COMPMID-617: Add validate support for NEON FullyConnectedLayer
Change-Id: I08987022c8d4cc335c00b8af27bd3edb8fe64d3b
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/111596
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Alexander Gilday <alexander.gilday@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
index 3dd59bd..cab3c7a 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixAccumulateBiasesKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -39,6 +39,42 @@
using namespace arm_compute;
+namespace
+{
+inline Status validate_arguments(const ITensorInfo *accum, const ITensorInfo *biases)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(biases, accum);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != accum->dimension(0));
+
+ return Status{};
+}
+
+inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *accum, ITensorInfo *biases)
+{
+ constexpr unsigned int num_elems_processed_per_iteration = 16;
+
+ // Configure kernel window
+ Window win = calculate_max_window(*accum, Steps(num_elems_processed_per_iteration));
+
+ bool window_changed = update_window_and_padding(win,
+ AccessWindowHorizontal(accum, 0, num_elems_processed_per_iteration),
+ AccessWindowStatic(biases, 0, 0, ceil_to_multiple(biases->dimension(0), num_elems_processed_per_iteration), biases->tensor_shape().y()));
+
+ AccessWindowHorizontal output_access(accum, 0, num_elems_processed_per_iteration);
+
+ // Set the valid region for the accum tensor
+ Coordinates coord;
+ coord.set_num_dimensions(accum->num_dimensions());
+ output_access.set_valid_region(win, ValidRegion(coord, accum->tensor_shape()));
+
+ Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+ return std::make_pair(err, win);
+}
+} // namespace
+
NEGEMMMatrixAccumulateBiasesKernel::NEGEMMMatrixAccumulateBiasesKernel()
: _accum(nullptr), _biases(nullptr)
{
@@ -46,31 +82,26 @@
void NEGEMMMatrixAccumulateBiasesKernel::configure(ITensor *accum, const ITensor *biases)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(accum, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(biases, accum);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(biases, accum);
- ARM_COMPUTE_ERROR_ON(biases->info()->num_dimensions() > 1);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(accum, biases);
+
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(accum->info(), biases->info()));
_biases = biases;
_accum = accum;
- constexpr unsigned int num_elems_processed_per_iteration = 16;
-
// Configure kernel window
- Window win = calculate_max_window(*accum->info(), Steps(num_elems_processed_per_iteration));
+ auto win_config = validate_and_configure_window(accum->info(), biases->info());
+ ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+ INEKernel::configure(win_config.second);
+}
- update_window_and_padding(win,
- AccessWindowHorizontal(accum->info(), 0, num_elems_processed_per_iteration),
- AccessWindowStatic(biases->info(), 0, 0, ceil_to_multiple(biases->info()->dimension(0), num_elems_processed_per_iteration), biases->info()->tensor_shape().y()));
+Status NEGEMMMatrixAccumulateBiasesKernel::validate(const ITensorInfo *accum, const ITensorInfo *biases)
+{
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(accum, biases));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(accum->clone().get(), biases->clone().get()).first);
- AccessWindowHorizontal output_access(accum->info(), 0, num_elems_processed_per_iteration);
-
- // Set the valid region for the accum tensor
- Coordinates coord;
- coord.set_num_dimensions(accum->info()->num_dimensions());
- output_access.set_valid_region(win, ValidRegion(coord, accum->info()->tensor_shape()));
-
- INEKernel::configure(win);
+ return Status{};
}
void NEGEMMMatrixAccumulateBiasesKernel::run(const Window &window, const ThreadInfo &info)
diff --git a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
index aa5e2dd..69b052a 100644
--- a/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
+++ b/src/core/NEON/kernels/NEGEMMMatrixMultiplyKernel.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -36,6 +36,8 @@
#include "arm_compute/core/Validate.h"
#include "arm_compute/core/Window.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
#include <arm_neon.h>
#include <cstddef>
#include <cstdint>
@@ -1409,27 +1411,73 @@
ina, inb, out);
}
-Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+inline Status validate_arguments(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
{
