From 44f5572f3d6ba8e39c4a18a991049992d590ce39 Mon Sep 17 00:00:00 2001 From: Giorgio Arena Date: Fri, 12 Jul 2019 14:49:49 +0100 Subject: COMPMID-2179 New generic depthwise convolution for NEON F32 NHWC Change-Id: I2b883785c0500d4bdb6ee4700382ee058be2cd36 Signed-off-by: Giorgio Arena Reviewed-on: https://review.mlplatform.org/c/1538 Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Reviewed-by: Gian Marco Iodice --- .../NEON/functions/NEDepthwiseConvolutionLayer.cpp | 414 +++++++++++---------- 1 file changed, 221 insertions(+), 193 deletions(-) (limited to 'src/runtime') diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp index 001bece933..c2ed901169 100644 --- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -689,9 +689,10 @@ void NEDepthwiseConvolutionLayerOptimized::prepare() } 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(), _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) + : _im2col_kernel(), _weights_reshape_kernel(), _v2mm_kernel(), _depthwise_conv_kernel(), _vector_to_tensor_kernel(), _output_stage_kernel(), _fill_border(), _v2mm_input_fill_border(), + _v2mm_weights_fill_border(), _permute_input(), _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), _is_optimized(false), + _original_weights(nullptr) { } @@ -703,123 +704,135 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh ARM_COMPUTE_ERROR_THROW_ON(NEDepthwiseConvolutionLayer::validate(input->info(), weights->info(), (biases == nullptr) ? nullptr : biases->info(), output->info(), conv_info, depth_multiplier, act_info, dilation)); - _is_nhwc = input->info()->data_layout() == DataLayout::NHWC; + _is_nhwc = input->info()->data_layout() == DataLayout::NHWC; + _is_optimized = _is_nhwc && input->info()->data_type() == DataType::F32; - ITensor *input_to_use = input; - const ITensor *weights_to_use = weights; - ITensor *output_to_use = output; - - if(_is_nhwc) + if(!_is_optimized) { - _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); - _permuted_input.info()->set_data_layout(DataLayout::NCHW); - input_to_use = &_permuted_input; + ITensor *input_to_use = input; + const ITensor *weights_to_use = weights; + ITensor *output_to_use = output; - _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); - _permuted_weights.info()->set_data_layout(DataLayout::NCHW); - weights_to_use = &_permuted_weights; - } + if(_is_nhwc) + { + _permute_input.configure(input, &_permuted_input, PermutationVector(1U, 2U, 0U)); + _permuted_input.info()->set_data_layout(DataLayout::NCHW); + input_to_use = &_permuted_input; - const size_t weights_w = weights_to_use->info()->dimension(0); - const size_t weights_h = weights_to_use->info()->dimension(1); - const size_t weights_z = weights_to_use->info()->dimension(2); + _permute_weights.configure(weights, &_permuted_weights, PermutationVector(1U, 2U, 0U)); + _permuted_weights.info()->set_data_layout(DataLayout::NCHW); + weights_to_use = &_permuted_weights; + } - _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); - _is_prepared = false; - _original_weights = weights_to_use; + const size_t weights_w = weights_to_use->info()->dimension(0); + const size_t weights_h = weights_to_use->info()->dimension(1); + const size_t weights_z = weights_to_use->info()->dimension(2); - // Should bias be appended ? - bool append_bias = (biases != nullptr) && !_is_quantized; + _is_quantized = is_data_type_quantized_asymmetric(input->info()->data_type()); + _is_prepared = false; + _original_weights = weights_to_use; - // Calculate output shape - TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation); + // Should bias be appended ? + bool append_bias = (biases != nullptr) && !_is_quantized; - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); + // Calculate output shape + TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation); - if(_is_nhwc) - { - permute(output_shape, PermutationVector(1U, 2U, 0U)); - _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); - _permuted_output.info()->set_data_layout(DataLayout::NCHW); - _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); - output_to_use = &_permuted_output; - } - - // Output width and height - const unsigned int conv_w = output_shape.x(); - const unsigned int conv_h = output_shape.y(); - - // Set up intermediate tensors - const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0); - const size_t conv_size = conv_w * conv_h; - - // Im2Col configuration - TensorShape shape_im2col = input_to_use->info()->tensor_shape(); - shape_im2col.set(0, patch_size); - shape_im2col.set(1, conv_size); - shape_im2col.set(2, weights_z); - _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); - _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation); - - // Weights reshape configuration - const TensorShape shape_weights_reshape(patch_size, weights_z); - _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); - _weights_reshape_kernel.configure(weights_to_use, &_weights_reshaped, append_bias ? biases : nullptr); - - // GEMV