@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d.d.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @license
 * Copyright 2019 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.
 * =============================================================================
 */
/// <amd-module name="@tensorflow/tfjs-core/dist/ops/fused/depthwise_conv2d" />
import { Tensor, Tensor3D, Tensor4D } from '../../tensor';
import { TensorLike } from '../../types';
import { Activation } from '../fused_types';
/**
 * Computes depthwise 2D convolution, optionally fused with adding a
 * bias and applying an activation.
 *
 * Given a 4D `input` array and a `filter` array of shape
 * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
 * `inChannels` convolutional filters of depth 1, this op applies a
 * different filter to each input channel (expanding from 1 channel to
 * `channelMultiplier` channels for each), then concatenates the results
 * together. The output has `inChannels * channelMultiplier` channels.
 *
 * See
 * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
 *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
 * for more details.
 *
 * @param obj An object with the following properties:
 * @param x The input tensor, of rank 4 or rank 3, of shape
 *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
 * assumed.
 * @param filter The filter tensor, rank 4, of shape
 *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
 * @param strides The strides of the convolution: `[strideHeight,
 * strideWidth]`. If strides is a single number, then `strideHeight ==
 * strideWidth`.
 * @param pad The type of padding algorithm.
 *   - `same` and stride 1: output will be of same size as input,
 *       regardless of filter size.
 *   - `valid`: output will be smaller than input if filter is larger
 *       than 1x1.
 *   - For more info, see this guide:
 *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](
 *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)
 * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
 *     in which we sample input values across the height and width dimensions
 *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
 *     number, then `dilationHeight == dilationWidth`. If it is greater than
 *     1, then all values of `strides` must be 1.
 * @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
 *     "NHWC". 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]. Only "NHWC" is currently supported.
 * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is
 *     provided, it will default to truncate.
 * @param bias Tensor to be added to the result.
 * @param activation Name of activation kernel (defaults to `linear`).
 * @param preluActivationWeights Tensor of prelu weights to be applied as part
 *     of a `prelu` activation, typically the same shape as `x`.
 * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
 *     activation.
 */
declare function fusedDepthwiseConv2d_<T extends Tensor3D | Tensor4D>({ x, filter, strides, pad, dataFormat, dilations, dimRoundingMode, bias, activation, preluActivationWeights, leakyreluAlpha }: {
    x: T | TensorLike;
    filter: Tensor4D | TensorLike;
    strides: [number, number] | number;
    pad: 'valid' | 'same' | number;
    dataFormat?: 'NHWC' | 'NCHW';
    dilations?: [number, number] | number;
    dimRoundingMode?: 'floor' | 'round' | 'ceil';
    bias?: Tensor | TensorLike;
    activation?: Activation;
    preluActivationWeights?: Tensor;
    leakyreluAlpha?: number;
}): T;
export declare const depthwiseConv2d: typeof fusedDepthwiseConv2d_;
export {};