+ ARM_COMPUTE_UNUSED(alpha);
+
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input0, 1, DataType::F16, DataType::F32, DataType::QS8, DataType::QS16);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, input1, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, input1, output);
- ARM_COMPUTE_UNUSED(input0);
- ARM_COMPUTE_UNUSED(input1);
- ARM_COMPUTE_UNUSED(output);
- if(output->dimension(1) == 1)
+ if(!is_interleaved)
{
ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(0) != input1->dimension(1));
+
+ if(output->total_size() != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(input1->dimension(0) != output->dimension(0));
+ ARM_COMPUTE_RETURN_ERROR_ON(input0->dimension(1) != output->dimension(1));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
+ }
+ }
+ else
+ {
+ const int m = reshape_info.m();
+ const int n = reshape_info.n();
+ const int k = reshape_info.k();
+ const int mult_transpose1xW_width = reshape_info.mult_transpose1xW_width();
+ const int mult_interleave4x4_height = reshape_info.mult_interleave4x4_height();
+
+ /* Interleave */
+ TensorShape tensor_shape0{ input0->tensor_shape() };
+ tensor_shape0.set(0, k);
+ tensor_shape0.set(1, m);
+
+ const TensorInfo tensor_info0 = input0->clone()->set_tensor_shape(tensor_shape0);
+ const TensorInfo tensor_info_reshaped0 = input0->clone()->set_tensor_shape(misc::shape_calculator::compute_interleaved_shape(tensor_info0, mult_interleave4x4_height));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input0, &tensor_info_reshaped0);
+
+ if(n != 0) /* Transpose */
+ {
+ TensorShape tensor_shape1{ input1->tensor_shape() };
+ tensor_shape1.set(0, n);
+ tensor_shape1.set(1, k);
+
+ const TensorInfo tensor_info1 = input1->clone()->set_tensor_shape(tensor_shape1);
+ const TensorInfo tensor_info_reshaped1 = input1->clone()->set_tensor_shape(misc::shape_calculator::compute_transpose1xW_with_element_size_shape(tensor_info1, mult_transpose1xW_width));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(input1, &tensor_info_reshaped1);
+ }
+
+ if(output->total_size() != 0)
+ {
+ if(n != 0)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != static_cast<size_t>(n));
+ }
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != static_cast<size_t>(m));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input0, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input0, output);
+ }
}
return Status{};
}
-std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
+inline std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input0, ITensorInfo *input1, ITensorInfo *output)
{
- Window win = Window();
- bool window_changed = false;
+ bool window_changed{};
+ Window win{};
unsigned int num_elems_processed_per_iteration_x = 0;
const unsigned int num_elems_processed_per_iteration_y = 4;
@@ -1538,11 +1586,19 @@
{
}
-void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha)
+void NEGEMMMatrixMultiplyKernel::configure(const ITensor *input0, const ITensor *input1, ITensor *output, float alpha, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
{
- // Perform validate step
ARM_COMPUTE_ERROR_ON_NULLPTR(input0, input1, output);
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info()));
+
+ // Output tensor auto inizialitation if not yet initialized
+ TensorShape tensor_shape{ input0->info()->tensor_shape() };
+ tensor_shape.set(0, is_interleaved ? reshape_info.n() : input1->info()->dimension(0));
+ tensor_shape.set(1, is_interleaved ? reshape_info.m() : input0->info()->dimension(1));
+
+ auto_init_if_empty(*output->info(), input0->info()->clone()->set_tensor_shape(tensor_shape));
+
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input0->info(), input1->info(), output->info(), alpha, is_interleaved, reshape_info));
_input0 = input0;
_input1 = input1;
@@ -1555,9 +1611,10 @@
INEKernel::configure(win_config.second);
}
-Status NEGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output)
+Status NEGEMMMatrixMultiplyKernel::validate(const ITensorInfo *input0, const ITensorInfo *input1, const ITensorInfo *output, float alpha, bool is_interleaved,
+ const GEMMReshapeInfo &reshape_info)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input0, input1, output, alpha, is_interleaved, reshape_info));
ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input0->clone().get(), input1->clone().get(), output->clone().get()).first);
return Status{};
diff --git a/src/core/NEON/kernels/NEIm2ColKernel.cpp b/src/core/NEON/kernels/NEIm2ColKernel.cpp