configuration - DataType v2mm_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type(); - TensorShape shape_v2mm_out = input_to_use->info()->tensor_shape(); - shape_v2mm_out.set(0, conv_size * weights_z); - shape_v2mm_out.set(1, 1); - shape_v2mm_out.set(2, 1); - _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); - _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output); - _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); - _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output_to_use, conv_w, conv_h); - - // Output staged configuration - if(_is_quantized) - { - const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); + ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(output->info()->tensor_shape(), output_shape); - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int output_multiplier; - int output_shift; - quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); - _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset); - _output_reshaped.allocator()->allocate(); - } + if(_is_nhwc) + { + permute(output_shape, PermutationVector(1U, 2U, 0U)); + _permuted_output.allocator()->init(output->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); + _permuted_output.info()->set_data_layout(DataLayout::NCHW); + _permuted_output.info()->set_quantization_info(output->info()->quantization_info()); + output_to_use = &_permuted_output; + } - if(_is_nhwc) - { - _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); + // Output width and height + const unsigned int conv_w = output_shape.x(); + const unsigned int conv_h = output_shape.y(); + + // Set up intermediate tensors + const size_t patch_size = weights_w * weights_h + (append_bias ? 1 : 0); + const size_t conv_size = conv_w * conv_h; + + // Im2Col configuration + TensorShape shape_im2col = input_to_use->info()->tensor_shape(); + shape_im2col.set(0, patch_size); + shape_im2col.set(1, conv_size); + shape_im2col.set(2, weights_z); + _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); + _im2col_kernel.configure(input_to_use, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation); + + // Weights reshape configuration + const TensorShape shape_weights_reshape(patch_size, weights_z); + _weights_reshaped.allocator()->init(weights->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); + _weights_reshape_kernel.configure(weights_to_use, &_weights_reshaped, append_bias ? biases : nullptr); + + // GEMV configuration + DataType v2mm_dt = (input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : input->info()->data_type(); + TensorShape shape_v2mm_out = input_to_use->info()->tensor_shape(); + shape_v2mm_out.set(0, conv_size * weights_z); + shape_v2mm_out.set(1, 1); + shape_v2mm_out.set(2, 1); + _v2mm_output.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); + _v2mm_kernel.configure(&_input_reshaped, &_weights_reshaped, &_v2mm_output); + _output_reshaped.allocator()->init(_v2mm_output.info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); + _vector_to_tensor_kernel.configure(&_v2mm_output, (_is_quantized) ? &_output_reshaped : output_to_use, conv_w, conv_h); + + // Output staged configuration + if(_is_quantized) + { + const UniformQuantizationInfo iq_info = input->info()->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->info()->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->info()->quantization_info().uniform(); - _permuted_input.allocator()->allocate(); - _permuted_weights.allocator()->allocate(); - _permuted_output.allocator()->allocate(); - } + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; + int output_multiplier; + int output_shift; + quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift); + _output_stage_kernel.configure(&_output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset); + _output_reshaped.allocator()->allocate(); + } - // Fill borders on inputs - PixelValue zero_in(static_cast(0)); - PixelValue zero_w(static_cast(0)); - if(_is_quantized) - { - zero_in = PixelValue(static_cast(input->info()->quantization_info().uniform().offset)); - zero_w = PixelValue(static_cast(weights->info()->quantization_info().uniform().offset)); - } - BorderSize border_size = _v2mm_kernel.border_size(); - _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in); + if(_is_nhwc) + { + _permute_output.configure(&_permuted_output, output, PermutationVector(2U, 0U, 1U)); + + _permuted_input.allocator()->allocate(); + _permuted_weights.allocator()->allocate(); + _permuted_output.allocator()->allocate(); + } + + // Fill borders on inputs + PixelValue zero_in(static_cast(0)); + PixelValue zero_w(static_cast(0)); + if(_is_quantized) + { + zero_in = PixelValue(static_cast(input->info()->quantization_info().uniform().offset)); + zero_w = PixelValue(static_cast(weights->info()->quantization_info().uniform().offset)); + } + BorderSize border_size = _v2mm_kernel.border_size(); + _v2mm_input_fill_border.configure(&_input_reshaped, border_size, BorderMode::CONSTANT, zero_in); + + border_size.bottom = 0; + _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w); - border_size.bottom = 0; - _v2mm_weights_fill_border.configure(&_weights_reshaped, border_size, BorderMode::CONSTANT, zero_w); + // Allocate intermediate tensors + _input_reshaped.allocator()->allocate(); + _v2mm_output.allocator()->allocate(); + } + else + { + // Configure kernel + _depthwise_conv_kernel.configure(input, weights, biases, output, conv_info, depth_multiplier, dilation); - // Allocate intermediate tensors - _input_reshaped.allocator()->allocate(); - _v2mm_output.allocator()->allocate(); + // Fill input borders + _fill_border.configure(input, _depthwise_conv_kernel.border_size(), BorderMode::CONSTANT, PixelValue(static_cast(0), input->info()->data_type())); + } //Configure Activation Layer _is_activationlayer_enabled = act_info.enabled(); @@ -845,89 +858,96 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(height_idx) + (weights->dimension(height_idx) - 1) * (dilation.y() - 1) > input->dimension(height_idx) + conv_info.pad_top() + conv_info.pad_bottom()); ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(channel_idx) * depth_multiplier) != weights->dimension(channel_idx)); - // Clone output to use auto init - auto output_clone = output->clone(); - - const ITensorInfo *input_to_use = input; - const ITensorInfo *weights_to_use = weights; - const ITensorInfo *output_to_use = output_clone.get(); - - TensorShape permuted_input_shape = input->tensor_shape(); - TensorShape permuted_weights_shape = weights->tensor_shape(); - TensorInfo permuted_input; - TensorInfo permuted_weights; - - if(input->data_layout() == DataLayout::NHWC) + if(input->data_layout() != DataLayout::NHWC || input->data_type() != DataType::F32) { - permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); - permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); + // Clone output to use auto init + auto output_clone = output->clone(); - permuted_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW)); - permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW)); + const ITensorInfo *input_to_use = input; + const ITensorInfo *weights_to_use = weights; + const ITensorInfo *output_to_use = output_clone.get(); - input_to_use = &permuted_input; - weights_to_use = &permuted_weights; - } + TensorShape permuted_input_shape = input->tensor_shape(); + TensorShape permuted_weights_shape = weights->tensor_shape(); + TensorInfo permuted_input; + TensorInfo permuted_weights; - const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); - const bool append_bias = (biases != nullptr) && !is_quantized; - TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); - 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[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; + if(input->data_layout() == DataLayout::NHWC) + { + permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); + permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*output_clone, input->clone()->set_tensor_shape(output_shape)); - ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + permuted_input = TensorInfo(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_input_shape).set_data_layout(DataLayout::NCHW)); + permuted_weights = TensorInfo(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW)); - TensorInfo permuted_output; - if(input->data_layout() == DataLayout::NHWC) - { - permute(output_shape, PermutationVector(1U, 2U, 0U)); - permuted_output = TensorInfo(output_clone->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_layout(DataLayout::NCHW)); - output_to_use = &permuted_output; - } - - // Im2Col configuration - TensorShape shape_im2col = input_to_use->tensor_shape(); - shape_im2col.set(0, patch_size); - shape_im2col.set(1, conv_size); - shape_im2col.set(2, weights_z); - TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation)); + input_to_use = &permuted_input; + weights_to_use = &permuted_weights; + } - // Weights reshape configuration - const TensorShape shape_weights_reshape(patch_size, weights_z); - TensorInfo weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseWeightsReshapeKernel::validate(weights_to_use, &weights_reshaped, append_bias ? biases : nullptr)); + const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); + const bool append_bias = (biases != nullptr) && !is_quantized; + TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); + 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[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; + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output_clone, input->clone()->set_tensor_shape(output_shape)); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + + TensorInfo permuted_output; + if(input->data_layout() == DataLayout::NHWC) + { + permute(output_shape, PermutationVector(1U, 2U, 0U)); + permuted_output = TensorInfo(output_clone->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_layout(DataLayout::NCHW)); + output_to_use = &permuted_output; + } - // GEMV configuration - DataType v2mm_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); - TensorShape shape_v2mm_out = input_to_use->tensor_shape(); - shape_v2mm_out.set(0, conv_size * weights_z); - shape_v2mm_out.set(1, 1); - shape_v2mm_out.set(2, 1); - TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); - ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output)); + // Im2Col configuration + TensorShape shape_im2col = input_to_use->tensor_shape(); + shape_im2col.set(0, patch_size); + shape_im2col.set(1, conv_size); + shape_im2col.set(2, weights_z); + TensorInfo input_reshaped(input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col).set_data_layout(DataLayout::NCHW)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseIm2ColKernel::validate(input_to_use, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation)); + + // Weights reshape configuration + const TensorShape shape_weights_reshape(patch_size, weights_z); + TensorInfo weights_reshaped(weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_weights_reshape).set_data_layout(DataLayout::NCHW)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseWeightsReshapeKernel::validate(weights_to_use, &weights_reshaped, append_bias ? biases : nullptr)); + + // GEMV configuration + DataType v2mm_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); + TensorShape shape_v2mm_out = input_to_use->tensor_shape(); + shape_v2mm_out.set(0, conv_size * weights_z); + shape_v2mm_out.set(1, 1); + shape_v2mm_out.set(2, 1); + TensorInfo v2mm_output(input->clone()->set_is_resizable(true).reset_padding().set_data_type(v2mm_dt).set_tensor_shape(shape_v2mm_out).set_data_layout(DataLayout::NCHW)); + ARM_COMPUTE_RETURN_ON_ERROR(NEGEMMMatrixVectorMultiplyKernel::validate(&input_reshaped, &weights_reshaped, &v2mm_output)); + + TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_to_use->tensor_shape())); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output_to_use, conv_w, conv_h)); - TensorInfo output_reshaped(v2mm_output.clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_to_use->tensor_shape())); - ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseVectorToTensorKernel::validate(&v2mm_output, (is_quantized) ? &output_reshaped : output_to_use, conv_w, conv_h)); + if(is_quantized) + { + const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); + const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); + const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - if(is_quantized) + float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; + int output_multiplier; + int output_shift; + ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset)); + } + } + else { - const UniformQuantizationInfo iq_info = input->quantization_info().uniform(); - const UniformQuantizationInfo wq_info = weights->quantization_info().uniform(); - const UniformQuantizationInfo oq_info = output->quantization_info().uniform(); - - float multiplier = (iq_info.scale * wq_info.scale) / oq_info.scale; - int output_multiplier; - int output_shift; - ARM_COMPUTE_RETURN_ON_ERROR(quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift)); - ARM_COMPUTE_RETURN_ON_ERROR(NEDirectConvolutionLayerOutputStageKernel::validate(&output_reshaped, biases, output_to_use, output_multiplier, output_shift, oq_info.offset)); + ARM_COMPUTE_RETURN_ON_ERROR(NEDepthwiseConvolutionLayerKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, dilation)); } // Validate Activation Layer @@ -941,25 +961,33 @@ Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe void NEDepthwiseConvolutionLayer::run() { - prepare(); - - if(_is_nhwc) + if(!_is_optimized) { - _permute_input.run(); - } + prepare(); - NEScheduler::get().schedule(&_im2col_kernel, Window::DimX); - NEScheduler::get().schedule(&_v2mm_input_fill_border, Window::DimX); - NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX); - NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX); - if(_is_quantized) - { - NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); - } + if(_is_nhwc) + { + _permute_input.run(); + } + + NEScheduler::get().schedule(&_im2col_kernel, Window::DimX); + NEScheduler::get().schedule(&_v2mm_input_fill_border, Window::DimX); + NEScheduler::get().schedule(&_v2mm_kernel, Window::DimX); + NEScheduler::get().schedule(&_vector_to_tensor_kernel, Window::DimX); + if(_is_quantized) + { + NEScheduler::get().schedule(&_output_stage_kernel, Window::DimX); + } - if(_is_nhwc) + if(_is_nhwc) + { + _permute_output.run(); + } + } + else { - _permute_output.run(); + NEScheduler::get().schedule(&_fill_border, Window::DimX); + NEScheduler::get().schedule(&_depthwise_conv_kernel, Window::DimY); } if(_is_activationlayer_enabled) @@ -970,7 +998,7 @@ void NEDepthwiseConvolutionLayer::run() void NEDepthwiseConvolutionLayer::prepare() { - if(!_is_prepared) + if(!_is_prepared && !_is_optimized) { ARM_COMPUTE_ERROR_ON(!_original_weights->is_used()); -- cgit v1.2.1