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“3352199127c596459c8c4c0d7584542c44a751fd”上不存在“mobile/src/operators/flatten2_op.cpp”
提交 0c11ff97 编写于 作者: L Liangliang He
浏览文件
操作

Merge branch 'bm_to_image' into 'master' 

Bm to image

See merge request !132
上级 ef167287 a9832dfb
隐藏空白更改
内联 并排
Showing with 249 addition and 142 deletion
mace/BUILD
浏览文件 @ 0c11ff97
...@@ -23,3 +23,11 @@ config_setting( ...@@ -23,3 +23,11 @@ config_setting(
}, },
visibility = ["//visibility:public"], visibility = ["//visibility:public"],
) )
config_setting(
name = "is_profiling",
define_values = {
"profiling": "true",
},
visibility = ["//visibility:public"],
)
mace/core/BUILD
浏览文件 @ 0c11ff97
...@@ -7,7 +7,7 @@ package( ...@@ -7,7 +7,7 @@ package(
licenses(["notice"]) # Apache 2.0 licenses(["notice"]) # Apache 2.0
load("//mace:mace.bzl", "if_android") load("//mace:mace.bzl", "if_android", "if_profiling")
cc_library( cc_library(
name = "opencl_runtime", name = "opencl_runtime",
...@@ -19,7 +19,7 @@ cc_library( ...@@ -19,7 +19,7 @@ cc_library(
"runtime/opencl/cl2.hpp", "runtime/opencl/cl2.hpp",
"runtime/opencl/*.h", "runtime/opencl/*.h",
]), ]),
copts = ["-std=c++11"], copts = ["-std=c++11"] + if_profiling(["-D__ENABLE_PROFILING"]),
deps = [ deps = [
":logging", ":logging",
"@opencl_headers//:opencl20_headers", "@opencl_headers//:opencl20_headers",
......
mace/core/runtime/opencl/opencl_runtime.cc
浏览文件 @ 0c11ff97
...@@ -79,14 +79,16 @@ OpenCLRuntime *OpenCLRuntime::Get() { ...@@ -79,14 +79,16 @@ OpenCLRuntime *OpenCLRuntime::Get() {
return; return;
} }
cl_command_queue_properties properties = 0;
#ifdef __ENABLE_PROFILING
enable_profiling_ = true;
profiling_ev_.reset(new cl::Event());
properties = CL_QUEUE_PROFILING_ENABLE;
#endif
// a context is like a "runtime link" to the device and platform; // a context is like a "runtime link" to the device and platform;
// i.e. communication is possible // i.e. communication is possible
cl::Context context({gpu_device}); cl::Context context({gpu_device});
cl_command_queue_properties properties = 0;
if (enable_profiling_) {
profiling_ev_.reset(new cl::Event());
properties = CL_QUEUE_PROFILING_ENABLE;
}
cl::CommandQueue command_queue(context, gpu_device, properties); cl::CommandQueue command_queue(context, gpu_device, properties);
instance = new OpenCLRuntime(context, gpu_device, command_queue); instance = new OpenCLRuntime(context, gpu_device, command_queue);
...@@ -104,12 +106,12 @@ cl::Event* OpenCLRuntime::GetDefaultEvent() { ...@@ -104,12 +106,12 @@ cl::Event* OpenCLRuntime::GetDefaultEvent() {
} }
cl_ulong OpenCLRuntime::GetEventProfilingStartInfo() { cl_ulong OpenCLRuntime::GetEventProfilingStartInfo() {
MACE_CHECK(enable_profiling_, "should enable profiling first."); MACE_CHECK(profiling_ev_, "is NULL, should enable profiling first.");
return profiling_ev_->getProfilingInfo<CL_PROFILING_COMMAND_START>(); return profiling_ev_->getProfilingInfo<CL_PROFILING_COMMAND_START>();
} }
cl_ulong OpenCLRuntime::GetEventProfilingEndInfo() { cl_ulong OpenCLRuntime::GetEventProfilingEndInfo() {
MACE_CHECK(enable_profiling_, "should enable profiling first."); MACE_CHECK(profiling_ev_, "is NULL, should enable profiling first.");
return profiling_ev_->getProfilingInfo<CL_PROFILING_COMMAND_END>(); return profiling_ev_->getProfilingInfo<CL_PROFILING_COMMAND_END>();
} }
......
