libnd4j/include/ops/declarable/headers/convo.h
/* ******************************************************************************
*
*
* This program and the accompanying materials are made available under the
* terms of the Apache License, Version 2.0 which is available at
* https://www.apache.org/licenses/LICENSE-2.0.
*
* See the NOTICE file distributed with this work for additional
* information regarding copyright ownership.
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
* WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
* License for the specific language governing permissions and limitations
* under the License.
*
* SPDX-License-Identifier: Apache-2.0
******************************************************************************/
//
//
//
#ifndef LIBND4J_HEADERS_CONVOL_H
#define LIBND4J_HEADERS_CONVOL_H
#include <ops/declarable/headers/common.h>
namespace sd {
namespace ops {
/**
* 1D temporal convolution implementation
* Expected input:
* x: 3D array
* weight: 3D Array
* bias: optional vector
*
* Int args:
* 0: kernel
* 1: stride
* 2: padding
*/
#if NOT_EXCLUDED(OP_conv1d)
DECLARE_CUSTOM_OP(conv1d, 2, 1, false, 0, 5);
DECLARE_CUSTOM_OP(conv1d_bp, 3, 2, false, 0, 5);
#endif
/**
* 2D convolution implementation
* Expected input:
* x: 4D array
* weight: 4D Array
* bias: optional vector, length of outputChannels
*
* IntArgs:
* 0: kernel height
* 1: kernel width
* 2: stride height
* 3: stride width
* 4: padding height
* 5: padding width
* 6: dilation height
* 7: dilation width
* 8: same mode: 1 true, 0 false
* 9: data format: 1 NHWC, 0 NCHW
*/
#if NOT_EXCLUDED(OP_conv2d)
DECLARE_CUSTOM_OP(conv2d, 2, 1, false, 0, 9);
DECLARE_CUSTOM_OP(conv2d_bp, 3, 2, false, 0, 9);
DECLARE_CUSTOM_OP(conv2d_input_bp, 3, 1, false, 0, 9);
#endif
/**
* Depthwise convolution2d op:
* Expected inputs:
* x: 4D array, NCHW format
* weightsDepth: 4D array,
* weightsPointwise: optional, 4D array
* bias: optional, vector
*/
#if NOT_EXCLUDED(OP_sconv2d)
DECLARE_CUSTOM_OP(sconv2d, 2, 1, false, 0, 9);
DECLARE_CUSTOM_OP(sconv2d_bp, 3, 2, false, 0, 9);
#endif
/**
* 2D deconvolution implementation
*
* IntArgs:
* 0: kernel height
* 1: kernel width
* 2: stride height
* 3: stride width
* 4: padding height
* 5: padding width
* 6: dilation height
* 7: dilation width
* 8: same mode: 0 false, 1 true
*/
#if NOT_EXCLUDED(OP_deconv2d)
DECLARE_CUSTOM_OP(deconv2d, 2, 1, false, 0, 9);
DECLARE_CUSTOM_OP(deconv2d_bp, 3, 2, false, 0, 9);
#endif
/**
* 3D deconvolution implementation
*
* IntArgs:
* 0: filter(kernel) depth
* 1: filter(kernel) height
* 2: filter(kernel) width
* 3: strides depth
* 4: strides height
* 5: strides width
* 6: paddings depth
* 7: paddings height
* 8: paddings width
* 9: dilations depth
* 10: dilations height
* 11: dilations width
* 12: same mode: 0 false, 1 true
* 13: data format (optional): 0-NDHWC, 1-NCDHW, default is 1
*/
#if NOT_EXCLUDED(OP_deconv3d)
DECLARE_CUSTOM_OP(deconv3d, 2, 1, false, 0, 13);
DECLARE_CUSTOM_OP(deconv3d_bp, 3, 2, false, 0, 13);
#endif
/**
* This op implements max pooling for convolution networks.
* Expected Input: 4D array, NCHW format.
*
* IntArgs:
* 0: kernel height
* 1: kernel width
* 2: stride height
* 3: stride width
* 4: padding height
* 5: padding width
* 6: dilation height
* 7: dilation width
* 8: same mode: 0 false, 1 true
*/
#if NOT_EXCLUDED(OP_maxpool2d)
DECLARE_CUSTOM_OP(maxpool2d, 1, 1, false, 0, 10);
DECLARE_CUSTOM_OP(maxpool2d_bp, 2, 1, false, 0, 10);
#endif
/**
* This op implements average pooling for convolution networks.
* Expected Input: 4D array, NCHW format.
*
* IntArgs:
* 0: kernel height
* 1: kernel width
* 2: stride height
* 3: stride width
* 4: padding height
* 5: padding width
* 6: dilation height
* 7: dilation width
* 8: same mode: 0 false, 1 true
*/
#if NOT_EXCLUDED(OP_avgpool2d)
DECLARE_CUSTOM_OP(avgpool2d, 1, 1, false, 0, 10);
DECLARE_CUSTOM_OP(avgpool2d_bp, 2, 1, false, 0, 10);
#endif
/**
* This op implements pnorm pooling for convolution networks.
* Expected Input: 4D array, NCHW format.
