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!4817 fix opencl winograd param invalid bug

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Merge pull request !4817 from 王东旭/opencl_winograd_fixbug
pull/4817/MERGE
mindspore-ci-bot 5 years ago committed by Gitee
parent 70a6e65305 2d699c93a9
commit 333af530fc
3 changed files with 167 additions and 176 deletions
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210
mindspore/lite/src/runtime/kernel/opencl/kernel/convolution.cc
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6
mindspore/lite/src/runtime/kernel/opencl/kernel/convolution.h
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@ -40,6 +40,12 @@ class ConvolutionOpenCLKernel : public OpenCLKernel {
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;
float *packed_weight_ = nullptr;

127
mindspore/lite/test/ut/src/runtime/kernel/opencl/convolution_tests.cc
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@ -41,7 +41,7 @@ void LoadData(void *dst, size_t dst_size, const std::string &file_path) {
void MyCompareOutput(lite::tensor::Tensor *output_tensor, const std::string &file_path) {
auto *output_data = reinterpret_cast<float *>(output_tensor->Data());
printf("output[0:10]:");
printf("\noutput[0:10]:");
for (int i = 0; i < 10; i++) {
printf("%d:%.3f ", i, output_data[i]);
}
@ -58,15 +58,15 @@ void MyCompareOutput(lite::tensor::Tensor *output_tensor, const std::string &fil
return;
}
}
printf("compare success!\n");
printf("compare success!\n");
printf("compare success!\n\n\n");
printf("COMPARE SUCCESS!\n\n\n");
}
void TEST_MAIN(schema::Format input_format, schema::Format output_format, const std::string &data_path,
std::string attr_str) {
assert(data_format == schema::Format_NHWC || data_format == schema::Format_NHWC4);
auto param = new ConvParameter;
auto param = new (std::nothrow) ConvParameter;
if (param == nullptr) {
return;
}
sscanf(attr_str.c_str(),
"inputNHWC_%dx%dx%dx%d_outputNHWC_%dx%dx%dx%d_kernelHW_%dx%d_strideHW_%dx%d_padTopBottomLeftRight_%dx%dx%dx%d_"
"dilationHW_%dx%d",
@ -79,67 +79,81 @@ void TEST_MAIN(schema::Format input_format, schema::Format output_format, const
auto weight_file = testcase_path + "weight_OHWI.bin";
auto bias_file = testcase_path + "bias_C.bin";
auto expect_file = testcase_path + (output_format == schema::Format_NHWC4 ? "expect_NHWC4.bin" : "expect_NHWC.bin");
std::cout << "input_file:" << input_file << std::endl;
std::cout << "weight_file:" << weight_file << std::endl;
std::cout << "bias_file:" << bias_file << std::endl;
std::cout << "expect_file:" << expect_file << std::endl;
std::cout << "input_file :" << input_file << std::endl;
std::cout << "weight_file :" << weight_file << std::endl;
std::cout << "bias_file :" << bias_file << std::endl;
std::cout << "expect_file :" << expect_file << std::endl;
std::cout << "initialize OpenCLRuntime";
std::cout << "initialize OpenCLRuntime and OpenCLAllocator";
auto ocl_runtime = lite::opencl::OpenCLRuntime::GetInstance();
ocl_runtime->Init();
auto allocator = ocl_runtime->GetAllocator();
std::cout << "create Tensors(framework will do!!!)";
std::cout << "create Tensors";
std::vector<int> input_shape = {param->input_batch_, param->input_h_, param->input_w_, param->input_channel_};