mace/kernels/batch_norm.h
浏览文件 @ 0c11ff97
...@@ -28,9 +28,10 @@ struct BatchNormFunctor { ...@@ -28,9 +28,10 @@ struct BatchNormFunctor {
// new_scale = \frac{ \scale } { \sqrt{var+\variance_epsilon} } // new_scale = \frac{ \scale } { \sqrt{var+\variance_epsilon} }
// new_offset = \offset - mean * common_val; // new_offset = \offset - mean * common_val;
// Y = new_scale * X + new_offset; // Y = new_scale * X + new_offset;
const index_t n = input->dim(0); const index_t batch = input->dim(0);
const index_t channel = input->dim(1); const index_t height = input->dim(1);
const index_t sample_size = input->dim(2) * input->dim(3); const index_t width = input->dim(2);
const index_t channels = input->dim(3);
Tensor::MappingGuard input_mapper(input); Tensor::MappingGuard input_mapper(input);
Tensor::MappingGuard scale_mapper(scale); Tensor::MappingGuard scale_mapper(scale);
...@@ -48,19 +49,26 @@ struct BatchNormFunctor { ...@@ -48,19 +49,26 @@ struct BatchNormFunctor {
const T *epsilon_ptr = epsilon->data<T>(); const T *epsilon_ptr = epsilon->data<T>();
T *output_ptr = output->mutable_data<T>(); T *output_ptr = output->mutable_data<T>();
vector<T> new_scale(channels);
vector<T> new_offset(channels);
#pragma omp parallel for #pragma omp parallel for
for (index_t c = 0; c < channel; ++c) { for (index_t c = 0; c < channels; ++c) {
T new_scale = scale_ptr[c] / std::sqrt(var_ptr[c] + *epsilon_ptr); new_scale[c] = scale_ptr[c] / std::sqrt(var_ptr[c] + *epsilon_ptr);
T new_offset = offset_ptr[c] - mean_ptr[c] * new_scale; new_offset[c] = offset_ptr[c] - mean_ptr[c] * new_scale[c];
index_t pos = c * sample_size; }
index_t pos = 0;
for (index_t i = 0; i < n; ++i) { #pragma omp parallel for
const T *input_sample_ptr = input_ptr + pos; for (index_t n = 0; n < batch; ++n) {
T *output_sample_ptr = output_ptr + pos; for (index_t h = 0; h < height; ++h) {
for (index_t j = 0; j < sample_size; ++j) { for (index_t w = 0; w < width; ++w) {
output_sample_ptr[j] = new_scale * input_sample_ptr[j] + new_offset; for (index_t c = 0; c < channels; ++c) {
output_ptr[pos] = new_scale[c] * input_ptr[pos] + new_offset[c];
++pos;
}
} }
pos += channel * sample_size;
} }
} }
} }
...@@ -76,15 +84,16 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()( ...@@ -76,15 +84,16 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
const Tensor *epsilon, const Tensor *epsilon,
Tensor *output); Tensor *output);
template <> template <typename T>
void BatchNormFunctor<DeviceType::OPENCL, float>::operator()( struct BatchNormFunctor<DeviceType::OPENCL, T> {
const Tensor *input, void operator()(const Tensor *input,
const Tensor *scale, const Tensor *scale,
const Tensor *offset, const Tensor *offset,
const Tensor *mean, const Tensor *mean,
const Tensor *var, const Tensor *var,
const Tensor *epsilon, const Tensor *epsilon,
Tensor *output); Tensor *output);
};
} // namepsace kernels } // namepsace kernels
} // namespace mace } // namespace mace
......
