COMPMID-810 Add NHWC data format support for NEON convolution
Change-Id: I2a7b49a12da7f3bc3f04749243b1dc111160de6e
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/129348
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
diff --git a/arm_compute/runtime/NEON/AssemblyHelper.h b/arm_compute/runtime/NEON/AssemblyHelper.h
index 3db419e..ecaf35a 100644
--- a/arm_compute/runtime/NEON/AssemblyHelper.h
+++ b/arm_compute/runtime/NEON/AssemblyHelper.h
@@ -84,7 +84,12 @@
const int ldb = _b->info()->strides_in_bytes().y() / sizeof(TypeInput);
const int ldd = _d->info()->strides_in_bytes().y() / sizeof(TypeOutput);
- const int batch_stride_a = _a->info()->strides_in_bytes().z() / sizeof(TypeInput);
+ // In the case of NHWC we want to interpret the output shape as 3D. Thus, the batch stride for A is
+ // the relevant multiple of the row stride.
+ const bool is_nhwc = _a->info()->data_layout() == DataLayout::NHWC;
+ const int stride_in_bytes_a = is_nhwc ? _a->info()->strides_in_bytes().y() * _d->info()->dimension(1) : _a->info()->strides_in_bytes().z();
+
+ const int batch_stride_a = stride_in_bytes_a / sizeof(TypeInput);
const int batch_stride_d = _d->info()->strides_in_bytes().z() / sizeof(TypeOutput);
const int multi_stride_a = _a->info()->strides_in_bytes()[3] / sizeof(TypeInput);
@@ -158,7 +163,7 @@
const int M = d->info()->tensor_shape().y();
const int N = d->info()->tensor_shape().x();
const int K = a->info()->tensor_shape().x();
- const int batches = a->info()->tensor_shape().total_size_upper(2);
+ const int batches = d->info()->tensor_shape().total_size_upper(2);
const int multis = b->info()->tensor_shape().z();
unsigned int num_threads = NEScheduler::get().num_threads();
diff --git a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
index 7526931..d64fd9e 100644
--- a/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEGEMMConvolutionLayer.h
@@ -26,6 +26,7 @@
#include "arm_compute/runtime/IFunction.h"
+#include "arm_compute/core/NEON/kernels/NEArithmeticAdditionKernel.h"
#include "arm_compute/core/NEON/kernels/NECol2ImKernel.h"
#include "arm_compute/core/NEON/kernels/NEFillBorderKernel.h"
#include "arm_compute/core/NEON/kernels/NEGEMMAssemblyBaseKernel.h"
@@ -176,6 +177,7 @@
NEGEMMLowpQuantizeDownInt32ToUint8ScaleByFixedPoint _gemmlowp_output_stage;
NECol2ImKernel _output_col2im_kernel;
NEActivationLayer _activationlayer_function;
+ NEArithmeticAdditionKernel _add_bias_kernel;
const ITensor *_original_weights;
@@ -187,12 +189,14 @@
Tensor _workspace;
Tensor _B_pretransposed;
- bool _append_bias;
- bool _is_fully_connected_convolution;
- bool _are_weights_reshaped;
- bool _is_quantized;
- bool _is_interleaved;
- bool _is_activationlayer_enabled;
+ DataLayout _data_layout;
+ bool _append_bias;
+ bool _is_fully_connected_convolution;
+ bool _are_weights_reshaped;
+ bool _is_quantized;
+ bool _is_interleaved;
+ bool _is_activationlayer_enabled;
+ bool _skip_im2col;
};
}
#endif /* __ARM_COMPUTE_NECONVOLUTIONGEMMLAYER_H__ */
diff --git a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
index 1501402..3031a87 100644
--- a/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
+++ b/src/core/NEON/kernels/NEWeightsReshapeKernel.cpp
@@ -34,12 +34,16 @@
namespace
{
-template <typename T>
+template <typename T, bool is_nhwc>
void weights_reshape(const ITensor *input, const ITensor *bias, ITensor *output, const Window &window)
{
- const unsigned int kernel_size_x = input->info()->dimension(0);
- const unsigned int kernel_size_y = input->info()->dimension(1);
- const unsigned int kernel_depth = input->info()->dimension(2);
+ DataLayout data_layout = input->info()->data_layout();
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+ const unsigned int kernel_size_x = input->info()->dimension(idx_width);
+ const unsigned int kernel_size_y = input->info()->dimension(idx_height);
+ const unsigned int kernel_depth = input->info()->dimension(idx_channel);
const unsigned int input_stride_x = input->info()->strides_in_bytes().x();
const unsigned int input_stride_y = input->info()->strides_in_bytes().y();
const unsigned int input_stride_z = input->info()->strides_in_bytes().z();
@@ -67,13 +71,13 @@
for(unsigned int i = 0; i < kernel_size_x; ++i)
{
*(reinterpret_cast<T *>(tmp_output_ptr)) = *(reinterpret_cast<const T *>(tmp_input_ptr));
- tmp_input_ptr += input_stride_x;
+ tmp_input_ptr += is_nhwc ? input_stride_y : input_stride_x;
tmp_output_ptr += output_stride_y;
}
- curr_input_row_ptr += input_stride_y;
+ curr_input_row_ptr += is_nhwc ? input_stride_z : input_stride_y;
tmp_input_ptr = curr_input_row_ptr;
}
- curr_input_depth_ptr += input_stride_z;
+ curr_input_depth_ptr += is_nhwc ? input_stride_x : input_stride_z;
curr_input_row_ptr = curr_input_depth_ptr;
tmp_input_ptr = curr_input_depth_ptr;
}
@@ -161,21 +165,24 @@
_bias = bias;
_output = output;
+ const DataLayout data_layout = input->info()->data_layout();
+ const bool is_nhwc = data_layout == DataLayout::NHWC;
