COMPMID-1676: Change CLROIAlign interface to accept ROIs as tensors
Change-Id: I69e995973597ba3927d29e4f6ed5438560e53d77
diff --git a/arm_compute/core/CL/kernels/CLROIAlignLayerKernel.h b/arm_compute/core/CL/kernels/CLROIAlignLayerKernel.h
index 6908675..b5e0232 100644
--- a/arm_compute/core/CL/kernels/CLROIAlignLayerKernel.h
+++ b/arm_compute/core/CL/kernels/CLROIAlignLayerKernel.h
@@ -52,7 +52,8 @@
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data types supported: F16/F32.
- * @param[in] rois Array containing @ref ROI.
+ * @param[in] rois ROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner
+ * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: same as @p input
* @param[out] output Destination tensor. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -61,11 +62,11 @@
* @note The z dimensions of @p output tensor and @p input tensor must be the same.
* @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
*/
- void configure(const ICLTensor *input, const ICLROIArray *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
+ void configure(const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLROIAlignLayerKernel
*
* @param[in] input Source tensor info. Data types supported: F16/F32.
- * @param[in] num_rois Length of the array containing @ref ROI.
+ * @param[in] rois ROIs tensor info. Data types supported: same as @p input
* @param[out] output Destination tensor info. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -76,7 +77,7 @@
*
* @return a Status
*/
- static Status validate(const ITensorInfo *input, size_t num_rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info);
// Inherited methods overridden:
void run(const Window &window, cl::CommandQueue &queue);
@@ -84,7 +85,7 @@
private:
const ICLTensor *_input;
ICLTensor *_output;
- const ICLROIArray *_rois;
+ const ICLTensor *_rois;
ROIPoolingLayerInfo _pool_info;
};
} // namespace arm_compute
diff --git a/arm_compute/runtime/CL/functions/CLROIAlignLayer.h b/arm_compute/runtime/CL/functions/CLROIAlignLayer.h
index 6cf9bd2..fec0dac 100644
--- a/arm_compute/runtime/CL/functions/CLROIAlignLayer.h
+++ b/arm_compute/runtime/CL/functions/CLROIAlignLayer.h
@@ -44,7 +44,8 @@
/** Set the input and output tensors.
*
* @param[in] input Source tensor. Data types supported: F16/F32.
- * @param[in] rois Array containing @ref ROI.
+ * @param[in] rois ROIs tensor, it is a 2D tensor of size [5, N] (where N is the number of ROIs) containing top left and bottom right corner
+ * as coordinate of an image and batch_id of ROI [ batch_id, x1, y1, x2, y2 ]. Data types supported: same as @p input
* @param[out] output Destination tensor. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -53,11 +54,11 @@
* @note The z dimensions of @p output tensor and @p input tensor must be the same.
* @note The fourth dimension of @p output tensor must be the same as the number of elements in @p rois array.
*/
- void configure(const ICLTensor *input, const ICLROIArray *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
+ void configure(const ICLTensor *input, const ICLTensor *rois, ICLTensor *output, const ROIPoolingLayerInfo &pool_info);
/** Static function to check if given info will lead to a valid configuration of @ref CLROIAlignLayer
*
* @param[in] input Source tensor info. Data types supported: F16/F32.
- * @param[in] num_rois Length of the array containing @ref ROI.
+ * @param[in] rois ROIs tensor info. Data types supported: same as @p input
* @param[out] output Destination tensor info. Data types supported: Same as @p input.
* @param[in] pool_info Contains pooling operation information described in @ref ROIPoolingLayerInfo.
