code: # -------------------------------------------------------------------------
  # Copyright (c) Microsoft Corporation. All rights reserved.
  # Licensed under the MIT License. See License.txt in the project root for
  # license information.
  # --------------------------------------------------------------------------
  
  import unittest
  import numpy as np
  from onnx import helper, TensorProto, numpy_helper, save
  from onnxruntime.quantization import quantize_static, QuantFormat
  from op_test_utils import InputFeedsNegOneZeroOne, check_model_correctness, check_op_type_count
  
  
  class TestONNXModel(unittest.TestCase):
      def construct_model(self, model_path):
          #          (input)
          #         /    |  \
          #        /     |   \
          #       /      |    \
          #      /       |     \
          #  Conv(1)  Conv(2)  conv(3)
          #       \      |     /
          #         \    |    /
          #           \  |   /
          #            Concat
          #              |
          #           Identity
          #              |
          #           (output)
          initializers = []
          input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 15, 15])
          output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 13, 13, 13])
  
          # Conv1 output [1, 2, 13, 13]
          conv1_weight_initializer = numpy_helper.from_array(
              np.random.randint(-1, 2, [2, 3, 3, 3]).astype(np.float32), name='conv1_weight')
          conv1_node = helper.make_node('Conv', ['input', 'conv1_weight'], ['conv1_output'], name='conv1_node')
  
          # Conv2 output [1, 5, 13, 13]
          conv2_weight_initializer = numpy_helper.from_array(
              np.random.randint(-1, 2, [5, 3, 3, 3]).astype(np.float32), name='conv2_weight')
          conv2_node = helper.make_node('Conv', ['input', 'conv2_weight'], ['conv2_output'], name='conv2_node')
  
          # Conv3 output [1, 6, 13, 13]
          conv3_weight_initializer = numpy_helper.from_array(
              np.random.randint(-1, 2, [6, 3, 3, 3]).astype(np.float32), name='conv3_weight')
          conv3_node = helper.make_node('Conv', ['input', 'conv3_weight'], ['conv3_output'], name='conv3_node')
  
          concat_node = helper.make_node('Concat', ['conv1_output', 'conv2_output', 'conv3_output'], [
                                              'concat_output'], name='concat_node', axis=1)
  
          identity_node = helper.make_node('Identity', ['concat_output'], ['output'], name='identity_node')
  
          initializers = [conv1_weight_initializer, conv2_weight_initializer, conv3_weight_initializer]
          graph = helper.make_graph([conv1_node, conv2_node, conv3_node, concat_node, identity_node],
                                    'qlinear_concat_op_test', [input], [output], initializer=initializers)
          model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
          save(model, model_path)
  
      def test_quantize_concat(self):
          np.random.seed(1)
  
          model_fp32_path = 'concat_fp32.onnx'
          model_uint8_path = 'concat_uint8.onnx'
          model_uint8_qdq_path = 'concat_uint8_qdq.onnx'
  
          self.construct_model(model_fp32_path)
  
          # Verify QOperator mode
          data_reader = InputFeedsNegOneZeroOne(1, {'input': [1, 3, 15, 15]})
          quantize_static(model_fp32_path, model_uint8_path, data_reader)
  
          qnode_counts = {'QLinearConv': 3, 'QuantizeLinear': 1, 'DequantizeLinear': 1, 'QLinearConcat': 1}
          check_op_type_count(self, model_uint8_path, **qnode_counts)
          data_reader.rewind()
          check_model_correctness(self, model_fp32_path, model_uint8_path, data_reader.get_next())
  
          # Verify QDQ mode
          data_reader.rewind()
          quantize_static(model_fp32_path, model_uint8_qdq_path, data_reader, quant_format=QuantFormat.QDQ)
          qdqnode_counts = {'Conv': 3, 'QuantizeLinear': 5, 'DequantizeLinear': 8, 'Concat': 1}
          check_op_type_count(self, model_uint8_qdq_path, **qdqnode_counts)
          data_reader.rewind()
          check_model_correctness(self, model_fp32_path, model_uint8_qdq_path, data_reader.get_next())
  
  
  if __name__ == '__main__':
      unittest.main()
  
apis: numpy.random.randint numpy.random.seed