+
switch(_input->info()->element_size())
{
case 4:
{
- _func = &weights_reshape<uint32_t>;
+ _func = is_nhwc ? &weights_reshape<uint32_t, true> : &weights_reshape<uint32_t, false>;
break;
}
case 2:
{
- _func = &weights_reshape<uint16_t>;
+ _func = is_nhwc ? &weights_reshape<uint16_t, true> : &weights_reshape<uint16_t, false>;
break;
}
case 1:
{
- _func = &weights_reshape<uint8_t>;
+ _func = is_nhwc ? &weights_reshape<uint8_t, true> : &weights_reshape<uint8_t, false>;
break;
}
default:
diff --git a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
index 5a35463..a5f3055 100644
--- a/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEGEMMConvolutionLayer.cpp
@@ -109,6 +109,14 @@
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
+ // Checks performed when biases are present
+ if(append_bias)
+ {
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(weights, biases);
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+ }
+
if(transpose1xW)
{
TensorInfo weights_reshaped = weights->clone()->set_tensor_shape(get_reshaped_weights_shape(weights, append_bias));
@@ -159,7 +167,7 @@
Status validate_and_initialize_values(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const PadStrideInfo &conv_info, const WeightsInfo &weights_info,
const ActivationLayerInfo &act_info, DataType &dt,
- bool &append_bias,
+ bool &append_bias, bool &skip_im2col,
bool &are_weights_reshaped, unsigned int &kernel_width, unsigned int &kernel_height,
bool &is_fully_connected_convolution, bool &is_interleaved, bool &is_quantized, bool &is_activationlayer_enabled,
unsigned int &mat_weights_cols, unsigned int &mat_weights_rows,
@@ -168,9 +176,17 @@
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, weights);
ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_FIXED_POINT(input, weights);
- ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(2) != input->dimension(2));
+ ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_LAYOUT(input, weights);
+
+ DataLayout data_layout = input->data_layout();
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+ const int idx_channel = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
+
+ ARM_COMPUTE_RETURN_ERROR_ON(!weights_info.are_reshaped() && weights->dimension(idx_channel) != input->dimension(idx_channel));
ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
ARM_COMPUTE_RETURN_ERROR_ON(weights_info.are_reshaped() && is_data_type_quantized_asymmetric(input->data_type()));
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG(data_layout == DataLayout::NHWC && input->data_type() != DataType::F32, "NHWC is only supported for FP32 data type.");
dt = input->data_type();
is_quantized = is_data_type_quantized_asymmetric(dt);
@@ -190,14 +206,16 @@
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
}
+ // If we have 1x1 convolution and data layout is NHWC we can disable im2col
append_bias = (biases != nullptr) && (!is_quantized);
are_weights_reshaped = weights_info.are_reshaped();
- kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(0);
- kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(1);
+ kernel_width = (are_weights_reshaped) ? weights_info.kernel_size().first : weights->dimension(idx_width);
+ kernel_height = (are_weights_reshaped) ? weights_info.kernel_size().second : weights->dimension(idx_height);
mat_weights_cols = weights->dimension(3);
- mat_weights_rows = weights->dimension(0) * weights->dimension(1) * weights->dimension(2) + (append_bias ? 1 : 0);
+ mat_weights_rows = weights->dimension(idx_width) * weights->dimension(idx_height) * weights->dimension(idx_channel) + ((append_bias && !skip_im2col) ? 1 : 0);
+ skip_im2col = (data_layout == DataLayout::NHWC && kernel_width == 1 && kernel_height == 1);
- std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(0), input->dimension(1), kernel_width, kernel_height,
+ std::tie(conv_w, conv_h) = scaled_dimensions(input->dimension(idx_width), input->dimension(idx_height), kernel_width, kernel_height,
conv_info, dilation);
// Check if its a "fully connected" convolution
@@ -211,9 +229,9 @@
NEGEMMConvolutionLayer::NEGEMMConvolutionLayer(const std::shared_ptr<IMemoryManager> &memory_manager)
: _asm_glue(), _memory_group(memory_manager), _input_im2col_kernel(), _input_interleave_kernel(), _reshape_weights(), _mm_kernel(), _mm_gemmlowp(memory_manager), _gemmlowp_output_stage(),
- _output_col2im_kernel(), _activationlayer_function(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(), _tmp_output(),
- _workspace(), _B_pretransposed(), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false), _is_interleaved(false),
- _is_activationlayer_enabled(false)
+ _output_col2im_kernel(), _activationlayer_function(), _add_bias_kernel(), _original_weights(nullptr), _input_im2col_reshaped(), _input_interleaved_reshaped(), _weights_reshaped(), _gemm_output(),
