diff options
| author | Usama Arif <usama.arif@arm.com> | 2019-04-08 17:30:48 +0100 |
|---|---|---|
| committer | Pablo Marquez <pablo.tello@arm.com> | 2019-04-18 10:07:37 +0000 |
| commit | e73686ac797be2d19cd9bed26d690e1431e3d848 (patch) | |
| tree | 84a65c520b3a7b3e0abef03b48f8bbc0882e1fa4 | |
| parent | 6631ac22efdb75438e8f35e836ae9f17cfd40c86 (diff) | |
| download | ComputeLibrary-e73686ac797be2d19cd9bed26d690e1431e3d848.tar.gz | |
COMPMID-2047: Add support for dilation in CLDepthwiseConvolution.
Change-Id: I3106aa34bd168985a56791613d95072756be6e9b
Signed-off-by: Usama Arif <usama.arif@arm.com>
Reviewed-on: https://review.mlplatform.org/c/958
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez <pablo.tello@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
22 files changed, 1079 insertions, 285 deletions
diff --git a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h index b1c730d9a7..3b7fc7b7dc 100644 --- a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h +++ b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2018 ARM Limited. + * Copyright (c) 2018-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -41,31 +41,33 @@ public: * * @param[in] input Source tensor. DataType supported: QASYMM8/F16/F32. * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: Same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for QASYMM8 supported. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). */ void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - ActivationLayerInfo act_info) override; + ActivationLayerInfo act_info, const Size2D &dilation) override; /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NCHWKernel * - * @param[in] input Source tensor. DataType supported: F16/F32/QASYMM8. - * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. + * @param[in] input Source tensor info. DataType supported: F16/F32/QASYMM8. + * @param[in] weights Weights tensor info. A 3D tensor with dimensions [3, 3, IFM]. Data type supported: Same as @p input. + * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[in] output Destination tensor. Data type supported: Same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. + * @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. * @param[in] gpu_target (Optional) GPU target to validate the kernel for. Defaults to midgard. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - ActivationLayerInfo act_info = ActivationLayerInfo(), GPUTarget gpu_target = GPUTarget::MIDGARD); + ActivationLayerInfo act_info = ActivationLayerInfo(), GPUTarget gpu_target = GPUTarget::MIDGARD, const Size2D &dilation = Size2D(1U, 1U)); void run(const Window &window, cl::CommandQueue &queue) override; BorderSize border_size() const override; diff --git a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h index 2fc9780a2f..7d0ecec13e 100644 --- a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h +++ b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h @@ -42,30 +42,32 @@ public: * * @param[in] input Source tensor. DataType supported: QASYMM8. * @param[in] weights Weights tensor. A 3D tensor with dimensions [IFM, 3, 3]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: Same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). */ void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - ActivationLayerInfo act_info) override; + ActivationLayerInfo act_info, const Size2D &dilation) override; /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3NHWCKernel * * @param[in] input Source tensor info. DataType supported: QASYMM8. * @param[in] weights Weights tensor info. A 3D tensor with dimensions [IFM, 3, 3]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor info. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[in] output Destination tensor info. Data type supported: Same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU are supported. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - ActivationLayerInfo act_info = ActivationLayerInfo()); + ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; diff --git a/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h index 00d9cb64e1..15798471a8 100644 --- a/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h +++ b/arm_compute/core/CL/kernels/CLDepthwiseIm2ColKernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -58,22 +58,26 @@ public: * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. * @param[in] has_bias Boolean that specifies if the depthwise convolution has bias. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). */ - void configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1); + void configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias = false, unsigned int depth_multiplier = 1, + const Size2D &dilation = Size2D(1U, 1U)); /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseIm2ColKernel * - * @param[in] input The input tensor to convert. 3 lower dimensions represent a single input [width, height, IFM], + * @param[in] input The input tensor info to convert. 3 lower dimensions represent a single input [width, height, IFM], * while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F32 - * @param[in] output The output tensor. First 3 lower dimensions represent a transform of each 3D input, + * @param[in] output The output tensor info. First 3 lower dimensions represent a transform of each 3D input, * while every dimension above 3 represents a batch. Data types supported: Same as @p input * @param[in] kernel_dims The kernel dimensions (width and height). * @param[in] conv_info Contains padding and stride information described in @ref PadStrideInfo. * @param[in] has_bias Boolean that specifies if the depthwise convolution has bias. - * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. + * @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * * @return a status */ - static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier); + static Status validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier, + const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overridden: void run(const Window &window, cl::CommandQueue &queue) override; diff --git a/arm_compute/core/CL/kernels/ICLDepthwiseConvolutionLayer3x3Kernel.h b/arm_compute/core/CL/kernels/ICLDepthwiseConvolutionLayer3x3Kernel.h index 3396de2e46..92eca89fd8 100644 --- a/arm_compute/core/CL/kernels/ICLDepthwiseConvolutionLayer3x3Kernel.h +++ b/arm_compute/core/CL/kernels/ICLDepthwiseConvolutionLayer3x3Kernel.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -52,15 +52,16 @@ public: * * @param[in] input Source tensor. DataType supported: QASYMM8/F16/F32. * @param[in] weights Weights tensor. A 3D tensor with dimensions [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with dimensions [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: Same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for QASYMM8 supported. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). */ virtual void configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo()) = 0; + ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)) = 0; protected: BorderSize _border_size; diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h index 26b337d5c5..b46b1b2535 100644 --- a/arm_compute/core/utils/misc/ShapeCalculator.h +++ b/arm_compute/core/utils/misc/ShapeCalculator.h @@ -402,10 +402,12 @@ inline TensorShape compute_transposed_shape(const ITensorInfo &input) * @param[in] weights Weights tensor info * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. + * @param[in] dilation Dilation, in elements, across x and y. Defaults to (1, 1). * * @return the calculated shape */ -inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info, unsigned int depth_multiplier) +inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const ITensorInfo &weights, PadStrideInfo conv_info, unsigned int depth_multiplier, const Size2D &dilation = Size2D(1U, + 1U)) { const TensorShape input_shape{ input.tensor_shape() }; const TensorShape weights_shape{ weights.tensor_shape() }; @@ -415,43 +417,15 @@ inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); - unsigned int output_width = 0; - unsigned int output_height = 0; - std::tie(output_width, output_height) = scaled_dimensions(input_shape[width_idx], input_shape[height_idx], - weights_shape[width_idx], weights_shape[height_idx], - conv_info); - - TensorShape output_shape{ input_shape }; - output_shape.set(width_idx, output_width); - output_shape.set(height_idx, output_height); - output_shape.set(channel_idx, input_shape[channel_idx] * depth_multiplier); - - return output_shape; -} - -/** Calculate the depthwise convolution output shape of a tensor - * - * @param[in] input Input tensor info - * @param[in] weights_width Weights width - * @param[in] weights_height Weights height - * @param[in] conv_info Padding and stride information to use for the convolution. - * @param[in] depth_multiplier Multiplier to apply to the input's depth in order to retrieve the output's depth. - * - * @return the calculated shape - */ -inline TensorShape compute_depthwise_convolution_shape(const ITensorInfo &input, int weights_width, int weights_height, PadStrideInfo conv_info, unsigned int depth_multiplier) -{ - const TensorShape input_shape{ input.tensor_shape() }; - - const DataLayout data_layout = input.data_layout(); - const int width_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH); - const int height_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT); - const int channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL); + const DataLayout weights_data_layout = weights.data_layout(); + const int weights_width_idx = get_data_layout_dimension_index(weights_data_layout, DataLayoutDimension::WIDTH); + const int weights_height_idx = get_data_layout_dimension_index(weights_data_layout, DataLayoutDimension::HEIGHT); unsigned int output_width = 0; unsigned int output_height = 0; std::tie(output_width, output_height) = scaled_dimensions(input_shape[width_idx], input_shape[height_idx], - weights_width, weights_width, conv_info); + weights_shape[weights_width_idx], weights_shape[weights_height_idx], + conv_info, dilation); TensorShape output_shape{ input_shape }; output_shape.set(width_idx, output_width); diff --git a/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h index 23034c2b7c..b25c36a930 100644 --- a/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h +++ b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h @@ -70,32 +70,34 @@ public: * * @param[in, out] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). * @param[in] weights Weights tensor. A 3D tensor with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). */ void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo()); + ActivationLayerInfo act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer3x3 * - * @param[in] input Source tensor. Data type supported: QASYMM8 for all layouts, F16/F32 for NCHW. - * @param[in] weights Weights tensor. A 3D tensor with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] input Source tensor info. Data type supported: QASYMM8 for all layouts, F16/F32 for NCHW. + * @param[in] weights Weights tensor info. A 3D tensor with shape [3, 3, IFM]. Data type supported: Same as @p input. + * @param[in] biases Biases tensor info. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input, S32 when input is QASYMM8. * @param[in] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. Only RELU, BOUNDED_RELU and LU_BOUNDED_RELU for 3x3 QASYMM8 supported. * @param[in] gpu_target (Optional) GPU target to validate the kernel for. Defaults to midgard. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier = 1, - ActivationLayerInfo act_info = ActivationLayerInfo(), GPUTarget gpu_target = GPUTarget::MIDGARD); + ActivationLayerInfo act_info = ActivationLayerInfo(), GPUTarget gpu_target = GPUTarget::MIDGARD, const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overriden: void run() override; void prepare() override; @@ -142,31 +144,33 @@ public: * * @param[in, out] input Source tensor. Data type supported: QASYMM8/F32. (Written to only for border filling). * @param[in] weights Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input, S32 when input is QASYMM8. * @param[out] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). */ void configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); /** Static function to check if given info will lead to a valid configuration of @ref CLDepthwiseConvolutionLayer * - * @param[in] input Source tensor. Data type supported: QASYMM8/F32. - * @param[in] weights Weights tensor. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input. - * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] input Source tensor info. Data type supported: QASYMM8/F32. + * @param[in] weights Weights tensor info. These are 3D tensors with shape [kernel_x, kernel_y, IFM]. Data type supported: Same as @p input. + * @param[in] biases Biases tensor info. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input, S32 when input is QASYMM8. * @param[in] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overriden: void run() override; diff --git a/arm_compute/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.h b/arm_compute/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.h index 5eccc4d9e8..28db5e1310 100644 --- a/arm_compute/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.h +++ b/arm_compute/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -50,15 +50,16 @@ public: * * @param[in, out] input Source tensor. Data type supported: F16. (Written to only for border filling). * @param[in] weights Weights tensor. A 3D tensor with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only. */ void configure(IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overridden: void run() override final; diff --git a/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h b/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h index 28f0560e93..c60233664d 100644 --- a/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h +++ b/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h @@ -67,31 +67,33 @@ public: * * @param[in, out] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). * @param[in] weights Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only. */ void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); /** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseConvolutionLayer3x3 * * @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). * @param[in] weights Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[in] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only. * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overriden: void run() override; @@ -102,7 +104,7 @@ private: * * @param[in, out] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). * @param[in] weights Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. @@ -115,7 +117,7 @@ private: * * @param[in] input Source tensor. Data type supported: QASYMM8/F16/F32. (Written to only for border filling). * @param[in] weights Weights tensor. These are 3D tensors with shape [3, 3, IFM]. Data type supported: Same as @p input. - * @param[in] biases (Optional) Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. + * @param[in] biases Biases tensor. A 1D tensor with shape [IFM]. Must be nullptr if not needed. * Data type supported: Same as @p input. * @param[out] output Destination tensor. Data type supported: same as @p input. * @param[in] conv_info Padding and stride information to use for the convolution. @@ -184,9 +186,10 @@ public: * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only. */ void configure(ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); /** Static function to check if given info will lead to a valid configuration of @ref NEDepthwiseConvolutionLayer * @@ -198,11 +201,12 @@ public: * @param[in] conv_info Padding and stride information to use for the convolution. * @param[in] depth_multiplier (Optional) Multiplier to apply to the input's depth in order to retrieve the output's depth. Defaults to 1. * @param[in] act_info (Optional) Activation layer information in case of a fused activation. + * @param[in] dilation (Optional) Dilation, in elements, across x and y. Defaults to (1, 1). Currently supports (1,1) only. * * @return a status */ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo()); + unsigned int depth_multiplier = 1, const ActivationLayerInfo &act_info = ActivationLayerInfo(), const Size2D &dilation = Size2D(1U, 1U)); // Inherited methods overriden: void run() override; diff --git a/src/core/CL/cl_kernels/depthwise_convolution.cl b/src/core/CL/cl_kernels/depthwise_convolution.cl index 4f6fdfafee..8ee0185fe6 100644 --- a/src/core/CL/cl_kernels/depthwise_convolution.cl +++ b/src/core/CL/cl_kernels/depthwise_convolution.cl @@ -51,13 +51,18 @@ inline float2 convolution1x3_stride_1(__global const uchar *left_pixel, const float middle_coeff, const float right_coeff) { +#if(DILATION_X == 1 && DILATION_Y == 1) float4 temp = vload4(0, (__global float *)left_pixel); float2 left = CONVERT(temp.s01, float2); float2 middle = CONVERT(temp.s12, float2); float2 right = CONVERT(temp.s23, float2); - return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff; +#else /* DILATION_X==1 && DILATION_Y==1 */ + return vload2(0, (__global float *)left_pixel) * (float2)left_coeff + + vload2(0, (__global float *)(left_pixel) + DILATION_X) * (float2)middle_coeff + + vload2(0, (__global float *)(left_pixel) + 2 * DILATION_X) * (float2)right_coeff; +#endif /* DILATION_X==1 && DILATION_Y==1 */ } /** Compute a 1D horizontal convolution of size 3 and stride 2 for floating point type. @@ -74,6 +79,7 @@ inline float2 convolution1x3_stride_2(__global const uchar *left_pixel, const float middle_coeff, const float right_coeff) { +#if(DILATION_X == 1 && DILATION_Y == 1) float4 temp0 = vload4(0, (__global float *)left_pixel); float temp1 = *((__global float *)(left_pixel + 4 * sizeof(float))); @@ -82,6 +88,14 @@ inline float2 convolution1x3_stride_2(__global const uchar *left_pixel, float2 right = CONVERT((float2)(temp0.s2, temp1), float2); return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff; +#else /* DILATION_X==1 && DILATION_Y==1 */ + __global float *left_pixel_float = (__global float *)left_pixel; + + return vload4(0, left_pixel_float).s02 * (float2)left_coeff + + vload4(0, left_pixel_float + DILATION_X).s02 * (float2)middle_coeff + + vload4(0, left_pixel_float + DILATION_X * 2).s02 * (float2)right_coeff; + +#endif /* DILATION_X==1 && DILATION_Y==1 */ } /** Compute a 1D horizontal convolution of size 3 and stride 3 for floating point type. @@ -98,6 +112,7 @@ inline float2 convolution1x3_stride_3(__global const uchar *left_pixel, const float middle_coeff, const float right_coeff) { +#if(DILATION_X == 1 && DILATION_Y == 1) float4 temp0 = vload4(0, (__global float *)left_pixel); float2 temp1 = vload2(0, (__global float *)(left_pixel + 4 * sizeof(float))); @@ -106,6 +121,13 @@ inline float2 convolution1x3_stride_3(__global const uchar *left_pixel, float2 right = CONVERT((float2)(temp0.s2, temp1.s1), float2); return left * (float2)left_coeff + middle * (float2)middle_coeff + right * (float2)right_coeff; +#else /* DILATION_X==1 && DILATION_Y==1 */ + __global float *left_pixel_float = (__global float *)left_pixel; + + return (float2)(*left_pixel_float, *(left_pixel_float + 3)) * (float2)left_coeff + + (float2)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3)) * (float2)middle_coeff + + (float2)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3)) * (float2)right_coeff; +#endif /* DILATION_X==1 && DILATION_Y==1 */ } /** Apply a 3x3 convolution matrix to a single channel F32 input image and return the result. @@ -139,8 +161,8 @@ inline float2 convolution3x3( float2 pixels; pixels = convolution1x3(offset(src, 0, 0), mat0, mat1, mat2); - pixels += convolution1x3(offset(src, 0, 1), mat3, mat4, mat5); - pixels += convolution1x3(offset(src, 0, 2), mat6, mat7, mat8); + pixels += convolution1x3(offset(src, 0, DILATION_Y), mat3, mat4, mat5); + pixels += convolution1x3(offset(src, 0, DILATION_Y * 2), mat6, mat7, mat8); return pixels; } @@ -216,6 +238,8 @@ __kernel void depthwise_convolution_3x3( } #endif //defined(CONV_STRIDE_X) +#if(DILATION_X == 1 && DILATION_Y == 1) + #define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0, weights_row0) \ ({ \ acc.s0 = fma(src0.s0, weights_row0.s0, acc.s0); \ @@ -268,6 +292,227 @@ __kernel void depthwise_convolution_3x3( acc.s3 = fma(src1.s0, weights_row0.s2, acc.s3); \ }) +#else /* DILATION_X==1 && DILATION_Y==1 */ + +#define CONVOLUTION1x3_BIFROST2X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \ + ({ \ + acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ + acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ + acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ + acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \ + acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \ + acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \ + }) + +#define CONVOLUTION1x3_BIFROST2X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \ + ({ \ + acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ + acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ + acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ + acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \ + acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \ + acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \ + }) + +#define CONVOLUTION1x3_BIFROST4X1_STRIDE1(acc, src0_left, src0_mid, src0_right, weights_row0) \ + ({ \ + acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ + acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ + acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ + acc.s1 = fma(src0_left.s1, weights_row0.s0, acc.s1); \ + acc.s1 = fma(src0_mid.s1, weights_row0.s1, acc.s1); \ + acc.s1 = fma(src0_right.s1, weights_row0.s2, acc.s1); \ + acc.s2 = fma(src0_left.s2, weights_row0.s0, acc.s2); \ + acc.s2 = fma(src0_mid.s2, weights_row0.s1, acc.s2); \ + acc.s2 = fma(src0_right.s2, weights_row0.s2, acc.s2); \ + acc.s3 = fma(src0_left.s3, weights_row0.s0, acc.s3); \ + acc.s3 = fma(src0_mid.s3, weights_row0.s1, acc.s3); \ + acc.s3 = fma(src0_right.s3, weights_row0.s2, acc.s3); \ + }) + +#define CONVOLUTION1x3_BIFROST4X1_STRIDE2(acc, src0_left, src0_mid, src0_right, weights_row0) \ + ({ \ + acc.s0 = fma(src0_left.s0, weights_row0.s0, acc.s0); \ + acc.s0 = fma(src0_mid.s0, weights_row0.s1, acc.s0); \ + acc.s0 = fma(src0_right.s0, weights_row0.s2, acc.s0); \ + acc.s1 = fma(src0_left.s2, weights_row0.s0, acc.s1); \ + acc.s1 = fma(src0_mid.s2, weights_row0.s1, acc.s1); \ + acc.s1 = fma(src0_right.s2, weights_row0.s2, acc.s1); \ + acc.s2 = fma(src0_left.s4, weights_row0.s0, acc.s2); \ + acc.s2 = fma(src0_mid.s4, weights_row0.s1, acc.s2); \ + acc.s2 = fma(src0_right.s4, weights_row0.s2, acc.s2); \ + acc.s3 = fma(src0_left.s6, weights_row0.s0, acc.s3); \ + acc.s3 = fma(src0_mid.s6, weights_row0.s1, acc.s3); \ + acc.s3 = fma(src0_right.s6, weights_row0.s2, acc.s3); \ + }) + +/** Get the pointer position at a certain offset in x and y direction. + * + * @param[in] ptr Pointer to the starting position of the buffer + * @param[in] x Relative X position + * @param[in] y Relative Y position + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + */ +inline __global uchar *ptr_offset(__global uchar *ptr, const int x, const int y, const int stride_x, const int stride_y) +{ + return ptr + x * stride_x + y * stride_y; +} + +/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 and DILATION_Y>1 for F32 + * + * @param[in] src_addr Pointer to the starting position of where to perform the convolution + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] y_offset Offset from the source tensor from which to start convolution + * @param[in] weights_addr Pointer from where to get weights + * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension + */ +inline float2 convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, + const int y_offset, __global uchar *weights_addr, const int weights_stride_y) +{ + // Load the weights + float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); + float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); + float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); + + float2 pixels0 = 0.0f; + + float2 src00_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 + float2 src00_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + float2 src00_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + + float2 src10_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 + float2 src10_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + float2 src10_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + + float2 src20_left = vload2(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 + float2 src20_mid = vload2(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + float2 src20_right = vload2(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src00_left, src00_mid, src00_right, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src10_left, src10_mid, src10_right, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels0, src20_left, src20_mid, src20_right, weights_row2); + + return pixels0; +} + +/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 and DILATION_Y>1 for F32 + * + * @param[in] src_addr Pointer to the starting position of where to perform the convolution + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] y_offset Offset from the source tensor from which to start convolution + * @param[in] weights_addr Pointer from where to get weights + * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension + */ +inline float2 convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, + const int y_offset, __global uchar *weights_addr, const int weights_stride_y) +{ + // Load the weights + float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); + float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); + float3 weights_row2 = vload3(0, (__global float *)(weights_addr + 2 * weights_stride_y)); + + float2 pixels0 = 0.0f; + + float3 src00_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 + float3 src00_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + float3 src00_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + + float3 src10_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 + float3 src10_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + float3 src10_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + + float3 src20_left = vload3(0, (__global float *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 + float3 src20_mid = vload3(0, (__global float *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + float3 src20_right = vload3(0, (__global float *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src00_left, src00_mid, src00_right, weights_row0); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src10_left, src10_mid, src10_right, weights_row1); + CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels0, src20_left, src20_mid, src20_right, weights_row2); + + return pixels0; +} + +/** Perform 3x3 convolution for stride_x=1 and stride_y=1 when DILATION_X>1 and DILATION_Y>1 for f16 + * + * @param[in] src_addr Pointer to the starting position of where to perform the convolution + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] y_offset Offset from the source tensor from which to start convolution + * @param[in] weights_addr Pointer from where to get weights + * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension + */ +inline half4 convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, + const int y_offset, __global uchar *weights_addr, const int weights_stride_y) +{ + // Load the weights + half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); + half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); + half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); + + half4 pixels0 = 0.0f; + + half4 src00_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 + half4 src00_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + half4 src00_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + + half4 src10_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 + half4 src10_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + half4 src10_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + + half4 src20_left = vload4(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 + half4 src20_mid = vload4(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + half4 src20_right = vload4(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + + CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src00_left, src00_mid, src00_right, weights_row0); + CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src10_left, src10_mid, src10_right, weights_row1); + CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels0, src20_left, src20_mid, src20_right, weights_row2); + + return pixels0; +} + +/** Perform 3x3 convolution for stride_x=2 and stride_y=2 when DILATION_X>1 and DILATION_Y>1 for F16 + * + * @param[in] src_addr Pointer to the starting position of where to perform the convolution + * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) + * @param[in] src_stride_y Stride of the source tensor in Y dimension (in bytes) + * @param[in] y_offset Offset from the source tensor from which to start convolution + * @param[in] weights_addr Pointer from where to get weights + * @param[in] weights_stride_y