"use strict";
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/**
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* @license
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* Copyright 2018 Google Inc. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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var engine_1 = require("../engine");
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var tensor_util_env_1 = require("../tensor_util_env");
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var util = require("../util");
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var conv_util = require("./conv_util");
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var operation_1 = require("./operation");
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/**
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* Computes a 1D convolution over the input x.
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*
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* @param x The input tensor, of rank 3 or rank 2, of shape
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* `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
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* @param filter The filter, rank 3, of shape
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* `[filterWidth, inDepth, outDepth]`.
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* @param stride The number of entries by which the filter is moved right at
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* each step.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
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* https://www.tensorflow.org/api_guides/python/nn#Convolution)
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* @param dataFormat An optional string from "NWC", "NCW". Defaults to "NWC",
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* the data is stored in the order of [batch, in_width, in_channels]. Only
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* "NWC" is currently supported.
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* @param dilation The dilation rate in which we sample input values in
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* atrous convolution. Defaults to `1`. If it is greater than 1, then
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* stride must be `1`.
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* @param dimRoundingMode The rounding mode used when computing output
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* dimensions if pad is a number. If none is provided, it will not round
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* and error if the output is of fractional size.
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*/
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/** @doc {heading: 'Operations', subheading: 'Convolution'} */
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function conv1d_(x, filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
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if (dataFormat === void 0) { dataFormat = 'NWC'; }
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if (dilation === void 0) { dilation = 1; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv1d');
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var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv1d');
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var x3D = $x;
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var reshapedTo3D = false;
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if ($x.rank === 2) {
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reshapedTo3D = true;
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x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);
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}
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util.assert(x3D.rank === 3, function () { return "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + "."; });
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util.assert($filter.rank === 3, function () { return "Error in conv1d: filter must be rank 3, but got rank " +
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($filter.rank + "."); });
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if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in conv1d: pad must be an integer when using, " +
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("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
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}
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util.assert(x3D.shape[2] === $filter.shape[1], function () { return "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " +
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("input depth for filter " + $filter.shape[1] + "."); });
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util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), function () { return 'Error in conv1D: Either stride or dilation must be 1. ' +
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("Got stride " + stride + " and dilation '" + dilation + "'"); });
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util.assert(dataFormat === 'NWC', function () { return "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported."; });
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var filter4D = $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);
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var input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);
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var strides = [1, stride];
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var dilations = [1, dilation];
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var conv2dDataFormat = 'NHWC';
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var res = exports.conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
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if (reshapedTo3D) {
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return res.as2D(res.shape[2], res.shape[3]);
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}
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return res.as3D(res.shape[0], res.shape[2], res.shape[3]);
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}
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/**
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* Computes a 2D convolution over the input x.
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*
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* @param x The input tensor, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter, rank 4, of shape
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* `[filterHeight, filterWidth, inDepth, outDepth]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
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* - For more info, see this guide:
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* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
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* https://www.tensorflow.org/api_guides/python/nn#Convolution)
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* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
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* "NHWC". Specify the data format of the input and output data. With the
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* default format "NHWC", the data is stored in the order of: [batch,
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* height, width, channels].
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* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
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* in which we sample input values across the height and width dimensions
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* in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single
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* number, then `dilationHeight == dilationWidth`. If it is greater than
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* 1, then all values of `strides` must be 1.
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* @param dimRoundingMode The rounding mode used when computing output
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* dimensions if pad is a number. If none is provided, it will not round
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* and error if the output is of fractional size.
