ai.djl.nn.convolutional.Convolution Maven / Gradle / Ivy
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* Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
*
* Licensed under the Apache License, Version 2.0 (the "License"). You may not use this file except in compliance
* with the License. A copy of the License is located at
*
* http://aws.amazon.com/apache2.0/
*
* or in the "license" file accompanying this file. This file 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.
*/
package ai.djl.nn.convolutional;
import ai.djl.Device;
import ai.djl.MalformedModelException;
import ai.djl.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.NDManager;
import ai.djl.ndarray.internal.NDArrayEx;
import ai.djl.ndarray.types.LayoutType;
import ai.djl.ndarray.types.Shape;
import ai.djl.nn.AbstractBlock;
import ai.djl.nn.Block;
import ai.djl.nn.Parameter;
import ai.djl.nn.ParameterType;
import ai.djl.training.ParameterStore;
import ai.djl.util.PairList;
import java.io.DataInputStream;
import java.io.IOException;
/**
* A convolution layer does a dot product calculation on each channel of \(k\)-channel input data by
* specified number of filters, each containing \(k\) kernels for calculating each channel in the
* input data and then summed per filter, hence the number of filters denote the number of output
* channels of a convolution layer. Some modifications may be set on a convolution layer, namely
* stride which shows the distance between each convolved input data in a channel, and padding which
* shows the preservation of input size (width and/or height and/or depth) by adding specified
* padding to the sides of the output. A convolution layer extracts features of input data with
* different representations where each representation lies per channel in the output, often known
* as feature map or feature vector.
*
* While convolution process itself has been around for quite some time in mathematics, in 1998
* LeCun et al. implemented the very first convolution layers forming a network called
* LeNet-5 for character recognition task; details of the network's implementation can be find in
* LeNet-5's paper. When other
* approaches at that time used handcrafted features with external stage of feature extraction,
* convolution layer performed feature extraction on its own with no human interference. This marks
* a new era of machine-extracted features, but it was not until 2012 that the published
* paper of AlexNet marked the beginning of convolutional neural networks, which by the name
* itself heavily relies on convolution layer.
*
*
Convolution layer is usually used in image-related tasks due to its well-renowned performance
* as shown by existing works and currently, other non-image-related fields of study are beginning
* to incorporate convolution layer as an addition or replacement of previous approaches, with one
* example being time series processing with 1-dimensional convolution layer. Due to the nature of
* convolution that processes all points in the input data, it is computationally expensive, hence
* the use of GPU is strongly recommended for faster performance as opposed to using CPU. Note that
* it is also common to stack convolution layers with different output channels for more
* representations of the input data.
*
*
Current implementations of {@code Convolution} are {@link Conv1D} with input dimension of
* {@link LayoutType#WIDTH}, {@link Conv2D} with input dimension of {@link LayoutType#WIDTH} and
* {@link LayoutType#HEIGHT}, and lastly {@link Conv3D} with input dimension of {@link
* LayoutType#WIDTH}, {@link LayoutType#HEIGHT}, and {@link LayoutType#DEPTH}. These implementations
* share the same core principal as a {@code Convolution} layer does, with the difference being the
* number of input dimension each operates on as denoted by {@code ConvXD} for {@code X}
* dimension(s).
*/
public abstract class Convolution extends AbstractBlock {
private static final byte VERSION = 2;
protected Shape kernel;
protected Shape stride;
protected Shape pad;
protected Shape dilate;
protected int numFilters;
protected int numGroups;
protected boolean includeBias;
protected Parameter weight;
protected Parameter bias;
/**
* Creates a {@link Convolution} object.
*
* @param builder the {@code Builder} that has the necessary configurations
*/
public Convolution(ConvolutionBuilder builder) {
super(VERSION);
kernel = builder.kernel;
stride = builder.stride;
pad = builder.pad;
dilate = builder.dilate;
numFilters = builder.numFilters;
numGroups = builder.numGroups;
includeBias = builder.includeBias;
weight =
addParameter(
new Parameter("weight", this, ParameterType.WEIGHT),
(inputShapes) ->
new Shape(numFilters, inputShapes[0].get(1)).addAll(kernel));
if (includeBias) {
bias =
addParameter(
new Parameter("bias", this, ParameterType.BIAS), new Shape(numFilters));
}
}
/**
* Returns the expected layout of the input.
*
* @return the expected layout of the input
*/
protected abstract LayoutType[] getExpectedLayout();
/**
* Returns the string representing the layout of the input.
