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@ -42,7 +42,7 @@ TEST_F(InferTest, TestConvNode) {
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node->primitive = std::make_unique<schema::PrimitiveT>();
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node->primitive->value.type = schema::PrimitiveType_Conv2D;
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auto primitive = new schema::Conv2DT;
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primitive->padMode = schema::PadMode_SAME;
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primitive->padMode = schema::PadMode_SAME_UPPER;
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primitive->channelIn = 3;
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primitive->channelOut = 32;
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primitive->format = schema::Format_NHWC;
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@ -231,18 +231,6 @@ TEST_F(InferTest, TestAddNode) {
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ASSERT_EQ(TypeId::kNumberTypeFloat32, outTensor->data_type());
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auto *outData = reinterpret_cast<float *>(outTensor->MutableData());
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ASSERT_NE(nullptr, outData);
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// //===================================================
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// size_t output_size;
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// std::string output_path = "./convfp32_out_1_28_28_32.bin";
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// ReadFile(output_path.c_str(), &output_size, buf);
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// ASSERT_NE(nullptr, buf[0]);
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// auto output_data = reinterpret_cast<float *>(buf[0]);
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// ASSERT_NE(nullptr, output_data);
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// //===================================================
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// ASSERT_EQ(output_size, outTensor->Size());
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// for (size_t i = 0; i < outTensor->ElementsNum(); i++) {
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// ASSERT_EQ(output_data[i], outData[i]);
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// }
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MS_LOG(INFO) << "Passed";
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}
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@ -366,141 +354,4 @@ TEST_F(InferTest, TestModel) {
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auto outputs = session->GetOutputs();
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MS_LOG(INFO) << "Passed";
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}
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// TEST_F(TrainTest, TestMultiNode) {
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// auto msGraph = std::make_shared<schema::GraphDefT>();
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// msGraph->name = "graph";
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// auto msSubgraph = std::make_unique<schema::SubGraphDefT>();
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// msSubgraph->name = "subGraph";
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//
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// auto conv = std::make_unique<schema::OpDefT>();
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// conv->inputIndex = {0, 1};
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// conv->outputIndex = {2};
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// conv->attr.type = schema::OpT_Conv2D;
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// auto conv_attr = new schema::Conv2DT;
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// conv_attr->padMode = schema::PadMode_SAME;
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// conv_attr->format = schema::Format_NHWC;
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// conv_attr->strideH = 1;
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// conv_attr->strideW = 1;
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// conv_attr->kernelH = 3;
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// conv_attr->kernelW = 3;
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// conv_attr->dilateH = 1;
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// conv_attr->dilateW = 1;
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//
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// conv->attr.value = conv_attr;
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// conv->name = "Conv2D";
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// conv->fmkType = schema::FmkType_CAFFE;
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// msSubgraph->nodes.emplace_back(std::move(conv));
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//
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// auto matMul1 = std::make_unique<schema::OpDefT>();
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// matMul1->inputIndex = {2, 3};
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// matMul1->outputIndex = {4};
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// matMul1->attr.type = schema::OpT_MatMul;
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// auto matMul_attr1 = new schema::MatMulT;
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// matMul_attr1->transposeA = false;
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// matMul_attr1->transposeB = true;
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// matMul1->attr.value = matMul_attr1;
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// matMul1->name = "matmul1";
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// matMul1->fmkType = schema::FmkType_CAFFE;
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// msSubgraph->nodes.emplace_back(std::move(matMul1));
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//
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// auto matMul2 = std::make_unique<schema::OpDefT>();
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// matMul2->inputIndex = {4, 5};
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// matMul2->outputIndex = {6};
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// matMul2->attr.type = schema::OpT_MatMul;
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// auto matMul_attr2 = new schema::MatMulT;
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// matMul_attr2->transposeA = false;
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// matMul_attr2->transposeB = true;
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// matMul2->attr.value = matMul_attr2;
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// matMul2->name = "matmul2";
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// matMul2->fmkType = schema::FmkType_CAFFE;
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// msSubgraph->nodes.emplace_back(std::move(matMul2));
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//
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// msSubgraph->inputIndex = {0};
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// msSubgraph->outputIndex = {6};
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//
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// auto input0 = std::make_unique<schema::TensorDefT>();
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// input0->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
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// input0->format = schema::Format_NHWC;
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// input0->dataType = TypeId::kNumberTypeFloat32;