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*/
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/** @doc {heading: 'Operations', subheading: 'Convolution'} */
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function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
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if (dataFormat === void 0) { dataFormat = 'NHWC'; }
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if (dilations === void 0) { dilations = [1, 1]; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2d');
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var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2d');
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var x4D = $x;
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var reshapedTo4D = false;
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if ($x.rank === 3) {
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reshapedTo4D = true;
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x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
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}
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util.assert(x4D.rank === 4, function () { return "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + "."; });
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util.assert($filter.rank === 4, function () { return "Error in conv2d: filter must be rank 4, but got rank " +
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($filter.rank + "."); });
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if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in conv2d: pad must be an integer when using, " +
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("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
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}
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var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
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util.assert(inDepth === $filter.shape[2], function () { return "Error in conv2d: depth of input (" + inDepth + ") must match " +
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("input depth for filter " + $filter.shape[2] + "."); });
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util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv2D: Either strides or dilations must be 1. ' +
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("Got strides " + strides + " and dilations '" + dilations + "'"); });
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var $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);
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var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
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var grad = function (dy, saved) {
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var _a = saved, $filter = _a[0], x4D = _a[1];
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util.assert(conv_util.tupleValuesAreOne(dilations), function () { return 'Error in gradient of conv2D: dilation rates greater than 1 ' +
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("are not yet supported in gradients. Got dilations '" + dilations + "'"); });
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return {
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x: function () { return exports.conv2dDerInput(x4D.shape, dy, $filter, strides, pad, dataFormat); },
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filter: function () {
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return exports.conv2dDerFilter(x4D, dy, $filter.shape, strides, pad, dataFormat);
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}
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};
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};
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var inputsToSave = [$filter, x4D];
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.conv2d(x4D, $filter, convInfo);
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save([$filter, x4D]);
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return res;
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}, { x: x4D, filter: $filter }, grad, 'Conv2D', convInfo, inputsToSave);
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
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}
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return res;
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}
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/**
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* Computes the derivative of the input of a 2D convolution.
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*
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* @param xShape The shape of the input: [batch, height, width, inDepth].
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* If length of 3, batch of 1 is assumed.
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* @param dy The derivative of the output, of rank 4 or rank 3 of shape
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* `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter, rank 4, of shape
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* `[filterHeight, filterWidth, inDepth, outDepth]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`.
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* @param pad The type of padding algorithm used:
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
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* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
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* "NHWC". Specify the data format of the input and output data. With the
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* default format "NHWC", the data is stored in the order of: [batch,
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* height, width, channels].
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* @param dimRoundingMode The rounding mode used when computing output
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* dimensions if pad is a number. If none is provided, it will not round
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* and error if the output is of fractional size.
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*/
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function conv2dDerInput_(xShape, dy, filter, strides, pad, dataFormat, dimRoundingMode) {
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if (dataFormat === void 0) { dataFormat = 'NHWC'; }
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util.assert(xShape.length === dy.rank, function () { return "Length of inShape " +
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("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"); });
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var xShape4D = xShape;
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var dy4D = dy;
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var reshapedTo4D = false;
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if (dy.rank === 3) {
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reshapedTo4D = true;
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dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
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xShape4D = [1, xShape[0], xShape[1], xShape[2]];
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}
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util.assert(xShape4D.length === 4, function () {
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return "Error in conv2dDerInput: inShape must be length 4, but got length " +
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(xShape4D.length + ".");
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});
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util.assert(dy4D.rank === 4, function () { return "Error in conv2dDerInput: dy must be rank 4, but got " +
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("rank " + dy4D.rank); });
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util.assert(filter.rank === 4, function () { return "Error in conv2dDerInput: filter must be rank 4, but got " +
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("rank " + filter.rank); });
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var inDepth = dataFormat === 'NHWC' ? xShape4D[3] : xShape4D[1];
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var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
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util.assert(inDepth === filter.shape[2], function () { return "Error in conv2dDerInput: depth of input (" + inDepth + ") must " +
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("match input depth for filter " + filter.shape[2] + "."); });
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util.assert(outDepth === filter.shape[3], function () { return "Error in conv2dDerInput: depth of output (" + outDepth + ") must " +
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("match output depth for filter " + filter.shape[3] + "."); });
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if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in conv2dDerInput: pad must be an integer when using, " +
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("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
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}
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var dilations = 1;
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var grad = function (ddx, saved) {
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var filter = saved[0], dy4D = saved[1];
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return {
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dy4D: function () { return exports.conv2d(ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode); },
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filter: function () { return exports.conv2dDerFilter(ddx, dy4D, filter.shape, strides, pad, dataFormat, dimRoundingMode); }
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};
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};
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var $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);
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var convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
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var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
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var res = backend.conv2dDerInput(dy4D, filter, convInfo);
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save([filter, dy4D]);
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return res;
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}, { dy4D: dy4D, filter: filter }, grad);
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if (reshapedTo4D) {
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return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
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}
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return res;
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}
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/**
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* Computes the derivative of the filter of a 2D convolution.
