diff --git a/mindspore/lite/src/common/op_utils.h b/mindspore/lite/src/common/op_utils.h deleted file mode 100644 index e81e5181bc..0000000000 --- a/mindspore/lite/src/common/op_utils.h +++ /dev/null @@ -1,31 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_LITE_COMMON_OP_UTILS_H_ -#define MINDSPORE_LITE_COMMON_OP_UTILS_H_ - -#include -#include -#include "schema/model_generated.h" - -namespace mindspore { -namespace lite { -inline schema::PrimitiveType GetOpType(const schema::CNode &opDef) { return opDef.primitive()->value_type(); } -inline std::string GetOpTypeName(const schema::CNode &opDef) { return schema::EnumNamePrimitiveType(GetOpType(opDef)); } -} // namespace lite -} // namespace mindspore - -#endif // MINDSPORE_LITE_COMMON_OP_UTILS_H_ diff --git a/mindspore/lite/src/executor.cc b/mindspore/lite/src/executor.cc index d48df63c8f..b80e92dca7 100644 --- a/mindspore/lite/src/executor.cc +++ b/mindspore/lite/src/executor.cc @@ -44,7 +44,7 @@ int Executor::Run(std::vector &in_tensors, std::vector &out_ } kernel::LiteKernelUtil::InitTensorRefCount(kernels); for (auto out_tensor : out_tensors) { // increase RefCount of output tensors, such that Run will not free them - out_tensor->SetRefCount(out_tensor->RefCount() + 1); + out_tensor->set_ref_count(out_tensor->ref_count() + 1); } for (auto *kernel : kernels) { @@ -101,7 +101,7 @@ int Executor::TransformTensorLayoutFp32(Tensor *tensor, schema::Format dst_forma return RET_ERROR; } PackNC4HW4ToNHWCFp32(src_data, dst_data, tensor->Batch(), tensor->Height() * tensor->Width(), tensor->Channel()); - tensor->SetData(dst_data); + tensor->set_data(dst_data); tensor->SetFormat(dst_format); allocator->Free(src_data); return RET_OK; diff --git a/mindspore/lite/src/lite_kernel.cc b/mindspore/lite/src/lite_kernel.cc index 957054cbf4..dbff82263b 100644 --- a/mindspore/lite/src/lite_kernel.cc +++ b/mindspore/lite/src/lite_kernel.cc @@ -39,14 +39,14 @@ void LiteKernel::FreeWorkspace() { void LiteKernel::InitOutTensorRefCount() { for (auto *tensor : this->out_tensors_) { - tensor->SetRefCount(this->out_kernels_.size()); + tensor->set_ref_count(this->out_kernels_.size()); } } int LiteKernel::DecOutTensorRefCount() { for (auto *tensor : this->out_tensors_) { - tensor->decRefCount(); - if (0 >= tensor->RefCount()) { + tensor->DecRefCount(); + if (0 >= tensor->ref_count()) { auto ret = tensor->FreeData(); if (0 != ret) { MS_LOG(ERROR) << "Free tensor data failed"; @@ -190,8 +190,7 @@ std::vector LiteKernelUtil::SubgraphInputTensors(const std::vect for (const auto &kernel : input_kernels) { for (const auto &tensor : kernel->in_tensors()) { auto iter = std::find(all_output_tensors.begin(), all_output_tensors.end(), tensor); - if (iter == all_output_tensors.end() && - !(tensor->category() == mindspore::lite::Tensor::CONST && tensor->data_c() != nullptr)) { + if (iter == all_output_tensors.end() && !tensor->IsConst()) { input_tensors.emplace_back(tensor); } } diff --git a/mindspore/lite/src/lite_session.cc b/mindspore/lite/src/lite_session.cc index cadf01bdd8..75b0bf7d3a 100644 --- a/mindspore/lite/src/lite_session.cc +++ b/mindspore/lite/src/lite_session.cc @@ -61,11 +61,12 @@ int LiteSession::ConvertTensors(const lite::Model *model) { MS_LOG(ERROR) << i << "th tensor in model is nullptr"; return RET_NULL_PTR; } + auto src_category = TensorCategory(srcTensor); std::vector shape; if (srcTensor->dims() == nullptr) { MS_LOG(DEBUG) << "Dims of " << i << "th tensor is nullptr"; } else { - if (TensorCategory(srcTensor) == Tensor::Category::CONST) { + if (src_category == Tensor::Category::CONST_TENSOR) { if (srcTensor->dataType() == kObjectTypeString && srcTensor->data() != nullptr) { shape.push_back(srcTensor->data()->size()); } else { @@ -76,18 +77,13 @@ int LiteSession::ConvertTensors(const lite::Model *model) { } } int dataType = srcTensor->dataType(); - auto *dstTensor = - new (std::nothrow) Tensor(TypeId(dataType), shape, srcTensor->format(), TensorCategory(srcTensor)); + auto *dstTensor = new (std::nothrow) Tensor(TypeId(dataType), shape, srcTensor->format(), src_category); if (dstTensor == nullptr) { MS_LOG(ERROR) << "new " << i << "th tensor failed"; return RET_NULL_PTR; } - if (TensorCategory(srcTensor) == Tensor::Category::CONST && srcTensor->data() != nullptr && - srcTensor->data()->size() > 0) { - if (shape.empty()) { - shape.push_back(1); - dstTensor->set_shape(shape); - } + if ((src_category == Tensor::Category::CONST_TENSOR || src_category == Tensor::Category::CONST_SCALAR) && + srcTensor->data() != nullptr && srcTensor->data()->size() > 0) { MS_ASSERT(dstTensor->Size() == srcTensor->data()->size()); if (WeightTensorNeedCopy(model, i)) { auto dst_data = dstTensor->MutableData(); @@ -99,7 +95,7 @@ int LiteSession::ConvertTensors(const lite::Model *model) { memcpy(dst_data, srcTensor->data()->data(), dstTensor->Size()); copyed_tensor_idxes_.emplace_back(i); } else { - dstTensor->SetData(const_cast(srcTensor->data()->data())); + dstTensor->set_data(const_cast(srcTensor->data()->data())); } } auto quant_params = srcTensor->quantParams(); @@ -395,7 +391,7 @@ void LiteSession::BindThread(bool if_bind) { MS_LOG(ERROR) << "Device list is empty."; return; } - if (this->context_->IsCpuEnabled()) { + if (!this->context_->IsCpuEnabled()) { return; } auto cpu_device_info = this->context_->GetCpuInfo(); @@ -415,9 +411,8 @@ LiteSession::~LiteSession() { auto *tensor = tensors_.at(i); MS_ASSERT(tensor != nullptr); // data of weight tensor of node in packed_op can not be to free, we will free weight data when freeing meta_graph - if (tensor->category() == Tensor::Category::CONST && !IsContain(this->inputs_, tensor) && - !IsContain(copyed_tensor_idxes_, i)) { - tensor->SetData(nullptr); + if (tensor->IsConst() && !IsContain(this->inputs_, tensor) && !IsContain(copyed_tensor_idxes_, i)) { + tensor->set_data(nullptr); } delete tensor; } diff --git a/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc b/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc index 4ac7633844..926f4c2e02 100644 --- a/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc +++ b/mindspore/lite/src/runtime/kernel/arm/base/fullconnection_base.cc @@ -50,14 +50,14 @@ kernel::LiteKernel *CpuFullConnectionFp32KernelCreator(const std::vectorSetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto kernel = new (std::nothrow) FullconnectionCPUKernel(opParameter, inputs, outputs, ctx, primitive); if (!kernel) { MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -69,13 +69,13 @@ kernel::LiteKernel *CpuFullConnectionFp32KernelCreator(const std::vector(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.cc b/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.cc index 28d38d2ab7..9c83ff3a3b 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_depthwise_fp16.cc @@ -150,7 +150,7 @@ kernel::LiteKernel *CpuConvDwFp16KernelCreator(const std::vector return nullptr; } weight_tensor->set_data_type(kNumberTypeFloat32); - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto conv_param = reinterpret_cast(opParameter); @@ -165,7 +165,7 @@ kernel::LiteKernel *CpuConvDwFp16KernelCreator(const std::vector MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -177,13 +177,13 @@ kernel::LiteKernel *CpuConvDwFp16KernelCreator(const std::vector << schema::EnumNamePrimitiveType(static_cast(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc b/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc index c8c0de972d..7eede99558 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp16/convolution_fp16.cc @@ -192,7 +192,7 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector & return nullptr; } weight_tensor->set_data_type(kNumberTypeFloat32); - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto conv_param = reinterpret_cast(opParameter); @@ -224,7 +224,7 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector & MS_LOG(DEBUG) << "Create conv fp16 kernel failed."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -236,13 +236,13 @@ kernel::LiteKernel *CpuConvFp16KernelCreator(const std::vector & << ", type: " << schema::EnumNamePrimitiveType(static_cast(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.cc b/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.cc index 7c334aace4..e06bec9f59 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_depthwise_fp16.cc @@ -214,7 +214,7 @@ kernel::LiteKernel *CpuDeconvDwFp16KernelCreator(const std::vectorset_data_type(kNumberTypeFloat32); - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto kernel = new (std::nothrow) DeconvolutionDepthwiseFp16CPUKernel(opParameter, inputs, outputs, ctx, primitive); @@ -222,7 +222,7 @@ kernel::LiteKernel *CpuDeconvDwFp16KernelCreator(const std::vectorFreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -234,13 +234,13 @@ kernel::LiteKernel *CpuDeconvDwFp16KernelCreator(const std::vector(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_fp16.cc b/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_fp16.cc index 998f87e5e7..52dce32804 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_fp16.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp16/deconvolution_fp16.cc @@ -226,7 +226,7 @@ kernel::LiteKernel *CpuDeConvFp16KernelCreator(const std::vector return nullptr; } weight_tensor->set_data_type(kNumberTypeFloat32); - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } kernel::LiteKernel *kernel; @@ -242,7 +242,7 @@ kernel::LiteKernel *CpuDeConvFp16KernelCreator(const std::vector MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -254,13 +254,13 @@ kernel::LiteKernel *CpuDeConvFp16KernelCreator(const std::vector << schema::EnumNamePrimitiveType(static_cast(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.cc b/mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.cc index 7f40e995e3..5c16ef685d 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp16/fullconnection_fp16.cc @@ -247,14 +247,14 @@ kernel::LiteKernel *CpuFullConnectionFp16KernelCreator(const std::vectorset_data_type(kNumberTypeFloat32); - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto *kernel = new (std::nothrow) FullconnectionFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive); if (kernel == nullptr) { MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -266,13 +266,13 @@ kernel::LiteKernel *CpuFullConnectionFp16KernelCreator(const std::vectorFreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.cc b/mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.cc index f8ae9de1cb..355bd1cd68 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp16/matmul_fp16.cc @@ -260,14 +260,14 @@ kernel::LiteKernel *CpuMatmulFp16KernelCreator(const std::vector return nullptr; } weight_tensor->set_data_type(kNumberTypeFloat32); - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto *kernel = new (std::nothrow) MatmulFP16CPUKernel(opParameter, inputs, outputs, ctx, primitive); if (kernel == nullptr) { MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -279,13 +279,13 @@ kernel::LiteKernel *CpuMatmulFp16KernelCreator(const std::vector delete kernel; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc b/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc index acbd327e46..0acaa7ec90 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp32/convolution.cc @@ -225,8 +225,8 @@ kernel::LiteKernel *CpuGroupConvFp32KernelCreator(const std::vectordata_type(), filter_shape, Format_NHWC, lite::Tensor::Category::CONST); + auto filter_tensor = new (std::nothrow) lite::Tensor(inputs.at(kWeightIndex)->data_type(), filter_shape, + Format_NHWC, lite::Tensor::Category::CONST_TENSOR); filter_tensor->MallocData(); int copy_length = kernel_h * kernel_w * new_in_channel * new_out_channel; memcpy(filter_tensor->data_c(), origin_weight + i * copy_length, copy_length * sizeof(float)); @@ -235,7 +235,7 @@ kernel::LiteKernel *CpuGroupConvFp32KernelCreator(const std::vectordata_type(), bias_shape, Format_NHWC, lite::Tensor::Category::CONST); + lite::Tensor(inputs.at(kBiasIndex)->data_type(), bias_shape, Format_NHWC, lite::Tensor::Category::CONST_TENSOR); bias_tensor->MallocData(); memcpy(bias_tensor->data_c(), origin_bias + i * new_out_channel, new_out_channel * sizeof(float)); new_inputs.emplace_back(bias_tensor); @@ -293,7 +293,7 @@ kernel::LiteKernel *CpuConvFp32KernelCreator(const std::vector & free(op_parameter); return nullptr; } - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } kernel::LiteKernel *kernel; @@ -307,7 +307,7 @@ kernel::LiteKernel *CpuConvFp32KernelCreator(const std::vector & MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(op_parameter); return nullptr; @@ -319,14 +319,14 @@ kernel::LiteKernel *CpuConvFp32KernelCreator(const std::vector & << schema::EnumNamePrimitiveType(static_cast(op_parameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; diff --git a/mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise.cc b/mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise.cc index 33b8156b74..ec71ec3f6e 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp32/convolution_depthwise.cc @@ -132,7 +132,7 @@ kernel::LiteKernel *CpuConvDwFp32KernelCreator(const std::vector free(opParameter); return nullptr; } - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto conv_param = reinterpret_cast(opParameter); @@ -146,7 +146,7 @@ kernel::LiteKernel *CpuConvDwFp32KernelCreator(const std::vector MS_LOG(ERROR) << "kernel is nullptr."; if (weight_tensor->data_type() == kNumberTypeInt8 || weight_tensor->data_type() == kNumberTypeInt16) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -158,14 +158,14 @@ kernel::LiteKernel *CpuConvDwFp32KernelCreator(const std::vector << schema::EnumNamePrimitiveType(static_cast(opParameter->type_)); if (weight_tensor->data_type() == kNumberTypeInt8 || weight_tensor->data_type() == kNumberTypeInt16) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (weight_tensor->data_type() == kNumberTypeInt8 || weight_tensor->data_type() == kNumberTypeInt16) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; diff --git a/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution.cc b/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution.cc index cd4f792171..56bc61bad4 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution.cc @@ -243,7 +243,7 @@ kernel::LiteKernel *CpuDeConvFp32KernelCreator(const std::vector free(opParameter); return nullptr; } - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } kernel::LiteKernel *kernel; @@ -259,7 +259,7 @@ kernel::LiteKernel *CpuDeConvFp32KernelCreator(const std::vector MS_LOG(ERROR) << "kernel is nullptr."; if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -271,14 +271,14 @@ kernel::LiteKernel *CpuDeConvFp32KernelCreator(const std::vector << schema::EnumNamePrimitiveType(static_cast(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; diff --git a/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise.cc b/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise.cc index d60c000a46..ef53cf2728 100644 --- a/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise.cc +++ b/mindspore/lite/src/runtime/kernel/arm/fp32/deconvolution_depthwise.cc @@ -205,7 +205,7 @@ kernel::LiteKernel *CpuDeconvDwFp32KernelCreator(const std::vectorSetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto kernel = new (std::nothrow) kernel::DeconvolutionDepthwiseCPUKernel(opParameter, inputs, outputs, ctx, primitive); @@ -213,7 +213,7 @@ kernel::LiteKernel *CpuDeconvDwFp32KernelCreator(const std::vectorFreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -225,13 +225,13 @@ kernel::LiteKernel *CpuDeconvDwFp32KernelCreator(const std::vector(opParameter->type_)); if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (dequant_flag) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; } diff --git a/mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc b/mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc index 8b1f80f8e5..1d0a4ed497 100644 --- a/mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc +++ b/mindspore/lite/src/runtime/kernel/arm/int8/matmul_int8.cc @@ -217,7 +217,7 @@ kernel::LiteKernel *CpuMatmulInt8KernelCreator(const std::vector free(opParameter); return nullptr; } - weight_tensor->SetData(dequant_weight); + weight_tensor->set_data(dequant_weight); } auto input_tensor = inputs.at(kInputIndex); @@ -230,7 +230,7 @@ kernel::LiteKernel *CpuMatmulInt8KernelCreator(const std::vector MS_LOG(ERROR) << "kernel is nullptr."; if (is_const_quant_weight) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } free(opParameter); return nullptr; @@ -242,14 +242,14 @@ kernel::LiteKernel *CpuMatmulInt8KernelCreator(const std::vector << schema::EnumNamePrimitiveType(static_cast(opParameter->type_)); if (is_const_quant_weight) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return nullptr; } if (is_const_quant_weight) { weight_tensor->FreeData(); - weight_tensor->SetData(restore_data); + weight_tensor->set_data(restore_data); } return kernel; diff --git a/mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.cc b/mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.cc index cfe68b7916..9850e40e51 100644 --- a/mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.cc +++ b/mindspore/lite/src/runtime/kernel/opencl/kernel/arithmetic.cc @@ -71,7 +71,7 @@ int ArithmeticOpenCLKernel::InitBuffer() { for (auto in_tensor_ : in_tensors_) { auto nhwc_shape = GetNHWCShape(in_tensor_->shape()); inputs_nhwc_shapes_.push_back(nhwc_shape); - if (in_tensor_->category() != lite::Tensor::Category::CONST || in_tensor_->data_c() == nullptr) { + if (!in_tensor_->IsConst()) { inputs_weight_ptrs_.push_back(nullptr); } else { auto allocator = ocl_runtime_->GetAllocator(); diff --git a/mindspore/lite/src/runtime/kernel/opencl/kernel/scale.cc b/mindspore/lite/src/runtime/kernel/opencl/kernel/scale.cc index 9c35520302..1170c67c88 100644 --- a/mindspore/lite/src/runtime/kernel/opencl/kernel/scale.cc +++ b/mindspore/lite/src/runtime/kernel/opencl/kernel/scale.cc @@ -63,7 +63,7 @@ int ScaleOpenCLKernel::InitBuffer() { if (!element_flag_) { return RET_OK; } - if (in_tensors_[1]->category() == lite::Tensor::Category::CONST && in_tensors_[1]->data_c() != nullptr) { + if (in_tensors_[1]->IsConst()) { auto allocator = ocl_runtime_->GetAllocator(); std::vector img_size; GetImageSize(0, &img_size); diff --git a/mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.cc b/mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.cc index 5c14f06ee6..b7b009a8e5 100644 --- a/mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.cc +++ b/mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.cc @@ -209,7 +209,7 @@ int SubGraphOpenCLKernel::MallocTensorWithReuse() { std::vector img_size; op_kernel->GetImageSize(i, &img_size); auto data_ptr = allocator_->Malloc(output->Size(), img_size); - output->SetData(data_ptr); + output->set_data(data_ptr); } else { output->MallocData(allocator_); } diff --git a/mindspore/lite/src/runtime/opencl/opencl_executor.cc b/mindspore/lite/src/runtime/opencl/opencl_executor.cc index 50dc1ba7f4..a62e09e0cc 100644 --- a/mindspore/lite/src/runtime/opencl/opencl_executor.cc +++ b/mindspore/lite/src/runtime/opencl/opencl_executor.cc @@ -46,7 +46,7 @@ int OpenCLExecutor::Run(std::vector &inputs, std::vector &ou std::vector img_size; op_kernel->GetImageSize(i, &img_size); auto data_ptr = allocator_->Malloc(output->Size(), img_size); - output->SetData(data_ptr); + output->set_data(data_ptr); } else { output->MallocData(allocator_); } diff --git a/mindspore/lite/src/scheduler.cc b/mindspore/lite/src/scheduler.cc index c5a8317ed0..369558e353 100644 --- a/mindspore/lite/src/scheduler.cc +++ b/mindspore/lite/src/scheduler.cc @@ -315,7 +315,7 @@ void Scheduler::SetKernelTensorDataType(kernel::LiteKernel *kernel) { } } else if (kernel->desc().data_type == kNumberTypeFloat32) { for (auto tensor : kernel->in_tensors()) { - if (tensor->category() != Tensor::Category::CONST && tensor->data_type() == kNumberTypeFloat16) { + if (!tensor->IsConst() && tensor->data_type() == kNumberTypeFloat16) { tensor->set_data_type(kNumberTypeFloat32); } } diff --git a/mindspore/lite/src/tensor.cc b/mindspore/lite/src/tensor.cc index 65ed534007..88c1bf9591 100644 --- a/mindspore/lite/src/tensor.cc +++ b/mindspore/lite/src/tensor.cc @@ -54,7 +54,7 @@ int Tensor::CopyTensorData(const Tensor &srcTensor) { } } memcpy(this->data_, srcTensor.data_, data_size); - return 0; + return RET_OK; } int Tensor::CopyTensor(const Tensor &srcTensor, bool copyData) { @@ -69,7 +69,7 @@ int Tensor::CopyTensor(const Tensor &srcTensor, bool copyData) { return RET_ERROR; } } - return 0; + return RET_OK; } Tensor::~Tensor() { @@ -102,7 +102,7 @@ bool Tensor::operator==(const Tensor &tensor) { int32_t Tensor::Batch() const { if (this->shape_.size() != 4 && this->shape_.size() != 2) { MS_LOG(ERROR) << "Unsupported tensor shape: " << this->shape().size(); - return -1; + return RET_ERROR; } switch (this->format_) { case schema::Format::Format_NHWC: @@ -123,14 +123,14 @@ int32_t Tensor::Batch() const { return this->shape_[1]; default: MS_LOG(ERROR) << "Unsupported format: " << EnumNameFormat(this->format_); - return -1; + return RET_ERROR; } } int32_t Tensor::Channel() const { if (this->shape_.size() != 4 && this->shape_.size() != 2) { MS_LOG(ERROR) << "Unsupported tensor shape: " << this->shape().size(); - return -1; + return RET_ERROR; } switch (this->format_) { case schema::Format::Format_NCHW: @@ -150,14 +150,14 @@ int32_t Tensor::Channel() const { case schema::Format::Format_CHWK: return this->shape_[0]; default: - return -1; + return RET_ERROR; } } int32_t Tensor::Height() const { if (this->shape_.size() != 4 && this->shape_.size() != 2) { MS_LOG(ERROR) << "Unsupported tensor shape: " << this->shape().size(); - return -1; + return RET_ERROR; } switch (this->format_) { case schema::Format::Format_NCHW: @@ -177,7 +177,7 @@ int32_t Tensor::Height() const { return this->shape_[0]; default: MS_LOG(ERROR) << "Unsupported format: " << EnumNameFormat(this->format_); - return -1; + return RET_ERROR; } } @@ -203,11 +203,28 @@ int32_t Tensor::Width() const { case schema::Format::Format_HW4: return this->shape_[1]; default: - return -1; + return RET_ERROR; } } +size_t Tensor::Size() const { + size_t size = DataTypeSize(this->data_type_); + size *= (format_ == schema::Format::Format_NC4HW4 || format_ == schema::Format::Format_NHWC4) ? ElementsC4Num() + : ElementsNum(); + return size; +} + +int Tensor::ElementsNum() const { + if (this->category_ == CONST_SCALAR) { + return 1; + } + return std::accumulate(shape_.begin(), shape_.end(), 1LL, std::multiplies()); +} + int32_t Tensor::ElementsC4Num() const { + if (this->category_ == CONST_SCALAR) { + return 1; + } int32_t result = 0; if (this->shape_.size() == 4) { result = Batch() * Height() * Width() * ((Channel() + 3) / 4 * 4); @@ -217,6 +234,16 @@ int32_t Tensor::ElementsC4Num() const { return result; } +int Tensor::DimensionSize(size_t index) const { + int dim_size = -1; + if (index < shape_.size()) { + dim_size = shape_[index]; + } else { + MS_LOG(ERROR) << "Dimension index is wrong: " << index; + } + return dim_size; +} + std::string Tensor::ToString() const { std::ostringstream oss; oss << "schema::Format: " << EnumNameFormat(this->format_); @@ -287,7 +314,7 @@ std::string Tensor::ToString() const { int Tensor::MallocData(mindspore::lite::Allocator *allocator) { if (nullptr != this->data_) { - return 0; + return RET_OK; } if (allocator != nullptr) { allocator_ = allocator; @@ -299,15 +326,15 @@ int Tensor::MallocData(mindspore::lite::Allocator *allocator) { } if (nullptr == this->data_) { MS_LOG(ERROR) << "Malloc tensor data failed, size=" << this->Size(); - return -1; + return RET_ERROR; } - return 0; + return RET_OK; } int Tensor::FreeData() { if (nullptr == this->data_) { - return 0; + return RET_OK; } if (nullptr == allocator_) { free(this->data_); @@ -316,7 +343,7 @@ int Tensor::FreeData() { allocator_->Free(this->data_); this->data_ = nullptr; } - return 0; + return RET_OK; } void *Tensor::MutableData() { @@ -330,6 +357,12 @@ void *Tensor::MutableData() { return this->data_; } +bool Tensor::IsConst() { + return (this->category_ == CONST_TENSOR || this->category_ == CONST_SCALAR) && this->data_ != nullptr; +} + +bool Tensor::IsScalar() { return this->category_ == CONST_SCALAR && this->data_ != nullptr; } + void Tensor::AddQuantParam(const QuantArg &quant_arg) { this->quant_params_.push_back(quant_arg); } std::vector Tensor::GetQuantParams() const { return this->quant_params_; } diff --git a/mindspore/lite/src/tensor.h b/mindspore/lite/src/tensor.h index 8c012526b7..fb36e78fed 100644 --- a/mindspore/lite/src/tensor.h +++ b/mindspore/lite/src/tensor.h @@ -42,8 +42,9 @@ struct QuantArg { class Tensor : public mindspore::tensor::MSTensor { public: enum Category { - CONST, // weight tensor - VAR // activation tensor + CONST_TENSOR, // weight tensor + CONST_SCALAR, // weight scalar + VAR // activation tensor }; Tensor() = default; @@ -70,19 +71,9 @@ class Tensor : public mindspore::tensor::MSTensor { void set_shape(const std::vector &shape) { shape_ = shape; } - int DimensionSize(size_t index) const override { - int dim_size = -1; - if (index < shape_.size()) { - dim_size = shape_[index]; - } else { - MS_LOG(ERROR) << "Dimension index is wrong: " << index; - } - return dim_size; - } + int DimensionSize(size_t index) const override; - int ElementsNum() const override { - return std::accumulate(shape_.begin(), shape_.end(), 1LL, std::multiplies()); - } + int ElementsNum() const override; int32_t Batch() const; @@ -94,58 +85,7 @@ class Tensor : public mindspore::tensor::MSTensor { int32_t ElementsC4Num() const; - size_t Size() const override { - size_t size = 0; - switch (this->data_type_) { - case kNumberTypeFloat64: - size = sizeof(double); - break; - case kNumberTypeFloat: - case kNumberTypeFloat32: - size = sizeof(float); - break; - case kNumberTypeInt8: - size = sizeof(int8_t); - break; - case kNumberTypeUInt8: - size = sizeof(uint8_t); - break; - case kNumberTypeFloat16: - size = sizeof(int16_t); - break; - case kNumberTypeInt16: - size = sizeof(int16_t); - break; - case kNumberTypeInt32: - size = sizeof(int32_t); - break; - case kNumberTypeInt64: - size = sizeof(int64_t); - break; - case kNumberTypeUInt16: - size = sizeof(uint16_t); - break; - case kNumberTypeUInt32: - size = sizeof(uint32_t); - break; - case kNumberTypeUInt64: - size = sizeof(uint64_t); - break; - case kNumberTypeBool: - size = sizeof(bool); - break; - case kObjectTypeString: - size = sizeof(char); - break; - default: - MS_LOG(ERROR) << "Not support the type: " << this->data_type_; - return 0; - } - size *= (format_ == schema::Format::Format_NC4HW4 || format_ == schema::Format::Format_NHWC4) ? ElementsC4Num() - : ElementsNum(); - - return size; - } + size_t Size() const override; void set_allocator(mindspore::lite::Allocator *allocator) { allocator_ = allocator; } @@ -157,7 +97,7 @@ class Tensor : public mindspore::tensor::MSTensor { void *data_c() const { return data_; } - void SetData(void *data) { this->data_ = data; } + void set_data(void *data) { this->data_ = data; } Category category() { return this->category_; } @@ -165,11 +105,11 @@ class Tensor : public mindspore::tensor::MSTensor { schema::Format GetFormat() { return this->format_; } - size_t RefCount() { return this->refCount; } + size_t ref_count() { return this->ref_count_; } - void SetRefCount(size_t refCount) { this->refCount = refCount; } + void set_ref_count(size_t ref_count) { this->ref_count_ = ref_count; } - void decRefCount() { this->refCount--; } + void DecRefCount() { this->ref_count_--; } std::string ToString() const; @@ -177,6 +117,10 @@ class Tensor : public mindspore::tensor::MSTensor { std::vector GetQuantParams() const; + bool IsConst(); + + bool IsScalar(); + void Prepare() { if (allocator_ != nullptr) { data_ = allocator_->Prepare(data_); @@ -190,17 +134,63 @@ class Tensor : public mindspore::tensor::MSTensor { std::vector shape_; schema::Format format_; Category category_; - size_t refCount = 0; + size_t ref_count_ = 0; std::vector quant_params_; mindspore::lite::Allocator *allocator_ = nullptr; }; -inline Tensor::Category TensorCategory(const schema::Tensor *tensor) { - return (tensor->nodeType() == schema::NodeType::NodeType_ValueNode) ? Tensor::Category::CONST : Tensor::Category::VAR; +inline size_t DataTypeSize(const TypeId type) { + switch (type) { + case kNumberTypeFloat64: + return sizeof(double); + case kNumberTypeFloat: + case kNumberTypeFloat32: + return sizeof(float); + case kNumberTypeInt8: + return sizeof(int8_t); + case kNumberTypeUInt8: + return sizeof(uint8_t); + case kNumberTypeFloat16: + case kNumberTypeInt16: + return sizeof(int16_t); + case kNumberTypeInt32: + return sizeof(int32_t); + case kNumberTypeInt64: + return sizeof(int64_t); + case kNumberTypeUInt16: + return sizeof(uint16_t); + case kNumberTypeUInt32: + return sizeof(uint32_t); + case kNumberTypeUInt64: + return sizeof(uint64_t); + case kNumberTypeBool: + return sizeof(bool); + case kObjectTypeString: + return sizeof(char); + default: + MS_LOG(ERROR) << "Not support the type: " << type; + return 0; + } +} + +inline Tensor::Category TensorCategory(const schema::NodeType node_type, const size_t shape_num, const TypeId data_type, + const size_t data_size) { + return (node_type == schema::NodeType::NodeType_ValueNode) + ? (shape_num == 0 && data_size == DataTypeSize(data_type) ? Tensor::Category::CONST_SCALAR + : Tensor::Category::CONST_TENSOR) + : Tensor::Category::VAR; } -inline Tensor::Category TensorCategory(const schema::NodeType type) { - return (type == schema::NodeType::NodeType_ValueNode) ? Tensor::Category::CONST : Tensor::Category::VAR; + +inline Tensor::Category TensorCategory(const schema::Tensor *tensor) { + if (tensor == nullptr) { + MS_LOG(ERROR) << "tensor is nullptr"; + return Tensor::VAR; + } + auto shape_num = tensor->dims() == nullptr ? 0 : tensor->dims()->size(); + auto data_size = tensor->data() == nullptr ? 