Huawei_Technology
/
mindspore
Not watched
1
0
Fork 0
Code
Releases 13 Wiki evaluate Activity
Issues 0 Pull Requests 0 Datasets Model Cloudbrain HPC
Browse Source

!5042 rm caffe_prelu 

Merge pull request !5042 from liuzhongkai/delete_caffe_prelu
tags/v1.0.0
mindspore-ci-bot Gitee 5 years ago
parent
3454a808c7 b0184e5935
commit
33fdc43f18
9 changed files with 79 additions and 447 deletions
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. +0
    -23
      mindspore/lite/src/runtime/kernel/opencl/cl/caffe_prelu.cl
  2. +30
    -0
      mindspore/lite/src/runtime/kernel/opencl/cl/prelu.cl
  3. +0
    -152
      mindspore/lite/src/runtime/kernel/opencl/kernel/caffe_prelu.cc
  4. +0
    -49
      mindspore/lite/src/runtime/kernel/opencl/kernel/caffe_prelu.h
  5. +36
    -10
      mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.cc
  6. +2
    -0
      mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.h
  7. +0
    -2
      mindspore/lite/test/CMakeLists.txt
  8. +0
    -203
      mindspore/lite/test/ut/src/runtime/kernel/opencl/caffe_prelu_tests.cc
  9. +11
    -8
      mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc

+ 0
- 23
mindspore/lite/src/runtime/kernel/opencl/cl/caffe_prelu.cl View File

@@ -1,23 +0,0 @@
#pragma OPENCL EXTENSION cl_arm_printf : enable

#define SLICES 4
#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;

__kernel void CaffePRelu(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
__global float *alpha) {
int C = input_shape.w; // channel size

int Y = get_global_id(0); // height id
int X = get_global_id(1); // weight id
for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y)); // NHWC4: H WC
FLT4 tmp;
int index = num * 4;
tmp.x = in_c4.x * alpha[index];
tmp.y = in_c4.y * alpha[index + 1];
tmp.z = in_c4.z * alpha[index + 2];
tmp.w = in_c4.w * alpha[index + 3];
WRITE_IMAGE(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp); // NHWC4: H WC
}
}

+ 30
- 0
mindspore/lite/src/runtime/kernel/opencl/cl/prelu.cl View File

@@ -0,0 +1,30 @@
#pragma OPENCL EXTENSION cl_arm_printf : enable

#define SLICES 4
#define UP_DIV(x, y) (((x) + (y) - (1)) / (y))
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;

__kernel void PRelu(__read_only image2d_t input, __write_only image2d_t output, const int4 input_shape,
__global float *alpha, const int dim) {
int C = input_shape.w; // channel size

int Y = get_global_id(0); // height id
int X = get_global_id(1); // weight id
for (int num = 0; num < UP_DIV(C, SLICES); ++num) {
FLT4 in_c4 = READ_IMAGE(input, smp_zero, (int2)(X * UP_DIV(C, SLICES) + num, Y)); // NHWC4: H WC
FLT4 tmp;
if (dim == 1) {
tmp.x = in_c4.x >= 0 ? in_c4.x : in_c4.x * (*alpha);
tmp.y = in_c4.y >= 0 ? in_c4.y : in_c4.y * (*alpha);
tmp.z = in_c4.z >= 0 ? in_c4.z : in_c4.z * (*alpha);
tmp.w = in_c4.w >= 0 ? in_c4.w : in_c4.w * (*alpha);
} else {
int index = num * 4;
tmp.x = in_c4.x >= 0 ? in_c4.x : in_c4.x * alpha[index];
tmp.y = in_c4.y >= 0 ? in_c4.y : in_c4.y * alpha[index + 1];
tmp.z = in_c4.z >= 0 ? in_c4.z : in_c4.z * alpha[index + 2];
tmp.w = in_c4.w >= 0 ? in_c4.w : in_c4.w * alpha[index + 3];
}
WRITE_IMAGE(output, (int2)(X * UP_DIV(C, SLICES) + num, Y), tmp); // NHWC4: H WC
}
}

