add register func for mean op

mindspore/lite/src/ops/mean.cc

@@ -0,0 +1,79 @@
+/**
+ * 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 {
+namespace {
+constexpr size_t kInputSize = 1;
+constexpr size_t kOutputSize = 1;
+}  // namespace
+int Mean::InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor::Tensor *> outputs_) {
+  if (inputs_.size() != kInputSize || outputs_.size() != kOutputSize) {
+    return RET_ERROR;
+  }
+  auto input = inputs_.front();
+  auto output = outputs_.front();
+  if (input == nullptr || output == nullptr) {
+    return RET_NULL_PTR;
+  }
+  if (this->primitive == nullptr) {
+    return RET_NULL_PTR;
+  }
+  auto mean_prim = this->primitive->value_as_Mean();
+  bool keep_dims = static_cast<bool>(mean_prim->keepDims());
+  std::vector<int> in_shape = input->shape();
+  std::vector<int> out_shape;
+  const auto &axes = mean_prim->axis();
+  auto num_axes = axes->size();
+  // reduce on all axes
+  if (num_axes == 0) {
+    if (keep_dims) {
+      for (auto i = 0; i < in_shape.size(); i++) {
+        out_shape.push_back(1);
+      }
+    }
+    output->set_shape(out_shape);
+    output->set_data_type(input->data_type());
+    return RET_OK;
+  }
+
+  // reduce on selected axes
+  for (size_t i = 0; i < in_shape.size(); i++) {
+    bool reduce_axis = false;
+    for (int idx = 0; idx < num_axes; ++idx) {
+      if (static_cast<size_t>((*axes)[idx]) == i) {
+        reduce_axis = true;
+        break;
+      }
+    }
+    if (reduce_axis) {
+      if (keep_dims) {
+        out_shape.push_back(1);
+      }
+    } else {
+      out_shape.push_back(in_shape[i]);
+    }
+  }
+  output->set_shape(out_shape);
+  output->set_data_type(input->data_type());
+  output->SetFormat(input->GetFormat());
+  return RET_OK;
+}
+}  // namespace mindspore::lite

mindspore/lite/src/ops/ops.h

@@ -384,6 +384,13 @@ class Fill : public Primitive {
   int InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor::Tensor *> outputs_) override;
 };
 
+class Mean : public Primitive {
+ public:
+  explicit Mean(schema::Primitive *primitive) : Primitive(primitive) {}
+  const schema::Mean *GetAttribute() const { return this->primitive->value_as_Mean(); }
+  int InferShape(std::vector<tensor::Tensor *> inputs_, std::vector<tensor::Tensor *> outputs_) override;
+};
+
 class ArgMax : public Primitive {
  public:
   explicit ArgMax(schema::Primitive *primitive) : Primitive(primitive) {}
@@ -601,10 +608,11 @@ class SpaceToBatch : public Primitive {
   explicit SpaceToBatch(schema::Primitive *primitive) : Primitive(primitive) {}
   const schema::SpaceToBatch *GetAttribute() const { return this->primitive->value_as_SpaceToBatch(); }
   int InferShape(std::vector<tensor::Tensor *> inputs, std::vector<tensor::Tensor *> outputs) override;
-  std::vector<int> BlockSizes() {return block_sizes_;}
-  std::vector<int> Paddings() {return block_sizes_;}
-  std::vector<int> InShape() {return block_sizes_;}
-  std::vector<int> PaddedInShape() {return block_sizes_;}
+  std::vector<int> BlockSizes() { return block_sizes_; }
+  std::vector<int> Paddings() { return block_sizes_; }
+  std::vector<int> InShape() { return block_sizes_; }
+  std::vector<int> PaddedInShape() { return block_sizes_; }
+
  private:
   std::vector<int> block_sizes_;
   std::vector<int> paddings_;

mindspore/lite/src/populate_parameter.cc

@@ -18,6 +18,7 @@
 #include <float.h>
 #include "src/ops/ops.h"
 #include "utils/log_adapter.h"
+#include "schema/ops_generated.h"
 #include "src/runtime/kernel/arm/opclib/op_base.h"
 #include "src/runtime/kernel/arm/opclib/fp32/arg_min_max.h"
 #include "src/runtime/kernel/arm/opclib/fp32/cast.h"
@@ -391,6 +392,30 @@ OpParameter *PopulateReduceParameter(const lite::Primitive *primitive) {
   return reinterpret_cast<OpParameter *>(reduce_param);
 }
 
