From 7784c837afd5844fb6dc4d166ff253d983abfd2d Mon Sep 17 00:00:00 2001 From: Abe Mbise Date: Thu, 31 May 2018 16:48:41 +0100 Subject: COMPMID-1167: Validation for NEDepthwiseConvolutionLayer Change-Id: I9689e1a0627dc015dd2ce98417e4c97bb55581bb Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/131327 Reviewed-by: Anthony Barbier Tested-by: Jenkins --- .../NEDepthwiseConvolutionLayer3x3Kernel.cpp | 204 +++++++++++++-------- 1 file changed, 128 insertions(+), 76 deletions(-) (limited to 'src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp') diff --git a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp index 09728e2a8d..62dabc8d32 100644 --- a/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp +++ b/src/core/NEON/kernels/NEDepthwiseConvolutionLayer3x3Kernel.cpp @@ -144,6 +144,112 @@ inline void convolve_3x3(const Window &window, unsigned int num_elems_written_pe ARM_COMPUTE_ERROR("Not implemented"); } } + +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, bool is_optimized) +{ + ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + + if(is_optimized) + { + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) != 3 || weights->dimension(2) != 3); + } + else + { + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(0) != 3 || weights->dimension(1) != 3); + ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1 || conv_info.stride().first > 3); + } + + if(output->total_size() != 0) + { + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); + + ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && (output->data_type() != DataType::S32)); + ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_float(input->data_type()) && (output->data_type() != DataType::F32)); + } + + return Status{}; +} + +std::pair validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, bool is_optimized, + IDepthwiseConvolution *convolver = nullptr) +{ + Window win; + bool window_changed = false; + + if(is_optimized) + { + if(convolver != nullptr) + { + auto win_last = convolver->get_window(); + win.set(Window::DimX, Window::Dimension(0, win_last, 1)); + + // Auto-configure output + bool same_padding = conv_info.has_padding(); + TensorShape output_shape{ input->tensor_shape() }; + + output_shape.set(1, convolver->output_size(output_shape.y(), same_padding)); // Set width + output_shape.set(2, convolver->output_size(output_shape.z(), same_padding)); // Set height + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); + + // Configure window (optimised) + // Set padding in channels + const int num_channels = weights->dimension(0); + if((num_channels >= 128) && (num_channels % 16 == 0)) + { + input->extend_padding(PaddingSize(0, 4, 0, 0)); + weights->extend_padding(PaddingSize(0, 4, 0, 0)); + output->extend_padding(PaddingSize(0, 4, 0, 0)); + } + } + } + else + { + // Get convolved dimensions + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const DataType output_dt = (input->data_type() == DataType::QASYMM8) ? DataType::S32 : input->data_type(); + + // Output auto inizialitation if not yet initialized + auto_init_if_empty(*output, input->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt)); + + // Configure kernel window (generic) + const unsigned int conv_stride_x = conv_info.stride().first; + const unsigned int conv_stride_y = conv_info.stride().second; + const unsigned int conv_pad_top = conv_info.pad_top(); + const unsigned int conv_pad_left = conv_info.pad_left(); + + unsigned int num_elems_written_per_iteration = 16 >> conv_stride_x; + unsigned int num_elems_read_per_iteration = 0; + + switch(input->data_type()) + { + case DataType::QASYMM8: + num_elems_read_per_iteration = 16; + break; + case DataType::F32: + num_elems_read_per_iteration = 12; + break; + default: + ARM_COMPUTE_ERROR("Data type not supported."); + } + + // Configure kernel window + win = calculate_max_window(*output, Steps(num_elems_written_per_iteration)); + + AccessWindowRectangle input_access(input, -conv_pad_left, -conv_pad_top, num_elems_read_per_iteration, 3, conv_stride_x, conv_stride_y); + AccessWindowStatic weights_access(weights, 0, 0, 3, 3); + AccessWindowHorizontal output_access(output, 0, num_elems_written_per_iteration); + + window_changed = update_window_and_padding(win, input_access, weights_access, output_access); + output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape())); + } + + Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{}; + return std::make_pair(err, win); +} } // namespace NEDepthwiseConvolutionLayer3x3Kernel::NEDepthwiseConvolutionLayer3x3Kernel() @@ -159,8 +265,7 @@ BorderSize NEDepthwiseConvolutionLayer3x3Kernel::border_size() const void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const ITensor *weights, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, DataLayout data_layout) { - ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F32); - ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights); + ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); _input = input; _output = output; @@ -177,6 +282,17 @@ void NEDepthwiseConvolutionLayer3x3Kernel::configure(const ITensor *input, const (_run_optimized) ? configure_optimized() : configure_generic(); } +Status