0 : tensor->data()->size(); + return TensorCategory(tensor->nodeType(), shape_num, TypeId(tensor->dataType()), data_size); } + std::vector TensorVectorCast(const std::vector &src); } // namespace lite } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/common/strided_slice_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/common/strided_slice_tests.cc index 7e01b4eabb..62201f021b 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/common/strided_slice_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/common/strided_slice_tests.cc @@ -49,8 +49,8 @@ TEST_F(TestStridedSlice, StridedSlice) { lite::Tensor out_tensor(kNumberTypeFloat32, {1, 1, 2}); float input_data[] = {0.2390374, 0.92039955, 0.05051243, 0.49574447, 0.8355223, 0.02647042, 0.08811307, 0.4566604}; float output_data[2] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor}; @@ -73,8 +73,8 @@ TEST_F(TestStridedSlice, StridedSlice) { float expect[2] = {0.2390374, 0.05051243}; CompareOutputData(output_data, expect, 2, 0.000001); - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } TEST_F(TestStridedSlice, StridedSliceInt8) { @@ -82,8 +82,8 @@ TEST_F(TestStridedSlice, StridedSliceInt8) { lite::Tensor out_tensor(kNumberTypeInt8, {2, 3, 4}); int8_t input_data[] = {-12, -11, -10, -9, -8, -7, -6, -5, -4, -3, -2, -1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; int8_t output_data[4] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor}; @@ -121,7 +121,7 @@ TEST_F(TestStridedSlice, StridedSliceInt8) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp16/reduce_fp16_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp16/reduce_fp16_tests.cc index 1961ecec84..dd33f7a3f9 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp16/reduce_fp16_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp16/reduce_fp16_tests.cc @@ -43,8 +43,8 @@ class TestReduceFp16 : public mindspore::CommonTest { }; void TestReduceFp16::TearDown() { - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestReduceFp16::Prepare(const std::vector &input_shape, const std::vector &output_shape, @@ -54,8 +54,8 @@ void TestReduceFp16::Prepare(const std::vector &input_shape, const std::vec in_tensor_.set_shape(input_shape); out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(output_shape); - in_tensor_.SetData(input_data); - out_tensor_.SetData(output_data); + in_tensor_.set_data(input_data); + out_tensor_.set_data(output_data); bool keep_axis = false; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/activation_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/activation_fp32_test.cc index 036ad0ee6a..2461880f17 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/activation_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/activation_fp32_test.cc @@ -98,7 +98,7 @@ TEST_F(TestActivationFp32, HSwishFp32) { lite::Tensor input0_tensor; inputs_tensor.push_back(&input0_tensor); - input0_tensor.SetData(input.data()); + input0_tensor.set_data(input.data()); input0_tensor.set_shape(in_shape); std::vector output(8); @@ -106,7 +106,7 @@ TEST_F(TestActivationFp32, HSwishFp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Activation}; auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc); @@ -123,8 +123,8 @@ TEST_F(TestActivationFp32, HSwishFp32) { std::vector expect_output = {-0, -0.33333334, -0.33333334, 0, 0.6666667, 5, 6, 7}; CompareOutputData(output.data(), expect_output.data(), 8, 0.00001); - input0_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestActivationFp32, HardTanh1) { @@ -142,7 +142,7 @@ TEST_F(TestActivationFp32, HardTanh1) { lite::Tensor input0_tensor; inputs_tensor.push_back(&input0_tensor); - input0_tensor.SetData(input.data()); + input0_tensor.set_data(input.data()); input0_tensor.set_shape(in_shape); std::vector output(8); @@ -150,7 +150,7 @@ TEST_F(TestActivationFp32, HardTanh1) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Activation}; auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc); @@ -167,8 +167,8 @@ TEST_F(TestActivationFp32, HardTanh1) { std::vector expect_output = {-1.0, -1.0, -0.5, 0.0, 0.5, 1.0, 1.0, 1.0}; CompareOutputData(output.data(), expect_output.data(), 8, 0.00001); - input0_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestActivationFp32, HardTanh2) { @@ -186,7 +186,7 @@ TEST_F(TestActivationFp32, HardTanh2) { lite::Tensor input0_tensor; inputs_tensor.push_back(&input0_tensor); - input0_tensor.SetData(input.data()); + input0_tensor.set_data(input.data()); input0_tensor.set_shape(in_shape); std::vector output(8); @@ -194,7 +194,7 @@ TEST_F(TestActivationFp32, HardTanh2) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Activation}; auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc); @@ -211,8 +211,8 @@ TEST_F(TestActivationFp32, HardTanh2) { std::vector expect_output = {-2.0, -2.0, -1.0, 0.0, 1.0, 2.0, 2.0, 2.0}; CompareOutputData(output.data(), expect_output.data(), 8, 0.00001); - input0_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/arithmetic_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/arithmetic_fp32_tests.cc index b83fde169c..1c39ff059d 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/arithmetic_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/arithmetic_fp32_tests.cc @@ -67,11 +67,11 @@ void TestArithmeticTestFp32::PrepareInt(const std::vector &input0_shape, co } in_tensor_0_.set_data_type(kNumberTypeInt); - in_tensor_0_.SetData(input0_data); + in_tensor_0_.set_data(input0_data); in_tensor_0_.set_shape(input0_shape); - in_tensor_1_.SetData(input1_data); + in_tensor_1_.set_data(input1_data); in_tensor_1_.set_shape(input1_shape); - out_tensor_.SetData(output_data); + out_tensor_.set_data(output_data); out_tensor_.set_shape(output_shape); auto creator = lite::KernelRegistry::GetInstance()->GetCreator(desc_); @@ -83,9 +83,9 @@ void TestArithmeticTestFp32::PrepareInt(const std::vector &input0_shape, co } void TestArithmeticTestFp32::TearDown() { - in_tensor_0_.SetData(nullptr); - in_tensor_1_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_0_.set_data(nullptr); + in_tensor_1_.set_data(nullptr); + out_tensor_.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, AddTest) { @@ -548,8 +548,8 @@ TEST_F(TestArithmeticTestFp32, MulFp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -560,7 +560,7 @@ TEST_F(TestArithmeticTestFp32, MulFp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -583,9 +583,9 @@ TEST_F(TestArithmeticTestFp32, MulFp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, MulReluFp32) { @@ -622,8 +622,8 @@ TEST_F(TestArithmeticTestFp32, MulReluFp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -634,7 +634,7 @@ TEST_F(TestArithmeticTestFp32, MulReluFp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -657,9 +657,9 @@ TEST_F(TestArithmeticTestFp32, MulReluFp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, MulRelu6Fp32) { @@ -696,8 +696,8 @@ TEST_F(TestArithmeticTestFp32, MulRelu6Fp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -708,7 +708,7 @@ TEST_F(TestArithmeticTestFp32, MulRelu6Fp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -730,9 +730,9 @@ TEST_F(TestArithmeticTestFp32, MulRelu6Fp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, MulInt0) { @@ -1021,8 +1021,8 @@ TEST_F(TestArithmeticTestFp32, AddReluFp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -1033,7 +1033,7 @@ TEST_F(TestArithmeticTestFp32, AddReluFp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -1055,9 +1055,9 @@ TEST_F(TestArithmeticTestFp32, AddReluFp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, AddRelu6Fp32) { @@ -1094,8 +1094,8 @@ TEST_F(TestArithmeticTestFp32, AddRelu6Fp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -1106,7 +1106,7 @@ TEST_F(TestArithmeticTestFp32, AddRelu6Fp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -1127,9 +1127,9 @@ TEST_F(TestArithmeticTestFp32, AddRelu6Fp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, DivReluFp32) { @@ -1166,8 +1166,8 @@ TEST_F(TestArithmeticTestFp32, DivReluFp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -1178,7 +1178,7 @@ TEST_F(TestArithmeticTestFp32, DivReluFp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -1201,9 +1201,9 @@ TEST_F(TestArithmeticTestFp32, DivReluFp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, DivRelu6Fp32) { @@ -1240,8 +1240,8 @@ TEST_F(TestArithmeticTestFp32, DivRelu6Fp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -1252,7 +1252,7 @@ TEST_F(TestArithmeticTestFp32, DivRelu6Fp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -1273,9 +1273,9 @@ TEST_F(TestArithmeticTestFp32, DivRelu6Fp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestArithmeticTestFp32, EqualFp32) { @@ -1311,8 +1311,8 @@ TEST_F(TestArithmeticTestFp32, EqualFp32) { lite::Tensor input0_tensor; lite::Tensor input1_tensor; input0_tensor.set_data_type(kNumberTypeFloat32); - input0_tensor.SetData(input0.data()); - input1_tensor.SetData(input1.data()); + input0_tensor.set_data(input0.data()); + input1_tensor.set_data(input1.data()); input0_tensor.set_shape(input0_shape); input1_tensor.set_shape(input1_shape); inputs_tensor.push_back(&input0_tensor); @@ -1323,7 +1323,7 @@ TEST_F(TestArithmeticTestFp32, EqualFp32) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(output_shape); kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_Eltwise}; @@ -1343,8 +1343,8 @@ TEST_F(TestArithmeticTestFp32, EqualFp32) { CompareOutputData(output.data(), correct_out_ptr, 24, 0.00001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc index c712f413c6..267bb716a6 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/batchnorm_fp32_tests.cc @@ -39,9 +39,9 @@ TEST_F(TestBatchnormFp32, BNTest) { lite::Tensor input0_tensor(kNumberTypeFloat32, {1, 2, 2, 3}); lite::Tensor input1_tensor(kNumberTypeFloat32, {3}); lite::Tensor input2_tensor(kNumberTypeFloat32, {3}); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); - input2_tensor.SetData(in_data2.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); + input2_tensor.set_data(in_data2.data()); std::vector inputs_tensor = {&input0_tensor, &input1_tensor, &input2_tensor}; std::vector output(12); @@ -49,7 +49,7 @@ TEST_F(TestBatchnormFp32, BNTest) { -3.5422924, -14.005781, -2.3525476, -6.7113695, -16.396551, -1.4275324}; lite::Tensor output0_tensor(kNumberTypeFloat32, {1, 2, 2, 3}); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); std::vector outputs_tensor = {&output0_tensor}; kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_BatchNorm}; @@ -71,10 +71,10 @@ TEST_F(TestBatchnormFp32, BNTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - input2_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + input2_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestBatchnormFp32, FusedBNTest) { @@ -94,11 +94,11 @@ TEST_F(TestBatchnormFp32, FusedBNTest) { lite::Tensor input2(kNumberTypeFloat32, {3}); lite::Tensor input3(kNumberTypeFloat32, {3}); lite::Tensor input4(kNumberTypeFloat32, {3}); - input0.SetData(in_data.data()); - input1.SetData(scale.data()); - input2.SetData(offset.data()); - input3.SetData(mean.data()); - input4.SetData(var.data()); + input0.set_data(in_data.data()); + input1.set_data(scale.data()); + input2.set_data(offset.data()); + input3.set_data(mean.data()); + input4.set_data(var.data()); std::vector inputs_tensor = {&input0, &input1, &input2, &input3, &input4}; std::vector output(12); @@ -106,7 +106,7 @@ TEST_F(TestBatchnormFp32, FusedBNTest) { 5.1857452, 56.60399, -77.215096, -181.18402, 49.81066, -59.204563}; lite::Tensor output0(kNumberTypeFloat32, {1, 2, 2, 3}); - output0.SetData(output.data()); + output0.set_data(output.data()); std::vector outputs_tensor = {&output0}; kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_FusedBatchNorm}; @@ -127,12 +127,12 @@ TEST_F(TestBatchnormFp32, FusedBNTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0.ElementsNum(), 0.001); - input0.SetData(nullptr); - input1.SetData(nullptr); - input2.SetData(nullptr); - input3.SetData(nullptr); - input4.SetData(nullptr); - output0.SetData(nullptr); + input0.set_data(nullptr); + input1.set_data(nullptr); + input2.set_data(nullptr); + input3.set_data(nullptr); + input4.set_data(nullptr); + output0.set_data(nullptr); } TEST_F(TestBatchnormFp32, easyTest) { @@ -147,9 +147,9 @@ TEST_F(TestBatchnormFp32, easyTest) { lite::Tensor input0(kNumberTypeFloat32, {1, 1, 6, 2}); lite::Tensor input1(kNumberTypeFloat32, {2}); lite::Tensor input2(kNumberTypeFloat32, {2}); - input0.SetData(in_data.data()); - input1.SetData(in_data1.data()); - input2.SetData(in_data2.data()); + input0.set_data(in_data.data()); + input1.set_data(in_data1.data()); + input2.set_data(in_data2.data()); std::vector inputs_tensor = {&input0, &input1, &input2}; std::vector output(12); @@ -157,7 +157,7 @@ TEST_F(TestBatchnormFp32, easyTest) { -0.63498, -2.29971, -1.21223, -2.79965, -1.78949, -3.29959}; lite::Tensor output0(kNumberTypeFloat32, {1, 1, 6, 2}); - output0.SetData(output.data()); + output0.set_data(output.data()); std::vector outputs_tensor = {&output0}; kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_BatchNorm}; @@ -178,10 +178,10 @@ TEST_F(TestBatchnormFp32, easyTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0.ElementsNum(), 0.001); - input0.SetData(nullptr); - input1.SetData(nullptr); - input2.SetData(nullptr); - output0.SetData(nullptr); + input0.set_data(nullptr); + input1.set_data(nullptr); + input2.set_data(nullptr); + output0.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/constant_of_shape_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/constant_of_shape_fp32_test.cc index 636b438086..532cb9b71e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/constant_of_shape_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/constant_of_shape_fp32_test.cc @@ -27,7 +27,7 @@ class TestConstantOfShapeFp32 : public mindspore::CommonTest { int ConstantOfShapeTestInit(std::vector *inputs_, std::vector *outputs_, float *a_ptr, std::vector a_shape) { - auto in_t = new lite::Tensor(kNumberTypeInt32, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeInt32, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), a_ptr, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); @@ -36,7 +36,7 @@ int ConstantOfShapeTestInit(std::vector *inputs_, std::vector
  • MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/conv1x1_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/conv1x1_fp32_tests.cc index d44a67eeb2..d16e4a0922 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/conv1x1_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/conv1x1_fp32_tests.cc @@ -172,8 +172,7 @@ TEST_F(TestConv1x1Fp32, Conv1x1WeightTest1) { int Conv1x1TestInit1(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, float **correct) { - lite::Tensor *in_t = new lite::Tensor(kNumberTypeFloat, {1, 2, 3, 4}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *in_t = new lite::Tensor(kNumberTypeFloat, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::VAR); in_t->MallocData(); float in[] = {12.216284, 3.3466918, 15.327419, 5.234958, 0.804376, 9.952188, 14.727955, -8.080715, 13.71383, 8.055829, 6.5845337, -9.25232, -4.24519, 11.550042, 9.262012, 1.2780352, @@ -181,23 +180,21 @@ int Conv1x1TestInit1(std::vector *inputs_, std::vectorMutableData(), in, sizeof(float) * 24); inputs_->push_back(in_t); - lite::Tensor *weight_t = new lite::Tensor(kNumberTypeFloat, {3, 1, 1, 4}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *weight_t = + new lite::Tensor(kNumberTypeFloat, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::CONST_TENSOR); weight_t->MallocData(); float weight[] = {-0.7308652, 0.5257509, -0.87825793, -1.123181, -1.2206168, 0.562695, 1.5382664, -0.5020635, 0.8591602, -0.26410004, 1.1262615, 0.073132955}; /* nhwc */ memcpy(weight_t->MutableData(), weight, sizeof(float) * 12); inputs_->push_back(weight_t); - lite::Tensor *bias_t = new lite::Tensor(kNumberTypeFloat, {3}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *bias_t = new lite::Tensor(kNumberTypeFloat, {3}, schema::Format_NHWC, lite::Tensor::CONST_TENSOR); bias_t->MallocData(); float bias[] = {2, 2, 2}; memcpy(bias_t->MutableData(), bias, sizeof(float) * 3); inputs_->push_back(bias_t); - lite::Tensor *out_t = new lite::Tensor(kNumberTypeFloat, {1, 2, 3, 3}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *out_t = new lite::Tensor(kNumberTypeFloat, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::VAR); out_t->MallocData(); outputs_->push_back(out_t); @@ -239,32 +236,29 @@ TEST_F(TestConv1x1Fp32, Conv1x1Test1) { int Conv1x1TestInit2(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, float **correct) { size_t buffer_size; - lite::Tensor *in_t = new lite::Tensor(kNumberTypeFloat, {1, 300, 300, 24}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *in_t = new lite::Tensor(kNumberTypeFloat, {1, 300, 300, 24}, schema::Format_NHWC, lite::Tensor::VAR); in_t->MallocData(); std::string input_path = "./conv/conv1x1fp32_input1_nhwc.bin"; auto in = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &buffer_size)); memcpy(in_t->MutableData(), in, buffer_size); inputs_->push_back(in_t); - lite::Tensor *weight_t = new lite::Tensor(kNumberTypeFloat, {40, 1, 1, 24}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *weight_t = + new lite::Tensor(kNumberTypeFloat, {40, 1, 1, 24}, schema::Format_NHWC, lite::Tensor::CONST_TENSOR); weight_t->MallocData(); std::string weight_path = "./conv/conv1x1fp32_weight1_nhwc.bin"; auto weight = reinterpret_cast(mindspore::lite::ReadFile(weight_path.c_str(), &buffer_size)); memcpy(weight_t->MutableData(), weight, buffer_size); inputs_->push_back(weight_t); - lite::Tensor *bias_t = new lite::Tensor(kNumberTypeFloat, {40}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *bias_t = new lite::Tensor(kNumberTypeFloat, {40}, schema::Format_NHWC, lite::Tensor::CONST_TENSOR); bias_t->MallocData(); std::string bias_path = "./conv/conv1x1fp32_bias1_nhwc.bin"; auto bias = mindspore::lite::ReadFile(bias_path.c_str(), &buffer_size); memcpy(bias_t->MutableData(), bias, buffer_size); inputs_->push_back(bias_t); - lite::Tensor *out_t = new lite::Tensor(kNumberTypeFloat, {1, 300, 300, 40}, schema::Format_NHWC, - lite::TensorCategory(static_cast(1))); + lite::Tensor *out_t = new lite::Tensor(kNumberTypeFloat, {1, 300, 300, 40}, schema::Format_NHWC, lite::Tensor::VAR); out_t->MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/crop_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/crop_fp32_test.cc index 07505c577e..f91b9c08c6 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/crop_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/crop_fp32_test.cc @@ -257,12 +257,12 @@ TEST_F(CropTestFp32, CropTest11) { std::vector out_shape = {1, 4, 2, 2}; std::vector inputs; std::vector outputs; - auto in_t = new lite::Tensor(kNumberTypeFloat, in_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeFloat, in_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), input, sizeof(float) * in_t->ElementsNum()); inputs.push_back(in_t); - auto out_t = new lite::Tensor(kNumberTypeFloat, out_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto out_t = new lite::Tensor(kNumberTypeFloat, out_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs.push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/deconvolution_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/deconvolution_fp32_tests.cc index f1929e1abd..6f08977780 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/deconvolution_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/deconvolution_fp32_tests.cc @@ -324,7 +324,7 @@ int DeConvTestInit1(std::vector *inputs_, std::vector in_dims_nhwc = {1, 5, 7, 2}; lite::Tensor *in_t = - new lite::Tensor(kNumberTypeFloat, in_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::CONST); + new lite::Tensor(kNumberTypeFloat, in_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); float in_nchw[] = { 0.39451003, 0.15045597, 0.5367726, 0.62690735, 0.113554195, 0.5402554, 0.5522764, 0.044319753, 0.25721782, @@ -340,7 +340,7 @@ int DeConvTestInit1(std::vector *inputs_, std::vector weight_dims_nhwc = {2, 3, 3, 6}; lite::Tensor *weight_t = - new lite::Tensor(kNumberTypeFloat, weight_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::CONST); + new lite::Tensor(kNumberTypeFloat, weight_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); float weight_nchw[] = { 0.061163727, -0.06261389, 0.07708351, -0.019354159, -0.3859104, -0.082844816, -0.21268463, -0.15746808, @@ -361,7 +361,8 @@ int DeConvTestInit1(std::vector *inputs_, std::vectorChannel()); inputs_->push_back(weight_t); - lite::Tensor *bias_t = new lite::Tensor(kNumberTypeFloat, {6}, schema::Format_NHWC, lite::Tensor::Category::CONST); + lite::Tensor *bias_t = + new lite::Tensor(kNumberTypeFloat, {6}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); bias_t->MallocData(); float bias[] = {-0.19064677, -0.0034778118, 0.63741624, -1.0311537, -1.0288948, 0.71384084}; memcpy(bias_t->MutableData(), bias, sizeof(float) * 6); @@ -369,7 +370,7 @@ int DeConvTestInit1(std::vector *inputs_, std::vector output_nhwc_dims = {1, 9, 13, 6}; lite::Tensor *out_t = - new lite::Tensor(kNumberTypeFloat, output_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST); + new lite::Tensor(kNumberTypeFloat, output_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); @@ -497,7 +498,7 @@ TEST_F(TestDeConvolutionFp32, DeConvTest1) { int DeConvTestInit2(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, float **correct) { - auto *in_t = new lite::Tensor(kNumberTypeFloat, {1, 4, 2, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *in_t = new lite::Tensor(kNumberTypeFloat, {1, 4, 2, 3}, schema::Format_NHWC, lite::Tensor::Category::VAR); in_t->MallocData(); float in[] = {7.7566547, 19.250782, 17.923292, 13.584222, 3.3293908, 9.734102, 18.83455, -1.5142503, -0.29382008, 18.686155, 0.087307654, 4.2010098, -2.2539594, 4.1795673, 13.142356, -3.5939367, @@ -505,7 +506,8 @@ int DeConvTestInit2(std::vector *inputs_, std::vectorMutableData(), in, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - auto *weight_t = new lite::Tensor(kNumberTypeFloat, {3, 3, 3, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *weight_t = + new lite::Tensor(kNumberTypeFloat, {3, 3, 3, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); float weight[] = {-0.39557076, 0.15087655, 0.35216075, -0.20893791, 0.28683448, 0.08006268, 0.9830812, 0.27212173, 0.5171944, -0.0014505, 0.78694165, 0.25425306, 0.16605458, -0.06127124, @@ -519,7 +521,7 @@ int DeConvTestInit2(std::vector *inputs_, std::vectorpush_back(weight_t); std::vector out_nhwc_dims = {1, 7, 3, 2}; - auto *out_t = new lite::Tensor(kNumberTypeFloat, out_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *out_t = new lite::Tensor(kNumberTypeFloat, out_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::VAR); out_t->MallocData(); outputs_->push_back(out_t); @@ -564,7 +566,7 @@ TEST_F(TestDeConvolutionFp32, DeConvTest2) { int DeConvTestInit3(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, float **correct) { std::vector in_dims_nhwc = {1, 3, 3, 2}; - auto *in_t = new lite::Tensor(kNumberTypeFloat, in_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *in_t = new lite::Tensor(kNumberTypeFloat, in_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::VAR); in_t->MallocData(); float in_nchw[] = {0.10411751, 0.24034509, 0.71456534, 0.75286126, 0.9778457, 0.21043599, 0.26498786, 0.6701024, 0.9744634, 0.49075702, 0.03877404, 0.48646277, @@ -574,8 +576,8 @@ int DeConvTestInit3(std::vector *inputs_, std::vectorpush_back(in_t); std::vector w_dims_nhwc = {2, 2, 2, 2}; - auto *weight_t = new lite::Tensor(kNumberTypeFloat, w_dims_nhwc, schema::Format_NHWC, - lite::TensorCategory(schema::NodeType_Parameter)); + auto *weight_t = + new lite::Tensor(kNumberTypeFloat, w_dims_nhwc, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); float w_nchw[] = {-0.108016446, -0.44254777, 0.29249913, 0.18764605, 1.1250675, 0.29441583, -0.34362152, 0.7557833, 0.16503833, 0.2418737, -0.26612744, 0.5072577, @@ -585,8 +587,7 @@ int DeConvTestInit3(std::vector *inputs_, std::vectorpush_back(weight_t); std::vector out_dims_nhwc = {1, 9, 9, 2}; - auto *out_t = new lite::Tensor(kNumberTypeFloat, out_dims_nhwc, schema::Format_NC4HW4, - lite::TensorCategory(schema::NodeType_Parameter)); + auto *out_t = new lite::Tensor(kNumberTypeFloat, out_dims_nhwc, schema::Format_NC4HW4, lite::Tensor::Category::VAR); out_t->MallocData(); outputs_->push_back(out_t); @@ -644,7 +645,7 @@ int DeConvTestInit4(std::vector *inputs_, std::vector in_nhwc_dims = {1, 300, 300, 30}; - auto *in_t = new lite::Tensor(kNumberTypeFloat, in_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *in_t = new lite::Tensor(kNumberTypeFloat, in_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::VAR); in_t->MallocData(); std::string in_nhwc_path = "./deconv/deconv_fp32_nhwc_input1.bin"; auto in_nhwc = reinterpret_cast(mindspore::lite::ReadFile(in_nhwc_path.c_str(), &buffer_size)); @@ -652,7 +653,8 @@ int DeConvTestInit4(std::vector *inputs_, std::vectorpush_back(in_t); std::vector w_nhwc_dims = {30, 3, 3, 40}; - auto *weight_t = new lite::Tensor(kNumberTypeFloat, w_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *weight_t = + new lite::Tensor(kNumberTypeFloat, w_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); std::string weight_path = "./deconv/deconv_fp32_nchw_weight1.bin"; auto weight_nchw = reinterpret_cast(mindspore::lite::ReadFile(weight_path.c_str(), &buffer_size)); @@ -660,7 +662,7 @@ int DeConvTestInit4(std::vector *inputs_, std::vectorChannel()); inputs_->push_back(weight_t); - auto *bias_t = new lite::Tensor(kNumberTypeFloat, {40}, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *bias_t = new lite::Tensor(kNumberTypeFloat, {40}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); bias_t->MallocData(); std::string bias_path = "./deconv/deconv_fp32_nchw_bias1.bin"; auto bias = mindspore::lite::ReadFile(bias_path.c_str(), &buffer_size); @@ -668,7 +670,7 @@ int DeConvTestInit4(std::vector *inputs_, std::vectorpush_back(bias_t); std::vector out_nhwc_dims = {1, 302, 302, 40}; - auto *out_t = new lite::Tensor(kNumberTypeFloat, out_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto *out_t = new lite::Tensor(kNumberTypeFloat, out_nhwc_dims, schema::Format_NHWC, lite::Tensor::Category::VAR); out_t->MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/elu_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/elu_fp32_test.cc index 21311edc7a..f246a3f008 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/elu_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/elu_fp32_test.cc @@ -30,13 +30,14 @@ class TestEluFp32 : public mindspore::CommonTest { }; void EluTestInit(std::vector *inputs_, std::vector *outputs_, EluParameter *elu_param) { - Tensor *in_t_first = new Tensor(kNumberTypeFloat32, {6, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t_first = + new Tensor(kNumberTypeFloat32, {6, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t_first->MallocData(); float in_first[] = {-1, 2, -3, 4, -5, 6, -7, 8, -9, 10, -11, 0}; memcpy(in_t_first->MutableData(), in_first, sizeof(float) * in_t_first->ElementsNum()); inputs_->push_back(in_t_first); - Tensor *outputs_t = new Tensor(kNumberTypeFloat32, {6, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *outputs_t = new Tensor(kNumberTypeFloat32, {6, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); outputs_t->MallocData(); outputs_->push_back(outputs_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/embedding_lookup_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/embedding_lookup_fp32_test.cc index 3566063e5f..367c502140 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/embedding_lookup_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/embedding_lookup_fp32_test.cc @@ -31,25 +31,28 @@ class TestEmbeddingLookupFp32 : public mindspore::CommonTest { void ElTestInit(std::vector *inputs_, std::vector *outputs_, EmbeddingLookupParameter *embedding_lookup_param) { - Tensor *in_t_first = new Tensor(kNumberTypeFloat32, {6, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t_first = + new Tensor(kNumberTypeFloat32, {6, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t_first->MallocData(); float in_first[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; memcpy(in_t_first->MutableData(), in_first, sizeof(float) * in_t_first->ElementsNum()); inputs_->push_back(in_t_first); - Tensor *in_t_second = new Tensor(kNumberTypeFloat32, {4, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t_second = + new Tensor(kNumberTypeFloat32, {4, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t_second->MallocData(); float in_second[] = {1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8}; memcpy(in_t_second->MutableData(), in_second, sizeof(float) * in_t_second->ElementsNum()); inputs_->push_back(in_t_second); - Tensor *ids_t = new Tensor(kNumberTypeFloat32, {2, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *ids_t = new Tensor(kNumberTypeFloat32, {2, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); ids_t->MallocData(); int ids[] = {1, 9, 2, 4, 6, 7}; memcpy(ids_t->MutableData(), ids, sizeof(int) * ids_t->ElementsNum()); inputs_->push_back(ids_t); - Tensor *outputs_t = new Tensor(kNumberTypeInt32, {2, 3, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *outputs_t = + new Tensor(kNumberTypeInt32, {2, 3, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); outputs_t->MallocData(); outputs_->push_back(outputs_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/fullconnection_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/fullconnection_fp32_tests.cc index 4d8a6b119c..818c7a4c7f 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/fullconnection_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/fullconnection_fp32_tests.cc @@ -33,14 +33,14 @@ class TestFcFp32 : public mindspore::CommonTest { int FcTestInit1(std::vector *inputs_, std::vector *outputs_, MatMulParameter *matmal_param, float **correct) { - Tensor *in_t = new Tensor(kNumberTypeFloat, {2, 2, 2, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t = new Tensor(kNumberTypeFloat, {2, 2, 2, 2}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); float in[] = {-3.2366564, -4.7733846, -7.8329225, 16.146885, 5.060793, -6.1471, -1.7680453, -6.5721383, 17.87506, -5.1192183, 