[参考译文] PROCESSOR-SDK-J721S2：模型测试

您好、

还可以上传配置文件以便于重现：

            "hsh_modify": create_model_config(
        source=AttrDict(
            model_url="dummy",
            infer_shape=True,
        ),
        preprocess=AttrDict(
            resize=256,
            crop=224,
            data_layout="NCHW",
            resize_with_pad=False,
            reverse_channels=False,
        ),
        session=AttrDict(
            session_name="onnxrt",
            model_path=os.path.join(models_base_path, "hsh_model_modified.onnx"),
            input_mean=[123.675, 116.28, 103.53],
            input_scale=[0.017125, 0.017507, 0.017429],
            input_optimization=True,
        ),
        task_type="classification",
        extra_info=AttrDict(num_images=numImages, num_classes=1000),
    ),

修改了 onnxrt_ep.py 的脚本：

def infer_image(sess, image_files, config):
    '''
    Invoke the runtime session

    :param sess: Runtime session
    :param image_files: List of input image filename
    :param config: Configuration dictionary
    :return: Input Images
    :return: Output tensors
    :return: Total Processing time
    :return: Subgraphs Processing time
    :return: Height of input tensor
    :return: Width of input tensor
    '''

    # Get input details from the session
    input_details = sess.get_inputs()
    input_name = input_details[0].name
    floating_model = input_details[0].type == "tensor(float)"
    height = input_details[0].shape[2]
    width = input_details[0].shape[3]
    channel = input_details[0].shape[1]
    batch = input_details[0].shape[0]
    imgs = []
    shape = [batch, channel, height, width]
    input_shape = input_details[0].shape
    input_data = np.random.random(input_shape).astype(np.float32) * (1 - 0) 
    print(len(input_details))
    if len(input_details)>1:
        print(len(input_details))
        input_name2 = input_details[1].name
        if input_details[1].type == "tensor(float)":
            input_data2 = np.random.random(input_details[1].shape).astype(np.float32) * (1 - 0) 
        else:
            input_data2 = np.random.randint(0, 2000, size=input_details[1].shape).astype(np.int32)
    # Prepare the input data
    # input_data = np.zeros(shape)
    # for i in range(batch):
    #     imgs.append(
    #         Image.open(image_files[i])
    #         .convert("RGB")
    #         .resize((width, height), PIL.Image.LANCZOS)
    #     )
    #     temp_input_data = np.expand_dims(imgs[i], axis=0)
    #     temp_input_data = np.transpose(temp_input_data, (0, 3, 1, 2))
    #     input_data[i] = temp_input_data[0]
    # if floating_model:
    #     input_data = np.float32(input_data)
    #     for mean, scale, ch in zip(
    #         config["session"]["input_mean"],
    #         config["session"]["input_scale"],
    #         range(input_data.shape[1]),
    #     ):
    #         input_data[:, ch, :, :] = (input_data[:, ch, :, :] - mean) * scale
    # else:
    #     input_data = np.uint8(input_data)
    #     config["session"]["input_mean"] = [0, 0, 0]
    #     config["session"]["input_scale"] = [1, 1, 1]

    data = np.fromfile("0.bin", dtype=np.uint16).astype(np.float32)
    raw_vis = data.reshape(1056, 1920) / 4096
    print(raw_vis)
    input_data[0,0,:,:] = raw_vis
    # Invoke the session
    start_time = time.time()
    
    if len(input_details)>1:
        print(len(input_details))
        output = list(sess.run(None, {input_name: input_data, input_name2: input_data2}))
    else:
        output = list(sess.run(None, {input_name: input_data}))
    stop_time = time.time()
    infer_time = stop_time - start_time

    copy_time, sub_graphs_proc_time, totaltime = get_benchmark_output(sess)
    proc_time = totaltime - copy_time

    return imgs, output, proc_time, sub_graphs_proc_time, height, width

请使用 /cfs-file/__key/communityserver-discussions-components-files/791/modified_5F00_model.zip 

输入文件0.bin 位于 /cfs-file/__key/communityserver-discussions-components-files/791/1425.model.zip 中 

此致、

Adam