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package org.deeplearning4j.nn.layers.convolution;

import org.deeplearning4j.nn.conf.CNN2DFormat;
import org.deeplearning4j.nn.conf.ConvolutionMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.AlgoMode;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdDataAlgo;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.BwdFilterAlgo;
import org.deeplearning4j.nn.conf.layers.ConvolutionLayer.FwdAlgo;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.layers.LayerHelper;
import org.nd4j.linalg.activations.IActivation;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.common.primitives.Pair;
import org.deeplearning4j.nn.workspace.LayerWorkspaceMgr;

public interface ConvolutionHelper extends LayerHelper {
    boolean checkSupported();

    Pair backpropGradient(INDArray input, INDArray weights, INDArray bias, INDArray delta, int[] kernel,
                                              int[] strides, int[] pad, INDArray biasGradView, INDArray weightGradView, IActivation afn,
                                              AlgoMode mode, BwdFilterAlgo bwdFilterAlgo, BwdDataAlgo bwdDataAlgo,
                                              ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);

    INDArray preOutput(INDArray input, INDArray weights, INDArray bias, int[] kernel, int[] strides, int[] pad,
                       AlgoMode mode, FwdAlgo fwdAlgo, ConvolutionMode convolutionMode, int[] dilation, CNN2DFormat format, LayerWorkspaceMgr workspaceMgr);

    INDArray activate(INDArray z, IActivation afn, boolean training);
}