Chiebot-Mirror
/
opencv
mirror of https://github.com/opencv/opencv.git
8
0
Fork 0
Code Issues Projects Releases Wiki Activity

fp16 ocl support for more layers

Browse Source 
Signed-off-by: Li Peng <peng.li@intel.com>
pull/11397/head
Li Peng 7 years ago
parent 3dd916882a
commit ba5e8befa9
14 changed files with 120 additions and 49 deletions
Whitespace
Show all changes Ignore whitespace when comparing lines Ignore changes in amount of whitespace Ignore changes in whitespace at EOL
Split View
Diff Options
Show Stats Download Patch File Download Diff File
  1. 13
      modules/dnn/src/layers/batch_norm_layer.cpp
  2. 2
      modules/dnn/src/layers/blank_layer.cpp
  3. 50
      modules/dnn/src/layers/detection_output_layer.cpp
  4. 5
      modules/dnn/src/layers/normalize_bbox_layer.cpp
  5. 6
      modules/dnn/src/layers/permute_layer.cpp
  6. 13
      modules/dnn/src/layers/prior_box_layer.cpp
  7. 5
      modules/dnn/src/layers/proposal_layer.cpp
  8. 4
      modules/dnn/src/layers/region_layer.cpp
  9. 5
      modules/dnn/src/layers/reorg_layer.cpp
  10. 2
      modules/dnn/src/layers/reshape_layer.cpp
  11. 29
      modules/dnn/src/opencl/batchnorm.cl
  12. 4
      modules/dnn/src/opencl/permute.cl
  13. 27
      modules/dnn/src/opencl/prior_box.cl
  14. 4
      modules/dnn/src/opencl/reorg.cl

13
modules/dnn/src/layers/batch_norm_layer.cpp
Unescape View File

@ -120,12 +120,16 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inputs_.depth() == CV_16S);
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
CV_Assert(blobs.size() >= 2);
CV_Assert(inputs.size() == 1);
if (use_half && inputs[0].dims == 2)
return false;
if (umat_weight.empty())
{
umat_weight = weights_.getUMat(ACCESS_READ);
@ -139,6 +143,7 @@ public:
int rows = inpBlob.dims > 2 ? inpBlob.size[2] : 1;
int cols = inpBlob.dims > 2 ? inpBlob.size[3] : 1;
String opts = (use_half) ? " -DDtype=half" : " -DDtype=float";
for (size_t ii = 0; ii < outputs.size(); ii++)
{
if (inpBlob.dims == 2)
@ -154,8 +159,12 @@ public:
UMat src = inputs[ii].reshape(1, s.size(), &s[0]);
UMat dst = outputs[ii].reshape(1, s.size(), &s[0]);
int number = (s[1] % 8 == 0) ? 8 : ((s[1] % 4 == 0) ? 4 : 1);
String buildopt = format("-DNUM=%d", number);
String buildopt = format("-DNUM=%d", number) + opts;
String kname = format("batch_norm%d", number);
if (number == 1)
buildopt += format(" -Dconvert_T=convert_%s", use_half ? "half" : "float");
else
buildopt += format(" -Dconvert_T=convert_%s%d", use_half ? "half" : "float", number);
ocl::Kernel kernel(kname.c_str(), ocl::dnn::batchnorm_oclsrc, buildopt);
if (kernel.empty())
return false;
@ -181,7 +190,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

2
modules/dnn/src/layers/blank_layer.cpp
Unescape View File

@ -95,7 +95,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

50
modules/dnn/src/layers/detection_output_layer.cpp
Unescape View File

@ -307,8 +307,24 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
bool use_half = (inps.depth() == CV_16S);
if (use_half)
{
std::vector<UMat> orig_inputs;
std::vector<UMat> orig_outputs;
inps.getUMatVector(orig_inputs);
outs.getUMatVector(orig_outputs);
inputs.resize(orig_inputs.size());
for (size_t i = 0; i < orig_inputs.size(); i++)
convertFp16(orig_inputs[i], inputs[i]);
}
else
{
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
}
std::vector<LabelBBox> allDecodedBBoxes;
std::vector<Mat> allConfidenceScores;
@ -342,7 +358,13 @@ public:
{
// Set confidences to zeros.
Range ranges[] = {Range::all(), Range::all(), Range::all(), Range(2, 3)};
outputs[0](ranges).setTo(0);
if (use_half)
{
std::vector<UMat> orig_outputs;
outs.getUMatVector(orig_outputs);
orig_outputs[0](ranges).setTo(0);
} else
outputs[0](ranges).setTo(0);
return true;
}
int outputShape[] = {1, 1, (int)numKept, 7};
@ -360,9 +382,23 @@ public:
}
CV_Assert(count == numKept);
}
outputs.clear();
outputs.push_back(umat);
outs.assign(outputs);
if (use_half)
{
UMat half_umat;
convertFp16(umat, half_umat);
std::vector<UMat> orig_outputs;
outs.getUMatVector(orig_outputs);
orig_outputs.clear();
orig_outputs.push_back(half_umat);
outs.assign(orig_outputs);
} else {
outputs.clear();
outputs.push_back(umat);
outs.assign(outputs);
}
return true;
}
#endif
@ -372,7 +408,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

