COMPMID-1676: Change CLROIAlign interface to accept ROIs as tensors
Change-Id: I69e995973597ba3927d29e4f6ed5438560e53d77
diff --git a/src/core/CL/cl_kernels/roi_align_layer.cl b/src/core/CL/cl_kernels/roi_align_layer.cl
index 4625e53..f52eb18 100644
--- a/src/core/CL/cl_kernels/roi_align_layer.cl
+++ b/src/core/CL/cl_kernels/roi_align_layer.cl
@@ -97,38 +97,40 @@
* @note Sampling ratio (i.e., the number of samples in each bin) may be passed using -DSAMPLING_RATIO. If not defined each roi
* will have a default sampling ratio of roi_dims/pooling_dims
*
- * @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_ptr Pointer to the source tensor. Supported data types: F16, F32
+ * @param[in] input_stride_x Stride of the source tensor 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_stride_y Stride of the source tensor 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 pooled region of the source image as specifed by ROI
- * @param[in] rois_ptr Pointer to the rois array. Layout: {x, y, width, height, batch_indx}
- * @param[in] rois_stride_x Stride of the rois array in X dimension (in bytes)
- * @param[in] rois_step_x rois_stride_x * number of elements along X processed per workitem(in bytes)
- * @param[in] rois_offset_first_element_in_bytes The offset of the first element in the rois array
- * @param[out] output_ptr Pointer to the destination image. Supported data types: F16, F32
- * @param[in] output_stride_x Stride of the destination image in X dimension (in bytes)
+ * @param[in] input_offset_first_element_in_bytes The offset of the first element in the pooled region of the source tensor as specifed by ROI
+ * @param[in] rois_ptr Pointer to the ROIs tensor. Layout: { batch_index, x1, y1, x2, y2 }. Supported data types: same as @p input_ptr
+ * @param[in] rois_stride_x Stride of the ROIs tensor in X dimension (in bytes)
+ * @param[in] rois_step_x Step of the ROIs tensor in X dimension (in bytes)
+ * @param[in] rois_stride_y Stride of the ROIs tensor in Y dimension (in bytes)
+ * @param[in] rois_step_y Step of the ROIs tensor in Y dimension (in bytes)
+ * @param[in] rois_offset_first_element_in_bytes The offset of the first element in the ROIs tensor
+ * @param[out] output_ptr Pointer to the destination tensor. Supported data types: Supported data types: same as @p input_ptr
+ * @param[in] output_stride_x Stride of the destination tensor 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_stride_y Stride of the destination tensor 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 destination 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
- * @param[in] input_stride_w Stride of the source image in W dimension (in bytes)
- * @param[in] output_stride_w Stride of the destination image in W dimension (in bytes)
+ * @param[in] output_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ * @param[in] input_stride_w Stride of the source tensor in W dimension (in bytes)
+ * @param[in] output_stride_w Stride of the destination tensor in W dimension (in bytes)
*/
__kernel void roi_align_layer(
TENSOR3D_DECLARATION(input),
- VECTOR_DECLARATION(rois),
+ IMAGE_DECLARATION(rois),
TENSOR3D_DECLARATION(output),
unsigned int input_stride_w, unsigned int output_stride_w)
{
// Get pixels pointer
Tensor3D input = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(input);
- Vector rois = CONVERT_TO_VECTOR_STRUCT_NO_STEP(rois);
+ Image rois = CONVERT_TO_IMAGE_STRUCT_NO_STEP(rois);
Tensor3D output = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(output);
const int px = get_global_id(0);
@@ -136,19 +138,19 @@
const int pw = get_global_id(2);
// Load roi parameters
- // roi is laid out as follows:
- // { x, y, width, height, batch_index }
- const ushort4 roi = vload4(0, (__global ushort *)vector_offset(&rois, pw));
- const ushort roi_batch = *((__global ushort *)vector_offset(&rois, pw) + 4);
+ // roi is laid out as follows { batch_index, x1, y1, x2, y2 }
+ const ushort roi_batch = (ushort) * ((__global DATA_TYPE *)offset(&rois, 0, pw));
+ const VEC_DATA_TYPE(DATA_TYPE, 4)
+ roi = vload4(0, (__global DATA_TYPE *)offset(&rois, 1, pw));
const float2 roi_anchor = convert_float2(roi.s01) * convert_float(SPATIAL_SCALE);
- const float2 roi_dims = fmax(convert_float2(roi.s23) * convert_float(SPATIAL_SCALE), 1.f);
+ const float2 roi_dims = fmax(convert_float2(roi.s23 - roi.s01) * convert_float(SPATIAL_SCALE), 1.f);
// Calculate pooled region start and end
const float2 spatial_indx = (float2)(px, py);
const float2 pooled_dims = (float2)(POOLED_DIM_X, POOLED_DIM_Y);
const float2 max_spatial_dims = (float2)(MAX_DIM_X, MAX_DIM_Y);
- const float2 bin_size = roi_dims / pooled_dims;
+ const float2 bin_size = (float2)((roi_dims.s0 / (float)POOLED_DIM_X), (roi_dims.s1 / (float)POOLED_DIM_Y));
float2 region_start = spatial_indx * bin_size + roi_anchor;
float2 region_end = (spatial_indx + 1) * bin_size + roi_anchor;
@@ -159,7 +161,7 @@
const float2 roi_bin_grid = SAMPLING_RATIO;
#else // !defined(SAMPLING_RATIO)
// Note that we subtract EPS_GRID before ceiling. This is to avoid situations where 1.000001 gets ceiled to 2.
