!4270 [MS][LITE]add op_roipooling and testcase

Merge pull request !4270 from songhonglei413/roi

mindspore/lite/schema/model.fbs

@@ -56,6 +56,7 @@ union PrimitiveType {
     BatchNorm,
     BiasAdd,
     Pooling,
+    ROIPooling,
     DepthwiseConv2D,
     DeDepthwiseConv2D,
     Resize,

mindspore/lite/schema/ops.fbs

@@ -262,6 +262,12 @@ table BiasAdd {
     axis: [int];
 }
 
+table ROIPooling {
+    pooledH: int;
+    pooledW: int;
+    scale: float;
+}
+
 table Pooling {
     format: Format = 0;
     poolingMode: PoolMode;

mindspore/lite/src/ops/ops.cc

@@ -35,6 +35,8 @@ Primitive *Primitive::CreatePrimitive(schema::Primitive *primitive) {
       return new lite::Reduce(const_cast<schema::Primitive *>(primitive));
     case schema::PrimitiveType_Pooling:
       return new lite::Pooling(const_cast<schema::Primitive *>(primitive));
+    case schema::PrimitiveType_ROIPooling:
+      return new lite::ROIPooling(const_cast<schema::Primitive *>(primitive));
     case schema::PrimitiveType_DepthwiseConv2D:
       return new lite::DepthwiseConv2D(const_cast<schema::Primitive *>(primitive));
     case schema::PrimitiveType_FusedBatchNorm:

mindspore/lite/src/ops/ops.h

@@ -56,6 +56,13 @@ class Primitive {
   bool infer_flag_ = true;
 };
 
+class ROIPooling : public Primitive {
+ public:
+  explicit ROIPooling(schema::Primitive *primitive) : Primitive(primitive) {}
+  const schema::ROIPooling *GetAttribute() const { return this->primitive->value_as_ROIPooling(); }
+  int InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor::Tensor *> outputs_) override;
+};
+
 class Conv2D : public Primitive {
  public:
   explicit Conv2D(schema::Primitive *primitive) : Primitive(primitive) {}

mindspore/lite/src/ops/roi_pooling.cc

@@ -0,0 +1,58 @@
+/**
+ * Copyright 2019-2020 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 "src/ops/ops.h"
+#include "include/errorcode.h"
+#include "utils/log_adapter.h"
+#include "src/ir/tensor.h"
+
+namespace mindspore::lite {
+int ROIPooling::InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor::Tensor *> outputs_) {
+  MS_ASSERT(this->primitive != nullptr);
+  if (inputs_.size() != kDoubleNum) {
+    MS_LOG(ERROR) << "inputs number is not equal to " << kDoubleNum;
+    return RET_ERROR;
+  }
+  auto input = inputs_.front();
+  if (input == nullptr) {
+    return RET_NULL_PTR;
+  }
+  auto roi = inputs_.at(1);
+  if (roi == nullptr) {
+    return RET_NULL_PTR;
+  }
+  auto output = outputs_.front();
+  if (output == nullptr) {
+    return RET_NULL_PTR;
+  }
+  auto ROIPooling = GetAttribute();
+  auto new_h = ROIPooling->pooledH();
+  auto new_w = ROIPooling->pooledW();
+
+  auto shape_data = roi->shape();
+
+  std::vector<int> output_shape;
+  output_shape.push_back(shape_data[0]);
+  output_shape.push_back(new_h);
+  output_shape.push_back(new_w);
+  output_shape.push_back(input->Channel());
+  output->set_shape(output_shape);
+  output->set_data_type(input->data_type());
+  output->SetFormat(input->GetFormat());
+
+  return RET_OK;
+}
+}  // namespace mindspore::lite

mindspore/lite/src/populate_parameter.cc

@@ -33,6 +33,7 @@
 #include "src/runtime/kernel/arm/nnacl/conv_parameter.h"
 #include "src/runtime/kernel/arm/nnacl/fp32/pooling.h"
 #include "src/runtime/kernel/arm/nnacl/matmul_parameter.h"
+#include "src/runtime/kernel/arm/nnacl/fp32/roi_pooling.h"
 #include "src/runtime/kernel/arm/nnacl/softmax_parameter.h"
 #include "src/runtime/kernel/arm/nnacl/tile.h"
 #include "src/runtime/kernel/arm/nnacl/fp32/topk.h"
@@ -74,6 +75,21 @@
 #include "src/runtime/kernel/arm/nnacl/fp32/elu.h"
 