10.742863, 1.4536934, 19.693445, 19.45783, 5.063163, 0.5234792}; memcpy(in_t->MutableData(), in, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - Tensor *weight_t = new Tensor(kNumberTypeFloat, {3, 8}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *weight_t = new Tensor(kNumberTypeFloat, {3, 8}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); float weight[] = {-0.0024438887, 0.0006738146, -0.008169129, 0.0021510671, -0.012470592, -0.0053063435, 0.006050155, 0.008656233, 0.012911413, -0.0028635843, -0.00034080597, -0.0010622552, @@ -49,13 +49,13 @@ int FcTestInit1(std::vector *inputs_, std::vectorMutableData(), weight, sizeof(float) * weight_t->ElementsNum()); inputs_->push_back(weight_t); - Tensor *bias_t = new Tensor(kNumberTypeFloat, {3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *bias_t = new Tensor(kNumberTypeFloat, {3}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); bias_t->MallocData(); float bias[] = {1.6103756, -0.9872417, 0.546849}; memcpy(bias_t->MutableData(), bias, sizeof(float) * bias_t->ElementsNum()); inputs_->push_back(bias_t); - Tensor *out_t = new Tensor(kNumberTypeFloat, {2, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *out_t = new Tensor(kNumberTypeFloat, {2, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); @@ -91,28 +91,29 @@ int FcTestInit2(std::vector *inputs_, std::vectorMallocData(); std::string in_path = "./matmul/FcFp32_input1.bin"; auto in_data = mindspore::lite::ReadFile(in_path.c_str(), &buffer_size); memcpy(in_t->MutableData(), in_data, buffer_size); inputs_->push_back(in_t); - Tensor *weight_t = new Tensor(kNumberTypeFloat, {30, 80}, schema::Format_NCHW, lite::Tensor::Category::CONST); + Tensor *weight_t = new Tensor(kNumberTypeFloat, {30, 80}, schema::Format_NCHW, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); std::string weight_path = "./matmul/FcFp32_weight1.bin"; auto w_data = mindspore::lite::ReadFile(weight_path.c_str(), &buffer_size); memcpy(weight_t->MutableData(), w_data, buffer_size); inputs_->push_back(weight_t); - Tensor *bias_t = new Tensor(kNumberTypeFloat, {30}, schema::Format_NCHW, lite::Tensor::Category::CONST); + Tensor *bias_t = new Tensor(kNumberTypeFloat, {30}, schema::Format_NCHW, lite::Tensor::Category::CONST_TENSOR); bias_t->MallocData(); std::string bias_path = "./matmul/FcFp32_bias1.bin"; auto bias_data = mindspore::lite::ReadFile(bias_path.c_str(), &buffer_size); memcpy(bias_t->MutableData(), bias_data, buffer_size); inputs_->push_back(bias_t); - Tensor *out_t = new Tensor(kNumberTypeFloat, {20, 30}, schema::Format_NCHW, lite::Tensor::Category::CONST); + Tensor *out_t = new Tensor(kNumberTypeFloat, {20, 30}, schema::Format_NCHW, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); @@ -147,13 +148,13 @@ TEST_F(TestFcFp32, FcTest2) { int FcTestInit3(std::vector *inputs_, std::vector *outputs_, MatMulParameter *matmal_param, float **correct) { - Tensor *in_t = new Tensor(kNumberTypeFloat, {1, 1, 1, 20}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t = new Tensor(kNumberTypeFloat, {1, 1, 1, 20}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); float in[] = {1, 0, 3, 0, 4, 5, 2, 5, 2, 5, 1, 5, 0, 1, 2, 0, 2, 1, 0, 5}; memcpy(in_t->MutableData(), in, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - Tensor *weight_t = new Tensor(kNumberTypeFloat, {16, 20}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *weight_t = new Tensor(kNumberTypeFloat, {16, 20}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); float weight[] = {0, 5, 5, 3, 0, 5, 3, 1, 0, 1, 3, 0, 5, 5, 2, 4, 0, 1, 1, 2, 3, 0, 5, 5, 4, 4, 1, 4, 1, 1, 5, 3, 3, 1, 0, 3, 1, 2, 4, 5, 3, 4, 4, 0, 3, 5, 0, 3, 4, 1, 0, 1, 3, 4, 0, 5, 2, 5, 0, 4, 2, 2, 2, 2, @@ -168,7 +169,7 @@ int FcTestInit3(std::vector *inputs_, std::vectorMutableData(), weight, sizeof(float) * weight_t->ElementsNum()); inputs_->push_back(weight_t); - Tensor *out_t = new Tensor(kNumberTypeFloat, {1, 16}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *out_t = new Tensor(kNumberTypeFloat, {1, 16}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/instance_norm_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/instance_norm_fp32_tests.cc index dcbf701e0c..5b4c1a2084 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/instance_norm_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/instance_norm_fp32_tests.cc @@ -39,9 +39,9 @@ TEST_F(TestInstanceNormFp32, INTest1) { lite::Tensor input0_tensor(kNumberTypeFloat32, {1, 2, 2, 3}); lite::Tensor input1_tensor(kNumberTypeFloat32, {3}); lite::Tensor input2_tensor(kNumberTypeFloat32, {3}); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); - input2_tensor.SetData(in_data2.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); + input2_tensor.set_data(in_data2.data()); std::vector inputs_tensor = {&input0_tensor, &input1_tensor, &input2_tensor}; std::vector output(12); @@ -49,7 +49,7 @@ TEST_F(TestInstanceNormFp32, INTest1) { -3.5422924, -14.005781, -2.3525476, -6.7113695, -16.396551, -1.4275324}; lite::Tensor output0_tensor(kNumberTypeFloat32, {1, 2, 2, 3}); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); std::vector outputs_tensor = {&output0_tensor}; kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_InstanceNorm}; @@ -71,10 +71,10 @@ TEST_F(TestInstanceNormFp32, INTest1) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - input2_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + input2_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestInstanceNormFp32, INTest2) { @@ -92,9 +92,9 @@ TEST_F(TestInstanceNormFp32, INTest2) { lite::Tensor input0_tensor(kNumberTypeFloat32, {2, 2, 2, 3}); lite::Tensor input1_tensor(kNumberTypeFloat32, {6}); lite::Tensor input2_tensor(kNumberTypeFloat32, {6}); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); - input2_tensor.SetData(in_data2.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); + input2_tensor.set_data(in_data2.data()); std::vector inputs_tensor = {&input0_tensor, &input1_tensor, &input2_tensor}; std::vector output(24); @@ -104,7 +104,7 @@ TEST_F(TestInstanceNormFp32, INTest2) { -3.5422924, -14.005781, -2.3525476, -6.7113695, -16.396551, -1.4275324}; lite::Tensor output0_tensor(kNumberTypeFloat32, {2, 2, 2, 3}); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); std::vector outputs_tensor = {&output0_tensor}; kernel::KernelKey desc = {kernel::KERNEL_ARCH::kCPU, kNumberTypeFloat32, schema::PrimitiveType_InstanceNorm}; @@ -126,9 +126,9 @@ TEST_F(TestInstanceNormFp32, INTest2) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - input2_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + input2_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/l2norm_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/l2norm_fp32_test.cc index cb9ef486ae..6cfbc3cac3 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/l2norm_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/l2norm_fp32_test.cc @@ -42,8 +42,8 @@ class TestL2NormFp32 : public mindspore::CommonTest { }; void TestL2NormFp32::TearDown() { - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestL2NormFp32::Init(const std::vector &input_shape, const std::vector &output_shape, float *input_data, @@ -53,8 +53,8 @@ void TestL2NormFp32::Init(const std::vector &input_shape, const std::vector in_tensor_.set_shape(input_shape); out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(output_shape); - in_tensor_.SetData(input_data); - out_tensor_.SetData(output_data); + in_tensor_.set_data(input_data); + out_tensor_.set_data(output_data); param_.axis_num_ = axis_num; if (axis_num == 1) { diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/lsh_projection_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/lsh_projection_fp32_tests.cc index e8d4996377..54fdc5bc1c 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/lsh_projection_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/lsh_projection_fp32_tests.cc @@ -45,10 +45,10 @@ TEST_F(TestLshProjectionFp32, Dense1DInputs) { int32_t input_data1[] = {12345, 54321, 67890, 9876, -12345678}; float input_data2[] = {1.0, 1.0, 1.0, 1.0, 1.0}; int32_t output_data[6] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - in_tensor2.SetData(input_data2); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + in_tensor2.set_data(input_data2); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1, &in_tensor2}; std::vector outputs = {&out_tensor}; @@ -73,9 +73,9 @@ TEST_F(TestLshProjectionFp32, Dense1DInputs) { PrintData("output data", output_data, 6); CompareOutputData(output_data, except_result.data(), 6, 0.000001); - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } TEST_F(TestLshProjectionFp32, Sparse1DInputs) { @@ -86,9 +86,9 @@ TEST_F(TestLshProjectionFp32, Sparse1DInputs) { float input_data0[] = {0.123, 0.456, -0.321, 1.234, 5.678, -4.321}; int32_t input_data1[] = {12345, 54321, 67890, 9876, -12345678}; int32_t output_data[3] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1}; std::vector outputs = {&out_tensor}; @@ -113,9 +113,9 @@ TEST_F(TestLshProjectionFp32, Sparse1DInputs) { PrintData("output data", output_data, 3); CompareOutputData(output_data, except_result.data(), 3, 0.000001); - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } TEST_F(TestLshProjectionFp32, Sparse3DInputs) { @@ -129,10 +129,10 @@ TEST_F(TestLshProjectionFp32, Sparse3DInputs) { 9123, 7890, -987, -876, -765, -987, -543, -432, -321, -543}; float input_data2[] = {0.12, 0.34, 0.56, 0.67, 0.78}; int32_t output_data[3] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - in_tensor2.SetData(input_data2); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + in_tensor2.set_data(input_data2); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1, &in_tensor2}; std::vector outputs = {&out_tensor}; @@ -157,8 +157,8 @@ TEST_F(TestLshProjectionFp32, Sparse3DInputs) { PrintData("output data", output_data, 3); CompareOutputData(output_data, except_result.data(), 3, 0.000001); - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/matmul_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/matmul_fp32_tests.cc index f4193f9d4c..882372fb62 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/matmul_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/matmul_fp32_tests.cc @@ -70,17 +70,18 @@ TEST_F(TestMatMulFp32, Row2Col8Test2) { int MMTestInit(std::vector *inputs_, std::vector *outputs_, float *a_ptr, float *b_ptr, std::vector a_shape, std::vector b_shape, std::vector c_shape) { - auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), a_ptr, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - auto weight_t = new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto weight_t = + new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); memcpy(weight_t->MutableData(), b_ptr, sizeof(float) * weight_t->ElementsNum()); inputs_->push_back(weight_t); - auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); @@ -90,22 +91,24 @@ int MMTestInit(std::vector *inputs_, std::vector int MMTestInit2(std::vector *inputs_, std::vector *outputs_, float *a_ptr, float *b_ptr, float *bias_ptr, std::vector a_shape, std::vector b_shape, std::vector bias_shape, std::vector c_shape) { - auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), a_ptr, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - auto weight_t = new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto weight_t = + new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); memcpy(weight_t->MutableData(), b_ptr, sizeof(float) * weight_t->ElementsNum()); inputs_->push_back(weight_t); - auto bias_t = new lite::Tensor(kNumberTypeFloat, bias_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto bias_t = + new lite::Tensor(kNumberTypeFloat, bias_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); bias_t->MallocData(); memcpy(bias_t->MutableData(), bias_ptr, sizeof(float) * bias_t->ElementsNum()); inputs_->push_back(bias_t); - auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/non_max_suppression_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/non_max_suppression_fp32_tests.cc index 989686aedf..432d86fd8e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/non_max_suppression_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/non_max_suppression_fp32_tests.cc @@ -51,11 +51,11 @@ class TestNMSFp32 : public mindspore::CommonTest { }; void TestNMSFp32::TearDown() { - box_tensor_.SetData(nullptr); - score_tensor_.SetData(nullptr); - max_output_box_per_class_tensor_.SetData(nullptr); - iou_threshold_tensor_.SetData(nullptr); - score_threshold_tensor_.SetData(nullptr); + box_tensor_.set_data(nullptr); + score_tensor_.set_data(nullptr); + max_output_box_per_class_tensor_.set_data(nullptr); + iou_threshold_tensor_.set_data(nullptr); + score_threshold_tensor_.set_data(nullptr); out_tensor_.FreeData(); } @@ -65,19 +65,19 @@ void TestNMSFp32::Init(const std::vector &box_tensor_shape, float *box_data box_tensor_.set_data_type(kNumberTypeFloat32); box_tensor_.SetFormat(Format_NHWC); box_tensor_.set_shape(box_tensor_shape); - box_tensor_.SetData(box_data); + box_tensor_.set_data(box_data); score_tensor_.set_data_type(kNumberTypeFloat32); score_tensor_.SetFormat(Format_NHWC); score_tensor_.set_shape(score_tensor_shape); - score_tensor_.SetData(score_data); + score_tensor_.set_data(score_data); max_output_ = max_output; - max_output_box_per_class_tensor_.SetData(&max_output_); + max_output_box_per_class_tensor_.set_data(&max_output_); iou_threshold_ = iou_threshold; - iou_threshold_tensor_.SetData(&iou_threshold_); + iou_threshold_tensor_.set_data(&iou_threshold_); score_threshold_ = score_threshold; - score_threshold_tensor_.SetData(&score_threshold_); + score_threshold_tensor_.set_data(&score_threshold_); out_tensor_.set_data_type(kNumberTypeInt32); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/pad_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/pad_fp32_test.cc index fdff2be2aa..03c0e2a2ab 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/pad_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/pad_fp32_test.cc @@ -52,9 +52,9 @@ class TestPadFp32 : public mindspore::CommonTest { }; void TestPadFp32::TearDown() { - paddings_tensor_.SetData(nullptr); - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + paddings_tensor_.set_data(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestPadFp32::Prepare(const std::vector &input_shape, const std::vector &output_shape, float *input_data, @@ -65,8 +65,8 @@ void TestPadFp32::Prepare(const std::vector &input_shape, const std::vector in_tensor_.set_shape(input_shape); out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(output_shape); - in_tensor_.SetData(input_data); - out_tensor_.SetData(output_data); + in_tensor_.set_data(input_data); + out_tensor_.set_data(output_data); param_.pad_mode_ = static_cast(mode); if (mode == PaddingMode_CONSTANT) { @@ -78,7 +78,7 @@ void TestPadFp32::Prepare(const std::vector &input_shape, const std::vector } else { paddings_tensor_.set_data_type(kNumberTypeInt32); paddings_tensor_.set_shape({4, 2}); - paddings_tensor_.SetData(paddings); + paddings_tensor_.set_data(paddings); inputs_.emplace_back(&paddings_tensor_); } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/power_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/power_fp32_tests.cc index cc58a1dda9..2630b2c99b 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/power_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/power_fp32_tests.cc @@ -27,17 +27,18 @@ class TestPowerFp32 : public mindspore::CommonTest { int PowerTestInit(std::vector *inputs_, std::vector *outputs_, float *a_ptr, float *b_ptr, std::vector a_shape, std::vector b_shape, std::vector c_shape) { - auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), a_ptr, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - auto weight_t = new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto weight_t = + new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); memcpy(weight_t->MutableData(), b_ptr, sizeof(float) * weight_t->ElementsNum()); inputs_->push_back(weight_t); - auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); @@ -46,12 +47,12 @@ int PowerTestInit(std::vector *inputs_, std::vector *inputs_, std::vector *outputs_, float *a_ptr, std::vector a_shape, std::vector c_shape) { - auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), a_ptr, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reduce_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reduce_fp32_tests.cc index 2c58cf6ee2..55fd7bc90e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reduce_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reduce_fp32_tests.cc @@ -61,8 +61,8 @@ class TestReduceFp32 : public mindspore::CommonTest { void TestReduceFp32::TearDown() { delete ctx_; - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestReduceFp32::Prepare(const std::vector &in_shape, const std::vector &out_shape, float *input_data, @@ -70,11 +70,11 @@ void TestReduceFp32::Prepare(const std::vector &in_shape, const std::vector bool reduce_to_end, float coeff) { in_tensor_.set_data_type(kNumberTypeFloat32); in_tensor_.set_shape(in_shape); - in_tensor_.SetData(input_data); + in_tensor_.set_data(input_data); out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(out_shape); - out_tensor_.SetData(output_data); + out_tensor_.set_data(output_data); param_.mode_ = static_cast(mode); param_.num_axes_ = num_axes; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_bilinear_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_bilinear_fp32_tests.cc index 35b9effe61..9d78eca946 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_bilinear_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_bilinear_fp32_tests.cc @@ -46,8 +46,8 @@ class TestResizeBilinearFp32 : public mindspore::CommonTest { }; void TestResizeBilinearFp32::TearDown() { - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestResizeBilinearFp32::Prepare(const std::vector &input_shape, const std::vector &output_shape, @@ -58,8 +58,8 @@ void TestResizeBilinearFp32::Prepare(const std::vector &input_shape, const in_tensor_.set_shape(input_shape); out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(output_shape); - in_tensor_.SetData(input_data); - out_tensor_.SetData(output_data); + in_tensor_.set_data(input_data); + out_tensor_.set_data(output_data); ResizeParameter param_ = { {}, static_cast(schema::ResizeMethod_LINEAR), output_shape[1], output_shape[2], align_corners}; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_nearest_neighbor_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_nearest_neighbor_fp32_tests.cc index 30eec684a5..81c6b3c4b4 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_nearest_neighbor_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/resize_nearest_neighbor_fp32_tests.cc @@ -42,8 +42,8 @@ class TestResizeNearestNeighborFp32 : public mindspore::CommonTest { }; void TestResizeNearestNeighborFp32::TearDown() { - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestResizeNearestNeighborFp32::Prepare(const std::vector &input_shape, const std::vector &output_shape, @@ -53,8 +53,8 @@ void TestResizeNearestNeighborFp32::Prepare(const std::vector &input_shape, in_tensor_.set_shape(input_shape); out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(output_shape); - in_tensor_.SetData(input_data); - out_tensor_.SetData(output_data); + in_tensor_.set_data(input_data); + out_tensor_.set_data(output_data); ResizeParameter param_ = { {}, static_cast(schema::ResizeMethod_NEAREST), output_shape[1], output_shape[2], align_corners}; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reverse_sequence_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reverse_sequence_fp32_tests.cc index 6791b8e55d..31c6820f10 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reverse_sequence_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/reverse_sequence_fp32_tests.cc @@ -35,9 +35,9 @@ TEST_F(TestReverseSequenceFp32, BatchLessSeq) { 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47}; int input_data1[] = {2, 3, 4}; float output_data[2 * 3 * 4 * 2] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1}; std::vector outputs = {&out_tensor}; @@ -65,9 +65,9 @@ TEST_F(TestReverseSequenceFp32, BatchLessSeq) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } TEST_F(TestReverseSequenceFp32, BatchGreaterSeq) { @@ -79,9 +79,9 @@ TEST_F(TestReverseSequenceFp32, BatchGreaterSeq) { 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47}; int input_data1[] = {2, 3, 3, 2}; float output_data[2 * 3 * 4 * 2] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1}; std::vector outputs = {&out_tensor}; @@ -109,9 +109,9 @@ TEST_F(TestReverseSequenceFp32, BatchGreaterSeq) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } TEST_F(TestReverseSequenceFp32, BatchSeqNotAdjacent) { @@ -123,9 +123,9 @@ TEST_F(TestReverseSequenceFp32, BatchSeqNotAdjacent) { 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47}; int input_data1[] = {2, 4}; float output_data[2 * 3 * 4 * 2] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1}; std::vector outputs = {&out_tensor}; @@ -153,8 +153,8 @@ TEST_F(TestReverseSequenceFp32, BatchSeqNotAdjacent) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/roi_pooling_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/roi_pooling_fp32_tests.cc index b40b8d429d..361dfd0984 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/roi_pooling_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/roi_pooling_fp32_tests.cc @@ -27,17 +27,17 @@ class TestROIPoolingFp32 : public mindspore::CommonTest { int ROIPoolingTestInit(std::vector *inputs_, std::vector *outputs_, float *a_ptr, float *b_ptr, std::vector a_shape, std::vector b_shape, std::vector c_shape) { - auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto in_t = new lite::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); in_t->MallocData(); memcpy(in_t->MutableData(), a_ptr, sizeof(float) * in_t->ElementsNum()); inputs_->push_back(in_t); - auto roi_t = new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto roi_t = new lite::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); roi_t->MallocData(); memcpy(roi_t->MutableData(), b_ptr, sizeof(float) * roi_t->ElementsNum()); inputs_->push_back(roi_t); - auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto out_t = new lite::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); outputs_->push_back(out_t); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/scale_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/scale_fp32_tests.cc index ff81660faa..08a9e93b0c 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/scale_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/scale_fp32_tests.cc @@ -55,10 +55,10 @@ class TestScaleFp32 : public mindspore::CommonTest { }; void TestScaleFp32::TearDown() { - in_tensor_.SetData(nullptr); - scale_tensor_.SetData(nullptr); - offset_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + scale_tensor_.set_data(nullptr); + offset_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestScaleFp32::Prepare(const std::vector &input_shape, const std::vector &scale_shape, @@ -77,10 +77,10 @@ void TestScaleFp32::Prepare(const std::vector &input_shape, const std::vect out_tensor_.set_data_type(kNumberTypeFloat32); out_tensor_.set_shape(output_shape); - in_tensor_.SetData(input_data); - scale_tensor_.SetData(scale_data); - offset_tensor_.SetData(offset_data); - out_tensor_.SetData(output_data); + in_tensor_.set_data(input_data); + scale_tensor_.set_data(scale_data); + offset_tensor_.set_data(offset_data); + out_tensor_.set_data(output_data); param_.activation_type_ = act_type; param_.axis_ = axis; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/skip_gram_fp32.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/skip_gram_fp32.cc index 6f68d6e30a..83118945b8 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/skip_gram_fp32.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/skip_gram_fp32.cc @@ -33,14 +33,14 @@ class TestSkipGramFp32 : public mindspore::CommonTest { void SkipGramTestInit(std::vector *inputs_, std::vector *outputs_, SkipGramParameter *skip_gram_param) { - Tensor *in_t_first = new Tensor(kObjectTypeString, {}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t_first = new Tensor(kObjectTypeString, {}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); char sentence[] = "The quick brown fox jumps over the lazy dog"; std::vector str; str.push_back({43, sentence}); mindspore::lite::WriteStringsToTensor(in_t_first, str); inputs_->push_back(in_t_first); - Tensor *output = new Tensor(kObjectTypeString, {}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *output = new Tensor(kObjectTypeString, {}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); outputs_->push_back(output); skip_gram_param->ngram_size = 3; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/space_to_depth_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/space_to_depth_fp32_tests.cc index 0f5bbc6f00..309a39309b 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/space_to_depth_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/space_to_depth_fp32_tests.cc @@ -51,7 +51,7 @@ TEST_F(SpaceToDepthTestFp32, SpaceToDepthTest2) { std::vector input = {1, 2, 5, 6, 10, 20, 3, 8, 18, 10, 3, 4, 11, 55, 15, 25}; std::vector in_shape = {1, 4, 4, 1}; lite::Tensor input_tensor; - input_tensor.SetData(input.data()); + input_tensor.set_data(input.data()); input_tensor.set_shape(in_shape); input_tensor.SetFormat(schema::Format_NHWC); input_tensor.set_data_type(kNumberTypeFloat32); @@ -63,7 +63,7 @@ TEST_F(SpaceToDepthTestFp32, SpaceToDepthTest2) { std::vector output(16); std::vector out_shape = {1, 2, 2, 4}; lite::Tensor output_tensor; - output_tensor.SetData(output.data()); + output_tensor.set_data(output.data()); output_tensor.set_shape(out_shape); output_tensor.SetFormat(schema::Format_NHWC); output_tensor.set_data_type(kNumberTypeFloat32); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/sparse_to_dense_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/sparse_to_dense_fp32_tests.cc index c319087678..ba96fc08d8 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/sparse_to_dense_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/sparse_to_dense_fp32_tests.cc @@ -42,22 +42,22 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test1) { TypeId tid = kNumberTypeFloat32; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->set_data_type(tid); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->set_data_type(tid); lite::Tensor *input_tensor3 = new lite::Tensor; - input_tensor3->SetData(input3.data()); + input_tensor3->set_data(input3.data()); input_tensor3->set_shape(shape3); input_tensor3->set_data_type(tid); lite::Tensor *input_tensor4 = new lite::Tensor; - input_tensor4->SetData(input4.data()); + input_tensor4->set_data(input4.data()); input_tensor4->set_shape(shape4); input_tensor4->set_data_type(tid); @@ -72,7 +72,7 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test1) { std::vector output_shape = {6, 10}; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->set_data_type(tid); std::vector outputs_tensor(1); @@ -101,11 +101,11 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test1) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - input_tensor3->SetData(nullptr); - input_tensor4->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + input_tensor3->set_data(nullptr); + input_tensor4->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete input_tensor3; @@ -126,22 +126,22 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test2) { TypeId tid = kNumberTypeFloat32; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->set_data_type(tid); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->set_data_type(tid); lite::Tensor *input_tensor3 = new lite::Tensor; - input_tensor3->SetData(input3.data()); + input_tensor3->set_data(input3.data()); input_tensor3->set_shape(shape3); input_tensor3->set_data_type(tid); lite::Tensor *input_tensor4 = new lite::Tensor; - input_tensor4->SetData(input4.data()); + input_tensor4->set_data(input4.data()); input_tensor4->set_shape(shape4); input_tensor4->set_data_type(tid); @@ -156,7 +156,7 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test2) { std::vector output_shape = {6, 10}; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->set_data_type(tid); std::vector outputs_tensor(1); @@ -185,11 +185,11 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - input_tensor3->SetData(nullptr); - input_tensor4->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + input_tensor3->set_data(nullptr); + input_tensor4->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete input_tensor3; @@ -210,22 +210,22 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test3) { TypeId tid = kNumberTypeFloat32; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->set_data_type(tid); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->set_data_type(tid); lite::Tensor *input_tensor3 = new lite::Tensor; - input_tensor3->SetData(input3.data()); + input_tensor3->set_data(input3.data()); input_tensor3->set_shape(shape3); input_tensor3->set_data_type(tid); lite::Tensor *input_tensor4 = new lite::Tensor; - input_tensor4->SetData(input4.data()); + input_tensor4->set_data(input4.data()); input_tensor4->set_shape(shape4); input_tensor4->set_data_type(tid); @@ -240,7 +240,7 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test3) { std::vector output_shape = {1, 10}; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->set_data_type(tid); std::vector outputs_tensor(1); @@ -267,11 +267,11 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test3) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - input_tensor3->SetData(nullptr); - input_tensor4->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + input_tensor3->set_data(nullptr); + input_tensor4->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete input_tensor3; @@ -292,22 +292,22 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test4) { TypeId tid = kNumberTypeFloat32; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->set_data_type(tid); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->set_data_type(tid); lite::Tensor *input_tensor3 = new lite::Tensor; - input_tensor3->SetData(input3.data()); + input_tensor3->set_data(input3.data()); input_tensor3->set_shape(shape3); input_tensor3->set_data_type(tid); lite::Tensor *input_tensor4 = new lite::Tensor; - input_tensor4->SetData(input4.data()); + input_tensor4->set_data(input4.data()); input_tensor4->set_shape(shape4); input_tensor4->set_data_type(tid); @@ -322,7 +322,7 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test4) { std::vector output_shape = {1, 10}; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->set_data_type(tid); std::vector outputs_tensor(1); @@ -349,11 +349,11 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test4) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - input_tensor3->SetData(nullptr); - input_tensor4->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + input_tensor3->set_data(nullptr); + input_tensor4->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete input_tensor3; @@ -374,22 +374,22 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test5) { TypeId tid = kNumberTypeFloat32; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->set_data_type(tid); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->set_data_type(tid); lite::Tensor *input_tensor3 = new lite::Tensor; - input_tensor3->SetData(input3.data()); + input_tensor3->set_data(input3.data()); input_tensor3->set_shape(shape3); input_tensor3->set_data_type(tid); lite::Tensor *input_tensor4 = new lite::Tensor; - input_tensor4->SetData(input4.data()); + input_tensor4->set_data(input4.data()); input_tensor4->set_shape(shape4); input_tensor4->set_data_type(tid); @@ -404,7 +404,7 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test5) { std::vector output_shape = {6, 10}; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->set_data_type(tid); std::vector outputs_tensor(1); @@ -433,11 +433,11 @@ TEST_F(TestSparseToDenseFp32, SparseToDense_test5) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - input_tensor3->SetData(nullptr); - input_tensor4->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + input_tensor3->set_data(nullptr); + input_tensor4->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete input_tensor3; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/strided_slice_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/strided_slice_fp32_tests.cc index 597c7ace71..8b3e2e65fd 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/strided_slice_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/strided_slice_fp32_tests.cc @@ -137,7 +137,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice3) { std::vector output_shape = {1, 1, 2}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(input_shape); std::vector inputs_tensor(1); inputs_tensor[0] = &input_tensor; @@ -145,7 +145,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice3) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -163,8 +163,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice3) { delete ctx; CompareOutputData(output_data, correct, 2, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } TEST_F(TestStridedSliceFp32, StridedSlice4) { @@ -187,7 +187,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice4) { std::vector output_shape = {2, 2}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(input_shape); std::vector inputs_tensor(1); inputs_tensor[0] = &input_tensor; @@ -195,7 +195,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice4) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -213,8 +213,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice4) { delete ctx; CompareOutputData(output_data, correct, 4, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } TEST_F(TestStridedSliceFp32, StridedSlice5) { @@ -244,7 +244,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice5) { std::vector output_shape = {3, 2, 2}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(input_shape); std::vector inputs_tensor(1); inputs_tensor[0] = &input_tensor; @@ -252,7 +252,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice5) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -270,8 +270,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice5) { delete ctx; CompareOutputData(output_data, correct, 12, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } TEST_F(TestStridedSliceFp32, StridedSlice6) { @@ -301,7 +301,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice6) { std::vector output_shape = {2, 2, 2}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(input_shape); std::vector inputs_tensor(1); inputs_tensor[0] = &input_tensor; @@ -309,7 +309,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice6) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -327,8 +327,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice6) { delete ctx; CompareOutputData(output_data, correct, 8, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } TEST_F(TestStridedSliceFp32, StridedSlice7) { @@ -350,7 +350,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice7) { std::vector output_shape = {1, 1}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(input_shape); std::vector inputs_tensor(1); inputs_tensor[0] = &input_tensor; @@ -358,7 +358,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice7) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -376,8 +376,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice7) { delete ctx; CompareOutputData(output_data, correct, 1, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } TEST_F(TestStridedSliceFp32, StridedSlice8) { @@ -407,7 +407,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice8) { std::vector output_shape = {1, 1, 5}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(input_shape); std::vector inputs_tensor(1); inputs_tensor[0] = &input_tensor; @@ -415,7 +415,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice8) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -433,8 +433,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice8) { delete ctx; CompareOutputData(output_data, correct, 5, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } // 5d input, multi inputs @@ -549,16 +549,16 @@ TEST_F(TestStridedSliceFp32, StridedSlice9) { std::vector output_shape = {1, 1, 10, 7, 7}; lite::Tensor input_tensor; - input_tensor.SetData(input_data); + input_tensor.set_data(input_data); input_tensor.set_shape(in_shape); lite::Tensor begins_tensor; - begins_tensor.SetData(begins.data()); + begins_tensor.set_data(begins.data()); begins_tensor.set_shape({1}); lite::Tensor ends_tensor; - ends_tensor.SetData(ends.data()); + ends_tensor.set_data(ends.data()); ends_tensor.set_shape({1}); lite::Tensor strides_tensor; - strides_tensor.SetData(strides.data()); + strides_tensor.set_data(strides.data()); strides_tensor.set_shape({1}); std::vector inputs_tensor{&input_tensor, &begins_tensor, &ends_tensor, &strides_tensor}; @@ -566,7 +566,7 @@ TEST_F(TestStridedSliceFp32, StridedSlice9) { std::vector outputs_tensor; lite::Tensor output_tensor; outputs_tensor.push_back(&output_tensor); - output_tensor.SetData(output_data); + output_tensor.set_data(output_data); output_tensor.set_data_type(input_tensor.data_type()); output_tensor.set_shape(output_shape); @@ -583,8 +583,8 @@ TEST_F(TestStridedSliceFp32, StridedSlice9) { delete ctx; CompareOutputData(output_data, correct, 490, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/tile_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/tile_fp32_tests.cc index 760abe2203..17f317ab49 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/tile_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/tile_fp32_tests.cc @@ -31,8 +31,8 @@ TEST_F(TestTileFp32, Tile) { lite::Tensor out_tensor(kNumberTypeFloat32, {4, 6}); float input_data[] = {1, 2, 3, 4}; float output_data[24] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor}; @@ -65,7 +65,7 @@ TEST_F(TestTileFp32, Tile) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/topk_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/topk_fp32_tests.cc index 7f4a73c8fa..4601a0a2cf 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/topk_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/topk_fp32_tests.cc @@ -33,9 +33,9 @@ TEST_F(TestTopKFp32, TopK) { float input_data[] = {1, 2, 3, 6, 5, 4, 9, 8, 7, 10, 12, 11}; float output_data0[8] = {0}; int32_t output_data1[8] = {0}; - in_tensor.SetData(input_data); - out_tensor0.SetData(output_data0); - out_tensor1.SetData(output_data1); + in_tensor.set_data(input_data); + out_tensor0.set_data(output_data0); + out_tensor1.set_data(output_data1); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor0, &out_tensor1}; @@ -60,8 +60,8 @@ TEST_F(TestTopKFp32, TopK) { EXPECT_EQ(output_data1[i], expect1[i]); } - in_tensor.SetData(nullptr); - out_tensor0.SetData(nullptr); - out_tensor1.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor0.set_data(nullptr); + out_tensor1.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/transpose_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/transpose_fp32_tests.cc index 1749fcc4c0..fe888a18a1 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/transpose_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/transpose_fp32_tests.cc @@ -183,7 +183,7 @@ TEST_F(TestTransposeFp32, TransposeFp32_test5) { } lite::Tensor input_tensor; - input_tensor.SetData(input.data()); + input_tensor.set_data(input.data()); input_tensor.set_shape(input_shape); input_tensor.SetFormat(schema::Format_NHWC); input_tensor.set_data_type(kNumberTypeFloat32); @@ -191,7 +191,7 @@ TEST_F(TestTransposeFp32, TransposeFp32_test5) { inputs_tensor.emplace_back(&input_tensor); lite::Tensor output_tensor; - output_tensor.SetData(output.data()); + output_tensor.set_data(output.data()); output_tensor.set_shape(output_shape); output_tensor.SetFormat(schema::Format_NHWC); output_tensor.set_data_type(kNumberTypeFloat32); @@ -214,8 +214,8 @@ TEST_F(TestTransposeFp32, TransposeFp32_test5) { } std::cout << "\n"; CompareOutputData(output.data(), correct, 24, 0.000001); - input_tensor.SetData(nullptr); - output_tensor.SetData(nullptr); + input_tensor.set_data(nullptr); + output_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unique_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unique_fp32_tests.cc index 8c2fd20599..10bc091870 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unique_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unique_fp32_tests.cc @@ -33,9 +33,9 @@ TEST_F(TestUniqueFp32, Unique) { float input_data[] = {1, 1, 2, 4, 4, 4, 7, 8, 8}; float output_data0[9] = {0}; int output_data1[9] = {0}; - in_tensor.SetData(input_data); - out_tensor0.SetData(output_data0); - out_tensor1.SetData(output_data1); + in_tensor.set_data(input_data); + out_tensor0.set_data(output_data0); + out_tensor1.set_data(output_data1); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor0, &out_tensor1}; @@ -64,8 +64,8 @@ TEST_F(TestUniqueFp32, Unique) { EXPECT_EQ(output_data1[i], expect1[i]); } - in_tensor.SetData(nullptr); - out_tensor0.SetData(nullptr); - out_tensor1.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor0.set_data(nullptr); + out_tensor1.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unstack_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unstack_fp32_tests.cc index 39258fa359..d1f7c5cfca 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unstack_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/unstack_fp32_tests.cc @@ -37,11 +37,11 @@ TEST_F(TestUnstackFp32, Unstack) { float output_data1[6] = {0}; float output_data2[6] = {0}; float output_data3[6] = {0}; - in_tensor.SetData(input_data); - out_tensor0.SetData(output_data0); - out_tensor1.SetData(output_data1); - out_tensor2.SetData(output_data2); - out_tensor3.SetData(output_data3); + in_tensor.set_data(input_data); + out_tensor0.set_data(output_data0); + out_tensor1.set_data(output_data1); + out_tensor2.set_data(output_data2); + out_tensor3.set_data(output_data3); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor0, &out_tensor1, &out_tensor2, &out_tensor3}; @@ -70,11 +70,11 @@ TEST_F(TestUnstackFp32, Unstack) { EXPECT_EQ(output_data3[i], expect3[i]); } - in_tensor.SetData(nullptr); - out_tensor0.SetData(nullptr); - out_tensor1.SetData(nullptr); - out_tensor2.SetData(nullptr); - out_tensor3.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor0.set_data(nullptr); + out_tensor1.set_data(nullptr); + out_tensor2.set_data(nullptr); + out_tensor3.set_data(nullptr); } TEST_F(TestUnstackFp32, Unstack2) { @@ -86,10 +86,10 @@ TEST_F(TestUnstackFp32, Unstack2) { float output_data0[8] = {0}; float output_data1[8] = {0}; float output_data2[8] = {0}; - in_tensor.SetData(input_data); - out_tensor0.SetData(output_data0); - out_tensor1.SetData(output_data1); - out_tensor2.SetData(output_data2); + in_tensor.set_data(input_data); + out_tensor0.set_data(output_data0); + out_tensor1.set_data(output_data1); + out_tensor2.set_data(output_data2); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor0, &out_tensor1, &out_tensor2}; @@ -116,9 +116,9 @@ TEST_F(TestUnstackFp32, Unstack2) { EXPECT_EQ(output_data2[i], expect2[i]); } - in_tensor.SetData(nullptr); - out_tensor0.SetData(nullptr); - out_tensor1.SetData(nullptr); - out_tensor2.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor0.set_data(nullptr); + out_tensor1.set_data(nullptr); + out_tensor2.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/arithmetic_grad_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/arithmetic_grad_fp32_tests.cc index 9f50ea2106..53455f9386 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/arithmetic_grad_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/arithmetic_grad_fp32_tests.cc @@ -82,23 +82,23 @@ std::vector GenerateTensorsForTest(const char *test, int test_id auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(test, &input_size)); lite::Tensor *dy_tensor = new lite::Tensor(TypeId::kNumberTypeFloat32, large_dim); - dy_tensor->SetData(dy_data); + dy_tensor->set_data(dy_data); auto x1_data = reinterpret_cast(mindspore::lite::ReadFile(dx1_file, &input_size)); lite::Tensor *x1_tensor = new lite::Tensor(TypeId::kNumberTypeFloat32, large_dim); - x1_tensor->SetData(x1_data); + x1_tensor->set_data(x1_data); auto x2_data = reinterpret_cast(mindspore::lite::ReadFile(dx2_file, &input_size)); lite::Tensor *x2_tensor = new lite::Tensor(TypeId::kNumberTypeFloat32, small_dim); - x2_tensor->SetData(x2_data); + x2_tensor->set_data(x2_data); auto dx1_data = new float[large_size]; lite::Tensor *dx1_tensor = new lite::Tensor(TypeId::kNumberTypeFloat32, large_dim); - dx1_tensor->SetData(dx1_data); + dx1_tensor->set_data(dx1_data); auto dx2_data = new float[small_size]; lite::Tensor *dx2_tensor = new lite::Tensor(TypeId::kNumberTypeFloat32, small_dim); - dx2_tensor->SetData(dx2_data); + dx2_tensor->set_data(dx2_data); std::vector ret_vector = {dy_tensor, x1_tensor, x2_tensor, dx1_tensor, dx2_tensor}; return ret_vector; @@ -135,7 +135,7 @@ TEST_F(TestArithmeticGradFp32, TestAddGradFp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete kernel_obj; @@ -173,7 +173,7 @@ TEST_F(TestArithmeticGradFp32, TestAddGrad2Fp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; //TODO tensor data is unique pointer @@ -214,7 +214,7 @@ TEST_F(TestArithmeticGradFp32, TestAddGrad3Fp32) { for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -255,7 +255,7 @@ TEST_F(TestArithmeticGradFp32, TestSubGradFp32) { for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -296,7 +296,7 @@ TEST_F(TestArithmeticGradFp32, TestSubGrad2Fp32) { for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete kernel_obj; @@ -344,7 +344,7 @@ TEST_F(TestArithmeticGradFp32, TestMulGradFp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete kernel_obj; @@ -383,7 +383,7 @@ TEST_F(TestArithmeticGradFp32, TestMulGrad2Fp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -423,7 +423,7 @@ TEST_F(TestArithmeticGradFp32, TestMulGrad3Fp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -463,7 +463,7 @@ TEST_F(TestArithmeticGradFp32, TestMulGrad4Fp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -503,7 +503,7 @@ TEST_F(TestArithmeticGradFp32, TestDivGradFp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, dx2_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -544,7 +544,7 @@ TEST_F(TestArithmeticGradFp32, TestDivGrad2Fp32) { for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -584,7 +584,7 @@ TEST_F(TestArithmeticGradFp32, TestDivGrad3Fp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, output_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } // for (int i = 0; i < 5; i++) delete all_tensors[i]; @@ -624,7 +624,7 @@ TEST_F(TestArithmeticGradFp32, Test3DDivGrad2Fp32) { EXPECT_EQ(0, lite::CompareRelativeOutput(output_ptr, output_path)); for (auto tensor : all_tensors) { delete[] reinterpret_cast(tensor->MutableData()); - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete kernel_obj; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bias_grad_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bias_grad_fp32_tests.cc index 67e67ca1db..155f20e44c 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bias_grad_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bias_grad_fp32_tests.cc @@ -36,13 +36,13 @@ TEST_F(TestBiasGradFp32, BiasGradFp32) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_dy({10, 28, 28, 7}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(input_data); + dy_tensor.set_data(input_data); std::vector inputs = {&dy_tensor}; auto output_data = new float[7]; std::vector dim_dw = {7}; lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(output_data); + dw_tensor.set_data(output_data); std::vector outputs = {&dw_tensor}; lite::InnerContext ctx; @@ -66,8 +66,8 @@ TEST_F(TestBiasGradFp32, BiasGradFp32) { delete[] input_data; delete[] output_data; // delete bias_param; - dy_tensor.SetData(nullptr); - dw_tensor.SetData(nullptr); + dy_tensor.set_data(nullptr); + dw_tensor.set_data(nullptr); delete kernel_obj; MS_LOG(INFO) << "BiasGradFp32 passed"; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bn_grad_fp32_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bn_grad_fp32_test.cc index 9cb40c05d4..c9c5601550 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bn_grad_fp32_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/bn_grad_fp32_test.cc @@ -36,7 +36,7 @@ lite::Tensor *TestBNGradFp32::CreateInTensor(std::string file_name, std::vector< size_t input_size = 0; auto input_data = reinterpret_cast(mindspore::lite::ReadFile(file_name.c_str(), &input_size)); auto tensor = new lite::Tensor(TypeId::kNumberTypeFloat32, dim); - tensor->SetData(input_data); + tensor->set_data(input_data); EXPECT_EQ(input_size, tensor->Size()); return tensor; } @@ -108,7 +108,7 @@ TEST_F(TestBNGradFp32, BNGradFp32) { EXPECT_EQ(res, 0); for (auto v : inputs) { delete[] reinterpret_cast(v->MutableData()); - v->SetData(nullptr); + v->set_data(nullptr); delete v; } mindspore::kernel::LiteKernel::FreeWorkspace(); @@ -197,7 +197,7 @@ TEST_F(TestBNGradFp32, BNTtrainFp32) { res = mindspore::lite::CompareRelativeOutput(save_var, "./test_data/bngrad/running_var_3.bin"); EXPECT_EQ(res, 0); - x_tensor->SetData(nullptr); + x_tensor->set_data(nullptr); delete x_tensor; mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel_obj; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/convolution_grad_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/convolution_grad_fp32_tests.cc index b3c1d60df4..26894ad973 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/convolution_grad_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/convolution_grad_fp32_tests.cc @@ -85,7 +85,7 @@ TEST_F(TestConvolutionGradFp32, ConvFp32FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({1, 28, 28, 32}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -98,12 +98,12 @@ TEST_F(TestConvolutionGradFp32, ConvFp32FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({1, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({32, 3, 3, 3}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -141,9 +141,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32FilterGrad) { mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); MS_LOG(INFO) << "TestConvolutionGradFp32 Filter Grad passed"; } @@ -157,21 +157,21 @@ TEST_F(TestConvolutionGradFp32, ConvFp32InputGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({1, 28, 28, 32}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); size_t w_size; std::string w_path = "./test_data/conv/convfp32_w_32_3_3_3.bin"; auto w_data = reinterpret_cast(mindspore::lite::ReadFile(w_path.c_str(), &w_size)); std::vector dim_dw({32, 3, 3, 3}); lite::Tensor w_tensor(TypeId::kNumberTypeFloat32, dim_dw); - w_tensor.SetData(w_data); + w_tensor.set_data(w_data); size_t output_data_size = conv_param->input_batch_ * conv_param->input_h_ * conv_param->input_w_ * conv_param->input_channel_; auto dx_data = new float[output_data_size]; std::vector dim_dx({1, 28, 28, 3}); lite::Tensor dx_tensor(TypeId::kNumberTypeFloat32, dim_dx); - dx_tensor.SetData(dx_data); + dx_tensor.set_data(dx_data); std::vector inputs = {&dy_tensor, &w_tensor}; std::vector outputs = {&dx_tensor}; @@ -210,9 +210,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32InputGrad) { delete[] dx_data; delete[] w_data; delete[] dy_data; - w_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); - dx_tensor.SetData(nullptr); + w_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); + dx_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel; // delete conv_param; @@ -230,7 +230,7 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupFilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({1, 28, 28, 18}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -243,12 +243,12 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupFilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({1, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({18, 3, 3, 1}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -282,9 +282,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupFilterGrad) { delete[] input_data; delete[] dy_data; delete[] dw_data; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel; // delete conv_param; @@ -301,21 +301,21 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupInputGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({1, 28, 28, 18}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); size_t w_size; std::string w_path = "./test_data/conv/convfp32_w_g3_18_3_3_3.bin"; auto w_data = reinterpret_cast(mindspore::lite::ReadFile(w_path.c_str(), &w_size)); std::vector dim_dw({18, 3, 3, 1}); lite::Tensor w_tensor(TypeId::kNumberTypeFloat32, dim_dw); - w_tensor.SetData(w_data); + w_tensor.set_data(w_data); size_t output_data_size = conv_param->input_batch_ * conv_param->input_h_ * conv_param->input_w_ * conv_param->input_channel_; auto dx_data = new float[output_data_size]; std::vector dim_dx({1, 28, 28, 3}); lite::Tensor dx_tensor(TypeId::kNumberTypeFloat32, dim_dx); - dx_tensor.SetData(dx_data); + dx_tensor.set_data(dx_data); std::vector inputs = {&dy_tensor, &w_tensor}; std::vector outputs = {&dx_tensor}; @@ -353,9 +353,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupInputGrad) { delete[] dx_data; delete[] w_data; delete[] dy_data; - dx_tensor.SetData(nullptr); - w_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dx_tensor.set_data(nullptr); + w_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); delete kernel; mindspore::kernel::LiteKernel::FreeWorkspace(); @@ -374,7 +374,7 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupDilationFilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({1, 26, 26, 18}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -387,12 +387,12 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupDilationFilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({1, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({18, 3, 3, 1}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -426,9 +426,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupDilationFilterGrad) { delete[] input_data; delete[] dy_data; delete[] dw_data; - dw_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel; // delete conv_param; @@ -445,21 +445,21 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupDilationInputGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({1, 26, 26, 18}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); size_t w_size; std::string w_path = "./test_data/conv/convfp32_w_g3_d2_18_3_3_3.bin"; auto w_data = reinterpret_cast(mindspore::lite::ReadFile(w_path.c_str(), &w_size)); std::vector dim_w({18, 3, 3, 1}); lite::Tensor w_tensor(TypeId::kNumberTypeFloat32, dim_w); - w_tensor.SetData(w_data); + w_tensor.set_data(w_data); size_t output_data_size = conv_param->input_batch_ * conv_param->input_h_ * conv_param->input_w_ * conv_param->input_channel_; auto dx_data = new float[output_data_size]; std::vector dim_dx({1, 28, 28, 3}); lite::Tensor dx_tensor(TypeId::kNumberTypeFloat32, dim_dx); - dx_tensor.SetData(dx_data); + dx_tensor.set_data(dx_data); std::vector inputs = {&dy_tensor, &w_tensor}; std::vector outputs = {&dx_tensor}; @@ -493,9 +493,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32GroupDilationInputGrad) { delete[] dx_data; delete[] w_data; delete[] dy_data; - dx_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); - w_tensor.SetData(nullptr); + dx_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); + w_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel; // delete conv_param; @@ -512,21 +512,21 @@ TEST_F(TestConvolutionGradFp32, ConvGroupDilation) { auto x_data = reinterpret_cast(mindspore::lite::ReadFile(x_path.c_str(), &x_size)); std::vector dim_x({1, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(x_data); + x_tensor.set_data(x_data); size_t w_size; std::string w_path = "./test_data/conv/convfp32_w_g3_d2_18_3_3_3.bin"; auto w_data = reinterpret_cast(mindspore::lite::ReadFile(w_path.c_str(), &w_size)); std::vector dim_w({18, 3, 3, 1}); lite::Tensor w_tensor(TypeId::kNumberTypeFloat32, dim_w); - w_tensor.SetData(w_data); + w_tensor.set_data(w_data); size_t output_data_size = conv_param->output_batch_ * conv_param->output_h_ * conv_param->output_w_ * conv_param->output_channel_; auto y_data = new float[output_data_size]; std::vector dim_y({1, 26, 26, 18}); lite::Tensor y_tensor(TypeId::kNumberTypeFloat32, dim_y); - y_tensor.SetData(y_data); + y_tensor.set_data(y_data); std::vector inputs = {&x_tensor, &w_tensor}; std::vector outputs = {&y_tensor}; @@ -569,9 +569,9 @@ TEST_F(TestConvolutionGradFp32, ConvGroupDilation) { delete[] y_data; delete[] x_data; delete[] w_data; - x_tensor.SetData(nullptr); - y_tensor.SetData(nullptr); - w_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + y_tensor.set_data(nullptr); + w_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel; @@ -614,7 +614,7 @@ TEST_F(TestConvolutionGradFp32, ConvFp32Dilation2Group2Stride2FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 15, 15, 12}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -627,12 +627,12 @@ TEST_F(TestConvolutionGradFp32, ConvFp32Dilation2Group2Stride2FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 4}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({12, 3, 3, 2}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -670,9 +670,9 @@ TEST_F(TestConvolutionGradFp32, ConvFp32Dilation2Group2Stride2FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestConvolutionGradFp32 Filter Grad passed"; } @@ -713,21 +713,21 @@ TEST_F(TestConvolutionGradFp32, ConvGroup2Dilation2Stride2) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 15, 15, 12}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); size_t w_size; std::string w_path = "./test_data/conv/convfp32_w_d2_g2_s2_12_2_3_3.bin"; auto w_data = reinterpret_cast(mindspore::lite::ReadFile(w_path.c_str(), &w_size)); std::vector dim_w({12, 3, 3, 2}); lite::Tensor w_tensor(TypeId::kNumberTypeFloat32, dim_w); - w_tensor.SetData(w_data); + w_tensor.set_data(w_data); size_t output_data_size = conv_param->input_batch_ * conv_param->input_h_ * conv_param->input_w_ * conv_param->input_channel_; auto dx_data = new float[output_data_size]; std::vector dim_dx({2, 32, 32, 4}); lite::Tensor dx_tensor(TypeId::kNumberTypeFloat32, dim_dx); - dx_tensor.SetData(dx_data); + dx_tensor.set_data(dx_data); std::vector inputs = {&dy_tensor, &w_tensor}; std::vector outputs = {&dx_tensor}; @@ -766,9 +766,9 @@ TEST_F(TestConvolutionGradFp32, ConvGroup2Dilation2Stride2) { delete[] dx_data; delete[] w_data; delete[] dy_data; - dx_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); - w_tensor.SetData(nullptr); + dx_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); + w_tensor.set_data(nullptr); delete kernel; mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestConvolutionGradFp32 Filter Grad passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/deconvolution_grad_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/deconvolution_grad_fp32_tests.cc index 96be0e6cf2..35d298e338 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/deconvolution_grad_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/deconvolution_grad_fp32_tests.cc @@ -67,7 +67,7 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 63, 63, 9}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -80,12 +80,12 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({3, 3, 3, 9}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -123,9 +123,9 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestDeConvolutionGradFp32 Filter Grad passed"; } @@ -166,7 +166,7 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 65, 65, 9}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -179,12 +179,12 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({9, 3, 3, 3}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -222,9 +222,9 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestDeConvolutionGradFp32 Filter Grad passed"; } @@ -265,7 +265,7 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group3FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 65, 65, 9}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -278,12 +278,12 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group3FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({3, 3, 3, 3}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -321,9 +321,9 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group3FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestDeConvolutionGradFp32 Filter Grad passed"; } @@ -364,7 +364,7 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group3Stride1FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 34, 34, 9}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -377,12 +377,12 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group3Stride1FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({3, 3, 3, 3}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -420,9 +420,9 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group3Stride1FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestDeConvolutionGradFp32 Filter Grad passed"; } @@ -463,7 +463,7 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group2Stride2FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 65, 65, 12}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -476,12 +476,12 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group2Stride2FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 4}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({6, 3, 3, 4}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -519,9 +519,9 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group2Stride2FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestDeConvolutionGradFp32 Filter Grad passed"; } @@ -562,7 +562,7 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group12Stride2FilterGrad) { auto dy_data = reinterpret_cast(mindspore::lite::ReadFile(dy_path.c_str(), &dy_size)); std::vector dim_dy({2, 65, 65, 12}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(dy_data); + dy_tensor.set_data(dy_data); // runtime part printf("Calculating runtime cost...