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new ops named pad

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pull/7067/head
Pengyongrong 4 years ago
parent 49c78de682
commit 78408c1a99
6 changed files with 417 additions and 4 deletions
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4
mindspore/lite/src/runtime/kernel/opencl/cl/concat.cl
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@ -516,7 +516,7 @@ __kernel void Concat4input_NC4HW4(__read_only image2d_t input0, __read_only imag
DOConcat##Inputnum##Axis##ToFormat; \
}
// nc4hw4 ?
// nc4hw4
CONCAT6(6input, axis1, _NC4HW4)
CONCAT6(6input, axis2, _NC4HW4)
CONCAT6(6input, axis3, _NC4HW4)
@ -530,7 +530,7 @@ CONCAT2(2input, axis1, _NC4HW4)
CONCAT2(2input, axis2, _NC4HW4)
CONCAT2(2input, axis3, _NC4HW4)
// nhwc4?
// nhwc4
CONCAT6(6input, axis1, _NHWC4)
CONCAT6(6input, axis2, _NHWC4)
CONCAT6(6input, axis3, _NHWC4)

36
mindspore/lite/src/runtime/kernel/opencl/cl/pad.cl
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@ -0,0 +1,36 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
__constant sampler_t smp_zero = CLK_NORMALIZED_COORDS_FALSE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
#define Pad(dataformat, in_x, in_y, out_x, out_y) \
__kernel void Pad_##dataformat(__read_only image2d_t input, __write_only image2d_t output, int4 input_shape, \
int4 output_shape, int2 pad, float constant_value) { \
int oh = get_global_id(0); \
int ow = get_global_id(1); \
int co_slice = get_global_id(2); \
int OH = output_shape.y; \
int OW = output_shape.z; \
int CO_SLICES = output_shape.w; \
\
if (oh >= OH || ow >= OW || co_slice >= CO_SLICES) { \
return; \
} \
\
int IH = input_shape.y; \
int IW = input_shape.z; \
int CI_SLICES = input_shape.w; \
\
int pad_top = pad.x; \
int pad_left = pad.y; \
int ih = oh - pad_top; \
int iw = ow - pad_left; \
\
FLT4 result = (FLT4)(constant_value); \
if (ih >= 0 && ih < IH && iw >= 0 && iw < IW) { \
result = READ_IMAGE(input, smp_zero, (int2)(in_x, in_y)); \
} \
WRITE_IMAGE(output, (int2)(out_x, out_y), result); \
}
Pad(NHWC4, iw *CI_SLICES + co_slice, ih, ow *CO_SLICES + co_slice, oh);
Pad(NC4HW4, iw, co_slice *IH + ih, ow, co_slice *OH + oh);

157
mindspore/lite/src/runtime/kernel/opencl/kernel/pad.cc
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@ -0,0 +1,157 @@
/**
* Copyright 2019 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 <algorithm>
#include "src/common/utils.h"
#include "src/runtime/kernel/opencl/kernel/pad.h"
#include "src/runtime/kernel/opencl/utils.h"
#include "src/kernel_registry.h"
#include "include/errorcode.h"
#include "src/runtime/kernel/opencl/cl/pad.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::PaddingMode_CONSTANT;
using mindspore::schema::PrimitiveType_Pad;
using mindspore::schema::Format::Format_NC4HW4;
using mindspore::schema::Format::Format_NCHW;
using mindspore::schema::Format::Format_NHWC;
using mindspore::schema::Format::Format_NHWC4;
namespace mindspore::kernel {
int PadOpenCLKernel::Init() {
auto param = reinterpret_cast<PadParameter *>(op_parameter_);
std::set<std::string> build_options;
if (op_format_ != Format_NHWC4 && op_format_ != Format_NC4HW4) {
MS_LOG(ERROR) << "op_format_ " << op_format_ << " not support!";
}
if (in_tensors_.empty()) {
MS_LOG(ERROR) << "PadOpenCLKernel in_tensors is empty";
return RET_ERROR;
}
if (out_tensors_.empty()) {
MS_LOG(ERROR) << "PadOpenCLKernel out_tensors is empty";
return RET_ERROR;
}