index 633f78d..4fa329b 100644
--- a/src/core/NEON/kernels/NEIm2ColKernel.cpp
+++ b/src/core/NEON/kernels/NEIm2ColKernel.cpp
@@ -32,6 +32,8 @@
#include "arm_compute/core/Types.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+
#include <arm_neon.h>
#include <cstddef>
#include <cstdint>
@@ -42,14 +44,34 @@
namespace
{
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
+ bool has_bias, bool is_fully_connected, bool is_flatten)
{
ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QASYMM8, DataType::QS16, DataType::F16, DataType::F32);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, output);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, output);
ARM_COMPUTE_RETURN_ERROR_ON(input->data_type() == DataType::QASYMM8 && has_bias);
- ARM_COMPUTE_UNUSED(kernel_dims);
- ARM_COMPUTE_UNUSED(conv_info);
+
+ if(is_flatten) /* Called by FlattenLayer */
+ {
+ size_t flatten_shape = input->tensor_shape().x() * input->tensor_shape().y() * input->tensor_shape().z();
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != flatten_shape);
+ }
+ else if(!is_fully_connected) /* Called by ConvolutionLayer */
+ {
+ std::pair<unsigned int, unsigned int> out_dims = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_dims.width, kernel_dims.height, conv_info);
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (input->dimension(2) * kernel_dims.area() + (has_bias ? 1 : 0)));
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(1) != (out_dims.first * out_dims.second));
+ ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(2) != 1);
+ }
+ else /* Called by FullyConnectedLayer */
+ {
+ const int num_batch_dimensions = std::max(0, static_cast<int>(output->tensor_shape().num_dimensions()) - 1);
+ const int num_input_dimensions = input->tensor_shape().num_dimensions() - num_batch_dimensions;
+
+ TensorInfo expected_output = output->clone()->set_tensor_shape(misc::shape_calculator::compute_im2col_shape(input, num_input_dimensions));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_SHAPES(&expected_output, output);
+ }
return Status{};
}
@@ -291,12 +313,15 @@
{
}
-void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+void NEIm2ColKernel::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
+ bool has_bias, bool is_fully_connected, bool is_flatten)
{
ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
// Perform validation step
- ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias));
+ ARM_COMPUTE_UNUSED(is_fully_connected);
+ ARM_COMPUTE_UNUSED(is_flatten);
+ ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten));
_input = input;
_output = output;
@@ -382,9 +407,10 @@
IKernel::configure(window);
}
-Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+Status NEIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info,
+ bool has_bias, bool is_fully_connected, bool is_flatten)
{
- ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias));
+ ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, is_fully_connected, is_flatten));
return Status{};
}
diff --git a/src/graph/nodes/FullyConnectedLayer.cpp b/src/graph/nodes/FullyConnectedLayer.cpp
index 219e0f9..3742150 100644
--- a/src/graph/nodes/FullyConnectedLayer.cpp
+++ b/src/graph/nodes/FullyConnectedLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
diff --git a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
index e9d14db..2b4670b 100644
--- a/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
+++ b/src/runtime/CL/functions/CLFullyConnectedLayer.cpp
@@ -114,7 +114,7 @@
// If the fully connected layer is called after a convolution layer, the input tensor must be linearized
// Initialize output tensor for im2col
- TensorShape shape_im2col = compute_im2col_shape(*input->info());
+ TensorShape shape_im2col = compute_im2col_shape(input->info());
_im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
// Configure im2col kernel
@@ -243,7 +243,7 @@
bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
const GPUTarget gpu_target = CLScheduler::get().target();
- const ITensorInfo &im2col_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_shape(*input)));
+ const ITensorInfo &im2col_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_shape(input)));
const ITensorInfo &reshaped_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*weights)));
const ITensorInfo &gemmlowp_output = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