 namespace mindspore::kernel {
+
+OpParameter *PopulateROIPoolingParameter(const lite::Primitive *primitive) {
+  auto pooling_primitive = primitive->Value()->value_as_ROIPooling();
+  ROIPoolingParameter *param = new (std::nothrow) ROIPoolingParameter();
+  if (param == nullptr) {
+    MS_LOG(ERROR) << "new PoolingParameter failed.";
+    return nullptr;
+  }
+  param->op_parameter_.type_ = primitive->Type();
+  param->pooledH_ = pooling_primitive->pooledH();
+  param->pooledW_ = pooling_primitive->pooledW();
+  param->scale_ = pooling_primitive->scale();
+  return reinterpret_cast<OpParameter *>(param);
+}
+
 OpParameter *PopulateBatchNorm(const lite::Primitive *primitive) {
   BatchNormParameter *batch_norm_param = new (std::nothrow) BatchNormParameter();
   if (batch_norm_param == nullptr) {
@@ -1270,6 +1286,7 @@ PopulateParameterRegistry::PopulateParameterRegistry() {
   populate_parameter_funcs_[schema::PrimitiveType_Reduce] = PopulateReduceParameter;
   populate_parameter_funcs_[schema::PrimitiveType_Mean] = PopulateMeanParameter;
   populate_parameter_funcs_[schema::PrimitiveType_Pooling] = PopulatePoolingParameter;
+  populate_parameter_funcs_[schema::PrimitiveType_ROIPooling] = PopulateROIPoolingParameter;
   populate_parameter_funcs_[schema::PrimitiveType_DepthwiseConv2D] = PopulateConvDwParameter;
   populate_parameter_funcs_[schema::PrimitiveType_DeDepthwiseConv2D] = PopulateDeconvDwParameter;
   populate_parameter_funcs_[schema::PrimitiveType_DeConv2D] = PopulateDeconvParameter;

