COMPMID-1047 Extract Flatten function from Im2Col for NEON
Change-Id: I80f3aaadc8cae8c9ca1a5a239e79bda302b89bd8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/144813
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index 98b1488..e5d3128 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -41,11 +41,12 @@
#include <tuple>
using namespace arm_compute;
+using namespace misc::shape_calculator;
namespace
{
Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
- bool has_bias, const Size2D &dilation, unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+ bool has_bias, const Size2D &dilation, unsigned int num_groups)
{
ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(input);
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
@@ -55,18 +56,7 @@
if(output->total_size() > 0)
{
- TensorShape expected_output_shape;
-
- if(is_flatten || is_fully_connected)
- {
- expected_output_shape = misc::shape_calculator::compute_flatten_shape(input);
- }
- else
- {
- expected_output_shape = misc::shape_calculator::compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false);
- }
-
- TensorInfo expected_output = output->clone()->set_tensor_shape(expected_output_shape);
+ TensorInfo expected_output = output->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false));
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
}
@@ -74,6 +64,31 @@
return Status{};
}
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
+ bool has_bias, const Size2D &dilation)
+{
+ const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+ const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
+
+ std::pair<unsigned int, unsigned int> convolved_dims = scaled_dimensions(input->dimension(width_idx), input->dimension(height_idx),
+ kernel_dims.width, kernel_dims.height,
+ conv_info, dilation);
+
+ // Output tensor auto initialization if not yet initialized
+ auto_init_if_empty(*output, input->clone()->set_tensor_shape(compute_im2col_conv_shape(input, kernel_dims, conv_info, has_bias, dilation, false)));
+
+ Window win = calculate_max_window(*input, Steps());
+ win.set(width_idx, Window::Dimension(0, convolved_dims.first, 1));
+ win.set(height_idx, Window::Dimension(0, convolved_dims.second, 1));
+ win.set(channel_idx, Window::Dimension(0, 1, 1));
+
+ // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
+ output->set_valid_region(ValidRegion(Coordinates(), output->tensor_shape()));
+
+ return std::make_pair(Status{}, win);
+}
+
template <typename T, bool has_pads>
inline void linearize_volume(const uint8_t *const in_ptr,
T *out_ptr,
@@ -174,7 +189,7 @@
} // namespace
template <typename T, bool has_pads>
-void NEIm2ColKernel::run_generic(const Window &window)
+void NEIm2ColKernel::run_im2col(const Window &window)
{
ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(INEKernel::window(), window);
@@ -244,66 +259,21 @@
in, out);
}
-template <typename T>
-void NEIm2ColKernel::run_reduced(const Window &window)
-{
- const size_t in_width = _input->info()->dimension(0);
- const size_t in_height = _input->info()->dimension(1);
- const size_t out_step_x = in_width * _input->info()->element_size();
- const size_t out_step_y = out_step_x * in_height;
- const size_t out_width = _output->info()->dimension(0);
-
- Window in_window(window);
- in_window.set(Window::DimX, Window::Dimension(0, 1, 1));
-
- Window out_window;
- out_window.use_tensor_dimensions(_output->info()->tensor_shape());
- out_window.set(Window::DimX, Window::Dimension(out_window.x().start(), out_window.x().end(), in_width));
-
- Window in_slice = in_window.first_slice_window_3D();
- Window out_slice = out_window.first_slice_window_1D();
-
- do
- {
- Iterator in(_input, in_slice);
- Iterator out(_output, out_slice);
-
- uint8_t *out_ptr = out.ptr();
-
- execute_window_loop(in_slice, [&](const Coordinates & id)
- {
- memcpy(out_ptr + id.y() * out_step_x + id.z() * out_step_y, in.ptr(), out_step_x);
- },
- in);
-
- // Add bias
- if(_has_bias)
- {
- *(reinterpret_cast<T *>(out_ptr) + out_width - 1) = static_cast<T>(1);
- }
- }
- while(in_window.slide_window_slice_3D(in_slice) && out_window.slide_window_slice_1D(out_slice));
-}
-
NEIm2ColKernel::NEIm2ColKernel()
: _func(), _input(nullptr), _output(nullptr), _convolved_dims(), _conv_info(), _kernel_width(0), _kernel_height(0), _has_bias(false), _dilation(1U, 1U)
{
}
void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
