mirror of
https://github.com/saymrwulf/onnxruntime.git
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ada0fbbd2d
* Implement qlinear concat and unit test. Add quantization tools for QLinearConcat and it quantization tests. * Add kernel def hash for QLinearConcat. * Change according to PR. Add qdq transformer support for QLinearConcat. * Add QDQ Transformer unittest. Fix typo on domain. * remove dup logic of no use. * fix x86 build error. * Update operator docs.
88 lines
4 KiB
Python
88 lines
4 KiB
Python
# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License. See License.txt in the project root for
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# license information.
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# --------------------------------------------------------------------------
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import unittest
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import numpy as np
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from onnx import helper, TensorProto, numpy_helper, save
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from onnxruntime.quantization import quantize_static, QuantFormat
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from op_test_utils import InputFeedsNegOneZeroOne, check_model_correctness, check_op_type_count
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class TestONNXModel(unittest.TestCase):
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def construct_model(self, model_path):
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# (input)
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# / | \
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# / | \
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# / | \
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# / | \
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# Conv(1) Conv(2) conv(3)
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# \ | /
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# \ | /
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# \ | /
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# Concat
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# |
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# Identity
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# |
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# (output)
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initializers = []
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input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [1, 3, 15, 15])
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output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [1, 13, 13, 13])
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# Conv1 output [1, 2, 13, 13]
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conv1_weight_initializer = numpy_helper.from_array(
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np.random.randint(-1, 2, [2, 3, 3, 3]).astype(np.float32), name='conv1_weight')
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conv1_node = helper.make_node('Conv', ['input', 'conv1_weight'], ['conv1_output'], name='conv1_node')
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# Conv2 output [1, 5, 13, 13]
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conv2_weight_initializer = numpy_helper.from_array(
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np.random.randint(-1, 2, [5, 3, 3, 3]).astype(np.float32), name='conv2_weight')
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conv2_node = helper.make_node('Conv', ['input', 'conv2_weight'], ['conv2_output'], name='conv2_node')
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# Conv3 output [1, 6, 13, 13]
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conv3_weight_initializer = numpy_helper.from_array(
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np.random.randint(-1, 2, [6, 3, 3, 3]).astype(np.float32), name='conv3_weight')
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conv3_node = helper.make_node('Conv', ['input', 'conv3_weight'], ['conv3_output'], name='conv3_node')
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concat_node = helper.make_node('Concat', ['conv1_output', 'conv2_output', 'conv3_output'], [
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'concat_output'], name='concat_node', axis=1)
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identity_node = helper.make_node('Identity', ['concat_output'], ['output'], name='identity_node')
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initializers = [conv1_weight_initializer, conv2_weight_initializer, conv3_weight_initializer]
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graph = helper.make_graph([conv1_node, conv2_node, conv3_node, concat_node, identity_node],
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'qlinear_concat_op_test', [input], [output], initializer=initializers)
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model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)])
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save(model, model_path)
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def test_quantize_concat(self):
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np.random.seed(1)
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model_fp32_path = 'concat_fp32.onnx'
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model_uint8_path = 'concat_uint8.onnx'
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model_uint8_qdq_path = 'concat_uint8_qdq.onnx'
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self.construct_model(model_fp32_path)
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# Verify QOperator mode
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data_reader = InputFeedsNegOneZeroOne(1, {'input': [1, 3, 15, 15]})
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quantize_static(model_fp32_path, model_uint8_path, data_reader)
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qnode_counts = {'QLinearConv': 3, 'QuantizeLinear': 1, 'DequantizeLinear': 1, 'QLinearConcat': 1}
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check_op_type_count(self, model_uint8_path, **qnode_counts)
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data_reader.rewind()
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check_model_correctness(self, model_fp32_path, model_uint8_path, data_reader.get_next())
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# Verify QDQ mode
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data_reader.rewind()
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quantize_static(model_fp32_path, model_uint8_qdq_path, data_reader, quant_format=QuantFormat.QDQ)
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qdqnode_counts = {'Conv': 3, 'QuantizeLinear': 5, 'DequantizeLinear': 8, 'Concat': 1}
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check_op_type_count(self, model_uint8_qdq_path, **qdqnode_counts)
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data_reader.rewind()
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check_model_correctness(self, model_fp32_path, model_uint8_qdq_path, data_reader.get_next())
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if __name__ == '__main__':
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unittest.main()
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