diff --git a/src/runtime/NEON/functions/NEFlattenLayer.cpp b/src/runtime/NEON/functions/NEFlattenLayer.cpp
index 408eff5..32edf93 100644
--- a/src/runtime/NEON/functions/NEFlattenLayer.cpp
+++ b/src/runtime/NEON/functions/NEFlattenLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -32,6 +32,6 @@
void NEFlattenLayer::configure(const ITensor *input, ITensor *output)
{
auto k = arm_compute::support::cpp14::make_unique<NEIm2ColKernel>();
- k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+ k->configure(input, output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, false, true);
_kernel = std::move(k);
}
\ No newline at end of file
diff --git a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
index fc04e28..26b7271 100644
--- a/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
+++ b/src/runtime/NEON/functions/NEFullyConnectedLayer.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -23,15 +23,18 @@
*/
#include "arm_compute/runtime/NEON/functions/NEFullyConnectedLayer.h"
+#include "arm_compute/core/Helpers.h"
#include "arm_compute/core/Size2D.h"
#include "arm_compute/core/Validate.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
#include "arm_compute/runtime/NEON/NEScheduler.h"
#include <algorithm>
#include <cmath>
-namespace arm_compute
-{
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
NEFullyConnectedLayerReshapeWeights::NEFullyConnectedLayerReshapeWeights(std::shared_ptr<IMemoryManager> memory_manager)
: _memory_group(std::move(memory_manager)), _transpose_kernel(), _transpose1xW_kernel(), _transpose_output(), _transpose_weights(false), _is_batched_fc_layer(false)
{
@@ -39,13 +42,10 @@
void NEFullyConnectedLayerReshapeWeights::configure(const ITensor *input, ITensor *output, bool transpose_weights, bool is_batched_fc_layer)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON(input->info()->num_dimensions() > 2);
- ARM_COMPUTE_ERROR_ON(output == nullptr);
- ARM_COMPUTE_ERROR_ON(!transpose_weights && !is_batched_fc_layer);
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
- const DataType data_type = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayerReshapeWeights::validate(input->info(), output->info(), transpose_weights, is_batched_fc_layer));
_transpose_weights = transpose_weights;
_is_batched_fc_layer = is_batched_fc_layer;
@@ -56,8 +56,7 @@
if(_is_batched_fc_layer)
{
// Initialize the output tensor for transpose
- TensorShape shape_transposed(input->info()->dimension(1), input->info()->dimension(0));
- _transpose_output.allocator()->init(TensorInfo(shape_transposed, 1, data_type, fixed_point_position));
+ _transpose_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*input->info())));
_memory_group.manage(&_transpose_output);
_transpose_kernel.configure(input, &_transpose_output);
@@ -79,11 +78,39 @@
// Configure transpose 1xW kernel
_transpose1xW_kernel.configure(input, output);
}
+ }
+}
+
+Status NEFullyConnectedLayerReshapeWeights::validate(const ITensorInfo *input, const ITensorInfo *output, bool transpose_weights, bool is_batched_fc_layer)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 2);
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(!transpose_weights && !is_batched_fc_layer, "Configuration transpose_weights=false & is_batched_fc_layer=false not supported");
+
+ if(transpose_weights)
+ {
+ if(is_batched_fc_layer)
+ {
+ std::unique_ptr<ITensorInfo> use_output = output->clone();
+ use_output->set_is_resizable(true).reset_padding().set_tensor_shape(compute_transposed_shape(*input));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NETransposeKernel::validate(input, use_output.get()));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(use_output.get(), output));
+ }
else
{
- ARM_COMPUTE_ERROR("Configuration transpose_weights=false & is_batched_fc_layer=false not supported");
+ ARM_COMPUTE_RETURN_ON_ERROR(NETransposeKernel::validate(input, output));
}
}
+ else
+ {
+ if(is_batched_fc_layer)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMTranspose1xWKernel::validate(input, output));
+ }
+ }
+
+ return Status{};
}
void NEFullyConnectedLayerReshapeWeights::run()
@@ -122,26 +149,25 @@
// Weights: flat(In) x Out
// Biases: Out
// Output: Out x B (B can be multi-dimensional)
+ ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
- ARM_COMPUTE_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);
+ // Perform validate step
+ ARM_COMPUTE_ERROR_THROW_ON(NEFullyConnectedLayer::validate(input->info(),
+ weights->info(),
