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Trainer Agnostic Deep Learning
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/*
 * Copyright (c) 2016, Peter Abeles. All Rights Reserved.
 *
 * This file is part of DeepBoof
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *   http://www.apache.org/licenses/LICENSE-2.0
 *
 * 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.
 */

package deepboof.impl.forward.standard;

import deepboof.forward.SpatialBatchNorm;
import deepboof.tensors.Tensor_F64;

/**
 * Implementation of {@link SpatialBatchNorm} for {@link Tensor_F64}
 *
 * @author Peter Abeles
 */
public class SpatialBatchNorm_F64 extends FunctionBatchNorm_F64 implements SpatialBatchNorm {

	public SpatialBatchNorm_F64(boolean requiresGammaBeta) {
		super(requiresGammaBeta);
	}

	@Override
	public void _initialize() {
		if( shapeInput.length != 3 )
			throw new IllegalArgumentException("Expected 3 DOF in a spatial shape (C,W,H)");
		this.shapeOutput = shapeInput.clone();

		int paramShape[] = new int[2];
		paramShape[0] = shapeInput[0];             // number of channels
		paramShape[1] = requiresGammaBeta ? 4 : 2; // number of variables

		this.shapeParameters.add(paramShape);
		this.params.reshape(paramShape);
	}

	@Override
	public void _forward(Tensor_F64 input, Tensor_F64 output) {
		int C = input.length(1);
		int W = input.length(2);
		int H = input.length(3);

		int D = W*H;

		int indexIn  = input.startIndex;
		int indexOut = output.startIndex;

		if( hasGammaBeta() ) {
			for (int batch = 0; batch < miniBatchSize; batch++) {
				int indexP  = params.startIndex;
				for( int channel = 0; channel < C; channel++ ) {
					double mean  = params.d[indexP++];
					double inv_stdev_eps = params.d[indexP++];
					double gamma = params.d[indexP++];
					double beta  = params.d[indexP++];

					int end = indexIn + D;
					while (indexIn < end) {
						output.d[indexOut++] = (input.d[indexIn++] - mean)*(gamma * inv_stdev_eps) + beta;
					}
				}
			}
		} else {
			for (int batch = 0; batch < miniBatchSize; batch++) {
				int indexP  = params.startIndex;
				for (int channel = 0; channel < C; channel++) {
					double mean = params.d[indexP++];
					double inv_stdev_eps = params.d[indexP++];

					int end = indexIn + D;
					while (indexIn < end) {
						output.d[indexOut++] = (input.d[indexIn++] - mean) * inv_stdev_eps;
					}
				}
			}
		}
	}
}