Stride of weights tesnsor in Y dimension + */ +inline half4 convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(__global uchar *src_addr, const int stride_x_bytes, const int stride_y_bytes, + const int y_offset, __global uchar *weights_addr, const int weights_stride_y) +{ + // Load the weights + half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); + half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); + half3 weights_row2 = vload3(0, (__global half *)(weights_addr + 2 * weights_stride_y)); + + half4 pixels0 = 0.0f; + + half8 src00_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset, stride_x_bytes, stride_y_bytes)); // Row0 + half8 src00_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + half8 src00_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset, stride_x_bytes, stride_y_bytes)); + + half8 src10_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); // Row1 + half8 src10_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + half8 src10_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y, stride_x_bytes, stride_y_bytes)); + + half8 src20_left = vload8(0, (__global half *)ptr_offset(src_addr, 0, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); // Row2 + half8 src20_mid = vload8(0, (__global half *)ptr_offset(src_addr, DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + half8 src20_right = vload8(0, (__global half *)ptr_offset(src_addr, 2 * DILATION_X, y_offset + DILATION_Y * 2, stride_x_bytes, stride_y_bytes)); + + CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src00_left, src00_mid, src00_right, weights_row0); + CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src10_left, src10_mid, src10_right, weights_row1); + CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels0, src20_left, src20_mid, src20_right, weights_row2); + + return pixels0; +} + +#endif /* DILATION_X==1 && DILATION_Y==1 */ + /** This OpenCL kernel is optimized for Bifrost architectures and computes the depthwise convolution 3x3 when both * stride_x and stride_y are equal to 1 * @@ -326,6 +571,7 @@ __kernel void depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32( __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; +#if(DILATION_X == 1 && DILATION_Y == 1) // Load the weights float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); @@ -352,6 +598,19 @@ __kernel void depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32( CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src40, weights_row1); CONVOLUTION1x3_BIFROST2X1_STRIDE1(pixels3, src50, weights_row2); +#else /* DILATION_X==1 && DILATION_Y==1 */ + + //3x3 Convolution of elements starting in 0th row + pixels0 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 1st row + pixels1 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 1, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 2nd row + pixels2 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 3rd row + pixels3 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f32(src_addr, src.stride_x, src.stride_y, 3, weights_addr, weights_stride_y); + +#endif /* DILATION_X==1 && DILATION_Y==1 */ + #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); @@ -425,6 +684,8 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32( __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; +#if(DILATION_X == 1 && DILATION_Y == 1) + // Load the weights float3 weights_row0 = vload3(0, (__global float *)(weights_addr + 0 * weights_stride_y)); float3 weights_row1 = vload3(0, (__global float *)(weights_addr + 1 * weights_stride_y)); @@ -449,6 +710,14 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f32( CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src30, src31, weights_row1); CONVOLUTION1x3_BIFROST2X1_STRIDE2(pixels1, src40, src41, weights_row2); +#else /* DILATION_X==1 && DILATION_Y==1 */ + + //3x3 Convolution of elements starting in 0th row + pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 2nd row + pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f32(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); +#endif /* DILATION_X==1 && DILATION_Y==1 */ + #ifdef HAS_BIAS Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases); @@ -632,11 +901,12 @@ __kernel void depthwise_convolution_reshape_weights_generic( } #endif //defined(SRC_WIDTH) && defined(DATA_TYPE) -#if defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DATA_TYPE) && defined(PAD_VALUE) && defined(DEPTH_MULTIPLIER) +#if defined(STRIDE_X) && defined(STRIDE_Y) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(PAD_RIGHT) && defined(PAD_BOTTOM) && defined(KERNEL_WIDTH) && defined(KERNEL_HEIGHT) && defined(SRC_WIDTH) && defined(SRC_HEIGHT) && defined(DATA_TYPE) && defined(PAD_VALUE) && defined(DEPTH_MULTIPLIER) && defined(DILATION_X) && defined(DILATION_Y) /** This kernel performs a reshaping of the input tensor to a tensor used to perform depthwise convolution using vector to matrix multiplication. * * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float * @note The convolution information must be passed at compile time using -DSTRIDE_X, -DSTRIDE_Y, -DPAD_LEFT, -DPAD_TOP, -DPAD_RIGHT, -DPAD_BOTTOM, -DKERNEL_WIDHT, -DKERNEL_HEIGHT, -DSRC_WIDTH, -DSRC_HEIGHT, -DDEPTH_MULTIPLIER + * @note The dilation_x and dilation_y must be passed at compile time using -DDILATION_X and -DDILATION_Y: e.g. -DDILATION_X=1, -DDILATION_Y=1 * * @param[in] src_ptr Pointer to the source tensor. Supported data types: F16/F32 * @param[in] src_stride_x Stride of the source tensor in X dimension (in bytes) @@ -661,7 +931,7 @@ __kernel void depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(d const int src_pixel_linear = get_global_id(1) * STRIDE_X; const int full_length = SRC_WIDTH + PAD_LEFT + PAD_RIGHT; - const int max_initial_x = STRIDE_X * (((full_length - KERNEL_WIDTH) / STRIDE_X) + 1); + const int max_initial_x = STRIDE_X * (((full_length - (KERNEL_WIDTH + (KERNEL_WIDTH - 1) * (DILATION_X - 1))) / STRIDE_X) + 1); const int src_x = -PAD_LEFT + src_pixel_linear % max_initial_x; const int src_y = -PAD_TOP + src_pixel_linear / max_initial_x * STRIDE_Y; @@ -670,9 +940,9 @@ __kernel void depthwise_im2col(TENSOR3D_DECLARATION(src), TENSOR3D_DECLARATION(d __global uchar *input_ptr = src_ptr + src_offset_first_element_in_bytes + src_z * in_stride_z; __global DATA_TYPE *output_ptr = ((__global DATA_TYPE *)(dst.ptr)); - for(int y = src_y; y < src_y + KERNEL_HEIGHT; ++y) + for(int y = src_y; y < src_y + KERNEL_HEIGHT + (KERNEL_HEIGHT - 1) * (DILATION_Y - 1); y += DILATION_Y) { - for(int x = src_x; x < src_x + KERNEL_WIDTH; ++x, ++output_ptr) + for(int x = src_x; x < src_x + KERNEL_WIDTH + (KERNEL_WIDTH - 1) * (DILATION_X - 1); x += DILATION_X, ++output_ptr) { if(x < 0 || x >= SRC_WIDTH || y < 0 || y >= SRC_HEIGHT) { @@ -754,6 +1024,8 @@ inline half4 convolution1x3_stride_1_f16(__global const uchar *left_pixel, const half middle_coeff, const half right_coeff) { +#if(DILATION_X == 1 && DILATION_Y == 1) + half8 temp = vload8(0, (__global half *)left_pixel); half4 left = CONVERT(temp.s0123, half4); @@ -761,6 +1033,12 @@ inline half4 convolution1x3_stride_1_f16(__global const uchar *left_pixel, half4 right = CONVERT(temp.s2345, half4); return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; +#else /* DILATION_X==1 && DILATION_Y==1 */ + return vload4(0, (__global half *)left_pixel) * (half4)left_coeff + + vload4(0, (__global half *)(left_pixel) + DILATION_X) * (half4)middle_coeff + + vload4(0, (__global half *)(left_pixel) + 2 * DILATION_X) * (half4)right_coeff; + +#endif /* DILATION_X==1 && DILATION_Y==1 */ } /** Compute a 1D horizontal convolution of size 3 and stride 2 for 16bit floating point type. @@ -777,6 +1055,8 @@ inline half4 convolution1x3_stride_2_f16(__global const uchar *left_pixel, const half middle_coeff, const half right_coeff) { +#if(DILATION_X == 1 && DILATION_Y == 1) + half8 temp0 = vload8(0, (__global half *)left_pixel); half temp1 = *((__global half *)(left_pixel + 8 * sizeof(half))); @@ -785,6 +1065,15 @@ inline half4 convolution1x3_stride_2_f16(__global const uchar *left_pixel, half4 right = CONVERT((half4)(temp0.s246, temp1), half4); return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; +#else /* DILATION_X==1 && DILATION_Y==1 */ + + __global half *left_pixel_float = (__global half *)left_pixel; + + return (half4)(*left_pixel_float, *(left_pixel_float + 2), *(left_pixel_float + 4), *(left_pixel_float + 6)) * (half4)left_coeff + + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 2), *(left_pixel_float + DILATION_X + 4), *(left_pixel_float + DILATION_X + 6)) * (half4)middle_coeff + + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 2), *(left_pixel_float + DILATION_X * 2 + 4), *(left_pixel_float + DILATION_X * 2 + 6)) * (half4)right_coeff; + +#endif /* DILATION_X==1 && DILATION_Y==1 */ } /** Compute a 1D horizontal convolution of size 3 and stride 3 for 16bit floating point type. @@ -801,6 +1090,8 @@ inline half4 convolution1x3_stride_3_f16(__global const uchar *left_pixel, const half middle_coeff, const half right_coeff) { +#if(DILATION_X == 1 && DILATION_Y == 1) + half16 temp0 = vload16(0, (__global half *)left_pixel); half4 left = CONVERT(temp0.s0369, half4); @@ -808,6 +1099,15 @@ inline half4 convolution1x3_stride_3_f16(__global const uchar *left_pixel, half4 right = CONVERT(temp0.s258B, half4); return left * (half4)left_coeff + middle * (half4)middle_coeff + right * (half4)right_coeff; +#else /* DILATION_X==1 && DILATION_Y==1 */ + + __global half *left_pixel_float = (__global half *)left_pixel; + + return (half4)(*left_pixel_float, *(left_pixel_float + 3), *(left_pixel_float + 6), *(left_pixel_float + 9)) * (half4)left_coeff + + (half4)(*(left_pixel_float + DILATION_X), *(left_pixel_float + DILATION_X + 3), *(left_pixel_float + DILATION_X + 6), *(left_pixel_float + DILATION_X + 9)) * (half4)middle_coeff + + (half4)(*(left_pixel_float + DILATION_X * 2), *(left_pixel_float + DILATION_X * 2 + 3), *(left_pixel_float + DILATION_X * 2 + 6), *(left_pixel_float + DILATION_X * 2 + 9)) * (half4)right_coeff; + +#endif /* DILATION_X==1 && DILATION_Y==1 */ } /** Apply a 3x3 convolution matrix to a single channel F16 input image and return the result. @@ -841,8 +1141,8 @@ inline half4 convolution3x3_f16( half4 pixels; pixels = convolution1x3_f16(offset(src, 0, 0), mat0, mat1, mat2); - pixels += convolution1x3_f16(offset(src, 0, 1), mat3, mat4, mat5); - pixels += convolution1x3_f16(offset(src, 0, 2), mat6, mat7, mat8); + pixels += convolution1x3_f16(offset(src, 0, DILATION_Y), mat3, mat4, mat5); + pixels += convolution1x3_f16(offset(src, 0, DILATION_Y * 2), mat6, mat7, mat8); return pixels; } @@ -986,6 +1286,7 @@ __kernel void depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16( __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; +#if(DILATION_X == 1 && DILATION_Y == 1) // Load the weights half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); @@ -1012,6 +1313,19 @@ __kernel void depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16( CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels3, src40, weights_row1); CONVOLUTION1x3_BIFROST4X1_STRIDE1(pixels3, src50, weights_row2); +#else /* DILATION_X==1 && DILATION_Y==1 */ + + //3x3 Convolution of elements starting in 0th row + pixels0 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 1st row + pixels1 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 1, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 2nd row + pixels2 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 3rd row + pixels3 = convolution_3x3_dilation_stridex1_stridey1_bifrost_f16(src_addr, src.stride_x, src.stride_y, 3, weights_addr, weights_stride_y); + +#endif /* DILATION_X==1 && DILATION_Y==1 */ + #ifdef HAS_BIAS pixels0 += (half4)bias; pixels1 += (half4)bias; @@ -1088,6 +1402,8 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16( __global uchar *weights_addr = weights.ptr + get_global_id(0) * weights_step_x + get_global_id(1) * weights_step_y + channel * weights_step_z; __global uchar *src_addr = src.ptr - batch * (DST_CHANNELS / DEPTH_MULTIPLIER) * (DEPTH_MULTIPLIER - 1) * src_step_z - (channel - (channel / DEPTH_MULTIPLIER)) * src_step_z; +#if(DILATION_X == 1 && DILATION_Y == 1) + // Load the weights half3 weights_row0 = vload3(0, (__global half *)(weights_addr + 0 * weights_stride_y)); half3 weights_row1 = vload3(0, (__global half *)(weights_addr + 1 * weights_stride_y)); @@ -1112,6 +1428,13 @@ __kernel void depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16( CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels1, src30, src31, weights_row1); CONVOLUTION1x3_BIFROST4X1_STRIDE2(pixels1, src40, src41, weights_row2); +#else /* DILATION_X==1 && DILATION_Y==1 */ + //3x3 Convolution of elements starting in 0th row + pixels0 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 0, weights_addr, weights_stride_y); + //3x3 Convolution of elements starting in 2nd row + pixels1 = convolution_3x3_dilation_stridex2_stridey2_bifrost_f16(src_addr, src.stride_x, src.stride_y, 2, weights_addr, weights_stride_y); +#endif /* DILATION_X==1 && DILATION_Y==1 */ + #ifdef HAS_BIAS pixels0 += (half4)bias; pixels1 += (half4)bias; @@ -1189,9 +1512,9 @@ __kernel void depthwise_convolution_3x3_nhwc( #if defined(DST_DEPTH) int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) +#else // defined(DST_DEPTH) + int z = get_global_id(2); // spatial coordinate y +#endif // defined(DST_DEPTH) Vector weights = CONVERT_TO_VECTOR_STRUCT(weights); @@ -1203,7 +1526,7 @@ __kernel void depthwise_convolution_3x3_nhwc( int z_coord = 0; int4 offset = 0; - int4 y_offset = ((int4)(y * CONV_STRIDE_X) + (int4)(0, 1, 2, 3) - CONV_PAD_LEFT) * (int4)src_stride_y; + int4 y_offset = ((int4)(y * CONV_STRIDE_X) + (int4)(0, DILATION_X * 1, DILATION_X * 2, DILATION_X * 3) - CONV_PAD_LEFT) * (int4)src_stride_y; // We compute 2x1x1 [C,W,H] elements VEC_FLOAT acc = 0; @@ -1236,16 +1559,16 @@ __kernel void depthwise_convolution_3x3_nhwc( // z == 1 // z_coord can be only negative for z = 0 so we do not need to clamp it // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset - z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + 1; + z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y; offset = y_offset + (int4)(z_coord * src_stride_z); VEC_FLOAT values3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); VEC_FLOAT values4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); VEC_FLOAT values5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s2)); // z == 2 - // After z = 1 we can simply add src_stride_z to offset without updating z_coord - // However offset can be out-of-bound so we need to check if it is greater than max_offset - offset += (int4)src_stride_z; + // Offset can be out-of-bound so we need to check if it is greater than max_offset + z_coord = z * CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y * 2; + offset = y_offset + (int4)(z_coord * src_stride_z); offset = min(offset, (int4)max_offset); VEC_FLOAT values6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s0)); VEC_FLOAT values7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(src_addr + offset.s1)); @@ -1338,9 +1661,9 @@ __kernel void depthwise_convolution_3x3_nhwc_stride1( #if defined(DST_DEPTH) int z = get_global_id(2) % (int)DST_DEPTH; // spatial coordinate y int b = get_global_id(2) / (int)DST_DEPTH; // batch -#else // defined(DST_DEPTH) - int z = get_global_id(2); // spatial coordinate y -#endif // defined(DST_DEPTH) +#else // defined(DST_DEPTH) + int z = get_global_id(2); // spatial coordinate y +#endif // defined(DST_DEPTH) Vector weights = CONVERT_TO_VECTOR_STRUCT(weights); diff --git a/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl b/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl index 503aa7e837..8d145a038e 100644 --- a/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl +++ b/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl @@ -53,6 +53,8 @@ #if !(defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)) +#if DILATION_X == 1 + #if CONV_STRIDE_X == 1 #define GET_VALUES(first_value, left, middle, right) \ ({ \ @@ -85,6 +87,46 @@ }) #endif /* CONV_STRIDE_X */ +#else /* DILATION_X == 1 */ + +#if CONV_STRIDE_X == 1 +#define GET_VALUES(first_value, left, middle, right) \ + ({ \ + left = CONVERT(vload8(0, first_value), int8); \ + middle = CONVERT(vload8(0, first_value + DILATION_X * sizeof(uchar)), int8); \ + right = CONVERT(vload8(0, first_value + 2 * DILATION_X * sizeof(uchar)), int8); \ + }) +#elif CONV_STRIDE_X == 2 +#define GET_VALUES(first_value, left, middle, right) \ + ({ \ + int16 temp0 = CONVERT(vload16(0, first_value), int16); \ + left = CONVERT(temp0.s02468ace, int8); \ + \ + temp0 = CONVERT(vload16(0, first_value + DILATION_X * sizeof(uchar)), int16); \ + middle = CONVERT(temp0.s02468ace, int8); \ + \ + temp0 = CONVERT(vload16(0, first_value + 2 * DILATION_X * sizeof(uchar)), int16); \ + right = CONVERT(temp0.s02468ace, int8); \ + }) +#else /* CONV_STRIDE_X */ +#define GET_VALUES(first_value, left, middle, right) \ + ({ \ + int16 temp0 = CONVERT(vload16(0, first_value), int16); \ + int8 temp1 = CONVERT(vload8(0, (first_value + 16 * sizeof(uchar))), int8); \ + left = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ + \ + temp0 = CONVERT(vload16(0, first_value + DILATION_X * sizeof(uchar)), int16); \ + temp1 = CONVERT(vload8(0, (first_value + (16 + DILATION_X) * sizeof(uchar))), int8); \ + middle = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ + \ + temp0 = CONVERT(vload16(0, first_value + 2 * DILATION_X * sizeof(uchar)), int16); \ + temp1 = CONVERT(vload8(0, (first_value + (16 + 2 * DILATION_X) * sizeof(uchar))), int8); \ + right = CONVERT((int8)(temp0.s0369, temp0.scf, temp1.s25), int8); \ + }) + +#endif /* CONV_STRIDE_X */ +#endif /* DILATION_X==1 */ + /** This function computes the depthwise convolution quantized. * * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8 @@ -151,10 +193,10 @@ __kernel void dwc_3x3_native_qasymm8_nchw( int8 values0 = 0; int8 sum0 = 0; -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 int8 values1 = 0; int8 sum1 = 0; -#endif /* CONV_STRIDE_Y */ +#endif /* CONV_STRIDE_Y &&DILATION_Y==1 */ // Row0 int8 left, middle, right; @@ -168,44 +210,44 @@ __kernel void dwc_3x3_native_qasymm8_nchw( #endif /* WEIGHTS_OFFSET != 0 */ // Row1 - GET_VALUES(src.ptr + 1 * src_stride_y, left, middle, right); + GET_VALUES(src.ptr + DILATION_Y * src_stride_y, left, middle, right); values0 += left * (int8)(w1.s0); values0 += middle * (int8)(w1.s1); values0 += right * (int8)(w1.s2); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += left * (int8)(w0.s0); values1 += middle * (int8)(w0.s1); values1 += right * (int8)(w0.s2); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y && DILATION_Y== 1 */ #if WEIGHTS_OFFSET != 0 int8 tmp = left + middle + right; sum0 += tmp; -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 sum1 += tmp; -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y &&DILATION_Y== 1 */ #endif /* WEIGHTS_OFFSET != 0 */ // Row2 - GET_VALUES(src.ptr + 2 * src_stride_y, left, middle, right); + GET_VALUES(src.ptr + 2 * DILATION_Y * src_stride_y, left, middle, right); values0 += left * (int8)(w2.s0); values0 += middle * (int8)(w2.s1); values0 += right * (int8)(w2.s2); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += left * (int8)(w1.s0); values1 += middle * (int8)(w1.s1); values1 += right * (int8)(w1.s2); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y &&DILATION_Y == 1 */ #if WEIGHTS_OFFSET != 0 tmp = left + middle + right; sum0 += tmp; -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 sum1 += tmp; -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ #endif /* WEIGHTS_OFFSET != 0 */ -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 // Row3 GET_VALUES(src.ptr + 3 * src_stride_y, left, middle, right); values1 += left * (int8)(w2.s0); @@ -215,20 +257,20 @@ __kernel void dwc_3x3_native_qasymm8_nchw( #if WEIGHTS_OFFSET != 0 sum1 += left + middle + right; #endif /* WEIGHTS_OFFSET != 0 */ -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y && DILATION_Y == 1 */ #if defined(HAS_BIAS) values0 += (int8)(bias_value); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += (int8)(bias_value); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y & &DILATION_Y == 1 */ #endif //defined(HAS_BIAS) #if WEIGHTS_OFFSET != 0 values0 += sum0 * (int8)(WEIGHTS_OFFSET); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += sum1 * (int8)(WEIGHTS_OFFSET); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ #endif /* WEIGHTS_OFFSET != 0 */ #if INPUT_OFFSET != 0 @@ -236,16 +278,16 @@ __kernel void dwc_3x3_native_qasymm8_nchw( ushort3 tmp_we = convert_ushort3(w0) + convert_ushort3(w1) + convert_ushort3(w2); sum_weights += tmp_we.s0 + tmp_we.s1 + tmp_we.s2; values0 += sum_weights * (int8)(INPUT_OFFSET); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += sum_weights * (int8)(INPUT_OFFSET); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ #endif /* INPUT_OFFSET != 0 */ #if K_OFFSET != 0 values0 += (int8)(K_OFFSET); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += (int8)(K_OFFSET); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ #endif /* K_OFFSET != 0 */ #if defined(REAL_MULTIPLIER) @@ -264,7 +306,7 @@ __kernel void dwc_3x3_native_qasymm8_nchw( res0 = min(res0, (uchar8)255); vstore8(ACTIVATION_FUNC(res0), 0, dst.ptr); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 #if defined(REAL_MULTIPLIER) values1 = CONVERT(round(CONVERT(values1, float8) * (float8)REAL_MULTIPLIER), int8); @@ -281,11 +323,11 @@ __kernel void dwc_3x3_native_qasymm8_nchw( res1 = min(res1, (uchar8)255); vstore8(ACTIVATION_FUNC(res1), 0, dst.ptr + dst_stride_y); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ } #else // !(defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8)) - +#if DILATION_X == 1 #if CONV_STRIDE_X == 1 #define GET_VALUES(first_value, left, middle, right) \ ({ \ @@ -317,6 +359,43 @@ __kernel void dwc_3x3_native_qasymm8_nchw( right = (uchar8)(temp0.s258b, temp0.se, temp1.s147); \ }) #endif /* CONV_STRIDE_X */ +#else /*DILATION_X==1*/ + +#if CONV_STRIDE_X == 1 +#define GET_VALUES(first_value, left, middle, right) \ + ({ \ + left = vload8(0, first_value); \ + middle = vload8(0, first_value + DILATION_X * sizeof(uchar)); \ + right = vload8(0, first_value + 2 * DILATION_X * sizeof(uchar)); \ + }) +#elif CONV_STRIDE_X == 2 +#define GET_VALUES(first_value, left, middle, right) \ + ({ \ + uchar16 temp0 = vload16(0, first_value); \ + left = temp0.s02468ace; \ + temp0 = vload16(0, first_value + DILATION_X * sizeof(uchar)); \ + middle = temp0.s02468ace; \ + temp0 = vload16(0, first_value + 2 * DILATION_X * sizeof(uchar)); \ + right = temp0.s02468ace; \ + }) +#else /* CONV_STRIDE_X */ +#define GET_VALUES(first_value, left, middle, right) \ + ({ \ + uchar16 temp0 = vload16(0, first_value); \ + uchar8 temp1 = vload8(0, (first_value + 16 * sizeof(uchar))); \ + left = (uchar8)(temp0.s0369, temp0.scf, temp1.s25); \ + \ + temp0 = vload16(0, first_value + DILATION_X * sizeof(uchar)); \ + temp1 = vload8(0, (first_value + (16 + DILATION_X) * sizeof(uchar))); \ + middle = (uchar8)(temp0.s0369, temp0.scf, temp1.s25); \ + \ + temp0 = vload16(0, first_value + 2 * DILATION_X * sizeof(uchar)); \ + temp1 = vload8(0, (first_value + (16 + 2 * DILATION_X) * sizeof(uchar))); \ + right = (uchar8)(temp0.s0369, temp0.scf, temp1.s25); \ + }) + +#endif /* CONV_STRIDE_X */ +#endif /*DILATION_X==1*/ /** This function computes the depthwise convolution quantized using dot product when the data layout is NCHW. * * @param[in] src_ptr Pointer to the source tensor. Supported data types: QASYMM8 @@ -389,8 +468,8 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( int8 sum0 = 0; GET_VALUES(src.ptr + 0 * src_stride_y, left0, middle0, right0); - GET_VALUES(src.ptr + 1 * src_stride_y, left1, middle1, right1); - GET_VALUES(src.ptr + 2 * src_stride_y, left2, middle2, right2); + GET_VALUES(src.ptr + DILATION_Y * src_stride_y, left1, middle1, right1); + GET_VALUES(src.ptr + 2 * DILATION_Y * src_stride_y, left2, middle2, right2); #if WEIGHTS_OFFSET != 0 sum0 += convert_int8(left0) + convert_int8(middle0) + convert_int8(right0); @@ -398,7 +477,7 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( sum0 += convert_int8(left2) + convert_int8(middle2) + convert_int8(right2); #endif /* WEIGHTS_OFFSET != 0 */ -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 // If conv_stride_y is equals to 1, we compute two output rows uchar8 left3, middle3, right3; @@ -412,7 +491,7 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( sum1 += convert_int8(left2) + convert_int8(middle2) + convert_int8(right2); sum1 += convert_int8(left3) + convert_int8(middle3) + convert_int8(right3); #endif /* WEIGHTS_OFFSET != 0 */ -#endif // CONV_STRIDE_Y == 1 +#endif // CONV_STRIDE_Y == 1 && DILATION_Y==1 ARM_DOT((uchar4)(left0.s0, middle0.s0, right0.s0, left1.s0), (uchar4)(w0.s0, w0.s1, w0.s2, w1.s0), values0.s0); ARM_DOT((uchar4)(middle1.s0, right1.s0, left2.s0, middle2.s0), (uchar4)(w1.s1, w1.s2, w2.s0, w2.s1), values0.s0); @@ -446,7 +525,7 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( ARM_DOT((uchar4)(middle1.s7, right1.s7, left2.s7, middle2.s7), (uchar4)(w1.s1, w1.s2, w2.s0, w2.s1), values0.s7); values0.s7 += right2.s7 * w2.s2; -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 ARM_DOT((uchar4)(left1.s0, middle1.s0, right1.s0, left2.s0), (uchar4)(w0.s0, w0.s1, w0.s2, w1.s0), values1.s0); ARM_DOT((uchar4)(middle2.s0, right2.s0, left3.s0, middle3.s0), (uchar4)(w1.s1, w1.s2, w2.s0, w2.s1), values1.s0); values1.s0 += right3.s0 * w2.s2; @@ -478,20 +557,20 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( ARM_DOT((uchar4)(left1.s7, middle1.s7, right1.s7, left2.s7), (uchar4)(w0.s0, w0.s1, w0.s2, w1.s0), values1.s7); ARM_DOT((uchar4)(middle2.s7, right2.s7, left3.s7, middle3.s7), (uchar4)(w1.s1, w1.s2, w2.s0, w2.s1), values1.s7); values1.s7 += right3.s7 * w2.s2; -#endif // CONV_STRIDE_Y == 1 +#endif // CONV_STRIDE_Y == 1 && DILATION_Y==1 #if defined(HAS_BIAS) values0 += (int8)(bias_value); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += (int8)(bias_value); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ #endif //defined(HAS_BIAS) #if WEIGHTS_OFFSET != 0 values0 += sum0 * (int8)(WEIGHTS_OFFSET); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += sum1 * (int8)(WEIGHTS_OFFSET); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1 */ #endif /* WEIGHTS_OFFSET != 0 */ #if INPUT_OFFSET != 0 @@ -499,16 +578,16 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( ushort3 tmp_we = convert_ushort3(w0) + convert_ushort3(w1) + convert_ushort3(w2); sum_weights += tmp_we.s0 + tmp_we.s1 + tmp_we.s2; values0 += sum_weights * (int8)(INPUT_OFFSET); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += sum_weights * (int8)(INPUT_OFFSET); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ #endif /* INPUT_OFFSET != 0 */ #if K_OFFSET != 0 values0 += (int8)(K_OFFSET); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 values1 += (int8)(K_OFFSET); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ #endif /* K_OFFSET != 0 */ #if defined(REAL_MULTIPLIER) @@ -527,7 +606,7 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( res0 = min(res0, (uchar8)255); vstore8(ACTIVATION_FUNC(res0), 0, dst.ptr); -#if CONV_STRIDE_Y == 1 +#if CONV_STRIDE_Y == 1 && DILATION_Y == 1 #if defined(REAL_MULTIPLIER) @@ -545,7 +624,7 @@ __kernel void dwc_3x3_native_qasymm8_dot8_nchw( res1 = min(res1, (uchar8)255); vstore8(ACTIVATION_FUNC(res1), 0, dst.ptr + dst_stride_y); -#endif /* CONV_STRIDE_Y == 1 */ +#endif /* CONV_STRIDE_Y == 1 && DILATION_Y==1*/ } #endif // defined(ARM_COMPUTE_OPENCL_DOT8_ENABLED) && defined(cl_arm_integer_dot_product_int8) @@ -669,7 +748,7 @@ __kernel void dwc_3x3_reshaped_qasymm8_nhwc( int z_coord = 0; int4 offset = 0; - int4 y_coord = ((int4)(y * CONV_STRIDE_X) + (int4)(0, 1, 2, 3)) - (int)CONV_PAD_LEFT; + int4 y_coord = ((int4)(y * CONV_STRIDE_X) + (int4)(0, DILATION_X * 1, DILATION_X * 2, DILATION_X * 3)) - (int)CONV_PAD_LEFT; // Only for y = 0 we can have a negative coordinate. If so, we convert it to SRC_DIM_1 y_coord.s0 = min((uint)y_coord.s0, (uint)SRC_DIM_1); @@ -720,16 +799,16 @@ __kernel void dwc_3x3_reshaped_qasymm8_nhwc( // z == 1 // z_coord can be only negative for z = 0 so we do not need to clamp it // Moreover z_coord cannot be out-of-bound for z = 1 so we do not need to clamp the offset - z_coord = z * (int)CONV_STRIDE_Y - (int)CONV_PAD_TOP + 1; + z_coord = z * (int)CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y; offset = y_offset + (int4)(z_coord * src_stride_z); VEC_UCHAR values3 = VLOAD(VEC_SIZE)(0, src_addr + offset.s0); VEC_UCHAR values4 = VLOAD(VEC_SIZE)(0, src_addr + offset.s1); VEC_UCHAR values5 = VLOAD(VEC_SIZE)(0, src_addr + offset.s2); // z == 2 - // After z = 1 we can simply add src_stride_z to offset without updating z_coord - // However offset can be out-of-bound so we need to check if it is greater than max_offset - offset += (int4)src_stride_z; + // Offset can be out-of-bound so we need to check if it is greater than max_offset + z_coord = z * (int)CONV_STRIDE_Y - (int)CONV_PAD_TOP + DILATION_Y * 2; + offset = y_offset + (int4)(z_coord * src_stride_z); offset = min(offset, (int4)max_offset); VEC_UCHAR values6 = VLOAD(VEC_SIZE)(0, src_addr + offset.s0); VEC_UCHAR values7 = VLOAD(VEC_SIZE)(0, src_addr + offset.s1); diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp index 83fac16d88..ec27e419c4 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp @@ -43,7 +43,7 @@ using namespace arm_compute::misc::shape_calculator; namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const ActivationLayerInfo &act_info) + const ActivationLayerInfo &act_info, const Size2D dilation) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32); @@ -56,6 +56,8 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, 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); + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); if(biases != nullptr) @@ -74,7 +76,7 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, if(output->total_size() != 0) { - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); } @@ -82,10 +84,10 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, } std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - GPUTarget gpu_target, std::string &kernel_name) + GPUTarget gpu_target, std::string &kernel_name, const Size2D dilation) { // Output auto inizialitation if not yet initialized - const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); auto_init_if_empty(*output, input->clone()->set_tensor_shape(output_shape)); const unsigned int conv_stride_x = conv_info.stride().first; @@ -176,10 +178,12 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen kernel_name += (is_qasymm ? "_nchw" : ""); num_elems_written_per_iteration_x = 8 / data_size_from_type(input->data_type()); - num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1) ? 