\n"); @@ -575,12 +575,12 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group12Stride2FilterGrad) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_x({2, 32, 32, 12}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input_data); + x_tensor.set_data(input_data); auto dw_data = new float[output_data_size]; std::vector dim_dw({1, 3, 3, 12}); lite::Tensor dw_tensor(TypeId::kNumberTypeFloat32, dim_dw); - dw_tensor.SetData(dw_data); + dw_tensor.set_data(dw_data); std::vector inputs = {&dy_tensor, &x_tensor}; std::vector outputs = {&dw_tensor}; @@ -618,9 +618,9 @@ TEST_F(TestDeConvolutionGradFp32, DeConvFp32Dilation2Group12Stride2FilterGrad) { delete[] dw_data; delete kernel; // delete conv_param; - dw_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dw_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); MS_LOG(INFO) << "TestDeConvolutionGradFp32 Filter Grad passed"; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/pooling_grad_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/pooling_grad_fp32_tests.cc index 70256d1054..355f54669d 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/pooling_grad_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/pooling_grad_fp32_tests.cc @@ -124,20 +124,20 @@ TEST_F(TestPoolingGradFp32, AvgPoolingKernelGradFp32) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_dy({1, 28, 28, 3}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(input_data); + dy_tensor.set_data(input_data); std::string input1_path = "./test_data/pooling/avgpoolgradfp32_1_x_1_28_28_3.bin"; auto input1_data = reinterpret_cast(mindspore::lite::ReadFile(input1_path.c_str(), &input_size)); std::vector dim_x({1, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input1_data); + x_tensor.set_data(input1_data); std::vector inputs = {&dy_tensor, &x_tensor}; auto output_data = new float[output_data_size]; std::vector dim_dx({1, 28, 28, 3}); lite::Tensor dx_tensor(TypeId::kNumberTypeFloat32, dim_dx); - dx_tensor.SetData(output_data); + dx_tensor.set_data(output_data); std::vector outputs = {&dx_tensor}; lite::InnerContext context; @@ -162,9 +162,9 @@ TEST_F(TestPoolingGradFp32, AvgPoolingKernelGradFp32) { delete[] input_data; delete[] input1_data; delete[] output_data; - dx_tensor.SetData(nullptr); - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + dx_tensor.set_data(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); // delete pooling_param; delete kernel_obj; MS_LOG(INFO) << "TestAvgPoolingGradFp32 passed"; @@ -188,13 +188,13 @@ TEST_F(TestPoolingGradFp32, AvgPoolingBatchGradFp32) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_dy({3, 28, 28, 3}); lite::Tensor dy_tensor(TypeId::kNumberTypeFloat32, dim_dy); - dy_tensor.SetData(input_data); + dy_tensor.set_data(input_data); std::string input1_path = "./test_data/pooling/avgpoolgradfp32_1_x_3_28_28_3.bin"; auto input1_data = reinterpret_cast(mindspore::lite::ReadFile(input1_path.c_str(), &input_size)); std::vector dim_x({3, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(input1_data); + x_tensor.set_data(input1_data); std::vector inputs = {&dy_tensor, &x_tensor}; @@ -226,8 +226,8 @@ TEST_F(TestPoolingGradFp32, AvgPoolingBatchGradFp32) { delete[] input_data; delete[] input1_data; - x_tensor.SetData(nullptr); - dy_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + dy_tensor.set_data(nullptr); // delete pooling_param; delete kernel_obj; MS_LOG(INFO) << "TestAvgPoolingGradBatchFp32 passed"; @@ -253,13 +253,13 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride2Fp32) { mindspore::lite::ReadFile("./test_data/pooling/avgpoolgradfp32_s2_x_3_28_28_3.bin", &input_size)); std::vector dim_x({pool->output_batch_, pool->input_h_, pool->input_w_, pool->input_channel_}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(x_data); + x_tensor.set_data(x_data); auto yt_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/avgpoolgradfp32_s2_dy_3_28_28_3.bin", &input_size)); std::vector dim_y({pool->output_batch_, pool->output_h_, pool->output_w_, pool->output_channel_}); lite::Tensor yt_tensor(TypeId::kNumberTypeFloat32, dim_y); - yt_tensor.SetData(yt_data); + yt_tensor.set_data(yt_data); lite::Tensor out_tensor(TypeId::kNumberTypeFloat32, dim_x); ASSERT_EQ(out_tensor.MallocData(), 0); float *out_data = static_cast(out_tensor.MutableData()); @@ -289,8 +289,8 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride2Fp32) { delete[] yt_data; // delete[] out_data; // delete conv_param; - x_tensor.SetData(nullptr); - yt_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + yt_tensor.set_data(nullptr); delete kernel; MS_LOG(INFO) << "AvgPoolGradStride2Fp32 Filter Grad passed"; } @@ -315,13 +315,13 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride3Fp32) { mindspore::lite::ReadFile("./test_data/pooling/avgpoolgradfp32_s3_x_3_28_28_3.bin", &input_size)); std::vector dim_x({pool->output_batch_, pool->input_h_, pool->input_w_, pool->input_channel_}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(x_data); + x_tensor.set_data(x_data); auto yt_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/avgpoolgradfp32_s3_dy_3_28_28_3.bin", &input_size)); std::vector dim_y({pool->output_batch_, pool->output_h_, pool->output_w_, pool->output_channel_}); lite::Tensor yt_tensor(TypeId::kNumberTypeFloat32, dim_y); - yt_tensor.SetData(yt_data); + yt_tensor.set_data(yt_data); lite::Tensor out_tensor(TypeId::kNumberTypeFloat32, dim_x); ASSERT_EQ(out_tensor.MallocData(), 0); @@ -354,8 +354,8 @@ TEST_F(TestPoolingGradFp32, AvgPoolGradStride3Fp32) { delete[] yt_data; // delete[] out_data; // delete conv_param; - x_tensor.SetData(nullptr); - yt_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + yt_tensor.set_data(nullptr); delete kernel; MS_LOG(INFO) << "AvgPoolGradStride3Fp32 Filter Grad passed"; } @@ -431,18 +431,18 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradBatchFp32) { mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_1_x_3_28_28_3.bin", &input_size)); std::vector dim_x({3, 28, 28, 3}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(x_data); + x_tensor.set_data(x_data); auto y_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_1_dx_3_28_28_3.bin", &input_size)); std::vector dim_y({3, 28, 28, 3}); lite::Tensor y_tensor(TypeId::kNumberTypeFloat32, dim_y); - y_tensor.SetData(y_data); + y_tensor.set_data(y_data); auto yt_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_1_dy_3_28_28_3.bin", &input_size)); lite::Tensor yt_tensor(TypeId::kNumberTypeFloat32, dim_y); - yt_tensor.SetData(yt_data); + yt_tensor.set_data(yt_data); lite::Tensor out_tensor(TypeId::kNumberTypeFloat32, dim_x); ASSERT_EQ(out_tensor.MallocData(), 0); @@ -476,9 +476,9 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradBatchFp32) { delete[] yt_data; // delete[] out_data; // delete conv_param; - x_tensor.SetData(nullptr); - y_tensor.SetData(nullptr); - yt_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + y_tensor.set_data(nullptr); + yt_tensor.set_data(nullptr); delete kernel; MS_LOG(INFO) << "MaxPoolGradBatchFp32 Filter Grad passed"; } @@ -504,18 +504,18 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride2Fp32) { mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_s2_x_3_28_28_3.bin", &input_size)); std::vector dim_x({maxpool->output_batch_, maxpool->input_h_, maxpool->input_w_, maxpool->input_channel_}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(x_data); + x_tensor.set_data(x_data); auto y_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_s2_dx_3_28_28_3.bin", &input_size)); std::vector dim_y({maxpool->output_batch_, maxpool->output_h_, maxpool->output_w_, maxpool->output_channel_}); lite::Tensor y_tensor(TypeId::kNumberTypeFloat32, dim_y); - y_tensor.SetData(y_data); + y_tensor.set_data(y_data); auto yt_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_s2_dy_3_28_28_3.bin", &input_size)); lite::Tensor yt_tensor(TypeId::kNumberTypeFloat32, dim_y); - yt_tensor.SetData(yt_data); + yt_tensor.set_data(yt_data); lite::Tensor out_tensor(TypeId::kNumberTypeFloat32, dim_x); ASSERT_EQ(out_tensor.MallocData(), 0); @@ -550,9 +550,9 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride2Fp32) { delete[] yt_data; // delete[] out_data; // delete conv_param; - x_tensor.SetData(nullptr); - y_tensor.SetData(nullptr); - yt_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + y_tensor.set_data(nullptr); + yt_tensor.set_data(nullptr); delete kernel; MS_LOG(INFO) << "MaxPoolGradStride2Fp32 Filter Grad passed"; } @@ -578,18 +578,18 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride3Fp32) { mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_s3_x_3_28_28_3.bin", &input_size)); std::vector dim_x({maxpool->output_batch_, maxpool->input_h_, maxpool->input_w_, maxpool->input_channel_}); lite::Tensor x_tensor(TypeId::kNumberTypeFloat32, dim_x); - x_tensor.SetData(x_data); + x_tensor.set_data(x_data); auto y_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_s3_dx_3_28_28_3.bin", &input_size)); std::vector dim_y({maxpool->output_batch_, maxpool->output_h_, maxpool->output_w_, maxpool->output_channel_}); lite::Tensor y_tensor(TypeId::kNumberTypeFloat32, dim_y); - y_tensor.SetData(y_data); + y_tensor.set_data(y_data); auto yt_data = reinterpret_cast( mindspore::lite::ReadFile("./test_data/pooling/maxpoolgradfp32_s3_dy_3_28_28_3.bin", &input_size)); lite::Tensor yt_tensor(TypeId::kNumberTypeFloat32, dim_y); - yt_tensor.SetData(yt_data); + yt_tensor.set_data(yt_data); lite::Tensor out_tensor(TypeId::kNumberTypeFloat32, dim_x); ASSERT_EQ(out_tensor.MallocData(), 0); @@ -624,9 +624,9 @@ TEST_F(TestPoolingGradFp32, MaxPoolGradStride3Fp32) { delete[] yt_data; // delete[] out_data; // delete conv_param; - x_tensor.SetData(nullptr); - y_tensor.SetData(nullptr); - yt_tensor.SetData(nullptr); + x_tensor.set_data(nullptr); + y_tensor.set_data(nullptr); + yt_tensor.set_data(nullptr); delete kernel; MS_LOG(INFO) << "MaxPoolGradStride3Fp32 Filter Grad passed"; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/softmax_crossentropy_fp32_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/softmax_crossentropy_fp32_tests.cc index e1254e7202..ae059f756a 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/softmax_crossentropy_fp32_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/fp32_grad/softmax_crossentropy_fp32_tests.cc @@ -37,7 +37,7 @@ TEST_F(TestSoftmaxCrossEntropyFp32, SoftmaxCrossEntropyFp32) { auto input_data = reinterpret_cast(mindspore::lite::ReadFile(input_path.c_str(), &input_size)); std::vector dim_y({6, 4}); lite::Tensor y_tensor(TypeId::kNumberTypeFloat32, dim_y); - y_tensor.SetData(input_data); + y_tensor.set_data(input_data); std::string label_path = "./test_data/operators/sce_fp32_1_l_6.bin"; auto ll_labels = reinterpret_cast(mindspore::lite::ReadFile(label_path.c_str(), &input_size)); @@ -47,17 +47,17 @@ TEST_F(TestSoftmaxCrossEntropyFp32, SoftmaxCrossEntropyFp32) { std::vector dim_l({6, 4}); lite::Tensor l_tensor(TypeId::kNumberTypeInt32, dim_l); - l_tensor.SetData(labels); + l_tensor.set_data(labels); std::vector inputs = {&y_tensor, &l_tensor}; auto loss = new float[1]; std::vector dim_dw({1}); lite::Tensor loss_tensor(TypeId::kNumberTypeFloat32, dim_dw); - loss_tensor.SetData(loss); + loss_tensor.set_data(loss); auto grad = new float[24]; lite::Tensor grad_tensor(TypeId::kNumberTypeFloat32, dim_y); - grad_tensor.SetData(grad); + grad_tensor.set_data(grad); std::vector outputs = {&loss_tensor, &grad_tensor}; lite::InnerContext context; @@ -94,10 +94,10 @@ TEST_F(TestSoftmaxCrossEntropyFp32, SoftmaxCrossEntropyFp32) { delete[] input_data; delete[] loss; delete[] grad; - l_tensor.SetData(nullptr); - y_tensor.SetData(nullptr); - loss_tensor.SetData(nullptr); - grad_tensor.SetData(nullptr); + l_tensor.set_data(nullptr); + y_tensor.set_data(nullptr); + loss_tensor.set_data(nullptr); + grad_tensor.set_data(nullptr); mindspore::kernel::LiteKernel::FreeWorkspace(); delete kernel_obj; MS_LOG(INFO) << "SoftmaxCrossEntropyFp32 passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/add_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/add_int8_tests.cc index 9f56de0218..f4e6fbbf6a 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/add_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/add_int8_tests.cc @@ -35,9 +35,9 @@ TEST_F(TestQuantizedAdd, Add) { int8_t input_data0[] = {-102, 25, -51, 89, -102, 25, -51, 89, -102, 25}; // -0.8 0.2 -0.4 0.7 int8_t input_data1[] = {38, 51, 64, -102, 38, 51, 64, -102, 38, 51}; // 0.3 0.4 0.5 -0.8 int8_t output_data[10] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); const lite::QuantArg quant_in0 = {0.00784314f, 0}; // -1.0--1.0 -> 0--255 const lite::QuantArg quant_in1 = {0.00784314f, 0}; @@ -68,8 +68,8 @@ TEST_F(TestQuantizedAdd, Add) { EXPECT_EQ(output_data[i], expect0[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/arithmetic_self_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/arithmetic_self_int8_tests.cc index fcb346c6b9..192f3c5d2c 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/arithmetic_self_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/arithmetic_self_int8_tests.cc @@ -47,7 +47,7 @@ TEST_F(TestArithmeticSelfInt8, floor_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -55,7 +55,7 @@ TEST_F(TestArithmeticSelfInt8, floor_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -82,8 +82,8 @@ TEST_F(TestArithmeticSelfInt8, floor_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -107,7 +107,7 @@ TEST_F(TestArithmeticSelfInt8, floor_quant1_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -115,7 +115,7 @@ TEST_F(TestArithmeticSelfInt8, floor_quant1_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -142,8 +142,8 @@ TEST_F(TestArithmeticSelfInt8, floor_quant1_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -167,7 +167,7 @@ TEST_F(TestArithmeticSelfInt8, round_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -175,7 +175,7 @@ TEST_F(TestArithmeticSelfInt8, round_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -202,8 +202,8 @@ TEST_F(TestArithmeticSelfInt8, round_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -227,7 +227,7 @@ TEST_F(TestArithmeticSelfInt8, round_quant1_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -235,7 +235,7 @@ TEST_F(TestArithmeticSelfInt8, round_quant1_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -262,8 +262,8 @@ TEST_F(TestArithmeticSelfInt8, round_quant1_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -287,7 +287,7 @@ TEST_F(TestArithmeticSelfInt8, ceil_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -295,7 +295,7 @@ TEST_F(TestArithmeticSelfInt8, ceil_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -322,8 +322,8 @@ TEST_F(TestArithmeticSelfInt8, ceil_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -347,7 +347,7 @@ TEST_F(TestArithmeticSelfInt8, ceil_quant1_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -355,7 +355,7 @@ TEST_F(TestArithmeticSelfInt8, ceil_quant1_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -382,8 +382,8 @@ TEST_F(TestArithmeticSelfInt8, ceil_quant1_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -407,7 +407,7 @@ TEST_F(TestArithmeticSelfInt8, abs_quant0_thread0) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -415,7 +415,7 @@ TEST_F(TestArithmeticSelfInt8, abs_quant0_thread0) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -442,8 +442,8 @@ TEST_F(TestArithmeticSelfInt8, abs_quant0_thread0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -467,7 +467,7 @@ TEST_F(TestArithmeticSelfInt8, abs_quant1_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -475,7 +475,7 @@ TEST_F(TestArithmeticSelfInt8, abs_quant1_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -502,8 +502,8 @@ TEST_F(TestArithmeticSelfInt8, abs_quant1_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -527,7 +527,7 @@ TEST_F(TestArithmeticSelfInt8, sin_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -535,7 +535,7 @@ TEST_F(TestArithmeticSelfInt8, sin_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -562,8 +562,8 @@ TEST_F(TestArithmeticSelfInt8, sin_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -587,7 +587,7 @@ TEST_F(TestArithmeticSelfInt8, cos_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -595,7 +595,7 @@ TEST_F(TestArithmeticSelfInt8, cos_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -622,8 +622,8 @@ TEST_F(TestArithmeticSelfInt8, cos_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -647,7 +647,7 @@ TEST_F(TestArithmeticSelfInt8, log_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -655,7 +655,7 @@ TEST_F(TestArithmeticSelfInt8, log_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -682,8 +682,8 @@ TEST_F(TestArithmeticSelfInt8, log_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -707,7 +707,7 @@ TEST_F(TestArithmeticSelfInt8, sqrt_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -715,7 +715,7 @@ TEST_F(TestArithmeticSelfInt8, sqrt_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -742,8 +742,8 @@ TEST_F(TestArithmeticSelfInt8, sqrt_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -767,7 +767,7 @@ TEST_F(TestArithmeticSelfInt8, rsqrt_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -775,7 +775,7 @@ TEST_F(TestArithmeticSelfInt8, rsqrt_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -802,8 +802,8 @@ TEST_F(TestArithmeticSelfInt8, rsqrt_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -827,7 +827,7 @@ TEST_F(TestArithmeticSelfInt8, square_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -835,7 +835,7 @@ TEST_F(TestArithmeticSelfInt8, square_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -862,8 +862,8 @@ TEST_F(TestArithmeticSelfInt8, square_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -887,7 +887,7 @@ TEST_F(TestArithmeticSelfInt8, square_quant1_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -895,7 +895,7 @@ TEST_F(TestArithmeticSelfInt8, square_quant1_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -922,8 +922,8 @@ TEST_F(TestArithmeticSelfInt8, square_quant1_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -947,7 +947,7 @@ TEST_F(TestArithmeticSelfInt8, logical_not_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -955,7 +955,7 @@ TEST_F(TestArithmeticSelfInt8, logical_not_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -982,8 +982,8 @@ TEST_F(TestArithmeticSelfInt8, logical_not_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc index 323b7a5a65..c5322be70e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/batchnorm_int8_test.cc @@ -73,11 +73,11 @@ TEST_F(TestBatchnormInt8, FusedTest) { inputs_tensor.push_back(&input2_tensor); inputs_tensor.push_back(&input3_tensor); inputs_tensor.push_back(&input4_tensor); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); - input2_tensor.SetData(in_data2.data()); - input3_tensor.SetData(in_data3.data()); - input4_tensor.SetData(in_data4.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); + input2_tensor.set_data(in_data2.data()); + input3_tensor.set_data(in_data3.data()); + input4_tensor.set_data(in_data4.data()); input0_tensor.set_shape(shape); input1_tensor.set_shape({2}); input2_tensor.set_shape({2}); @@ -94,7 +94,7 @@ TEST_F(TestBatchnormInt8, FusedTest) { std::vector corr_out = {-22, -28, -20, -26, -17, -24, -28, -42, -30, -44, -33, -46}; lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(shape); output0_tensor.AddQuantParam(output_quant_arg); @@ -118,12 +118,12 @@ TEST_F(TestBatchnormInt8, FusedTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - input2_tensor.SetData(nullptr); - input3_tensor.SetData(nullptr); - input4_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + input2_tensor.set_data(nullptr); + input3_tensor.set_data(nullptr); + input4_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); MS_LOG(INFO) << "TestBathNormFp32 accuracy passed"; } @@ -160,9 +160,9 @@ TEST_F(TestBatchnormInt8, BNTest) { inputs_tensor.push_back(&input0_tensor); inputs_tensor.push_back(&input1_tensor); inputs_tensor.push_back(&input2_tensor); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); - input2_tensor.SetData(in_data2.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); + input2_tensor.set_data(in_data2.data()); input0_tensor.set_shape(shape); input1_tensor.set_shape({2}); input2_tensor.set_shape({2}); @@ -175,7 +175,7 @@ TEST_F(TestBatchnormInt8, BNTest) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(shape); output0_tensor.AddQuantParam(output_quant_arg); @@ -199,10 +199,10 @@ TEST_F(TestBatchnormInt8, BNTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - input2_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + input2_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); MS_LOG(INFO) << "TestBathNormFp32 accuracy passed"; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/bias_add_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/bias_add_int8_tests.cc index 518db24583..e1ac3b57f9 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/bias_add_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/bias_add_int8_tests.cc @@ -36,9 +36,9 @@ TEST_F(TestBiasAddInt8, BiasAdd) { int8_t input_data0[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; int8_t input_data1[] = {1, 1}; int8_t output_data[12] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor0, &in_tensor1}; std::vector outputs = {&out_tensor}; @@ -70,8 +70,8 @@ TEST_F(TestBiasAddInt8, BiasAdd) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/concat_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/concat_int8_tests.cc index 5ff97603e1..b354f43b92 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/concat_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/concat_int8_tests.cc @@ -51,13 +51,13 @@ TEST_F(TestConcatInt8, Concat1_axis0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -68,7 +68,7 @@ TEST_F(TestConcatInt8, Concat1_axis0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -93,9 +93,9 @@ TEST_F(TestConcatInt8, Concat1_axis0) { std::vector except_result = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}; PrintData("output data", output, input1.size() + input2.size()); CompareOutputData(output, except_result.data(), input1.size() + input2.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; @@ -123,13 +123,13 @@ TEST_F(TestConcatInt8, Concat1_axis1_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -140,7 +140,7 @@ TEST_F(TestConcatInt8, Concat1_axis1_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -166,9 +166,9 @@ TEST_F(TestConcatInt8, Concat1_axis1_thread2) { PrintData("output data", output, input1.size() + input2.size()); CompareOutputData(output, except_result.data(), input1.size() + input2.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; @@ -196,13 +196,13 @@ TEST_F(TestConcatInt8, Concat1_axis1_thread2_quant1) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -213,7 +213,7 @@ TEST_F(TestConcatInt8, Concat1_axis1_thread2_quant1) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -239,9 +239,9 @@ TEST_F(TestConcatInt8, Concat1_axis1_thread2_quant1) { PrintData("output data", output, input1.size() + input2.size()); CompareOutputData(output, except_result.data(), input1.size() + input2.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/conv_1x1_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/conv_1x1_int8_tests.cc index ff8c7e1754..2d75af9f92 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/conv_1x1_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/conv_1x1_int8_tests.cc @@ -71,7 +71,7 @@ TEST_F(TestConv1x1Int8, Input1x1PrePack2) { int Conv1x1Int8TestInit1_perchannel(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, int8_t **correct) { - Tensor *in_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); auto in_quant_arg = new mindspore::lite::QuantArg(); in_quant_arg->zeroPoint = -42, in_quant_arg->scale = 0.117647; in_t->AddQuantParam(*in_quant_arg); @@ -81,7 +81,8 @@ int Conv1x1Int8TestInit1_perchannel(std::vector *inputs_, std::v memcpy(in_t->MutableData(), in, in_t->ElementsNum() * sizeof(int8_t)); inputs_->push_back(in_t); - Tensor *weight_t = new Tensor(kNumberTypeInt8, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *weight_t = + new Tensor(kNumberTypeInt8, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); auto weight_quant_arg1 = new mindspore::lite::QuantArg(); weight_quant_arg1->zeroPoint = 66, weight_quant_arg1->scale = 0.96439215686275; @@ -96,7 +97,7 @@ int Conv1x1Int8TestInit1_perchannel(std::vector *inputs_, std::v memcpy(weight_t->MutableData(), weight, weight_t->ElementsNum() * sizeof(int8_t)); inputs_->push_back(weight_t); - Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); auto output_quant_arg = new mindspore::lite::QuantArg(); output_quant_arg->zeroPoint = 7, output_quant_arg->scale = 0.294321233; @@ -139,7 +140,7 @@ TEST_F(TestConv1x1Int8, Conv1x1TestPerChannel) { int Conv1x1Int8TestInit1(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, int8_t **correct) { - Tensor *in_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); auto in_quant_arg = new mindspore::lite::QuantArg(); in_quant_arg->zeroPoint = -42, in_quant_arg->scale = 0.117647; in_t->AddQuantParam(*in_quant_arg); @@ -151,7 +152,8 @@ int Conv1x1Int8TestInit1(std::vector *inputs_, std::vector(in_t->MutableData())); inputs_->push_back(in_t); - Tensor *weight_t = new Tensor(kNumberTypeInt8, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *weight_t = + new Tensor(kNumberTypeInt8, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); auto weight_quant_arg = new mindspore::lite::QuantArg(); weight_quant_arg->zeroPoint = 66, weight_quant_arg->scale = 0.036439215686275; weight_t->AddQuantParam(*weight_quant_arg); @@ -162,7 +164,7 @@ int Conv1x1Int8TestInit1(std::vector *inputs_, std::vector(weight_t->MutableData())); inputs_->push_back(weight_t); - Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); auto output_quant_arg = new mindspore::lite::QuantArg(); output_quant_arg->zeroPoint = 7, output_quant_arg->scale = 0.234321233; @@ -208,7 +210,7 @@ TEST_F(TestConv1x1Int8, Conv1x1Int8Test1) { int Conv1x1Int8TestInit2(std::vector *inputs_, std::vector *outputs_, ConvParameter *conv_param, int8_t **correct) { size_t buffer_size; - Tensor *in_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *in_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); auto in_quant_arg = new mindspore::lite::QuantArg(); in_quant_arg->zeroPoint = -42, in_quant_arg->scale = 0.117647; in_t->AddQuantParam(*in_quant_arg); @@ -219,7 +221,8 @@ int Conv1x1Int8TestInit2(std::vector *inputs_, std::vectorpush_back(in_t); delete[] input; - Tensor *weight_t = new Tensor(kNumberTypeInt8, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *weight_t = + new Tensor(kNumberTypeInt8, {3, 1, 1, 4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); auto weight_quant_arg = new mindspore::lite::QuantArg(); weight_quant_arg->zeroPoint = 66, weight_quant_arg->scale = 0.036439215686275; weight_t->AddQuantParam(*weight_quant_arg); @@ -230,7 +233,7 @@ int Conv1x1Int8TestInit2(std::vector *inputs_, std::vectorpush_back(weight_t); delete[] weight; - Tensor *bias_t = new Tensor(kNumberTypeInt32, {4}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *bias_t = new Tensor(kNumberTypeInt32, {4}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); std::string bias_path = "./bias"; auto bias = mindspore::lite::ReadFile(bias_path.c_str(), &buffer_size); @@ -238,7 +241,7 @@ int Conv1x1Int8TestInit2(std::vector *inputs_, std::vectorpush_back(bias_t); delete[] bias; - Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST); + Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 2, 3, 3}, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); out_t->MallocData(); auto output_quant_arg = new mindspore::lite::QuantArg(); output_quant_arg->zeroPoint = 7, output_quant_arg->scale = 0.234321233; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/crop_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/crop_int8_tests.cc index edbf7e2422..40ef98d6be 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/crop_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/crop_int8_tests.cc @@ -48,7 +48,7 @@ TEST_F(TestCropInt8, crop_1d_axis0_offset0_quant0_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -58,7 +58,7 @@ TEST_F(TestCropInt8, crop_1d_axis0_offset0_quant0_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -87,8 +87,8 @@ TEST_F(TestCropInt8, crop_1d_axis0_offset0_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -112,7 +112,7 @@ TEST_F(TestCropInt8, crop_2d_axis1_offset0_quant0_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -122,7 +122,7 @@ TEST_F(TestCropInt8, crop_2d_axis1_offset0_quant0_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -151,8 +151,8 @@ TEST_F(TestCropInt8, crop_2d_axis1_offset0_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -176,7 +176,7 @@ TEST_F(TestCropInt8, crop_3d_axis1_offset0_quant0_thread0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -186,7 +186,7 @@ TEST_F(TestCropInt8, crop_3d_axis1_offset0_quant0_thread0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -215,8 +215,8 @@ TEST_F(TestCropInt8, crop_3d_axis1_offset0_quant0_thread0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -241,7 +241,7 @@ TEST_F(TestCropInt8, crop_3d_axis1_offset0_quant0_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -251,7 +251,7 @@ TEST_F(TestCropInt8, crop_3d_axis1_offset0_quant0_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -280,8 +280,8 @@ TEST_F(TestCropInt8, crop_3d_axis1_offset0_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -305,7 +305,7 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -315,7 +315,7 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -344,8 +344,8 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -369,7 +369,7 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset0_quant0_thread0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -379,7 +379,7 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset0_quant0_thread0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -408,8 +408,8 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset0_quant0_thread0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -433,7 +433,7 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset1_quant0_thread0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -443,7 +443,7 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset1_quant0_thread0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -475,8 +475,8 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset1_quant0_thread0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -500,7 +500,7 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset1_quant1_thread0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -510,7 +510,7 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset1_quant1_thread0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -542,8 +542,8 @@ TEST_F(TestCropInt8, crop_4d_axis1_offset1_quant1_thread0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -569,7 +569,7 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -579,7 +579,7 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -608,8 +608,8 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -635,7 +635,7 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread3) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -645,7 +645,7 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread3) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -674,8 +674,8 @@ TEST_F(TestCropInt8, crop_4d_axis0_offset0_quant0_thread3) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc index 306b32d498..d633005e1a 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/deconv_int8_tests.cc @@ -310,7 +310,7 @@ int DeConvInt8TestInit1(std::vector *inputs_, std::vectorMallocData(); int8_t in[] = {6, 43, 38, 24, -8, 12, 41, -24, -20, 41, -19, -6, -26, -6, 23, -31, 34, 45, 8, 45, -39, -27, -48, 12}; memcpy(in_t->MutableData(), in, sizeof(int8_t) * in_t->ElementsNum()); @@ -319,7 +319,7 @@ int DeConvInt8TestInit1(std::vector *inputs_, std::vectorAddQuantParam(*in_quant_arg); inputs_->push_back(in_t); - Tensor *weight_t = new Tensor(kNumberTypeInt8, {3, 3, 3, 2}, Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *weight_t = new Tensor(kNumberTypeInt8, {3, 3, 3, 2}, Format_NHWC, lite::Tensor::Category::CONST_TENSOR); weight_t->MallocData(); int8_t weight[] = {66, 89, 98, 74, 95, 86, 125, 95, 105, 83, 116, 94, 90, 80, 86, 59, 72, 92, 64, 76, 92, 80, 90, 87, 106, 55, 105, 60, 75, 53, 81, 81, 98, 81, 86, 59, @@ -330,7 +330,7 @@ int DeConvInt8TestInit1(std::vector *inputs_, std::vectorAddQuantParam(*w_quant_arg); inputs_->push_back(weight_t); - Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 7, 3, 2}, Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *out_t = new Tensor(kNumberTypeInt8, {1, 7, 3, 2}, Format_NHWC, lite::Tensor::Category::VAR); out_t->MallocData(); QuantArg *out_quant_arg = new QuantArg(); out_quant_arg->zeroPoint = 31, out_quant_arg->scale = 0.3439215686275; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/div_int8_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/div_int8_test.cc index 4bb836a01b..a019ab6390 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/div_int8_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/div_int8_test.cc @@ -36,9 +36,9 @@ TEST_F(TestDivInt8, DivInt8) { int8_t input_data0[] = {105, 35, -27, 0, -63, 99, 16, 45, 67, -49}; int8_t input_data1[] = {126, -38, -115, 106, -98, 119, 103, 81, -114, 68}; int8_t output_data[10] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); const lite::QuantArg quant_in0 = {0.00784314f, 0}; // -1.0--1.0 -> 0--255 const lite::QuantArg quant_in1 = {0.00784314f, 0}; @@ -69,8 +69,8 @@ TEST_F(TestDivInt8, DivInt8) { EXPECT_EQ(output_data[i], expect0[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc index 066e77719f..4dc18b145e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/fullconnection_int8_tests.cc @@ -42,7 +42,7 @@ extern void QuantProcess(float *input, int len, float min, float max, float *sca extern lite::Tensor *MakeQuantTensor(int8_t *data, int len, std::vector *shape, float scale, int zp); lite::Tensor *MakeIntTensor(int *data, int len, std::vector *shape) { - auto tensor = new lite::Tensor(kNumberTypeInt32, *shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto tensor = new lite::Tensor(kNumberTypeInt32, *shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); tensor->MallocData(); auto tensor_ptr = reinterpret_cast(tensor->MutableData()); memcpy(tensor_ptr, data, len * sizeof(int)); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gatherNd_int8_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gatherNd_int8_test.cc index 6ec95b84fc..361cead544 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gatherNd_int8_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gatherNd_int8_test.cc @@ -57,8 +57,8 @@ TEST_F(TestGatherNdInt8, GatherNdTest) { inputs_tensor.push_back(&input0_tensor); inputs_tensor.push_back(&input1_tensor); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); input0_tensor.set_shape(shape); input1_tensor.set_shape({3, 3}); @@ -71,7 +71,7 @@ TEST_F(TestGatherNdInt8, GatherNdTest) { std::vector corr_out = {6, 7, 8, 9, 0, 6, 7, 8, 9, 0, 1, 2, 3, 4, 5}; lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(out_shape); output0_tensor.AddQuantParam(output_quant_arg); @@ -94,9 +94,9 @@ TEST_F(TestGatherNdInt8, GatherNdTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); MS_LOG(INFO) << "TestGatherNd accuracy passed"; } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gather_int8_test.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gather_int8_test.cc index 9174875ce7..1cedf1241d 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gather_int8_test.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/gather_int8_test.cc @@ -55,8 +55,8 @@ TEST_F(TestGatherInt8, GatherTest) { inputs_tensor.push_back(&input0_tensor); inputs_tensor.push_back(&input1_tensor); - input0_tensor.SetData(in_data.data()); - input1_tensor.SetData(in_data1.data()); + input0_tensor.set_data(in_data.data()); + input1_tensor.set_data(in_data1.data()); input0_tensor.set_shape(shape); input1_tensor.set_shape({2}); @@ -69,7 +69,7 @@ TEST_F(TestGatherInt8, GatherTest) { std::vector corr_out = {-11, -41, -21, -51, -31, -61, 11, 41, 21, 51, 31, 61}; lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.set_shape(shape); output0_tensor.AddQuantParam(output_quant_arg); @@ -92,9 +92,9 @@ TEST_F(TestGatherInt8, GatherTest) { std::cout << std::endl; CompareOutputData(output.data(), corr_out.data(), output0_tensor.ElementsNum(), 0.001); - input0_tensor.SetData(nullptr); - input1_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + input1_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); MS_LOG(INFO) << "TestGather_int8 accuracy passed"; } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/hswish_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/hswish_int8_tests.cc index 30e5933e45..4e81f27a21 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/hswish_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/hswish_int8_tests.cc @@ -36,8 +36,8 @@ TEST_F(TestHSwishInt8, HSwish) { int8_t input_data[] = {-116, -105, -93, -35, 23, 35, 46, 104}; // -3.5f, -3.0f, -2.5f, 0.f, 2.5f, 3.0f, 3.5f, 6.0f int8_t output_data[8] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); const lite::QuantArg quant_in = {0.0431373f, -35}; // -4.0 -- 7.0 const lite::QuantArg quant_out = {0.0392157f, -52}; // -3.0 -- 7.0 @@ -69,7 +69,7 @@ TEST_F(TestHSwishInt8, HSwish) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc index 061bebebc1..b24b5893c6 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/matmul_int8_tests.cc @@ -47,7 +47,7 @@ void QuantProcess(float *input, int len, float min, float max, float *scale, int } lite::Tensor *MakeQuantTensor(int8_t *data, int len, std::vector *shape, float scale, int zp) { - auto tensor = new lite::Tensor(kNumberTypeInt8, *shape, schema::Format_NHWC, lite::Tensor::Category::CONST); + auto tensor = new lite::Tensor(kNumberTypeInt8, *shape, schema::Format_NHWC, lite::Tensor::Category::CONST_TENSOR); tensor->MallocData(); if (data) { auto tensor_ptr = reinterpret_cast(tensor->MutableData()); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/mul_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/mul_int8_tests.cc index a80faffe86..95a3ecc602 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/mul_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/mul_int8_tests.cc @@ -51,13 +51,13 @@ TEST_F(TestMulInt8, Mul_quant0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -68,7 +68,7 @@ TEST_F(TestMulInt8, Mul_quant0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -92,9 +92,9 @@ TEST_F(TestMulInt8, Mul_quant0) { std::vector except_result = {1, 4, 3, 8, 5, 12, 21, 32, 27, 40, 33, 48}; PrintData("output data", output, input1.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; @@ -122,13 +122,13 @@ TEST_F(TestMulInt8, Mul_quant0_thread0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -139,7 +139,7 @@ TEST_F(TestMulInt8, Mul_quant0_thread0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -163,9 +163,9 @@ TEST_F(TestMulInt8, Mul_quant0_thread0) { std::vector except_result = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18}; PrintData("output data", output, input1.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; @@ -193,13 +193,13 @@ TEST_F(TestMulInt8, Mul_quant1) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -210,7 +210,7 @@ TEST_F(TestMulInt8, Mul_quant1) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -234,9 +234,9 @@ TEST_F(TestMulInt8, Mul_quant1) { std::vector except_result = {1, 2, 2, 4, 3, 6, 11, 16, 14, 20, 17, 24}; PrintData("output data", output, input1.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; @@ -264,13 +264,13 @@ TEST_F(TestMulInt8, Mul_quant1_thread1) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -281,7 +281,7 @@ TEST_F(TestMulInt8, Mul_quant1_thread1) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -305,9 +305,9 @@ TEST_F(TestMulInt8, Mul_quant1_thread1) { std::vector except_result = {1, 2, 2, 4, 3, 6, 11, 16, 14, 20, 17, 24}; PrintData("output data", output, input1.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; @@ -335,13 +335,13 @@ TEST_F(TestMulInt8, test) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); lite::Tensor *input_tensor2 = new lite::Tensor; - input_tensor2->SetData(input2.data()); + input_tensor2->set_data(input2.data()); input_tensor2->set_shape(shape2); input_tensor2->AddQuantParam(input_quant_arg); input_tensor2->set_data_type(tid_int8); @@ -352,7 +352,7 @@ TEST_F(TestMulInt8, test) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -376,9 +376,9 @@ TEST_F(TestMulInt8, test) { std::vector except_result = {1, 4, 9, 16, 25, 36, 7, 16, 27, 40, 55, 72}; PrintData("output data", output, input1.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - input_tensor2->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + input_tensor2->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete input_tensor2; delete output0_tensor; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/pad_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/pad_int8_tests.cc index f12e6d5e47..2ea9242673 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/pad_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/pad_int8_tests.cc @@ -34,7 +34,7 @@ class TestPadInt8 : public mindspore::CommonTest { int PadInt8TestInit1(std::vector *inputs_, std::vector *outputs_, PadParameter *pad_param, int8_t **correct) { - Tensor *in_t = new Tensor(kNumberTypeInt8, {3}, schema::Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *in_t = new Tensor(kNumberTypeInt8, {3}, schema::Format_NHWC, lite::Tensor::CONST_TENSOR); in_t->MallocData(); int8_t in[] = {1, 1, 1}; memcpy(in_t->MutableData(), in, sizeof(int8_t) * in_t->ElementsNum()); @@ -43,7 +43,7 @@ int PadInt8TestInit1(std::vector *inputs_, std::vector *outp in_t->AddQuantParam(*in_quant_arg); inputs_->push_back(in_t); - Tensor *out_t = new Tensor(kNumberTypeInt8, {7}, schema::Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *out_t = new Tensor(kNumberTypeInt8, {7}, schema::Format_NHWC, lite::Tensor::CONST_TENSOR); out_t->MallocData(); QuantArg *out_quant_arg = new QuantArg(); out_quant_arg->zeroPoint = 10, out_quant_arg->scale = 0.31228156; @@ -85,7 +85,7 @@ TEST_F(TestPadInt8, PadInt8Test1) { int PadInt8TestInit2(std::vector *inputs_, std::vector *outputs_, PadParameter *pad_param, int8_t **correct) { - Tensor *in_t = new Tensor(kNumberTypeInt8, {6, 2}, schema::Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *in_t = new Tensor(kNumberTypeInt8, {6, 2}, schema::Format_NHWC, lite::Tensor::VAR); in_t->MallocData(); int8_t in[] = {18, 71, 99, -6, 5, -119, 86, 13, 15, -85, -41, -77}; memcpy(in_t->MutableData(), in, sizeof(int8_t) * in_t->ElementsNum()); @@ -94,7 +94,7 @@ int PadInt8TestInit2(std::vector *inputs_, std::vector *outp in_t->AddQuantParam(*in_quant_arg); inputs_->push_back(in_t); - Tensor *out_t = new Tensor(kNumberTypeInt8, {10, 5}, schema::Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *out_t = new Tensor(kNumberTypeInt8, {10, 5}, schema::Format_NHWC, lite::Tensor::VAR); out_t->MallocData(); QuantArg *out_quant_arg = new QuantArg(); out_quant_arg->zeroPoint = 10, out_quant_arg->scale = 0.31228156; @@ -138,8 +138,7 @@ TEST_F(TestPadInt8, PadInt8Test2) { int PadInt8TestInit4(std::vector *inputs_, std::vector *outputs_, PadParameter *pad_param, int8_t **correct) { - Tensor *in_t = - new Tensor(kNumberTypeInt8, {2, 3, 2, 1}, schema::Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *in_t = new Tensor(kNumberTypeInt8, {2, 3, 2, 1}, schema::Format_NHWC, lite::Tensor::VAR); in_t->MallocData(); int8_t in[] = {73, 24, 7, -31, -109, -2, 69, -64, 51, -45, 38, 53}; memcpy(in_t->MutableData(), in, sizeof(int8_t) * in_t->ElementsNum()); @@ -148,8 +147,7 @@ int PadInt8TestInit4(std::vector *inputs_, std::vector *outp in_t->AddQuantParam(*in_quant_arg); inputs_->push_back(in_t); - Tensor *out_t = - new Tensor(kNumberTypeInt8, {6, 6, 4, 3}, schema::Format_NHWC, lite::TensorCategory(NodeType_Parameter)); + Tensor *out_t = new Tensor(kNumberTypeInt8, {6, 6, 4, 3}, schema::Format_NHWC, lite::Tensor::VAR); out_t->MallocData(); QuantArg *out_quant_arg = new QuantArg(); out_quant_arg->zeroPoint = 10, out_quant_arg->scale = 0.31228156; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/power_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/power_int8_tests.cc index 5e4d1b661b..c207ac73a4 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/power_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/power_int8_tests.cc @@ -52,7 +52,7 @@ TEST_F(TestPowerInt8, PowerInt8) { lite::Tensor input0_tensor; TypeId tid_int8 = kNumberTypeInt8; inputs_tensor.push_back(&input0_tensor); - input0_tensor.SetData(input.data()); + input0_tensor.set_data(input.data()); input0_tensor.set_shape(in_shape); input0_tensor.AddQuantParam(input_quant_arg); input0_tensor.set_data_type(tid_int8); @@ -62,7 +62,7 @@ TEST_F(TestPowerInt8, PowerInt8) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.AddQuantParam(output_quant_arg); output0_tensor.set_data_type(tid_int8); @@ -81,8 +81,8 @@ TEST_F(TestPowerInt8, PowerInt8) { std::vector except_result = {-112, -65, 15, 127}; CompareOutputData(output.data(), except_result.data(), input.size(), 0.000001); - input0_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } TEST_F(TestPowerInt8, normal) { @@ -116,12 +116,12 @@ TEST_F(TestPowerInt8, normal) { TypeId tid_int8 = kNumberTypeInt8; inputs_tensor.push_back(&input0_tensor); inputs_tensor.push_back(&input1_tensor); - input0_tensor.SetData(input.data()); + input0_tensor.set_data(input.data()); input0_tensor.set_shape(in_shape); input0_tensor.AddQuantParam(input_quant_arg); input0_tensor.set_data_type(tid_int8); - input1_tensor.SetData(input1.data()); + input1_tensor.set_data(input1.data()); input1_tensor.set_shape(in_shape1); input1_tensor.AddQuantParam(exp_quant_arg); input1_tensor.set_data_type(tid_int8); @@ -131,7 +131,7 @@ TEST_F(TestPowerInt8, normal) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.AddQuantParam(output_quant_arg); output0_tensor.set_data_type(tid_int8); @@ -150,7 +150,7 @@ TEST_F(TestPowerInt8, normal) { std::vector except_result = {-99, 95, 124, -14}; CompareOutputData(output.data(), except_result.data(), input.size(), 0.000001); - input0_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/prelu_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/prelu_int8_tests.cc index e457c7026e..556c33956a 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/prelu_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/prelu_int8_tests.cc @@ -48,7 +48,7 @@ TEST_F(TestPreluInt8, prelu_1) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -58,7 +58,7 @@ TEST_F(TestPreluInt8, prelu_1) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -87,8 +87,8 @@ TEST_F(TestPreluInt8, prelu_1) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete ctx; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/quant_dtype_cast_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/quant_dtype_cast_tests.cc index ba66766127..67193f0382 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/quant_dtype_cast_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/quant_dtype_cast_tests.cc @@ -40,7 +40,7 @@ TEST_F(QuantDTypeCastTestFp32, QuantDTypeCastTest1) { std::vector input = {10, 14, 29, 33, 52, 99, 19, 43, 90, 52, 19, 24, 57, 127, 76, 123}; std::vector in_shape = {1, 4, 4, 1}; lite::Tensor input_tensor; - input_tensor.SetData(input.data()); + input_tensor.set_data(input.data()); input_tensor.set_shape(in_shape); input_tensor.set_data_type(kNumberTypeInt8); input_tensor.SetFormat(schema::Format_NHWC); @@ -55,7 +55,7 @@ TEST_F(QuantDTypeCastTestFp32, QuantDTypeCastTest1) { std::vector output(16); std::vector out_shape = {1, 4, 4, 1}; lite::Tensor output_tensor; - output_tensor.SetData(output.data()); + output_tensor.set_data(output.data()); output_tensor.set_shape(out_shape); output_tensor.set_data_type(kNumberTypeFloat32); // output_tensor.SetFormat(schema::Format_NHWC); @@ -89,7 +89,7 @@ TEST_F(QuantDTypeCastTestFp32, QuantDTypeCastTest2) { std::vector input = {1, 2, 5, 6, 10, -20, 3, 8, 18, 10, 3, 4, 11, 16, 15, 25}; std::vector in_shape = {1, 4, 4, 1}; lite::Tensor input_tensor; - input_tensor.SetData(input.data()); + input_tensor.set_data(input.data()); input_tensor.set_shape(in_shape); // input_tensor.SetFormat(schema::Format_NHWC); input_tensor.set_data_type(kNumberTypeFloat32); @@ -102,7 +102,7 @@ TEST_F(QuantDTypeCastTestFp32, QuantDTypeCastTest2) { std::vector output(16); std::vector out_shape = {1, 4, 4, 1}; lite::Tensor output_tensor; - output_tensor.SetData(output.data()); + output_tensor.set_data(output.data()); output_tensor.set_shape(out_shape); output_tensor.SetFormat(schema::Format_NHWC); output_tensor.set_data_type(kNumberTypeInt8); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reduce_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reduce_int8_tests.cc index 56bc7c9c2a..947a307c70 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reduce_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reduce_int8_tests.cc @@ -57,20 +57,20 @@ class TestReduceInt8 : public mindspore::CommonTest { }; void TestReduceInt8::TearDown() { - in_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestReduceInt8::Prepare(const std::vector &in_shape, const std::vector &out_shape, int8_t *input_data, int8_t *output_data, ReduceMode mode, const int *axes, const int num_axes) { in_tensor_.set_data_type(kNumberTypeInt8); in_tensor_.set_shape(in_shape); - in_tensor_.SetData(input_data); + in_tensor_.set_data(input_data); in_tensor_.AddQuantParam(quant_in_); out_tensor_.set_data_type(kNumberTypeInt8); out_tensor_.set_shape(out_shape); - out_tensor_.SetData(output_data); + out_tensor_.set_data(output_data); out_tensor_.AddQuantParam(quant_out_); param_.mode_ = static_cast(mode); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/relux_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/relux_int8_tests.cc index 7297afbf85..159990deb5 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/relux_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/relux_int8_tests.cc @@ -34,8 +34,8 @@ TEST_F(TestReluXInt8, Relu) { int8_t input_data[] = {-102, 25, -51, 89}; // -0.8 0.2 -0.4 0.7 int8_t output_data[4] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); const lite::QuantArg quant_in = {0.00784314f, 0}; // -1.0--1.0 -> const lite::QuantArg quant_out = {0.00784314f, 0}; @@ -67,8 +67,8 @@ TEST_F(TestReluXInt8, Relu) { EXPECT_EQ(output_data[i], expect0[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } TEST_F(TestReluXInt8, Relu6) { @@ -78,8 +78,8 @@ TEST_F(TestReluXInt8, Relu6) { // -2.5f, -1.5f, 1.25f, 3.0f, 4.5f, 6.0f, 6.5f, 9.0f int8_t input_data[] = {-118, -98, -44, -10, 19, 49, 59, 108}; int8_t output_data[8] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); const lite::QuantArg quant_in = {0.0509804f, -69}; // -3.0 -- 10.0 const lite::QuantArg quant_out = {0.0392157f, -128}; // 0.0 -- 10.0 @@ -112,7 +112,7 @@ TEST_F(TestReluXInt8, Relu6) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reshape_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reshape_int8_tests.cc index 29a8c698f0..90f000485e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reshape_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/reshape_int8_tests.cc @@ -47,7 +47,7 @@ TEST_F(TestReshapeInt8, reshape_quant0) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -57,7 +57,7 @@ TEST_F(TestReshapeInt8, reshape_quant0) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -83,8 +83,8 @@ TEST_F(TestReshapeInt8, reshape_quant0) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; @@ -107,7 +107,7 @@ TEST_F(TestReshapeInt8, reshape_quant1_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -117,7 +117,7 @@ TEST_F(TestReshapeInt8, reshape_quant1_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -143,8 +143,8 @@ TEST_F(TestReshapeInt8, reshape_quant1_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), input1.size(), 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_bilinear_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_bilinear_int8_tests.cc index a2c7bd2deb..981fe92e7f 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_bilinear_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_bilinear_int8_tests.cc @@ -47,8 +47,8 @@ class TestResizeBilinearInt8 : public mindspore::CommonTest { }; void TestResizeBilinearInt8::TearDown() { - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } void TestResizeBilinearInt8::Prepare(const std::vector &in_shape, const std::vector &out_shape, @@ -57,12 +57,12 @@ void TestResizeBilinearInt8::Prepare(const std::vector &in_shape, const std const int thread_num) { in_tensor.set_data_type(kNumberTypeInt8); in_tensor.set_shape(in_shape); - in_tensor.SetData(input_data); + in_tensor.set_data(input_data); in_tensor.AddQuantParam(quant_in); out_tensor.set_data_type(kNumberTypeInt8); out_tensor.set_shape(out_shape); - out_tensor.SetData(output_data); + out_tensor.set_data(output_data); out_tensor.AddQuantParam(quant_out); inputs.push_back(&in_tensor); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_nearest_neighbor_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_nearest_neighbor_int8_tests.cc index a14d6dc3ab..b4259aadd9 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_nearest_neighbor_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/resize_nearest_neighbor_int8_tests.cc @@ -52,12 +52,12 @@ void TestResizeNearestNeighborInt8::Prepare(const std::vector &in_shape, co const QuantArg quant_out, const bool align_corners, const int thread_num) { in_tensor.set_data_type(kNumberTypeInt8); in_tensor.set_shape(in_shape); - in_tensor.SetData(input_data); + in_tensor.set_data(input_data); in_tensor.AddQuantParam(quant_in); out_tensor.set_data_type(kNumberTypeInt8); out_tensor.set_shape(out_shape); - out_tensor.SetData(output_data); + out_tensor.set_data(output_data); out_tensor.AddQuantParam(quant_out); inputs.push_back(&in_tensor); @@ -76,8 +76,8 @@ void TestResizeNearestNeighborInt8::Prepare(const std::vector &in_shape, co } void TestResizeNearestNeighborInt8::TearDown() { - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } // 2*2*1 -> 4*4*1 diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/scale_int8.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/scale_int8.cc index 20fc96b8ba..fe400d754d 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/scale_int8.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/scale_int8.cc @@ -55,10 +55,10 @@ class TestScaleInt8 : public mindspore::CommonTest { }; void TestScaleInt8::TearDown() { - in_tensor_.SetData(nullptr); - scale_tensor_.SetData(nullptr); - bias_tensor_.SetData(nullptr); - out_tensor_.SetData(nullptr); + in_tensor_.set_data(nullptr); + scale_tensor_.set_data(nullptr); + bias_tensor_.set_data(nullptr); + out_tensor_.set_data(nullptr); } void TestScaleInt8::Prepare(const std::vector &in_shape, int8_t *input_data, const std::vector &scale_shape, @@ -66,11 +66,11 @@ void TestScaleInt8::Prepare(const std::vector &in_shape, int8_t *input_data const std::vector &out_shape, int8_t *output_data, int axis, bool has_bias) { in_tensor_.set_data_type(kNumberTypeInt8); in_tensor_.set_shape(in_shape); - in_tensor_.SetData(input_data); + in_tensor_.set_data(input_data); in_tensor_.AddQuantParam(quant_in_); scale_tensor_.set_data_type(kNumberTypeInt8); scale_tensor_.set_shape(scale_shape); - scale_tensor_.SetData(scale_data); + scale_tensor_.set_data(scale_data); scale_tensor_.AddQuantParam(quant_scale_); inputs.clear(); @@ -79,14 +79,14 @@ void TestScaleInt8::Prepare(const std::vector &in_shape, int8_t *input_data if (has_bias) { bias_tensor_.set_data_type(kNumberTypeInt8); bias_tensor_.set_shape(bias_shape); - bias_tensor_.SetData(bias_data); + bias_tensor_.set_data(bias_data); bias_tensor_.AddQuantParam(quant_bias_); inputs.emplace_back(&bias_tensor_); } out_tensor_.set_data_type(kNumberTypeInt8); out_tensor_.set_shape(out_shape); - out_tensor_.SetData(output_data); + out_tensor_.set_data(output_data); out_tensor_.AddQuantParam(quant_out_); param_.axis_ = axis; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sigmoid_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sigmoid_int8_tests.cc index 806f781af2..0677d024fa 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sigmoid_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sigmoid_int8_tests.cc @@ -33,8 +33,8 @@ TEST_F(TestSigmoidInt8, Sigmoid) { int8_t input_data[] = {0, 0, 0, 0, 1, 1, 1, 1}; // -3.5f, -3.0f, -2.5f, 0.f, 2.5f, 3.0f, 3.5f, 6.0f int8_t output_data[8] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); const lite::QuantArg quant_in = {1.0, 0}; // -4.0 -- 7.0 const lite::QuantArg quant_out = {1.0, 0}; // -3.0 -- 7.0 @@ -66,7 +66,7 @@ TEST_F(TestSigmoidInt8, Sigmoid) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/slice_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/slice_int8_tests.cc index 91c5ef2802..0801d945fd 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/slice_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/slice_int8_tests.cc @@ -34,8 +34,8 @@ TEST_F(TestSliceInt8, SliceInt8) { int8_t input_data[] = {105, 35, -27, 0, -63, 99, 16, 45, 67, -49, -115, 106, -98, 119, 103, 81, -114, 68}; int8_t output_data[12]; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); const lite::QuantArg quant_in0 = {0.00784314f, 0}; // -1.0--1.0 -> 0--255 const lite::QuantArg quant_out = {0.00784314f, 0}; @@ -71,7 +71,7 @@ TEST_F(TestSliceInt8, SliceInt8) { EXPECT_EQ(output_data[i], expect0[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/softmax_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/softmax_int8_tests.cc index 828c32c8ae..57937d8ca1 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/softmax_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/softmax_int8_tests.cc @@ -56,7 +56,7 @@ TEST_F(TestSoftmaxInt8, SoftmaxInt8) { lite::Tensor input0_tensor; TypeId tid_int8 = kNumberTypeInt8; inputs_tensor.push_back(&input0_tensor); - input0_tensor.SetData(input.data()); + input0_tensor.set_data(input.data()); input0_tensor.set_shape(in_shape); input0_tensor.AddQuantParam(input_quant_arg); input0_tensor.set_data_type(tid_int8); @@ -66,7 +66,7 @@ TEST_F(TestSoftmaxInt8, SoftmaxInt8) { lite::Tensor output0_tensor; outputs_tensor.push_back(&output0_tensor); - output0_tensor.SetData(output.data()); + output0_tensor.set_data(output.data()); output0_tensor.AddQuantParam(output_quant_arg); output0_tensor.set_data_type(tid_int8); @@ -86,8 +86,8 @@ TEST_F(TestSoftmaxInt8, SoftmaxInt8) { -127, -127, -127, -127, -59, -59, -61, -59, 58, 58, 59, 58}; CompareOutputData(output.data(), except_result.data(), input.size(), 0.000001); - input0_tensor.SetData(nullptr); - output0_tensor.SetData(nullptr); + input0_tensor.set_data(nullptr); + output0_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/space_to_batch_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/space_to_batch_int8_tests.cc index 2f07423979..a4ad5b1631 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/space_to_batch_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/space_to_batch_int8_tests.cc @@ -29,8 +29,8 @@ TEST_F(SpaceToBatchTestInt8, test1) { lite::Tensor out_tensor(kNumberTypeInt8, {4, 2, 2, 1}); int8_t input_data[] = {1, 2, 3, 4}; int8_t output_data[16] = {0}; - in_tensor.SetData(input_data); - out_tensor.SetData(output_data); + in_tensor.set_data(input_data); + out_tensor.set_data(output_data); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor}; @@ -52,7 +52,7 @@ TEST_F(SpaceToBatchTestInt8, test1) { for (int i = 0; i < 8; ++i) { EXPECT_EQ(output_data[i], expect[i]); } - in_tensor.