COMPMID-522 - Added support for GlobalPooling in CLPoolingLayer and CLFlattening for 3D tensor
Change-Id: Ifc7db1e4d4af322a4dcbfeb3e132e5c326596872
Reviewed-on: http://mpd-gerrit.cambridge.arm.com/86618
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Tested-by: Kaizen <jeremy.johnson+kaizengerrit@arm.com>
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 6602b4d..4cd0a78 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -239,6 +239,7 @@
{ "pooling_layer_3", "pooling_layer.cl" },
{ "pooling_layer_3_optimized", "pooling_layer.cl" },
{ "pooling_layer_7", "pooling_layer.cl" },
+ { "pooling_layer_N", "pooling_layer.cl" },
{ "quantization_layer", "quantization_layer.cl" },
{ "reduction_operation", "reduction_operation.cl" },
{ "remap_nearest_neighbour", "remap.cl" },
diff --git a/src/core/CL/cl_kernels/pooling_layer.cl b/src/core/CL/cl_kernels/pooling_layer.cl
index 18ad4a6..0497bf4 100644
--- a/src/core/CL/cl_kernels/pooling_layer.cl
+++ b/src/core/CL/cl_kernels/pooling_layer.cl
@@ -415,3 +415,101 @@
// Store result
*(__global DATA_TYPE *)output.ptr = res;
}
+
+#if defined(POOL_SIZE)
+
+// Set the initial value for the pooling operation accordingly with the data type
+#if defined(POOL_AVG)
+#define INITIAL_VALUE 0
+#else // POOL_AVG
+#ifdef FIXED_POINT_POSITION
+#define MIN_VAL_EXPAND(type) type##_MIN
+#define MIN_VAL(type) MIN_VAL_EXPAND(type)
+#define INITIAL_VALUE MIN_VAL(DATA_TYPE)
+#define INITIAL_VALUE 0
+#else // FIXED_POINT_POSITION
+#if FP16
+#define INITIAL_VALUE -HALF_MAX
+#else // FP16
+#define INITIAL_VALUE -FLT_MAX
+#endif // FP16
+#endif // FIXED_POINT_POSITION
+
+#endif // POOL_AVG
+
+/** Performs a pooling function of pool size equal to N
+ *
+ * @note Datatype must be passed using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types are F16/F32;
+ * @note -DFP16 must be passed at compile time if half float data type is used
+ * @note Pool size must be passed using -DPOOL_SIZE e.g. -DPOOL_SIZE=13;
+ * @note In case of average pooling the following information must be passed at compile time:
+ * -DPOOL_AVG must be provided otherwise max pooling will be performed.
+ * -DMAX_WIDTH and -DMAX_HEIGHT which are the maximum accessible indeces in x and y dimensions (width + pad)
+ * -DSTRIDE_X and -DSTRIDE_Y which are the steps of the window along the x and y directions
+ * -DPAD_X and -DPAD_Y which are the pooling paddings in x and y dimension
+ *
+ * @param[in] input_ptr Pointer to the source image. Supported data types: F16/F32
+ * @param[in] input_stride_x Stride of the source image in X dimension (in bytes)
+ * @param[in] input_step_x input_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] input_stride_y Stride of the source image in Y dimension (in bytes)
+ * @param[in] input_step_y input_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] input_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] input_step_z input_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] input_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[out] output_ptr Pointer to the destination image. Supported data types: same as @p input_ptr
+ * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes)
+ * @param[in] output_step_x output_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] output_stride_y Stride of the destination image in Y dimension (in bytes)
+ * @param[in] output_step_y output_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] output_stride_z Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] output_step_z output_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination image
+ */
+__kernel void pooling_layer_N(
+ TENSOR3D_DECLARATION(input),
+ TENSOR3D_DECLARATION(output))
+{
+ // Get pixels pointer
+ Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT(input);
+ Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT(output);
+
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ vdata = INITIAL_VALUE;
+ DATA_TYPE sdata = INITIAL_VALUE;
+
+ // Load data
+ for(int y = 0; y < POOL_SIZE; y++)
+ {
+ int x = 0;
+ for(; x <= ((int)POOL_SIZE - 8); x += 8)
+ {
+ VEC_DATA_TYPE(DATA_TYPE, 8)
+ data0 = vload8(0, (__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0));
+ vdata = POOL_OP(vdata, data0);
+ }
+
+ // Leftover
+ for(; x < (int)POOL_SIZE; ++x)
+ {
+ DATA_TYPE data0 = *((__global DATA_TYPE *)tensor3D_offset(&input, x, y, 0));
+ sdata = POOL_OP(sdata, data0);
+ }
+ }
+
+ // Reduce result
+ VEC_DATA_TYPE(DATA_TYPE, 4)
+ reduce4 = POOL_OP(vdata.s0123, vdata.s4567);
+ VEC_DATA_TYPE(DATA_TYPE, 2)
+ reduce2 = POOL_OP(reduce4.s01, reduce4.s23);
+ DATA_TYPE res = POOL_OP(reduce2.s0, reduce2.s1);
+ res = POOL_OP(res, sdata);
+
+ // Divide by pool region in case of average pooling
+#ifdef POOL_AVG
