@tensorflow/tfjs-core/dist/gradients/DepthwiseConv2dNative_grad.js

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2020 Google LLC. 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.
 * 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.
 * =============================================================================
 */
import { DepthwiseConv2dNative } from '../kernel_names';
import * as conv_util from '../ops/conv_util';
import { depthwiseConv2dNativeBackpropFilter } from '../ops/depthwise_conv2d_native_backprop_filter';
import { depthwiseConv2dNativeBackpropInput } from '../ops/depthwise_conv2d_native_backprop_input';
import * as util from '../util';
export const depthwiseConv2dNativeGradConfig = {
    kernelName: DepthwiseConv2dNative,
    inputsToSave: ['x', 'filter'],
    gradFunc: (dy, saved, attrs) => {
        const { dilations, strides, pad, dimRoundingMode } = attrs;
        const $dilations = dilations == null ? [1, 1] : dilations;
        util.assert(conv_util.tupleValuesAreOne($dilations), () => 'Error in gradient of depthwiseConv2dNative: dilation rates ' +
            `greater than 1 are not yet supported. Got dilations ` +
            `'${$dilations}'`);
        const [x, filter] = saved;
        util.assert(x.rank === 4, () => `Error in gradient of depthwiseConv2dNative: input must be ` +
            `rank 4, but got rank ${x.rank}.`);
        util.assert(filter.rank === 4, () => `Error in gradient of depthwiseConv2dNative: filter must be ` +
            `rank 4, but got rank ${filter.rank}.`);
        util.assert(x.shape[3] === filter.shape[2], () => `Error in gradient of depthwiseConv2d: number of input ` +
            `channels (${x.shape[3]}) must match the inChannels dimension ` +
            `in filter ${filter.shape[2]}.`);
        util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, $dilations), () => 'Error in gradient of depthwiseConv2d: Either strides or ' +
            `dilations must be  1. Got strides ${strides} and dilations ` +
            `'${$dilations}'.`);
        if (dimRoundingMode != null) {
            util.assert(util.isInt(pad), () => `Error in depthwiseConv2d: pad must be an integer when using, ` +
                `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
        }
        return {
            x: () => depthwiseConv2dNativeBackpropInput(x.shape, dy, filter, strides, pad, dilations, dimRoundingMode),
            filter: () => depthwiseConv2dNativeBackpropFilter(x, dy, filter.shape, strides, pad, dilations, dimRoundingMode),
        };
    }
};
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