+OpParameter *PopulateMeanParameter(const lite::Primitive *primitive) {
+    ReduceParameter *mean_param = new (std::nothrow) ReduceParameter();
+    if (mean_param == nullptr) {
+        MS_LOG(ERROR) << "new ReduceParameter failed.";
+        return nullptr;
+    }
+    mean_param->op_parameter_.type_ = primitive->Type();
+    auto mean = primitive->Value()->value_as_Mean();
+    mean_param->keep_dims_ = mean->keepDims();
+    auto axisVector = mean->axis();
+    if (axisVector->size() > REDUCE_MAX_AXES_NUM) {
+        MS_LOG(ERROR) << "Reduce axes size " << axisVector->size() << " exceed limit " << REDUCE_MAX_AXES_NUM;
+        delete (mean_param);
+        return nullptr;
+    }
+    mean_param->num_axes_ = static_cast<int>(axisVector->size());
+    int i = 0;
+    for (auto iter = axisVector->begin(); iter != axisVector->end(); iter++) {
+        mean_param->axes_[i++] = *iter;
+    }
+    mean_param->mode_ = static_cast<int>(schema::ReduceMode_ReduceMean);
+    return reinterpret_cast<OpParameter *>(mean_param);
+}
+
 OpParameter *PopulatePadParameter(const lite::Primitive *primitive) {
   PadParameter *pad_param = new (std::nothrow) PadParameter();
   if (pad_param == nullptr) {
@@ -1131,6 +1156,7 @@ PopulateParameterRegistry::PopulateParameterRegistry() {
   populate_parameter_funcs_[schema::PrimitiveType_Activation] = PopulateActivationParameter;
   populate_parameter_funcs_[schema::PrimitiveType_Conv2D] = PopulateConvParameter;
   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_DepthwiseConv2D] = PopulateConvDwParameter;
   populate_parameter_funcs_[schema::PrimitiveType_DeDepthwiseConv2D] = PopulateDeconvDwParameter;

mindspore/lite/src/runtime/kernel/arm/base/reshape_base.cc

@@ -92,7 +92,7 @@ kernel::LiteKernel *CpuReshapeFp32KernelCreator(const std::vector<lite::tensor::
   MS_ASSERT(desc.type == schema::PrimitiveType_Reshape);
   auto *kernel = new (std::nothrow) ReshapeCPUKernel(opParameter, inputs, outputs, ctx);
   if (kernel == nullptr) {
-    MS_LOG(ERROR) << "new ConcatCPUKernel fail!";
+    MS_LOG(ERROR) << "new ReshapeCPUKernel fail!";
     return nullptr;
   }
   auto ret = kernel->Init();

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

@@ -99,8 +99,10 @@ kernel::LiteKernel *CpuActivationFp32KernelCreator(const std::vector<lite::tenso
   }
   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;
 }

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

@@ -61,6 +61,7 @@ kernel::LiteKernel *CpuFusedBatchnormKernelCreator(const std::vector<lite::tenso
   }
   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;
@@ -70,4 +71,3 @@ kernel::LiteKernel *CpuFusedBatchnormKernelCreator(const std::vector<lite::tenso
 
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_FusedBatchNorm, CpuFusedBatchnormKernelCreator)
 }  // namespace mindspore::kernel
-

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

@@ -46,12 +46,13 @@ kernel::LiteKernel *CpuMatmulFp32KernelCreator(const std::vector<lite::tensor::T
   }
   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_MatMul, CpuMatmulFp32KernelCreator)
 }  // namespace mindspore::kernel
-

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

@@ -74,8 +74,10 @@ kernel::LiteKernel *CpuPowerFp32KernelCreator(const std::vector<lite::tensor::Te
   }
   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;
 }

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

@@ -26,6 +26,7 @@ using mindspore::lite::KernelRegistrar;
 using mindspore::lite::RET_ERROR;
 using mindspore::lite::RET_NULL_PTR;
 using mindspore::lite::RET_OK;
+using mindspore::schema::PrimitiveType_Mean;
 using mindspore::schema::PrimitiveType_Reduce;
 using mindspore::schema::ReduceMode;
 using mindspore::schema::ReduceMode_ReduceMax;
@@ -195,6 +196,27 @@ int ReduceCPUKernel::Run() {
   return RET_OK;
 }
 
