Function main

CV/Pytorch_classification/DenseNet/predict.py:11–55

()

from model import densenet121

def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
    
    data_transform = transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    
    # load image
    img_path = ''
    assert os.path.exists(img_path), "file: '{}' does not exist.".format(img_path)
    img = Image.open(img_path)
    plt.imshow(img)
    # [N, C, H, W]
    img = data_transform(img)
    # expand batch dimension
    img = torch.unsqueeze(img, dim=0)
    
    # read class_indict
    json_path = ''
    assert os.path.exists(json_path), "file: '{}' does not exist.".format(json_path)
    
    with open(json_path, "r") as f:
        class_indict = json.load(f)
        
    # create model
    model = densenet121(num_classes=5).to(device)
    # load model weights
    model_weight_path = "./weights/model-3.pth"
    model.load_state_dict(torch.load(model_weight_path, map_location=device))
    model.eval()
    with torch.no_grad():
        # predict class
        output = torch.squezze(model(img.to(device)).cpu())
        predict = torch.softmax(output, dim=0)
        predict_cla = torch.argmax(predict).numpy()
        
    print_res = "class:{}  prob:{:.3}".format(class_indict[str(predict_cla)], predict[predict_cla].numpy())
    plt.title(print_res)
    for i in range(len(predict)):
        print("class:{:.10}  prob:{:.3}".format(class_indict[str(predict_cla)], predict[i].numpy()))
        
    plt.show()
    
if __name__ == '__main__':
    main()