- const float2 roi_bin_grid = ceil(roi_dims / pooled_dims - EPS_GRID);
+ const float2 roi_bin_grid = ceil(bin_size - EPS_GRID);
#endif // defined(SAMPLING_RATIO)
// Move input and output pointer across the fourth dimension
diff --git a/src/core/CL/kernels/CLROIAlignLayerKernel.cpp b/src/core/CL/kernels/CLROIAlignLayerKernel.cpp
index 2e1e854..2d2ac07 100644
--- a/src/core/CL/kernels/CLROIAlignLayerKernel.cpp
+++ b/src/core/CL/kernels/CLROIAlignLayerKernel.cpp
@@ -39,24 +39,47 @@
{
namespace
{
-Status validate_arguments(const ITensorInfo *input, size_t num_rois, const ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
{
- ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, rois, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, rois);
+ ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(0) != 5);
+ ARM_COMPUTE_RETURN_ERROR_ON(rois->num_dimensions() > 2);
ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
ARM_COMPUTE_RETURN_ERROR_ON((pool_info.pooled_width() == 0) || (pool_info.pooled_height() == 0));
- ARM_COMPUTE_RETURN_ERROR_ON(num_rois == 0);
if(output->total_size() != 0)
{
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON((output->dimension(0) != pool_info.pooled_width()) || (output->dimension(1) != pool_info.pooled_height()));
ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(2) != output->dimension(2));
- ARM_COMPUTE_RETURN_ERROR_ON(num_rois != output->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(rois->dimension(1) != output->dimension(3));
}
return Status{};
}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+{
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
+
+ // Output auto inizialitation if not yet initialized
+ TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->dimension(2), rois->dimension(1));
+ auto_init_if_empty((*output), output_shape, 1, input->data_type());
+
+ // Configure kernel window
+ const unsigned int num_elems_processed_per_iteration = 1;
+ Window win = calculate_max_window(*output, Steps(num_elems_processed_per_iteration));
+
+ AccessWindowHorizontal output_access(output, 0, num_elems_processed_per_iteration);
+ AccessWindowHorizontal input_access(input, input->valid_region().start(0), num_elems_processed_per_iteration);
+
+ bool window_changed = update_window_and_padding(win, input_access, output_access);
+ output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
} // namespace
CLROIAlignLayerKernel::CLROIAlignLayerKernel()
@@ -64,13 +87,14 @@
{
}
-void CLROIAlignLayerKernel::configure(const ICLTensor *input, const ICLROIArray *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
+void CLROIAlignLayerKernel::configure(const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output, rois);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), rois->num_values(), output->info(), pool_info));
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), rois->info(), output->info(), pool_info));
- TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), input->info()->dimension(2), rois->num_values());
- auto_init_if_empty(*output->info(), output_shape, 1, input->info()->data_type());
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), rois->info(), output->info(), pool_info);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
_input = input;
_output = output;
@@ -78,46 +102,27 @@
_pool_info = pool_info;
// Set build options
- std::set<std::string> build_opts;
- build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type())));
- build_opts.emplace(("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type())));
- build_opts.emplace(("-DMAX_DIM_X=" + support::cpp11::to_string(_input->info()->dimension(Window::DimX))));
- build_opts.emplace(("-DMAX_DIM_Y=" + support::cpp11::to_string(_input->info()->dimension(Window::DimY))));
- build_opts.emplace(("-DMAX_DIM_Z=" + support::cpp11::to_string(_input->info()->dimension(Window::DimZ))));
- build_opts.emplace(("-DPOOLED_DIM_X=" + support::cpp11::to_string(pool_info.pooled_width())));
- build_opts.emplace(("-DPOOLED_DIM_Y=" + support::cpp11::to_string(pool_info.pooled_height())));
- build_opts.emplace(("-DSPATIAL_SCALE=" + float_to_string_with_full_precision(pool_info.spatial_scale())));
- if(pool_info.sampling_ratio() > 0)
- {
- build_opts.emplace(("-DSAMPLING_RATIO=" + support::cpp11::to_string(pool_info.sampling_ratio())));
- }
+ CLBuildOptions build_opts;
+ build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(input->info()->data_type()));
