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• Conv2D.java

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org.bytedeco.tensorflow.Conv2D Maven / Gradle / Ivy

// Targeted by JavaCPP version 1.5.8: DO NOT EDIT THIS FILE

package org.bytedeco.tensorflow;

import org.bytedeco.tensorflow.Allocator;
import java.nio.*;
import org.bytedeco.javacpp.*;
import org.bytedeco.javacpp.annotation.*;

import static org.bytedeco.javacpp.presets.javacpp.*;

import static org.bytedeco.tensorflow.global.tensorflow.*;


/** Computes a 2-D convolution given 4-D {@code input} and {@code filter} tensors.
 * 
 *  Given an input tensor of shape {@code [batch, in_height, in_width, in_channels]}
 *  and a filter / kernel tensor of shape
 *  {@code [filter_height, filter_width, in_channels, out_channels]}, this op
 *  performs the following:
 * 
 *  1. Flattens the filter to a 2-D matrix with shape
 *     {@code [filter_height * filter_width * in_channels, output_channels]}.
 *  2. Extracts image patches from the input tensor to form a *virtual*
 *     tensor of shape {@code [batch, out_height, out_width,
 *     filter_height * filter_width * in_channels]}.
 *  3. For each patch, right-multiplies the filter matrix and the image patch
 *     vector.
 * 
 *  In detail, with the default NHWC format,
 * 
 *      output[b, i, j, k] =
 *          sum_{di, dj, q} input[b, strides[1] * i + di, strides[2] * j + dj, q] *
 *                          filter[di, dj, q, k]
 * 
 *  Must have {@code strides[0] = strides[3] = 1}.  For the most common case of the same
 *  horizontal and vertices strides, {@code strides = [1, stride, stride, 1]}.
 * 
 *  Arguments:
 *  * scope: A Scope object
 *  * input: A 4-D tensor. The dimension order is interpreted according to the value
 *  of {@code data_format}, see below for details.
 *  * filter: A 4-D tensor of shape
 *  {@code [filter_height, filter_width, in_channels, out_channels]}
 *  * strides: 1-D tensor of length 4.  The stride of the sliding window for each
 *  dimension of {@code input}. The dimension order is determined by the value of
 *  {@code data_format}, see below for details.
 *  * padding: The type of padding algorithm to use.
 * 
 *  Optional attributes (see {@code Attrs}):
 *  * explicit_paddings: If {@code padding} is {@code "EXPLICIT"}, the list of explicit padding amounts. For the ith
 *  dimension, the amount of padding inserted before and after the dimension is
 *  {@code explicit_paddings[2 * i]} and {@code explicit_paddings[2 * i + 1]}, respectively. If
 *  {@code padding} is not {@code "EXPLICIT"}, {@code explicit_paddings} must be empty.
 *  * data_format: Specify the data format of the input and output data. With the
 *  default format "NHWC", the data is stored in the order of:
 *      [batch, height, width, channels].
 *  Alternatively, the format could be "NCHW", the data storage order of:
 *      [batch, channels, height, width].
 *  * dilations: 1-D tensor of length 4.  The dilation factor for each dimension of
 *  {@code input}. If set to k > 1, there will be k-1 skipped cells between each
 *  filter element on that dimension. The dimension order is determined by the
 *  value of {@code data_format}, see above for details. Dilations in the batch and
 *  depth dimensions must be 1.
 * 
 *  Returns:
 *  * {@code Output}: A 4-D tensor. The dimension order is determined by the value of
 *  {@code data_format}, see below for details. */
@Namespace("tensorflow::ops") @NoOffset @Properties(inherit = org.bytedeco.tensorflow.presets.tensorflow.class)
public class Conv2D extends Pointer {
    static { Loader.load(); }
    /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
    public Conv2D(Pointer p) { super(p); }

  /** Optional attribute setters for Conv2D */
  public static class Attrs extends Pointer {
      static { Loader.load(); }
      /** Default native constructor. */
      public Attrs() { super((Pointer)null); allocate(); }
      /** Native array allocator. Access with {@link Pointer#position(long)}. */
      public Attrs(long size) { super((Pointer)null); allocateArray(size); }
      /** Pointer cast constructor. Invokes {@link Pointer#Pointer(Pointer)}. */
      public Attrs(Pointer p) { super(p); }
      private native void allocate();
      private native void allocateArray(long size);
      @Override public Attrs position(long position) {
          return (Attrs)super.position(position);
      }
      @Override public Attrs getPointer(long i) {
          return new Attrs((Pointer)this).offsetAddress(i);
      }
  
    /** Defaults to true */
    
    ///
    public native @ByVal Attrs UseCudnnOnGpu(@Cast("bool") boolean x);

    /** If {@code padding} is {@code "EXPLICIT"}, the list of explicit padding amounts. For the ith
     *  dimension, the amount of padding inserted before and after the dimension is
     *  {@code explicit_paddings[2 * i]} and {@code explicit_paddings[2 * i + 1]}, respectively. If
     *  {@code padding} is not {@code "EXPLICIT"}, {@code explicit_paddings} must be empty.
     * 
     *  Defaults to [] */
    
