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enable concat layer fuse for OCL target

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Signed-off-by: Li Peng <peng.li@intel.com>
pull/11959/head
Li Peng 7 years ago
parent 625d20b9b4
commit f0cadaa6e3
3 changed files with 64 additions and 7 deletions
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  1. 42
      modules/dnn/src/dnn.cpp
  2. 9
      modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
  3. 20
      modules/dnn/src/opencl/conv_layer_spatial.cl

42
modules/dnn/src/dnn.cpp
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@ -1492,7 +1492,8 @@ struct Net::Impl
// TODO: OpenCL target support more fusion styles.
if ( preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget) &&
(!cv::ocl::useOpenCL() || (ld.layerInstance->type != "Convolution" &&
ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling")) )
ld.layerInstance->type != "MVN" && ld.layerInstance->type != "Pooling" &&
ld.layerInstance->type != "Concat")) )
continue;
Ptr<Layer>& currLayer = ld.layerInstance;
@ -1701,6 +1702,31 @@ struct Net::Impl
ld.outputBlobs.size() == 1 )
{
Mat& output = ld.outputBlobs[0];
UMat umat_output;
if (!ld.outputBlobsWrappers.empty() &&
(preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget)))
{
size_t i, ninputs = ld.inputBlobsId.size();
bool conv_layer = true;
for( i = 0; i < ninputs; i++ )
{
LayerPin pin = ld.inputBlobsId[i];
LayerData* inp_i_data = &layers[pin.lid];
while(inp_i_data->skip &&
inp_i_data->inputBlobsId.size() == 1 &&
inp_i_data->consumers.size() == 1)
{
pin = inp_i_data->inputBlobsId[0];
inp_i_data = &layers[pin.lid];
}
conv_layer = conv_layer && (inp_i_data->getLayerInstance()->type == "Convolution");
}
if (!conv_layer)
continue;
std::vector<UMat> umat_outputBlobs;
umat_outputBlobs = OpenCLBackendWrapper::getUMatVector(ld.outputBlobsWrappers);
umat_output = umat_outputBlobs[0];
}
// TODO: in general, this optimization can always be done, but
// many layers currently check that the input/output blobs are
@ -1737,6 +1763,14 @@ struct Net::Impl
// Allocate new memory to prevent collisions during memory
// reusing (see https://github.com/opencv/opencv/pull/10456).
output = output.clone();
if (preferableBackend == DNN_BACKEND_OPENCV &&
IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(1);
umat_output = umat_output.clone();
umats[0] = umat_output;
OpenCLBackendWrapper::update(ld.outputBlobsWrappers, umats);
}
Range chrange[] = { Range::all(), Range::all(), Range::all(), Range::all() };
int ofs = 0;
for( i = 0; i < ninputs; i++ )
@ -1753,6 +1787,12 @@ struct Net::Impl
CV_Assert(output_slice.isContinuous() && output_slice.size == curr_output.size);
Mat* oldPtr = &curr_output;
curr_output = output_slice;
if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
{
std::vector<UMat> umats(inp_i_data->outputBlobsWrappers.size());
umats[pin.oid] = umat_output(chrange);
OpenCLBackendWrapper::update(inp_i_data->outputBlobsWrappers, umats);
}
// Layers that refer old input Mat will refer to the
// new data but the same Mat object.
CV_Assert(curr_output.data == output_slice.data, oldPtr == &curr_output);