mindspore/lite/src/runtime/kernel/arm/fp32/roi_pooling.cc

@@ -0,0 +1,112 @@
+/**
+ * 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 "src/runtime/kernel/arm/fp32/roi_pooling.h"
+#include <vector>
+#include "schema/model_generated.h"
+#include "src/kernel_registry.h"
+#include "src/runtime/runtime_api.h"
+#include "include/errorcode.h"
+
+using mindspore::kernel::KERNEL_ARCH::kCPU;
+using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_ERROR;
+using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_ROIPooling;
+
+namespace mindspore::kernel {
+
+int ROIPoolingCPUKernel::Init() {
+  if (!InferShapeDone()) {
+    return RET_OK;
+  }
+  return ReSize();
+}
+
+int ROIPoolingCPUKernel::ReSize() { return RET_OK; }
+
+int ROIPoolingCPUKernel::DoExecute(int task_id) {
+  auto ret = ROIPooling(in_ptr_, out_ptr_, roi_ptr_, in_shape_, out_shape_, dim_, task_id, param_);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "ROIPooling Execute error task_id[" << task_id << "] error_code[" << ret << "]";
+    return ret;
+  }
+  return RET_OK;
+}
+
+int ROIPoolingRun(int task_id, LiteParallelGroupEnv *penv, void *cdata) {
+  auto Data = reinterpret_cast<ROIPoolingCPUKernel *>(cdata);
+  auto ret = Data->DoExecute(task_id);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "ROIPooling Run error task_id[" << task_id << "] error_code[" << ret << "]";
+    return ret;
+  }
+  return RET_OK;
+}
+
+int ROIPoolingCPUKernel::Run() {
+  auto ret = Prepare();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Prepare fail! ret: " << ret;
+    return ret;
+  }
+  in_ptr_ = reinterpret_cast<float *>(inputs_.front()->Data());
+  out_ptr_ = reinterpret_cast<float *>(outputs_.front()->Data());
+  roi_ptr_ = reinterpret_cast<float *>(inputs_.at(1)->Data());
+  in_shape_ = reinterpret_cast<const int *>(inputs_.front()->shape().data());
+  out_shape_ = reinterpret_cast<const int *>(outputs_.front()->shape().data());
+  dim_ = inputs_.front()->shape().size();
+  thread_count_ = 1;
+  ret = LiteBackendParallelLaunch(ROIPoolingRun, this, thread_count_);
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "ROIPooling error: error_code[" << ret << "]";
+    return ret;
+  }
+  return ret;
+}
+
+kernel::LiteKernel *CpuROIPoolingFp32KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
+                                                   const std::vector<lite::tensor::Tensor *> &outputs,
+                                                   OpParameter *opParameter, const lite::Context *ctx,
+                                                   const kernel::KernelKey &desc, const lite::Primitive *primitive) {
+  if (opParameter == nullptr) {
+    MS_LOG(ERROR) << "Input opParameter is nullptr!";
+    return nullptr;
+  }
+  if (ctx == nullptr) {
+    MS_LOG(ERROR) << "Input context is nullptr!";
+    return nullptr;
+  }
+  if (ctx->thread_num_ == 0) {
+    MS_LOG(ERROR) << "context thread num is 0!";
+    return nullptr;
+  }
+  auto *kernel = new (std::nothrow) ROIPoolingCPUKernel(opParameter, inputs, outputs, ctx, primitive);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "new ROIPoolingCPUKernel fail!";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    delete kernel;
+    MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
+                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
+    return nullptr;
+  }
+  return kernel;
+}
+
+REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_ROIPooling, CpuROIPoolingFp32KernelCreator)
+}  // namespace mindspore::kernel

mindspore/lite/src/runtime/kernel/arm/fp32/roi_pooling.h

@@ -0,0 +1,51 @@
+/**
+ * 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_SRC_RUNTIME_KERNEL_ARM_FP32_ROI_POOLING_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_ROI_POOLING_H_
+
+#include <vector>
+#include "src/lite_kernel.h"
+#include "src/runtime/kernel/arm/nnacl/fp32/roi_pooling.h"
+
+namespace mindspore::kernel {
+class ROIPoolingCPUKernel : public LiteKernel {
+ public:
+  ROIPoolingCPUKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
+                      const std::vector<lite::tensor::Tensor *> &outputs, const lite::Context *ctx,
+                      const lite::Primitive *primitive)
+      : LiteKernel(parameter, inputs, outputs, ctx, primitive) {
+    param_ = reinterpret_cast<ROIPoolingParameter *>(parameter);
+  }
+  ~ROIPoolingCPUKernel() override = default;
+
+  int Init() override;
+  int ReSize() override;
+  int Run() override;
+  int DoExecute(int task_id);
+
+ private:
+  float *in_ptr_;
+  float *out_ptr_;
+  float *roi_ptr_;
+  const int *in_shape_;
+  const int *out_shape_;
+  ROIPoolingParameter *param_;
+  int dim_;
+  int thread_count_;
+};
+}  // namespace mindspore::kernel
+
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_FP32_REVERSE_H_

mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/roi_pooling.cc

@@ -0,0 +1,96 @@
+/**
+ * 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 "nnacl/fp32/roi_pooling.h"
+#include <math.h>
+#include "nnacl/errorcode.h"
+
+int ROIPooling(float *in_ptr, float *out_ptr, float *roi, const int *in_shape, const int *out_shape, int dim, int tid,
+               ROIPoolingParameter *param) {
+  int num_rois = out_shape[kNHWC_N];
+  int batch_size = in_shape[kNHWC_N];
+  int height_ = in_shape[kNHWC_H];
+  int width_ = in_shape[kNHWC_W];
+  int channels_ = in_shape[kNHWC_C];
+  int scale = param->scale_;
+  int pooled_height = param->pooledH_;
+  int pooled_width = param->pooledW_;
+  int in_stride[DIMENSION_4D];
+  int out_stride[DIMENSION_4D];
+  int roi_stride = 5;
+  in_stride[DIMENSION_4D - 1] = 1;
+  out_stride[DIMENSION_4D - 1] = 1;
+  for (int i = dim - 2; i >= 0; --i) {
+    in_stride[i] = in_stride[i + 1] * in_shape[i + 1];
+    out_stride[i] = out_stride[i + 1] * out_shape[i + 1];
+  }
+  int roi_ind_st = 0;
+  for (int i = 0; i < num_rois; ++i) {
+    int roi_batch_ind = (int)roi[roi_ind_st];  // batch_index
+    if (roi_batch_ind >= batch_size) {
+      return NNACL_ERRCODE_INDEX_OUT_OF_RANGE;
+    }
+    int roi_start_h = (int)roundf(roi[roi_ind_st + 1] * scale);  // top-left x1
+    int roi_start_w = (int)roundf(roi[roi_ind_st + 2] * scale);  // top-left y1
+    int roi_end_h = (int)roundf(roi[roi_ind_st + 3] * scale);    // bottom-right x2
+    int roi_end_w = (int)roundf(roi[roi_ind_st + 4] * scale);    // bottom-fight y2
+
+    int roi_height = MSMAX(roi_end_h - roi_start_h + 1, 1);
+    int roi_width = MSMAX(roi_end_w - roi_start_w + 1, 1);
+
+    float bin_size_h = (float)roi_height / (float)pooled_height;
+    float bin_size_w = (float)roi_width / (float)pooled_width;
+    float *batch_data = in_ptr + in_stride[kNHWC_N] * roi_batch_ind;
+
+    int out_ind = i * out_stride[0];
+    for (int c = kNHWC_N; c < channels_; ++c) {
+      float max_v = -__FLT_MAX__;
+      for (int ph = 0; ph < pooled_height; ++ph) {
+        for (int pw = 0; pw < pooled_width; ++pw) {
+          int pooled_index =
+            i * out_stride[kNHWC_N] + ph * out_stride[kNHWC_H] + pw * out_stride[kNHWC_W] + c * out_stride[kNHWC_C];
+          int hstart = (int)floorf(ph * bin_size_h);     // block xi_1
+          int wstart = (int)floorf(pw * bin_size_w);     // block yi_1
+          int hend = (int)ceilf((ph + 1) * bin_size_h);  // block xi_2
+          int wend = (int)ceilf((pw + 1) * bin_size_w);  // block yi_2
+
+          hstart = MSMIN(MSMAX(hstart + roi_start_h, 0), height_);
+          hend = MSMIN(MSMAX(hend + roi_start_h, 0), height_);
+          wstart = MSMIN(MSMAX(wstart + roi_start_w, 0), width_);
+          wend = MSMIN(MSMAX(wend + roi_start_w, 0), width_);
+
+          bool is_empty = (hend <= hstart) || (wend <= wstart);
+          if (is_empty) {
+            max_v = 0;
+          }
+          int bd_index = c * in_stride[kNHWC_C] + hstart * in_stride[kNHWC_H];
+          for (int h = hstart; h < hend; ++h) {
+            int wi = bd_index + wstart * in_stride[kNHWC_W];
+            for (int w = wstart; w < wend; ++w) {
+              max_v = MSMAX(batch_data[wi], max_v);
+              // printf("bd:index: %d, data: %f, max_v: %f\n",wi,batch_data[wi],max_v);
+              wi += in_stride[kNHWC_W];
+            }
+            bd_index += in_stride[kNHWC_H];
+          }
+          out_ptr[pooled_index] = max_v;
+        }
+      }
+    }
+    roi_ind_st += roi_stride;
+  }
+  return NNACL_OK;
+}

mindspore/lite/src/runtime/kernel/arm/nnacl/fp32/roi_pooling.h

@@ -0,0 +1,30 @@
+/**
+ * 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_SRC_RUNTIME_KERNEL_ARM_NNACL_FP32_ROI_POOLING_H_
+#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_FP32_ROI_POOLING_H_
+
+#include "nnacl/op_base.h"
+
+typedef struct ROIPoolingParameter {
+  OpParameter op_parameter_;
+  int pooledW_;
+  int pooledH_;
+  float scale_;
+} ROIPoolingParameter;
+
+int ROIPooling(float *in_ptr, float *out_ptr, float *roi, const int *in_shape, const int *out_shape, int dim, int tid,
+                ROIPoolingParameter *param);
+#endif  // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_ARM_NNACL_FP32_ROI_POOLING_H_

