predict.py

import torch
from model import AlexNet
from PIL import Image
from torchvision import transforms
import matplotlib.pyplot as plt
import json

def predict_flower(path):
    # 预处理
    data_transform = transforms.Compose(
        [transforms.Resize((224, 224)),
         transforms.ToTensor(),
         transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

    # load image
    img = Image.open(path)
    # plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)

    # read class_indict
    try:
        json_file = open('./class_indices.json', 'r')
        class_indict = json.load(json_file)
    except Exception as e:
        print(e)
        exit(-1)

    # create model
    model = AlexNet(num_classes=5)
    # load model weights
    model_weight_path = "./AlexNet.pth"
    model.load_state_dict(torch.load(model_weight_path))

    # 关闭 Dropout
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squeeze(model(img))  # 将输出压缩，即压缩掉 batch 这个维度
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()
    return class_indict[str(predict_cla)],predict[predict_cla].item()
    # print(class_indict[str(predict_cla)], predict[predict_cla].item())
    # plt.show()