arm_compute v18.11
diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
index 24b12f4..a2f0094 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -36,14 +36,16 @@
using namespace arm_compute::misc::shape_calculator;
NEDepthwiseConvolutionLayer3x3::NEDepthwiseConvolutionLayer3x3()
- : _dwc_kernel(), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), _accumulator(), _permuted_input(), _permuted_weights(), _permuted_output(),
- _has_bias(false), _is_quantized(false), _is_optimized(false), _are_weights_reshaped(false), _is_nchw(true), _is_first_run(true), _permute(false)
+ : _dwc_kernel(), _output_stage_kernel(), _border_handler(), _permute_input(), _permute_weights(), _permute_output(), _activationlayer_function(), _accumulator(), _permuted_input(),
+ _permuted_weights(), _permuted_output(), _has_bias(false), _is_quantized(false), _is_optimized(false), _are_weights_reshaped(false), _is_nchw(true), _is_first_run(true), _permute(false),
+ _is_activationlayer_enabled(false)
{
}
-void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
{
- ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32);
+ ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
PixelValue zero_value(0.f);
@@ -59,8 +61,25 @@
_is_nchw = input->info()->data_layout() == DataLayout::NCHW;
_permute = _is_optimized == _is_nchw;
+ // Initialize the intermediate accumulator tensor in case of quantized input
+ if(_is_quantized)
+ {
+ TensorShape accum_shape = output->info()->tensor_shape();
+ DataLayout accum_layout = output->info()->data_layout();
+ if(!_is_optimized && !_is_nchw)
+ {
+ permute(accum_shape, PermutationVector(1U, 2U, 0U));
+ accum_layout = DataLayout::NCHW;
+ }
+
+ _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32, input->info()->quantization_info()));
+ _accumulator.info()->set_data_layout(accum_layout);
+ zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().offset));
+ }
+
if(_is_optimized)
{
+ ITensor *optimized_output = (_is_quantized) ? &_accumulator : output;
if(_is_nchw)
{
// Configure the function to transform the input tensor from NCHW -> NHWC
@@ -75,8 +94,8 @@
_dwc_kernel.configure(&_permuted_input, &_permuted_weights, &_permuted_output, conv_info, depth_multiplier, DataLayout::NHWC);
// Configure the function to transform the convoluted output to ACL's native ordering format NCHW
- _permute_output.configure(&_permuted_output, output, PermutationVector(1U, 2U, 0U));
- _permuted_output.info()->set_data_layout(DataLayout::NCHW);
+ _permuted_output.info()->set_data_layout(DataLayout::NHWC);
+ _permute_output.configure(&_permuted_output, optimized_output, PermutationVector(1U, 2U, 0U));
// Allocate tensors
_permuted_input.allocator()->allocate();
@@ -85,26 +104,11 @@
}
else
{
- _dwc_kernel.configure(input, weights, output, conv_info, depth_multiplier, DataLayout::NHWC);
+ _dwc_kernel.configure(input, weights, optimized_output, conv_info, depth_multiplier, DataLayout::NHWC);
}
}
else
{
- // Allocate the intermediate accumulator tensor in case of quantized input
- if(_is_quantized)
- {
- TensorShape accum_shape = output->info()->tensor_shape();
-
- if(!_is_nchw)
- {
- permute(accum_shape, PermutationVector(1U, 2U, 0U));
- }
-
- _accumulator.allocator()->init(TensorInfo(accum_shape, 1, DataType::S32));
- _accumulator.info()->set_quantization_info(input->info()->quantization_info());
- zero_value = PixelValue(static_cast<uint32_t>(input->info()->quantization_info().offset));
- }
-
if(!_is_nchw)
{
// Configure the function to transform the input tensor from NHWC -> NCHW
@@ -143,7 +147,7 @@
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);
- _output_stage_kernel.configure(&_accumulator, biases, _is_nchw ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset);
+ _output_stage_kernel.configure(&_accumulator, biases, (_is_nchw || _is_optimized) ? output : &_permuted_output, output_multiplier, output_shift, output_quant_info.offset);
_accumulator.allocator()->allocate();
}
else if(_has_bias)
@@ -157,21 +161,46 @@
_permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U));
_permuted_output.allocator()->allocate();
}
+
+ //Configure Activation Layer
+ _is_activationlayer_enabled = act_info.enabled();
+
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.configure(output, nullptr, act_info);
+ }
}
Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- unsigned int depth_multiplier)