+ _tmp_output(), _workspace(), _B_pretransposed(), _data_layout(DataLayout::NCHW), _append_bias(false), _is_fully_connected_convolution(false), _are_weights_reshaped(false), _is_quantized(false),
+ _is_interleaved(false), _is_activationlayer_enabled(false), _skip_im2col(false)
{
}
@@ -255,7 +273,13 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, act_info, dt, _append_bias,
+ _data_layout = input->info()->data_layout();
+ const bool is_nhwc = _data_layout == DataLayout::NHWC;
+ const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+ const int idx_channel = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+
+ Status status = validate_and_initialize_values(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), conv_info, weights_info, act_info, dt, _append_bias, _skip_im2col,
_are_weights_reshaped,
kernel_width, kernel_height,
_is_fully_connected_convolution, _is_interleaved, _is_quantized, _is_activationlayer_enabled,
@@ -272,20 +296,12 @@
// Reshape weights if needed
if(run_optimised)
{
- if(_are_weights_reshaped)
- {
- mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(1);
- }
- else
- {
- TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
+ TensorShape reshaped_weights_shape{ mat_weights_cols, mat_weights_rows };
- // 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, &_weights_reshaped, false /* 1xW transpose */);
- weights = &_weights_reshaped;
- }
+ // 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, &_weights_reshaped, false /* 1xW transpose */);
+ weights = &_weights_reshaped;
}
else
{
@@ -294,12 +310,12 @@
if(_is_fully_connected_convolution || _is_quantized)
{
mat_weights_cols = weights_info.num_kernels();
- mat_weights_rows = weights->info()->dimension(1);
+ mat_weights_rows = weights->info()->dimension(idx_height);
}
else
{
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);
+ mat_weights_rows = weights_info.kernel_size().first * weights_info.kernel_size().second * input->info()->dimension(idx_channel) + (_append_bias ? 1 : 0);
}
}
else
@@ -325,48 +341,56 @@
}
}
- // Create tensor to store im2col reshaped inputs
- const unsigned int mat_input_cols = mat_weights_rows;
- const unsigned int mat_input_rows = conv_w * conv_h;
-
- TensorShape shape_im2col(input->info()->tensor_shape());
- shape_im2col.set(0, mat_input_cols);
- shape_im2col.set(1, mat_input_rows);
- shape_im2col.set(2, 1);
- _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
- _memory_group.manage(&_input_im2col_reshaped);
-
- // Create tensor (interleave) to prepare input tensor for GEMM
- if(!_is_fully_connected_convolution && !run_optimised && _is_interleaved)
+ // In case we skip im2col we have to add bias
+ if(!_skip_im2col)
{
- TensorShape shape_interleaved(shape_im2col);
- shape_interleaved.set(0, shape_interleaved.x() * 4);
- shape_interleaved.set(1, std::ceil(shape_interleaved.y() / 4.f));
- _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
- _memory_group.manage(&_input_interleaved_reshaped);
+ const unsigned int mat_input_cols = mat_weights_rows;
+ const unsigned int mat_input_rows = conv_w * conv_h;
+
+ // Create tensor to store im2col reshaped inputs
+ TensorShape shape_im2col(input->info()->tensor_shape());
+ shape_im2col.set(0, mat_input_cols);
+ shape_im2col.set(1, mat_input_rows);
+ shape_im2col.set(2, 1);
+ _input_im2col_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col));
+ _memory_group.manage(&_input_im2col_reshaped);
+
+ // Create tensor (interleave) to prepare input tensor for GEMM
+ if(!_is_fully_connected_convolution && !run_optimised && _is_interleaved)
+ {
+ TensorShape shape_interleaved(shape_im2col);
+ shape_interleaved.set(idx_width, shape_interleaved.x() * 4);
+ shape_interleaved.set(idx_height, std::ceil(shape_interleaved[idx_height] / 4.f));
+ _input_interleaved_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_interleaved));
+ _memory_group.manage(&_input_interleaved_reshaped);
+ }
+
+ // Create GEMM output tensor
+ TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
+ shape_gemm.set(0, mat_weights_cols);
+ shape_gemm.set(1, mat_input_rows);
+ const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
+ // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
+ TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
+ info_gemm.set_quantization_info(output->info()->quantization_info());
+ _gemm_output.allocator()->init(info_gemm);
+
+ // FIXME: enabling memory manager for _gemm_output gives incorrect results (maybe bound to the assembly kernel in GEMMLowp?)
+ // _memory_group.manage(&_gemm_output);
+
+ // Configure im2col
+ _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
}
-
- // Create GEMM output tensor
- TensorShape shape_gemm(_input_im2col_reshaped.info()->tensor_shape());
- shape_gemm.set(0, mat_weights_cols);
- shape_gemm.set(1, mat_input_rows);
- const DataType gemm_data_type = _is_quantized ? DataType::S32 : dt;
- // GEMM output should be S32 for acquiring raw integer accumulator without quantized postprocessing for quantized asymmetric input.