+ biases != nullptr ? biases->info() : nullptr,
+ output->info(),
+ transpose_weights,
+ are_weights_reshaped));
- const DataType data_type = input->info()->data_type();
- const int fixed_point_position = input->info()->fixed_point_position();
- const int num_batch_dimensions = std::max(0, static_cast<int>(output->info()->tensor_shape().num_dimensions()) - 1);
- const int num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions;
- const size_t linear_input_size = input->info()->tensor_shape().total_size_lower(num_input_dimensions);
+ const int num_batch_dimensions = std::max(0, static_cast<int>(output->info()->tensor_shape().num_dimensions()) - 1);
+ const int num_input_dimensions = input->info()->tensor_shape().num_dimensions() - num_batch_dimensions;
+ const size_t linear_input_size = input->info()->tensor_shape().total_size_lower(num_input_dimensions);
_linearize_input = (input->info()->tensor_shape().x() != linear_input_size) || (num_input_dimensions > 1 && linear_input_size == 1);
_are_weights_reshaped = are_weights_reshaped;
_accumulate_biases = biases != nullptr;
_is_batched_fc_layer = num_batch_dimensions > 0;
- // Check if number of batches match
- ARM_COMPUTE_ERROR_ON(input->info()->tensor_shape().total_size_upper(num_input_dimensions) != output->info()->tensor_shape().total_size_upper(1));
- ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 2);
-
const size_t interleave_width = 16 / input->info()->element_size();
const ITensor *weights_to_use = weights;
@@ -149,65 +175,33 @@
{
weights_to_use = &_reshape_weights_output;
- TensorShape reshaped_weights_shape(weights->info()->tensor_shape());
-
- // Transpose weights if the user hasn't done it
- if(transpose_weights)
- {
- const size_t shape_x = reshaped_weights_shape.x();
- reshaped_weights_shape.set(0, reshaped_weights_shape.y());
- reshaped_weights_shape.set(1, shape_x);
- }
-
- // If the we run multiple batches we need 1xW transpose, too.
- if(_is_batched_fc_layer)
- {
- const float shape_x = reshaped_weights_shape.x();
- reshaped_weights_shape.set(0, reshaped_weights_shape.y() * interleave_width);
- reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(shape_x / interleave_width)));
- }
-
- _reshape_weights_output.allocator()->init(TensorInfo(reshaped_weights_shape, 1, data_type, fixed_point_position));
+ _reshape_weights_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_fully_connected_reshaped_weights_shape(weights->info(),
+ transpose_weights,
+ _is_batched_fc_layer, interleave_width)));
// Reshape the weights
_reshape_weights_kernel.configure(weights, &_reshape_weights_output, transpose_weights, _is_batched_fc_layer);
}
- // Check correct shape of weights
- if(_is_batched_fc_layer)
- {
- // Transpose + Transpose1xW
- ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != linear_input_size * interleave_width);
- ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != static_cast<unsigned int>(std::ceil(static_cast<float>(output->info()->tensor_shape().x()) / interleave_width)));
- }
- else
- {
- // Transpose
- ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().x() != output->info()->tensor_shape().x());
- ARM_COMPUTE_ERROR_ON(weights_to_use->info()->tensor_shape().y() != linear_input_size);
- }
-
const ITensor *multiply_input = input;
if(_linearize_input)
{
- TensorShape shape_im2col(input->info()->tensor_shape());
- shape_im2col.collapse(num_input_dimensions);
- _im2col_output.allocator()->init(TensorInfo(shape_im2col, 1, data_type, fixed_point_position));
+ _im2col_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_im2col_shape(input->info(), num_input_dimensions)));
// Configure im2col kernel
_memory_group.manage(&_im2col_output);
- _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false);
+ _im2col_kernel.configure(input, &_im2col_output, Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, true);
multiply_input = &_im2col_output;
}
+ int m = multiply_input->info()->dimension(1);
+ int k = multiply_input->info()->dimension(0);
+
if(_is_batched_fc_layer)
{
- TensorShape shape_interleaved(multiply_input->info()->tensor_shape());
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- _interleave4x4_output.allocator()->init(TensorInfo(shape_interleaved, 1, data_type, fixed_point_position));
+ _interleave4x4_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(compute_interleaved_shape(*multiply_input->info())));
// Configure interleave4x4 kernel
