济南 网站制作/成人技术培训学校

b站小土堆pytorch教程学习笔记

import torch
import torchvision
from PIL import Image
from torch import nnimg_path= '../imgs/dog.png'
image=Image.open(img_path)
print(image)
# image=image.convert('RGB')transform=torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),torchvision.transforms.ToTensor()])
image=transform(image)
print(image.shape)#加载模型
class Han(nn.Module):def __init__(self):super(Han, self).__init__()self.model = nn.Sequential(nn.Conv2d(3, 32, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(2),nn.Conv2d(32, 32, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(2),nn.Conv2d(32, 64, kernel_size=5, stride=1, padding=2),nn.MaxPool2d(2),nn.Flatten(),nn.Linear(64 * 4 * 4, 64),nn.Linear(64, 10))def forward(self, x):x = self.model(x)return xmodel=torch.load('../han_9.pth',map_location=torch.device('cpu'))#将GPU上运行的模型转移到CPU
print(model)#对图片进行reshap
image=torch.reshape(image,(-1,3,32,32))#将模型转化为测试类型
model.eval()
with torch.no_grad():#节约内存output=model(image)
print(output)print(output.argmax(1))

<PIL.PngImagePlugin.PngImageFile image mode=RGB size=306x283 at 0x250B0006EE0>
torch.Size([3, 32, 32])
Han(
(model): Sequential(
(0): Conv2d(3, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(2): Conv2d(32, 32, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(4): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2))
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Flatten(start_dim=1, end_dim=-1)
(7): Linear(in_features=1024, out_features=64, bias=True)
(8): Linear(in_features=64, out_features=10, bias=True)
)
)
tensor([[-2.0302, -0.6256, 0.7483, 1.5765, 0.2651, 2.2243, -0.7037, -0.5262,
-1.4401, -0.6563]])
tensor([5])
Process finished with exit code 0
预测正确！