mace/kernels/opencl/batch_norm_opencl.cc
浏览文件 @ 0c11ff97
...@@ -11,8 +11,8 @@ ...@@ -11,8 +11,8 @@
namespace mace { namespace mace {
namespace kernels { namespace kernels {
template <> template <typename T>
void BatchNormFunctor<DeviceType::OPENCL, float>::operator()( void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(
const Tensor *input, const Tensor *input,
const Tensor *scale, const Tensor *scale,
const Tensor *offset, const Tensor *offset,
...@@ -21,35 +21,39 @@ void BatchNormFunctor<DeviceType::OPENCL, float>::operator()( ...@@ -21,35 +21,39 @@ void BatchNormFunctor<DeviceType::OPENCL, float>::operator()(
const Tensor *epsilon, const Tensor *epsilon,
Tensor *output) { Tensor *output) {
index_t pixel_size = input->dim(2) * input->dim(3); const index_t batch = input->dim(0);
index_t blocks = (pixel_size + 3) / 4; const index_t height = input->dim(1);
const index_t width = input->dim(2);
const index_t channels = input->dim(3);
const uint32_t gws[3] = {static_cast<uint32_t>(input->dim(0)), const index_t channel_blocks = RoundUpDiv4(channels);
static_cast<uint32_t>(input->dim(1)),
static_cast<uint32_t>(blocks)}; const uint32_t gws[3] = {static_cast<uint32_t>(channel_blocks),
static_cast<uint32_t>(width),
static_cast<uint32_t>(height * batch)};
auto runtime = OpenCLRuntime::Get(); auto runtime = OpenCLRuntime::Get();
std::set<std::string> built_options; std::set<std::string> built_options;
built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(input->dtype())); auto dt = DataTypeToEnum<T>::value;
built_options.emplace("-DDATA_TYPE=" + DtToUpstreamCLDt(dt));
built_options.emplace("-DCMD_DATA_TYPE=" + DtToUpstreamCLCMDDt(dt));
auto bm_kernel = runtime->BuildKernel("batch_norm", "batch_norm", built_options); auto bm_kernel = runtime->BuildKernel("batch_norm", "batch_norm", built_options);
const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(bm_kernel); const uint32_t kwg_size = runtime->GetKernelMaxWorkGroupSize(bm_kernel);
const std::vector<uint32_t> lws = {1, 1, kwg_size}; const std::vector<uint32_t> lws = {1, kwg_size, 1};
uint32_t idx = 0; uint32_t idx = 0;
bm_kernel.setArg(idx++, *(static_cast<const cl::Buffer *>(input->buffer()))); bm_kernel.setArg(idx++, *(static_cast<const cl::Image2D *>(input->buffer())));
bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(scale->buffer()))); bm_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(scale->buffer())));
bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(offset->buffer()))); bm_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(offset->buffer())));
bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(mean->buffer()))); bm_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(mean->buffer())));
bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(var->buffer()))); bm_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(var->buffer())));
bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(epsilon->buffer()))); bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(epsilon->buffer())));
bm_kernel.setArg(idx++, static_cast<uint32_t>(pixel_size)); bm_kernel.setArg(idx++, *(static_cast<cl::Image2D *>(output->buffer())));
bm_kernel.setArg(idx++, *(static_cast<cl::Buffer *>(output->buffer())));
bm_kernel.setArg(idx++, lws[1] * sizeof(float) * 4, nullptr);