_memory_group.manage(&_interleave4x4_output);
@@ -217,13 +211,10 @@
}
// Configure matrix multiply kernel
- _mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f);
+ _mm_kernel.configure(multiply_input, weights_to_use, output, 1.0f, _is_batched_fc_layer, GEMMReshapeInfo(m, 0 /* no transpose */, k));
if(_accumulate_biases)
{
- ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
- ARM_COMPUTE_ERROR_ON(biases->info()->tensor_shape().x() != output->info()->tensor_shape().x());
-
// Configure accumulate biases kernel
_accumulate_biases_kernel.configure(output, biases);
}
@@ -246,6 +237,88 @@
}
}
+Status NEFullyConnectedLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, bool transpose_weights, bool are_weights_reshaped)
+{
+ ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QS8, DataType::QS16, DataType::F16, DataType::F32);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, output);
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT_POSITION(input, weights, output);
+
+ const int num_batch_dimensions = std::max(0, static_cast<int>(output->tensor_shape().num_dimensions()) - 1);
+ const int num_input_dimensions = input->tensor_shape().num_dimensions() - num_batch_dimensions;
+ const size_t linear_input_size = input->tensor_shape().total_size_lower(num_input_dimensions);
+
+ const bool linearize_input = (input->tensor_shape().x() != linear_input_size) || (num_input_dimensions > 1 && linear_input_size == 1);
+ const bool accumulate_biases = biases != nullptr;
+ const bool is_batched_fc_layer = num_batch_dimensions > 0;
+
+ ARM_COMPUTE_RETURN_ERROR_ON(input->tensor_shape().total_size_upper(num_input_dimensions) != output->tensor_shape().total_size_upper(1));
+ ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 2);
+
+ const size_t interleave_width = 16 / input->element_size();
+ const ITensorInfo *weights_to_use = weights;
+ std::unique_ptr<ITensorInfo> reshape_weights_output = input->clone();
+
+ if(!are_weights_reshaped && (transpose_weights || is_batched_fc_layer))
+ {
+ reshape_weights_output->set_tensor_shape(compute_fully_connected_reshaped_weights_shape(weights, transpose_weights, is_batched_fc_layer, interleave_width));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEFullyConnectedLayerReshapeWeights::validate(weights, reshape_weights_output.get(), transpose_weights, is_batched_fc_layer));
+
+ weights_to_use = reshape_weights_output.get();
+ }
+
+ // Check correct shape of weights
+ if(is_batched_fc_layer)
+ {
+ // Transpose + Transpose1xW
+ ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().x() != linear_input_size * interleave_width);
+ ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().y() != static_cast<unsigned int>(std::ceil(static_cast<float>(output->tensor_shape().x()) / interleave_width)));
+ }
+ else
+ {
+ // Transpose
+ ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().x() != output->tensor_shape().x());
+ ARM_COMPUTE_RETURN_ERROR_ON(weights_to_use->tensor_shape().y() != linear_input_size);
+ }
+
+ const ITensorInfo *multiply_input = input;
+ std::unique_ptr<ITensorInfo> im2col_output = input->clone();
+ std::unique_ptr<ITensorInfo> interleave4x4_output = input->clone();
+
+ if(linearize_input)
+ {
+ im2col_output->set_tensor_shape(compute_im2col_shape(input, num_input_dimensions));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, im2col_output.get(), Size2D(1, 1), PadStrideInfo(1, 1, 0, 0), false, true));
+
+ multiply_input = im2col_output.get();
+ }
+
+ int m = multiply_input->dimension(1);
+ int k = multiply_input->dimension(0);
+
+ if(is_batched_fc_layer)
+ {
+ interleave4x4_output->set_tensor_shape(compute_interleaved_shape(*multiply_input));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(multiply_input, interleave4x4_output.get()));
+
+ multiply_input = interleave4x4_output.get();
+ }
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(multiply_input, weights_to_use, output, 1.0f, is_batched_fc_layer, GEMMReshapeInfo(m, 0 /* no transpose */, k)));
+
+ if(accumulate_biases)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->tensor_shape().x() != output->tensor_shape().x());
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixAccumulateBiasesKernel::validate(output, biases));
+ }
+
+ return Status{};
+}
+
void NEFullyConnectedLayer::run()
{
// Reshape of the weights (happens only once)
@@ -280,4 +353,3 @@
_memory_group.release();
}
-} // namespace arm_compute
diff --git a/src/runtime/NEON/functions/NEGEMM.cpp b/src/runtime/NEON/functions/NEGEMM.cpp