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/split_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/split_int8_tests.cc index 05ee62c78b..2f0d84084b 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/split_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/split_int8_tests.cc @@ -52,7 +52,7 @@ TEST_F(TestSplitInt8, Split_quant0_thread2) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -60,12 +60,12 @@ TEST_F(TestSplitInt8, Split_quant0_thread2) { inputs_tensor[0] = input_tensor1; lite::Tensor *output1_tensor = new lite::Tensor; - output1_tensor->SetData(output1); + output1_tensor->set_data(output1); output1_tensor->set_shape(output1_shape); output1_tensor->AddQuantParam(output_quant_arg); output1_tensor->set_data_type(tid_int8); lite::Tensor *output2_tensor = new lite::Tensor; - output2_tensor->SetData(output2); + output2_tensor->set_data(output2); output2_tensor->set_shape(output2_shape); output2_tensor->AddQuantParam(output_quant_arg); output2_tensor->set_data_type(tid_int8); @@ -103,9 +103,9 @@ TEST_F(TestSplitInt8, Split_quant0_thread2) { CompareOutputData(output1, except_result1.data(), output1_size, 0.000001); CompareOutputData(output2, except_result2.data(), output2_size, 0.000001); - input_tensor1->SetData(nullptr); - output1_tensor->SetData(nullptr); - output2_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output1_tensor->set_data(nullptr); + output2_tensor->set_data(nullptr); delete input_tensor1; delete output1_tensor; delete output2_tensor; @@ -137,7 +137,7 @@ TEST_F(TestSplitInt8, Split_quant0_thread2_num) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -145,17 +145,17 @@ TEST_F(TestSplitInt8, Split_quant0_thread2_num) { inputs_tensor[0] = input_tensor1; lite::Tensor *output1_tensor = new lite::Tensor; - output1_tensor->SetData(output1); + output1_tensor->set_data(output1); output1_tensor->set_shape(output1_shape); output1_tensor->AddQuantParam(output_quant_arg); output1_tensor->set_data_type(tid_int8); lite::Tensor *output2_tensor = new lite::Tensor; - output2_tensor->SetData(output2); + output2_tensor->set_data(output2); output2_tensor->set_shape(output2_shape); output2_tensor->AddQuantParam(output_quant_arg); output2_tensor->set_data_type(tid_int8); lite::Tensor *output3_tensor = new lite::Tensor; - output3_tensor->SetData(output3); + output3_tensor->set_data(output3); output3_tensor->set_shape(output3_shape); output3_tensor->AddQuantParam(output_quant_arg); output3_tensor->set_data_type(tid_int8); @@ -198,10 +198,10 @@ TEST_F(TestSplitInt8, Split_quant0_thread2_num) { CompareOutputData(output2, except_result2.data(), output2_size, 0.000001); CompareOutputData(output3, except_result3.data(), output3_size, 0.000001); - input_tensor1->SetData(nullptr); - output1_tensor->SetData(nullptr); - output2_tensor->SetData(nullptr); - output3_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output1_tensor->set_data(nullptr); + output2_tensor->set_data(nullptr); + output3_tensor->set_data(nullptr); delete input_tensor1; delete output1_tensor; delete output2_tensor; @@ -234,7 +234,7 @@ TEST_F(TestSplitInt8, Split_quant1_thread2_num) { TypeId tid_int8 = kNumberTypeInt8; lite::Tensor *input_tensor1 = new lite::Tensor; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -242,17 +242,17 @@ TEST_F(TestSplitInt8, Split_quant1_thread2_num) { inputs_tensor[0] = input_tensor1; lite::Tensor *output1_tensor = new lite::Tensor; - output1_tensor->SetData(output1); + output1_tensor->set_data(output1); output1_tensor->set_shape(output1_shape); output1_tensor->AddQuantParam(output_quant_arg); output1_tensor->set_data_type(tid_int8); lite::Tensor *output2_tensor = new lite::Tensor; - output2_tensor->SetData(output2); + output2_tensor->set_data(output2); output2_tensor->set_shape(output2_shape); output2_tensor->AddQuantParam(output_quant_arg); output2_tensor->set_data_type(tid_int8); lite::Tensor *output3_tensor = new lite::Tensor; - output3_tensor->SetData(output3); + output3_tensor->set_data(output3); output3_tensor->set_shape(output3_shape); output3_tensor->AddQuantParam(output_quant_arg); output3_tensor->set_data_type(tid_int8); @@ -295,10 +295,10 @@ TEST_F(TestSplitInt8, Split_quant1_thread2_num) { CompareOutputData(output2, except_result2.data(), output2_size, 0.000001); CompareOutputData(output3, except_result3.data(), output3_size, 0.000001); - input_tensor1->SetData(nullptr); - output1_tensor->SetData(nullptr); - output2_tensor->SetData(nullptr); - output3_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output1_tensor->set_data(nullptr); + output2_tensor->set_data(nullptr); + output3_tensor->set_data(nullptr); delete input_tensor1; delete output1_tensor; delete output2_tensor; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/squeeze_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/squeeze_int8_tests.cc index 98eda9024e..a30f3552f2 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/squeeze_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/squeeze_int8_tests.cc @@ -48,7 +48,7 @@ TEST_F(TestSqueezeInt8, Squeeze_1d_axis0_offset0_quant0_thread2) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -58,7 +58,7 @@ TEST_F(TestSqueezeInt8, Squeeze_1d_axis0_offset0_quant0_thread2) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -87,8 +87,8 @@ TEST_F(TestSqueezeInt8, Squeeze_1d_axis0_offset0_quant0_thread2) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sub_int_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sub_int_tests.cc index eac24d33c3..fdba01f381 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sub_int_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/sub_int_tests.cc @@ -36,9 +36,9 @@ TEST_F(TestSubInt8, SubInt8) { int8_t input_data0[] = {105, 35, -27, 0, -63, 99, 16, 122, 67, -49}; int8_t input_data1[] = {24, -38, -115, 106, -98}; int8_t output_data[10] = {0}; - in_tensor0.SetData(input_data0); - in_tensor1.SetData(input_data1); - out_tensor.SetData(output_data); + in_tensor0.set_data(input_data0); + in_tensor1.set_data(input_data1); + out_tensor.set_data(output_data); const lite::QuantArg quant_in0 = {0.00784314f, 0}; // -1.0--1.0 -> 0--255 const lite::QuantArg quant_in1 = {0.00784314f, 0}; @@ -69,8 +69,8 @@ TEST_F(TestSubInt8, SubInt8) { EXPECT_EQ(output_data[i], expect0[i]); } - in_tensor0.SetData(nullptr); - in_tensor1.SetData(nullptr); - out_tensor.SetData(nullptr); + in_tensor0.set_data(nullptr); + in_tensor1.set_data(nullptr); + out_tensor.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/topk_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/topk_int8_tests.cc index 541d25907d..a9345f0a8a 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/topk_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/topk_int8_tests.cc @@ -34,9 +34,9 @@ TEST_F(TestTopKInt8, TopK) { int8_t input_data[] = {1, 2, 3, 6, 5, 4, 9, 8, 7, 10, 12, 11}; int8_t output_data0[8] = {0}; int32_t output_data1[8] = {0}; - in_tensor.SetData(input_data); - out_tensor0.SetData(output_data0); - out_tensor1.SetData(output_data1); + in_tensor.set_data(input_data); + out_tensor0.set_data(output_data0); + out_tensor1.set_data(output_data1); std::vector inputs = {&in_tensor}; std::vector outputs = {&out_tensor0, &out_tensor1}; @@ -59,8 +59,8 @@ TEST_F(TestTopKInt8, TopK) { EXPECT_EQ(output_data1[i], expect1[i]); } - in_tensor.SetData(nullptr); - out_tensor0.SetData(nullptr); - out_tensor1.SetData(nullptr); + in_tensor.set_data(nullptr); + out_tensor0.set_data(nullptr); + out_tensor1.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/unsqueeze_int8_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/unsqueeze_int8_tests.cc index 0546438c89..f1f8d74edc 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/unsqueeze_int8_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/int8/unsqueeze_int8_tests.cc @@ -47,7 +47,7 @@ TEST_F(TestUnsqueezeInt8, Unsqueeze_1) { lite::Tensor *input_tensor1 = new lite::Tensor; TypeId tid_int8 = kNumberTypeInt8; - input_tensor1->SetData(input1.data()); + input_tensor1->set_data(input1.data()); input_tensor1->set_shape(shape1); input_tensor1->AddQuantParam(input_quant_arg); input_tensor1->set_data_type(tid_int8); @@ -57,7 +57,7 @@ TEST_F(TestUnsqueezeInt8, Unsqueeze_1) { std::vector outputs_tensor(1); lite::Tensor *output0_tensor = new lite::Tensor; - output0_tensor->SetData(output); + output0_tensor->set_data(output); output0_tensor->set_shape(output_shape); output0_tensor->AddQuantParam(output_quant_arg); output0_tensor->set_data_type(tid_int8); @@ -86,8 +86,8 @@ TEST_F(TestUnsqueezeInt8, Unsqueeze_1) { PrintData("output data shape", output_tensor_shape.data(), output_tensor_shape.size()); CompareOutputData(output, except_result.data(), output_size, 0.000001); - input_tensor1->SetData(nullptr); - output0_tensor->SetData(nullptr); + input_tensor1->set_data(nullptr); + output0_tensor->set_data(nullptr); delete input_tensor1; delete output0_tensor; delete ctx; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/arm/string/normalize.cc b/mindspore/lite/test/ut/src/runtime/kernel/arm/string/normalize.cc index 5237373a44..e236839c0a 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/arm/string/normalize.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/arm/string/normalize.cc @@ -80,8 +80,8 @@ TEST_F(TestNormalize, TestSentence) { printf("\n"); } - input_tensor_.SetData(nullptr); - output_tensor_.SetData(nullptr); + input_tensor_.set_data(nullptr); + output_tensor_.set_data(nullptr); } } // namespace mindspore diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/activation_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/activation_tests.cc index 4f11b2e94e..dd341616b5 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/activation_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/activation_tests.cc @@ -92,7 +92,7 @@ TEST_F(TestActivationOpenCL, ReluFp_dim4) { std::vector input_shape = {1, 9}; schema::Format format = schema::Format_NC; schema::Format op_format = schema::Format_NC4; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type); if (input_tensor == nullptr) { MS_LOG(ERROR) << "new input tensor error!"; @@ -199,7 +199,7 @@ TEST_F(TestActivationOpenCL, Relu6Fp_dim4) { std::vector input_shape = {1, 9}; schema::Format format = schema::Format_NC; schema::Format op_format = schema::Format_NC4; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type); if (input_tensor == nullptr) { MS_LOG(ERROR) << "new input tensor error!"; @@ -309,7 +309,7 @@ TEST_F(TestActivationOpenCL, SigmoidFp_dim4) { std::vector input_shape = {1, 9}; schema::Format format = schema::Format_NC; schema::Format op_format = schema::Format_NC4; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type); if (input_tensor == nullptr) { MS_LOG(ERROR) << "new input tensor error!"; @@ -417,7 +417,7 @@ TEST_F(TestActivationOpenCL, LeakyReluFp_dim4) { MS_LOG(INFO) << "Init tensors."; std::vector input_shape = {1, 9}; // need modify - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; schema::Format format = schema::Format_NC; // need modify schema::Format op_format = schema::Format_NHWC4; // need modify auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type); @@ -528,7 +528,7 @@ TEST_F(TestActivationOpenCLTanh, TanhFp_dim4) { std::vector input_shape = {1, 2, 3, 9}; schema::Format format = schema::Format_NHWC; schema::Format op_format = schema::Format_NC4HW4; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type); if (input_tensor == nullptr) { MS_LOG(ERROR) << "new input tensor error!"; @@ -618,9 +618,9 @@ TEST_F(TestActivationOpenCLTanh, TanhFp_dim4) { printf_tensor("Tanh:FP32--output data---", outputs[0]); CompareRes(output_tensor, out_file); } - input_tensor->SetData(nullptr); + input_tensor->set_data(nullptr); delete input_tensor; - output_tensor->SetData(nullptr); + output_tensor->set_data(nullptr); delete output_tensor; delete sub_graph; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/arithmetic_self_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/arithmetic_self_tests.cc index c06909743e..7e9f71ff89 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/arithmetic_self_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/arithmetic_self_tests.cc @@ -60,7 +60,7 @@ TEST_F(TestArithmeticSelfOpenCLfp16, ArithmeticSelfOpenCLFp16) { std::vector shape = {1, 2, 2, 144}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); auto *output_tensor = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); if (input_tensor == nullptr || output_tensor == nullptr) { @@ -125,11 +125,11 @@ TEST_F(TestArithmeticSelfOpenCLfp16, ArithmeticSelfOpenCLFp16) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(input_data1, output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -148,7 +148,7 @@ TEST_F(TestArithmeticSelfOpenCLCI, ArithmeticSelfRound) { MS_LOG(INFO) << " init tensors "; std::vector shape = {1, 1, 4, 4}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); auto *output_tensor = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); if (input_tensor == nullptr || output_tensor == nullptr) { @@ -214,11 +214,11 @@ TEST_F(TestArithmeticSelfOpenCLCI, ArithmeticSelfRound) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(input_data1, output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -243,7 +243,7 @@ TEST_F(TestArithmeticSelfOpenCLfp16, ArithmeticSelfdim2Fp16) { std::vector shape = {1, 512}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NC, tensor_type); auto *output_tensor = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NC, tensor_type); if (input_tensor == nullptr || output_tensor == nullptr) { @@ -307,11 +307,11 @@ TEST_F(TestArithmeticSelfOpenCLfp16, ArithmeticSelfdim2Fp16) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(input_data1, output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc index 532c7fc9e5..ed796e3116 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/avg_pooling_tests.cc @@ -118,10 +118,10 @@ void RunTestCaseAvgPooling(const std::vector &shape, void *input_data, void } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test AvgPool2d passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc index 59ce5d59ef..de7a306dba 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/batchnorm_tests.cc @@ -46,7 +46,7 @@ TEST_F(TestBatchnormOpenCLCI, Batchnormfp32CI) { std::vector input_shape = {1, 2, 2, 8}; std::vector output_shape = {1, 2, 2, 8}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; float input_data[] = {2.471454, -2.1379554, -0.0904604, 1.2928944, -0.19215967, -0.8677279, -0.12759617, 1.2242758, -0.06398406, -0.4041858, 0.20352598, -2.067808, 0.52113044, -1.567617, @@ -143,11 +143,11 @@ TEST_F(TestBatchnormOpenCLCI, Batchnormfp32CI) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -164,7 +164,7 @@ TEST_F(TestBatchnormOpenCLfp16, Batchnormfp16input_dim4) { std::vector input_shape = {1, 256, 256, 48}; std::vector output_shape = {1, 256, 256, 48}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; // get the input from .bin size_t input_size, output_size; @@ -262,11 +262,11 @@ TEST_F(TestBatchnormOpenCLfp16, Batchnormfp16input_dim4) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.01); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -282,7 +282,7 @@ TEST_F(TestBatchnormOpenCLfp32, Batchnormfp32input_dim4) { std::vector input_shape = {1, 256, 256, 47}; std::vector output_shape = {1, 256, 256, 47}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; // get the input from .bin size_t input_size, output_size; @@ -380,11 +380,11 @@ TEST_F(TestBatchnormOpenCLfp32, Batchnormfp32input_dim4) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/biasadd_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/biasadd_tests.cc index 2755067fa8..d93655cb18 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/biasadd_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/biasadd_tests.cc @@ -81,7 +81,7 @@ TEST_F(TestBiasAddOpenCL, BiasAddFp32_dim4) { ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16); std::vector input_shape = {1, 9}; // need modify std::vector output_shape = {1, 9}; // need modify - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; schema::Format type = schema::Format_NC; // need modify schema::Format op_format = schema::Format_NC4; // need modify int weight_shape = 0; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/cast_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/cast_tests.cc index bb92f4b4a3..2f30341eb0 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/cast_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/cast_tests.cc @@ -61,7 +61,7 @@ TEST_F(TestCastSelfOpenCL, Castfp32tofp16) { MS_LOG(INFO) << " init tensors "; std::vector shape = {1, 23, 39, 47}; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(kNumberTypeFloat32, shape, schema::Format_NHWC, tensor_type); auto *output_tensor = new (std::nothrow) lite::Tensor(kNumberTypeFloat16, shape, schema::Format_NHWC, tensor_type); if (input_tensor == nullptr || output_tensor == nullptr) { @@ -113,11 +113,11 @@ TEST_F(TestCastSelfOpenCL, Castfp32tofp16) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -147,7 +147,7 @@ TEST_F(TestCastSelfOpenCL, Castfp16tofp32) { MS_LOG(INFO) << " init tensors "; std::vector shape = {1, 23, 39, 47}; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto *input_tensor = new (std::nothrow) lite::Tensor(kNumberTypeFloat16, shape, schema::Format_NHWC, tensor_type); auto *output_tensor = new (std::nothrow) lite::Tensor(kNumberTypeFloat32, shape, schema::Format_NHWC, tensor_type); if (input_tensor == nullptr || output_tensor == nullptr) { @@ -199,11 +199,11 @@ TEST_F(TestCastSelfOpenCL, Castfp16tofp32) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc index 2236b9c16a..68738ba866 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc @@ -48,7 +48,7 @@ TEST_F(TestConcatOpenCLCI, ConcatFp32_2inputforCI) { std::array, INPUT_NUM> input_shapes = {std::vector{1, 1, 1, 8}, std::vector{1, 1, 1, 8}}; std::vector output_shape = {2, 1, 1, 8}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; float input_data1[] = {0.75f, 0.06f, 0.74f, 0.30f, 0.9f, 0.59f, 0.03f, 0.37f}; float input_data2[] = {0.5f, 0.6f, 0.74f, 0.23f, 0.46f, 0.69f, 0.13f, 0.47f}; float correctOutput[] = {0.75f, 0.06f, 0.74f, 0.30f, 0.9f, 0.59f, 0.03f, 0.37f, @@ -126,11 +126,11 @@ TEST_F(TestConcatOpenCLCI, ConcatFp32_2inputforCI) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.00001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -164,7 +164,7 @@ TEST_F(TestConcatOpenCLfp16, ConcatFp16_4input_dim4_axis1) { std::vector{1, 19, 19, 96}}; std::vector output_shape = {1, 76, 19, 96}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; std::vector inputs; for (auto &shape : input_shapes) { auto input_temp = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); @@ -254,11 +254,11 @@ TEST_F(TestConcatOpenCLfp16, ConcatFp16_4input_dim4_axis1) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -287,7 +287,7 @@ TEST_F(TestConcatOpenCLfp32, ConcatFp32_3input_dim4_axis1) { std::vector{1, 16, 256, 80}, std::vector{1, 16, 256, 80}, std::vector{1, 16, 256, 80}}; std::vector output_shape = {1, 48, 256, 80}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; std::vector inputs; for (auto &shape : input_shapes) { auto input_temp = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); @@ -373,11 +373,11 @@ TEST_F(TestConcatOpenCLfp32, ConcatFp32_3input_dim4_axis1) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.00001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -415,7 +415,7 @@ TEST_F(TestConcatOpenCLfp16, ConcatFp16_6input_dim4_axis1) { std::vector{1, 50, 3, 4}, std::vector{1, 30, 3, 4}, std::vector{1, 4, 3, 4}}; std::vector output_shape = {1, 2034, 3, 4}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; std::vector inputs; for (auto &shape : input_shapes) { auto input_temp = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); @@ -512,11 +512,11 @@ TEST_F(TestConcatOpenCLfp16, ConcatFp16_6input_dim4_axis1) { auto *output_data_gpu = reinterpret_cast(output_tensor->MutableData()); CompareOutputData(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/conv2d_transpose_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/conv2d_transpose_tests.cc index cc4b507882..7283c60468 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/conv2d_transpose_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/conv2d_transpose_tests.cc @@ -64,7 +64,7 @@ void RunTestCaseConv2dTranspose(const std::vector &shape, void *input_data, MS_LOG(ERROR) << "tensor_w create error."; return; } - tensor_w->SetData(weight_data); + tensor_w->set_data(weight_data); std::vector bias_shape = {co}; auto tensor_bias_ptr = @@ -74,7 +74,7 @@ void RunTestCaseConv2dTranspose(const std::vector &shape, void *input_data, MS_LOG(ERROR) << "tensor_bias create error."; return; } - tensor_bias->SetData(bias_data); + tensor_bias->set_data(bias_data); std::vector out_shape = {1, oh, ow, co}; auto tensor_out_ptr = @@ -129,10 +129,10 @@ void RunTestCaseConv2dTranspose(const std::vector &shape, void *input_data, } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/convolution_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/convolution_tests.cc index da72fe4b21..3a0f10b389 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/convolution_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/convolution_tests.cc @@ -120,10 +120,10 @@ void TEST_MAIN(const std::string &attr, const TypeId data_type, const float atol std::vector weight_shape = {param->output_channel_, param->kernel_h_, param->kernel_w_, param->input_channel_}; std::vector bias_shape = {param->output_channel_}; std::vector output_shape = {param->output_batch_, param->output_h_, param->output_w_, param->output_channel_}; - auto input = Tensor(data_type, input_shape, Format_NHWC, lite::TensorCategory(NodeType_ValueNode)); - auto weight = Tensor(data_type, weight_shape, Format_KHWC, lite::TensorCategory(NodeType_ValueNode)); - auto bias = Tensor(data_type, bias_shape, Format_KHWC, lite::TensorCategory(NodeType_ValueNode)); - auto output = Tensor(data_type, output_shape, Format_NHWC, lite::TensorCategory(NodeType_ValueNode)); + auto input = Tensor(data_type, input_shape, Format_NHWC, lite::Tensor::CONST_TENSOR); + auto weight = Tensor(data_type, weight_shape, Format_KHWC, lite::Tensor::CONST_TENSOR); + auto bias = Tensor(data_type, bias_shape, Format_KHWC, lite::Tensor::CONST_TENSOR); + auto output = Tensor(data_type, output_shape, Format_NHWC, lite::Tensor::CONST_TENSOR); MS_LOG(DEBUG) << "allocate memory and initialize weight/bias"; weight.MallocData(); diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/depthwise_conv2d_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/depthwise_conv2d_tests.cc index b952c5fe67..3b3b9e626c 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/depthwise_conv2d_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/depthwise_conv2d_tests.cc @@ -92,8 +92,8 @@ void DepthWiseTestMain(ConvParameter *conv_param, T2 *input_data, T1 *weight_dat std::vector outputs{&tensor_d}; // freamework to do!!! - inputs[1]->SetData(packed_weight); - inputs[2]->SetData(bias_data); + inputs[1]->set_data(packed_weight); + inputs[2]->set_data(bias_data); OpParameter *parameter = reinterpret_cast(conv_param); auto pKernel = std::make_unique(parameter, inputs, outputs); @@ -163,11 +163,11 @@ void DepthWiseTestMain(ConvParameter *conv_param, T2 *input_data, T1 *weight_dat delete[] packed_correct_data; } - inputs[1]->SetData(nullptr); - inputs[2]->SetData(nullptr); + inputs[1]->set_data(nullptr); + inputs[2]->set_data(nullptr); delete[] packed_input; - inputs[0]->SetData(nullptr); - outputs[0]->SetData(nullptr); + inputs[0]->set_data(nullptr); + outputs[0]->set_data(nullptr); return; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/fullconnection_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/fullconnection_tests.cc index ccaae1b1e0..1538f6ecd4 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/fullconnection_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/fullconnection_tests.cc @@ -78,7 +78,7 @@ void RunTestCaseFullConnection(const std::vector &shape, void *input_data, MS_LOG(ERROR) << "tensor_w create error."; return; } - tensor_w->SetData(weight_data); + tensor_w->set_data(weight_data); auto tensor_bias_ptr = std::make_unique(TypeId(enable_fp16 ? kNumberTypeFloat16 : kNumberTypeFloat32), bias_shape, schema::Format_NC); @@ -87,7 +87,7 @@ void RunTestCaseFullConnection(const std::vector &shape, void *input_data, MS_LOG(ERROR) << "tensor_w create error."; return; } - tensor_bias->SetData(bias_data); + tensor_bias->set_data(bias_data); auto tensor_out_ptr = std::make_unique(TypeId(enable_fp16 ? kNumberTypeFloat16 : kNumberTypeFloat32), output_shape, schema::Format_NC); @@ -128,10 +128,10 @@ void RunTestCaseFullConnection(const std::vector &shape, void *input_data, } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "TestFullConnection passed"; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/matmul_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/matmul_tests.cc index 97ebefcb65..284b89cd59 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/matmul_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/matmul_tests.cc @@ -75,7 +75,7 @@ void RunTestCaseMatMul(const std::vector &shape, void *input_data, void *we MS_LOG(ERROR) << "tensor_w create error."; return; } - tensor_w->SetData(weight_data); + tensor_w->set_data(weight_data); auto tensor_out_ptr = std::make_unique(TypeId(enable_fp16 ? kNumberTypeFloat16 : kNumberTypeFloat32), output_shape, @@ -117,10 +117,10 @@ void RunTestCaseMatMul(const std::vector &shape, void *input_data, void *we } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "TestMatMul passed"; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/max_pooling_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/max_pooling_tests.cc index c89fd1bdbd..d9011dca6d 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/max_pooling_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/max_pooling_tests.cc @@ -117,10 +117,10 @@ void RunTestCaseMaxPooling(const std::vector &shape, void *input_data, void CompareOutput(outputs[0]->MutableData(), output_data, outputs[0]->ElementsNum(), static_cast(1e-5)); } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test MaxPool2d passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/pad_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/pad_tests.cc index 481839bb14..91c52ba4e8 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/pad_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/pad_tests.cc @@ -50,8 +50,8 @@ void TEST_MAIN(PadParameter *param, Format input_format, Format output_format, F auto allocator = ocl_runtime->GetAllocator(); MS_LOG(DEBUG) << "create Tensors"; - auto input = Tensor(kNumberTypeFloat32, input_shape, input_format, lite::TensorCategory(NodeType_ValueNode)); - auto output = Tensor(kNumberTypeFloat32, output_shape, output_format, lite::TensorCategory(NodeType_ValueNode)); + auto input = Tensor(kNumberTypeFloat32, input_shape, input_format, lite::Tensor::CONST_TENSOR); + auto output = Tensor(kNumberTypeFloat32, output_shape, output_format, lite::Tensor::CONST_TENSOR); MS_LOG(DEBUG) << "create OpenCL Kernel"; std::vector inputs{&input}; @@ -77,8 +77,8 @@ void TEST_MAIN(PadParameter *param, Format input_format, Format output_format, F } MS_LOG(DEBUG) << "release resources"; - input.SetData(nullptr); - output.SetData(nullptr); + input.set_data(nullptr); + output.set_data(nullptr); delete sub_graph; } diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc index 6b99182280..47e72f465e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc @@ -87,7 +87,7 @@ TEST_F(TestPReluOpenCL, PReluFp32_dim4) { ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16); schema::Format format = schema::Format_NHWC; schema::Format op_format = schema::Format_NC4HW4; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; auto input_tensor = new (std::nothrow) lite::Tensor(data_type, input_shape, format, tensor_type); if (input_tensor == nullptr) { MS_LOG(ERROR) << "new input_tensor error!"