NEDepthwiseConvolutionLayer3x3Kernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier) +{ + ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); + + bool is_optimized = NEDepthwiseConvolutionLayer3x3Kernel::is_optimized_execution_possible(input->tensor_shape(), conv_info, input->data_type(), depth_multiplier, input->data_layout()); + + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, output, conv_info, depth_multiplier, is_optimized)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, is_optimized).first); + return Status{}; +} + void NEDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, const ThreadInfo &info) { ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this); @@ -227,90 +343,26 @@ void NEDepthwiseConvolutionLayer3x3Kernel::generate_convolver() void NEDepthwiseConvolutionLayer3x3Kernel::configure_generic() { - ARM_COMPUTE_ERROR_ON(_weights->info()->dimension(0) != 3 || _weights->info()->dimension(1) != 3); - - // Get convolved dimensions - const TensorShape output_shape = compute_depthwise_convolution_shape(*_input->info(), *_weights->info(), _conv_info, _depth_multiplier); - const DataType output_dt = (_input->info()->data_type() == DataType::QASYMM8) ? DataType::S32 : _input->info()->data_type(); - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*_output->info(), - _input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape).set_data_type(output_dt)); - - ARM_COMPUTE_ERROR_ON_MISMATCHING_DIMENSIONS(_output->info()->tensor_shape(), output_shape); - - const unsigned int conv_stride_x = _conv_info.stride().first; - const unsigned int conv_stride_y = _conv_info.stride().second; - const unsigned int conv_pad_top = _conv_info.pad_top(); - const unsigned int conv_pad_right = _conv_info.pad_right(); - const unsigned int conv_pad_bottom = _conv_info.pad_bottom(); - const unsigned int conv_pad_left = _conv_info.pad_left(); - - ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 3); - - unsigned int num_elems_read_per_iteration = 0; - switch(_input->info()->data_type()) - { - case DataType::QASYMM8: - num_elems_read_per_iteration = 16; - _num_elems_written_per_iteration = 16 >> conv_stride_x; - break; - case DataType::F32: - num_elems_read_per_iteration = 12; - _num_elems_written_per_iteration = 16 >> conv_stride_x; - break; - default: - ARM_COMPUTE_ERROR("Data type not supported."); - } - _border_size = BorderSize(conv_pad_top, conv_pad_right, conv_pad_bottom, conv_pad_left); - - // Configure kernel window - Window win = calculate_max_window(*_output->info(), Steps(_num_elems_written_per_iteration)); - - AccessWindowRectangle input_access(_input->info(), -conv_pad_left, -conv_pad_top, - num_elems_read_per_iteration, 3, - conv_stride_x, conv_stride_y); - AccessWindowStatic weights_access(_weights->info(), 0, 0, 3, 3); - AccessWindowHorizontal output_access(_output->info(), 0, _num_elems_written_per_iteration); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, _run_optimized)); - update_window_and_padding(win, input_access, weights_access, output_access); - output_access.set_valid_region(win, ValidRegion(Coordinates(), _output->info()->tensor_shape())); + _num_elems_written_per_iteration = 16 >> _conv_info.stride().first; + _border_size = BorderSize(_conv_info.pad_top(), _conv_info.pad_right(), _conv_info.pad_bottom(), _conv_info.pad_left()); - INEKernel::configure(win); + auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, false); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + INEKernel::configure(win_config.second); } void NEDepthwiseConvolutionLayer3x3Kernel::configure_optimized() { - ARM_COMPUTE_ERROR_ON(_weights->info()->dimension(1) != 3 || _weights->info()->dimension(2) != 3); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, _run_optimized)); _border_size = BorderSize(0, 0); _convolver = create_convolver_object(_conv_info, _weights, _input, _output); - // Auto-configure output - bool same_padding = _conv_info.has_padding(); - TensorShape output_shape{ _input->info()->tensor_shape() }; - - output_shape.set(1, _convolver->output_size(output_shape.y(), same_padding)); // Set width - output_shape.set(2, _convolver->output_size(output_shape.z(), same_padding)); // Set height - - // Output auto inizialitation if not yet initialized - auto_init_if_empty(*_output->info(), - _input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(output_shape)); - - // Set padding in channels - const int num_channels = _weights->info()->dimension(0); - if((num_channels >= 128) && (num_channels % 16 == 0)) - { - _input->info()->extend_padding(PaddingSize(0, 4, 0, 0)); - _weights->info()->extend_padding(PaddingSize(0, 4, 0, 0)); - _output->info()->extend_padding(PaddingSize(0, 4, 0, 0)); - } - - // Configure window - Window win; - auto win_last = _convolver->get_window(); - win.set(Window::DimX, Window::Dimension(0, win_last, 1)); - INEKernel::configure(win); + auto win_config = validate_and_configure_window(_input->info(), _weights->info(), _output->info(), _conv_info, _depth_multiplier, true, _convolver.get()); + ARM_COMPUTE_ERROR_THROW_ON(win_config.first); + INEKernel::configure(win_config.second); } void NEDepthwiseConvolutionLayer3x3Kernel::run_generic(const Window &window, const ThreadInfo &info) -- cgit v1.2.1