    ///
    public native @ByVal Attrs ExplicitPaddings(@ArraySlice IntPointer x);
    public native @ByVal Attrs ExplicitPaddings(@ArraySlice IntBuffer x);
    public native @ByVal Attrs ExplicitPaddings(@ArraySlice int... x);

    /** Specify the data format of the input and output data. With the
     *  default format "NHWC", the data is stored in the order of:
     *      [batch, height, width, channels].
     *  Alternatively, the format could be "NCHW", the data storage order of:
     *      [batch, channels, height, width].
     * 
     *  Defaults to "NHWC" */
    
    ///
    public native @ByVal Attrs DataFormat(@StringPiece BytePointer x);
    public native @ByVal Attrs DataFormat(@StringPiece String x);

    /** 1-D tensor of length 4.  The dilation factor for each dimension of
     *  {@code input}. If set to k > 1, there will be k-1 skipped cells between each
     *  filter element on that dimension. The dimension order is determined by the
     *  value of {@code data_format}, see above for details. Dilations in the batch and
     *  depth dimensions must be 1.
     * 
     *  Defaults to [1, 1, 1, 1] */
    public native @ByVal Attrs Dilations(@ArraySlice IntPointer x);
    public native @ByVal Attrs Dilations(@ArraySlice IntBuffer x);
    public native @ByVal Attrs Dilations(@ArraySlice int... x);

    public native @Cast("bool") boolean use_cudnn_on_gpu_(); public native Attrs use_cudnn_on_gpu_(boolean setter);
    public native @ArraySlice IntPointer explicit_paddings_(); public native Attrs explicit_paddings_(IntPointer setter);
    public native @StringPiece BytePointer data_format_(); public native Attrs data_format_(BytePointer setter);
    public native @ArraySlice IntPointer dilations_(); public native Attrs dilations_(IntPointer setter);
  }
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece BytePointer padding) { super((Pointer)null); allocate(scope, input, filter, strides, padding); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece BytePointer padding);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece String padding) { super((Pointer)null); allocate(scope, input, filter, strides, padding); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece String padding);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece BytePointer padding) { super((Pointer)null); allocate(scope, input, filter, strides, padding); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece BytePointer padding);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece String padding) { super((Pointer)null); allocate(scope, input, filter, strides, padding); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece String padding);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece BytePointer padding) { super((Pointer)null); allocate(scope, input, filter, strides, padding); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece BytePointer padding);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece String padding) { super((Pointer)null); allocate(scope, input, filter, strides, padding); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece String padding);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece BytePointer padding, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, input, filter, strides, padding, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece BytePointer padding, @Const @ByRef Attrs attrs);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece String padding, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, input, filter, strides, padding, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece String padding, @Const @ByRef Attrs attrs);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece BytePointer padding, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, input, filter, strides, padding, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece BytePointer padding, @Const @ByRef Attrs attrs);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece String padding, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, input, filter, strides, padding, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntPointer strides,
         @StringPiece String padding, @Const @ByRef Attrs attrs);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece BytePointer padding, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, input, filter, strides, padding, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice IntBuffer strides,
         @StringPiece BytePointer padding, @Const @ByRef Attrs attrs);
  public Conv2D(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece String padding, @Const @ByRef Attrs attrs) { super((Pointer)null); allocate(scope, input, filter, strides, padding, attrs); }
  private native void allocate(@Const @ByRef Scope scope, @ByVal Input input,
         @ByVal Input filter, @ArraySlice int[] strides,
         @StringPiece String padding, @Const @ByRef Attrs attrs);
  public native @ByVal @Name("operator tensorflow::Output") Output asOutput();
  public native @ByVal @Name("operator tensorflow::Input") Input asInput();
  public native Node node();

  public static native @ByVal Attrs UseCudnnOnGpu(@Cast("bool") boolean x);
  public static native @ByVal Attrs ExplicitPaddings(@ArraySlice IntPointer x);
  public static native @ByVal Attrs ExplicitPaddings(@ArraySlice IntBuffer x);
  public static native @ByVal Attrs ExplicitPaddings(@ArraySlice int... x);
  public static native @ByVal Attrs DataFormat(@StringPiece BytePointer x);
  public static native @ByVal Attrs DataFormat(@StringPiece String x);
  public static native @ByVal Attrs Dilations(@ArraySlice IntPointer x);
  public static native @ByVal Attrs Dilations(@ArraySlice IntBuffer x);
  public static native @ByVal Attrs Dilations(@ArraySlice int... x);

  public native @ByRef Operation operation(); public native Conv2D operation(Operation setter);
  public native @ByRef Output output(); public native Conv2D output(Output setter);
}