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/reduce_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/reduce_tests.cc index cedd0367e4..33eb04c555 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/reduce_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/reduce_tests.cc @@ -96,10 +96,10 @@ void RunTestCaseReduce(const std::vector &shape, void *input_data, void *ou CompareOutput(outputs[0]->MutableData(), output_data, outputs[0]->ElementsNum(), static_cast(1e-5)); } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test Reduce passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/reshape_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/reshape_tests.cc index dcbfcf4fdb..46f1636373 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/reshape_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/reshape_tests.cc @@ -82,10 +82,10 @@ void RunTestCaseReshape(const std::vector &shape_in, const std::vector CompareOutput(outputs[0]->MutableData(), output_data, outputs[0]->ElementsNum(), static_cast(1e-5)); } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test Reshape passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/resize_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/resize_tests.cc index ae6dd01ebe..be76efb40d 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/resize_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/resize_tests.cc @@ -98,10 +98,10 @@ void RunTestCaseResize(const std::vector &shape, void *input_data, void *ou CompareOutput(outputs[0]->MutableData(), output_data, outputs[0]->ElementsNum(), static_cast(1e-5)); } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test Resize passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/slice_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/slice_tests.cc index 0fb44f3332..454a319841 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/slice_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/slice_tests.cc @@ -52,7 +52,7 @@ TEST_F(TestSliceOpenCLfp32, Slicefp32CI) { std::vector begin = {0, 0, 0, 2}; std::vector size = {1, 2, 2, 5}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; float input_data[] = {-0.45816937, 0.92391545, -0.9135602, -1.4002057, 1.1080881, 0.40712625, -0.28128958, 0.09470133, 0.19801073, 0.04927751, -1.2808367, 0.1470597, 0.03393711, -0.33282498, @@ -140,11 +140,11 @@ TEST_F(TestSliceOpenCLfp32, Slicefp32CI) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -162,7 +162,7 @@ TEST_F(TestSliceOpenCLfp32, Slicefp32input_dim4) { std::vector begin = {0, 2, 3, 4}; std::vector size = {1, 10, 10, 13}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; // get the input from .bin size_t input_size, output_size; @@ -248,11 +248,11 @@ TEST_F(TestSliceOpenCLfp32, Slicefp32input_dim4) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -271,7 +271,7 @@ TEST_F(TestSliceOpenCLfp16, Slicefp16input_dim4) { std::vector begin = {0, 1, 1, 7}; std::vector size = {1, 24, 24, 15}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; // get the input from .bin size_t input_size, output_size; @@ -357,11 +357,11 @@ TEST_F(TestSliceOpenCLfp16, Slicefp16input_dim4) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData1(output_data_gpu, correct_data, output_tensor->ElementsNum(), 0.0001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/softmax_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/softmax_tests.cc index 006577634b..eb39c950b4 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/softmax_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/softmax_tests.cc @@ -104,10 +104,10 @@ void RunTestCaseSoftmax(const std::vector &shape, void *input_data, void *o CompareOutput(outputs[0]->MutableData(), output_data, outputs[0]->ElementsNum(), static_cast(1e-5)); } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test Softmax passed"; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/stack_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/stack_tests.cc index ab03d31f3d..6434505599 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/stack_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/stack_tests.cc @@ -44,7 +44,7 @@ TEST_F(TestStackOpenCLCI, StackFp32_8inputforCI) { std::vector{1, 1, 8}, std::vector{1, 1, 8}, std::vector{1, 1, 8}, std::vector{1, 1, 8}}; std::vector output_shape = {8, 1, 1, 8}; auto data_type = kNumberTypeFloat32; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; float input_data1[] = {0.75f, 0.06f, 0.74f, 0.30f, 0.9f, 0.59f, 0.03f, 0.37f}; float input_data2[] = {0.5f, 0.6f, 0.74f, 0.23f, 0.46f, 0.69f, 0.13f, 0.47f}; float input_data3[] = {0.31f, 0.63f, 0.84f, 0.43f, 0.56f, 0.79f, 0.12f, 0.57f}; @@ -137,11 +137,11 @@ TEST_F(TestStackOpenCLCI, StackFp32_8inputforCI) { auto *output_data_gpu = reinterpret_cast(output_tensor->data_c()); CompareOutputData(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.00001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; @@ -183,7 +183,7 @@ TEST_F(TestStackOpenCLfp16, StackFp32_8inputaxis1) { std::vector{1, 17, 18}, std::vector{1, 17, 18}, std::vector{1, 17, 18}, std::vector{1, 17, 18}}; std::vector output_shape = {1, 8, 17, 18}; auto data_type = kNumberTypeFloat16; - auto tensor_type = lite::TensorCategory(schema::NodeType_ValueNode); + auto tensor_type = lite::Tensor::CONST_TENSOR; std::vector inputs; for (auto &shape : input_shapes) { auto input_temp = new (std::nothrow) lite::Tensor(data_type, shape, schema::Format_NHWC, tensor_type); @@ -270,11 +270,11 @@ TEST_F(TestStackOpenCLfp16, StackFp32_8inputaxis1) { auto *output_data_gpu = reinterpret_cast(output_tensor->MutableData()); CompareOutputData(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001); for (auto tensor : inputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } for (auto tensor : outputs) { - tensor->SetData(nullptr); + tensor->set_data(nullptr); delete tensor; } delete sub_graph; diff --git a/mindspore/lite/test/ut/src/runtime/kernel/opencl/transpose_tests.cc b/mindspore/lite/test/ut/src/runtime/kernel/opencl/transpose_tests.cc index 311c0b0ce6..c5aec7176e 100644 --- a/mindspore/lite/test/ut/src/runtime/kernel/opencl/transpose_tests.cc +++ b/mindspore/lite/test/ut/src/runtime/kernel/opencl/transpose_tests.cc @@ -96,10 +96,10 @@ void RunTestTranspose(const std::vector &shape, void *input_data, void *out } for (auto t : inputs) { - t->SetData(nullptr); + t->set_data(nullptr); } for (auto t : outputs) { - t->SetData(nullptr); + t->set_data(nullptr); } MS_LOG(INFO) << "Test TransposeFp32 passed"; diff --git a/mindspore/lite/tools/anf_importer/import_from_protobuf.cc b/mindspore/lite/tools/anf_importer/import_from_protobuf.cc index ddb0abea8b..013385fb58 100644 --- a/mindspore/lite/tools/anf_importer/import_from_protobuf.cc +++ b/mindspore/lite/tools/anf_importer/import_from_protobuf.cc @@ -248,7 +248,7 @@ int AnfImporterFromProtobuf::BuildParameterForFuncGraph(const ParameterPtr &node if (tensor_data_buf == nullptr) { return RET_MEMORY_FAILED; } - tensor_info->SetData(nullptr); + tensor_info->set_data(nullptr); auto ret = memcpy_s(tensor_data_buf, tensor_info->Size(), initial_data.data(), initial_data.size()); if (EOK != ret) { MS_LOG(ERROR) << "memcpy_s error"; diff --git a/mindspore/lite/tools/common/converter_op_utils.h b/mindspore/lite/tools/common/converter_op_utils.h deleted file mode 100644 index c51386c734..0000000000 --- a/mindspore/lite/tools/common/converter_op_utils.h +++ /dev/null @@ -1,33 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef PREDICT_CONVERTER_COMMON_OP_UTILS_H_ -#define PREDICT_CONVERTER_COMMON_OP_UTILS_H_ - -#include -#include -#include "schema/inner/model_generated.h" - -namespace mindspore { -namespace lite { -inline schema::PrimitiveType GetCNodeTType(const schema::CNodeT &cNodeT) { return cNodeT.primitive->value.type; } -inline std::string GetCNodeTTypeName(const schema::CNodeT &cNodeT) { - return schema::EnumNamePrimitiveType(GetCNodeTType(cNodeT)); -} -} // namespace lite -} // namespace mindspore - -#endif // PREDICT_CONVERTER_COMMON_OP_UTILS_H_ diff --git a/mindspore/lite/tools/common/node_util.h b/mindspore/lite/tools/common/node_util.h index 72e499d9eb..51d85ab1a5 100644 --- a/mindspore/lite/tools/common/node_util.h +++ b/mindspore/lite/tools/common/node_util.h @@ -19,6 +19,7 @@ #include #include +#include #include #include "schema/inner/model_generated.h" #include "src/common/common.h" @@ -31,6 +32,16 @@ namespace lite { using STATUS = int; STATUS BroadCastQuantParam(schema::MetaGraphT *graphT, const std::unique_ptr &node); +inline schema::PrimitiveType GetCNodeTType(const schema::CNodeT &cNodeT) { return cNodeT.primitive->value.type; } + +inline std::string GetCNodeTTypeName(const schema::CNodeT &cNodeT) { + return schema::EnumNamePrimitiveType(GetCNodeTType(cNodeT)); +} + +inline schema::PrimitiveType GetOpType(const schema::CNode &opDef) { return opDef.primitive()->value_type(); } + +inline std::string GetOpTypeName(const schema::CNode &opDef) { return schema::EnumNamePrimitiveType(GetOpType(opDef)); } + std::unordered_map GetNc2NhAxisMap(); std::vector GetInsertOpList(); diff --git a/mindspore/lite/tools/converter/legacy_optimizer/fusion/batchnorm_fold_fusion_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/fusion/batchnorm_fold_fusion_pass.cc index e51a5b0ed7..1490485eb7 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/fusion/batchnorm_fold_fusion_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/fusion/batchnorm_fold_fusion_pass.cc @@ -26,7 +26,6 @@ #include "tools/common/tensor_util.h" #include "include/errorcode.h" #include "schema/inner/model_generated.h" -#include "src/common/op_utils.h" namespace mindspore { namespace lite { diff --git a/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.cc index 82ed5b0dc9..e978d49878 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.cc @@ -25,7 +25,6 @@ #include "tools/converter/legacy_optimizer/fusion/fusion_pass.h" #include "src/common/log_adapter.h" -#include "tools/common/converter_op_utils.h" #include "src/common/utils.h" #include "tools/common/graph_util.h" #include "include/errorcode.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.h b/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.h index 191d2d7c31..9e78588c30 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.h +++ b/mindspore/lite/tools/converter/legacy_optimizer/fusion/fusion_pass.h @@ -22,7 +22,7 @@ #include #include #include -#include "tools/common/converter_op_utils.h" +#include "tools/common/node_util.h" #include "tools/converter/optimizer.h" #include "tools/converter/legacy_optimizer/fusion/fusion_pattern.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/fusion/matmul_biasadd_fusion_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/fusion/matmul_biasadd_fusion_pass.cc index c5dd766865..b310c736bd 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/fusion/matmul_biasadd_fusion_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/fusion/matmul_biasadd_fusion_pass.cc @@ -21,11 +21,9 @@ #include #include "tools/converter/legacy_optimizer/fusion/matmul_biasadd_fusion_pass.h" #include "src/common/log_adapter.h" -#include "securec/include/securec.h" #include "tools/common/graph_util.h" #include "include/errorcode.h" #include "schema/inner/model_generated.h" -#include "src/common/op_utils.h" namespace mindspore { namespace lite { diff --git a/mindspore/lite/tools/converter/legacy_optimizer/fusion/mul_add_fusion_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/fusion/mul_add_fusion_pass.cc index 10f3467c49..c775b7f2d3 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/fusion/mul_add_fusion_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/fusion/mul_add_fusion_pass.cc @@ -17,15 +17,12 @@ #include #include #include -#include #include #include "tools/converter/legacy_optimizer/fusion/mul_add_fusion_pass.h" #include "src/common/log_adapter.h" -#include "securec/include/securec.h" #include "tools/common/graph_util.h" #include "include/errorcode.h" #include "schema/inner/model_generated.h" -#include "src/common/op_utils.h" namespace mindspore { namespace lite { diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.cc index 6fc4bcdad1..36a09f5bcd 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.cc @@ -15,19 +15,15 @@ */ #include "tools/converter/legacy_optimizer/graph/batchnorm_convert_scale_pass.h" -#include #include #include -#include #include #include #include "third_party/securec/include/securec.h" #include "src/common/log_adapter.h" -#include "tools/common/graph_util.h" #include "tools/common/tensor_util.h" #include "include/errorcode.h" #include "schema/inner/model_generated.h" -#include "src/common/op_utils.h" namespace mindspore { namespace lite { diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc index 20852c670f..1084bcad91 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/dtype_trans_pass.cc @@ -17,7 +17,6 @@ #include "tools/converter/legacy_optimizer/graph/dtype_trans_pass.h" #include #include -#include "tools/common/converter_op_utils.h" #include "tools/common/node_util.h" #include "src/common/common.h" #include "src/common/utils.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/format_trans_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/format_trans_pass.cc index 15be667c70..1009d71fec 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/format_trans_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/format_trans_pass.cc @@ -18,7 +18,6 @@ #include #include #include "tools/converter/legacy_optimizer/graph/format_trans_pass.h" -#include "tools/common/converter_op_utils.h" #include "tools/common/node_util.h" #include "src/common/log_adapter.h" #include "src/common/common.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/infer_quant_param_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/infer_quant_param_pass.cc index 7a83ac517f..c49af020ec 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/infer_quant_param_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/infer_quant_param_pass.cc @@ -19,7 +19,6 @@ #include "tools/converter/legacy_optimizer/graph/infer_quant_param_pass.h" #include "tools/converter/quantizer/calc_quant_param.h" #include "tools/common/node_util.h" -#include "tools/common/converter_op_utils.h" namespace mindspore::lite { STATUS InferQuantParamPass::Run(schema::MetaGraphT *graph) { diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/infershape_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/infershape_pass.cc index 61f4f08e64..cc9e3e9f6d 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/infershape_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/infershape_pass.cc @@ -35,8 +35,9 @@ std::vector ConvertTensorToLiteTensor(MetaGraphT *graph, const std::ve for (size_t i = 0; i < tensor_indexs.size(); i++) { auto &tensorT = graph->allTensors.at(tensor_indexs[i]); auto tensor_shape = tensorT->dims; - auto lite_tensor = std::make_unique(TypeId(tensorT->dataType), tensor_shape, tensorT->format, - TensorCategory(tensorT->nodeType)); + auto lite_tensor = std::make_unique( + TypeId(tensorT->dataType), tensor_shape, tensorT->format, + TensorCategory(tensorT->nodeType, tensorT->dims.size(), TypeId(tensorT->dataType), tensorT->data.size())); if (lite_tensor == nullptr) { MS_LOG(ERROR) << "lite tensor is nullptr"; return std::vector(); diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/isolated_node_remove_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/isolated_node_remove_pass.cc index a8197895d6..54ccceaf24 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/isolated_node_remove_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/isolated_node_remove_pass.cc @@ -20,8 +20,6 @@ #include "tools/converter/legacy_optimizer/graph/isolated_node_remove_pass.h" #include "src/common/log_adapter.h" -#include "tools/common/converter_op_utils.h" -#include "src/common/utils.h" #include "tools/common/graph_util.h" #include "include/errorcode.h" #include "schema/inner/model_generated.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.cc deleted file mode 100644 index a16cdbb889..0000000000 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.cc +++ /dev/null @@ -1,46 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include -#include "tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.h" -#include "src/common/log_adapter.h" -#include "tools/common/converter_op_utils.h" -#include "tools/common/node_util.h" -#include "include/errorcode.h" - -namespace mindspore { -namespace lite { -STATUS ModelInputFormatPreProcessPass::Run(schema::MetaGraphT *graph) { - MS_ASSERT(graph != nullptr); - for (auto inputIndex : graph->inputIndex) { - if (graph->allTensors[inputIndex]->dims.size() == 4) { - std::vector tmpDims(graph->allTensors[inputIndex]->dims); - auto status = NodeUtils::ConvertDims(schema::Format::Format_NCHW, tmpDims, schema::Format::Format_NHWC, - &graph->allTensors[inputIndex]->dims); - if (status == RET_OK) { - graph->allTensors[inputIndex]->format = schema::Format::Format_NHWC; - } else { - MS_LOG(ERROR) << "ConvertDims from NHWC to NCHW error: " << status; - return RET_ERROR; - } - } else { - graph->allTensors[inputIndex]->format = schema::Format::Format_NHWC; - } - } - return RET_OK; -} -} // namespace lite -} // namespace mindspore diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.h b/mindspore/lite/tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.h deleted file mode 100644 index 82248960bf..0000000000 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/model_input_format_preprocess_pass.h +++ /dev/null @@ -1,37 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#ifndef MINDSPORE_PREDICT_MODEL_FORMAT_PREPROCESS_PASS_H -#define MINDSPORE_PREDICT_MODEL_FORMAT_PREPROCESS_PASS_H - -#include -#include "tools/converter/optimizer.h" -#include "include/errorcode.h" - -namespace mindspore { -namespace lite { -class ModelInputFormatPreProcessPass : public GraphPass { - public: - ModelInputFormatPreProcessPass() = default; - - ~ModelInputFormatPreProcessPass() override = default; - - STATUS Run(schema::MetaGraphT *graph) override; -}; -} // namespace lite -} // namespace mindspore - -#endif // MINDSPORE_PREDICT_MODEL_FORMAT_PREPROCESS_PASS_H diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/topological_sort_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/topological_sort_pass.cc index 6e55d6228f..1f40888b9d 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/topological_sort_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/topological_sort_pass.cc @@ -19,7 +19,7 @@ #include #include #include "tools/converter/legacy_optimizer/graph/topological_sort_pass.h" -#include "tools/common/converter_op_utils.h" +#include "tools/common/node_util.h" #include "src/common/log_adapter.h" #include "src/common/utils.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/trans_format_insert_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/trans_format_insert_pass.cc index 94d61e0376..0eaad698cf 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/trans_format_insert_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/trans_format_insert_pass.cc @@ -19,7 +19,6 @@ #include #include #include "tools/converter/legacy_optimizer/graph/trans_format_insert_pass.h" -#include "tools/common/converter_op_utils.h" #include "tools/common/node_util.h" #include "src/common/log_adapter.h" #include "src/common/utils.h" diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/unused_node_remove_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/unused_node_remove_pass.cc index 25cd2540ef..a25e05bd7f 100644 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/unused_node_remove_pass.cc +++ b/mindspore/lite/tools/converter/legacy_optimizer/graph/unused_node_remove_pass.cc @@ -14,18 +14,12 @@ * limitations under the License. */ +#include "tools/converter/legacy_optimizer/graph/unused_node_remove_pass.h" #include -#include -#include - -#include "mindspore/lite/tools/converter/legacy_optimizer/graph/unused_node_remove_pass.h" #include "src/common/log_adapter.h" -#include "tools/common/converter_op_utils.h" -#include "src/common/utils.h" #include "tools/common/graph_util.h" #include "include/errorcode.h" #include "schema/inner/model_generated.h" -#include "mindspore/core/ir/dtype/type_id.h" namespace mindspore { namespace lite { diff --git a/mindspore/lite/tools/converter/legacy_optimizer/graph/weight_format_hardcode_pass.cc b/mindspore/lite/tools/converter/legacy_optimizer/graph/weight_format_hardcode_pass.cc deleted file mode 100644 index 92d9e11a0d..0000000000 --- a/mindspore/lite/tools/converter/legacy_optimizer/graph/weight_format_hardcode_pass.cc +++ /dev/null @@ -1,220 +0,0 @@ -/** - * Copyright 2020 Huawei Technologies Co., Ltd - * - * Licensed under the Apache License, Version 2.0 (the "License"); - * you may not use this file except in compliance with the License. - * You may obtain a copy of the License at - * - * http://www.apache.org/licenses/LICENSE-2.0 - * - * Unless required by applicable law or agreed to in writing, software - * distributed under the License is distributed on an "AS IS" BASIS, - * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. - * See the License for the specific language governing permissions and - * limitations under the License. - */ - -#include "tools/converter/legacy_optimizer/graph/weight_format_hardcode_pass.h" -#include "tools/common/converter_op_utils.h" -#include "src/common/log_adapter.h" -#include "src/common/utils.h" -#include "tools/common/node_util.h" - -namespace mindspore { -namespace lite { -void WeightFormatHardCodePass::SetQuantType(QuantType quantType) { this->quantType = quantType; } - -void WeightFormatHardCodePass::SetFmkType(converter::FmkType fmkType) { this->fmkType = fmkType; } - -// pre set tensor format -// non quant, filterFormat: -// conv deconv depth dedepth -// caffe K(C/g)HW C(K/g)HW / / -// tf HWCK HWKC HWCK HWKC -// onnx K(C/g)HW C(K/g)HW / / - -// awareing quant, filterFormat: -// conv deconv depth dedepth -// onnx KHWC ? CHWK ? -// tf HWCK ? HWCK ? -STATUS WeightFormatHardCodePass::Run(MetaGraphT *graph) { - MS_ASSERT(graph != nullptr); - for (auto &node : graph->nodes) { - MS_ASSERT(node != nullptr); - MS_ASSERT(node->primitive != nullptr); - auto opType = node->primitive->value.type; - if (opType != PrimitiveType_Conv2D && opType != PrimitiveType_DepthwiseConv2D && opType != PrimitiveType_DeConv2D && - opType != PrimitiveType_DeDepthwiseConv2D) { - continue; - } - MS_ASSERT(node->inputIndex.size() >= 2); - auto weightIndex = node->inputIndex.at(1); - MS_ASSERT(subGraph->allTensors.size() > weightIndex); - auto &weightTensor = graph->allTensors[weightIndex]; - MS_ASSERT(weightTensor->dims.size() == 4 || weightTensor->dims.empty()); // for conv with fakqQuant before weight - STATUS status; - switch (fmkType) { - case converter::FmkType_CAFFE: - status = HardCodeCAFFE(node, weightTensor); - break; - case converter::FmkType_TFLITE: - status = HardCodeTFLITE(node, weightTensor); - break; - case converter::FmkType_ONNX: - status = HardCodeONNX(node, weightTensor); - break; - case converter::FmkType_MS: - status = HardCodeMS(node, weightTensor); - break; - default: - MS_LOG(ERROR) << "Unsupported fmkType: " << fmkType << ", node: " << node->name; - return RET_ERROR; - } - if (status != RET_OK) { - MS_LOG(ERROR) << "schema::Format hardCode faild: " << status << ", node: " << node->name; - return RET_ERROR; - } - } - return RET_OK; -} - -STATUS WeightFormatHardCodePass::HardCodeCAFFE(const std::unique_ptr &node, - const std::unique_ptr &weightTensor) { - MS_ASSERT(node != nullptr); - MS_ASSERT(weightTensor != nullptr); - MS_ASSERT(node != nullptr); - MS_ASSERT(node->primitive != nullptr); - auto opType = node->primitive->value.type; - switch (this->quantType) { - case QuantType_WeightQuant: - case QuantType_QUANT_NONE: { - if (opType == schema::PrimitiveType_Conv2D || opType == schema::PrimitiveType_DepthwiseConv2D || - opType == schema::PrimitiveType_DeConv2D || opType == schema::PrimitiveType_DeDepthwiseConv2D) { - weightTensor->format = schema::Format::Format_KCHW; - } else { - MS_LOG(ERROR) << "Unsupported opType: " << EnumNamePrimitiveType(opType) << ", node: " << node->name; - } - } break; - default: { - MS_LOG(ERROR) << "Unsupported quantType: " << EnumNameQuantType(node->quantType) << ", node: " << node->name; - return RET_ERROR; - } - } - return RET_OK; -} - -STATUS WeightFormatHardCodePass::HardCodeONNX(const std::unique_ptr &node, - const std::unique_ptr &weightTensor) { - MS_ASSERT(node != nullptr); - MS_ASSERT(weightTensor != nullptr); - MS_ASSERT(node != nullptr); - MS_ASSERT(node->primitive != nullptr); - auto opType = node->primitive->value.type; - switch (this->quantType) { - case QuantType_AwareTraining: { - // sum up from current onnx quant models - if (opType == PrimitiveType_Conv2D) { - weightTensor->format = schema::Format::Format_KHWC; - } else if (opType == PrimitiveType_DepthwiseConv2D) { - weightTensor->format = schema::Format::Format_CHWK; - } else if (opType == PrimitiveType_DeConv2D) { - weightTensor->format = schema::Format::Format_KCHW; - } else { - MS_LOG(ERROR) << "Unsupported opType: " << EnumNamePrimitiveType(opType) << ", node: " << node->name; - return RET_ERROR; - } - } break; - case QuantType_WeightQuant: - case QuantType_QUANT_NONE: { - // conv (K x C/group x kH x kW) group = 1 - // depth (K x C/group x kH x kW) group = channelOut ==> (K, multiplier, H, W) - // deconv (C x K/group x kH x kW) group = 1 - // dedepth (C x K/group x kH x kW) group = channelIn ==> (C, multiplier, H, W) - if (opType == PrimitiveType_Conv2D || opType == PrimitiveType_DepthwiseConv2D) { - weightTensor->format = schema::Format::Format_KCHW; - } else if (opType == PrimitiveType_DeConv2D) { - weightTensor->format = schema::Format::Format_KCHW; - } else { - MS_LOG(ERROR) << "Unsupported opType: " << EnumNamePrimitiveType(opType) << ", node: " << node->name; - return RET_ERROR; - } - } break; - default: { - MS_LOG(ERROR) << "Unsupported quantType: " << EnumNameQuantType(node->quantType) << ", node: " << node->name; - return RET_ERROR; - } - } - return RET_OK; -} - -STATUS WeightFormatHardCodePass::HardCodeMS(const std::unique_ptr &node, - const std::unique_ptr &weightTensor) { - MS_ASSERT(node != nullptr); - MS_ASSERT(weightTensor != nullptr); - MS_ASSERT(node != nullptr); - MS_ASSERT(node->primitive != nullptr); - auto opType = node->primitive->value.type; - switch (this->quantType) { - case QuantType_AwareTraining: { - if (opType == schema::PrimitiveType_Conv2D) { - weightTensor->format = schema::Format::Format_KCHW; - } else if (opType == PrimitiveType_DepthwiseConv2D) { - weightTensor->format = schema::Format::Format_CKHW; - } else { - weightTensor->format = schema::Format::Format_KCHW; - } - } break; - case QuantType_WeightQuant: - case QuantType_QUANT_NONE: { - // sum up from current ms quant models - if (opType == PrimitiveType_Conv2D) { - weightTensor->format = schema::Format::Format_KCHW; - } else if (opType == PrimitiveType_DepthwiseConv2D) { - weightTensor->format = Format_CKHW; - } else if (opType == PrimitiveType_DeConv2D) { - weightTensor->format = Format_KCHW; - } else { - MS_LOG(ERROR) << "Unsupported opType: " << EnumNamePrimitiveType(opType) << ", node: " << node->name; - return RET_ERROR; - } - } break; - default: { - MS_LOG(ERROR) << "Unsupported quantType: " << EnumNameQuantType(node->quantType) << ", node: " << node->name; - return RET_ERROR; - } - } - return RET_OK; -} - -STATUS WeightFormatHardCodePass::HardCodeTFLITE(const std::unique_ptr &node, - const std::unique_ptr &weightTensor) { - MS_ASSERT(node != nullptr); - MS_ASSERT(weightTensor != nullptr); - MS_ASSERT(node != nullptr); - MS_ASSERT(node->primitive != nullptr); - auto opType = node->primitive->value.type; - switch (this->quantType) { - case QuantType_AwareTraining: - case QuantType_PostTraining: - case QuantType_WeightQuant: - case QuantType_QUANT_NONE: { - if (opType == schema::PrimitiveType_Conv2D) { - weightTensor->format = schema::Format::Format_KHWC; - } else if (opType == schema::PrimitiveType_DepthwiseConv2D) { - weightTensor->format = schema::Format::Format_CHWK; - } else if (opType == schema::PrimitiveType_DeConv2D) { - weightTensor->format = schema::Format::Format_CHWK; - } else { - MS_LOG(ERROR) << "Unsupported opType: " << EnumNamePrimitiveType(opType) << ", node: " << node->name; - return RET_ERROR; - } - } break; - default: { - MS_LOG(ERROR) << "Unsupported opType: " << EnumNamePrimitiveType(opType) << ", node: " << node->name; - return RET_ERROR; - } - } - return RET_OK; -} -} // namespace lite -} // namespace mindspore diff --git a/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc b/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc index d3587181cc..6190de2dd3 100644 --- a/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc +++ b/mindspore/lite/tools/converter/quantizer/aware_quantizer.cc @@ -25,7 +25,6 @@ #include "schema/inner/model_generated.h" #include "securec/include/securec.h" #include "src/common/utils.h" -#include "tools/common/converter_op_utils.h" #include "tools/common/node_util.h" #include "tools/common/tensor_util.h" #include "tools/converter/quantizer/calc_quant_param.h" diff --git a/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc b/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc index ffbafa4113..b676c5aee6 100644 --- a/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc +++ b/mindspore/lite/tools/optimizer/fusion/constant_folding_fusion.cc @@ -41,8 +41,9 @@ std::vector GetCNodeInputTensors(const CNodePtr &CNode) { for (auto input_index : tmp_fb_node->inputIndex) { auto tensorT = tmp_meta_graph->allTensors.at(input_index).get(); auto tensor_shape = tensorT->dims; - auto lite_tensor = new (std::nothrow) - Tensor(TypeId(tensorT->dataType), tensor_shape, tensorT->format, lite::TensorCategory(tensorT->nodeType)); + auto lite_tensor = new (std::nothrow) Tensor( + TypeId(tensorT->dataType), tensor_shape, tensorT->format, + lite::TensorCategory(tensorT->nodeType, tensorT->dims.size(), TypeId(tensorT->dataType), tensorT->data.size())); if (lite_tensor == nullptr) { MS_LOG(ERROR) << "lite tensor is nullptr"; return input_tensors; @@ -67,7 +68,7 @@ std::vector GetCNodeInputTensors(const CNodePtr &CNode) { lite::ReturnCode::GetSingleReturnCode()->UpdateReturnCode(lite::RET_MEMORY_FAILED); return {}; } - lite_tensor->SetData(tensor_data); + lite_tensor->set_data(tensor_data); input_tensors.emplace_back(lite_tensor); } return input_tensors;