ai.djl.nn.convolutional.Conv3d Maven / Gradle / Ivy
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* Copyright 2019 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.ndarray.NDArray;
import ai.djl.ndarray.NDList;
import ai.djl.ndarray.types.LayoutType;
import ai.djl.ndarray.types.Shape;
import ai.djl.nn.Block;
import ai.djl.util.Preconditions;
/**
* {@code Conv3d} layer behaves just as {@link Convolution} does, with the distinction being it
* operates of 3-dimensional data such as medical images or video data. The traversal of each filter
* begins from {@link LayoutType#WIDTH} then to {@link LayoutType#HEIGHT}, and lastly across each
* {@link LayoutType#DEPTH} in the specified {@code depth} size of the data.
*
* The utilization of {@code Conv3d} layer allows deeper analysis of visual data such as those in
* medical images, or even analysis on temporal data such as video data as a whole instead of
* processing each frame with a {@link Conv2d} layer, despite this being a common practice in
* computer vision researches. The benefit of utilizing this kind of layer is the maintaining of
* serial data across 2-dimensional data, hence could be beneficial for research focus on such as
* object tracking. The drawback is that this kind of layer is more costly compared to other
* convolution layer types since dot product operation is performed on all three dimensions.
*
*
The input to a {@code Conv3d} is an {@link ai.djl.ndarray.NDList} with a single 5-D {@link
* ai.djl.ndarray.NDArray}. The layout of the {@link ai.djl.ndarray.NDArray} must be "NCDHW". The
* shapes are
*
*
* - {@code data: (batch_size, channel, depth, height, width)}
*
- {@code weight: (num_filter, channel, kernel[0], kernel[1], kernel[2])}
*
- {@code bias: (num_filter,)}
*
- {@code out: (batch_size, num_filter, out_depth, out_height, out_width)}
* {@code out_depth = f(depth, kernel[0], pad[0], stride[0], dilate[0])}
* {@code out_height = f(height, kernel[1], pad[1], stride[1], dilate[1])}
* {@code out_width = f(width, kernel[2], pad[2], stride[2], dilate[2])}
* {@code where f(x, k, p, s, d) = floor((x + 2 * p - d * (k - 1) - 1)/s) + 1}
*
*
* Both {@code weight} and {@code bias} are learn-able parameters.
*
* @see Convolution
*/
public class Conv3d extends Convolution {
private static final LayoutType[] EXPECTED_LAYOUT = {
LayoutType.BATCH, LayoutType.CHANNEL, LayoutType.DEPTH, LayoutType.HEIGHT, LayoutType.WIDTH
};
private static final String STRING_LAYOUT = "NCDHW";
private static final int NUM_DIMENSIONS = 5;
Conv3d(Builder builder) {
super(builder);
}
/** {@inheritDoc} */
@Override
protected LayoutType[] getExpectedLayout() {
return EXPECTED_LAYOUT;
}
/** {@inheritDoc} */
@Override
protected String getStringLayout() {
return STRING_LAYOUT;
}
/** {@inheritDoc} */
@Override
protected int numDimensions() {
return NUM_DIMENSIONS;
}
/**
* Applies 3D convolution over an input signal composed of several input planes.
*
* @param input the input {@code NDArray} of shape (batchSize, inputChannel, depth, height,
* width)
* @param weight filters {@code NDArray} of shape (outChannel, inputChannel/groups, depth,
* height, width)
* @return the output of the conv3d operation
*/
public static NDList conv3d(NDArray input, NDArray weight) {
return conv3d(
input, weight, null, new Shape(1, 1, 1), new Shape(0, 0, 0), new Shape(1, 1, 1));
}
/**
* Applies 3D convolution over an input signal composed of several input planes.
*
* @param input the input {@code NDArray} of shape (batchSize, inputChannel, depth, height,
* width)
* @param weight filters {@code NDArray} of shape (outChannel, inputChannel/groups, depth,
* height, width)
* @param bias bias {@code NDArray} of shape (outChannel)
* @return the output of the conv3d operation
*/
public static NDList conv3d(NDArray input, NDArray weight, NDArray bias) {
return conv3d(
input, weight, bias, new Shape(1, 1, 1), new Shape(0, 0, 0), new Shape(1, 1, 1));
}
/**
* Applies 3D convolution over an input signal composed of several input planes.