+ 0
- 152
mindspore/lite/src/runtime/kernel/opencl/kernel/caffe_prelu.cc View File

@@ -1,152 +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 <string>
#include <set>
#include <vector>

#include "src/kernel_registry.h"
#include "include/errorcode.h"
#include "src/runtime/kernel/opencl/kernel/caffe_prelu.h"
#include "src/runtime/opencl/opencl_runtime.h"
#include "src/runtime/kernel/opencl/cl/caffe_prelu.cl.inc"

using mindspore::kernel::KERNEL_ARCH::kGPU;
using mindspore::lite::KernelRegistrar;
using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_OK;
using mindspore::schema::PrimitiveType_CaffePReLU;

namespace mindspore::kernel {

void CaffePReluOpenCLKernel::CaffeWeight() {
int C = in_tensors_[1]->shape()[0];
int div_ci = UP_DIV(C, C4NUM);
std::cout << div_ci << std::endl;
auto allocator = lite::opencl::OpenCLRuntime::GetInstance()->GetAllocator();
CaffeWeight_ = reinterpret_cast<FLOAT_t *>(allocator->Malloc(div_ci * C4NUM * sizeof(FLOAT_t)));
CaffeWeight_ = reinterpret_cast<FLOAT_t *>(allocator->MapBuffer(CaffeWeight_, CL_MAP_WRITE, nullptr, true));
memset(CaffeWeight_, 0x00, div_ci * C4NUM * sizeof(FLOAT_t));
auto origin_weight = reinterpret_cast<FLOAT_t *>(in_tensors_[1]->Data());
for (int i = 0; i < in_tensors_[1]->ElementsNum(); ++i) {
CaffeWeight_[i] = origin_weight[i];
}
allocator->UnmapBuffer(CaffeWeight_);
}

int CaffePReluOpenCLKernel::Init() {
if (in_tensors_[0]->shape().size() != 4) {
MS_LOG(ERROR) << "Caffe PRelu only support dim=4, but your dim=" << in_tensors_[0]->shape().size();
return RET_ERROR;
}
CaffeWeight();
std::set<std::string> build_options;
std::string source = caffe_prelu_source;
std::string program_name = "CaffePRelu";
std::string kernel_name = "CaffePRelu";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->LoadSource(program_name, source);
ocl_runtime->BuildKernel(kernel_, program_name, kernel_name, build_options);
in_ori_format_ = in_tensors_[0]->GetFormat();
in_tensors_[0]->SetFormat(schema::Format_NHWC4);
out_ori_format_ = out_tensors_[0]->GetFormat();
out_tensors_[0]->SetFormat(schema::Format_NHWC4);
MS_LOG(DEBUG) << program_name << " Init Done!";
return RET_OK;
}

int CaffePReluOpenCLKernel::Run() {
int N = in_tensors_[0]->shape()[0];
int H = in_tensors_[0]->shape()[1];
int W = in_tensors_[0]->shape()[2];
int C = in_tensors_[0]->shape()[3];

cl_int4 input_shape = {N, H, W, C};
int C_Weight = in_tensors_[1]->shape()[0];
if (UP_DIV(C_Weight, C4NUM) != UP_DIV(C, C4NUM)) {
MS_LOG(ERROR) << "CaffePRelu weight channel size:" << C_Weight
<< " must be equal with in_teneors channel size:" << C;
return RET_ERROR;
}