index 48a0d2a..05907ba 100644
--- a/src/runtime/NEON/functions/NEGEMM.cpp
+++ b/src/runtime/NEON/functions/NEGEMM.cpp
@@ -120,7 +120,7 @@
#endif /* defined(__aarch64__) */
{
// Configure the matrix multiply kernel
- _mm_kernel.configure(a, b, d, alpha);
+ _mm_kernel.configure(a, b, d, alpha, false);
}
// Configure matrix addition kernel
@@ -212,6 +212,10 @@
_memory_group.manage(&_tmp_a);
_memory_group.manage(&_tmp_b);
+ int m = a->info()->dimension(1);
+ int n = b->info()->dimension(0);
+ int k = a->info()->dimension(0);
+
// Configure interleave kernel
_interleave_kernel.configure(a, &_tmp_a);
@@ -219,7 +223,7 @@
_transpose_kernel.configure(b, &_tmp_b);
// Configure matrix multiplication kernel
- _mm_kernel.configure(&_tmp_a, &_tmp_b, d, alpha);
+ _mm_kernel.configure(&_tmp_a, &_tmp_b, d, alpha, true, GEMMReshapeInfo(m, n, k));
// Allocate once the all configure methods have been called
_tmp_a.allocator()->allocate();
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index d0a16ef..a85078c 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -178,7 +178,7 @@
Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info, DataType &dt,
bool &append_bias,
bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
- bool &is_fully_connected_convolution, bool &is_interleaved_transposed, bool &is_quantized,
+ bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized,
unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
unsigned int &conv_w, unsigned int &conv_h)
{
@@ -219,7 +219,7 @@
// Check if its a "fully connected" convolution
is_fully_connected_convolution = ((conv_w == 1) && (conv_h == 1));
- is_interleaved_transposed = (!is_fully_connected_convolution && !is_quantized);
+ is_interleaved = (!is_fully_connected_convolution && !is_quantized);
return Status{};
}
@@ -228,11 +228,11 @@
NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
: _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_optimised_kernel(nullptr), _mm_gemmlowp(memory_manager),
_gemmlowp_output_stage(), _output_col2im_kernel(), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(), _workspace(), _append_bias(false),
- _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved_transposed(false)
+ _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false)
{
}
-void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output)
+void NEGEMMConvolutionLayer::configure_mm(const ITensor *input, const ITensor *weights, ITensor *output, bool is_interleaved, const GEMMReshapeInfo &reshape_info)
{
if(_is_quantized)
{
@@ -252,7 +252,7 @@
}
else
{
- _mm_kernel.configure(input, weights, output, 1.f);
+ _mm_kernel.configure(input, weights, output, 1.f, is_interleaved, reshape_info);
}
}
@@ -290,7 +290,7 @@
Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, dt, _append_bias, _are_weights_reshaped,
kernel_width, kernel_height,
- _is_fully_connected_convolution, _is_interleaved_transposed, _is_quantized,
+ _is_fully_connected_convolution, _is_interleaved, _is_quantized,
mat_weights_cols, mat_weights_rows, conv_w, conv_h);
ARM_COMPUTE_ERROR_THROW_ON(status);
@@ -339,9 +339,8 @@
}
else
{
- const unsigned int transpose_width = 16 / input->info()->element_size();
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(0) / transpose_width + (_append_bias ? 1 : 0);
+ mat_weights_cols = weights_info.num_kernels();
+ mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(2) + (_append_bias ? 1 : 0);
}
}
else
@@ -362,7 +361,7 @@
// Create tensor to store the reshaped weights
_weights_reshaped.allocator()->init(TensorInfo(reshaped_weights_shape, 1, dt, fixed_point_position));
- _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved_transposed /* 1xW transpose */);
+ _reshape_weights.configure(weights, biases_to_use, &_weights_reshaped, _is_interleaved /* 1xW transpose */);
weights = &_weights_reshaped;
}
}
@@ -430,18 +429,19 @@
}
else
{
- if(_is_interleaved_transposed)
+ if(_is_interleaved)
{
// Configure GEMMInterleave4x4. _input_interleaved_reshaped will be auto configured in the kernel
_input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
// Configure GEMM
- configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output);
+ configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(1), 0 /* no transpose */,
+ _input_im2col_reshaped.info()->dimension(0)));
_input_interleaved_reshaped.allocator()->allocate();
}
else
{
- configure_mm(&_input_im2col_reshaped, weights, &_gemm_output);
