模型验证套路

输入图像 一只猫预测结果 类别3
类别对应索引

完整代码

import torch
import torchvision.transforms
from torch import nn
from PIL import Image
from torch.nn import Conv2d,MaxPool2d,Flatten,Linear,Sequential

img_path = "./cat.png"
image = Image.open(img_path)
# 将一个4通道转化为rgb三通道
img = image.convert("RGB")

transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32,32)),
                                            torchvision.transforms.ToTensor()],)

img = transform(image)
print(img.shape)

# img = img.reshape(1,3,32,32)

class MyModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.model1 = Sequential(
        Conv2d(3,32,5,padding=2),
        MaxPool2d(2),
        Conv2d(32,32,5,padding=2),
        MaxPool2d(2),
        Conv2d(32, 64, 5, padding= 2),
        MaxPool2d(2),
        Flatten(),
        Linear(1024,64),
        Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x

model = torch.load("./models/mymodule_9.pth")
# model 要求输入是4维
img = torch.reshape(img,(1,3,32,32))
model.eval()
with torch.no_grad():
    output = model(img)
print(output)

print(output.argmax(1))