*
* @return the string representing the layout of the input
*/
protected abstract String getStringLayout();
/**
* Returns the number of dimensions of the input.
*
* @return the number of dimensions of the input
*/
protected abstract int numDimensions();
/** {@inheritDoc} */
@Override
public NDList forward(
ParameterStore parameterStore,
NDList inputs,
boolean training,
PairList params) {
inputs = opInputs(parameterStore, inputs);
NDArrayEx ex = inputs.head().getNDArrayInternal();
return ex.convolution(
inputs,
kernel,
stride,
pad,
dilate,
numFilters,
numGroups,
getStringLayout(),
!includeBias,
params);
}
/** {@inheritDoc} */
@Override
protected void beforeInitialize(Shape[] inputs) {
this.inputShapes = inputs;
Shape inputShape = inputs[0];
Block.validateLayout(getExpectedLayout(), inputShape.getLayout());
}
/** {@inheritDoc} */
@Override
public Shape[] getOutputShapes(NDManager manager, Shape[] inputs) {
long[] shape = new long[numDimensions()];
shape[0] = inputs[0].get(0);
shape[1] = numFilters;
for (int i = 0; i < numDimensions() - 2; i++) {
shape[2 + i] =
(inputs[0].get(2 + i)
+ 2 * pad.get(i)
- dilate.get(0) * (kernel.get(i) - 1)
- 1)
/ stride.get(0)
+ 1;
}
return new Shape[] {new Shape(shape)};
}
/** {@inheritDoc} */
@Override
public void loadMetadata(byte version, DataInputStream is)
throws IOException, MalformedModelException {
if (version == VERSION) {
readInputShapes(is);
} else if (version != 1) {
throw new MalformedModelException("Unsupported encoding version: " + version);
}
}
private NDList opInputs(ParameterStore parameterStore, NDList inputs) {
NDArray data = inputs.singletonOrThrow();
Device device = data.getDevice();
NDList ret = new NDList(3);
ret.add(data);
ret.add(parameterStore.getValue(weight, device));
if (bias != null) {
ret.add(parameterStore.getValue(bias, device));
}
return ret;
}
/**
* A builder that can build any {@code Convolution} block.
*
* @param the type of {@code Convolution} block to build
*/
@SuppressWarnings("rawtypes")
public abstract static class ConvolutionBuilder {
protected Shape kernel;
protected Shape stride;
protected Shape pad;
protected Shape dilate;
protected int numFilters;
protected int numGroups = 1;
protected boolean includeBias = true;
/**
* Sets the shape of the kernel.
*
* @param kernel the shape of the kernel
* @return this Builder
*/
public T setKernel(Shape kernel) {
this.kernel = kernel;
return self();
}
/**
* Sets the stride of the convolution. Defaults to 1 in each dimension.
*
* @param stride the shape of the stride
* @return this Builder
*/
public T optStride(Shape stride) {
this.stride = stride;
return self();
}
/**
* Sets the padding along each dimension. Defaults to 0 along each dimension.
*
* @param pad the shape of padding along each dimension
* @return this Builder
*/
public T optPad(Shape pad) {
this.pad = pad;
return self();
}
/**
* Sets the dilation along each dimension. Defaults to 1 along each dimension.
*
* @param dilate the shape of dilation along each dimension
* @return this Builder
*/
public T optDilate(Shape dilate) {
this.dilate = dilate;
return self();
}
/**
* Sets the Required number of filters.
*
* @param numFilters the number of convolution filters(channels)
* @return this Builder
*/
public T setNumFilters(int numFilters) {
this.numFilters = numFilters;
return self();
}
/**
* Sets the number of group partitions.
*
* @param numGroups the number of group partitions
* @return this Builder
*/
public T optNumGroups(int numGroups) {
this.numGroups = numGroups;
return self();
}
/**
* Sets the optional parameter of whether to include a bias vector. Includes bias by
* default.
*
* @param includeBias whether to use a bias vector parameter
* @return this Builder
*/
public T optBias(boolean includeBias) {
this.includeBias = includeBias;
return self();
}
/**
* Validates that the required arguments are set.
*
* @throws IllegalArgumentException if the required arguments are not set
*/
protected void validate() {
if (kernel == null || numFilters == 0) {
throw new IllegalArgumentException("Kernel and numFilters must be set");
}
}
protected abstract T self();
}
}