch
import torchvision
from torchvision import datasets,transforms
import os
import matplotlib.pyplot as plt
import time

data_dir = os.getcwd()

data_dir = os.path.join(data_dir,”DogsVSCats”)

data_dir = “./DogsVSCats/“

data_transform = {
x:transforms.Compose(
[
transforms.Scale([64,64]), #Scale类将原始图片的大小统一缩放至64×64
transforms.ToTensor()
]
)
for x in [“train”,”valid”]
}

image_datasets = {
x:datasets.ImageFolder(
root=os.path.join(data_dir,x),
#将输入参数中的两个名字拼接成一个完整的文件路径
transform=data_transform[x]
)
for x in [“train”,”valid”]
}

dataloader = {
x:torch.utils.data.DataLoader(
dataset=image_datasets[x],
batch_size=16,
shuffle=True
)
for x in [“train”,”valid”]
}

X_example, Y_example = next(iter(dataloader[‘train’]))
print(‘X_example个数{}’.format(len(X_example))) #X_example个数16
print(‘Y_example个数{}’.format(len(Y_example))) #Y_example个数16

index_classes = image_datasets[‘train’].class_to_idx # 显示类别对应的独热编码
print(index_classes) #{‘cat’: 0, ‘dog’: 1}

example_classes = image_datasets[‘train’].classes # 将原始图像的类别保存起来
print(example_classes) #[‘cat’, ‘dog’]

img = torchvision.utils.make_grid(X_example)
img = img.numpy().transpose([1,2,0])
print([example_classes[i] for i in Y_example])
#[‘cat’, ‘cat’, ‘cat’, ‘cat’, ‘dog’, ‘cat’, ‘cat’, ‘dog’, ‘cat’, ‘cat’, ‘dog’, ‘dog’, ‘cat’, ‘dog’, ‘dog’, ‘cat’]
plt.imshow(img)
plt.show()