- bool has_bias, const Size2D &dilation, unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+ bool has_bias, const Size2D &dilation, unsigned int num_groups)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
-
- // Perform validation step
- ARM_COMPUTE_UNUSED(is_fully_connected, is_flatten);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups));
ARM_COMPUTE_UNUSED(num_groups);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation, num_groups, is_fully_connected, is_flatten));
const DataLayout data_layout = input->info()->data_layout();
const unsigned int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
const unsigned int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
- const unsigned int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
_input = input;
_output = output;
@@ -316,73 +286,35 @@
_conv_info, _dilation);
_has_bias = has_bias;
- unsigned int stride_x = 0;
- unsigned int stride_y = 0;
- std::tie(stride_x, stride_y) = conv_info.stride();
-
- bool run_img2col_reduced = (output->info()->dimension(0) == (input->info()->dimension(0) * input->info()->dimension(1) * input->info()->dimension(2))) && (TensorShape::num_max_dimensions >= 4)
- && (std::equal(input->info()->tensor_shape().cbegin() + 3,
- input->info()->tensor_shape().cend(),
- output->info()->tensor_shape().cbegin() + 1))
- && ((stride_x == 1) && (stride_y == 1) && !conv_info.has_padding())
- && ((dilation.x() == 1) && (dilation.y() == 1));
-
- Window window = calculate_max_window(*input->info(), Steps());
-
- if(run_img2col_reduced)
+ switch(_input->info()->data_type())
{
- switch(_input->info()->data_type())
- {
- case DataType::F32:
- _func = &NEIm2ColKernel::run_reduced<float>;
- break;
+ case DataType::F32:
+ _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float, false> : &NEIm2ColKernel::run_im2col<float, true>;
+ break;
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- case DataType::F16:
- _func = &NEIm2ColKernel::run_reduced<float16_t>;
- break;
+ case DataType::F16:
+ _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<float16_t, false> : &NEIm2ColKernel::run_im2col<float16_t, true>;
+ break;
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QASYMM8:
- _func = &NEIm2ColKernel::run_reduced<qasymm8_t>;
- break;
- default:
- ARM_COMPUTE_ERROR("Data type not supported");
- break;
- }
- }
- else
- {
- switch(_input->info()->data_type())
- {
- case DataType::F32:
- _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float, false> : &NEIm2ColKernel::run_generic<float, true>;
- break;
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
- case DataType::F16:
- _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<float16_t, false> : &NEIm2ColKernel::run_generic<float16_t, true>;
- break;
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
- case DataType::QASYMM8:
- _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_generic<qasymm8_t, false> : &NEIm2ColKernel::run_generic<qasymm8_t, true>;
- break;
- default:
- ARM_COMPUTE_ERROR("Data type not supported");
- break;
- }
- window.set(width_idx, Window::Dimension(0, _convolved_dims.first, 1));
- window.set(height_idx, Window::Dimension(0, _convolved_dims.second, 1));
- window.set(channel_idx, Window::Dimension(0, 1, 1));
+ case DataType::QASYMM8:
+ _func = (!conv_info.has_padding()) ? &NEIm2ColKernel::run_im2col<qasymm8_t, false> : &NEIm2ColKernel::run_im2col<qasymm8_t, true>;
+ break;
+ default:
+ ARM_COMPUTE_ERROR("Data type not supported");
+ break;
}
- // The NEIm2ColKernel doesn't need padding so update_window_and_padding() can be skipped
- output->info()->set_valid_region(ValidRegion(Coordinates(), output->info()->tensor_shape()));
-
- IKernel::configure(window);
+ // Configure kernel window
+ auto win_config = validate_and_configure_window(input->info(), output->info(), kernel_dims, conv_info, has_bias, dilation);
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ INEKernel::configure(win_config.second);
}
Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
- bool has_bias, const Size2D &dilation, unsigned int num_groups, bool is_fully_connected, bool is_flatten)
+ bool has_bias, const Size2D &dilation, unsigned int num_groups)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups, is_fully_connected, is_flatten));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, dilation, num_groups));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), output->clone().get(), kernel_dims, conv_info, has_bias, dilation).first);
return Status{};
}