- TensorInfo info_gemm(shape_gemm, 1, gemm_data_type, input->info()->fixed_point_position());
- info_gemm.set_quantization_info(output->info()->quantization_info());
- _gemm_output.allocator()->init(info_gemm);
-
- // FIXME: enabling memory manager for _gemm_output gives incorrect results (maybe bound to the assembly kernel in GEMMLowp?)
- // _memory_group.manage(&_gemm_output);
-
- // Configure kernels
- // Configure im2col
- _input_im2col_kernel.configure(input, &_input_im2col_reshaped, Size2D(kernel_width, kernel_height), conv_info, _append_bias, false, false, dilation);
+ else if(_append_bias)
+ {
+ // Configure add bias kernel
+ _add_bias_kernel.configure(output, biases, output, ConvertPolicy::SATURATE);
+ }
// Configure matrix multiply
if(run_optimised)
{
- if(!setup_assembly_kernel(&_input_im2col_reshaped, weights, &_gemm_output, 1.f, 0.f, true, _workspace, _B_pretransposed, _memory_group, _asm_glue))
+ if(!setup_assembly_kernel(_skip_im2col ? input : &_input_im2col_reshaped, weights, is_nhwc ? output : &_gemm_output, 1.f, 0.f, true, _workspace, _B_pretransposed, _memory_group, _asm_glue))
{
ARM_COMPUTE_ERROR("setup_assembly_kernel failed.");
}
@@ -379,8 +403,8 @@
_input_interleave_kernel.configure(&_input_im2col_reshaped, &_input_interleaved_reshaped);
// Configure GEMM
- 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)));
+ configure_mm(&_input_interleaved_reshaped, weights, &_gemm_output, _is_interleaved, GEMMReshapeInfo(_input_im2col_reshaped.info()->dimension(idx_height), 0 /* no transpose */,
+ _input_im2col_reshaped.info()->dimension(idx_width)));
_input_interleaved_reshaped.allocator()->allocate();
}
else
@@ -389,29 +413,36 @@
}
}
- _input_im2col_reshaped.allocator()->allocate();
-
- // Configure output stage for quantized case
- if(_is_quantized)
+ if(!_skip_im2col)
{
- const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+ _input_im2col_reshaped.allocator()->allocate();
- float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
- int output_multiplier, output_shift;
- quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
- _memory_group.manage(&_tmp_output);
- _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+ // Configure output stage for quantized case
+ if(_is_quantized)
+ {
+ const QuantizationInfo output_quant_info = (output->info()->total_size() == 0) ? input->info()->quantization_info() : output->info()->quantization_info();
+
+ float multiplier = input->info()->quantization_info().scale * weights->info()->quantization_info().scale / output_quant_info.scale;
+ int output_multiplier, output_shift;
+ quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+ _memory_group.manage(&_tmp_output);
+ _gemmlowp_output_stage.configure(&_gemm_output, biases, &_tmp_output, output_multiplier, output_shift, output_quant_info.offset);
+ }
+
+ // Configure Col2Im
+ if(!is_nhwc)
+ {
+ _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
+ }
+
+ if(_is_quantized)
+ {
+ _tmp_output.allocator()->allocate();
+ }
+ _gemm_output.allocator()->allocate();
}
- // Configure Col2Im
- _output_col2im_kernel.configure(_is_quantized ? &_tmp_output : &_gemm_output, output, Size2D(conv_w, conv_h));
- if(_is_quantized)
- {
- _tmp_output.allocator()->allocate();
- }
- _gemm_output.allocator()->allocate();
-
- ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(0) != conv_w) || (output->info()->dimension(1) != conv_h), "Output shape does not match the expected one");
+ ARM_COMPUTE_ERROR_ON_MSG((output->info()->dimension(idx_width) != conv_w) || (output->info()->dimension(idx_height) != conv_h), "Output shape does not match the expected one");
// Allocate intermediate tensor
if(!_are_weights_reshaped)
@@ -433,6 +464,7 @@
DataType dt{};
bool append_bias{};
+ bool skip_im2col{};
bool are_weights_reshaped{};
bool is_fully_connected_convolution{};
bool is_interleaved{};
@@ -445,7 +477,12 @@
unsigned int conv_w = 0;
unsigned int conv_h = 0;
- Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, act_info, dt, append_bias, are_weights_reshaped, kernel_width, kernel_height,
+ const DataLayout data_layout = input->data_layout();
+ const bool is_nhwc = data_layout == DataLayout::NHWC;
+ const int idx_width = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT);
+
+ Status status = validate_and_initialize_values(input, weights, biases, conv_info, weights_info, act_info, dt, append_bias, skip_im2col, are_weights_reshaped, kernel_width, kernel_height,
is_fully_connected_convolution, is_interleaved, is_quantized, is_activationlayer_enabled, mat_weights_cols, mat_weights_rows,
conv_w, conv_h, dilation);
@@ -461,7 +498,6 @@
optimised_kernel = true;
}
- // Validate im2col
const unsigned int mat_input_cols = mat_weights_rows;
const unsigned int mat_input_rows = conv_w * conv_h;
TensorShape shape_im2col = input->tensor_shape();
@@ -469,7 +505,17 @@
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, kernel_weights, conv_info, append_bias, false, false, dilation));
+
+ if(!skip_im2col)
+ {
+ // Validate im2col