*
@@ -68,7 +69,7 @@
*
* @return a Status
*/
- static Status validate(const ITensorInfo *input, size_t num_rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info);
+ static Status validate(const ITensorInfo *input, const ITensorInfo *rois, ITensorInfo *output, const ROIPoolingLayerInfo &pool_info);
};
} // namespace arm_compute
#endif /* __ARM_COMPUTE_CLROIALIGNLAYER_H__ */
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>();
diff --git a/tests/datasets/ROIAlignLayerDataset.h b/tests/datasets/ROIAlignLayerDataset.h
new file mode 100644
index 0000000..27c6ee4
--- /dev/null
+++ b/tests/datasets/ROIAlignLayerDataset.h
@@ -0,0 +1,143 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_TEST_ROI_ALIGN_LAYER_DATASET
+#define ARM_COMPUTE_TEST_ROI_ALIGN_LAYER_DATASET
+
+#include "utils/TypePrinter.h"
+
+#include "arm_compute/core/TensorShape.h"
+#include "arm_compute/core/Types.h"
+
+namespace arm_compute
+{
+namespace test
+{
+namespace datasets
+{
+class ROIAlignLayerDataset
+{
+public:
+ using type = std::tuple<TensorShape, ROIPoolingLayerInfo, TensorShape>;
+
+ struct iterator
+ {
+ iterator(std::vector<TensorShape>::const_iterator tensor_shape_it,
+ std::vector<ROIPoolingLayerInfo>::const_iterator infos_it,
+ std::vector<TensorShape>::const_iterator rois_shape_it)
+ : _tensor_shape_it{ std::move(tensor_shape_it) },
+ _infos_it{ std::move(infos_it) },
+ _rois_shape_it{ std::move(rois_shape_it) }
+ {
+ }
+
+ std::string description() const
+ {
+ std::stringstream description;
+ description << "In=" << *_tensor_shape_it << ":";
+ description << "Info=" << *_infos_it << ":";
+ description << "ROIS=" << *_rois_shape_it;
+ return description.str();
+ }
+
+ ROIAlignLayerDataset::type operator*() const
+ {
+ return std::make_tuple(*_tensor_shape_it, *_infos_it, *_rois_shape_it);
+ }
+
+ iterator &operator++()
+ {
+ ++_tensor_shape_it;
+ ++_infos_it;
+ ++_rois_shape_it;
+
+ return *this;
+ }
+
+ private:
+ std::vector<TensorShape>::const_iterator _tensor_shape_it;
+ std::vector<ROIPoolingLayerInfo>::const_iterator _infos_it;
+ std::vector<TensorShape>::const_iterator _rois_shape_it;
+ };
+
+ iterator begin() const
+ {
+ return iterator(_tensor_shapes.begin(), _infos.begin(), _rois_shape.begin());
+ }
+
+ int size() const
+ {
+ return std::min(std::min(_tensor_shapes.size(), _infos.size()), _rois_shape.size());
+ }
+
+ void add_config(TensorShape tensor_shape, ROIPoolingLayerInfo info, TensorShape rois_shape)
+ {
+ _tensor_shapes.emplace_back(std::move(tensor_shape));
+ _infos.emplace_back(std::move(info));
+ _rois_shape.emplace_back(std::move(rois_shape));
+ }
+
+protected:
+ ROIAlignLayerDataset() = default;
+ ROIAlignLayerDataset(ROIAlignLayerDataset &&) = default;
+
+private:
+ std::vector<TensorShape> _tensor_shapes{};
+ std::vector<ROIPoolingLayerInfo> _infos{};
+ std::vector<TensorShape> _rois_shape{};
+};
+
+class SmallROIAlignLayerDataset final : public ROIAlignLayerDataset
+{
+public:
+ SmallROIAlignLayerDataset()
+ {
+ add_config(TensorShape(50U, 47U, 1U, 1U), ROIPoolingLayerInfo(7U, 7U, 1.f / 4.f), TensorShape(5U, 1U));
+ add_config(TensorShape(50U, 47U, 3U, 4U), ROIPoolingLayerInfo(7U, 7U, 1.f / 4.f), TensorShape(5U, 1U));
