!12693 Setting of dynamic_batch_size supported
From: @zhupuxu Reviewed-by: Signed-off-by:pull/12693/MERGE
mindspore-ci-bot
4 years ago
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Gitee
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/**
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* Copyright 2021 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 <map>
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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;
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static const char tensor_add_file[] = "/home/workspace/mindspore_dataset/mindir/add/add.mindir";
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static const float input_data_1[2][2] = {{1,2},{3,4}};
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static const float input_data_2[2][2] = {{2,3},{4,5}};
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static const float input_data_3[1] ={2};
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class TestDynamicBatchSize : public ST::Common {
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public:
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TestDynamicBatchSize() {}
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};
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TEST_F(TestDynamicBatchSize, InferMindIR) {
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mindspore::GlobalContext::SetGlobalDeviceTarget(mindspore::kDeviceTypeAscend310);
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mindspore::GlobalContext::SetGlobalDeviceID(2);
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std::map<int,std::vector<int>> input_shape;
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input_shape.insert(std::make_pair(0,std::vector<int>{-1,2}));
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input_shape.insert(std::make_pair(1,std::vector<int>{-1,2}));
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auto model_context = std::make_shared<ModelContext>();
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std::vector<size_t> dynamic_batch_size ={1,2,4,8};
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ModelContext::SetDynamicBatchSize(model_context,dynamic_batch_size);
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ModelContext::SetInputShapeMap(model_context,input_shape);
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auto graph = Serialization::LoadModel(tensor_add_file, ModelType::kMindIR);
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Model tensor_add(GraphCell(graph),model_context);
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ASSERT_TRUE(tensor_add.Build() == kSuccess);
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// get model inputs
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std::vector<MSTensor> origin_inputs = tensor_add.GetInputs();
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ASSERT_EQ(origin_inputs.size()-1, 2);
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// prepare input
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std::vector<MSTensor> outputs;
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std::vector<MSTensor> inputs;
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size_t row = sizeof(input_data_1)/sizeof(input_data_1[0]);
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size_t col = sizeof(input_data_1[0])/sizeof(input_data_1[0][0]);;
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inputs.emplace_back(origin_inputs[0].Name(), origin_inputs[0].DataType(), origin_inputs[0].Shape(),
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input_data_1, sizeof(float) * row*col);
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inputs.emplace_back(origin_inputs[1].Name(), origin_inputs[1].DataType(), origin_inputs[1].Shape(),
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input_data_2, sizeof(float) * row*col);
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inputs.emplace_back(origin_inputs[2].Name(), origin_inputs[2].DataType(), origin_inputs[2].Shape(),
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input_data_3, sizeof(float) * 1);
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// infer
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ASSERT_TRUE(tensor_add.Predict(inputs, &outputs) == kSuccess);
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// assert input
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inputs = tensor_add.GetInputs();
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ASSERT_EQ(inputs.size()-1, 2);
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auto after_input_data_1 = inputs[0].Data();
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auto after_input_data_2 = inputs[1].Data();
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const float *p = reinterpret_cast<const float *>(after_input_data_1.get());
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float input_data1[inputs[0].DataSize() / sizeof(float)] ={0};
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float input_data2[inputs[1].DataSize() / sizeof(float)] ={0};
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size_t k=0,t=0;
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for(size_t i=0;i<row;i++)
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for(size_t j=0;j<col;j++){
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input_data1[k++]=input_data_1[i][j];
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input_data2[t++]=input_data_2[i][j];
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}
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for (size_t i = 0; i < inputs[0].DataSize() / sizeof(float); ++i) {
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ASSERT_LE(std::abs(p[i] - input_data1[i]), 1e-4);
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}
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p = reinterpret_cast<const float *>(after_input_data_2.get());
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for (size_t i = 0; i < inputs[1].DataSize() / sizeof(float); ++i) {
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ASSERT_LE(std::abs(p[i] - input_data2[i]), 1e-4);
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}
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// assert output
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for (auto &buffer : outputs) {
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auto buffer_data = buffer.Data();
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p = reinterpret_cast<const float *>(buffer_data.get());
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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_data1[i] + input_data2[i])), 1e-4);
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}
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}
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}
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