+ ARM_COMPUTE_RETURN_ON_ERROR(NEIm2ColKernel::validate(input, &im2_col_info, kernel_weights, conv_info, append_bias, false, false, dilation));
+ }
+ else if(append_bias)
+ {
+ // Validate add bias kernel
+ ARM_COMPUTE_RETURN_ON_ERROR(NEArithmeticAdditionKernel::validate(output, biases, output, ConvertPolicy::SATURATE));
+ }
// Create GEMM output tensor
TensorShape shape_gemm(im2_col_info.tensor_shape());
@@ -511,8 +557,8 @@
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));
+ shape_interleaved.set(idx_width, shape_interleaved.x() * 4);
+ shape_interleaved.set(idx_height, 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, 1.f, is_interleaved, GEMMReshapeInfo(shape_im2col[1], // m
@@ -524,10 +570,12 @@
ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixMultiplyKernel::validate(&im2_col_info, weights, &gemm_output_info, 1.f, is_interleaved, GEMMReshapeInfo()));
}
}
+ if(!is_nhwc)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NECol2ImKernel::validate(&gemm_output_info, output, Size2D(conv_w, conv_h)));
+ }
- 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");
+ ARM_COMPUTE_RETURN_ERROR_ON_MSG((output->dimension(idx_width) != conv_w) || (output->dimension(idx_height) != conv_h), "Output shape does not match the expected one");
if(act_info.enabled())
{
@@ -553,8 +601,12 @@
_memory_group.acquire();
- // Run input reshaping
- NEScheduler::get().schedule(&_input_im2col_kernel, Window::DimY);
+ if(!_skip_im2col)
+ {
+ // Run input reshaping
+ unsigned int _y_dim = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+ NEScheduler::get().schedule(&_input_im2col_kernel, _y_dim);
+ }
// Runs matrix multiply on reshaped matrices
if(_asm_glue._optimised_kernel != nullptr)
@@ -585,6 +637,11 @@
}
}
+ if(_skip_im2col && _append_bias)
+ {
+ NEScheduler::get().schedule(&_add_bias_kernel, Window::DimY);
+ }
+
// Run output stage for quantized case
if(_is_quantized)
{
@@ -592,7 +649,10 @@
}
// Reshape output matrix
- NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+ if(_data_layout == DataLayout::NCHW)
+ {
+ NEScheduler::get().schedule(&_output_col2im_kernel, Window::DimY);
+ }
if(_is_activationlayer_enabled)
{
diff --git a/tests/validation/CL/ConvolutionLayer.cpp b/tests/validation/CL/ConvolutionLayer.cpp
index a2b55a8..935a6eb 100644
--- a/tests/validation/CL/ConvolutionLayer.cpp
+++ b/tests/validation/CL/ConvolutionLayer.cpp
@@ -198,20 +198,22 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
ActivationFunctionsDataset))
{
// Validate output
@@ -221,20 +223,22 @@
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
ActivationFunctionsDataset))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
ActivationFunctionsDataset))
{
// Validate output
diff --git a/tests/validation/CL/DilatedConvolutionLayer.cpp b/tests/validation/CL/DilatedConvolutionLayer.cpp
index 9ee002c..d02497d 100644
--- a/tests/validation/CL/DilatedConvolutionLayer.cpp
+++ b/tests/validation/CL/DilatedConvolutionLayer.cpp
@@ -164,17 +164,19 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
@@ -183,17 +185,19 @@
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(CLAccessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, CLGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
diff --git a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
index bc0170f..0f81512 100644
--- a/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
+++ b/tests/validation/GLES_COMPUTE/ConvolutionLayer.cpp
@@ -117,19 +117,23 @@
TEST_SUITE(Float)
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
+ framework::dataset::make("DataLayout",
+ DataLayout::NCHW)),
ActivationFunctionsDataset))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_f16, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType",
DataType::F16)),
+ framework::dataset::make("DataLayout",
+ DataLayout::NCHW)),
ActivationFunctionsDataset))
{
// Validate output
@@ -138,17 +142,21 @@
TEST_SUITE_END()
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunSmall, GCConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout",
+ DataLayout::NCHW)),
ActivationFunctionsDataset))
{
// Validate output
validate(GCAccessor(_target), _reference, tolerance_f32, tolerance_num);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+FIXTURE_DATA_TEST_CASE(RunLarge, GCConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout",
+ DataLayout::NCHW)),
ActivationFunctionsDataset))
{
// Validate output
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 8b2e21e..4f59345 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -194,17 +194,19 @@
TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
ActivationFunctionsDataset))
{
// Validate output
@@ -214,17 +216,19 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
ActivationFunctionsDataset))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
ActivationFunctionsDataset))
{
// Validate output
@@ -240,7 +244,7 @@
TEST_SUITE(QS8)
// We test for fixed point precision [4,6]
FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS8)),
