Optimize CL DeconvolutionLayer-Part II: Add CLDirectDeconvolution function to be used by CLDeconvolution.
This is only a code refactoring (no optimizations have been added)
Change-Id: I78488f4aecfe1cce93c31dba31489dcee4c85c67
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/895
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>
diff --git a/arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h b/arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h
new file mode 100644
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+++ b/arm_compute/runtime/CL/functions/CLDirectDeconvolutionLayer.h
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+/*
+ * 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_CLDIRECTDECONVOLUTIONLAYER_H__
+#define __ARM_COMPUTE_CLDIRECTDECONVOLUTIONLAYER_H__
+
+#include "arm_compute/runtime/CL/functions/CLConvolutionLayer.h"
+#include "arm_compute/runtime/CL/functions/CLDeconvolutionLayerUpsample.h"
+#include "arm_compute/runtime/CL/functions/CLTranspose.h"
+
+#include "arm_compute/core/CPP/kernels/CPPFlipWeightsKernel.h"
+
+#include "arm_compute/runtime/CL/CLMemoryGroup.h"
+#include "arm_compute/runtime/CL/CLTensor.h"
+#include "arm_compute/runtime/IFunction.h"
+#include "arm_compute/runtime/IMemoryManager.h"
+
+#include <memory>
+
+namespace arm_compute
+{
+class ICLTensor;
+/** Function to run the deconvolution layer.
+ *
+ * Deconvolution Layer is the backward pass of Convolution Layer. First we transform the input depending on the stride and pad info and then perform a 1x1
+ * convolution pass. Input stride defines how many zeroes we should put between each element of the input and pad is the amount of padding.
+ *
+ * The relation between input to output is as follows:
+ * \f[
+ * width\_output = (width\_input - 1) \cdot stride\_x - 2 \cdot padding\_x + kernel\_x
+ * \f]
+ * \f[
+ * height\_output = (height\_input - 1) \cdot stride\_y - 2 \cdot padding\_y + kernel\_y
+ * \f]
+ *
+ * where:
+ * width_input is the size of the first input dimension.
+ * height_input is the size of the second input dimension.
+ * width_output is the size of the first output dimension.
+ * height_output is the size of the second output dimension.
+ * kernel_x and kernel_y are the convolution sizes in x and y.
+ * stride_x and stride_y is the input stride of the first and second dimension.
+ *
+ * The weights used by Deconvolution are supposed to be the same as the ones used for Convolution. Therefore, it will be necessary to use the weights in the
+ * reverse order to perform an actual convolution. This is achieved by using the @ref CPPFlipWeightsKernel.
+ *
+ * This function calls the following OpenCL kernels/functions:
+ *
+ * -# @ref CLDeconvolutionLayerUpsample
+ * -# @ref CLConvolutionLayer
+ *
+ * And the following CPP kernels:
+ * -# @ref CPPFlipWeightsKernel
+ *
+ */
+class CLDirectDeconvolutionLayer : public IFunction
+{
+public:
+ /** Constructor */
+ CLDirectDeconvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLDirectDeconvolutionLayer(const CLDirectDeconvolutionLayer &) = delete;
+ /** Default move constructor */
+ CLDirectDeconvolutionLayer(CLDirectDeconvolutionLayer &&) = default;
+ /** Prevent instances of this class from being copied (As this class contains pointers) */
+ CLDirectDeconvolutionLayer &operator=(const CLDirectDeconvolutionLayer &) = delete;
+ /** Default move assignment operator */
+ CLDirectDeconvolutionLayer &operator=(CLDirectDeconvolutionLayer &&) = default;
+ /** Set the input, weights, biases and output tensors.
+ *
+ * @param[in,out] input Input tensor. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
+ * @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
+ * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input.
+ * @param[out] output Output tensor. The output has the same number of dimensions as the @p input.
+ * @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+ * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
+ *
+ */
+ void configure(ICLTensor *input, ICLTensor *weights, const ICLTensor *bias, ICLTensor *output, const PadStrideInfo &info, const WeightsInfo &weights_info = WeightsInfo());
+ /** Static function to check if given info will lead to a valid configuration of @ref CLDirectDeconvolutionLayer
+ *
+ * @param[in] input Input tensor info. 3 lower dimensions represent a single input, and an optional 4th dimension for batch of inputs. Data types supported: QASYMM8/F16/F32.
+ * @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type supported: Same as @p input.
+ * @param[in] bias (Optional) The biases have one dimension. Data type supported: Same as @p input.
+ * @param[in] output Output tensor info. The output has the same number of dimensions as the @p input.
+ * @param[in] info Contains padding and policies to be used in the deconvolution, this is decribed in @ref PadStrideInfo.
+ * @param[in] weights_info (Optional) Weights information needed for @ref CLConvolutionLayer, specifies if the weights tensor has been reshaped with @ref CLWeightsReshapeKernel.
+ *
+ * @return a status
+ */
+ static Status validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *bias, ITensorInfo *output, const PadStrideInfo &info,
+ const WeightsInfo &weights_info = WeightsInfo());
+
+ // Inherited methods overridden:
+ void run() override;
+ void prepare() override;
+
+private:
+ CLMemoryGroup _memory_group;
+ CLDeconvolutionLayerUpsample _scale_f;
+ CLConvolutionLayer _conv_f;
+ CPPFlipWeightsKernel _flip_weights;
+
+ CLTensor _scaled_output;
+ ICLTensor *_original_weights;
+ CLTensor _weights_flipped;
+
+ bool _is_prepared;
+};
+} // namespace arm_compute
+#endif /* __ARM_COMPUTE_CLDECONVOLUTIONLAYER_H__ */