import torch
import torchvision
from PIL import Image
from torch import nn

img_path = "./imgs/dog.jpg"
img = Image.open(img_path)

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

img = transform(img)

# 模型文件
class Tudui(nn.Module):
    def __init__(self):
        super(Tudui, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, 1, 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 x

# model = torch.load("./tudui_44.pth", map_location=torch.device("cuda"))
model = torch.load("./tudui_44.pth")
model.to("cpu")
print(model)
img = torch.reshape(img, (1, 3, 32, 32))
model.eval()
with torch.no_grad():
    output = model(img)
print(output)

print(output.argmax(1))