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handle optional input in quant topo sort (#7223)
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#!/usr/bin/env python | ||
# coding: utf-8 | ||
# ------------------------------------------------------------------------- | ||
# Copyright (c) Microsoft Corporation. All rights reserved. | ||
# Licensed under the MIT License. See License.txt in the project root for | ||
# license information. | ||
# -------------------------------------------------------------------------- | ||
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import unittest | ||
import onnx | ||
import onnxruntime | ||
import numpy as np | ||
from onnx import helper, TensorProto, numpy_helper | ||
from onnxruntime.quantization.onnx_model import ONNXModel | ||
from op_test_utils import TestDataFeeds, check_model_correctness, check_op_type_count, check_op_type_order | ||
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def generate_input_initializer(tensor_shape, tensor_dtype, input_name): | ||
''' | ||
Helper function to generate initializers for test inputs | ||
''' | ||
tensor = np.random.normal(0, 0.3, tensor_shape).astype(tensor_dtype) | ||
init = numpy_helper.from_array(tensor, input_name) | ||
return init | ||
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class TestONNXModel(unittest.TestCase): | ||
def construct_model(self, model_path): | ||
# (input) | ||
# | | ||
# GRU | ||
# / \ | ||
# Conv(1) \ | ||
# | \ | ||
# Relu Conv(2) | ||
# | | | ||
# \ / | ||
# Add | ||
# | | ||
# (output) | ||
initializers = [] | ||
input = helper.make_tensor_value_info('input', TensorProto.FLOAT, [4, 8, 12]) | ||
output = helper.make_tensor_value_info('output', TensorProto.FLOAT, [4, 2, 8, 8]) | ||
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# make GRU | ||
initializers.append(generate_input_initializer([2, 24, 12], np.float32, 'W_GRU')) | ||
initializers.append(generate_input_initializer([2, 24, 8], np.float32, 'R_GRU')) | ||
initializers.append(generate_input_initializer([2, 8, 8], np.float32, 'H_GRU')) | ||
gru_node = onnx.helper.make_node( | ||
'GRU', | ||
['input', 'W_GRU', 'R_GRU', '', '', 'H_GRU'], | ||
['GRU_O'], | ||
hidden_size = 8, | ||
direction = 'bidirectional') | ||
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initializers.append(generate_input_initializer([2, 2, 1, 1], np.float32, 'W1')) | ||
initializers.append(generate_input_initializer([2, 2, 1, 1], np.float32, 'W2')) | ||
initializers.append(generate_input_initializer([2], np.float32, 'B1')) | ||
initializers.append(generate_input_initializer([2], np.float32, 'B2')) | ||
conv_node_1 = onnx.helper.make_node('Conv', ['GRU_O', 'W1', 'B1'], ['Conv1_O'], name='Conv1') | ||
conv_node_2 = onnx.helper.make_node('Conv', ['GRU_O', 'W2', 'B2'], ['Conv2_O'], name='Conv2') | ||
relu_node = onnx.helper.make_node('Relu', ['Conv1_O'], ['Relu_O'], name='Relu') | ||
add_node = onnx.helper.make_node('Add', ['Relu_O', 'Conv2_O'], ['output'], name='Add') | ||
graph = helper.make_graph([conv_node_1, relu_node, conv_node_2, gru_node, add_node], | ||
'onnx_model_test', [input], [output], initializer=initializers) | ||
model = helper.make_model(graph, opset_imports=[helper.make_opsetid("", 13)]) | ||
onnx.save(model, model_path) | ||
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def test_topo_sort(self): | ||
test_model_path = 'onnx_model_topo_sort.onnx' | ||
self.construct_model(test_model_path) | ||
onnx_model = ONNXModel(onnx.load(test_model_path)) | ||
check_op_type_order(self, onnx_model.model, ['Conv', 'Relu', 'Conv', 'GRU', 'Add']) | ||
onnx_model.topological_sort() | ||
check_op_type_order(self, onnx_model.model, ['GRU', 'Conv', 'Conv', 'Relu', 'Add']) | ||
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if __name__ == '__main__': | ||
unittest.main() |