+ res = DIV_OP(res, calculate_avg_scale(POOL_SIZE, MAX_WIDTH, MAX_HEIGHT, PAD_X, PAD_Y, STRIDE_X, STRIDE_Y));
+#endif /* POOL_AVG */
+
+ // Store result
+ *(__global DATA_TYPE *)output.ptr = res;
+}
+#endif // defined(POOL_SIZE)
\ No newline at end of file
diff --git a/src/core/CL/kernels/CLPoolingLayerKernel.cpp b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
index 22d29c6..22c7730 100644
--- a/src/core/CL/kernels/CLPoolingLayerKernel.cpp
+++ b/src/core/CL/kernels/CLPoolingLayerKernel.cpp
@@ -64,13 +64,10 @@
std::tie(pool_pad_x, pool_pad_y) = pad_stride_info.pad();
std::tie(pool_stride_x, pool_stride_y) = pad_stride_info.stride();
- static const std::set<int> supported_pool_sizes = { 2, 3, 7 };
- ARM_COMPUTE_UNUSED(supported_pool_sizes);
-
ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_NULLPTR(output);
- ARM_COMPUTE_ERROR_ON(supported_pool_sizes.find(pool_size) == supported_pool_sizes.end());
ARM_COMPUTE_ERROR_ON(pool_pad_x >= pool_size || pool_pad_y >= pool_size);
+ ARM_COMPUTE_ERROR_ON(pool_size > 7 && is_data_type_fixed_point(input->info()->data_type()));
// Check output dimensions
std::tie(pooled_w, pooled_h) = scaled_dimensions(input->info()->dimension(0),
@@ -92,29 +89,14 @@
ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT(input, output);
ARM_COMPUTE_ERROR_ON((output->info()->dimension(0) != pooled_w) || (output->info()->dimension(1) != pooled_h));
- // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
- // each thread computes 4 output elements
- const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type());
-
- int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;
- if(is_pool3x3_stride_le3)
- {
- // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
- _num_elems_processed_per_iteration = 4;
- num_elements_read_per_iteration = pool_size * (pool_stride_x + 1);
- }
- const int input_width = input->info()->dimension(0);
- const int input_height = input->info()->dimension(1);
- const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
- const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+ const int input_width = input->info()->dimension(0);
+ const int input_height = input->info()->dimension(1);
// Set instance variables
- _input = input;
- _output = output;
- _pool_info = pool_info;
- _border_size = BorderSize(pool_pad_y, pool_pad_x);
- _border_size.right = std::max(upper_bound_w, pool_pad_x);
- _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+ _input = input;
+ _output = output;
+ _pool_info = pool_info;
+ _border_size = BorderSize(pool_pad_y, pool_pad_x);
// Set build options
std::set<std::string> build_opts;
@@ -136,14 +118,52 @@
}
// Create kernel
- std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
- if(is_pool3x3_stride_le3)
+ if(pool_size <= 7)
{
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
+ // Check if we have pool3x3 with stride_x less equal than 3. In these cases, run an optimized OpenCL kernel where
+ // each thread computes 4 output elements
+ const bool is_pool3x3_stride_le3 = (pool_size == 3) && (pool_stride_x <= 3) && !is_data_type_fixed_point(input->info()->data_type());
+
+ int num_elements_read_per_iteration = (pool_size == 7) ? 8 : pool_size;
+ if(is_pool3x3_stride_le3)
+ {
+ // Change the number of elements processed and number of elements read per iteration for pooling 3x3 with stride less equal than 3
+ _num_elems_processed_per_iteration = 4;
+ num_elements_read_per_iteration = pool_size * (pool_stride_x + 1);
+ }
+
+ const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + num_elements_read_per_iteration) - input_width;
+ const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+ _border_size.right = std::max(upper_bound_w, pool_pad_x);
+ _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+ std::string kernel_name = "pooling_layer_" + support::cpp11::to_string(pool_size);
+ if(is_pool3x3_stride_le3)
+ {
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name + "_optimized", build_opts));
+ }
+ else
+ {
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
+ }
}
- else
+ else // Run general case
{
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
+ _num_elems_processed_per_iteration = 1;
+
+ const int upper_bound_w = ((pooled_w - 1) * pool_stride_x - pool_pad_x + pool_size) - input_width;
+ const int upper_bound_h = ((pooled_h - 1) * pool_stride_y - pool_pad_y + pool_size) - input_height;
+
+ _border_size.right = std::max(upper_bound_w, pool_pad_x);
+ _border_size.bottom = std::max(upper_bound_h, pool_pad_y);
+
+ build_opts.emplace(("-DPOOL_SIZE=" + support::cpp11::to_string(pool_size)));
+ if(input->info()->data_type() == DataType::F16)
+ {
+ build_opts.emplace("-DFP16");
+ }
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel("pooling_layer_N", build_opts));
}
// Configure kernel window