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*
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* @param x The input tensor, of rank 4 or rank 3 of shape
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* [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.
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* @param dy The dy image, of rank 4 or rank 3, of shape
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* [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
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* @param filterShape The shape of the filter, length 4,
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* [filterHeight, filterWidth, inDepth, outDepth].
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* @param strides The strides of the convolution: [strideHeight,
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* strideWidth].
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* @param pad A string from: 'same', 'valid'. The type of padding algorithm
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* used in the forward prop of the op.
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* @param dataFormat: An optional string from: "NHWC", "NCHW". Defaults to
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* "NHWC". Specify the data format of the input and output data. With the
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* default format "NHWC", the data is stored in the order of: [batch,
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* height, width, channels].
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* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The
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* rounding mode used when computing output dimensions if pad is a
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* number. If none is provided, it will not round and error if the output
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* is of fractional size.
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*/
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function conv2dDerFilter_(x, dy, filterShape, strides, pad, dataFormat, dimRoundingMode) {
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if (dataFormat === void 0) { dataFormat = 'NHWC'; }
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var x4D = x;
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if (x.rank === 3) {
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x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
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}
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var dy4D = dy;
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if (dy4D.rank === 3) {
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dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
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}
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util.assert(x4D.rank === 4, function () { return "Error in conv2dDerFilter: input must be rank 4, but got shape " +
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(x4D.shape + "."); });
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util.assert(dy4D.rank === 4, function () { return "Error in conv2dDerFilter: dy must be rank 4, but got shape " +
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(dy4D.shape + "."); });
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util.assert(filterShape.length === 4, function () { return "Error in conv2dDerFilter: filterShape must be length 4, but got " +
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(filterShape + "."); });
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var inDepth = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
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var outDepth = dataFormat === 'NHWC' ? dy4D.shape[3] : dy4D.shape[1];
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util.assert(inDepth === filterShape[2], function () { return "Error in conv2dDerFilter: depth of input " + inDepth + ") must " +
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("match input depth in filter (" + filterShape[2] + "."); });
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util.assert(outDepth === filterShape[3], function () { return "Error in conv2dDerFilter: depth of dy (" + outDepth + ") must " +
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("match output depth for filter (" + filterShape[3] + ")."); });
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if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in conv2dDerFilter: pad must be an integer when using, " +
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("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
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}
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var dilations = 1;
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var $dataFormat = conv_util.convertConv2DDataFormat(dataFormat);
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var convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode, false, $dataFormat);
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return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.conv2dDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
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}
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/**
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* Computes the transposed 2D convolution of an image, also known as a
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* deconvolution.
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*
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* @param x The input image, of rank 4 or rank 3, of shape
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* `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.
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* @param filter The filter, rank 4, of shape
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* `[filterHeight, filterWidth, outDepth, inDepth]`.
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* `inDepth` must match `inDepth` in `x`.
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* @param outputShape Output shape, of rank 4 or rank 3:
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* `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.
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* @param strides The strides of the original convolution:
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* `[strideHeight, strideWidth]`.
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* @param pad The type of padding algorithm used in the non-transpose version
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* of the op.
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* @param dimRoundingMode The rounding mode used when computing output
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* dimensions if pad is a number. If none is provided, it will not round
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* and error if the output is of fractional size.
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*/
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/** @doc {heading: 'Operations', subheading: 'Convolution'} */
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function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2dTranspose');
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var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2dTranspose');
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return conv2dDerInput_(outputShape, $x, $filter, strides, pad, 'NHWC', dimRoundingMode);
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}
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/**
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* Depthwise 2D convolution.
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*
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* Given a 4D `input` array and a `filter` array of shape
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* `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
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* `inChannels` convolutional filters of depth 1, this op applies a
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* different filter to each input channel (expanding from 1 channel to
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* `channelMultiplier` channels for each), then concatenates the results
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* together. The output has `inChannels * channelMultiplier` channels.