2 : 1; + num_elems_written_per_iteration_y = (is_qasymm && conv_stride_y == 1 && dilation.y() == 1) ? 2 : 1; num_elems_read_per_iteration_x = 3 + (num_elems_written_per_iteration_x - 1) * conv_stride_x; num_elems_read_per_iteration_y = num_elems_written_per_iteration_y + 2; } + num_elems_read_per_iteration_x += (num_elems_read_per_iteration_x - 1) * (dilation.x() - 1); + num_elems_read_per_iteration_y += (num_elems_read_per_iteration_y - 1) * (dilation.y() - 1); // Create window and update padding Window win = calculate_max_window(*output, Steps(num_elems_written_per_iteration_x, num_elems_written_per_iteration_y)); @@ -210,10 +214,10 @@ BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const } void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info) + unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info, dilation)); bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); @@ -231,7 +235,7 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, std::string kernel_name; const GPUTarget gpu_target = get_target(); - auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, gpu_target, kernel_name); + auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info, depth_multiplier, gpu_target, kernel_name, dilation); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); ICLKernel::configure_internal(win_config.second); @@ -240,6 +244,8 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, build_opts.add_option("-DDST_CHANNELS=" + support::cpp11::to_string(_output->info()->tensor_shape().z())); build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); build_opts.add_option("-DCONV_STRIDE_X=" + support::cpp11::to_string(_conv_stride_x)); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS"); if(is_qasymm) @@ -292,12 +298,11 @@ void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, } Status CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - ActivationLayerInfo act_info, GPUTarget gpu_target) + unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation) { std::string kernel_name; - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info)); - ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, gpu_target, kernel_name).first); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info, depth_multiplier, gpu_target, kernel_name, dilation).first); return Status{}; } diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp index 431039c31f..86d186b95e 100644 --- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp @@ -42,7 +42,7 @@ namespace arm_compute namespace { Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const ActivationLayerInfo &act_info) + const ActivationLayerInfo &act_info, const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input); ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F16, DataType::F32, DataType::QASYMM8); @@ -57,6 +57,8 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, ARM_COMPUTE_RETURN_ERROR_ON(conv_info.stride().first < 1); ARM_COMPUTE_RETURN_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 1); + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || (dilation.y() < 1)); + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); const size_t weights_width = 3; const size_t weights_height = 3; @@ -89,7 +91,8 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, if(output->total_size() != 0) { - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, weights_width, weights_height, conv_info, depth_multiplier); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape( + *input, TensorInfo(TensorShape(weights_width, weights_height), 1, weights->data_type()).set_data_layout(DataLayout::NCHW), conv_info, depth_multiplier, dilation); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape); } @@ -97,13 +100,14 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, } std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *bias, ITensorInfo *output, - const PadStrideInfo &conv_info, unsigned int depth_multiplier) + const PadStrideInfo &conv_info, unsigned int depth_multiplier, const Size2D &dilation) { const size_t weights_width = 3; const size_t weights_height = 3; // Get convolved dimensions - const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape(*input, weights_width, weights_height, conv_info, depth_multiplier); + const TensorShape output_shape = arm_compute::misc::shape_calculator::compute_depthwise_convolution_shape( + *input, TensorInfo(TensorShape(weights_width, weights_height), 1, weights->data_type()).set_data_layout(DataLayout::NCHW), conv_info, depth_multiplier, dilation); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output, @@ -112,10 +116,10 @@ std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITen input->data_type(), input->quantization_info()); - const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); - const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); + const bool is_qasymm = is_data_type_quantized_asymmetric(input->data_type()); + const bool is_stride_1_dilation_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1) && dilation.x() == 1 && dilation.y() == 1); - const unsigned int num_rows_processed_per_iteration = is_stride_1 ? 2 : 1; + const unsigned int num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; const unsigned int num_elems_accessed_per_iteration = is_qasymm ? 4 : (8 / input->element_size()); const unsigned int num_rows_read_per_iteration = num_rows_processed_per_iteration + 2; const unsigned int num_rows_written_per_iteration = std::ceil(num_rows_processed_per_iteration / static_cast<float>(conv_info.stride().first)); @@ -166,15 +170,17 @@ BorderSize CLDepthwiseConvolutionLayer3x3NHWCKernel::border_size() const } void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, ActivationLayerInfo act_info) + unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info)); - auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, act_info, dilation)); + auto win_config = validate_and_configure_window(input->info(), weights->info(), biases != nullptr ? biases->info() : nullptr, output->info(), conv_info, depth_multiplier, dilation); ARM_COMPUTE_ERROR_THROW_ON(win_config.first); - const bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); - const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); + const bool is_qasymm = is_data_type_quantized_asymmetric(input->info()->data_type()); + const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); + const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); _input = input; @@ -182,8 +188,8 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, _weights = weights; _biases = biases; _conv_stride_y = conv_info.stride().second; - _num_rows_processed_per_iteration = is_stride_1 ? 2 : 1; - _num_planes_processed_per_iteration = is_stride_1 ? 2 : 1; + _num_rows_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; + _num_planes_processed_per_iteration = is_stride_1_dilation_1 ? 2 : 1; // If QASYMM8 and the 8 bit dot product is available, force _num_planes_processed_per_iteration to 1 if(is_dot8_supported && is_qasymm) @@ -201,6 +207,8 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, build_opts.add_option("-DSRC_DIM_2=" + support::cpp11::to_string(_input->info()->dimension(2))); build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())); build_opts.add_option("-DCONV_PAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); if(is_qasymm) { @@ -238,7 +246,7 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, build_opts.add_option("-DDATA_TYPE=" + get_cl_type_from_data_type(_input->info()->data_type())); } - if(is_stride_1) + if(is_stride_1_dilation_1) { build_opts.add_option("-DNUM_ROWS_PROCESSED=" + support::cpp11::to_string(_num_rows_processed_per_iteration)); build_opts.add_option("-DNUM_PLANES_PROCESSED=" + support::cpp11::to_string(_num_planes_processed_per_iteration)); @@ -257,14 +265,14 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, if(is_qasymm) { kernel_name = std::string("dwc_3x3_reshaped_qasymm8"); - kernel_name += (is_dot8_supported && is_stride_1 ? "_dot8" : ""); - kernel_name += (is_stride_1 ? "_stride1" : ""); + kernel_name += (is_dot8_supported && is_stride_1_dilation_1 ? "_dot8" : ""); + kernel_name += (is_stride_1_dilation_1 ? "_stride1" : ""); kernel_name += "_nhwc"; } else { kernel_name = std::string("depthwise_convolution_3x3_nhwc"); - kernel_name += (is_stride_1 ? "_stride1" : ""); + kernel_name += (is_stride_1_dilation_1 ? "_stride1" : ""); } ICLKernel::configure_internal(win_config.second); @@ -287,13 +295,12 @@ void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, } Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - ActivationLayerInfo act_info) + unsigned int depth_multiplier, ActivationLayerInfo act_info, const Size2D &dilation) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), biases != nullptr ? biases->clone().get() : nullptr, - output->clone().get(), conv_info, depth_multiplier) + output->clone().get(), conv_info, depth_multiplier, dilation) .first); return Status{}; diff --git a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp index beff7ae8c4..28d4ff2759 100644 --- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp +++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp @@ -44,7 +44,8 @@ CLDepthwiseIm2ColKernel::CLDepthwiseIm2ColKernel() namespace { -Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier) +Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier, + const Size2D &dilation) { const size_t idx_c = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::CHANNEL); @@ -55,16 +56,18 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *output, c ARM_COMPUTE_RETURN_ERROR_ON(is_data_type_quantized_asymmetric(input->data_type()) && has_bias); ARM_COMPUTE_RETURN_ERROR_ON((input->dimension(idx_c) * depth_multiplier) != output->dimension(2)); ARM_COMPUTE_RETURN_ERROR_ON(output->dimension(0) != (kernel_dims.width * kernel_dims.height + ((has_bias) ? 1 : 0))); + ARM_COMPUTE_RETURN_ERROR_ON((dilation.x() < 1) || dilation.y() < 1); ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_QUANTIZATION_INFO(input, output); return Status{}; } } // namespace -void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier) +void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier, + const Size2D &dilation) { ARM_COMPUTE_ERROR_ON_NULLPTR(input, output); - ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, depth_multiplier)); + ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), output->info(), kernel_dims, conv_info, has_bias, depth_multiplier, dilation)); _input = input; _output = output; @@ -88,6 +91,8 @@ void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *outpu build_opts.add_option("-DKERNEL_WIDTH=" + support::cpp11::to_string(kernel_dims.width)); build_opts.add_option("-DKERNEL_HEIGHT=" + support::cpp11::to_string(kernel_dims.height)); build_opts.add_option("-DDEPTH_MULTIPLIER=" + support::cpp11::to_string(depth_multiplier)); + build_opts.add_option("-DDILATION_X=" + support::cpp11::to_string(dilation.x())); + build_opts.add_option("-DDILATION_Y=" + support::cpp11::to_string(dilation.y())); build_opts.add_option("-D" + string_from_data_layout(input->info()->data_layout())); build_opts.add_option_if(has_bias, "-DHAS_BIAS"); build_opts.add_option_if_else(is_data_type_quantized_asymmetric(input->info()->data_type()), @@ -104,9 +109,10 @@ void CLDepthwiseIm2ColKernel::configure(const ICLTensor *input, ICLTensor *outpu ICLKernel::configure_internal(win); } -Status CLDepthwiseIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier) +Status CLDepthwiseIm2ColKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const Size2D &kernel_dims, const PadStrideInfo &conv_info, bool has_bias, unsigned int depth_multiplier, + const Size2D &dilation) { - ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, depth_multiplier)); + ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, output, kernel_dims, conv_info, has_bias, depth_multiplier, dilation)); return Status{}; } diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp index 65d3f5ffe4..23c3f81edc 100644 --- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp +++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp @@ -45,7 +45,7 @@ CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3(std::shared_ptr<I } void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - ActivationLayerInfo act_info) + ActivationLayerInfo act_info, const Size2D &dilation) { 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); @@ -62,11 +62,13 @@ void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor const ICLTensor *weights_to_use = weights; ICLTensor *output_to_use = output; - const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); + const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); + const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); + DepthwiseConvolutionReshapeInfo info; info.c0 = 4; - info.transpose = is_stride_1 && is_dot8_supported; + info.transpose = is_stride_1_dilation_1 && is_dot8_supported; if(_needs_permute) { @@ -103,7 +105,7 @@ void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor // Configure kernel _kernel->set_target(CLScheduler::get().target()); - _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, act_info); + _kernel->configure(input_to_use, weights_to_use, biases, output_to_use, conv_info, depth_multiplier, act_info, dilation); // Permute