+ build_opts.add_option("-DDATA_SIZE=" + get_data_size_from_data_type(input->info()->data_type()));
+ build_opts.add_option("-DMAX_DIM_X=" + support::cpp11::to_string(_input->info()->dimension(Window::DimX)));
+ build_opts.add_option("-DMAX_DIM_Y=" + support::cpp11::to_string(_input->info()->dimension(Window::DimY)));
+ build_opts.add_option("-DMAX_DIM_Z=" + support::cpp11::to_string(_input->info()->dimension(Window::DimZ)));
+ build_opts.add_option("-DPOOLED_DIM_X=" + support::cpp11::to_string(pool_info.pooled_width()));
+ build_opts.add_option("-DPOOLED_DIM_Y=" + support::cpp11::to_string(pool_info.pooled_height()));
+ build_opts.add_option("-DSPATIAL_SCALE=" + float_to_string_with_full_precision(pool_info.spatial_scale()));
+ build_opts.add_option_if(pool_info.sampling_ratio() > 0, "-DSAMPLING_RATIO=" + support::cpp11::to_string(pool_info.sampling_ratio()));
// Create kernel
std::string kernel_name = "roi_align_layer";
- _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts));
+ _kernel = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
- // Set static kernel arguments
- unsigned int idx = 2 * num_arguments_per_3D_tensor() + num_arguments_per_1D_array();
- add_argument<cl_uint>(idx, _input->info()->strides_in_bytes()[3]);
- add_argument<cl_uint>(idx, _output->info()->strides_in_bytes()[3]);
-
- // Configure kernel window
- const unsigned int num_elems_processed_per_iteration = 1;
- Window window = calculate_max_window(*output->info(), Steps(num_elems_processed_per_iteration));
- AccessWindowStatic input_access(input->info(),
- input->info()->valid_region().start(0),
- input->info()->valid_region().start(1),
- input->info()->valid_region().end(0),
- input->info()->valid_region().end(1));
- AccessWindowStatic output_access(output->info(), 0, 0, pool_info.pooled_width(), pool_info.pooled_height());
-
- output_access.set_valid_region(window, ValidRegion(Coordinates(), output->info()->tensor_shape()));
- ICLKernel::configure_internal(window);
+ ICLKernel::configure_internal(win_config.second);
}
-Status CLROIAlignLayerKernel::validate(const ITensorInfo *input, size_t num_rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+Status CLROIAlignLayerKernel::validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, num_rois, output, pool_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, rois, output, pool_info));
return Status{};
}
@@ -126,16 +131,20 @@
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
- Window slice = window.first_slice_window_3D();
- // Parallelize spatially and across the fourth dimension of the output tensor (also across ROIArray)
+ Window slice = window.first_slice_window_3D();
+ Window slice_rois = slice;
+ // Parallelize spatially and across the fourth dimension of the output tensor (also across ROITensor)
+ slice_rois.set_dimension_step(Window::DimX, _rois->info()->dimension(0));
slice.set(Window::DimZ, window[3]);
// Set arguments
unsigned int idx = 0;
add_3D_tensor_argument(idx, _input, slice);
- add_1D_array_argument<ROI>(idx, _rois, Strides(sizeof(ROI)), 1U, slice);
+ add_2D_tensor_argument(idx, _rois, slice_rois);
add_3D_tensor_argument(idx, _output, slice);
+ add_argument<cl_uint>(idx, _input->info()->strides_in_bytes()[3]);
+ add_argument<cl_uint>(idx, _output->info()->strides_in_bytes()[3]);
+
enqueue(queue, *this, slice);
}
-
} // namespace arm_compute
diff --git a/src/runtime/CL/functions/CLROIAlignLayer.cpp b/src/runtime/CL/functions/CLROIAlignLayer.cpp
index 1528759..5bfd594 100644
--- a/src/runtime/CL/functions/CLROIAlignLayer.cpp
+++ b/src/runtime/CL/functions/CLROIAlignLayer.cpp
@@ -29,14 +29,14 @@
namespace arm_compute
{
-Status CLROIAlignLayer::validate(const ITensorInfo *input, size_t num_rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
+Status CLROIAlignLayer::validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info)
{
- ARM_COMPUTE_RETURN_ON_ERROR(CLROIAlignLayerKernel::validate(input, num_rois, output, pool_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(CLROIAlignLayerKernel::validate(input, rois, output, pool_info));
return Status{};
}
-void CLROIAlignLayer::configure(const ICLTensor *input, const ICLROIArray *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
+void CLROIAlignLayer::configure(const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info)
{
// Configure ROI pooling kernel
auto k = arm_compute::support::cpp14::make_unique<CLROIAlignLayerKernel>();