if (param->paddings_[0] || param->paddings_[1] || param->paddings_[6] || param->paddings_[7]) {
MS_LOG(ERROR) << "PadOpenCLKernel not support pad at Batch/Channel axis";
return RET_ERROR;
}
if (param->pad_mode_ != PaddingMode_CONSTANT) {
MS_LOG(ERROR) << "PadOpenCLKernel only support CONSTANT MODE";
return RET_ERROR;
}
auto input_tensor = in_tensors_[0];
auto output_tensor = out_tensors_[0];
in_ori_format_ = input_tensor->GetFormat();
out_ori_format_ = output_tensor->GetFormat();
input_tensor->SetFormat(op_format_);
output_tensor->SetFormat(op_format_);
CI_ = input_tensor->Channel();
IH_ = input_tensor->Height();
IW_ = input_tensor->Width();
CO_ = output_tensor->Channel();
OH_ = output_tensor->Height();
OW_ = output_tensor->Width();
CI_SLICES_ = UP_DIV(CI_, C4NUM);
CO_SLICES_ = UP_DIV(CO_, C4NUM);
const std::string source = pad_source;
const std::string kernel_name = op_format_ == Format_NHWC4 ? "Pad_NHWC4" : "Pad_NC4HW4";
const std::string &program_name = kernel_name;
ocl_runtime_->LoadSource(program_name, source);
ocl_runtime_->BuildKernel(kernel_, program_name, kernel_name, build_options);
MS_LOG(DEBUG) << "Pad Init Done!";
return RET_OK;
}
int PadOpenCLKernel::GetImageSize(size_t idx, std::vector<size_t> *img_size) {
size_t im_dst_x, im_dst_y;
if (in_tensors_[0]->GetFormat() == Format_NHWC4) {
if (OW_ * CO_SLICES_ <= MAX_IMAGE2D_SIZE) {
{
im_dst_x = OW_ * CO_SLICES_;
im_dst_y = OH_;
}
} else {
im_dst_x = OH_ * CO_SLICES_;
im_dst_y = OW_;
}
} else {
im_dst_y = OH_ * CO_SLICES_;
im_dst_x = OW_;
}
size_t img_dtype = ocl_runtime_->GetFp16Enable() ? CL_HALF_FLOAT : CL_FLOAT;
img_size->clear();
img_size->push_back(im_dst_x);
img_size->push_back(im_dst_y);
img_size->push_back(img_dtype);
return RET_OK;
}
int PadOpenCLKernel::Run() {
MS_LOG(DEBUG) << this->name() << " Running!";
auto param = reinterpret_cast<PadParameter *>(op_parameter_);
cl_int4 input_shape = {1, IH_, IW_, CI_SLICES_};
cl_int4 output_shape = {1, OH_, OW_, CO_SLICES_};
cl_int2 pad_top_left = {param->paddings_[2], param->paddings_[4]};
int arg_cn = 0;
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, in_tensors_[0]->data_c(), lite::opencl::MemType::IMG);
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, out_tensors_[0]->data_c(), lite::opencl::MemType::IMG);
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, input_shape);
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, output_shape);
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, pad_top_left);
ocl_runtime_->SetKernelArg(kernel_, arg_cn++, static_cast<cl_float>(param->constant_value_));
std::vector<size_t> global = {static_cast<size_t>(OH_), static_cast<size_t>(OW_), static_cast<size_t>(CO_SLICES_)};
std::vector<size_t> local = {8, 4, 1};
ocl_runtime_->RunKernel(kernel_, global, local, nullptr);
return RET_OK;
}
kernel::LiteKernel *OpenCLPadKernelCreator(const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs, OpParameter *opParameter,
const lite::InnerContext *ctx, const kernel::KernelKey &desc,
const mindspore::lite::PrimitiveC *primitive) {
auto *kernel = new (std::nothrow) PadOpenCLKernel(reinterpret_cast<OpParameter *>(opParameter), inputs, outputs);
if (kernel == nullptr) {
MS_LOG(ERROR) << "Create OpenCL Pad kernel failed!";
return nullptr;
}
auto ret = kernel->Init();
if (ret != RET_OK) {
MS_LOG(ERROR) << "Init kernel failed, name: Pad";
delete kernel;
return nullptr;
}
return kernel;
}
REG_KERNEL(kGPU, kNumberTypeFloat32, PrimitiveType_Pad, OpenCLPadKernelCreator)
