mirror of
https://github.com/saymrwulf/onnxruntime.git
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1541784f6c
* remove default python ep registration. raise exception if providers are not explicitly set if there are available providers * temporarily disable exception * fix python tests * explicitly set CUDAProvider for python iobinding tests * explicitly set providers param for InferenceSession()) * onnxrt * raise ValueError if not explicitly set providers when creating InferenceSession * add required providers param * explicitly set providers * typo
98 lines
5.1 KiB
Python
98 lines
5.1 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# -*- coding: UTF-8 -*-
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import unittest
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import os
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import numpy as np
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import gc
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import onnxruntime as onnxrt
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import threading
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import sys
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from helper import get_name
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from onnxruntime.capi.onnxruntime_pybind11_state import Fail
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class TestSparseToDenseMatmul(unittest.TestCase):
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def testRunSparseOutputOnly(self):
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'''
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Try running models using the new run_with_ort_values
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sparse_initializer_as_output.onnx - requires no inputs, but only one output
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that comes from the initializer
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'''
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# The below values are a part of the model
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dense_shape = [3,3]
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values = np.array([1.764052391052246, 0.40015721321105957, 0.978738009929657], np.float)
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indices = np.array([2, 3, 5], np.int64)
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sess = onnxrt.InferenceSession(get_name("sparse_initializer_as_output.onnx"),
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providers=onnxrt.get_available_providers())
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res = sess.run_with_ort_values(["values"], {})
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self.assertEqual(len(res), 1)
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ort_value = res[0]
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self.assertTrue(isinstance(ort_value, onnxrt.OrtValue))
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sparse_output = ort_value.as_sparse_tensor()
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self.assertTrue(isinstance(sparse_output, onnxrt.SparseTensor))
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self.assertEqual(dense_shape, sparse_output.dense_shape())
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self.assertTrue(np.array_equal(values, sparse_output.values()))
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self.assertTrue(np.array_equal(indices, sparse_output.as_coo_rep().indices()))
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def testRunContribSparseMatMul(self):
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'''
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Mutliple sparse COO tensor to dense
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'''
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common_shape = [9,9] # inputs and oputputs same shape
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A_values = np.array([1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0,
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10.0, 11.0, 12.0, 13.0, 14.0, 15.0, 16.0, 17.0,
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18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0, 25.0,
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26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0,
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34.0, 35.0, 36.0, 37.0, 38.0, 39.0, 40.0, 41.0,
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42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 48.0, 49.0,
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50.0, 51.0, 52.0, 53.0], np.float32)
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# 2-D index
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A_indices = np.array([0, 1, 0, 2, 0, 6, 0, 7, 0, 8, 1, 0, 1,
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1, 1, 2, 1, 6, 1, 7, 1, 8, 2, 0, 2, 1,
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2, 2, 2, 6, 2, 7, 2, 8, 3, 3, 3, 4, 3,
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5, 3, 6, 3, 7, 3, 8, 4, 3, 4, 4, 4, 5,
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4, 6, 4, 7, 4, 8, 5, 3, 5, 4, 5, 5, 5,
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6, 5, 7, 5, 8, 6, 0, 6, 1, 6, 2, 6, 3,
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6, 4, 6, 5, 7, 0, 7, 1, 7, 2, 7, 3, 7,
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4, 7, 5, 8, 0, 8, 1, 8, 2, 8, 3, 8, 4,
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8, 5], np.int64).reshape((len(A_values), 2))
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cpu_device = onnxrt.OrtDevice.make('cpu', 0)
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sparse_tensor = onnxrt.SparseTensor.sparse_coo_from_numpy(common_shape, A_values, A_indices, cpu_device)
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A_ort_value = onnxrt.OrtValue.ort_value_from_sparse_tensor(sparse_tensor)
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B_data = np.array([0, 1, 2, 0, 0, 0, 3, 4, 5,
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6, 7, 8, 0, 0, 0, 9, 10, 11,
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12, 13, 14, 0, 0, 0, 15, 16, 17,
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0, 0, 0, 18, 19, 20, 21, 22, 23,
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0, 0, 0, 24, 25, 26, 27, 28, 29,
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0, 0, 0, 30, 31, 32, 33, 34, 35,
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36, 37, 38, 39, 40, 41, 0, 0, 0,
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42, 43, 44, 45, 46, 47, 0, 0, 0,
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48, 49, 50, 51, 52, 53, 0, 0, 0], np.float32).reshape(common_shape)
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B_ort_value = onnxrt.OrtValue.ortvalue_from_numpy(B_data)
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Y_result = np.array([546, 561, 576, 552, 564, 576, 39, 42, 45,
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1410, 1461, 1512, 1362, 1392, 1422, 201, 222, 243,
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2274, 2361, 2448, 2172, 2220, 2268, 363, 402, 441,
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2784, 2850, 2916, 4362, 4485, 4608, 1551, 1608, 1665,
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3540, 3624, 3708, 5604, 5763, 5922, 2037, 2112, 2187,
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4296, 4398, 4500, 6846, 7041, 7236, 2523, 2616, 2709,
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678, 789, 900, 2892, 3012, 3132, 4263, 4494, 4725,
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786, 915, 1044, 3324, 3462, 3600, 4911, 5178, 5445,
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894, 1041, 1188, 3756, 3912, 4068, 5559, 5862, 6165], np.float).reshape(common_shape)
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sess = onnxrt.InferenceSession(get_name("sparse_to_dense_matmul.onnx"),
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providers=onnxrt.get_available_providers())
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res = sess.run_with_ort_values(["dense_Y"], { "sparse_A" : A_ort_value, "dense_B" : B_ort_value })
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self.assertEqual(len(res), 1)
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ort_value = res[0]
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self.assertTrue(isinstance(ort_value, onnxrt.OrtValue))
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self.assertTrue(ort_value.is_tensor())
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self.assertEqual(ort_value.data_type(), "tensor(float)")
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self.assertEqual(ort_value.shape(), common_shape)
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result = ort_value.numpy()
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self.assertEqual(list(result.shape), common_shape)
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self.assertTrue(np.array_equal(Y_result, result))
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