*/
/*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
*
* SPDX-License-Identifier: MIT
*
* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
* SOFTWARE.
*/
-
#ifndef __ARM_COMPUTE_NETRANSPOSECONVLAYER_H__
#define __ARM_COMPUTE_NETRANSPOSECONVLAYER_H__
-#include "arm_compute/runtime/CPP/functions/CPPUpsampleEx.h"
+#include "arm_compute/runtime/CPP/functions/CPPUpsample.h"
#include "arm_compute/runtime/NEON/functions/NEConvolutionLayer.h"
#include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h"
-#include "arm_compute/runtime/NEON/functions/NEPermute.h"
+#include "arm_compute/runtime/NEON/functions/NEReverse.h"
-#include "arm_compute/core/CPP/kernels/CPPFlipWeightsKernel.h"
#include "arm_compute/core/Types.h"
#include "arm_compute/runtime/IFunction.h"
#include "arm_compute/runtime/IMemoryManager.h"
{
/** Function to run the deconvolution layer.
*
- * Transpose convolution Layer is the backward pass of Convolution Layer. First we transform the
- * input depending on the stride and pad info and then perfrom a 1x1
+ * Deconvolution Layer is the backward pass of Convolution Layer. First we transform the input
+ * depending on the stride and pad info and then perfrom a 1x1
* convolution pass. Input stride defines how many zeroes we should put between each element of the
* input, pad is the amount of padding and finaly a is a user
* specified value where a < stride - 1 that increases the padding top and right of the input image.
* 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 Transpose convolution 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.
+ * 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 @ref NEReverse.
*
* This function calls the following NEON kernels/functions:
*
- * -# @ref CPPUpsample
+ * -# @ref CPPUpsampleEx
* -# @ref NEConvolutionLayer
+ * -# @ref NEPermute
+ * -# @ref NEReverse
*
*/
class NETransposeConvLayer : public IFunction
{
public:
- /** Default constructor */
+ /** Constructor */
NETransposeConvLayer(std::shared_ptr<IMemoryManager> memory_manager = nullptr);
/** Prevent instances of this class from being copied (As this class contains pointers) */
/** 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: F32/F16/QASYMM8.
+ * optional 4th dimension for batch of inputs. Data types supported: F32/F16/QASYMM8/QASYMM8_SIGNED.
* @param[in] weights The 4d weights with dimensions [width, height, IFM, OFM]. Data type
- * supported: Same as @p input.
+ * supported: Same as @p input.
* @param[in] bias Optional, ignored if NULL. The biases have one dimension. Data type
- * supported: Data types supported: S32 for QASYMM8 input, F32 for F32 input, F16 for F16 input.
+ * supported: Data types supported: S32 for QASYMM8 and QASYMM8_SIGNED input, F32 for F32 input, F16
+ * for F16 input.
* @param[out] output Output tensor. The output has the same number of dimensions as the @p
- * input.
+ * input.
* @param[in] info Contains padding and policies to be used in the deconvolution, this is
- * decribed in @ref PadStrideInfo.
- * @param[in] invalid_right The number of zeros added to right edge of the output.
- * @param[in] invalid_bottom The number of zeros added to top edge of the output.
+ * decribed in @ref PadStrideInfo.
+ * @param[in] invalid_right The number of zeros added to right edge of the output.
+ * @param[in] invalid_bottom The number of zeros added to bottom edge of the output.
*
*/
void configure(ITensor *input, const ITensor *weights, const ITensor *bias, ITensor *output,
const PadStrideInfo &info, unsigned int invalid_right,
unsigned int invalid_bottom);
/** Static function to check if given info will lead to a valid configuration of @ref
- * NETransposeConvLayer
+ * NETransposeConvLayer
*
* @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: F32/F16/QASYMM8.
+ * optional 4th dimension for batch of inputs. Data types supported: F32/F16/QASYMM8/QASYMM8_SIGNED.
* @param[in] weights The 4d weights info with dimensions [width, height, IFM, OFM]. Data type
- * supported: Same as @p input.
+ * supported: Same as @p input.
* @param[in] bias (Optional) The biases have one dimension. Data type supported: Data types
- * supported: S32 for QASYMM8 input, F32 for F32 input, F16 for F16 input.
+ * supported: S32 for QASYMM8 and QASYMM8_SIGNED input, F32 for F32 input, F16 for F16 input.
* @param[in] output Output tensor info. The output has the same number of dimensions as the @p
- * input.
+ * input.
* @param[in] info Contains padding and policies to be used in the deconvolution, this is
- * decribed in @ref PadStrideInfo.
- * @param[in] innvalid_right The number of zeros added to right edge of the output.
- * @param[in] invalid_bottom The number of zeros added to top edge of the output.
+ * decribed in @ref PadStrideInfo.
+ * @param[in] innvalid_right The number of zeros added to right edge of the output.
+ * @param[in] invalid_bottom The number of zeros added to bottom edge of the output.
*
* @return a status
*/
private:
MemoryGroup _memory_group;
NEConvolutionLayer _conv_f;
- CPPUpsampleEx _upsample_f;
- CPPFlipWeightsKernel _flip_weights;
- NEPermute _permute_input;
- NEPermute _permute_weights;
- NEPermute _permute_output;
+ CPPUpsample _upsample_f;
+ NEReverse _flip_weights;
Tensor _scaled_output;
Tensor _weights_flipped;
- Tensor _permuted_input;
- Tensor _permuted_weights;
- Tensor _permuted_output;
- bool _is_nchw;
+ Tensor _flip_axis;
const ITensor *_original_weights;
ITensor *_input;
PadStrideInfo _info;
projects / platform / core / ml / nnfw.git / blobdiff