+ configure_mm(&_input_im2col_reshaped, weights, &_gemm_output, _is_interleaved);
}
}
@@ -479,11 +479,13 @@
Status NEGEMMConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
const WeightsInfo &weights_info)
{
+ ARM_COMPUTE_UNUSED(output);
+
DataType dt{};
bool append_bias{};
bool are_weights_reshaped{};
bool is_fully_connected_convolution{};
- bool is_interleaved_transposed{};
+ bool is_interleaved{};
bool is_quantized{};
unsigned int kernel_width = 0;
unsigned int kernel_height = 0;
@@ -493,9 +495,11 @@
unsigned int conv_h = 0;
Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
- is_fully_connected_convolution, is_interleaved_transposed, is_quantized, mat_weights_cols, mat_weights_rows,
+ is_fully_connected_convolution, is_interleaved, is_quantized, mat_weights_cols, mat_weights_rows,
conv_w, conv_h);
+ const Size2D kernel_weights = Size2D(kernel_width, kernel_height);
+
ARM_COMPUTE_RETURN_ON_ERROR(status);
std::unique_ptr<ITensorInfo> reshaped_weights = weights->clone();
@@ -570,7 +574,7 @@
shape_im2col.set(1, mat_input_rows);
shape_im2col.set(2, 1);
TensorInfo im2_col_info = input->clone()->set_tensor_shape(shape_im2col);
- ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, Size2D(weights->dimension(0), weights->dimension(1)), conv_info, append_bias));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false));
// Create GEMM output tensor
TensorShape shape_gemm(im2_col_info.tensor_shape());
@@ -579,24 +583,20 @@
TensorInfo gemm_output_info = input->clone()->set_tensor_shape(shape_gemm);
// Validate GEMM interleave and multiply
- if(is_interleaved_transposed)
+ if(is_interleaved)
{
TensorShape shape_interleaved = shape_im2col;
shape_interleaved.set(0, shape_interleaved.x() * 4);
shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
TensorInfo input_interleaved_info = input->clone()->set_tensor_shape(shape_interleaved);
ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMInterleave4x4Kernel::validate(&im2_col_info, &input_interleaved_info));
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&input_interleaved_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
}
else
{
- ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info));
+ ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
}
- ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
-
- ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(0) != conv_w) || (output->dimension(1) != conv_h), "Output shape does not match the expected one");
-
return Status{};
}
@@ -621,7 +621,7 @@
}
else
{
- if(_is_interleaved_transposed)
+ if(_is_interleaved)
{
// Run interleave
NEScheduler::get().schedule(&_input_interleave_kernel, Window::DimY);
diff --git a/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp b/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp
index 571bf2b..802b946 100644
--- a/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp
+++ b/src/runtime/NEON/functions/NEGEMMTranspose1xW.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -38,3 +38,7 @@
k->configure(input, output);
_kernel = std::move(k);
}
+Status NEGEMMTranspose1xW::validate(const ITensorInfo *input, const ITensorInfo *output)
+{
+ return NEGEMMTranspose1xWKernel::validate(input, output);
+}
diff --git a/src/runtime/NEON/functions/NEIm2Col.cpp b/src/runtime/NEON/functions/NEIm2Col.cpp
index 8e90e66..b962db9 100644
--- a/src/runtime/NEON/functions/NEIm2Col.cpp
+++ b/src/runtime/NEON/functions/NEIm2Col.cpp
@@ -1,5 +1,5 @@
/*
- * Copyright (c) 2017 ARM Limited.
+ * Copyright (c) 2017-2018 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
@@ -28,14 +28,14 @@
using namespace arm_compute;
-void NEIm2Col::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+void NEIm2Col::configure(const ITensor *input, ITensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, bool is_fully_connected)
{
auto k = arm_compute::support::cpp14::make_unique<NEIm2ColKernel>();
- k->configure(input, output, kernel_dims, conv_info, has_bias);
+ k->configure(input, output, kernel_dims, conv_info, has_bias, is_fully_connected);
_kernel = std::move(k);
}
-Status NEIm2Col::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias)
+Status NEIm2Col::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, bool is_fully_connected)
{
- return NEIm2ColKernel::validate(input, output, kernel_dims, conv_info, has_bias);
+ return NEIm2ColKernel::validate(input, output, kernel_dims, conv_info, has_bias, is_fully_connected);
}