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fp16 ocl support for more layers

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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
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  1. 13
      modules/dnn/src/layers/batch_norm_layer.cpp
  2. 2
      modules/dnn/src/layers/blank_layer.cpp
  3. 38
      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
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@ -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
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@ -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))

38
modules/dnn/src/layers/detection_output_layer.cpp
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@ -307,8 +307,24 @@ public:
std::vector<UMat> inputs;
std::vector<UMat> 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;
@ -342,6 +358,12 @@ public:
{
// Set confidences to zeros.
Range ranges[] = {Range::all(), Range::all(), Range::all(), Range(2, 3)};
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;
}
@ -360,9 +382,23 @@ public:
}
CV_Assert(count == numKept);
}
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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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
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@ -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,

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