MS_LOG(DEBUG) << op_parameter_->name_ << " Running!";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
int arg_idx = 0;
ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, input_shape);
ocl_runtime->SetKernelArg(kernel_, arg_idx++, CaffeWeight_);
std::vector<size_t> local = {1, 1};
std::vector<size_t> global = {static_cast<size_t>(H), static_cast<size_t>(W)};
auto ret = ocl_runtime->RunKernel(kernel_, global, local, nullptr);
if (ret != RET_OK) {
MS_LOG(ERROR) << "Run kernel " << op_parameter_->name_ << " error.";
return RET_ERROR;
}
return RET_OK;
}

int CaffePReluOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
int H = in_tensors_[0]->shape()[1];
int W = in_tensors_[0]->shape()[2];
int C = in_tensors_[0]->shape()[3];

#ifdef ENABLE_FP16
size_t img_dtype = CL_HALF_FLOAT;
#else
size_t img_dtype = CL_FLOAT;
#endif

img_size->clear();
img_size->push_back(W * UP_DIV(C, C4NUM));
img_size->push_back(H);
img_size->push_back(img_dtype);
return RET_OK;
}

kernel::LiteKernel *OpenCLCaffePReluKernelCreator(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 mindspore::lite::PrimitiveC *primitive) {
if (inputs.size() == 0) {
MS_LOG(ERROR) << "Input data size must be greater than 0, but your size is " << inputs.size();
return nullptr;
}
if (inputs[0]->shape()[0] > 1) {
MS_LOG(ERROR) << "Init CaffePRelu kernel failed: Unsupported multi-batch.";
return nullptr;
}
auto *kernel =
new (std::nothrow) CaffePReluOpenCLKernel(reinterpret_cast<OpParameter *>(opParameter), inputs, outputs);
if (kernel == nullptr) {
MS_LOG(ERROR) << "Kernel " << opParameter->name_ << "is nullptr.";
return nullptr;
}

auto ret = kernel->Init();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Init CaffePRelu kernel failed!";
delete kernel;
return nullptr;
}
return kernel;
}

REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_CaffePReLU, OpenCLCaffePReluKernelCreator)
} // namespace mindspore::kernel

+ 0
- 49
mindspore/lite/src/runtime/kernel/opencl/kernel/caffe_prelu.h View File

@@ -1,49 +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_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_CAFFEPRELU_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_CAFFEPRELU_H_

#include <vector>
#include <string>

#include "src/ir/tensor.h"
#include "src/runtime/kernel/opencl/opencl_kernel.h"
#include "schema/model_generated.h"
#include "src/runtime/opencl/opencl_runtime.h"

namespace mindspore::kernel {

class CaffePReluOpenCLKernel : public OpenCLKernel {
public:
explicit CaffePReluOpenCLKernel(OpParameter *parameter, const std::vector<lite::tensor::Tensor *> &inputs,
const std::vector<lite::tensor::Tensor *> &outputs)
: OpenCLKernel(parameter, inputs, outputs) {}
~CaffePReluOpenCLKernel() override{};

int Init() override;
int Run() override;
int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
void CaffeWeight();

private:
cl::Kernel kernel_;
FLOAT_t *CaffeWeight_;
};

} // namespace mindspore::kernel

#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_CAFFEPRELU_H_

+ 36
- 10
mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.cc View File

@@ -23,8 +23,7 @@
#include "include/errorcode.h"
#include "src/runtime/kernel/opencl/kernel/prelu.h"
#include "src/runtime/opencl/opencl_runtime.h"
#include "src/runtime/kernel/opencl/cl/activation.cl.inc"
#include "nnacl/prelu_parameter.h"
#include "src/runtime/kernel/opencl/cl/prelu.cl.inc"

using mindspore::kernel::KERNEL_ARCH::kGPU;
using mindspore::lite::KernelRegistrar;
@@ -34,15 +33,41 @@ using mindspore::schema::PrimitiveType_Prelu;