output if needed if(_needs_permute) @@ -126,26 +128,26 @@ void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor } Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, - ActivationLayerInfo act_info, GPUTarget gpu_target) + unsigned int depth_multiplier, ActivationLayerInfo act_info, GPUTarget gpu_target, const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); - const bool is_nhwc = input->data_layout() == DataLayout::NHWC; - const bool needs_permute = is_nhwc && (depth_multiplier > 1); - const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1); - const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); - const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); + const bool is_nhwc = input->data_layout() == DataLayout::NHWC; + const bool needs_permute = is_nhwc && (depth_multiplier > 1); + const bool needs_weights_reshape = is_nhwc && (depth_multiplier == 1); + const bool is_stride_1 = ((conv_info.stride().first == conv_info.stride().second) && (conv_info.stride().first == 1)); + const bool is_stride_1_dilation_1 = (is_stride_1 && dilation.x() == 1 && dilation.y() == 1); + const bool is_dot8_supported = dot8_supported(CLKernelLibrary::get().get_device()); DepthwiseConvolutionReshapeInfo info; info.c0 = 4; - info.transpose = is_stride_1 && is_dot8_supported; + info.transpose = is_stride_1_dilation_1 && is_dot8_supported; if(needs_permute) { TensorShape permuted_input_shape = input->tensor_shape(); TensorShape permuted_weights_shape = weights->tensor_shape(); - TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + TensorShape permuted_output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); permute(permuted_input_shape, PermutationVector(1U, 2U, 0U)); permute(permuted_weights_shape, PermutationVector(1U, 2U, 0U)); @@ -155,7 +157,8 @@ Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const const TensorInfo permuted_weights = weights->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_weights_shape).set_data_layout(DataLayout::NCHW); const TensorInfo permuted_output = output->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(permuted_output_shape).set_data_layout(DataLayout::NCHW); - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, act_info, gpu_target)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(&permuted_input, &permuted_weights, biases, &permuted_output, conv_info, depth_multiplier, act_info, gpu_target, + dilation)); } else if(is_nhwc) { @@ -163,13 +166,13 @@ Status CLDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input, const { auto reshaped_weights_shape = arm_compute::misc::shape_calculator::compute_reshaped_depthwise_weights_shape(*weights, info); ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, &weights->clone()->set_tensor_shape(reshaped_weights_shape), biases, output, conv_info, depth_multiplier, - act_info)); + act_info, dilation)); } - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation)); } else { - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseConvolutionLayer3x3NCHWKernel::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, gpu_target, dilation)); } return Status{}; @@ -227,7 +230,7 @@ CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer() } void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info) + unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) { 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); @@ -241,7 +244,7 @@ void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *w if(bool(can_run_optimised_3x3_kernel)) { auto f = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3>(); - f->configure(input, weights, biases, output, conv_info, depth_multiplier, act_info); + f->configure(input, weights, biases, output, conv_info, depth_multiplier, act_info, dilation); _optimised_function = std::move(f); } else @@ -260,7 +263,7 @@ void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *w const GPUTarget gpu_target = CLScheduler::get().target(); // Calculate output shape - TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier); + TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info, depth_multiplier, dilation); // Output auto inizialitation if not yet initialized auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(output_shape)); @@ -281,7 +284,7 @@ void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *w shape_im2col.set(2, weights_z); _input_reshaped.allocator()->init(input->info()->clone()->set_is_resizable(true).reset_padding().set_tensor_shape(shape_im2col)); _im2col_kernel.set_target(gpu_target); - _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier); + _im2col_kernel.configure(input, &_input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation); CLScheduler::get().tune_kernel_static(_im2col_kernel); // Weights reshape configuration @@ -343,11 +346,14 @@ void CLDepthwiseConvolutionLayer::configure(ICLTensor *input, const ICLTensor *w } Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info) + unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) { const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH); const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_w) + (weights->dimension(idx_w) - 1) * (dilation.x() - 1) > input->dimension(idx_w) + conv_info.pad_left() + conv_info.pad_right()); + ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(idx_h) + (weights->dimension(idx_h) - 1) * (dilation.y() - 1) > input->dimension(idx_h) + conv_info.pad_top() + conv_info.pad_bottom()); + const bool can_run_optimised_3x3_kernel = (weights->dimension(idx_w) == 3) && (weights->dimension(idx_h) == 3); if(can_run_optimised_3x3_kernel) @@ -359,7 +365,7 @@ Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe const bool is_quantized = is_data_type_quantized_asymmetric(input->data_type()); const bool append_bias = (biases != nullptr) && !is_quantized; - const TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier); + const TensorShape output_shape = shape_calculator::compute_depthwise_convolution_shape(*input, *weights, conv_info, depth_multiplier, dilation); const size_t weights_w = weights->dimension(idx_w); const size_t weights_h = weights->dimension(idx_h); const size_t weights_z = weights->dimension(idx_c); @@ -373,7 +379,7 @@ Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe 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)); - ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseIm2ColKernel::validate(input, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier)); + ARM_COMPUTE_RETURN_ON_ERROR(CLDepthwiseIm2ColKernel::validate(input, &input_reshaped, Size2D(weights_w, weights_h), conv_info, append_bias, depth_multiplier, dilation)); 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)); @@ -403,7 +409,7 @@ Status CLDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITe } else { - CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info); + CLDepthwiseConvolutionLayer3x3::validate(input, weights, biases, output, conv_info, depth_multiplier, act_info, GPUTarget::MIDGARD, dilation); } return Status{}; } diff --git a/src/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.cpp b/src/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.cpp index ba05838920..0f772bdc94 100644 --- a/src/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.cpp +++ b/src/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.cpp @@ -36,8 +36,10 @@ GCDepthwiseConvolutionLayer3x3::GCDepthwiseConvolutionLayer3x3() } void GCDepthwiseConvolutionLayer3x3::configure(IGCTensor *input, const IGCTensor *weights, const IGCTensor *biases, IGCTensor *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info) + unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) { + ARM_COMPUTE_ERROR_ON(dilation.x() != 1 || dilation.y() != 1); + ARM_COMPUTE_UNUSED(dilation); auto k = arm_compute::support::cpp14::make_unique<GCDepthwiseConvolutionLayer3x3Kernel>(); k->configure(input, weights, biases, output, conv_info, depth_multiplier); _kernel = std::move(k); diff --git a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp index 4f632a20a0..4c602b3640 100644 --- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp +++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp @@ -182,8 +182,11 @@ void NEDepthwiseConvolutionLayer3x3::configure(ITensor *input, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const ActivationLayerInfo &act_info) + const ActivationLayerInfo &act_info, + const Size2D &dilation) { + ARM_COMPUTE_ERROR_ON(dilation.x() != 1 || dilation.y() != 1); + ARM_COMPUTE_UNUSED(dilation); 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); @@ -222,10 +225,12 @@ Status NEDepthwiseConvolutionLayer3x3::validate(const ITensorInfo *input const ITensorInfo *output, const PadStrideInfo &conv_info, unsigned int depth_multiplier, - const ActivationLayerInfo &act_info) + const ActivationLayerInfo &act_info, + const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); + ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() != 1 || dilation.y() != 1); if(biases != nullptr) { @@ -347,10 +352,12 @@ NEDepthwiseConvolutionLayer::NEDepthwiseConvolutionLayer() } 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) + unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) { const unsigned int channel_idx = get_data_layout_dimension_index(input->info()->data_layout(), DataLayoutDimension::CHANNEL); ARM_COMPUTE_UNUSED(channel_idx); + ARM_COMPUTE_ERROR_ON(dilation.x() != 1 || dilation.y() != 1); + ARM_COMPUTE_UNUSED(dilation); 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); @@ -480,10 +487,11 @@ void NEDepthwiseConvolutionLayer::configure(ITensor *input, const ITensor *weigh } Status NEDepthwiseConvolutionLayer::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info, - unsigned int depth_multiplier, const ActivationLayerInfo &act_info) + unsigned int depth_multiplier, const ActivationLayerInfo &act_info, const Size2D &dilation) { ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input, weights, output); ARM_COMPUTE_RETURN_ERROR_ON(input->data_layout() == DataLayout::UNKNOWN); + ARM_COMPUTE_RETURN_ERROR_ON(dilation.x() != 1 || dilation.y() != 1); 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); diff --git a/tests/benchmark/fixtures/DepthwiseConvolutionLayerFixture.h b/tests/benchmark/fixtures/DepthwiseConvolutionLayerFixture.h index 48ea03810f..33753bcd07 100644 --- a/tests/benchmark/fixtures/DepthwiseConvolutionLayerFixture.h +++ b/tests/benchmark/fixtures/DepthwiseConvolutionLayerFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -45,7 +45,7 @@ class DepthwiseConvolutionLayerFixture : public framework::Fixture { public: template <typename...> - void setup(TensorShape src_shape, Size2D kernel_size, PadStrideInfo info, DataType data_type, int batches) + void setup(TensorShape src_shape, Size2D kernel_size, PadStrideInfo info, Size2D Dilation, DataType data_type, int batches) { // Get shapes TensorShape weights_shape(kernel_size.width, kernel_size.height); diff --git a/tests/datasets/DepthwiseConvolutionLayerDataset.h b/tests/datasets/DepthwiseConvolutionLayerDataset.h index 8a1f3b6f39..4c78eb87ea 100644 --- a/tests/datasets/DepthwiseConvolutionLayerDataset.h +++ b/tests/datasets/DepthwiseConvolutionLayerDataset.h @@ -38,16 +38,18 @@ namespace datasets class DepthwiseConvolutionLayerDataset { public: - using type = std::tuple<TensorShape, Size2D, PadStrideInfo>; + using type = std::tuple<TensorShape, Size2D, PadStrideInfo, Size2D>; struct iterator { iterator(std::vector<TensorShape>::const_iterator src_it, std::vector<Size2D>::const_iterator weights_it, - std::vector<PadStrideInfo>::const_iterator infos_it) + std::vector<PadStrideInfo>::const_iterator infos_it, + std::vector<Size2D>::const_iterator dilation_it) : _src_it{ std::move(src_it) }, _weights_it{ std::move(weights_it) }, - _infos_it{ std::move(infos_it) } + _infos_it{ std::move(infos_it) }, + _dilation_it{ std::move(dilation_it) } { } @@ -56,13 +58,14 @@ public: std::stringstream description; description << "In=" << *_src_it << ":"; description << "Weights=" << *_weights_it << ":"; - description << "Info=" << *_infos_it; + description << "Info=" << *_infos_it << ":"; + description << "Dilation=" << *_dilation_it; return description.str(); } DepthwiseConvolutionLayerDataset::type operator*() const { - return std::make_tuple(*_src_it, *_weights_it, *_infos_it); + return std::make_tuple(*_src_it, *_weights_it, *_infos_it, *_dilation_it); } iterator &operator++() @@ -70,6 +73,7 @@ public: ++_src_it; ++_weights_it; ++_infos_it; + ++_dilation_it; return *this; } @@ -78,23 +82,25 @@ public: std::vector<TensorShape>::const_iterator _src_it; std::vector<Size2D>::const_iterator _weights_it; std::vector<PadStrideInfo>::const_iterator _infos_it; + std::vector<Size2D>::const_iterator _dilation_it; }; iterator begin() const { - return iterator(_src_shapes.begin(), _weight_shapes.begin(), _infos.begin()); + return iterator(_src_shapes.begin(), _weight_shapes.begin(), _infos.begin(), _dilations.begin()); } int size() const { - return std::min(_src_shapes.size(), std::min(_weight_shapes.size(), _infos.size())); + return std::min(_src_shapes.size(), std::min(_weight_shapes.size(), std::min(_infos.size(), _dilations.size()))); } - void add_config(TensorShape src, Size2D weights, PadStrideInfo info) + void add_config(TensorShape src, Size2D weights, PadStrideInfo info, Size2D dilation = Size2D(1U, 1U)) { _src_shapes.emplace_back(std::move(src)); _weight_shapes.emplace_back(std::move(weights)); _infos.emplace_back(std::move(info)); + _dilations.emplace_back(std::move(dilation)); } protected: @@ -105,6 +111,7 @@ private: std::vector<TensorShape> _src_shapes{}; std::vector<Size2D> _weight_shapes{}; std::vector<PadStrideInfo> _infos{}; + std::vector<Size2D> _dilations{}; }; /** Dataset containing small, generic depthwise convolution shapes. */ diff --git a/tests/datasets/DilatedDepthwiseConvolutionLayerDataset.h b/tests/datasets/DilatedDepthwiseConvolutionLayerDataset.h new file mode 100644 index 0000000000..df054de501 --- /dev/null +++ b/tests/datasets/DilatedDepthwiseConvolutionLayerDataset.h @@ -0,0 +1,139 @@ +/* + * Copyright (c) 2019 ARM Limited. + * + * SPDX-License-Identifier: MIT + * + * Permission is hereby granted, free of charge, to any person obtaining a copy + * of this software and associated documentation files (the "Software"), to + * deal in the Software without restriction, including without limitation the + * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or + * sell copies of the Software, and to permit persons to whom the Software is + * furnished to do so, subject to the following conditions: + * + * The above copyright notice and this permission notice shall be included in all + * copies or substantial