*
* IntArgs:
* 0: kernel height
* 1: kernel width
* 2: stride height
* 3: stride width
* 4: padding height
* 5: padding width
* 6: dilation height
* 7: dilation width
* 8: same mode: 0 false, 1 true
* 9: p for p-norm
*/
#if NOT_EXCLUDED(OP_pnormpool2d)
DECLARE_CUSTOM_OP(pnormpool2d, 1, 1, false, 0, 10);
DECLARE_CUSTOM_OP(pnormpool2d_bp, 2, 1, false, 1, 10);
#endif
/**
* This op implements im2col algorithm, widely used in convolution neural networks
* Input: 4D input expected
*
* Int args:
* 0: kernel height
* 1: kernel width
* 2: stride height
* 3: stride width
* 4: padding height
* 5: padding width
* 6: dilation height
* 7: dilation width
* 8: isSameMode
*/
#if NOT_EXCLUDED(OP_im2col)
DECLARE_CUSTOM_OP(im2col, 1, 1, false, 0, 9);
DECLARE_CUSTOM_OP(im2col_bp, 2, 1, false, 0, 9);
#endif
/**
* This op implements col2im algorithm, widely used in convolution neural networks
* Input: 6D input expected (like output of im2col op)
*
* Int args:
* 0: stride height
* 1: stride width
* 2: padding height
* 3: padding width
* 4: image height
* 5: image width
* 6: dilation height
* 7: dilation width
*/
#if NOT_EXCLUDED(OP_col2im)
DECLARE_CUSTOM_OP(col2im, 1, 1, false, 0, 9);
#endif
/**
* Expected input: 4D array
*
* IntArgs:
* 0: scale factor for rows (height)
* 1: scale factor for columns (width)
* 2: data format: 0 NHWC (default), 1 NCHW
*/
#if NOT_EXCLUDED(OP_upsampling2d)
DECLARE_CUSTOM_OP(upsampling2d, 1, 1, false, 0, 2);
DECLARE_CUSTOM_OP(upsampling2d_bp, 2, 1, false, 0, 0);
#endif
/**
* Expected input: 4D array
*
* IntArgs:
* 0: scale factor for depth
* 1: scale factor for rows (height)
* 2: scale factor for columns (width)
* 3: data format: 0 NDHWC (default), 1 NCDHW
*/
#if NOT_EXCLUDED(OP_upsampling3d)
DECLARE_CUSTOM_OP(upsampling3d, 1, 1, false, 0, 3);
DECLARE_CUSTOM_OP(upsampling3d_bp, 2, 1, false, 0, 0);
#endif
/**
* This op produces binary matrix wrt to target dimension.
* Maximum value within each TAD is replaced with 1, other values are set to true.
*
* Int args:
* 0: axis
*/
#if NOT_EXCLUDED(OP_ismax)
DECLARE_CONFIGURABLE_OP(ismax, 1, 1, true, 0, -2);
#endif
/**
* Dilation2D op
*
* Int args:
* 0: isSameMode
*/
#if NOT_EXCLUDED(OP_dilation2d)
DECLARE_CUSTOM_OP(dilation2d, 2, 1, false, 0, 1);
#endif
#if NOT_EXCLUDED(OP_conv3dnew)
DECLARE_CUSTOM_OP(conv3dnew, 2, 1, false, 0, 13);
DECLARE_CUSTOM_OP(conv3dnew_bp, 3, 2, false, 0, 13);
#endif
#if NOT_EXCLUDED(OP_avgpool3dnew)
DECLARE_CUSTOM_OP(avgpool3dnew, 1, 1, false, 0, 10);
DECLARE_CUSTOM_OP(avgpool3dnew_bp, 2, 1, false, 0, 10);
#endif
#if NOT_EXCLUDED(OP_maxpool3dnew)
DECLARE_CUSTOM_OP(maxpool3dnew, 1, 1, false, 0, 10);
DECLARE_CUSTOM_OP(maxpool3dnew_bp, 2, 1, false, 0, 10);
#endif
/**
* This op same as maxpool2d with a variant to return a matrix of indexes for max values
*
* Input - 4D tensor
* Output:
* 0 - 4D tensor as input
* 1 - 4D tensor with max value indexes
*
* Int params:
* 9 int with 2x4 vectors and 1 bool value
*/
#if NOT_EXCLUDED(OP_max_pool_with_argmax)
DECLARE_CUSTOM_OP(max_pool_with_argmax, 1, 2, false, 0, 9);
#endif
#if NOT_EXCLUDED(OP_depthwise_conv2d)
DECLARE_CUSTOM_OP(depthwise_conv2d, 2, 1, false, 0, 9);
DECLARE_CUSTOM_OP(depthwise_conv2d_bp, 3, 2, false, 0, 9);
#endif
/**
* point-wise 2D convolution
* Expected input:
* x: 4D array
* weight: 4D Array [1, 1, iC, oC] (NHWC) or [oC, iC, 1, 1] (NCHW)
* bias: optional vector, length of oC
*
* IntArgs:
* 0: data format: 1 NHWC, 0 NCHW (optional, by default = NHWC)
*/
#if NOT_EXCLUDED(OP_pointwise_conv2d)
DECLARE_CUSTOM_OP(pointwise_conv2d, 2, 1, false, 0, 0);
#endif
#if NOT_EXCLUDED(OP_deconv2d_tf)
DECLARE_CUSTOM_OP(deconv2d_tf, 2, 1, false, 0, 0);
#endif
} // namespace ops
} // namespace sd
#endif