framework::dataset::make("FractionalBits", 4, 7)),
ActivationFunctionsDataset))
@@ -249,7 +253,7 @@
validate(Accessor(_target), _reference, tolerance_q);
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS8)),
framework::dataset::make("FractionalBits", 4, 7)),
ActivationFunctionsDataset))
@@ -262,7 +266,7 @@
TEST_SUITE(QS16)
// Testing for fixed point position [1,14)
FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::TinyConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS16)),
framework::dataset::make("FractionalBits", 1, 14)),
ActivationFunctionsDataset))
@@ -271,7 +275,7 @@
validate(Accessor(_target), _reference, tolerance_q);
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS16)),
framework::dataset::make("FractionalBits", 1, 14)),
ActivationFunctionsDataset))
diff --git a/tests/validation/NEON/DilatedConvolutionLayer.cpp b/tests/validation/NEON/DilatedConvolutionLayer.cpp
index 358cec3..d9fd093 100644
--- a/tests/validation/NEON/DilatedConvolutionLayer.cpp
+++ b/tests/validation/NEON/DilatedConvolutionLayer.cpp
@@ -157,17 +157,19 @@
TEST_SUITE(Float)
#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
TEST_SUITE(FP16)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f16);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F16)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
@@ -177,17 +179,19 @@
#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
TEST_SUITE(FP32)
-FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
validate(Accessor(_target), _reference, tolerance_f32);
}
-FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+FIXTURE_DATA_TEST_CASE(RunLarge, NEGEMMDilatedConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(datasets::LargeDilatedConvolutionLayerDataset(),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::F32)),
+ framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
{
// Validate output
@@ -204,7 +208,7 @@
// We test for fixed point precision [4,6]
FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS8)),
framework::dataset::make("FractionalBits", 4, 7)),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
@@ -214,7 +218,7 @@
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixedPointFixture<int8_t>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS8)),
framework::dataset::make("FractionalBits", 4, 7)),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
@@ -228,7 +232,7 @@
// Testing for fixed point position [1,14)
FIXTURE_DATA_TEST_CASE(RunTiny, NEGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::PRECOMMIT,
combine(combine(combine(combine(datasets::TinyDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS16)),
framework::dataset::make("FractionalBits", 1, 14)),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
@@ -238,7 +242,7 @@
}
FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMDilatedConvolutionLayerFixedPointFixture<int16_t>, framework::DatasetMode::NIGHTLY,
combine(combine(combine(combine(datasets::SmallDilatedConvolutionLayerDataset(),
- framework::dataset::make("ReshapeWeights", { true, false })),
+ framework::dataset::make("ReshapeWeights", { true })),
framework::dataset::make("DataType", DataType::QS16)),
framework::dataset::make("FractionalBits", 1, 14)),
framework::dataset::make("ActivationLayerInfo", ActivationLayerInfo())))
diff --git a/tests/validation/fixtures/ConvolutionLayerFixture.h b/tests/validation/fixtures/ConvolutionLayerFixture.h
index 1bcffed..93de24d 100644
--- a/tests/validation/fixtures/ConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/ConvolutionLayerFixture.h
@@ -35,6 +35,7 @@
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/ActivationLayer.h"
#include "tests/validation/reference/ConvolutionLayer.h"
+#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/Utils.h"
#include <random>
@@ -56,13 +57,14 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights,
- DataType data_type, int fractional_bits, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
+ DataType data_type, DataLayout data_layout, int fractional_bits, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
_data_type = data_type;
_is_quantized = is_data_type_quantized_asymmetric(data_type);
_bias_data_type = _is_quantized ? DataType::S32 : data_type;
_fractional_bits = fractional_bits;
_quantization_info = quantization_info;
+ _data_layout = data_layout;
_target = compute_target(input_shape, weights_shape, bias_shape, output_shape, info, reshape_weights, dilation, act_info);
_reference = compute_reference(input_shape, weights_shape, bias_shape, output_shape, info, dilation, act_info);
@@ -98,46 +100,27 @@
}
}
- TensorType compute_target(const TensorShape &input_shape, const TensorShape &weights_shape, const TensorShape &bias_shape, const TensorShape &output_shape, const PadStrideInfo &info,
+ TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, const TensorShape &bias_shape, TensorShape output_shape, const PadStrideInfo &info,
bool reshape_weights, const Size2D &dilation, const ActivationLayerInfo act_info)
{