+int ReduceCPUKernel::MallocTmpBuffer() {
+  auto input_shape = inputs_.at(0)->shape();
+  for (auto i = 0; i < num_axes_ - 1; i++) {
+    int axis = axes_[i];
+    size_t size = 1;
+    for (auto j = 0; j < input_shape.size(); j++) {
+      if (static_cast<size_t>(axis) != j) {
+        size *= input_shape[j];
+      }
+    }
+    float *buffer = reinterpret_cast<float *>(malloc(size * sizeof(float)));
+    if (buffer == nullptr) {
+      MS_LOG(ERROR) << "Malloc data failed.";
+      return RET_ERROR;
+    }
+    data_buffers_.emplace_back(buffer);
+    input_shape[axis] = 1;
+  }
+  return RET_OK;
+}
+
 kernel::LiteKernel *CpuReduceFp32KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
                                                const std::vector<lite::tensor::Tensor *> &outputs,
                                                OpParameter *opParameter, const lite::Context *ctx,
@@ -219,30 +241,42 @@ kernel::LiteKernel *CpuReduceFp32KernelCreator(const std::vector<lite::tensor::T
   if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
                   << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
+    delete kernel;
+    return nullptr;
   }
   return kernel;
 }
 
-int ReduceCPUKernel::MallocTmpBuffer() {
-  auto input_shape = inputs_.at(0)->shape();
-  for (auto i = 0; i < num_axes_ - 1; i++) {
-    int axis = axes_[i];
-    size_t size = 1;
-    for (auto j = 0; j < input_shape.size(); j++) {
-      if (static_cast<size_t>(axis) != j) {
-        size *= input_shape[j];
-      }
-    }
-    float *buffer = reinterpret_cast<float *>(malloc(size * sizeof(float)));
-    if (buffer == nullptr) {
-      MS_LOG(ERROR) << "Malloc data failed.";
-      return RET_ERROR;
-    }
-    data_buffers_.emplace_back(buffer);
-    input_shape[axis] = 1;
+kernel::LiteKernel *CpuMeanFp32KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
+                                             const std::vector<lite::tensor::Tensor *> &outputs,
+                                             OpParameter *opParameter, const lite::Context *ctx,
+                                             const kernel::KernelKey &desc) {
+  MS_ASSERT(opParameter != nullptr);
+  MS_ASSERT(desc.type == schema::PrimitiveType_Mean);
+  if (opParameter == nullptr) {
+    MS_LOG(ERROR) << "Reduce opParameter nullptr";
+    return nullptr;
   }
-  return RET_OK;
+  if (desc.type != schema::PrimitiveType_Mean) {
+    MS_LOG(ERROR) << "Reduce op desc.type should be PrimitiveType_Mean, got " << desc.type;
+    return nullptr;
+  }
+  auto *kernel =
+    new (std::nothrow) ReduceCPUKernel(reinterpret_cast<ReduceParameter *>(opParameter), inputs, outputs, ctx);
+  if (kernel == nullptr) {
+    MS_LOG(ERROR) << "Reduce new ReduceCPUKernel failed.";
+    return nullptr;
+  }
+  auto ret = kernel->Init();
+  if (ret != RET_OK) {
+    MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
+                  << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
+    delete kernel;
+    return nullptr;
+  }
+  return kernel;
 }
 
 REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Reduce, CpuReduceFp32KernelCreator)
+REG_KERNEL(kCPU, kNumberTypeFloat32, PrimitiveType_Mean, CpuMeanFp32KernelCreator)
 }  // namespace mindspore::kernel

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

@@ -69,7 +69,7 @@ kernel::LiteKernel *CpuShapeFp32KernelCreator(const std::vector<lite::tensor::Te
   }
 
   auto ret = kernel->Init();
-  if (ret != 0) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
                   << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
     delete kernel;

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

@@ -73,7 +73,7 @@ kernel::LiteKernel *CpuStridedSliceFp32KernelCreator(const std::vector<lite::ten
   }
 
   auto ret = kernel->Init();
-  if (ret != 0) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: " << opParameter->name_ << ", type: "
                   << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(opParameter->type_));
     delete kernel;