mindspore/lite/test/ut/src/runtime/kernel/arm/fp32/roi_pooling_fp32_tests.cc

@@ -0,0 +1,80 @@
+/**
+ * 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 "mindspore/core/utils/log_adapter.h"
+#include "common/common_test.h"
+#include "mindspore/lite/src/runtime/kernel/arm/fp32/roi_pooling.h"
+#include "src/kernel_registry.h"
+#include "src/lite_kernel.h"
+
+namespace mindspore {
+class TestROIPoolingFp32 : public mindspore::Common {
+ public:
+  TestROIPoolingFp32() {}
+};
+
+int ROIPoolingTestInit(std::vector<lite::tensor::Tensor *> *inputs_, std::vector<lite::tensor::Tensor *> *outputs_,
+                       float *a_ptr, float *b_ptr, std::vector<int> a_shape, std::vector<int> b_shape,
+                       std::vector<int> c_shape) {
+  auto in_t =
+    new lite::tensor::Tensor(kNumberTypeFloat, a_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
+  in_t->MallocData();
+  memcpy(in_t->Data(), a_ptr, sizeof(float) * in_t->ElementsNum());
+  inputs_->push_back(in_t);
+
+  auto roi_t =
+    new lite::tensor::Tensor(kNumberTypeFloat, b_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
+  roi_t->MallocData();
+  memcpy(roi_t->Data(), b_ptr, sizeof(float) * roi_t->ElementsNum());
+  inputs_->push_back(roi_t);
+
+  auto out_t =
+    new lite::tensor::Tensor(kNumberTypeFloat, c_shape, schema::Format_NHWC, static_cast<schema::NodeType>(1));
+  out_t->MallocData();
+  outputs_->push_back(out_t);
+
+  return out_t->ElementsNum();
+}
+
+TEST_F(TestROIPoolingFp32, Simple) {
+  std::vector<lite::tensor::Tensor *> inputs_;
+  std::vector<lite::tensor::Tensor *> outputs_;
+  auto param = new ROIPoolingParameter();
+  param->scale_ = 1;
+  param->pooledW_ = 2;
+  param->pooledH_ = 2;
+  float a[] = {1, 2, 3, 4, 5, 11, 12, 13, 14, 15, 21, 22, 23, 24, 25, 31, 32, 33, 34, 35,
+               1, 2, 3, 4, 5, 11, 12, 13, 14, 15, 21, 22, 23, 24, 25, 31, 32, 33, 34, 35};
+  float b[] = {0, 1, 1, 3, 4, 1, 1, 1, 3, 4};
+  std::vector<int> a_shape = {2, 4, 5, 1};
+  std::vector<int> b_shape = {2, 5};
+  std::vector<int> c_shape = {2, 2, 2, 1};
+  int total_size = ROIPoolingTestInit(&inputs_, &outputs_, a, b, a_shape, b_shape, c_shape);
+  auto ctx = new lite::Context;
+  ctx->thread_num_ = 1;
+  kernel::ROIPoolingCPUKernel *op =
+    new kernel::ROIPoolingCPUKernel(reinterpret_cast<OpParameter *>(param), inputs_, outputs_, ctx, nullptr);
+  op->Init();
+  op->Run();
+  float correct[] = {23, 25, 33, 35, 23, 25, 33, 35};
+  float *output = reinterpret_cast<float *>(outputs_[0]->Data());
+  for (int i = 0; i < 8; ++i) printf("%f ", output[i]);
+  printf("\n");
+  CompareOutputData(reinterpret_cast<float *>(outputs_[0]->Data()), correct, total_size, 0.0001);
+  delete op;
+  for (auto t : inputs_) delete t;
+  for (auto t : outputs_) delete t;
+}
+}  // namespace mindspore