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59 lines
2.0 KiB
59 lines
2.0 KiB
4 years ago
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/**
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* Copyright 2020 Huawei Technologies Co., Ltd
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <string>
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#include <vector>
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#include "common/common_test.h"
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#include "include/api/model.h"
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#include "include/api/serialization.h"
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#include "include/api/context.h"
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using namespace mindspore::api;
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static const char tensor_add_file[] = "/home/workspace/mindspore_dataset/tensor_add/tensor_add.mindir";
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static const std::vector<float> input_data_1 = {1, 2, 3, 4};
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static const std::vector<float> input_data_2 = {2, 3, 4, 5};
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class TestTensorAdd : public ST::Common {
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public:
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TestTensorAdd() {}
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};
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TEST_F(TestTensorAdd, InferMindIR) {
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Context::Instance().SetDeviceTarget(kDeviceTypeAscend310).SetDeviceID(1);
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auto graph = Serialization::LoadModel(tensor_add_file, ModelType::kMindIR);
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Model tensor_add((GraphCell(graph)));
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Status ret = tensor_add.Build({});
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ASSERT_TRUE(ret == SUCCESS);
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// prepare input
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std::vector<Buffer> outputs;
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std::vector<Buffer> inputs;
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inputs.emplace_back(Buffer(input_data_1.data(), sizeof(float) * input_data_1.size()));
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inputs.emplace_back(Buffer(input_data_2.data(), sizeof(float) * input_data_2.size()));
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// infer
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ret = tensor_add.Predict(inputs, &outputs);
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ASSERT_TRUE(ret == SUCCESS);
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// print
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for (auto &buffer : outputs) {
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const float *p = reinterpret_cast<const float *>(buffer.Data());
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for (size_t i = 0; i < buffer.DataSize() / sizeof(float); ++i) {
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ASSERT_LE(std::abs(p[i] - (input_data_1[i] + input_data_2[i])), 1e-4);
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}
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}
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}
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