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

@@ -82,7 +82,7 @@ kernel::LiteKernel *CpuUnsqueezeFp32KernelCreator(const std::vector<lite::tensor
   MS_ASSERT(desc.type == schema::PrimitiveType_Unsqueeze);
   auto *kernel = new (std::nothrow) UnsqueezeCPUKernel(opParameter, inputs, outputs, ctx);
   if (kernel == nullptr) {
-    MS_LOG(ERROR) << "new AddNCPUKernel fail!";
+    MS_LOG(ERROR) << "new UnsqueezeCPUKernel fail!";
     return nullptr;
   }
   auto ret = kernel->Init();

mindspore/lite/src/runtime/kernel/arm/int8/activation.cc

@@ -30,9 +30,9 @@ using mindspore::schema::PrimitiveType_Activation;
 
 namespace mindspore::kernel {
 kernel::LiteKernel *CpuActivationInt8KernelCreator(const std::vector<lite::tensor::Tensor *> &inputs,
-                                               const std::vector<lite::tensor::Tensor *> &outputs,
-                                               OpParameter *parameter, const lite::Context *ctx,
-                                               const KernelKey &desc) {
+                                                   const std::vector<lite::tensor::Tensor *> &outputs,
+                                                   OpParameter *parameter, const lite::Context *ctx,
+                                                   const KernelKey &desc) {
   if (parameter == nullptr) {
     MS_LOG(ERROR) << "parameter is nullptr";
     return nullptr;
@@ -56,8 +56,10 @@ kernel::LiteKernel *CpuActivationInt8KernelCreator(const std::vector<lite::tenso
   }
   auto ret = kernel->Init();
   if (ret != RET_OK) {
+    delete kernel;
     MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
                   << ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
+    return nullptr;
   }
   return kernel;
 }

mindspore/lite/src/runtime/kernel/arm/int8/add_int8.cc

@@ -23,6 +23,7 @@
 #include "include/errorcode.h"
 
 using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_Add;
 
 namespace mindspore::kernel {
@@ -135,7 +136,7 @@ kernel::LiteKernel *CpuAddInt8KernelCreator(const std::vector<lite::tensor::Tens
     return nullptr;
   }
   auto ret = kernel->Init();
-  if (0 != ret) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
                   << ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
     delete kernel;
@@ -146,4 +147,3 @@ kernel::LiteKernel *CpuAddInt8KernelCreator(const std::vector<lite::tensor::Tens
 
 REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_Add, CpuAddInt8KernelCreator)
 }  // namespace mindspore::kernel
-

mindspore/lite/src/runtime/kernel/arm/int8/bias_add_int8.cc

@@ -18,8 +18,10 @@
 #include "src/runtime/kernel/arm/opclib/fp32/arithmetic.h"
 #include "src/runtime/kernel/arm/opclib/errorcode.h"
 #include "src/kernel_registry.h"
+#include "include/errorcode.h"
 
 using mindspore::lite::KernelRegistrar;
+using mindspore::lite::RET_OK;
 using mindspore::schema::PrimitiveType_BiasAdd;
 
 namespace mindspore::kernel {
@@ -71,7 +73,7 @@ kernel::LiteKernel *CpuBiasAddInt8KernelCreator(const std::vector<lite::tensor::
   }
 
   auto ret = kernel->Init();
-  if (0 != ret) {
+  if (ret != RET_OK) {
     MS_LOG(ERROR) << "Init kernel failed, name: " << parameter->name_
                   << ", type: " << schema::EnumNamePrimitiveType(static_cast<schema::PrimitiveType>(parameter->type_));
     delete kernel;
@@ -82,4 +84,3 @@ kernel::LiteKernel *CpuBiasAddInt8KernelCreator(const std::vector<lite::tensor::
 
 REG_KERNEL(kCPU, kNumberTypeInt8, PrimitiveType_BiasAdd, CpuBiasAddInt8KernelCreator)
 }  // namespace mindspore::kernel
-

