1 files changed, 170 insertions, 0 deletions
diff --git a/libs/ARMComputeEx/src/core/CL/kernels/CLSquaredDifferenceKernel.cpp b/libs/ARMComputeEx/src/core/CL/kernels/CLSquaredDifferenceKernel.cpp
new file mode 100644
index 000000000..260bc39f1
--- /dev/null
+++ b/libs/ARMComputeEx/src/core/CL/kernels/CLSquaredDifferenceKernel.cpp
@@ -0,0 +1,170 @@
+/*
+ * Copyright (c) 2018 Samsung Electronics Co., Ltd. All Rights Reserved
+ * Copyright (c) 2016-2018 ARM Limited.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *      http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+#include "arm_compute/core/CL/kernels/CLSquaredDifferenceKernel.h"
+
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibraryEx.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+
+using namespace arm_compute;
+
+namespace
+{
+constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+Status validate(const ITensorInfo *input1, const ITensorInfo *input2, const ITensorInfo *output)
+{
+  const TensorShape &out_shape =
+      TensorShape::broadcast_shape(input1->tensor_shape(), input2->tensor_shape());
+
+  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input1, 1, DataType::F16, DataType::F32);
+  ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input2, 1, DataType::F16, DataType::F32);
+
+  ARM_COMPUTE_RETURN_ERROR_ON_MSG(out_shape.total_size() == 0,
+                                  "Inputs are not broadcast compatible");
+  // Validate in case of configured output
+  if (output->total_size() > 0)
+  {
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(output, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(
+        detail::have_different_dimensions(out_shape, output->tensor_shape(), 0),
+        "Wrong shape for output");
+  }
+  return Status{};
+}
+} // namespace
+
+CLSquaredDifferenceKernel::CLSquaredDifferenceKernel()
+    : _input1(nullptr), _input2(nullptr), _output(nullptr)
+{
+}
+
+void CLSquaredDifferenceKernel::configure(const ICLTensor *input1, const ICLTensor *input2,
+                                          ICLTensor *output)
+{
+  ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, input2);
+  ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input1, output);
+  ARM_COMPUTE_ERROR_THROW_ON(validate(input1->info(), input2->info(), output->info()));
+
+  _input1 = input1;
+  _input2 = input2;
+  _output = output;
+
+  // Create kernel
+  std::set<std::string> build_opts;
+  build_opts.emplace(("-DDATA_TYPE=" + get_cl_type_from_data_type(input1->info()->data_type())));
+  build_opts.emplace(
+      ("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_processed_per_iteration)));
+  _kernel = static_cast<cl::Kernel>(
+      CLKernelLibraryEx::get().create_kernel("squared_difference", build_opts));
+
+  const std::pair<TensorShape, ValidRegion> broadcast_pair =
+      ITensorInfo::broadcast_shape_and_valid_region(*input1->info(), *input2->info());
+
+  const TensorShape &out_shape = broadcast_pair.first;
+  const ValidRegion &valid_region = broadcast_pair.second;
+
+  // Auto initialize output if not initialized
+  {
+    set_shape_if_empty(*output->info(), out_shape);
+
+    if (input1->info()->data_type() == DataType::F16 &&
+        input2->info()->data_type() == DataType::F16)
+    {
+      set_format_if_unknown(*output->info(), Format::F16);
+    }
+    else if (input1->info()->data_type() == DataType::F32 ||
+             input2->info()->data_type() == DataType::F32)
+    {
+      set_format_if_unknown(*output->info(), Format::F32);
+    }
+  }
+
+  Window win = calculate_max_window(valid_region, Steps(num_elems_processed_per_iteration));
+  Window win_input1 = win.broadcast_if_dimension_le_one(*input1->info());
+  Window win_input2 = win.broadcast_if_dimension_le_one(*input2->info());
+
+  AccessWindowHorizontal input1_access(input1->info(), 0, num_elems_processed_per_iteration);
+  AccessWindowHorizontal input2_access(input2->info(), 0, num_elems_processed_per_iteration);
+  AccessWindowHorizontal output_access(output->info(), 0, num_elems_processed_per_iteration);
+
+  bool window_changed = update_window_and_padding(win_input1, input1_access) ||
+                        update_window_and_padding(win_input2, input2_access) ||
+                        update_window_and_padding(win, output_access);
+
+  output_access.set_valid_region(win, valid_region);
+
+  ICLKernel::configure_internal(win);
+}
+
+void CLSquaredDifferenceKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+  ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+  ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(ICLKernel::window(), window);
+
+  const TensorShape &in_shape1 = _input1->info()->tensor_shape();
+  const TensorShape &in_shape2 = _input2->info()->tensor_shape();
+  const TensorShape &out_shape = _output->info()->tensor_shape();
+
+  bool can_collapse = true;
+  if (std::min(in_shape1.total_size(), in_shape2.total_size()) > 1)
+  {
+    can_collapse =
+        (std::min(in_shape1.num_dimensions(), in_shape2.num_dimensions()) > Window::DimZ);
+    for (size_t d = Window::DimZ; can_collapse && (d < out_shape.num_dimensions()); d++)
+    {
+      can_collapse = (in_shape1[d] == in_shape2[d]);
+    }
+  }
+
+  bool has_collapsed = false;
+  Window collapsed =
+      can_collapse ? window.collapse_if_possible(ICLKernel::window(), Window::DimZ, &has_collapsed)
+                   : window;
+
+  const TensorShape &in_shape1_collapsed =
+      has_collapsed ? in_shape1.collapsed_from(Window::DimZ) : in_shape1;
+  const TensorShape &in_shape2_collapsed =
+      has_collapsed ? in_shape2.collapsed_from(Window::DimZ) : in_shape2;
+
+  Window slice = collapsed.first_slice_window_3D();
+  Window slice_input1 = slice.broadcast_if_dimension_le_one(in_shape1_collapsed);
+  Window slice_input2 = slice.broadcast_if_dimension_le_one(in_shape2_collapsed);
+
+  do
+  {
+    unsigned int idx = 0;
+    add_3D_tensor_argument(idx, _input1, slice_input1);
+    add_3D_tensor_argument(idx, _input2, slice_input2);
+    add_3D_tensor_argument(idx, _output, slice);
+
+    enqueue(queue, *this, slice);
+
+    collapsed.slide_window_slice_3D(slice_input1);
+    collapsed.slide_window_slice_3D(slice_input2);
+  } while (collapsed.slide_window_slice_3D(slice));
+}
+
+BorderSize CLSquaredDifferenceKernel::border_size() const
+{
+  const unsigned int replicateSize =
+      _output->info()->dimension(0) -
+      std::min(_input1->info()->dimension(0), _input2->info()->dimension(0));
+  const unsigned int border =
+      std::min<unsigned int>(num_elems_processed_per_iteration - 1U, replicateSize);
+  return BorderSize(0, border, 0, 0);
+}