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW && input->data_layout() != DataLayout::NHWC);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
if(biases != nullptr)
{
+ const unsigned int channel_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL);
ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
- ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(3));
+ ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(channel_idx));
}
- return NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, output, conv_info, depth_multiplier);
+ const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type());
+ TensorInfo accumulator = TensorInfo(output->clone()->set_is_resizable(true).reset_padding().set_data_type(DataType::S32));
+
+ ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayer3x3Kernel::validate(input, weights, is_quantized ? &accumulator : output, conv_info, depth_multiplier));
+
+ if(is_quantized)
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&accumulator, biases, output));
+ }
+
+ //Validate Activation Layer
+ if(act_info.enabled())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info));
+ }
+
+ return Status{};
}
void NEDepthwiseConvolutionLayer3x3::run()
@@ -222,16 +251,22 @@
{
_permute_output.run();
}
+
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.run();
+ }
}
NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer()
: _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _v2mm_input_fill_border(), _v2mm_weights_fill_border(), _permute_input(),
- _permute_weights(), _permute_output(), _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _permuted_input(), _permuted_weights(), _permuted_output(), _is_prepared(false),
- _is_quantized(false), _is_nhwc(false), _original_weights(nullptr)
+ _permute_weights(), _permute_output(), _activationlayer_function(), _input_reshaped(), _weights_reshaped(), _v2mm_output(), _output_reshaped(), _permuted_input(), _permuted_weights(),
+ _permuted_output(), _is_prepared(false), _is_quantized(false), _is_nhwc(false), _is_activationlayer_enabled(false), _original_weights(nullptr)
{
}
-void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier)
+void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info,
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
{
const unsigned int channel_idx = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL);
ARM_COMPUTE_UNUSED(channel_idx);
@@ -353,13 +388,24 @@
// Allocate intermediate tensors
_input_reshaped.allocator()->allocate();
_v2mm_output.allocator()->allocate();
+
+ //Configure Activation Layer
+ _is_activationlayer_enabled = act_info.enabled();
+
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.configure(output, nullptr, act_info);
+ }
}
Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
- unsigned int depth_multiplier)
+ unsigned int depth_multiplier, const ActivationLayerInfo &act_info)
{
ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output);
- ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() != DataLayout::NCHW && input->data_layout() != DataLayout::NHWC);
+ ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN);
+
+ const unsigned int width_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+ const unsigned int height_idx = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
// Clone output to use auto init
auto output_clone = output->clone();
@@ -391,8 +437,8 @@
const size_t weights_w = weights_to_use->dimension(0);
const size_t weights_h = weights_to_use->dimension(1);
const size_t weights_z = weights_to_use->dimension(2);
- const unsigned int conv_w = output_shape.x();
- const unsigned int conv_h = output_shape.y();
+ const unsigned int conv_w = output_shape[width_idx];
+ const unsigned int conv_h = output_shape[height_idx];
const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0);
const size_t conv_size = conv_w * conv_h;
@@ -438,6 +484,12 @@
ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output_to_use));
}
+ // Validate Activation Layer
+ if(act_info.enabled())
+ {
+ ARM_COMPUTE_RETURN_ON_ERROR(NEActivationLayer::validate(output, nullptr, act_info));
+ }
+
return Status{};
}
@@ -463,6 +515,11 @@
{
_permute_output.run();
}
+
+ if(_is_activationlayer_enabled)
+ {
+ _activationlayer_function.run();
+ }
}
void NEDepthwiseConvolutionLayer::prepare()