5
modules/dnn/src/layers/normalize_bbox_layer.cpp
Unescape View File

@ -87,6 +87,9 @@ public:
std::vector<UMat> outputs;
std::vector<UMat> internals;
if (inputs_.depth() == CV_16S)
return false;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
@ -162,7 +165,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

6
modules/dnn/src/layers/permute_layer.cpp
Unescape View File

@ -288,9 +288,11 @@ public:
if (!_needsPermute)
return false;
bool use_half = (inps.depth() == CV_16S);
String opts = format("-DDtype=%s", use_half ? "half" : "float");
for (size_t i = 0; i < inputs.size(); i++)
{
ocl::Kernel kernel("permute", ocl::dnn::permute_oclsrc);
ocl::Kernel kernel("permute", ocl::dnn::permute_oclsrc, opts);
kernel.set(0, (int)_count);
kernel.set(1, ocl::KernelArg::PtrReadOnly(inputs[i]));
@ -313,7 +315,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

13
modules/dnn/src/layers/prior_box_layer.cpp
Unescape View File

@ -316,6 +316,7 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
@ -340,9 +341,15 @@ public:
heights.copyTo(umat_heights);
}
String opts;
if (use_half)
opts = "-DDtype=half -DDtype4=half4 -Dconvert_T=convert_half4";
else
opts = "-DDtype=float -DDtype4=float4 -Dconvert_T=convert_float4";
size_t nthreads = _layerHeight * _layerWidth;
ocl::Kernel kernel("prior_box", ocl::dnn::prior_box_oclsrc, opts);
ocl::Kernel kernel("prior_box", ocl::dnn::prior_box_oclsrc);
kernel.set(0, (int)nthreads);
kernel.set(1, (float)_stepX);
kernel.set(2, (float)_stepY);
@ -375,7 +382,7 @@ public:
// set the variance.
{
ocl::Kernel kernel("set_variance", ocl::dnn::prior_box_oclsrc);
ocl::Kernel kernel("set_variance", ocl::dnn::prior_box_oclsrc, opts);
int offset = total(shape(outputs[0]), 2);
size_t nthreads = _layerHeight * _layerWidth * _numPriors;
kernel.set(0, (int)nthreads);
@ -395,7 +402,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

5
modules/dnn/src/layers/proposal_layer.cpp
Unescape View File

@ -158,6 +158,9 @@ public:
std::vector<UMat> outputs;
std::vector<UMat> internals;
if (inputs_.depth() == CV_16S)
return false;
inputs_.getUMatVector(inputs);
outputs_.getUMatVector(outputs);
internals_.getUMatVector(internals);
@ -237,7 +240,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

4
modules/dnn/src/layers/region_layer.cpp
Unescape View File

@ -127,7 +127,7 @@ public:
std::vector<UMat> outputs;
// TODO: implement a logistic activation to classification scores.
if (useLogistic)
if (useLogistic || inps.depth() == CV_16S)
return false;
inps.getUMatVector(inputs);
@ -191,7 +191,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

5
modules/dnn/src/layers/reorg_layer.cpp
Unescape View File

@ -96,9 +96,10 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> outputs;
bool use_half = (inps.depth() == CV_16S);
inps.getUMatVector(inputs);
outs.getUMatVector(outputs);
String buildopt = String("-DDtype=") + ocl::typeToStr(inputs[0].type()) + String(" ");
String buildopt= format("-DDtype=%s ", use_half ? "half" : "float");
for (size_t i = 0; i < inputs.size(); i++)
{
@ -134,7 +135,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

2
modules/dnn/src/layers/reshape_layer.cpp
Unescape View File

@ -219,7 +219,7 @@ public:
CV_TRACE_FUNCTION();
CV_TRACE_ARG_VALUE(name, "name", name.c_str());
CV_OCL_RUN((preferableTarget == DNN_TARGET_OPENCL) &&
CV_OCL_RUN(IS_DNN_OPENCL_TARGET(preferableTarget) &&
OCL_PERFORMANCE_CHECK(ocl::Device::getDefault().isIntel()),
forward_ocl(inputs_arr, outputs_arr, internals_arr))