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*
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* See
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* [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
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* https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
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* for more details.
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*
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* @param x The input tensor, of rank 4 or rank 3, of shape
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* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
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* assumed.
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* @param filter The filter tensor, rank 4, of shape
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* `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
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* @param strides The strides of the convolution: `[strideHeight,
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* strideWidth]`. If strides is a single number, then `strideHeight ==
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* strideWidth`.
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* @param pad The type of padding algorithm.
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* - `same` and stride 1: output will be of same size as input,
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* regardless of filter size.
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* - `valid`: output will be smaller than input if filter is larger
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* than 1x1.
|
* - For more info, see this guide:
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* [https://www.tensorflow.org/api_guides/python/nn#Convolution](
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* https://www.tensorflow.org/api_guides/python/nn#Convolution)
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* @param dilations The dilation rates: `[dilationHeight, dilationWidth]`
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* in which we sample input values across the height and width dimensions
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* in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single
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* number, then `dilationHeight == dilationWidth`. If it is greater than
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* 1, then all values of `strides` must be 1.
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* @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 The rounding mode used when computing output
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* dimensions if pad is a number. If none is provided, it will not round
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* and error if the output is of fractional size.
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*/
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/** @doc {heading: 'Operations', subheading: 'Convolution'} */
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function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
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if (dataFormat === void 0) { dataFormat = 'NHWC'; }
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if (dilations === void 0) { dilations = [1, 1]; }
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var $x = tensor_util_env_1.convertToTensor(x, 'x', 'depthwiseConv2d');
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var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'depthwiseConv2d');
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var x4D = $x;
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var reshapedTo4D = false;
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if ($x.rank === 3) {
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reshapedTo4D = true;
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x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
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}
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util.assert(x4D.rank === 4, function () { return "Error in depthwiseConv2d: input must be rank 4, but got " +
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("rank " + x4D.rank + "."); });
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util.assert($filter.rank === 4, function () { return "Error in depthwiseConv2d: filter must be rank 4, but got rank " +
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($filter.rank + "."); });
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util.assert(x4D.shape[3] === $filter.shape[2], function () { return "Error in depthwiseConv2d: number of input channels " +
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("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
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("filter " + $filter.shape[2] + "."); });
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if (dilations == null) {
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dilations = [1, 1];
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}
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util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), function () {
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return 'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +
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("Got strides " + strides + " and dilations '" + dilations + "'");
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});
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if (dimRoundingMode != null) {
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util.assert(util.isInt(pad), function () { return "Error in depthwiseConv2d: pad must be an integer when using, " +
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("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."); });
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}
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var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
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var grad = function (dy, saved) {
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util.assert(conv_util.tupleValuesAreOne(dilations), function () { return 'Error in gradient of depthwiseConv2d: dilation rates ' +
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"greater than 1 are not yet supported. Got dilations " +
|
("'" + dilations + "'"); });
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var x4D = saved[0], $filter = saved[1];
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return {
|
x: function () { return exports.depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo); },
|
filter: function () { return exports.depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo); },
|
};
|
};
|
var inputsToSave = [x4D, $filter];
|
var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.depthwiseConv2D(x4D, $filter, convInfo);
|
save([x4D, $filter]);
|
return res;
|
}, { x: x4D, filter: $filter }, grad, 'DepthwiseConv2dNative', convInfo, inputsToSave);
|
if (reshapedTo4D) {
|
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
|
}
|
return res;
|
}
|
/**
|
* 2-D convolution with separable filters.
|
*
|
* Performs a depthwise convolution that acts separately on channels followed
|
* by a pointwise convolution that mixes channels. Note that this is
|
* separability between dimensions [1, 2] and 3, not spatial separability
|
* between dimensions 1 and 2.
|
*
|
* See
|
* [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](
|
* https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)
|
* for more details.
|
*
|
* @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 depthwiseFilter The depthwise filter tensor, rank 4, of shape
|
* `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is
|
* the filter used in the first step.
|
* @param pointwiseFilter The pointwise filter tensor, rank 4, of shape
|
* `[1, 1, inChannels * channelMultiplier, outChannels]`. This is
|
* the filter used in the second step.