+ add_config(TensorShape(50U, 47U, 3U, 1U), ROIPoolingLayerInfo(7U, 7U, 1.f / 4.f), TensorShape(5U, 10U));
+ add_config(TensorShape(50U, 47U, 10U, 1U), ROIPoolingLayerInfo(7U, 7U, 1.f / 4.f), TensorShape(5U, 80U));
+
+ //Spatial Scale 1/4
+ add_config(TensorShape(50U, 47U, 80U, 4U), ROIPoolingLayerInfo(7U, 7U, 1.f / 4.f), TensorShape(5U, 80U));
+ add_config(TensorShape(50U, 47U, 3U, 1U), ROIPoolingLayerInfo(9U, 9U, 1.f / 4.f), TensorShape(5U, 40U));
+ add_config(TensorShape(50U, 47U, 10U, 1U), ROIPoolingLayerInfo(9U, 9U, 1.f / 4.f), TensorShape(5U, 80U));
+ add_config(TensorShape(50U, 47U, 80U, 8U), ROIPoolingLayerInfo(9U, 9U, 1.f / 4.f), TensorShape(5U, 80U));
+
+ //Spatial Scale 1/8
+ add_config(TensorShape(50U, 47U, 80U, 4U), ROIPoolingLayerInfo(7U, 7U, 1.f / 8.f), TensorShape(5U, 80U));
+ add_config(TensorShape(50U, 47U, 3U, 1U), ROIPoolingLayerInfo(9U, 9U, 1.f / 8.f), TensorShape(5U, 40U));
+ add_config(TensorShape(50U, 47U, 10U, 1U), ROIPoolingLayerInfo(9U, 9U, 1.f / 8.f), TensorShape(5U, 80U));
+ add_config(TensorShape(50U, 47U, 80U, 8U), ROIPoolingLayerInfo(9U, 9U, 1.f / 8.f), TensorShape(5U, 80U));
+
+ //Spatial Scale 1/16
+ add_config(TensorShape(50U, 47U, 80U, 4U), ROIPoolingLayerInfo(7U, 7U, 1.f / 16.f), TensorShape(5U, 80U));
+ add_config(TensorShape(50U, 47U, 3U, 1U), ROIPoolingLayerInfo(9U, 9U, 1.f / 16.f), TensorShape(5U, 40U));
+ add_config(TensorShape(50U, 47U, 10U, 1U), ROIPoolingLayerInfo(9U, 9U, 1.f / 16.f), TensorShape(5U, 80U));
+ add_config(TensorShape(50U, 47U, 80U, 8U), ROIPoolingLayerInfo(9U, 9U, 1.f / 16.f), TensorShape(5U, 80U));
+ }
+};
+
+} // namespace datasets
+} // namespace test
+} // namespace arm_compute
+#endif /* ARM_COMPUTE_TEST_ROI_ALIGN_LAYER_DATASET */
diff --git a/tests/validation/CL/ROIAlignLayer.cpp b/tests/validation/CL/ROIAlignLayer.cpp
index acea6d4..f3fc381 100644
--- a/tests/validation/CL/ROIAlignLayer.cpp
+++ b/tests/validation/CL/ROIAlignLayer.cpp
@@ -24,9 +24,8 @@
#include "arm_compute/runtime/CL/CLScheduler.h"
#include "arm_compute/runtime/CL/functions/CLROIAlignLayer.h"
#include "tests/CL/CLAccessor.h"
-#include "tests/CL/CLArrayAccessor.h"
#include "tests/Globals.h"
-#include "tests/datasets/ROIPoolingLayerDataset.h"
+#include "tests/datasets/ROIAlignLayerDataset.h"
#include "tests/datasets/ShapeDatasets.h"
#include "tests/framework/Macros.h"
#include "tests/framework/datasets/Datasets.h"
@@ -43,7 +42,10 @@
namespace
{
RelativeTolerance<float> relative_tolerance_f32(0.01f);
-RelativeTolerance<float> absolute_tolerance_f32(0.001f);
+AbsoluteTolerance<float> absolute_tolerance_f32(0.001f);
+
+RelativeTolerance<float> relative_tolerance_f16(0.01f);
+AbsoluteTolerance<float> absolute_tolerance_f16(0.001f);
} // namespace
TEST_SUITE(CL)
@@ -53,17 +55,28 @@
// clang-format off
DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
framework::dataset::make("InputInfo", { TensorInfo(TensorShape(250U, 128U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(250U, 128U, 3U), 1, DataType::F32), // Mismatching data type input/rois
TensorInfo(TensorShape(250U, 128U, 3U), 1, DataType::F32), // Mismatching data type input/output
