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