|
* @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_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/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.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
|
if (dilation === void 0) { dilation = [1, 1]; }
|
if (dataFormat === void 0) { dataFormat = 'NHWC'; }
|
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'separableConv2d');
|
var $depthwiseFilter = tensor_util_env_1.convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
|
var $pointwiseFilter = tensor_util_env_1.convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
|
var x4D = $x;
|
var reshapedTo4D = false;
|
if ($x.rank === 3) {
|
reshapedTo4D = true;
|
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
|
}
|
if (dataFormat === 'NCHW') {
|
throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +
|
'NHWC is supported');
|
}
|
util.assert(x4D.rank === 4, function () { return "Error in separableConv2d: input must be rank 4, but got " +
|
("rank " + x4D.rank + "."); });
|
util.assert($depthwiseFilter.rank === 4, function () { return "Error in separableConv2d: depthwise filter must be rank 4, but " +
|
("got rank " + $depthwiseFilter.rank + "."); });
|
util.assert($pointwiseFilter.rank === 4, function () { return "Error in separableConv2d: pointwise filter must be rank 4, but " +
|
("got rank " + $depthwiseFilter.rank + "."); });
|
util.assert($pointwiseFilter.shape[0] === 1, function () {
|
return "Error in separableConv2d: the first dimension of pointwise filter " +
|
(" must be 1, but got " + $pointwiseFilter.shape[0] + ".");
|
});
|
util.assert($pointwiseFilter.shape[1] === 1, function () { return "Error in separableConv2d: the second dimension of pointwise " +
|
("filter must be 1, but got " + $pointwiseFilter.shape[1] + "."); });
|
var inChannels = $depthwiseFilter.shape[2];
|
var channelMultiplier = $depthwiseFilter.shape[3];
|
util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, function () {
|
return "Error in separableConv2d: the third dimension of pointwise filter " +
|
("must be " + inChannels * channelMultiplier + ", ") +
|
("but got " + $pointwiseFilter.shape[2] + ".");
|
});
|
var depthwise = exports.depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
|
var pointwiseStride = 1;
|
var res = exports.conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
|
if (reshapedTo4D) {
|
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
|
}
|
return res;
|
}
|
function parseTupleParam(param) {
|
if (typeof param === 'number') {
|
return [param, param, param];
|
}
|
if (param.length === 2) {
|
return [param[0], param[1], 1];
|
}
|
return param;
|
}
|
function tupleValuesAreOne(param) {
|
var _a = parseTupleParam(param), dimA = _a[0], dimB = _a[1], dimC = _a[2];
|
return dimA === 1 && dimB === 1 && dimC === 1;
|
}
|
function eitherStridesOrDilationsAreOne(strides, dilations) {
|
return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
|
}
|
function depthwiseConv2dDerInput_(xShape, dy, filter, convInfo) {
|
var dy4D = dy;
|
var reshapedTo4D = false;
|
if (dy.rank === 3) {
|
reshapedTo4D = true;
|
dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
|
}
|
var res = engine_1.ENGINE.runKernelFunc(function (backend) { return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D });
|
if (reshapedTo4D) {
|
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
|
}
|
return res;
|
}
|
function depthwiseConv2dDerFilter_(x, dy, filterShape, convInfo) {
|
var x4D = x;
|
if (x.rank === 3) {
|
x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
|
}
|
var dy4D = dy;
|
if (dy4D.rank === 3) {
|
dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
|
}
|
return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
|
}
|
/**
|
* Computes a 3D convolution over the input x.
|
*
|
* @param x The input tensor, of rank 5 or rank 4, of shape
|
* `[batch, depth, height, width, channels]`. If rank 4,
|
* batch of 1 is assumed.
|
* @param filter The filter, rank 5, of shape
|
* `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.
|
* inChannels must match between input and filter.
|
* @param strides The strides of the convolution: `[strideDepth, 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_guides/python/nn#Convolution](
|
* https://www.tensorflow.org/api_guides/python/nn#Convolution)
|
* @param dataFormat: An optional string from: "NDHWC", "NCDHW". Defaults to
|
* "NDHWC". Specify the data format of the input and output data. With the
|
* default format "NDHWC", the data is stored in the order of: [batch,
|
* depth, height, width, channels]. Only "NDHWC" is currently supported.