TensorInfo(TensorShape(250U, 128U, 2U), 1, DataType::F32), // Mismatching depth size input/output
TensorInfo(TensorShape(250U, 128U, 2U), 1, DataType::F32), // Mismatching number of rois and output batch size
+ TensorInfo(TensorShape(250U, 128U, 3U), 1, DataType::F32), // Invalid number of values per ROIS
TensorInfo(TensorShape(250U, 128U, 2U), 1, DataType::F32), // Mismatching height and width input/output
}),
- framework::dataset::make("NumRois", { 3U, 3U, 4U, 10U, 4U})),
+ framework::dataset::make("RoisInfo", { TensorInfo(TensorShape(5, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(5, 3U), 1, DataType::F16),
+ TensorInfo(TensorShape(5, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(5, 4U), 1, DataType::F32),
+ TensorInfo(TensorShape(5, 10U), 1, DataType::F32),
+ TensorInfo(TensorShape(4, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(5, 4U), 1, DataType::F32),
+ })),
framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(7U, 7U, 3U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(7U, 7U, 3U, 3U), 1, DataType::F32),
TensorInfo(TensorShape(7U, 7U, 3U, 3U), 1, DataType::F16),
TensorInfo(TensorShape(7U, 7U, 4U, 3U), 1, DataType::F32),
TensorInfo(TensorShape(7U, 7U, 2U, 3U), 1, DataType::F32),
+ TensorInfo(TensorShape(7U, 7U, 3U, 3U), 1, DataType::F32),
TensorInfo(TensorShape(5U, 5U, 2U, 4U), 1, DataType::F32),
})),
framework::dataset::make("PoolInfo", { ROIPoolingLayerInfo(7U, 7U, 1./8),
@@ -71,30 +84,35 @@
ROIPoolingLayerInfo(7U, 7U, 1./8),
ROIPoolingLayerInfo(7U, 7U, 1./8),
ROIPoolingLayerInfo(7U, 7U, 1./8),
+ ROIPoolingLayerInfo(7U, 7U, 1./8),
+ ROIPoolingLayerInfo(7U, 7U, 1./8),
})),
- framework::dataset::make("Expected", { true, false, false, false, false })),
- input_info, num_rois, output_info, pool_info, expected)
+ framework::dataset::make("Expected", { true, false, false, false, false, false, false })),
+ input_info, rois_info, output_info, pool_info, expected)
{
- ARM_COMPUTE_EXPECT(bool(CLROIAlignLayer::validate(&input_info.clone()->set_is_resizable(true), num_rois, &output_info.clone()->set_is_resizable(true), pool_info)) == expected, framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(bool(CLROIAlignLayer::validate(&input_info.clone()->set_is_resizable(true), &rois_info.clone()->set_is_resizable(true), &output_info.clone()->set_is_resizable(true), pool_info)) == expected, framework::LogLevel::ERRORS);
}
// clang-format on
// *INDENT-ON*
template <typename T>
-using CLROIAlignLayerFixture = ROIAlignLayerFixture<CLTensor, CLAccessor, CLROIAlignLayer, CLArray<ROI>, CLArrayAccessor<ROI>, T>;
+using CLROIAlignLayerFixture = ROIAlignLayerFixture<CLTensor, CLAccessor, CLROIAlignLayer, T>;
TEST_SUITE(Float)
-TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(SmallROIAlignLayer, CLROIAlignLayerFixture<float>, framework::DatasetMode::ALL,
- framework::dataset::combine(framework::dataset::combine(datasets::SmallROIPoolingLayerDataset(),
- framework::dataset::make("DataType", { DataType::F32 })),
- framework::dataset::make("Batches", { 1, 4, 8 })))
+FIXTURE_DATA_TEST_CASE(SmallROIAlignLayerFloat, CLROIAlignLayerFixture<float>, framework::DatasetMode::ALL,
+ framework::dataset::combine(datasets::SmallROIAlignLayerDataset(),