namespace mindspore::kernel {

void PReluOpenCLKernel::InitBuffer() {
int C = in_tensors_[1]->shape()[0];
int div_ci = UP_DIV(C, C4NUM);
std::cout << div_ci << std::endl;
auto allocator = lite::opencl::OpenCLRuntime::GetInstance()->GetAllocator();
PReluWeight_ = reinterpret_cast<FLOAT_t *>(allocator->Malloc(div_ci * C4NUM * sizeof(FLOAT_t)));
PReluWeight_ = reinterpret_cast<FLOAT_t *>(allocator->MapBuffer(PReluWeight_, CL_MAP_WRITE, nullptr, true));
memset(PReluWeight_, 0x00, div_ci * C4NUM * sizeof(FLOAT_t));
auto origin_weight = reinterpret_cast<FLOAT_t *>(in_tensors_[1]->Data());
for (int i = 0; i < in_tensors_[1]->ElementsNum(); ++i) {
PReluWeight_[i] = origin_weight[i];
}
allocator->UnmapBuffer(PReluWeight_);
}

int PReluOpenCLKernel::Init() {
if (in_tensors_[0]->shape().size() != 4) {
MS_LOG(ERROR) << "PRelu only support dim=4, but your dim=" << in_tensors_[0]->shape().size();
return RET_ERROR;
}
int C_Weight = in_tensors_[1]->shape()[0];
int C = in_tensors_[0]->shape()[3];
if (C_Weight != 1 && UP_DIV(C_Weight, C4NUM) != UP_DIV(C, C4NUM)) {
MS_LOG(ERROR)
<< "PRelu weight channel size must be 1 or must be equal with in_teneors channel size, but your weight size is "
<< C_Weight << " and your input channel size is " << C;
return RET_ERROR;
}
if (C_Weight != 1) {
InitBuffer();
}
std::set<std::string> build_options;
std::string source = activation_source;
std::string source = prelu_source;
std::string program_name = "PRelu";
std::string kernel_name = "ReluScalar";
std::string kernel_name = "PRelu";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->LoadSource(program_name, source);
ocl_runtime->BuildKernel(kernel_, program_name, kernel_name, build_options);
@@ -61,17 +86,18 @@ int PReluOpenCLKernel::Run() {
int W = in_tensors_[0]->shape()[2];
int C = in_tensors_[0]->shape()[3];
cl_int4 input_shape = {N, H, W, C};
if (in_tensors_[1]->ElementsNum() < 1) {
MS_LOG(ERROR) << "PRelu weight size must be greater than 1! But your weight size is "
<< in_tensors_[1]->ElementsNum();
return RET_ERROR;
}

auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
int arg_idx = 0;
ocl_runtime->SetKernelArg(kernel_, arg_idx++, in_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, out_tensors_[0]->Data());
ocl_runtime->SetKernelArg(kernel_, arg_idx++, input_shape);
ocl_runtime->SetKernelArg(kernel_, arg_idx++, reinterpret_cast<float *>(in_tensors_[1]->Data())[0]);
if (in_tensors_[1]->shape()[0] == 1) {
ocl_runtime->SetKernelArg(kernel_, arg_idx++, reinterpret_cast<float *>(in_tensors_[1]->Data()));
} else {
ocl_runtime->SetKernelArg(kernel_, arg_idx++, PReluWeight_);
}
ocl_runtime->SetKernelArg(kernel_, arg_idx++, reinterpret_cast<int>(in_tensors_[1]->shape()[0]));

std::vector<size_t> local = {1, 1};
std::vector<size_t> global = {static_cast<size_t>(H), static_cast<size_t>(W)};


+ 2
- 0
mindspore/lite/src/runtime/kernel/opencl/kernel/prelu.h View File

@@ -36,9 +36,11 @@ class PReluOpenCLKernel : public OpenCLKernel {
int Init() override;
int Run() override;
int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
void InitBuffer();

private:
cl::Kernel kernel_;
FLOAT_t *PReluWeight_;
};