REG_KERNEL(kGPU, kNumberTypeFloat16, PrimitiveType_Pad, OpenCLPadKernelCreator)
} // namespace mindspore::kernel

53
mindspore/lite/src/runtime/kernel/opencl/kernel/pad.h
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@ -0,0 +1,53 @@
/**
* Copyright 2019 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_PAD_H_
#define MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_PAD_H_
#include <vector>
#include <string>
#include "src/tensor.h"
#include "src/runtime/kernel/opencl/opencl_kernel.h"
#include "schema/model_generated.h"
#include "nnacl/pad_parameter.h"
namespace mindspore::kernel {
class PadOpenCLKernel : public OpenCLKernel {
public:
explicit PadOpenCLKernel(OpParameter *parameter, const std::vector<lite::Tensor *> &inputs,
const std::vector<lite::Tensor *> &outputs)
: OpenCLKernel(parameter, inputs, outputs) {}
~PadOpenCLKernel() override{};
int Init() override;
int Run() override;
int GetImageSize(size_t idx, std::vector<size_t> *img_size) override;
private:
int CI_{};
int IH_{};
int IW_{};
int CO_{};
int OH_{};
int OW_{};
int CI_SLICES_{};
int CO_SLICES_{};
cl::Kernel kernel_;
};
} // namespace mindspore::kernel
#endif // MINDSPORE_LITE_SRC_RUNTIME_KERNEL_OPENCL_KERNEL_PAD_H_

3
mindspore/lite/test/ut/src/runtime/kernel/opencl/concat_tests.cc
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@ -396,7 +396,7 @@ TEST_F(TestConcatOpenCLfp32, ConcatFp32_3input_dim4_axis1) {
TEST_F(TestConcatOpenCLfp16, ConcatFp16_6input_dim4_axis1) {
MS_LOG(INFO) << " begin test ";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
auto ocl_runtime = lite::opencl::OpenCLRuntimeWrapper().GetInstance();
ocl_runtime->SetFp16Enable(true);
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
@ -523,7 +523,6 @@ TEST_F(TestConcatOpenCLfp16, ConcatFp16_6input_dim4_axis1) {
sub_graph->Run();
auto *output_data_gpu = reinterpret_cast<float16_t *>(output_tensor->MutableData());
CompareOutputData1(output_data_gpu, correctOutput, output_tensor->ElementsNum(), 0.000001);
lite::opencl::OpenCLRuntime::DeleteInstance();
for (auto tensor : inputs) {
tensor->SetData(nullptr);
delete tensor;

168
mindspore/lite/test/ut/src/runtime/kernel/opencl/pad_tests.cc
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@ -0,0 +1,168 @@
/**
* 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 <memory>
#include "src/common/log_adapter.h"
#include "common/common_test.h"
#include "mindspore/lite/src/common/file_utils.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/pad.h"
#include "nnacl/pack.h"
using mindspore::kernel::LiteKernel;
using mindspore::kernel::PadOpenCLKernel;
using mindspore::kernel::SubGraphOpenCLKernel;
using mindspore::lite::Tensor;
using mindspore::schema::Format;
using mindspore::schema::Format_NC4HW4;
using mindspore::schema::Format_NHWC;
using mindspore::schema::Format_NHWC4;
using mindspore::schema::NodeType_ValueNode;
using mindspore::schema::PaddingMode;
using mindspore::schema::PaddingMode_CONSTANT;
using mindspore::schema::PaddingMode_REFLECT;
using mindspore::schema::PaddingMode_SYMMETRIC;
namespace mindspore {
class TestPadOpenCL : public mindspore::CommonTest {};
void TEST_MAIN(PadParameter *param, Format input_format, Format output_format, Format op_format, const TypeId data_type,
const std::vector<int> &input_shape, const std::vector<int> &output_shape, const float *input_data,
const float *expect_data) {
auto ocl_runtime_wrapper = lite::opencl::OpenCLRuntimeWrapper();
auto ocl_runtime = ocl_runtime_wrapper.GetInstance();
ocl_runtime->Init();