std::vector<int> weight_shape = {param->output_channel_, param->kernel_h_, param->kernel_w_, param->input_channel_};
std::vector<int> bias_shape = {param->output_channel_};
std::vector<int> output_shape = {param->output_batch_, param->output_h_, param->output_w_, param->output_channel_};
auto data_type = kNumberTypeFloat32;
auto tensor_type = schema::NodeType_ValueNode;
auto input_tensor = new lite::tensor::Tensor(data_type, input_shape, input_format, tensor_type);
auto weight_tensor = new lite::tensor::Tensor(data_type, weight_shape, schema::Format_KHWC, tensor_type);
auto bias_tensor = new lite::tensor::Tensor(data_type, bias_shape, schema::Format_KHWC, tensor_type);
auto output_tensor = new lite::tensor::Tensor(data_type, output_shape, output_format, tensor_type);
std::vector<lite::tensor::Tensor *> inputs{input_tensor, weight_tensor, bias_tensor};
std::vector<lite::tensor::Tensor *> outputs{output_tensor};
std::cout << "allocate and initialize weight/bias memory by hand here(framework will do!!!)";
std::vector<float> weight_vec(weight_tensor->ElementsNum());
std::vector<float> bias_vec(weight_tensor->ElementsNum());
weight_tensor->SetData(weight_vec.data());
bias_tensor->SetData(bias_vec.data());
LoadData(weight_tensor->Data(), weight_tensor->Size(), weight_file);
LoadData(bias_tensor->Data(), bias_tensor->Size(), bias_file);
std::cout << "create OpenCL Kernel"; // weight/bias has been allcated by framework
auto *conv_kernel = new ConvolutionOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
conv_kernel->Init();
std::cout << "create SubGraphOpenCLKernel";
inputs[0]->MallocData(allocator); // allocate input memory by hand here, framework will do!!!
auto *sub_graph = new SubGraphOpenCLKernel({input_tensor}, outputs, {conv_kernel}, {conv_kernel}, {conv_kernel});
auto input_tensor = lite::tensor::Tensor(data_type, input_shape, input_format, tensor_type);
auto weight_tensor = lite::tensor::Tensor(data_type, weight_shape, schema::Format_KHWC, tensor_type);
auto bias_tensor = lite::tensor::Tensor(data_type, bias_shape, schema::Format_KHWC, tensor_type);
auto output_tensor = lite::tensor::Tensor(data_type, output_shape, output_format, tensor_type);
std::vector<lite::tensor::Tensor *> inputs{&input_tensor, &weight_tensor, &bias_tensor};
std::vector<lite::tensor::Tensor *> outputs{&output_tensor};
std::cout << "allocate memory and initialize weight/bias";
weight_tensor.MallocData();
bias_tensor.MallocData();
LoadData(weight_tensor.Data(), weight_tensor.Size(), weight_file);
LoadData(bias_tensor.Data(), bias_tensor.Size(), bias_file);
std::cout << "create OpenCL Kernel";
auto kernel = ConvolutionOpenCLKernel(reinterpret_cast<OpParameter *>(param), inputs, outputs);
kernel.Init();
std::cout << "create SubGraph";
auto sub_graph = new (std::nothrow) SubGraphOpenCLKernel({&input_tensor}, outputs, {&kernel}, {&kernel}, {&kernel});
if (sub_graph == nullptr) {
return;
}
input_tensor.MallocData(allocator); // before MapBuffer()
sub_graph->Init();
LoadData(input_tensor->Data(), input_tensor->Size(), input_file); // initialize input Tensors data
printf("input[0] =%.3f\n", reinterpret_cast<float *>(input_tensor->Data())[0]);
printf("weight[0]=%.3f\n", reinterpret_cast<float *>(weight_tensor->Data())[0]);