} // namespace mindspore::kernel


+ 0
- 2
mindspore/lite/test/CMakeLists.txt View File

@@ -155,7 +155,6 @@ if (SUPPORT_GPU)
${LITE_DIR}/src/runtime/kernel/opencl/kernel/transpose.cc
${LITE_DIR}/src/runtime/kernel/opencl/kernel/reshape.cc
${LITE_DIR}/src/runtime/kernel/opencl/kernel/to_format.cc
${LITE_DIR}/src/runtime/kernel/opencl/kernel/caffe_prelu.cc
${LITE_DIR}/src/runtime/kernel/opencl/kernel/prelu.cc
${LITE_DIR}/src/runtime/kernel/opencl/kernel/to_format.cc
${LITE_DIR}/src/runtime/kernel/opencl/kernel/biasadd.cc
@@ -336,7 +335,6 @@ if (SUPPORT_GPU)
${TEST_DIR}/ut/src/runtime/kernel/opencl/convolution_tests.cc
${TEST_DIR}/ut/src/runtime/kernel/opencl/activation_tests.cc
${TEST_DIR}/ut/src/runtime/kernel/opencl/to_format_tests.cc
${TEST_DIR}/ut/src/runtime/kernel/opencl/caffe_prelu_tests.cc
${TEST_DIR}/ut/src/runtime/kernel/opencl/prelu_tests.cc
${TEST_DIR}/ut/src/runtime/kernel/opencl/reshape_tests.cc
${TEST_DIR}/ut/src/runtime/kernel/opencl/biasadd_tests.cc


+ 0
- 203
mindspore/lite/test/ut/src/runtime/kernel/opencl/caffe_prelu_tests.cc View File

@@ -1,203 +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 <iostream>

#include "utils/log_adapter.h"
#include "common/common_test.h"
#include "mindspore/lite/src/common/file_utils.h"
#include "nnacl/pack.h"
#include "mindspore/lite/src/runtime/opencl/opencl_runtime.h"
#include "mindspore/lite/src/runtime/kernel/opencl/subgraph_opencl_kernel.h"
#include "mindspore/lite/src/runtime/kernel/opencl/kernel/caffe_prelu.h"
#include "mindspore/lite/nnacl/prelu_parameter.h"

using mindspore::kernel::CaffePReluOpenCLKernel;
using mindspore::kernel::LiteKernel;
using mindspore::kernel::SubGraphOpenCLKernel;
using mindspore::lite::RET_ERROR;
using mindspore::lite::RET_OK;

namespace mindspore {
class TestCaffePReluOpenCL : public mindspore::CommonTest {};

void LoadDataCaffePRelu(void *dst, size_t dst_size, const std::string &file_path) {
if (file_path.empty()) {
memset(dst, 0x00, dst_size);
} else {
auto src_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(file_path.c_str(), &dst_size));
memcpy(dst, src_data, dst_size);
}
}

void CompareOutCaffePRelu(lite::tensor::Tensor *output_tensor, const std::string &standard_answer_file) {
auto *output_data = reinterpret_cast<float *>(output_tensor->Data());
size_t output_size = output_tensor->ElementsC4Num();
auto expect_data = reinterpret_cast<float *>(mindspore::lite::ReadFile(standard_answer_file.c_str(), &output_size));
constexpr float atol = 0.0002;
for (int i = 0; i < output_tensor->ElementsC4Num(); ++i) {
if (std::fabs(output_data[i] - expect_data[i]) > atol) {
printf("error at idx[%d] expect=%.3f output=%.3f\n", i, expect_data[i], output_data[i]);
printf("error at idx[%d] expect=%.3f output=%.3f\n", i, expect_data[i], output_data[i]);
printf("error at idx[%d] expect=%.3f output=%.3f\n\n\n", i, expect_data[i], output_data[i]);
return;
}
}
printf("compare success!\n");
printf("compare success!\n");
printf("compare success!\n\n\n");
}

void printf_tensor_caffeprelu(mindspore::lite::tensor::Tensor *in_data, int size) {
auto input_data = reinterpret_cast<float *>(in_data->Data());
for (int i = 0; i < size; ++i) {
printf("%f ", input_data[i]);
}
printf("\n");
MS_LOG(INFO) << "Print tensor done";
}

void printf_float(float *data, int num = 0) {
float *temp = data;
for (int i = 0; i < num; ++i) {
std::cout << *temp << " ";
temp++;
}
std::cout << std::endl;
}