- const bool is_optimised = std::is_same<FunctionType, NEConvolutionLayer>::value && _data_type == DataType::F32;
-
- WeightsInfo weights_info(!reshape_weights, weights_shape.x(), weights_shape.y(), weights_shape[3]);
- TensorShape reshaped_weights_shape(weights_shape);
-
- if(!reshape_weights)
+ if(_data_layout == DataLayout::NHWC)
{
- // Check if its a "fully connected" convolution
- const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
-
- reshaped_weights_shape.collapse(3);
-
- if(bias_shape.total_size() > 0 && !_is_quantized)
- {
- // Add bias to the weights reshaped matrix
- reshaped_weights_shape.set(0, reshaped_weights_shape.x() + 1);
- }
-
- if(is_fully_connected_convolution || is_optimised)
- {
- 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);
- }
- else
- {
- const int interleave_width = 16 / data_size_from_type(_data_type);
- reshaped_weights_shape.set(0, reshaped_weights_shape.x() * interleave_width);
- reshaped_weights_shape.set(1, static_cast<unsigned int>(std::ceil(reshaped_weights_shape.y() / static_cast<float>(interleave_width))));
- }
+ permute(input_shape, PermutationVector(2U, 0U, 1U));
+ permute(weights_shape, PermutationVector(2U, 0U, 1U));
+ permute(output_shape, PermutationVector(2U, 0U, 1U));
}
+ const int idx_width = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
+ const int idx_height = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
+
+ WeightsInfo weights_info(!reshape_weights, weights_shape[idx_width], weights_shape[idx_height], weights_shape[3]);
+ TensorShape reshaped_weights_shape(weights_shape);
+
// Create tensors
- TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _fractional_bits, _quantization_info);
- TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _fractional_bits, _quantization_info);
- TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info);
- TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _fractional_bits, _quantization_info);
+ TensorType src = create_tensor<TensorType>(input_shape, _data_type, 1, _fractional_bits, _quantization_info, _data_layout);
+ TensorType weights = create_tensor<TensorType>(reshaped_weights_shape, _data_type, 1, _fractional_bits, _quantization_info, _data_layout);
+ TensorType bias = create_tensor<TensorType>(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info, _data_layout);
+ TensorType dst = create_tensor<TensorType>(output_shape, _data_type, 1, _fractional_bits, _quantization_info, _data_layout);
// Create and configure function
FunctionType conv;
@@ -161,48 +144,8 @@
// Fill tensors
fill(AccessorType(src), 0);
-
- if(!reshape_weights)
- {
- const bool is_fully_connected_convolution = (output_shape.x() == 1 && output_shape.y() == 1);
- TensorShape tmp_weights_shape(weights_shape);
- SimpleTensor<T> tmp_weights(tmp_weights_shape, _data_type, 1, _fractional_bits, _quantization_info);
-
- // Fill with original shape
- fill(tmp_weights, 1);
-
- if(_is_quantized)
- {
- fill(AccessorType(bias), 2);
- tmp_weights = linearise_weights(tmp_weights);
- }
- else
- {
- SimpleTensor<T> tmp_bias(bias_shape, _bias_data_type, 1, _fractional_bits, _quantization_info);
- fill(tmp_bias, 2);
- tmp_weights = linearise_weights(tmp_weights, &tmp_bias);
- }
-
- if(!is_fully_connected_convolution && !is_optimised)
- {
- // Transpose with interleave
- const int interleave_size = 16 / tmp_weights.element_size();
- tmp_weights = transpose(std::move(tmp_weights), interleave_size);
- }
-
- AccessorType weights_accessor(weights);
-
- for(int i = 0; i < tmp_weights.num_elements(); ++i)
- {
- Coordinates coord = index2coord(tmp_weights.shape(), i);
- std::copy_n(static_cast<const T *>(tmp_weights(coord)), 1, static_cast<T *>(weights_accessor(coord)));
- }
- }
- else
- {
- fill(AccessorType(weights), 1);
- fill(AccessorType(bias), 2);
- }
+ fill(AccessorType(weights), 1);
+ fill(AccessorType(bias), 2);
// Compute NEConvolutionLayer function
conv.run();
@@ -232,53 +175,10 @@
SimpleTensor<T> _reference{};
DataType _data_type{};
DataType _bias_data_type{};
+ DataLayout _data_layout{};
int _fractional_bits{};
QuantizationInfo _quantization_info{};
bool _is_quantized = false;
-
-private:
- template <typename U>
- SimpleTensor<U> linearise_weights(const SimpleTensor<U> &weights, const SimpleTensor<U> *biases = nullptr)
- {
- TensorShape dst_shape(weights.shape());
- dst_shape.collapse(3);
-
- if(biases != nullptr)
- {
- dst_shape.set(0, dst_shape.x() + 1);
- }
-
- const size_t shape_x = dst_shape.x();
- dst_shape.set(0, dst_shape.y());
- dst_shape.set(1, shape_x);
-
- SimpleTensor<U> dst(dst_shape, weights.data_type());
-
- // Don't iterate over biases yet
- for(int weights_idx = 0; weights_idx < weights.num_elements(); ++weights_idx)
- {
- Coordinates weights_coord = index2coord(weights.shape(), weights_idx);
- const int dst_row = weights_idx % weights.shape().total_size_lower(3);
- Coordinates dst_coord{ weights_coord[3], dst_row, weights_coord[4] };
- const int dst_idx = coord2index(dst.shape(), dst_coord);
-