+ framework::dataset::make("DataType", { DataType::F32 })))
{
// Validate output
validate(CLAccessor(_target), _reference, relative_tolerance_f32, .02f, absolute_tolerance_f32);
}
-TEST_SUITE_END() // FP32
-
+FIXTURE_DATA_TEST_CASE(SmallROIAlignLayerHalf, CLROIAlignLayerFixture<half>, framework::DatasetMode::ALL,
+ framework::dataset::combine(datasets::SmallROIAlignLayerDataset(),
+ framework::dataset::make("DataType", { DataType::F16 })))
+{
+ // Validate output
+ validate(CLAccessor(_target), _reference, relative_tolerance_f16, .02f, absolute_tolerance_f16);
+}
TEST_SUITE_END() // Float
TEST_SUITE_END() // RoiAlign
diff --git a/tests/validation/fixtures/ROIAlignLayerFixture.h b/tests/validation/fixtures/ROIAlignLayerFixture.h
index d327b09..c029fba 100644
--- a/tests/validation/fixtures/ROIAlignLayerFixture.h
+++ b/tests/validation/fixtures/ROIAlignLayerFixture.h
@@ -41,18 +41,15 @@
{
namespace validation
{
-template <typename TensorType, typename AccessorType, typename FunctionType, typename Array_T, typename ArrayAccessor, typename T>
+template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
class ROIAlignLayerFixture : public framework::Fixture
{
public:
template <typename...>
- void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, unsigned int num_rois, DataType data_type, int batches)
+ void setup(TensorShape input_shape, const ROIPoolingLayerInfo pool_info, TensorShape rois_shape, DataType data_type)
{
- input_shape.set(2, batches);
- std::vector<ROI> rois = generate_random_rois(input_shape, pool_info, num_rois, 0U);
-
- _target = compute_target(input_shape, data_type, rois, pool_info);
- _reference = compute_reference(input_shape, data_type, rois, pool_info);
+ _target = compute_target(input_shape, data_type, pool_info, rois_shape);
+ _reference = compute_reference(input_shape, data_type, pool_info, rois_shape);
}
protected:
@@ -62,37 +59,78 @@
library->fill_tensor_uniform(tensor, 0);
}
+ template <typename U>
+ void generate_rois(U &&rois, const TensorShape &shape, const ROIPoolingLayerInfo &pool_info, TensorShape rois_shape)
+ {
+ const size_t values_per_roi = rois_shape.x();
+ const size_t num_rois = rois_shape.y();
+
+ std::mt19937 gen(library->seed());
+ T *rois_ptr = static_cast<T *>(rois.data());
+
+ const float pool_width = pool_info.pooled_width();
+ const float pool_height = pool_info.pooled_height();
+ const float roi_scale = pool_info.spatial_scale();
+
+ // Calculate distribution bounds
+ const auto scaled_width = static_cast<T>((shape.x() / roi_scale) / pool_width);
+ const auto scaled_height = static_cast<T>((shape.y() / roi_scale) / pool_height);
+ const auto min_width = static_cast<T>(pool_width / roi_scale);
+ const auto min_height = static_cast<T>(pool_height / roi_scale);
+
+ // Create distributions
+ std::uniform_int_distribution<int> dist_batch(0, shape[3] - 1);
+ std::uniform_int_distribution<> dist_x1(0, scaled_width);
+ std::uniform_int_distribution<> dist_y1(0, scaled_height);
+ std::uniform_int_distribution<> dist_w(min_width, std::max(float(min_width), (pool_width - 2) * scaled_width));
+ std::uniform_int_distribution<> dist_h(min_height, std::max(float(min_height), (pool_height - 2) * scaled_height));