TEST_F(TestCaffePReluOpenCL, CaffePReluFp32_dim4) {
std::string in_file = "/data/local/tmp/in_data.bin";
std::string weight_file = "/data/local/tmp/weight_data.bin";
std::string standard_answer_file = "/data/local/tmp/caffeprelu.bin";
MS_LOG(INFO) << "CaffePRelu Begin test:";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();

MS_LOG(INFO) << "CaffePRelu init tensors.";

std::vector<int> input_shape = {1, 4, 3, 9};
std::vector<int> output_shape = {1, 4, 3, 9};
auto data_type = kNumberTypeFloat32;
auto tensor_type = schema::NodeType_ValueNode;
auto *input_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
if (input_tensor == nullptr) {
MS_LOG(ERROR) << "new input tensor error";
return;
}
auto *output_tensor = new lite::tensor::Tensor(data_type, output_shape, schema::Format_NHWC4, tensor_type);
if (output_tensor == nullptr) {
MS_LOG(ERROR) << "new output_tensor error";
delete input_tensor;
return;
}
auto *weight_tensor = new (std::nothrow)
lite::tensor::Tensor(data_type, std::vector<int>{input_shape[3]}, schema::Format_NHWC, tensor_type);
if (weight_tensor == nullptr) {
MS_LOG(ERROR) << "new weight_tensor error";
delete input_tensor;
delete output_tensor;
return;
}

std::vector<lite::tensor::Tensor *> inputs{input_tensor, weight_tensor};
std::vector<lite::tensor::Tensor *> outputs{output_tensor};
inputs[0]->MallocData(allocator);
inputs[1]->MallocData(allocator);
std::cout << input_tensor->Size() << std::endl;
LoadDataCaffePRelu(input_tensor->Data(), input_tensor->Size(), in_file);
MS_LOG(INFO) << "CaffePRelu==================input data================";
printf_tensor_caffeprelu(inputs[0], input_tensor->ElementsNum());

LoadDataCaffePRelu(weight_tensor->Data(), weight_tensor->Size(), weight_file);
MS_LOG(INFO) << "CaffePRelu==================weight data================";
printf_tensor_caffeprelu(inputs[1], weight_tensor->ElementsNum());

auto param = new (std::nothrow) PReluParameter();
if (param == nullptr) {
MS_LOG(ERROR) << "new param error!";
delete input_tensor;
delete output_tensor;
delete weight_tensor;
return;
}
param->channel_num_ = input_shape[3];
auto *caffeprelu_kernel =
new (std::nothrow) kernel::CaffePReluOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
if (caffeprelu_kernel == nullptr) {
delete param;
delete input_tensor;
delete output_tensor;
delete weight_tensor;
MS_LOG(ERROR) << "Create caffe prelu kernel error.";
return;
}

auto ret = caffeprelu_kernel->Init();
if (ret != RET_OK) {
delete param;
delete input_tensor;
delete output_tensor;
delete weight_tensor;
delete caffeprelu_kernel;
MS_LOG(ERROR) << "caffeprelu_kernel init error.";
return;
}

MS_LOG(INFO) << "initialize sub_graph";
std::vector<kernel::LiteKernel *> kernels{caffeprelu_kernel};
auto *sub_graph = new (std::nothrow) kernel::SubGraphOpenCLKernel({input_tensor}, outputs, kernels, kernels, kernels);
if (sub_graph == nullptr) {
delete param;
delete input_tensor;
delete output_tensor;
delete weight_tensor;
delete caffeprelu_kernel;
MS_LOG(ERROR) << "Create sub_graph kernel error.";
return;
}
ret = sub_graph->Init();
if (ret != RET_OK) {
delete param;
delete input_tensor;
delete output_tensor;
delete weight_tensor;
delete caffeprelu_kernel;
delete sub_graph;
MS_LOG(ERROR) << "sub_graph init error.";
return;
}
MS_LOG(INFO) << "Sub graph begin running!";
ret = sub_graph->Run();
if (ret != RET_OK) {
delete input_tensor;
delete output_tensor;
delete weight_tensor;
delete sub_graph;
MS_LOG(ERROR) << "sub_graph run error.";
return;
}

