PeterYoung777/EfficientNetV2-For-Flower-Detection

1import os2import json3 4import torch5from PIL import Image6from torchvision import transforms7import matplotlib.pyplot as plt8 9from model import efficientnetv2_m as create_model10 11 12def main():13    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")14 15    img_size = {"s": [300, 384],  # train_size, val_size16                "m": [384, 480],17                "l": [384, 480]}18    num_model = "s"19 20    data_transform = transforms.Compose(21        [transforms.Resize(img_size[num_model][1]),22         transforms.CenterCrop(img_size[num_model][1]),23         transforms.ToTensor(),24         transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])])25 26    # load image27    img_path = "../d.jpg"28    assert os.path.exists(img_path), "file: '{}' dose not exist.".format(img_path)29    img = Image.open(img_path)30    plt.imshow(img)31    # [N, C, H, W]32    img = data_transform(img)33    # expand batch dimension34    img = torch.unsqueeze(img, dim=0)35 36    # read class_indict37    json_path = './class_indices.json'38    assert os.path.exists(json_path), "file: '{}' dose not exist.".format(json_path)39 40    json_file = open(json_path, "r")41    class_indict = json.load(json_file)42 43    # create model44    model = create_model(num_classes=5).to(device)45    # load model weights46    model_weight_path = "./weights/model-20.pth"47    model.load_state_dict(torch.load(model_weight_path, map_location=device))48    model.eval()49    with torch.no_grad():50        # predict class51        output = torch.squeeze(model(img.to(device))).cpu()52        predict = torch.softmax(output, dim=0)53        predict_cla = torch.argmax(predict).numpy()54 55    print_res = "class: {}   prob: {:.3}".format(class_indict[str(predict_cla)],56                                                 predict[predict_cla].numpy())57    plt.title(print_res)58    for i in range(len(predict)):59        print("class: {:10}   prob: {:.3}".format(class_indict[str(i)],60                                                  predict[i].numpy()))61    plt.show()62 63 64if __name__ == '__main__':65    main()66