pytorch如何使用训练好的模型预测新数据

import torch, glob, cv2
from torchvision import transforms
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):  # 神经网络部分用你自己的
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 2, 1)  # nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv2 = nn.Conv2d(32, 64, 3, 2, 1)
        self.conv3 = nn.Conv2d(64, 128, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(6272, 128)  # 6272=128*7*7
        self.fc2 = nn.Linear(128, 8)
    def forward(self, x):
        x = self.conv1(x)
        x = F.relu(x)
        x = self.conv2(x)
        x = F.relu(x)
        x = self.conv3(x)
        x = F.relu(x)
        x = F.max_pool2d(x, 2)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = F.relu(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        self.output = F.log_softmax(x, dim=1)
        out1 = x
        return self.output,out1
def predict():
    model = Net()
    model.load_state_dict(torch.load('test.pt'))
    torch.no_grad()
    imgfile = glob.glob(r"")  # 输入要预测的图片所在路径
    print(len(imgfile), imgfile)
    for i in imgfile:
        imgfile1 = i.replace("\\", "/")
        img = cv2.imdecode(np.fromfile(imgfile1, dtype=np.uint8), cv2.IMREAD_GRAYSCALE)
        img = cv2.resize(img, (64, 64))  # 是否需要resize取决于新图片格式与训练时的是否一致
        tran = transforms.ToTensor()
        img = img.reshape((*img.shape, -1))
        img = tran(img)
        img = img.unsqueeze(0)
        outputs, out1 = model(img)  # outputs，out1修改为你的网络的输出
        predicted, index  = torch.max(out1, 1)
        degre = int(index[0])
        list = [0, 45, -45, -90, 90, 135, -135, 180]
        print(predicted, list[degre])
if __name__ == '__main__':
    predict()

model.load_state_dict(torch.load('test.pt'))

list = ['裤子', '套衫', '连衣裙', '外套', '凉鞋', '衬衫', '运动鞋', '短靴']

import torchvision
from model import Tudui
import torch
from PIL import Image
# 读取图像
img = Image.open("./data/train/Dog/9.jpg")
# 数据预处理
# 缩放
transform = torchvision.transforms.Compose([torchvision.transforms.Resize((32, 32)),
                                            torchvision.transforms.ToTensor()])
image = transform(img)
print(image.shape)
# 根据保存方式加载
model = torch.load("tudui_99.pth", map_location=torch.device('cpu'))
# 注意维度转换，单张图片
image1 = torch.reshape(image, (1, 3, 32, 32))
# 测试开关
model.eval()
# 节约性能
with torch.no_grad():
    output = model(image1)
print(output)
# print(output.argmax(1))
# 定义类别对应字典
dist = {0: "飞机", 1: "汽车", 2: "鸟", 3: "猫", 4: "鹿", 5: "狗", 6: "青蛙", 7: "马", 8: "船", 9: "卡车"}
# 转numpy格式,列表内取第一个
a = dist[output.argmax(1).numpy()[0]]
img.show()
print(a)