ocl_runtime->SetFp16Enable(data_type == kNumberTypeFloat16);
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));
MS_LOG(DEBUG) << "create OpenCL Kernel";
std::vector<lite::Tensor *> inputs{&input};
std::vector<lite::Tensor *> outputs{&output};
auto kernel = std::make_unique<PadOpenCLKernel>(reinterpret_cast<OpParameter *>(param), inputs, outputs);
if (kernel == nullptr) {
return;
}
kernel->SetFormatType(op_format);
kernel->Init();
MS_LOG(DEBUG) << "create SubGraph";
std::vector<kernel::LiteKernel *> kernels{kernel.release()};
auto sub_graph = new (std::nothrow) SubGraphOpenCLKernel({&input}, {&output}, kernels, kernels, kernels);
input.MallocData(allocator);
sub_graph->Init();
memcpy(input.data_c(), input_data, input.Size());
sub_graph->Run();
if (lite::CompareOutputData(reinterpret_cast<float *>(output.data_c()), output.ElementsNum(),
const_cast<float *>(expect_data), output.ElementsNum())) {
FAIL();
} else {
std::cout << "COMPARE SUCCESS!\n";
}
MS_LOG(DEBUG) << "release resources";
input.SetData(nullptr);
output.SetData(nullptr);
delete sub_graph;
}
TEST_F(TestPadOpenCL, TestPad3) {
auto param = static_cast<PadParameter *>(malloc(sizeof(PadParameter)));
if (param == nullptr) {
MS_LOG(ERROR) << "PadParameter create error.";
return;
}
param->pad_mode_ = PaddingMode_CONSTANT;
param->constant_value_ = 0.0f;
param->padding_length = MAX_PAD_SIZE;
int paddings[MAX_PAD_SIZE] = {0, 0, 3, 3, 3, 3, 0, 0};
memcpy(param->paddings_, paddings, sizeof(paddings));
float input_data[48] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0,
12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,
24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0};
float expect_data[300] = {
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 12.0, 13.0, 14.0, 15.0,
16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 36.0,
37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0};
TEST_MAIN(param, Format_NHWC, Format_NHWC, Format_NHWC4, kNumberTypeFloat32, {1, 4, 4, 3}, {1, 10, 10, 3}, input_data,
expect_data);
TEST_MAIN(param, Format_NHWC, Format_NHWC, Format_NC4HW4, kNumberTypeFloat32, {1, 4, 4, 3}, {1, 10, 10, 3},
input_data, expect_data);
TEST_MAIN(param, Format_NHWC, Format_NHWC, Format_NHWC4, kNumberTypeFloat16, {1, 4, 4, 3}, {1, 10, 10, 3}, input_data,
expect_data);
TEST_MAIN(param, Format_NHWC, Format_NHWC, Format_NC4HW4, kNumberTypeFloat16, {1, 4, 4, 3}, {1, 10, 10, 3},
input_data, expect_data);
}
TEST_F(TestPadOpenCL, TestPad4) {
auto param = static_cast<PadParameter *>(malloc(sizeof(PadParameter)));
if (param == nullptr) {
MS_LOG(ERROR) << "PadParameter create error.";
return;
}
param->pad_mode_ = PaddingMode_CONSTANT;
param->constant_value_ = 1.0f;
param->padding_length = MAX_PAD_SIZE;
int paddings[MAX_PAD_SIZE] = {0, 0, 3, 3, 3, 3, 0, 0};
memcpy(param->paddings_, paddings, sizeof(paddings));
float input_data[48] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0,
12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,
24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0};
float expect_data[300] = {
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 12.0, 13.0, 14.0, 15.0,
16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 36.0,
37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
TEST_MAIN(param, Format_NHWC, Format_NHWC, Format_NHWC4, kNumberTypeFloat32, {1, 4, 4, 3}, {1, 10, 10, 3}, input_data,
expect_data);
}
} // namespace mindspore
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