9
modules/dnn/src/ocl4dnn/src/ocl4dnn_conv_spatial.cpp
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@ -821,7 +821,7 @@ void OCL4DNNConvSpatial<float>::CreateSubBuffer(const UMat& buffer, UMat& sub_bu
cl_int err;
size_t element_size = (use_half_) ? sizeof(short) : sizeof(float);
region.origin = offset * element_size;
region.origin = offset * element_size + buffer.offset;
region.size = size * element_size;
sub_mem = clCreateSubBuffer((cl_mem)buffer.handle(ACCESS_READ),
write_only ? CL_MEM_WRITE_ONLY : CL_MEM_READ_ONLY,
@ -853,6 +853,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
return false;
int32_t bias_offset;
int32_t element_size = use_half_ ? sizeof(short) : sizeof(float);
if (config->kernelType == KERNEL_TYPE_INTEL_IDLF) {
if (!swizzleWeight(weight, config->workItem_output[2], false))
@ -931,10 +932,12 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
return false;
kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(out_buffer));
kernel.set(argIdx++, (int)(out_buffer.offset / element_size));
}
else
{
kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(top));
kernel.set(argIdx++, (int)(top.offset / element_size));
}
kernel.set(argIdx++, (uint16_t)width_);
@ -1024,10 +1027,12 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
return false;
kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(out_buffer));
kernel.set(argIdx++, (int)(out_buffer.offset / element_size));
}
else
{
kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(top));
kernel.set(argIdx++, (int)(top.offset / element_size));
}
kernel.set(argIdx++, (uint16_t)width_);
@ -1079,6 +1084,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
if (bias_term_)
kernel.set(argIdx++, ocl::KernelArg::PtrReadOnly(bias));
kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(top));
kernel.set(argIdx++, (int)(top.offset / element_size));
kernel.set(argIdx++, (uint16_t)width_);
kernel.set(argIdx++, (uint16_t)height_);
kernel.set(argIdx++, (uint16_t)output_w_);
@ -1126,6 +1132,7 @@ bool OCL4DNNConvSpatial<float>::convolve(const UMat &bottom, UMat &top,
kernel.set(argIdx++, (void *)NULL);
kernel.set(argIdx++, bias_offset);
kernel.set(argIdx++, ocl::KernelArg::PtrWriteOnly(top));
kernel.set(argIdx++, (int)(top.offset / element_size));
kernel.set(argIdx++, output_image_offset);
kernel.set(argIdx++, (uint16_t)width_);
kernel.set(argIdx++, (uint16_t)height_);

20
modules/dnn/src/opencl/conv_layer_spatial.cl
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@ -136,7 +136,8 @@ __kernel void ConvolveBasic(
int kernel_offset,
__global Dtype* bias,
const int bias_offset,
__global Dtype* convolved_image,
__global Dtype* convolved_image_base,
const int convolved_image_base_offset,
const int convolved_image_offset,
const ushort input_width,
const ushort input_height,
@ -146,6 +147,7 @@ __kernel void ConvolveBasic(
const ushort pad_h
)
{
__global Dtype* convolved_image = convolved_image_base + convolved_image_base_offset;
const int outputX = get_global_id(0);
const int outputY = get_global_id(1);
const int kernelNum = get_global_id(2) * ZPAR;
@ -220,12 +222,14 @@ convolve_simd(
__global Dtype* inputs,
__global Dtype* weights,
BIAS_KERNEL_ARG
__global Dtype* outputs,
__global Dtype* outputs_base,
const int outputs_offset,
const ushort input_width,
const ushort input_height,
const ushort output_width,
const ushort output_height)
{
__global Dtype* outputs = outputs_base + outputs_offset;
unsigned int oc = get_global_id(0) * OUT_BLOCK_WIDTH; // oc = Output Column
unsigned int or = get_global_id(1) * OUT_BLOCK_HEIGHT; // or = Output Row
unsigned int fm = get_global_id(2); // fm = Feature Map = od = Output Depth
@ -395,7 +399,8 @@ typedef struct half0 { half s0; } half0; //never used but makes compiler happy.
const __global Dtype *src0, \
const __global Dtype *src1, \
BIAS_KERNEL_ARG \
__global Dtype *dst, \
__global Dtype *dst_base, \
const int dst_offset, \
const ushort input_width, \
const ushort input_height, \
const ushort output_width, \
@ -425,6 +430,7 @@ typedef struct half0 { half s0; } half0; //never used but makes compiler happy.
__attribute__((intel_reqd_sub_group_size(8)))
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
__global Dtype *dst = dst_base + dst_offset;
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
@ -813,6 +819,7 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
__attribute__((intel_reqd_sub_group_size(8)))
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
__global Dtype *dst = dst_base + dst_offset;
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
@ -1374,6 +1381,7 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
__attribute__((intel_reqd_sub_group_size(16)))
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
__global Dtype *dst = dst_base + dst_offset;
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
@ -1559,6 +1567,7 @@ __kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
__attribute__((intel_reqd_sub_group_size(16)))
__kernel void Conv_Interleaved(GEMM_LIKE_KERNEL_ARGS)
{
__global Dtype *dst = dst_base + dst_offset;
const int group_x = get_group_id(0);
const int group_y = get_group_id(1);
const int global_x = get_global_id(0);
@ -1770,12 +1779,13 @@ __kernel void DWCONV(
__global Dtype* image_data,
__global Dtype* kernel_data,
BIAS_KERNEL_ARG
__global Dtype* convolved_image,
__global Dtype* convolved_image_base,
const int convolved_image_offset,
const ushort input_width,
const ushort input_height,
const ushort output_width,
const ushort output_height) {
__global Dtype* convolved_image = convolved_image_base + convolved_image_offset;
const int outputX = get_global_id(0);
const int outputY = get_global_id(1);
const int outputZ = get_global_id(2);

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