bm_kernel.setArg(idx++, lws[1] * sizeof(float) * 4, nullptr);
auto params_generator = [&kwg_size]()->std::vector<std::vector<uint32_t>> { auto params_generator = [&kwg_size]()->std::vector<std::vector<uint32_t>> {
return {{1, 1, 64}, return {{8, 128, 1}, //SNPE size
{1, 1, 64},
{1, 1, 128}, {1, 1, 128},
{1, kwg_size/16, 16}, {1, kwg_size/16, 16},
{1, kwg_size/32, 32}, {1, kwg_size/32, 32},
...@@ -80,5 +84,9 @@ void BatchNormFunctor<DeviceType::OPENCL, float>::operator()( ...@@ -80,5 +84,9 @@ void BatchNormFunctor<DeviceType::OPENCL, float>::operator()(
func); func);
} }
template
struct BatchNormFunctor<DeviceType::OPENCL, float>;
template
struct BatchNormFunctor<DeviceType::OPENCL, half>;
} // namespace kernels } // namespace kernels
} // namespace mace } // namespace mace
mace/kernels/opencl/cl/batch_norm.cl
浏览文件 @ 0c11ff97
#include <common.h> #include <common.h>
// Supported data types: half/float // Supported data types: half/float
void kernel batch_norm(global const DATA_TYPE *input, __kernel void batch_norm(__read_only image2d_t input,
global const DATA_TYPE *scale, __read_only image2d_t scale,
global const DATA_TYPE *offset, __read_only image2d_t offset,
global const DATA_TYPE *mean, __read_only image2d_t mean,
global const DATA_TYPE *var, __read_only image2d_t var,
global const DATA_TYPE *epsilon, __global const DATA_TYPE *epsilon,
private const int pixels, __write_only image2d_t output) {
global DATA_TYPE *output, const int ch_blk = get_global_id(0);
__local VEC_DATA_TYPE(DATA_TYPE, 4) *new_scale, const int w = get_global_id(1);
__local VEC_DATA_TYPE(DATA_TYPE, 4) *new_offset) { const int hb = get_global_id(2);
const int batch = get_global_id(0); const int width = get_global_size(1);
const int channel = get_global_id(1);
const int channels = get_global_size(1);
const int pixel_offset = get_global_id(2);
const int local_channel = get_local_id(1);
const int local_pixel_idx = get_local_id(2);
if(local_pixel_idx == 0) { DATA_TYPE4 scale_value = READ_IMAGET(scale, SAMPLER, (int2)(ch_blk, 0));
new_scale[local_channel] = (float4)(scale[channel] * rsqrt(var[channel] + *epsilon)); DATA_TYPE4 offset_value = READ_IMAGET(offset, SAMPLER, (int2)(ch_blk, 0));
new_offset[local_channel] = (float4)(offset[channel] - mean[channel] * new_scale[local_channel].x); DATA_TYPE4 mean_value = READ_IMAGET(mean, SAMPLER, (int2)(ch_blk, 0));
} DATA_TYPE4 var_value = READ_IMAGET(var, SAMPLER, (int2)(ch_blk, 0));
barrier(CLK_LOCAL_MEM_FENCE); DATA_TYPE4 new_scale = scale_value * rsqrt(var_value + (DATA_TYPE4)(*epsilon));
DATA_TYPE4 new_offset = offset_value - mean_value * new_scale;
const int image_offset = (batch * channels + channel) * pixels + pixel_offset*4; const int pos = ch_blk * width + w;
const DATA_TYPE *input_ptr = input + image_offset;
DATA_TYPE *output_ptr = output + image_offset;
const int end = (batch * channels + channel + 1) * pixels;
if ((image_offset+4) > end) {
for (int i = image_offset; i < end; ++i) {
*output_ptr = new_scale[local_channel].x * *input_ptr + new_offset[local_channel].x;
++input_ptr;
++output_ptr;
}
} else {
VEC_DATA_TYPE(DATA_TYPE, 4) values = vload4(0, input_ptr);
values = values * new_scale[local_channel] + new_offset[local_channel];
vstore4(values, 0, output_ptr);