29
modules/dnn/src/opencl/batchnorm.cl
Unescape View File

@ -40,24 +40,27 @@
//
//M*/
#define Dtype float
#define Dtype4 float4
#define Dtype8 float8
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
#if NUM == 8
#define load(src, index) vload8(0, src + index)
#define store(vec, dst, index) vstore8(vec, 0, dst + index)
#define vec_type Dtype8
#define float_type float8
#define convert_f convert_float8
#define BATCH_NORM batch_norm8
#elif NUM == 4
#define load(src, index) vload4(0, src + index)
#define store(vec, dst, index) vstore4(vec, 0, dst + index)
#define vec_type Dtype4
#define float_type float4
#define convert_f convert_float4
#define BATCH_NORM batch_norm4
#elif NUM == 1
#define load(src, index) src[index]
#define store(vec, dst, index) dst[index] = vec
#define vec_type Dtype
#define float_type float
#define convert_f convert_float
#define BATCH_NORM batch_norm1
#endif
@ -65,8 +68,8 @@ __kernel void BATCH_NORM(__global const Dtype* src,
const int rows,
const int cols,
const int channels,
__global const Dtype* weight,
__global const Dtype* bias,
__global const float* weight,
__global const float* bias,
__global Dtype* dst)
{
int x = get_global_id(0);
@ -76,9 +79,9 @@ __kernel void BATCH_NORM(__global const Dtype* src,
if (x >= rows || y >= cols)
return;
Dtype w = weight[x % channels];
Dtype b = bias[x % channels];
vec_type src_vec = load(src, index);
vec_type dst_vec = src_vec * w + (vec_type)b;
store(dst_vec, dst, index);
float w = weight[x % channels];
float b = bias[x % channels];
float_type src_vec = convert_f(load(src, index));
float_type dst_vec = src_vec * w + (float_type)b;
store(convert_T(dst_vec), dst, index);
}

4
modules/dnn/src/opencl/permute.cl
Unescape View File

@ -40,7 +40,9 @@
//
//M*/
#define Dtype float
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void permute(const int nthreads,
__global Dtype* bottom_data,

27
modules/dnn/src/opencl/prior_box.cl
Unescape View File

@ -39,17 +39,18 @@
//
//M*/
#define Dtype float
#define Dtype4 float4
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void prior_box(const int nthreads,
const Dtype stepX,
const Dtype stepY,
__global const Dtype* _offsetsX,
__global const Dtype* _offsetsY,
const float stepX,
const float stepY,
__global const float* _offsetsX,
__global const float* _offsetsY,
const int offsetsX_size,
__global const Dtype* _widths,
__global const Dtype* _heights,
__global const float* _widths,
__global const float* _heights,
const int widths_size,
__global Dtype* dst,
const int _layerHeight,
@ -65,7 +66,7 @@ __kernel void prior_box(const int nthreads,
outputPtr = dst + index * 4 * offsetsX_size * widths_size;
Dtype _boxWidth, _boxHeight;
float _boxWidth, _boxHeight;
Dtype4 vec;
for (int i = 0; i < widths_size; ++i)
{
@ -73,8 +74,8 @@ __kernel void prior_box(const int nthreads,
_boxHeight = _heights[i];
for (int j = 0; j < offsetsX_size; ++j)
{
float center_x = (w + _offsetsX[j]) * stepX;
float center_y = (h + _offsetsY[j]) * stepY;
Dtype center_x = (w + _offsetsX[j]) * (Dtype)stepX;
Dtype center_y = (h + _offsetsY[j]) * (Dtype)stepY;
vec.x = (center_x - _boxWidth * 0.5f) / imgWidth; // xmin
vec.y = (center_y - _boxHeight * 0.5f) / imgHeight; // ymin
@ -91,7 +92,7 @@ __kernel void prior_box(const int nthreads,
__kernel void set_variance(const int nthreads,
const int offset,
const int variance_size,
__global const Dtype* variance,
__global const float* variance,
__global Dtype* dst)
{
for (int index = get_global_id(0); index < nthreads; index += get_global_size(0))
@ -101,7 +102,7 @@ __kernel void set_variance(const int nthreads,
if (variance_size == 1)
var_vec = (Dtype4)(variance[0]);
else
var_vec = vload4(0, variance);
var_vec = convert_T(vload4(0, variance));
vstore4(var_vec, 0, dst + offset + index * 4);
}

4
modules/dnn/src/opencl/reorg.cl
Unescape View File

@ -39,6 +39,10 @@
//
//M*/
#if defined(cl_khr_fp16)
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#endif
__kernel void reorg(const int count,
__global const Dtype* src,
const int channels,

Write Preview
Loading…
Cancel
Save