portions of the Software. + * + * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR + * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, + * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE + * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER + * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * SOFTWARE. + */ +#ifndef ARM_COMPUTE_TEST_DILATED_CONVOLUTION_LAYER_DATASET +#define ARM_COMPUTE_TEST_DILATED_CONVOLUTION_LAYER_DATASET + +#include "utils/TypePrinter.h" + +#include "arm_compute/core/TensorShape.h" +#include "arm_compute/core/Types.h" +#include "tests/datasets/DepthwiseConvolutionLayerDataset.h" + +namespace arm_compute +{ +namespace test +{ +namespace datasets +{ +/** Dataset containing small, generic depthwise convolution shapes with dilation. */ +class SmallDepthwiseDilatedConvolutionLayerDataset final : public DepthwiseConvolutionLayerDataset +{ +public: + SmallDepthwiseDilatedConvolutionLayerDataset() + { + // Different strides and dilations + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 2U), PadStrideInfo(1, 1, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 2U), PadStrideInfo(1, 2, 0, 0), Size2D(2U, 1U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 2U), PadStrideInfo(1, 1, 0, 0), Size2D(2U, 1U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 2U), PadStrideInfo(2, 1, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 2U), PadStrideInfo(2, 2, 0, 0), Size2D(1U, 2U)); + + add_config(TensorShape(7U, 8U, 1U), Size2D(2U, 3U), PadStrideInfo(1, 2, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(23U, 27U, 5U), Size2D(3U, 5U), PadStrideInfo(2, 1, 0, 0), Size2D(2U, 1U)); + add_config(TensorShape(33U, 27U, 7U), Size2D(7U, 3U), PadStrideInfo(3, 2, 1, 0), Size2D(1U, 2U)); + // Asymmetric padding + add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 1, 2, 0, DimensionRoundingType::FLOOR), Size2D(2U, 2U)); + add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 1, 0, 2, DimensionRoundingType::FLOOR), Size2D(2U, 2U)); + } +}; + +/** Dataset containing small, 3x3 depthwise convolution shapes with dilation. */ +class SmallDepthwiseDilatedConvolutionLayerDataset3x3 final : public DepthwiseConvolutionLayerDataset +{ +public: + SmallDepthwiseDilatedConvolutionLayerDataset3x3() + { + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(1, 1, 2, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(1, 1, 3, 0), Size2D(2U, 2U)); + + // Different strides and dilations + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 0), Size2D(2U, 1U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(2, 1, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 1U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 0), Size2D(1U, 2U)); + + add_config(TensorShape(11U, 11U, 1U), Size2D(3U, 3U), PadStrideInfo(3, 3, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(7U, 7U, 3U, 2U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 0), Size2D(2U, 2U)); + + add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 0), Size2D(1U, 1U)); + + add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 0), Size2D(2U, 2U)); + // Asymmetric padding + add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), Size2D(2U, 2U)); + } +}; + +/** Dataset containing large, generic depthwise convolution shapes with dilation. */ +class LargeDepthwiseDilatedConvolutionLayerDataset final : public DepthwiseConvolutionLayerDataset +{ +public: + LargeDepthwiseDilatedConvolutionLayerDataset() + { + add_config(TensorShape(33U, 27U, 11U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 1), Size2D(2U, 1U)); + add_config(TensorShape(17U, 31U, 2U), Size2D(5U, 9U), PadStrideInfo(1, 2, 1, 1), Size2D(1U, 2U)); + add_config(TensorShape(23U, 27U, 5U), Size2D(11U, 3U), PadStrideInfo(1, 2, 0, 0), Size2D(3U, 3U)); + add_config(TensorShape(17U, 31U, 2U, 3U), Size2D(5U, 9U), PadStrideInfo(1, 2, 1, 1), Size2D(2U, 2U)); + add_config(TensorShape(233U, 277U, 55U), Size2D(3U, 3U), PadStrideInfo(2, 1, 0, 0), Size2D(2U, 2U)); + add_config(TensorShape(333U, 277U, 77U), Size2D(3U, 3U), PadStrideInfo(3, 2, 1, 0), Size2D(3U, 2U)); + add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 1), Size2D(2U, 2U)); + add_config(TensorShape(233U, 277U, 55U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 0), Size2D(5U, 2U)); + add_config(TensorShape(333U, 277U, 77U), Size2D(3U, 3U), PadStrideInfo(2, 3, 0, 1), Size2D(2U, 2U)); + add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 1), Size2D(2U, 5U)); + // Asymmetric padding + add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 2, 1, 2, 0, DimensionRoundingType::FLOOR), Size2D(3U, 2U)); + add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 3, 0, 2, DimensionRoundingType::FLOOR), Size2D(4U, 4U)); + add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 1, 0, 1, 0, DimensionRoundingType::FLOOR), Size2D(2U, 2U)); + add_config(TensorShape(33U, 27U, 7U), Size2D(5U, 7U), PadStrideInfo(3, 2, 0, 1, 0, 1, DimensionRoundingType::FLOOR), Size2D(3U, 3U)); + } +}; + +/** Dataset containing large, 3x3 depthwise convolution shapes with dilation. */ +class LargeDepthwiseDilatedConvolutionLayerDataset3x3 final : public DepthwiseConvolutionLayerDataset +{ +public: + LargeDepthwiseDilatedConvolutionLayerDataset3x3() + { + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 1, 0, 1), Size2D(2U, 1U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 1, 1, 1), Size2D(2U, 2U)); + add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 0), Size2D(2U, 2U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 1), Size2D(2U, 1U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 1), Size2D(2U, 3U)); + add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 0), Size2D(2U, 1U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 0, 1), Size2D(3U, 3U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 1), Size2D(2U, 2U)); + add_config(TensorShape(21U, 31U, 9U, 4U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 0), Size2D(2U, 2U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 2, 0, 1), Size2D(4U, 4U)); + add_config(TensorShape(33U, 27U, 11U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 2, 1, 1), Size2D(2U, 5U)); + add_config(TensorShape(233U, 277U, 55U, 3U), Size2D(3U, 3U), PadStrideInfo(2, 1, 0, 0), Size2D(3U, 3U)); + add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 3U), PadStrideInfo(1, 2, 1, 1), Size2D(4U, 4U)); + add_config(TensorShape(233U, 277U, 55U), Size2D(3U, 3U), PadStrideInfo(1, 2, 0, 0), Size2D(5U, 5U)); + add_config(TensorShape(333U, 277U, 77U, 5U), Size2D(3U, 3U), PadStrideInfo(2, 3, 0, 1), Size2D(4U, 4U)); + add_config(TensorShape(177U, 311U, 22U), Size2D(3U, 3U), PadStrideInfo(2, 1, 1, 1), Size2D(3U, 3U)); + } +}; +} // namespace datasets +} // namespace test +} // namespace arm_compute +#endif /* ARM_COMPUTE_TEST_DILATED_CONVOLUTION_LAYER_DATASET */ diff --git a/tests/validation/CL/DepthwiseConvolutionLayer.cpp b/tests/validation/CL/DepthwiseConvolutionLayer.cpp index dd2d9f344a..94f64e19b4 100644 --- a/tests/validation/CL/DepthwiseConvolutionLayer.cpp +++ b/tests/validation/CL/DepthwiseConvolutionLayer.cpp @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -18,7 +18,7 @@ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, - * OUT OF OR IN CONCLCTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE + * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE * SOFTWARE. */ #include "arm_compute/core/Types.h" @@ -28,6 +28,7 @@ #include "tests/CL/CLAccessor.h" #include "tests/PaddingCalculator.h" #include "tests/datasets/DepthwiseConvolutionLayerDataset.h" +#include "tests/datasets/DilatedDepthwiseConvolutionLayerDataset.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" @@ -55,7 +56,7 @@ TEST_SUITE(DepthwiseConvolutionLayer) // *INDENT-OFF* // clang-format off -DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( +DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Mismatching data type input/weights TensorInfo(TensorShape(32U, 18U, 3U), 1, DataType::F32), // Mismatching input feature maps TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Unsupported weights dimensions @@ -66,6 +67,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid biases dimensions TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), // Invalid output size TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Window shrink + TensorInfo(TensorShape(32U, 18U, 8U), 1, DataType::F32), // patch size bigger than input width + TensorInfo(TensorShape(32U, 18U, 8U), 1, DataType::F32), // dilation < 1 TensorInfo(TensorShape(32U, 18U, 8U), 1, DataType::F32), TensorInfo(TensorShape(50U, 32U, 8U), 1, DataType::QASYMM8), }), @@ -80,6 +83,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8), })), framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32), @@ -93,6 +98,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip TensorInfo(TensorShape(2U), 1, DataType::F32), TensorInfo(TensorShape(2U), 1, DataType::F32), TensorInfo(TensorShape(16U), 1, DataType::F32), + TensorInfo(TensorShape(16U), 1, DataType::F32), + TensorInfo(TensorShape(16U), 1, DataType::F32), TensorInfo(TensorShape(24U), 1, DataType::S32), })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(30U, 16U, 2U), 1, DataType::F32), @@ -106,6 +113,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip TensorInfo(TensorShape(32U, 18U, 2U), 1, DataType::F32), TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(30U, 16U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(30U, 16U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(30U, 16U, 16U), 1, DataType::F32), TensorInfo(TensorShape(48U, 30U, 24U), 1, DataType::QASYMM8), })), framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), @@ -120,6 +129,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), })), framework::dataset::make("DepthMultiplier", { 1, 1, @@ -132,6 +143,8 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip 1, 1, 2, + 2, + 2, 3, })), framework::dataset::make("ActivationInfo", { ActivationLayerInfo(), @@ -145,22 +158,41 @@ DATA_TEST_CASE(Validate3x3, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip ActivationLayerInfo(), ActivationLayerInfo(), ActivationLayerInfo(), + ActivationLayerInfo(), + ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), })), - framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, true, true })), - input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, act_info, expected) + framework::dataset::make("Dilation", { Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(20U, 1U), + Size2D(0U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + })), + framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false, false, false, false, true, true })), + input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, act_info, dilation, expected) { - bool is_valid = bool(CLDepthwiseConvolutionLayer3x3::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier, act_info)); + bool is_valid = bool(CLDepthwiseConvolutionLayer3x3::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier, act_info,GPUTarget::MIDGARD, dilation)); ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); } -DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( +DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(zip( framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights TensorInfo(TensorShape(27U, 13U, 3U), 1, DataType::F32), // Mismatching input feature maps TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching depth multiplier TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size + TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32), // patch size bigger than input width + TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32), // dilation < 1 TensorInfo(TensorShape(27U, 13U, 8U), 1, DataType::F32), TensorInfo(TensorShape(32U, 13U, 8U), 1, DataType::QASYMM8), }), @@ -171,6 +203,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 2U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(3U, 3U, 16U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 24U), 1, DataType::QASYMM8), })), framework::dataset::make("BiasesInfo", { TensorInfo(TensorShape(2U), 1, DataType::F32), @@ -180,6 +214,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip TensorInfo(TensorShape(2U, 2U), 1, DataType::F32), TensorInfo(TensorShape(2U), 1, DataType::F32), TensorInfo(TensorShape(16U), 1, DataType::F32), + TensorInfo(TensorShape(16U), 1, DataType::F32), + TensorInfo(TensorShape(16U), 1, DataType::F32), TensorInfo(TensorShape(24U), 1, DataType::S32), })), framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), @@ -189,6 +225,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip TensorInfo(TensorShape(25U, 11U, 2U), 1, DataType::F32), TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32), + TensorInfo(TensorShape(25U, 11U, 16U), 1, DataType::F32), TensorInfo(TensorShape(32U, 11U, 24U), 1, DataType::QASYMM8), })), framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), @@ -198,6 +236,8 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 1, 0), })), framework::dataset::make("DepthMultiplier", { 1, @@ -207,12 +247,25 @@ DATA_TEST_CASE(ValidateGeneric, framework::DatasetMode::ALL, zip(zip(zip(zip(zip 1, 1, 2, + 2, + 2, 3, })), - framework::dataset::make("Expected", { false, false, false, false, false, false, true, true })), - input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, expected) + framework::dataset::make("Dilation", { Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + Size2D(20U, 1U), + Size2D(0U, 1U), + Size2D(1U, 1U), + Size2D(1U, 1U), + })), + framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, true, true })), + input_info, weights_info, biases_info, output_info, conv_info, depth_multiplier, dilation, expected) { - bool is_valid = bool(CLDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier)); + bool is_valid = bool(CLDepthwiseConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, depth_multiplier,ActivationLayerInfo(), dilation)); ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); } // clang-format on @@ -243,7 +296,25 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<half>, frame { validate(CLAccessor(_target), _reference, tolerance_f16); } -TEST_SUITE_END() +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NCHW }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // NCHW TEST_SUITE(NHWC) FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, @@ -263,8 +334,26 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<half>, frame { validate(CLAccessor(_target), _reference, tolerance_f16); } -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // NHWC +TEST_SUITE_END() // W3x3 TEST_SUITE(Generic) FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers), @@ -282,8 +371,27 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<half>, frame { validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); } -TEST_SUITE_END() -TEST_SUITE_END() + +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f16, tolerance_num); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // Generic +TEST_SUITE_END() // FP16 TEST_SUITE(FP32) TEST_SUITE(W3x3) @@ -306,7 +414,26 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<float>, fram { validate(CLAccessor(_target), _reference, tolerance_f32); } -TEST_SUITE_END() +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, + combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(), depth_multipliers), + framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("DataLayout", DataLayout::NCHW))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("DataLayout", DataLayout::NCHW))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32); +} + +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // NCHW TEST_SUITE(NHWC) FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(), @@ -325,8 +452,28 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<float>, fram { validate(CLAccessor(_target), _reference, tolerance_f32); } -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE(Dilation) + +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, + combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("DataLayout", DataLayout::NHWC))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("DataLayout", DataLayout::NHWC))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // NHWC +TEST_SUITE_END() // W3x3 TEST_SUITE(Generic) FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset(), depth_multipliers), @@ -344,9 +491,28 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<float>, fram { validate(CLAccessor(_target), _reference, tolerance_f32); } -TEST_SUITE_END() -TEST_SUITE_END() -TEST_SUITE_END() + +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::ALL, combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", + DataType::F32)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_f32); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // Generic +TEST_SUITE_END() // FP32 +TEST_SUITE_END() // Float template <typename T> using CLDepthwiseConvolutionLayerQuantizedFixture = DepthwiseConvolutionLayerValidationQuantizedFixture<CLTensor, CLAccessor, CLDepthwiseConvolutionLayer, T>; @@ -372,7 +538,27 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerQuantizedFixture<uin { validate(CLAccessor(_target), _reference, tolerance_qasymm8); } -TEST_SUITE_END() +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset(), + depth_multipliers), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset(), + depth_multipliers), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // Generic TEST_SUITE(W3x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(datasets::SmallDepthwiseConvolutionLayerDataset3x3(), @@ -392,12 +578,32 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerQuantizedFixture<uin { validate(CLAccessor(_target), _reference, tolerance_qasymm8); } -TEST_SUITE_END() -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE(Dilation) +FIXTURE_DATA_TEST_CASE(RunSmall, CLDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, + combine(combine(combine(combine(datasets::SmallDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +FIXTURE_DATA_TEST_CASE(RunLarge, CLDepthwiseConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, + combine(combine(combine(combine(datasets::LargeDepthwiseDilatedConvolutionLayerDataset3x3(), + depth_multipliers), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.5f, 10) })), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) +{ + validate(CLAccessor(_target), _reference, tolerance_qasymm8); +} +TEST_SUITE_END() // Dilation +TEST_SUITE_END() // W3x3 +TEST_SUITE_END() // QASYMM8 +TEST_SUITE_END() // Quantized -TEST_SUITE_END() -TEST_SUITE_END() +TEST_SUITE_END() // DepthwiseConvolutionLayer +TEST_SUITE_END() // CL } // namespace validation } // namespace test } // namespace arm_compute diff --git a/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h b/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h index 5428154a2b..dd8bf232be 100644 --- a/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h +++ b/tests/validation/fixtures/DepthwiseConvolutionLayerFixture.h @@ -1,5 +1,5 @@ /* - * Copyright (c) 2017-2018 ARM Limited. + * Copyright (c) 2017-2019 ARM Limited. * * SPDX-License-Identifier: MIT * @@ -55,7 +55,8 @@ public: public: template <typename...> - void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, unsigned int depth_multiplier, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout) + void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, unsigned int depth_multiplier, DataType data_type, QuantizationInfo quantization_info, + DataLayout data_layout) { _quantization_info = quantization_info; _data_type = data_type; @@ -65,13 +66,13 @@ public: const TensorInfo in_info(in_shape, 1, data_type); const TensorInfo we_info(weights_shape, 1, data_type); - TensorShape out_shape = compute_depthwise_convolution_shape(in_info, we_info, pad_stride_info, depth_multiplier); + const TensorShape out_shape = compute_depthwise_convolution_shape(in_info, we_info, pad_stride_info, depth_multiplier, dilation); weights_shape.set(2, out_shape.z()); const TensorShape biases_shape(weights_shape[2]); - _target = compute_target(in_shape, weights_shape, biases_shape, out_shape, pad_stride_info, depth_multiplier, data_type, bias_data_type, quantization_info, data_layout); - _reference = compute_reference(in_shape, weights_shape, biases_shape, out_shape, pad_stride_info, depth_multiplier, data_type, bias_data_type, quantization_info); + _target = compute_target(in_shape, weights_shape, biases_shape, out_shape, pad_stride_info, dilation, depth_multiplier, data_type, bias_data_type, quantization_info, data_layout); + _reference = compute_reference(in_shape, weights_shape, biases_shape, out_shape, pad_stride_info, dilation, depth_multiplier, data_type, bias_data_type, quantization_info); } protected: @@ -104,7 +105,8 @@ protected: } } - TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape output_shape, PadStrideInfo &pad_stride_info, unsigned int depth_multiplier, + TensorType compute_target(TensorShape input_shape, TensorShape weights_shape, TensorShape biases_shape, TensorShape output_shape, PadStrideInfo &pad_stride_info, Size2D dilation, + unsigned int depth_multiplier, const DataType data_type, const DataType bias_data_type, const QuantizationInfo quantization_info, const DataLayout data_layout) { if(data_layout == DataLayout::NHWC) @@ -122,7 +124,7 @@ protected: // Create Depthwise Convolution configure function FunctionType dwc; - dwc.configure(&src, &weights, &biases, &dst, pad_stride_info, depth_multiplier); + dwc.configure(&src, &weights, &biases, &dst, pad_stride_info, depth_multiplier, ActivationLayerInfo(), dilation); ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS); ARM_COMPUTE_EXPECT(weights.info()->is_resizable(), framework::LogLevel::ERRORS); @@ -152,7 +154,7 @@ protected: } SimpleTensor<T> compute_reference(const TensorShape &in_shape, const TensorShape &weights_shape, const TensorShape &biases_shape, const TensorShape &out_shape, const PadStrideInfo &pad_stride_info, - unsigned int depth_multiplier, + const Size2D &dilation, unsigned int depth_multiplier, const DataType data_type, const DataType bias_data_type, const QuantizationInfo quantization_info) { SimpleTensor<T> src{ in_shape, data_type, 1, quantization_info }; @@ -163,7 +165,7 @@ protected: fill(weights, 1); fill(biases, 2); - return reference::depthwise_convolution(src, weights, biases, out_shape, pad_stride_info, depth_multiplier); + return reference::depthwise_convolution(src, weights, biases, out_shape, pad_stride_info, depth_multiplier, dilation); } TensorType _target{}; @@ -177,9 +179,9 @@ class DepthwiseConvolutionLayerValidationFixture : public DepthwiseConvolutionLa { public: template <typename...> - void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, unsigned int depth_multiplier, DataType data_type, DataLayout data_layout) + void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, unsigned int depth_multiplier, DataType data_type, DataLayout data_layout) { - DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(in_shape, kernel_size, pad_stride_info, depth_multiplier, + DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(in_shape, kernel_size, pad_stride_info, dilation, depth_multiplier, data_type, QuantizationInfo(), data_layout); } }; @@ -189,9 +191,10 @@ class DepthwiseConvolutionLayerValidationQuantizedFixture : public DepthwiseConv { public: template <typename...> - void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, unsigned int depth_multiplier, DataType data_type, QuantizationInfo quantization_info, DataLayout data_layout) + void setup(TensorShape in_shape, Size2D kernel_size, PadStrideInfo pad_stride_info, Size2D dilation, unsigned int depth_multiplier, DataType data_type, QuantizationInfo quantization_info, + DataLayout data_layout) { - DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(in_shape, kernel_size, pad_stride_info, depth_multiplier, + DepthwiseConvolutionLayerValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(in_shape, kernel_size, pad_stride_info, dilation, depth_multiplier, data_type, quantization_info, data_layout); } }; diff --git a/tests/validation/reference/DepthwiseConvolutionLayer.cpp b/tests/validation/reference/DepthwiseConvolutionLayer.cpp index 122dbd4d98..90ecffbbca 100644 --- a/tests/validation/reference/DepthwiseConvolutionLayer.cpp +++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp @@ -70,13 +70,19 @@ SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTe const int pad_right = conv_info.pad_right(); const int pad_bottom = conv_info.pad_bottom(); - const int patch_half_width = (filter_width + (dilation.x() - 1) * (filter_width - 1)) / 2; - const int patch_half_height = (filter_height + (dilation.y() - 1) * (filter_height - 1)) / 2; + const float patch_width = (filter_width + (dilation.x() - 1) * (filter_width - 1)); + const float patch_height = (filter_height + (dilation.y() - 1) * (filter_height - 1)); - const int minimum_x = -pad_left + patch_half_width; - const int minimum_y = -pad_top + patch_half_height; - const int maximum_x = input_width + pad_left + pad_right - patch_half_width * 2; - const int maximum_y = input_height + pad_top + pad_bottom - patch_half_height * 2; + const int patch_half_width_floor = patch_width / 2; + const int patch_half_height_floor = patch_height / 2; + + const auto patch_half_width_ceil = static_cast<int>(std::ceil(patch_width / 2)); + const auto patch_half_height_ceil = static_cast<int>(std::ceil(patch_height / 2)); + + const int minimum_x = -pad_left + patch_half_width_floor; + const int minimum_y = -pad_top + patch_half_height_floor; + const int maximum_x = input_width + pad_left + pad_right - static_cast<int>(patch_width); + const int maximum_y = input_height + pad_top + pad_bottom - static_cast<int>(patch_height); const T border_value(0); @@ -89,21 +95,20 @@ SimpleTensor<T> depthwise_convolution(const SimpleTensor<T> &src, const SimpleTe { const int out_z = z * depth_multiplier + m; - for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second) + for(int y = minimum_y; y <= minimum_y + maximum_y; y += conv_info.stride().second) { - for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first) + for(int x = minimum_x; x <= minimum_x + maximum_x; x += conv_info.stride().first) { Coordinates coords(static_cast<int>(x), static_cast<int>(y), static_cast<int>(z), static_cast<int>(r)); size_t filter_offset = filter_plane * out_z; T val(0); - for(int j = y - patch_half_height; j <= y + patch_half_height; j += dilation.y()) + for(int j = y - patch_half_height_floor; j < y + patch_half_height_ceil; j += dilation.y()) { - for(int i = x - patch_half_width; i <= x + patch_half_width; i += dilation.x()) + for(int i = x - patch_half_width_floor; i < x + patch_half_width_ceil; i += dilation.x()) { coords.set(0, i); coords.set(1, j); - val += *(weights.data() + filter_offset) * tensor_elem_at(src, coords, BorderMode::CONSTANT, border_value); ++filter_offset; } @@ -157,13 +162,19 @@ SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, co const int pad_right = conv_info.pad_right(); const int pad_bottom = conv_info.pad_bottom(); - const int patch_half_width = (filter_width + (dilation.x() - 1) * (filter_width - 1)) / 2; - const int patch_half_height = (filter_height + (dilation.y() - 1) * (filter_height - 1)) / 2; + const float patch_width = (filter_width + (dilation.x() - 1) * (filter_width - 1)); + const float patch_height = (filter_height + (dilation.y() - 1) * (filter_height - 1)); + + const int patch_half_width_floor = patch_width / 2; + const int patch_half_height_floor = patch_height / 2; + + const auto patch_half_width_ceil = static_cast<int>(std::ceil(patch_width / 2)); + const auto patch_half_height_ceil = static_cast<int>(std::ceil(patch_height / 2)); - const int minimum_x = -pad_left + patch_half_width; - const int minimum_y = -pad_top + patch_half_height; - const int maximum_x = input_width + pad_left + pad_right - patch_half_width * 2; - const int maximum_y = input_height + pad_top + pad_bottom - patch_half_height * 2; + const int minimum_x = -pad_left + patch_half_width_floor; + const int minimum_y = -pad_top + patch_half_height_floor; + const int maximum_x = input_width + pad_left + pad_right - static_cast<int>(patch_width); + const int maximum_y = input_height + pad_top + pad_bottom - static_cast<int>(patch_height); int out_pos = 0; for(int r = 0; r < num_batches; ++r) @@ -175,17 +186,17 @@ SimpleTensor<uint8_t> depthwise_convolution(const SimpleTensor<uint8_t> &src, co const int out_z = z * depth_multiplier + m; const int32_t bias_val = *static_cast<const int32_t *>(biases(Coordinates(out_z))); - for(int y = minimum_y; y < minimum_y + maximum_y; y += conv_info.stride().second) + for(int y = minimum_y; y <= minimum_y + maximum_y; y += conv_info.stride().second) { - for(int x = minimum_x; x < minimum_x + maximum_x; x += conv_info.stride().first) + for(int x = minimum_x; x <= minimum_x + maximum_x; x += conv_info.stride().first) { Coordinates coords(x, y, z, r); int filter_offset = filter_plane * out_z; int32_t val = 0; - for(int j = y - patch_half_height; j <= y + patch_half_height; j += dilation.y()) + for(int j = y - patch_half_height_floor; j < y + patch_half_height_ceil; j += dilation.y()) { - for(int i = x - patch_half_width; i <= x + patch_half_width; i += dilation.x()) + for(int i = x - patch_half_width_floor; i < x + patch_half_width_ceil; i += dilation.x()) { coords.set(0, i); coords.set(1, j); |