}
}
DATA_TYPE4 in = READ_IMAGET(input, SAMPLER, (int2)(pos, hb));
DATA_TYPE4 out = in * new_scale + new_offset;
WRITE_IMAGET(output, (int2)(pos, hb), out);
}
mace/mace.bzl
浏览文件 @ 0c11ff97
...@@ -22,4 +22,10 @@ def if_android_arm64(a): ...@@ -22,4 +22,10 @@ def if_android_arm64(a):
return select({ return select({
"//mace:android_arm64": a, "//mace:android_arm64": a,
"//conditions:default": [], "//conditions:default": [],
}) })
\ No newline at end of file
def if_profiling(a):
return select({
"//mace:is_profiling": a,
"//conditions:default": [],
})
mace/ops/batch_norm.cc
浏览文件 @ 0c11ff97
...@@ -23,4 +23,9 @@ REGISTER_OPENCL_OPERATOR(OpKeyBuilder("BatchNorm") ...@@ -23,4 +23,9 @@ REGISTER_OPENCL_OPERATOR(OpKeyBuilder("BatchNorm")
.Build(), .Build(),
BatchNormOp<DeviceType::OPENCL, float>); BatchNormOp<DeviceType::OPENCL, float>);
} // namespace mace REGISTER_OPENCL_OPERATOR(OpKeyBuilder("BatchNorm")
\ No newline at end of file .TypeConstraint<half>("T")
.Build(),
BatchNormOp<DeviceType::OPENCL, half>);
} // namespace mace
mace/ops/batch_norm_benchmark.cc
浏览文件 @ 0c11ff97
...@@ -13,28 +13,45 @@ static void BatchNorm( ...@@ -13,28 +13,45 @@ static void BatchNorm(
int iters, int batch, int channels, int height, int width) { int iters, int batch, int channels, int height, int width) {
mace::testing::StopTiming(); mace::testing::StopTiming();
if ( D == OPENCL )
OpenCLRuntime::EnableProfiling();
OpsTestNet net; OpsTestNet net;
OpDefBuilder("BatchNorm", "BatchNormBM")
.Input("Input")
.Input("Scale")
.Input("Offset")
.Input("Mean")
.Input("Var")
.Input("Epsilon")
.Output("Output")
.Finalize(net.NewOperatorDef());
// Add input data // Add input data
net.AddRandomInput<D, T>("Input", {batch, channels, height, width}); net.AddRandomInput<D, T>("Input", {batch, height, width, channels});
net.AddRandomInput<D, T>("Scale", {channels}); net.AddRandomInput<D, T>("Scale", {channels});
net.AddRandomInput<D, T>("Offset", {channels}); net.AddRandomInput<D, T>("Offset", {channels});
net.AddRandomInput<D, T>("Mean", {channels}); net.AddRandomInput<D, T>("Mean", {channels});
net.AddRandomInput<D, T>("Var", {channels}, true); net.AddRandomInput<D, T>("Var", {channels}, true);
net.AddInputFromArray<D, float>("Epsilon", {}, {1e-3}); net.AddInputFromArray<D, float>("Epsilon", {}, {1e-3});
if (D == DeviceType::OPENCL) {
BufferToImage<D, float>(net, "Input", "InputImage", kernels::BufferType::IN_OUT);
BufferToImage<D, float>(net, "Scale", "ScaleImage", kernels::BufferType::ARGUMENT);
BufferToImage<D, float>(net, "Offset", "OffsetImage", kernels::BufferType::ARGUMENT);
BufferToImage<D, float>(net, "Mean", "MeanImage", kernels::BufferType::ARGUMENT);
BufferToImage<D, float>(net, "Var", "VarImage", kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormBM")
.Input("InputImage")
.Input("ScaleImage")
.Input("OffsetImage")
.Input("MeanImage")
.Input("VarImage")
.Input("Epsilon")
.Output("Output")
.Finalize(net.NewOperatorDef());
}
else {
OpDefBuilder("BatchNorm", "BatchNormBM")
.Input("Input")
.Input("Scale")
.Input("Offset")
.Input("Mean")
.Input("Var")
.Input("Epsilon")
.Output("Output")
.Finalize(net.NewOperatorDef());
}
// tuning // tuning
setenv("MACE_TUNING", "1", 1); setenv("MACE_TUNING", "1", 1);
net.RunOp(D); net.RunOp(D);
......