|
* @param dilations The dilation rates: `[dilationDepth, dilationHeight,
|
* dilationWidth]` in which we sample input values across the height
|
* and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.
|
* If `dilations` is a single number, then
|
* `dilationDepth == dilationHeight == dilationWidth`. If it is greater
|
* than 1, then all values of `strides` must be 1.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
function conv3d_(x, filter, strides, pad, dataFormat, dilations) {
|
if (dataFormat === void 0) { dataFormat = 'NDHWC'; }
|
if (dilations === void 0) { dilations = [1, 1, 1]; }
|
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv3d');
|
var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv3d');
|
var x5D = $x;
|
var reshapedTo5D = false;
|
if ($x.rank === 4) {
|
reshapedTo5D = true;
|
x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);
|
}
|
util.assert(x5D.rank === 5, function () { return "Error in conv3d: input must be rank 5, but got rank " + x5D.rank + "."; });
|
util.assert($filter.rank === 5, function () { return "Error in conv3d: filter must be rank 5, but got rank " +
|
($filter.rank + "."); });
|
util.assert(x5D.shape[4] === $filter.shape[3], function () { return "Error in conv3d: depth of input (" + x5D.shape[4] + ") must match " +
|
("input depth for filter " + $filter.shape[3] + "."); });
|
util.assert(eitherStridesOrDilationsAreOne(strides, dilations), function () { return 'Error in conv3D: Either strides or dilations must be 1. ' +
|
("Got strides " + strides + " and dilations '" + dilations + "'"); });
|
util.assert(dataFormat === 'NDHWC', function () { return "Error in conv3d: got dataFormat of " + dataFormat + " but only NDHWC is currently supported."; });
|
var convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);
|
var grad = function (dy, saved) {
|
util.assert(tupleValuesAreOne(dilations), function () {
|
return 'Error in gradient of conv3D: dilation rates greater than 1 are ' +
|
("not yet supported in gradients. Got dilations '" + dilations + "'");
|
});
|
var x5D = saved[0], $filter = saved[1];
|
return {
|
x: function () { return conv3dDerInput_(x5D.shape, dy, $filter, strides, pad); },
|
$filter: function () { return conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad); }
|
};
|
};
|
var res = engine_1.ENGINE.runKernelFunc(function (backend, save) {
|
var res = backend.conv3d(x5D, $filter, convInfo);
|
save([x5D, $filter]);
|
return res;
|
}, { x: x5D, $filter: $filter }, grad);
|
if (reshapedTo5D) {
|
return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
|
}
|
return res;
|
}
|
/**
|
* Computes the derivative of the input of a 3D convolution.
|
*
|
* @param xShape The shape of the input: [batch, depth, height, width,
|
* in_channels]. If length of 4, batch of 1 is assumed.
|
* @param dy The derivative of the output, of rank 5 or rank 4 of shape
|
* `[batch, outDepth, outHeight, outWidth, in_channels]`.
|
* If rank 4, batch of 1 is assumed.
|
* @param filter The filter, rank 5, of shape
|
* `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.
|
* @param strides The strides of the convolution: `[strideDepth, strideHeight,
|
* strideWidth]`.
|
* @param pad The type of padding algorithm used:
|
* - `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.