*
* @param input the input {@code NDArray} of shape (batchSize, inputChannel, depth, height,
* width)
* @param weight filters {@code NDArray} of shape (outChannel, inputChannel/groups, depth,
* height, width)
* @param bias bias {@code NDArray} of shape (outChannel)
* @param stride the stride of the convolving kernel: Shape(depth, height, width)
* @return the output of the conv3d operation
*/
public static NDList conv3d(NDArray input, NDArray weight, NDArray bias, Shape stride) {
return conv3d(input, weight, bias, stride, new Shape(0, 0, 0), new Shape(1, 1, 1));
}
/**
* Applies 3D convolution over an input signal composed of several input planes.
*
* @param input the input {@code NDArray} of shape (batchSize, inputChannel, depth, height,
* width)
* @param weight filters {@code NDArray} of shape (outChannel, inputChannel/groups, depth,
* height, width)
* @param bias bias {@code NDArray} of shape (outChannel)
* @param stride the stride of the convolving kernel: Shape(depth, height, width)
* @param padding implicit paddings on both sides of the input: Shape(depth, height, width)
* @return the output of the conv3d operation
*/
public static NDList conv3d(
NDArray input, NDArray weight, NDArray bias, Shape stride, Shape padding) {
return conv3d(input, weight, bias, stride, padding, new Shape(1, 1, 1));
}
/**
* Applies 3D convolution over an input signal composed of several input planes.
*
* @param input the input {@code NDArray} of shape (batchSize, inputChannel, depth, height,
* width)
* @param weight filters {@code NDArray} of shape (outChannel, inputChannel/groups, depth,
* height, width)
* @param bias bias {@code NDArray} of shape (outChannel)
* @param stride the stride of the convolving kernel: Shape(depth, height, width)
* @param padding implicit paddings on both sides of the input: Shape(depth, height, width)
* @param dilation the spacing between kernel elements: Shape(depth, height, width)
* @return the output of the conv3d operation
*/
public static NDList conv3d(
NDArray input,
NDArray weight,
NDArray bias,
Shape stride,
Shape padding,
Shape dilation) {
return conv3d(input, weight, bias, stride, padding, dilation, 1);
}
/**
* Applies 3D convolution over an input signal composed of several input planes.
*
* @param input the input {@code NDArray} of shape (batchSize, inputChannel, depth, height,
* width)
* @param weight filters {@code NDArray} of shape (outChannel, inputChannel/groups, depth,
* height, width)
* @param bias bias {@code NDArray} of shape (outChannel)
* @param stride the stride of the convolving kernel: Shape(depth, height, width)
* @param padding implicit paddings on both sides of the input: Shape(depth, height, width)
* @param dilation the spacing between kernel elements: Shape(depth, height, width)
* @param groups split input into groups: input channel(input.size(1)) should be divisible by
* the number of groups
* @return the output of the conv3d operation
*/
public static NDList conv3d(
NDArray input,
NDArray weight,
NDArray bias,
Shape stride,
Shape padding,
Shape dilation,
int groups) {
Preconditions.checkArgument(
input.getShape().dimension() == 5 && weight.getShape().dimension() == 5,
"the shape of input or weight doesn't match the conv2d");
Preconditions.checkArgument(
stride.dimension() == 3 && padding.dimension() == 3 && dilation.dimension() == 3,
"the shape of stride or padding or dilation doesn't match the conv2d");
return Convolution.convolution(input, weight, bias, stride, padding, dilation, groups);
}
/**
* Creates a builder to build a {@code Conv3d}.
*
* @return a new builder
*/
public static Builder builder() {
return new Builder();
}
/** The Builder to construct a {@link Conv3d} type of {@link Block}. */
public static final class Builder extends ConvolutionBuilder {
/** Creates a builder that can build a {@link Conv3d} block. */
Builder() {
stride = new Shape(1, 1, 1);
padding = new Shape(0, 0, 0);
dilation = new Shape(1, 1, 1);
}
/** {@inheritDoc} */
@Override
protected Builder self() {
return this;
}
/**
* Builds a {@link Conv3d} block.
*
* @return the {@link Conv3d} block
*/
public Conv3d build() {
validate();
return new Conv3d(this);
}
}
}