mace/ops/batch_norm_test.cc
浏览文件 @ 0c11ff97
...@@ -11,20 +11,10 @@ class BatchNormOpTest : public OpsTestBase {}; ...@@ -11,20 +11,10 @@ class BatchNormOpTest : public OpsTestBase {};
template <DeviceType D> template <DeviceType D>
void Simple() { void Simple() {
// Construct graph
OpsTestNet net; OpsTestNet net;
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("Input")
.Input("Scale")
.Input("Offset")
.Input("Mean")
.Input("Var")
.Input("Epsilon")
.Output("Output")
.Finalize(net.NewOperatorDef());
// Add input data // Add input data
net.AddInputFromArray<D, float>("Input", {1, 1, 6, 2}, net.AddInputFromArray<D, float>("Input", {1, 6, 2, 1},
{5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15}); {5, 5, 7, 7, 9, 9, 11, 11, 13, 13, 15, 15});
net.AddInputFromArray<D, float>("Scale", {1}, {4.0f}); net.AddInputFromArray<D, float>("Scale", {1}, {4.0f});
net.AddInputFromArray<D, float>("Offset", {1}, {2.0}); net.AddInputFromArray<D, float>("Offset", {1}, {2.0});
...@@ -32,12 +22,44 @@ void Simple() { ...@@ -32,12 +22,44 @@ void Simple() {
net.AddInputFromArray<D, float>("Var", {1}, {11.67f}); net.AddInputFromArray<D, float>("Var", {1}, {11.67f});
net.AddInputFromArray<D, float>("Epsilon", {}, {1e-3}); net.AddInputFromArray<D, float>("Epsilon", {}, {1e-3});
// Run if (D == DeviceType::OPENCL) {
net.RunOp(D); BufferToImage<D, float>(net, "Input", "InputImage", kernels::BufferType::IN_OUT);
BufferToImage<D, float>(net, "Scale", "ScaleImage", kernels::BufferType::ARGUMENT);
BufferToImage<D, float>(net, "Offset", "OffsetImage", kernels::BufferType::ARGUMENT);
BufferToImage<D, float>(net, "Mean", "MeanImage", kernels::BufferType::ARGUMENT);
BufferToImage<D, float>(net, "Var", "VarImage", kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputImage")
.Input("ScaleImage")
.Input("OffsetImage")
.Input("MeanImage")
.Input("VarImage")
.Input("Epsilon")
.Output("OutputImage")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
// Transfer output
ImageToBuffer<D, float>(net, "OutputImage", "Output", kernels::BufferType::IN_OUT);
} else {
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("Input")
.Input("Scale")
.Input("Offset")
.Input("Mean")
.Input("Var")
.Input("Epsilon")
.Output("Output")
.Finalize(net.NewOperatorDef());
// Run
net.RunOp(D);
}
// Check // Check
auto expected = auto expected =
CreateTensor<float>({1, 1, 6, 2}, {-3.86, -3.86, -1.51, -1.51, 0.83, 0.83, CreateTensor<float>({1, 6, 2, 1}, {-3.86, -3.86, -1.51, -1.51, 0.83, 0.83,
3.17, 3.17, 5.51, 5.51, 7.86, 7.86}); 3.17, 3.17, 5.51, 5.51, 7.86, 7.86});
ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-2); ExpectTensorNear<float>(*expected, *net.GetOutput("Output"), 1e-2);
...@@ -47,14 +69,17 @@ TEST_F(BatchNormOpTest, SimpleCPU) { ...@@ -47,14 +69,17 @@ TEST_F(BatchNormOpTest, SimpleCPU) {
Simple<DeviceType::CPU>(); Simple<DeviceType::CPU>();
} }
/*
TEST_F(BatchNormOpTest, SimpleNEON) { TEST_F(BatchNormOpTest, SimpleNEON) {
Simple<DeviceType::NEON>(); Simple<DeviceType::NEON>();
} }
*/
TEST_F(BatchNormOpTest, SimpleOPENCL) { TEST_F(BatchNormOpTest, SimpleOPENCL) {
Simple<DeviceType::OPENCL>(); Simple<DeviceType::OPENCL>();
} }
/*
TEST_F(BatchNormOpTest, SimpleRandomNeon) { TEST_F(BatchNormOpTest, SimpleRandomNeon) {
srand(time(NULL)); srand(time(NULL));
...@@ -136,6 +161,7 @@ TEST_F(BatchNormOpTest, ComplexRandomNeon) { ...@@ -136,6 +161,7 @@ TEST_F(BatchNormOpTest, ComplexRandomNeon) {
ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 1e-2); ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 1e-2);
} }
*/
TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
srand(time(NULL)); srand(time(NULL));
...@@ -145,6 +171,7 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { ...@@ -145,6 +171,7 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
index_t channels = 3 + rand() % 50; index_t channels = 3 + rand() % 50;
index_t height = 64; index_t height = 64;
index_t width = 64; index_t width = 64;
// Construct graph // Construct graph
auto &net = test_net(); auto &net = test_net();
OpDefBuilder("BatchNorm", "BatchNormTest") OpDefBuilder("BatchNorm", "BatchNormTest")
...@@ -158,30 +185,48 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) { ...@@ -158,30 +185,48 @@ TEST_F(BatchNormOpTest, SimpleRandomOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>("Input", {batch, channels, height, width}); net.AddRandomInput<DeviceType::OPENCL, float>("Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels}); net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}, true); net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}, true);
net.AddInputFromArray<DeviceType::OPENCL, float>("Epsilon", {}, {1e-3}); net.AddInputFromArray<DeviceType::OPENCL, float>("Epsilon", {}, {1e-3});
// TODO : there is a bug for tuning // run cpu
// tuning net.RunOp();
// setenv("MACE_TUNING", "1", 1);
// net.RunOp(DeviceType::OPENCL);
// unsetenv("MACE_TUNING");
// Run on opencl
net.RunOp(DeviceType::OPENCL);
// Check // Check
Tensor expected; Tensor expected;
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// run cpu // Run on opencl
net.RunOp(); BufferToImage<DeviceType::OPENCL, float>(net, "Input", "InputImage", kernels::BufferType::IN_OUT);
BufferToImage<DeviceType::OPENCL, float>(net, "Scale", "ScaleImage", kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(net, "Offset", "OffsetImage", kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(net, "Mean", "MeanImage", kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(net, "Var", "VarImage", kernels::BufferType::ARGUMENT);
ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 1e-2); OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputImage")
.Input("ScaleImage")
.Input("OffsetImage")
.Input("MeanImage")
.Input("VarImage")
.Input("Epsilon")
.Output("OutputImage")
.Finalize(net.NewOperatorDef());
// Tuning
setenv("MACE_TUNING", "1", 1);
net.RunOp(DeviceType::OPENCL);
unsetenv("MACE_TUNING");
// Run on opencl
net.RunOp(DeviceType::OPENCL);
net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(net, "OutputImage", "OPENCLOutput", kernels::BufferType::IN_OUT);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2);
} }
TEST_F(BatchNormOpTest, ComplexRandomOPENCL) { TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
...@@ -192,6 +237,7 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) { ...@@ -192,6 +237,7 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
index_t channels = 3 + rand() % 50; index_t channels = 3 + rand() % 50;
index_t height = 103; index_t height = 103;
index_t width = 113; index_t width = 113;
// Construct graph // Construct graph
auto &net = test_net(); auto &net = test_net();
OpDefBuilder("BatchNorm", "BatchNormTest") OpDefBuilder("BatchNorm", "BatchNormTest")