- dst[dst_idx] = weights[weights_idx];
- }
- if(biases != nullptr)
- {
- // Fill last row with biases
- for(int bias_idx = 0; bias_idx < biases->num_elements(); ++bias_idx)
- {
- Coordinates bias_coord = index2coord(biases->shape(), bias_idx);
- Coordinates dst_coord{ bias_coord.x(), static_cast<int>(dst.shape().y()) - 1, bias_coord.y() };
- int dst_idx = coord2index(dst.shape(), dst_coord);
-
- dst[dst_idx] = (*biases)[bias_idx];
- }
- }
-
- return dst;
- }
};
template <typename TensorType, typename AccessorType, typename FunctionType, typename T>
@@ -287,11 +187,10 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
- ActivationLayerInfo act_info)
+ DataLayout data_layout, ActivationLayerInfo act_info)
{
- ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, 0,
- QuantizationInfo(),
- act_info);
+ ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, data_layout, 0,
+ QuantizationInfo(), act_info);
}
};
@@ -301,11 +200,11 @@
public:
template <typename...>
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
- int fractional_bits,
- ActivationLayerInfo act_info)
+ int fractional_bits, ActivationLayerInfo act_info)
{
- ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, fractional_bits,
- QuantizationInfo(), act_info);
+ ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type,
+ DataLayout::NCHW,
+ fractional_bits, QuantizationInfo(), act_info);
}
};
@@ -317,7 +216,8 @@
void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation, bool reshape_weights, DataType data_type,
QuantizationInfo quantization_info, ActivationLayerInfo act_info)
{
- ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type, 0,
+ ConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, reshape_weights, data_type,
+ DataLayout::NCHW, 0,
quantization_info, act_info);
}
};
diff --git a/tests/validation/reference/ConvolutionLayer.cpp b/tests/validation/reference/ConvolutionLayer.cpp
index 617e85c..fe558ba 100644
--- a/tests/validation/reference/ConvolutionLayer.cpp
+++ b/tests/validation/reference/ConvolutionLayer.cpp
@@ -26,6 +26,7 @@
#include "tests/validation/FixedPoint.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/Convolution3d.h"
+#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/Utils.h"
#include "tests/validation/reference/UtilsQuantizedAsymm.h"
@@ -46,12 +47,9 @@
} // namespace
template <typename T, typename TB>
-SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info,
- const Size2D &dilation)
+SimpleTensor<T> convolution_layer_nchw(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, SimpleTensor<T> &dst, const PadStrideInfo &info,
+ const Size2D &dilation)
{
- // Create reference
- SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
-
// Compute reference
const int width_in = src.shape().x();
const int height_in = src.shape().y();
@@ -105,6 +103,26 @@
return dst;
}
+template <typename T, typename TB>
+SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor<T> &weights, const SimpleTensor<TB> &bias, const TensorShape &output_shape, const PadStrideInfo &info,
+ const Size2D &dilation)
+{
+ // Create reference
+ SimpleTensor<T> dst{ output_shape, src.data_type(), 1, src.fixed_point_position(), src.quantization_info() };
+
+ if(src.data_layout() == DataLayout::NHWC)
+ {
+ SimpleTensor<T> src_nchw = reference::permute<T>(src, PermutationVector(1U, 2U, 0U));
+ SimpleTensor<T> weights_nchw = reference::permute<T>(weights, PermutationVector(1U, 2U, 0U));
+ SimpleTensor<T> dst_nchw = reference::permute<T>(dst, PermutationVector(1U, 2U, 0U));
+
+ return reference::permute<T>(convolution_layer_nchw(src_nchw, weights_nchw, bias, dst_nchw, info, dilation), PermutationVector(2U, 0U, 1U));
+ }
+ else
+ {
+ return convolution_layer_nchw(src, weights, bias, dst, info, dilation);
+ }
+}
template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,
const PadStrideInfo &info, const Size2D &dilation);
diff --git a/tests/validation/reference/Permute.cpp b/tests/validation/reference/Permute.cpp
index c670c3e..bbb2e8d 100644
--- a/tests/validation/reference/Permute.cpp
+++ b/tests/validation/reference/Permute.cpp
@@ -57,11 +57,11 @@
return dst;
}
+template SimpleTensor<int8_t> permute(const SimpleTensor<int8_t> &src, PermutationVector perm);
template SimpleTensor<uint8_t> permute(const SimpleTensor<uint8_t> &src, PermutationVector perm);
+template SimpleTensor<int16_t> permute(const SimpleTensor<int16_t> &src, PermutationVector perm);
template SimpleTensor<uint16_t> permute(const SimpleTensor<uint16_t> &src, PermutationVector perm);
template SimpleTensor<uint32_t> permute(const SimpleTensor<uint32_t> &src, PermutationVector perm);
-template SimpleTensor<int8_t> permute(const SimpleTensor<int8_t> &src, PermutationVector perm);
-template SimpleTensor<int16_t> permute(const SimpleTensor<int16_t> &src, PermutationVector perm);
template SimpleTensor<float> permute(const SimpleTensor<float> &src, PermutationVector perm);
template SimpleTensor<half> permute(const SimpleTensor<half> &src, PermutationVector perm);
} // namespace reference