mirror of
https://github.com/saymrwulf/onnxruntime.git
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8e0fff7b8d
1. Support the new external data extension in ONNX 1.4 onnx/onnx#678 2. Enable onnxruntime_perf_test in Mac Build 3. move path_lib.h from onnx_test_runner source dir to onnxruntime_framework 4. Enable memory planner for string tensors 5. Make memory planner always enabled, to simplify model loading logic 6. Delete some duplicated code between onnxruntime_perf_test and onnx_test_runner 7. Delete win_getopt_mb lib. 8. Remove the dependency on Pathcch lib, which is only available on Windows 8 and newer.
75 lines
2.5 KiB
C++
75 lines
2.5 KiB
C++
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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#include "core/framework/tensorprotoutils.h"
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#include "core/graph/onnx_protobuf.h"
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#include "gtest/gtest.h"
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using namespace ::onnxruntime::utils;
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using namespace ONNX_NAMESPACE;
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namespace onnxruntime {
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namespace test {
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namespace {
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template <typename T>
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Status UnpackTensorWrapper(const ONNX_NAMESPACE::TensorProto& tensor, /*out*/ T* p_data, int64_t expected_size) {
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if (tensor.has_raw_data())
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return UnpackTensor(tensor, tensor.raw_data().data(), tensor.raw_data().size(), p_data, expected_size);
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return UnpackTensor(tensor, nullptr, 0, p_data, expected_size);
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}
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} // namespace
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//T must be float for double, and it must match with the 'type' argument
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template <typename T>
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void test_unpack_float_tensor(TensorProto_DataType type) {
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TensorProto float_tensor_proto;
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float_tensor_proto.set_data_type(type);
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T f[4] = {1.1f, 2.2f, 3.3f, 4.4f};
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const size_t len = sizeof(T) * 4;
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char rawdata[len];
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for (int i = 0; i < 4; ++i) {
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memcpy(rawdata + i * sizeof(T), &(f[i]), sizeof(T));
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}
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float_tensor_proto.set_raw_data(rawdata, len);
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T float_data2[4];
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auto status = UnpackTensorWrapper(float_tensor_proto, float_data2, 4);
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EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
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EXPECT_EQ(1.1f, float_data2[0]);
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EXPECT_EQ(2.2f, float_data2[1]);
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EXPECT_EQ(3.3f, float_data2[2]);
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EXPECT_EQ(4.4f, float_data2[3]);
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}
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TEST(TensorParseTest, TensorUtilsTest) {
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TensorProto bool_tensor_proto;
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bool_tensor_proto.set_data_type(TensorProto_DataType_BOOL);
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bool_tensor_proto.add_int32_data(1);
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bool bool_data[1];
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auto status = UnpackTensorWrapper(bool_tensor_proto, bool_data, 1);
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EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
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EXPECT_TRUE(bool_data[0]);
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float float_data[1];
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status = UnpackTensorWrapper(bool_tensor_proto, float_data, 1);
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EXPECT_FALSE(status.IsOK());
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test_unpack_float_tensor<float>(TensorProto_DataType_FLOAT);
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test_unpack_float_tensor<double>(TensorProto_DataType_DOUBLE);
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TensorProto string_tensor_proto;
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string_tensor_proto.set_data_type(TensorProto_DataType_STRING);
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string_tensor_proto.add_string_data("a");
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string_tensor_proto.add_string_data("b");
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std::string string_data[2];
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status = UnpackTensorWrapper(string_tensor_proto, string_data, 2);
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EXPECT_TRUE(status.IsOK()) << status.ErrorMessage();
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EXPECT_EQ("a", string_data[0]);
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EXPECT_EQ("b", string_data[1]);
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status = UnpackTensorWrapper(bool_tensor_proto, string_data, 2);
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EXPECT_FALSE(status.IsOK());
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}
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} // namespace test
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} // namespace onnxruntime
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