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>
diff --git a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h
index b1c730d..3b7fc7b 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 @@
*
* @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 2fc9780..7d0ecec 100644
--- a/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h
+++ b/arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h
@@ -42,30 +42,32 @@
*
* @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 00d9cb6..1579847 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 @@
* @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 3396de2..92eca89 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 @@
*
* @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 26b337d..b46b1b2 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -402,10 +402,12 @@
* @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 @@
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 23034c2..b25c36a 100644
--- a/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLDepthwiseConvolutionLayer.h
@@ -70,32 +70,34 @@
*
* @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 @@
*
* @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 5eccc4d..28db5e1 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 @@
*
* @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 28f0560..c602336 100644
--- a/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h
+++ b/arm_compute/runtime/NEON/functions/NEDepthwiseConvolutionLayer.h
@@ -67,31 +67,33 @@
*
* @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 @@
*
* @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 @@
*
* @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 @@
* @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 @@
* @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 4f6fdfa..8ee0185 100644
--- a/src/core/CL/cl_kernels/depthwise_convolution.cl
+++ b/src/core/CL/cl_kernels/depthwise_convolution.cl
@@ -51,13 +51,18 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
}
#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 @@
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 @@
__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 @@
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 @@
__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 @@
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 @@
}
#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 @@
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 @@
__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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
__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 @@
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 @@
__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 @@
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 @@
#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 @@
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 @@
// 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 @@
#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 503aa7e..8d145a0 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 @@
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 @@
#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 @@
#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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
// 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 83fac16..ec27e41 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
@@ -43,7 +43,7 @@
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 @@
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 @@
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 @@
}
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 @@
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 @@
}
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 @@
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 @@
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 @@
}
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 431039c..86d186b 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
@@ -42,7 +42,7 @@
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 @@
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 @@
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 @@
}
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 @@
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 @@
}
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 @@
_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 @@
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 @@
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 @@
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 @@
}
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 beff7ae..28d4ff2 100644
--- a/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseIm2ColKernel.cpp
@@ -44,7 +44,8 @@
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 @@
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 @@
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 @@
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 65d3f5f..23c3f81 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -45,7 +45,7 @@
}
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 @@
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 @@
// 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 @@
}
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 @@
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 @@
{
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 @@
}
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 @@
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 @@
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 @@
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 @@
}
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 @@
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 @@
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 @@
}
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 ba05838..0f772bd 100644
--- a/src/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/GLES_COMPUTE/functions/GCDepthwiseConvolutionLayer.cpp
@@ -36,8 +36,10 @@
}
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 4f632a2..4c602b3 100644
--- a/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEDepthwiseConvolutionLayer.cpp
@@ -182,8 +182,11 @@
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 @@
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 @@
}
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 @@
}
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 48ea038..33753bc 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 @@
{
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 8a1f3b6..4c78eb8 100644
--- a/tests/datasets/DepthwiseConvolutionLayerDataset.h
+++ b/tests/datasets/DepthwiseConvolutionLayerDataset.h
@@ -38,16 +38,18 @@
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 @@
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 @@
++_src_it;
++_weights_it;
++_infos_it;
+ ++_dilation_it;
return *this;
}
@@ -78,23 +82,25 @@
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 @@
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 0000000..df054de
--- /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 dd2d9f3..94f64e1 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 @@
// *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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
1,
1,
2,
+ 2,
+ 2,
3,
})),
framework::dataset::make("ActivationInfo", { ActivationLayerInfo(),
@@ -145,22 +158,41 @@
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 @@
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 @@
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 @@
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 @@
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 @@
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 @@
{
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 @@
{
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 @@
{
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 @@
{
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 @@
{
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 @@
{
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 @@
{
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 @@
{
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 5428154..dd8bf23 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:
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 @@
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 @@
}
}
- 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 @@
// 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 @@
}
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 @@
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 @@
{
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 @@
{
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 122dbd4..90ecffb 100644
--- a/tests/validation/reference/DepthwiseConvolutionLayer.cpp
+++ b/tests/validation/reference/DepthwiseConvolutionLayer.cpp
@@ -70,13 +70,19 @@
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 @@
{
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 @@
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);
int out_pos = 0;
for(int r = 0; r < num_batches; ++r)
@@ -175,17 +186,17 @@
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);