+
+ for(unsigned int pw = 0; pw < num_rois; ++pw)
+ {
+ const auto batch_idx = dist_batch(gen);
+ const auto x1 = dist_x1(gen);
+ const auto y1 = dist_y1(gen);
+ const auto x2 = x1 + dist_w(gen);
+ const auto y2 = y1 + dist_h(gen);
+
+ rois_ptr[values_per_roi * pw] = batch_idx;
+ rois_ptr[values_per_roi * pw + 1] = x1;
+ rois_ptr[values_per_roi * pw + 2] = y1;
+ rois_ptr[values_per_roi * pw + 3] = x2;
+ rois_ptr[values_per_roi * pw + 4] = y2;
+ }
+ }
+
TensorType compute_target(const TensorShape &input_shape,
DataType data_type,
- std::vector<ROI> const &rois,
- const ROIPoolingLayerInfo &pool_info)
+ const ROIPoolingLayerInfo &pool_info,
+ const TensorShape rois_shape)
{
// Create tensors
- TensorType src = create_tensor<TensorType>(input_shape, data_type);
+ TensorType src = create_tensor<TensorType>(input_shape, data_type);
+ TensorType rois_tensor = create_tensor<TensorType>(rois_shape, data_type);
TensorType dst;
- size_t num_rois = rois.size();
-
- // Create roi arrays
- std::unique_ptr<Array_T> rois_array = arm_compute::support::cpp14::make_unique<Array_T>(num_rois);
- fill_array(ArrayAccessor(*rois_array), rois);
-
// Create and configure function
FunctionType roi_align_layer;
- roi_align_layer.configure(&src, rois_array.get(), &dst, pool_info);
+ roi_align_layer.configure(&src, &rois_tensor, &dst, pool_info);
ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Allocate tensors
src.allocator()->allocate();
+ rois_tensor.allocator()->allocate();
dst.allocator()->allocate();
ARM_COMPUTE_EXPECT(!src.info()->is_resizable(), framework::LogLevel::ERRORS);
+ ARM_COMPUTE_EXPECT(!rois_tensor.info()->is_resizable(), framework::LogLevel::ERRORS);
ARM_COMPUTE_EXPECT(!dst.info()->is_resizable(), framework::LogLevel::ERRORS);
// Fill tensors
fill(AccessorType(src));
+ generate_rois(AccessorType(rois_tensor), input_shape, pool_info, rois_shape);
// Compute function
roi_align_layer.run();
@@ -102,16 +140,18 @@
SimpleTensor<T> compute_reference(const TensorShape &input_shape,
DataType data_type,
- std::vector<ROI> const &rois,
- const ROIPoolingLayerInfo &pool_info)
+ const ROIPoolingLayerInfo &pool_info,
+ const TensorShape rois_shape)
{
// Create reference tensor
SimpleTensor<T> src{ input_shape, data_type };
+ SimpleTensor<T> rois_tensor{ rois_shape, data_type };
// Fill reference tensor
fill(src);
+ generate_rois(rois_tensor, input_shape, pool_info, rois_shape);
- return reference::roi_align_layer(src, rois, pool_info);
+ return reference::roi_align_layer(src, rois_tensor, pool_info);
}
TensorType _target{};
diff --git a/tests/validation/reference/ROIAlignLayer.cpp b/tests/validation/reference/ROIAlignLayer.cpp
index 68a465d..8a76983 100644
--- a/tests/validation/reference/ROIAlignLayer.cpp
+++ b/tests/validation/reference/ROIAlignLayer.cpp
@@ -114,30 +114,35 @@
}
} // namespace
template <typename T>
-SimpleTensor<T> roi_align_layer(const SimpleTensor<T> &src, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info)
+SimpleTensor<T> roi_align_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &rois, const ROIPoolingLayerInfo &pool_info)
{
- const size_t num_rois = rois.size();