mindspore/lite/src/runtime/kernel/arm/opclib/common_func.cc

@@ -105,7 +105,46 @@ void IndirectGemmFp32_8x8(float *output, const float *input, const float *weight
 #ifndef ENABLE_ARM32
 void IndirectGemmFp32_8x4(float *output, const float *input, const float *weight, const float *bias, size_t step,
                           size_t ic4, size_t output_channel, size_t offset, size_t mode, size_t writeC4, size_t relu,
-                          size_t relu6) {}
+                          size_t relu6) {
+  for (int i = 0; i < TILE_NUM; i++) {
+    int input_tile_offset = i * C4NUM;
+    int output_tile_offset = i * output_channel;
+    for (int j = 0; j < output_channel; j++) {
+      int oc4_block = j / C4NUM;
+      int oc4_res = j % C4NUM;
+      int weight_oc_offset = oc4_block * step * ic4 * C4NUM * C4NUM + oc4_res;
+      int out_oc_offset = output_tile_offset + j;
+
+      float acc = 0;
+      for (int n = 0; n < step; n++) {
+        int input_kw_offset = input_tile_offset + n * ic4 * C4NUM * TILE_NUM;
+        int weight_kw_offset = weight_oc_offset + n * ic4 * C4NUM * C4NUM;
+
+        for (int k = 0; k < ic4; k++) {
+          int input_ic4_offset = input_kw_offset + k * TILE_NUM * C4NUM;
+          int weight_ic4_offset = weight_kw_offset + k * C4NUM * C4NUM;
+          for (int m = 0; m < C4NUM; m++) {
+            int input_ic_offset = input_ic4_offset + m;
+            int weight_ic_offset = weight_ic4_offset + m * C4NUM;
+            acc += (weight + weight_ic_offset)[0] * (input + input_ic_offset)[0];
+          }
+        }
+      }
+      acc += bias[j];
+      if (relu) {
+        acc = acc > 0 ? acc : 0;
+      } else if (relu6) {
+        if (acc < 0) {
+          acc = 0;
+        } else if (acc > 6) {
+          acc = 6;
+        } else {
+        }
+      }
+      (output + out_oc_offset)[0] = acc;
+    }
+  }
+}
 #endif
 
 int8_t MinInt8(int8_t a, int8_t b) { return b ^ ((a ^ b) & -(a < b)); }

mindspore/lite/src/scheduler.cc

@@ -175,9 +175,11 @@ kernel::LiteKernel *Scheduler::ScheduleNode(const std::vector<tensor::Tensor *>
     kernel::KernelKey key{desc.arch, kNumberTypeFloat16, desc.type};
     kernel = KernelFactory::GetInstance()->GetKernel(inputs, outputs, primitive, context_, key);
     if (kernel != nullptr) {
+      MS_LOG(INFO) << "Get fp16 op success.";
       kernel->set_desc(desc);
       return kernel;
     }
+    MS_LOG(INFO) << "Get fp16 op failed, back to fp32 op.";
     kernel = KernelFactory::GetInstance()->GetKernel(inputs, outputs, primitive, context_, desc);
   } else {
     kernel = KernelFactory::GetInstance()->GetKernel(inputs, outputs, primitive, context_, desc);

mindspore/lite/test/ut/src/infer_test.cc

@@ -73,7 +73,7 @@ TEST_F(InferTest, TestConvNode) {
   auto buf = new char *[1];
   //================================================================
   size_t weight_size;
-  std::string weight_path = "./convfp32_weight_32_3_3_3.bin";
+  std::string weight_path = "./test_data/conv/convfp32_weight_32_3_3_3.bin";
   ReadFile(weight_path.c_str(), &weight_size, buf);
   ASSERT_NE(nullptr, buf[0]);
   auto weight_data_temp = reinterpret_cast<float *>(buf[0]);
@@ -118,7 +118,7 @@ TEST_F(InferTest, TestConvNode) {
   auto data = inTensor->MutableData();
   //===================================================
   size_t input_size;
-  std::string input_path = "./convfp32_input_1_28_28_3.bin";
+  std::string input_path = "./test_data/conv/convfp32_input_1_28_28_3.bin";
   ReadFile(input_path.c_str(), &input_size, buf);
   ASSERT_NE(nullptr, buf[0]);
   auto input_data = reinterpret_cast<float *>(buf[0]);
@@ -138,7 +138,7 @@ TEST_F(InferTest, TestConvNode) {
   ASSERT_NE(nullptr, outData);
   //===================================================
   size_t output_size;
-  std::string output_path = "./convfp32_out_1_28_28_32.bin";
+  std::string output_path = "./test_data/conv/convfp32_out_1_28_28_32.bin";
   ReadFile(output_path.c_str(), &output_size, buf);
   ASSERT_NE(nullptr, buf[0]);
   auto output_data = reinterpret_cast<float *>(buf[0]);
@@ -146,7 +146,7 @@ TEST_F(InferTest, TestConvNode) {
   //===================================================
   ASSERT_EQ(output_size, outTensor->Size());
   for (size_t i = 0; i < outTensor->ElementsNum(); i++) {
-    ASSERT_EQ(output_data[i], outData[i]);
+    ASSERT_LE((output_data[i]- outData[i]), 0.001);
   }
   MS_LOG(INFO) << "Passed";
 }