...@@ -205,31 +251,49 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) { ...@@ -205,31 +251,49 @@ TEST_F(BatchNormOpTest, ComplexRandomOPENCL) {
.Finalize(net.NewOperatorDef()); .Finalize(net.NewOperatorDef());
// Add input data // Add input data
net.AddRandomInput<DeviceType::OPENCL, float>("Input", {batch, channels, height, width}); net.AddRandomInput<DeviceType::OPENCL, float>("Input", {batch, height, width, channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels}); net.AddRandomInput<DeviceType::OPENCL, float>("Scale", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels}); net.AddRandomInput<DeviceType::OPENCL, float>("Offset", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels}); net.AddRandomInput<DeviceType::OPENCL, float>("Mean", {channels});
net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}, true); net.AddRandomInput<DeviceType::OPENCL, float>("Var", {channels}, true);
net.AddInputFromArray<DeviceType::OPENCL, float>("Epsilon", {}, {1e-3}); net.AddInputFromArray<DeviceType::OPENCL, float>("Epsilon", {}, {1e-3});
// TODO : there is a bug for tuning // run cpu
// tuning net.RunOp();
// setenv("MACE_TUNING", "1", 1);
// net.RunOp(DeviceType::OPENCL);
// unsetenv("MACE_TUNING");
// Run on opencl
net.RunOp(DeviceType::OPENCL);
net.Sync();
// Check // Check
Tensor expected; Tensor expected;
expected.Copy(*net.GetOutput("Output")); expected.Copy(*net.GetOutput("Output"));
// run cpu
net.RunOp();
ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 1e-2); // Run on opencl
BufferToImage<DeviceType::OPENCL, float>(net, "Input", "InputImage", kernels::BufferType::IN_OUT);
BufferToImage<DeviceType::OPENCL, float>(net, "Scale", "ScaleImage", kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(net, "Offset", "OffsetImage", kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(net, "Mean", "MeanImage", kernels::BufferType::ARGUMENT);
BufferToImage<DeviceType::OPENCL, float>(net, "Var", "VarImage", kernels::BufferType::ARGUMENT);
OpDefBuilder("BatchNorm", "BatchNormTest")
.Input("InputImage")
.Input("ScaleImage")
.Input("OffsetImage")
.Input("MeanImage")
.Input("VarImage")
.Input("Epsilon")
.Output("OutputImage")
.Finalize(net.NewOperatorDef());
// tuning
setenv("MACE_TUNING", "1", 1);
net.RunOp(DeviceType::OPENCL);
unsetenv("MACE_TUNING");
// Run on opencl
net.RunOp(DeviceType::OPENCL);
net.Sync();
ImageToBuffer<DeviceType::OPENCL, float>(net, "OutputImage", "OPENCLOutput", kernels::BufferType::IN_OUT);
ExpectTensorNear<float>(expected, *net.GetOutput("OPENCLOutput"), 1e-2);
} }
} }
tools/bazel-adb-run.sh
浏览文件 @ 0c11ff97
...@@ -22,7 +22,10 @@ ANDROID_ABI=arm64-v8a ...@@ -22,7 +22,10 @@ ANDROID_ABI=arm64-v8a
STRIP="" STRIP=""
STRIP="--strip always" STRIP="--strip always"
bazel build -c opt $STRIP --verbose_failures $BAZEL_TARGET --crosstool_top=//external:android/crosstool --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cpu=$ANDROID_ABI # for profiling
bazel build -c opt $STRIP --verbose_failures $BAZEL_TARGET --crosstool_top=//external:android/crosstool --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cpu=$ANDROID_ABI --define profiling=true
#bazel build -c opt $STRIP --verbose_failures $BAZEL_TARGET --crosstool_top=//external:android/crosstool --host_crosstool_top=@bazel_tools//tools/cpp:toolchain --cpu=$ANDROID_ABI
if [ $? -ne 0 ]; then if [ $? -ne 0 ]; then
exit 1 exit 1
fi fi
......
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