- DataType dst_data_type = src.data_type();
+ const size_t values_per_roi = rois.shape()[0];
+ const size_t num_rois = rois.shape()[1];
+ DataType dst_data_type = src.data_type();
+
+ const auto *rois_ptr = static_cast<const T *>(rois.data());
TensorShape input_shape = src.shape();
TensorShape output_shape(pool_info.pooled_width(), pool_info.pooled_height(), src.shape()[2], num_rois);
SimpleTensor<T> dst(output_shape, dst_data_type);
// Iterate over every pixel of the input image
- for(size_t px = 0; px < pool_info.pooled_width(); px++)
+ for(size_t px = 0; px < pool_info.pooled_width(); ++px)
{
- for(size_t py = 0; py < pool_info.pooled_height(); py++)
+ for(size_t py = 0; py < pool_info.pooled_height(); ++py)
{
- for(size_t pw = 0; pw < num_rois; pw++)
+ for(size_t pw = 0; pw < num_rois; ++pw)
{
- ROI roi = rois[pw];
- const int roi_batch = roi.batch_idx;
+ const unsigned int roi_batch = rois_ptr[values_per_roi * pw];
+ const auto x1 = float(rois_ptr[values_per_roi * pw + 1]);
+ const auto y1 = float(rois_ptr[values_per_roi * pw + 2]);
+ const auto x2 = float(rois_ptr[values_per_roi * pw + 3]);
+ const auto y2 = float(rois_ptr[values_per_roi * pw + 4]);
- const float roi_anchor_x = roi.rect.x * pool_info.spatial_scale();
- const float roi_anchor_y = roi.rect.y * pool_info.spatial_scale();
- const float roi_dims_x = std::max(roi.rect.width * pool_info.spatial_scale(), 1.0f);
- const float roi_dims_y = std::max(roi.rect.height * pool_info.spatial_scale(), 1.0f);
- ;
+ const float roi_anchor_x = x1 * pool_info.spatial_scale();
+ const float roi_anchor_y = y1 * pool_info.spatial_scale();
+ const float roi_dims_x = std::max((x2 - x1) * pool_info.spatial_scale(), 1.0f);
+ const float roi_dims_y = std::max((y2 - y1) * pool_info.spatial_scale(), 1.0f);
float bin_size_x = roi_dims_x / pool_info.pooled_width();
float bin_size_y = roi_dims_y / pool_info.pooled_height();
@@ -178,8 +183,8 @@
}
return dst;
}
-template SimpleTensor<float> roi_align_layer(const SimpleTensor<float> &src, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info);
-template SimpleTensor<half> roi_align_layer(const SimpleTensor<half> &src, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info);
+template SimpleTensor<float> roi_align_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &rois, const ROIPoolingLayerInfo &pool_info);
+template SimpleTensor<half> roi_align_layer(const SimpleTensor<half> &src, const SimpleTensor<half> &rois, const ROIPoolingLayerInfo &pool_info);
} // namespace reference
} // namespace validation
} // namespace test
diff --git a/tests/validation/reference/ROIAlignLayer.h b/tests/validation/reference/ROIAlignLayer.h
index 818f9b1..b67ff42 100644
--- a/tests/validation/reference/ROIAlignLayer.h
+++ b/tests/validation/reference/ROIAlignLayer.h
@@ -37,7 +37,7 @@
namespace reference
{
template <typename T>
-SimpleTensor<T> roi_align_layer(const SimpleTensor<T> &src, const std::vector<ROI> &rois, const ROIPoolingLayerInfo &pool_info);
+SimpleTensor<T> roi_align_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &rois, const ROIPoolingLayerInfo &pool_info);
} // namespace reference
} // namespace validation
} // namespace test