printf("bias[0] =%.3f\n", reinterpret_cast<float *>(bias_tensor->Data())[0]);
LoadData(input_tensor.Data(), input_tensor.Size(), input_file); // after MapBuffer()
printf("input[0-2] =%.3f\n", reinterpret_cast<float *>(input_tensor.Data())[0]);
printf("weight[0-2]=%.3f\n", reinterpret_cast<float *>(weight_tensor.Data())[0]);
printf("bias[0-2] =%.3f\n", reinterpret_cast<float *>(bias_tensor.Data())[0]);
sub_graph->Run();
std::cout << "compare result";
MyCompareOutput(output_tensor, expect_file);
// lite::CompareOutput(reinterpret_cast<float *>(output_tensor->Data()), expect_file);
for (auto tensor : inputs) {
delete tensor;
}
for (auto tensor : outputs) {
delete tensor;
}
delete conv_kernel;
MyCompareOutput(&output_tensor, expect_file);
std::cout << "release resources";
weight_tensor.FreeData();
bias_tensor.FreeData();
input_tensor.SetData(nullptr);
output_tensor.SetData(nullptr);
weight_tensor.SetData(nullptr);
bias_tensor.SetData(nullptr);
delete param;
delete sub_graph;
lite::opencl::OpenCLRuntime::DeleteInstance();
}
TEST_F(TestConvolutionOpenCL, in1x1x64x512_out1x1x64x7358_k11_s11_p0000) {
// change W/H
TEST_MAIN(
schema::Format_NHWC, schema::Format_NHWC4, "testcases/02_fp32/",
"inputNHWC_1x1x64x512_outputNHWC_1x1x64x7358_kernelHW_1x1_strideHW_1x1_padTopBottomLeftRight_0x0x0x0_dilationHW_"
"1x1");
}
TEST_F(TestConvolutionOpenCL, winograd_02_other_inputNHWC_1x32x512x1_outputNHWC_1x32x512x50) {
// speed up
TEST_MAIN(schema::Format_NHWC, schema::Format_NHWC4, "testcases/test_fp32/",
"inputNHWC_1x32x512x1_outputNHWC_1x32x512x50_kernelHW_3x3_strideHW_1x1_padTopBottomLeftRight_1x1x1x1_"
"dilationHW_1x1");
}
TEST_F(TestConvolutionOpenCL, in1x224x224x3_out1x112x112x32_k33_s22_p0101) {
TEST_MAIN(
schema::Format_NHWC, schema::Format_NHWC4, "testcases/mobilenetv2_fp32/",
@ -147,13 +161,6 @@ TEST_F(TestConvolutionOpenCL, in1x224x224x3_out1x112x112x32_k33_s22_p0101) {
"1x1");
}
// TEST_F(TestConvolutionOpenCL, in1x1x64x512_out1x1x64x7358_k11_s11_p0000) {
// TEST_MAIN(
// schema::Format_NHWC, schema::Format_NHWC4, "testcases/02_fp32/",
// "inputNHWC_1x1x64x512_outputNHWC_1x1x64x7358_kernelHW_1x1_strideHW_1x1_padTopBottomLeftRight_0x0x0x0_dilationHW_"
// "1x1");
//}
TEST_F(TestConvolutionOpenCL, winograd_02_origin_inputNHWC_1x16x256x96_outputNHWC_1x16x256x80) {
TEST_MAIN(schema::Format_NHWC, schema::Format_NHWC4, "testcases/test_fp32/",
"inputNHWC_1x16x256x96_outputNHWC_1x16x256x80_kernelHW_3x3_strideHW_1x1_padTopBottomLeftRight_1x1x1x1_"
@ -165,12 +172,6 @@ TEST_F(TestConvolutionOpenCL, winograd_02_origin_inputNHWC_1x16x256x100_outputNH
"dilationHW_1x1");
}
// TEST_F(TestConvolutionOpenCL, winograd_02_other_inputNHWC_1x32x512x1_outputNHWC_1x32x512x50) {
// TEST_MAIN(schema::Format_NHWC, schema::Format_NHWC4, "testcases/test_fp32/",
// "inputNHWC_1x32x512x1_outputNHWC_1x32x512x50_kernelHW_3x3_strideHW_1x1_padTopBottomLeftRight_1x1x1x1_"
// "dilationHW_1x1");
//}
TEST_F(TestConvolutionOpenCL, winograd_02_other_inputNHWC_1x32x512x50_outputNHWC_1x32x512x48) {
TEST_MAIN(schema::Format_NHWC, schema::Format_NHWC4, "testcases/02_fp32/",
"inputNHWC_1x32x512x50_outputNHWC_1x32x512x48_kernelHW_3x3_strideHW_1x1_padTopBottomLeftRight_1x1x1x1_"

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