MS_LOG(INFO) << "CaffePRelu==================output data================";
printf_tensor_caffeprelu(outputs[0], output_tensor->ElementsC4Num());
CompareOutCaffePRelu(output_tensor, standard_answer_file);
delete input_tensor;
delete output_tensor;
delete weight_tensor;
delete sub_graph;
}
} // namespace mindspore

+ 11
- 8
mindspore/lite/test/ut/src/runtime/kernel/opencl/prelu_tests.cc View File

@@ -62,26 +62,27 @@ void CompareOutPRelu(lite::tensor::Tensor *output_tensor, const std::string &sta

TEST_F(TestPReluOpenCL, PReluFp32_dim4) {
std::string in_file = "/data/local/tmp/in_data.bin";
std::string standard_answer_file = "/data/local/tmp/leaky_relu.bin";
std::string weight_file = "/data/local/tmp/weight_data.bin";
std::string standard_answer_file = "/data/local/tmp/caffe_prelu.bin";
MS_LOG(INFO) << "-------------------->> Begin test PRelu!";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();

MS_LOG(INFO) << "Init tensors.";
std::vector<int> input_shape = {1, 4, 3, 8};
std::vector<int> input_shape = {1, 4, 3, 9};

auto data_type = kNumberTypeFloat32;
auto tensor_type = schema::NodeType_ValueNode;
auto input_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensor_type);
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
if (input_tensor == nullptr) {
MS_LOG(ERROR) << "new input_tensor error!";
return;
}

auto output_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC4, tensor_type);
new (std::nothrow) lite::tensor::Tensor(data_type, input_shape, schema::Format_NHWC, tensor_type);
if (output_tensor == nullptr) {
MS_LOG(ERROR) << "new output_tensor error";
delete input_tensor;
@@ -89,7 +90,7 @@ TEST_F(TestPReluOpenCL, PReluFp32_dim4) {
}

auto weight_tensor =
new (std::nothrow) lite::tensor::Tensor(data_type, std::vector<int>{1}, schema::Format_NHWC, tensor_type);
new (std::nothrow) lite::tensor::Tensor(data_type, std::vector<int>{9}, schema::Format_NHWC, tensor_type);
if (weight_tensor == nullptr) {
MS_LOG(ERROR) << "new weight_tensor error";
delete input_tensor;
@@ -105,11 +106,13 @@ TEST_F(TestPReluOpenCL, PReluFp32_dim4) {

MS_LOG(INFO) << "initialize input data";
LoadDataPRelu(input_tensor->Data(), input_tensor->Size(), in_file);
LoadDataPRelu(weight_tensor->Data(), weight_tensor->Size(), weight_file);
auto weight_data = reinterpret_cast<float *>(weight_tensor->Data());
weight_data[0] = 0.3;
PrintData("Weight data", weight_data, inputs[1]->ElementsNum());
auto *input_data = reinterpret_cast<float *>(inputs[0]->Data());
PrintData("PRelu input data", input_data, inputs[0]->ElementsC4Num());

PrintData("PRelu input data", input_data, inputs[0]->ElementsNum());
std::cout << inputs[0]->ElementsNum() << std::endl;
std::cout << "--------------------------------------------" << std::endl;
auto param = new (std::nothrow) PReluParameter();
if (param == nullptr) {
MS_LOG(ERROR) << "new PreluParameter error";


Write Preview
Loading…
Cancel
Save