ONNX Runtime 打印每层的输出

import os
import onnx
import copy
import numpy as np
import logging
import onnxruntime
from collections import OrderedDict
from onnx import shape_inference
logging.basicConfig(level=logging.INFO)
from onnx import shape_inference, TensorProto, version_converter, numpy_helper
logger = logging.getLogger("[ONNXOPTIMIZER]")

def test_model_by_onnxruntime(model):
    logger.info("Test model by onnxruntime")

    input_shape = model.graph.input[0].type.tensor_type.shape.dim

    image_shape = [x.dim_value for x in input_shape]
    image_shape_new = []
    for x in image_shape:
        if x == 0:
            image_shape_new.append(1)
        else:
            image_shape_new.append(x)
    image_shape = image_shape_new
    img_array = np.array(np.random.random(image_shape), dtype = np.float32)
    img = img_array
    for node in model.graph.node:
        for output in node.output:
            model.graph.output.extend([onnx.