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// input0->dims = {1, 5, 5, 3};
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// input0->offset = -1;
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// msSubgraph->allTensors.emplace_back(std::move(input0));
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//
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// auto conv_weight = std::make_unique<schema::TensorDefT>();
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// conv_weight->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
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// conv_weight->format = schema::Format_KHWC;
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// conv_weight->dataType = TypeId::kNumberTypeFloat32;
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// conv_weight->dims = {8, 3, 3, 3};
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// conv_weight->data.resize(8*3*3*3*sizeof(float));
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// msSubgraph->allTensors.emplace_back(std::move(conv_weight));
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//
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// auto conv_output = std::make_unique<schema::TensorDefT>();
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// conv_output->refCount = 0;
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// conv_output->format = schema::Format_NHWC;
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// conv_output->dataType = TypeId::kNumberTypeFloat32;
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// conv_output->dims = {1, 5, 5, 8};
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// msSubgraph->allTensors.emplace_back(std::move(conv_output));
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//
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// auto add_weight = std::make_unique<schema::TensorDefT>();
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// add_weight->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
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// add_weight->format = schema::Format_NHWC;
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// add_weight->dataType = TypeId::kNumberTypeFloat32;
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// add_weight->dims = {1, 5, 5, 8};
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// add_weight->data.resize(5*5*8*sizeof(float));
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// msSubgraph->allTensors.emplace_back(std::move(add_weight));
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//
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// auto add_output = std::make_unique<schema::TensorDefT>();
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// add_output->refCount = 0;
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// add_output->format = schema::Format_NHWC;
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// add_output->dataType = TypeId::kNumberTypeFloat32;
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// add_output->dims = {1, 5, 5, 8};
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// msSubgraph->allTensors.emplace_back(std::move(add_output));
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//
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// auto mul_weight = std::make_unique<schema::TensorDefT>();
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// mul_weight->refCount = schema::MSCONST_WEIGHT_REFCOUNT;
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// mul_weight->format = schema::Format_NHWC;
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// mul_weight->dataType = TypeId::kNumberTypeFloat32;
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// mul_weight->dims = {1, 5, 5, 8};
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// mul_weight->data.resize(5*5*8*sizeof(float));
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// msSubgraph->allTensors.emplace_back(std::move(mul_weight));
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//
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// auto mul_output = std::make_unique<schema::TensorDefT>();
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// mul_output->refCount = 0;
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// mul_output->format = schema::Format_NHWC;
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// mul_output->dataType = TypeId::kNumberTypeFloat32;
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// mul_output->dims = {1, 5, 5, 8};
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// msSubgraph->allTensors.emplace_back(std::move(mul_output));
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// msGraph->subgraphs.emplace_back(std::move(msSubgraph));
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//
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// flatbuffers::FlatBufferBuilder builder(1024);
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// auto offset = schema::GraphDef::Pack(builder, msGraph.get());
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// builder.Finish(offset);
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// size_t size = builder.GetSize();
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// const char *content = (char *)builder.GetBufferPointer();
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// const std::string strstub = "";
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//
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// auto func_graph = inference::LoadModel(content, size, strstub);
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// ASSERT_NE(nullptr, func_graph);
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// auto session = inference::MSSession::CreateSession(kCPUDevice, 0);
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// ASSERT_NE(nullptr, session);
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// auto graphId = session->CompileGraph(func_graph);
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//
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// auto inTensor =
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// std::shared_ptr<inference::MSTensor>(inference::MSTensor::CreateTensor(TypeId::kNumberTypeFloat32, {1, 5, 5, 3}));
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// ASSERT_NE(nullptr, inTensor);
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// ASSERT_EQ(sizeof(float) * (5 * 5 * 3), inTensor->Size());
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// (void)inTensor->MutableData();
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//
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// std::vector<std::shared_ptr<inference::MSTensor>> inputs;
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// inputs.emplace_back(inTensor);
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// auto outputs = session->RunGraph(graphId, inputs);
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// ASSERT_EQ(1, outputs.size());
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// ASSERT_EQ(1, outputs.front().size());
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// auto runOutput = outputs.front().front();
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// ASSERT_NE(nullptr, runOutput);
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// ASSERT_EQ(5 * 5 * 8, runOutput->ElementsNum());
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// ASSERT_EQ(TypeId::kNumberTypeFloat32, runOutput->data_type());
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// MS_LOG(INFO) << "Passed";
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//}
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} // namespace mindspore
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xuanyue
5 years ago