|
*/
|
function conv3dDerInput_(xShape, dy, filter, strides, pad) {
|
util.assert(xShape.length === dy.rank, function () { return "Length of inShape " +
|
("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"); });
|
var xShape5D = xShape;
|
var dy5D = dy;
|
var reshapedTo5D = false;
|
if (dy.rank === 4) {
|
reshapedTo5D = true;
|
dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
|
xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
|
}
|
var inDepth = xShape5D[4];
|
var outDepth = dy5D.shape[4];
|
util.assert(xShape5D.length === 5, function () {
|
return "Error in conv3dDerInput: inShape must be length 5, but got length " +
|
(xShape5D.length + ".");
|
});
|
util.assert(dy5D.rank === 5, function () { return "Error in conv3dDerInput: dy must be rank 5, but got " +
|
("rank " + dy5D.rank); });
|
util.assert(filter.rank === 5, function () { return "Error in conv3dDerInput: filter must be rank 5, but got " +
|
("rank " + filter.rank); });
|
util.assert(inDepth === filter.shape[3], function () { return "Error in conv3dDerInput: depth of input (" + inDepth + ") must " +
|
("match input depth for filter " + filter.shape[3] + "."); });
|
util.assert(outDepth === filter.shape[4], function () { return "Error in conv3dDerInput: depth of output (" + outDepth + ") must " +
|
("match output depth for filter " + filter.shape[4] + "."); });
|
var dilations = 1;
|
var convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);
|
var res = engine_1.ENGINE.runKernelFunc(function (backend) { return backend.conv3dDerInput(dy5D, filter, convInfo); }, { dy5D: dy5D });
|
if (reshapedTo5D) {
|
return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
|
}
|
return res;
|
}
|
/**
|
* Computes the derivative of the filter of a 3D convolution.
|
*
|
* @param x The input tensor, of rank 5 or rank 4 of shape
|
* [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is
|
* assumed.
|
* @param dy The dy image, of rank 5 or rank 4, of shape
|
* [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is
|
* assumed.
|
* @param filterShape The shape of the filter, length 5,
|
* [filterDepth, filterHeight, filterWidth, inDepth, outDepth].
|
* @param strides The strides of the convolution: [strideDepth, strideHeight,
|
* strideWidth].
|
* @param pad A string from: 'same', 'valid'. The type of padding algorithm
|
* used in the forward prop of the op.
|
*/
|
function conv3dDerFilter_(x, dy, filterShape, strides, pad) {
|
var x5D = x;
|
if (x.rank === 4) {
|
x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);
|
}
|
var dy5D = dy;
|
if (dy5D.rank === 4) {
|
dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
|
}
|
util.assert(x5D.rank === 5, function () { return "Error in conv3dDerFilter: input must be rank 5, but got shape " +
|
(x5D.shape + "."); });
|
util.assert(dy5D.rank === 5, function () { return "Error in conv3dDerFilter: dy must be rank 5, but got shape " +
|
(dy5D.shape + "."); });
|
util.assert(filterShape.length === 5, function () { return "Error in conv3dDerFilter: filterShape must be length 5, but got " +
|
(filterShape + "."); });
|
util.assert(x5D.shape[4] === filterShape[3], function () { return "Error in conv3dDerFilter: depth of input " + x5D.shape[4] + ") must " +
|
("match input depth in filter (" + filterShape[3] + "."); });
|
util.assert(dy5D.shape[4] === filterShape[4], function () { return "Error in conv3dDerFilter: depth of dy (" + dy5D.shape[4] + ") must " +
|
("match output depth for filter (" + filterShape[4] + ")."); });
|
var dilations = 1;
|
var convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);
|
return engine_1.ENGINE.runKernelFunc(function (backend) { return backend.conv3dDerFilter(x5D, dy5D, convInfo); }, { x5D: x5D, dy5D: dy5D });
|
}
|
/**
|
* Computes the transposed 3D convolution of a volume, also known as a
|
* deconvolution.
|
*
|
* @param x The input image, of rank 5 or rank 4, of shape
|
* `[batch, depth, height, width, inDepth]`. If rank 4, batch of 1 is assumed.
|
* @param filter The filter, rank 4, of shape
|
* `[depth, filterHeight, filterWidth, outDepth, inDepth]`.
|
* `inDepth` must match `inDepth` in `x`.
|
* @param outputShape Output shape, of rank 5 or rank 4:
|
* `[batch, depth, height, width, outDepth]`. If rank 3, batch of 1 is
|
* assumed.
|
* @param strides The strides of the original convolution:
|
* `[strideDepth, strideHeight, strideWidth]`.
|
* @param pad The type of padding algorithm used in the non-transpose version
|
* of the op.
|
*/
|
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
|
function conv3dTranspose_(x, filter, outputShape, strides, pad) {
|
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv3dTranspose');
|
var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv3dTranspose');
|
return conv3dDerInput_(outputShape, $x, $filter, strides, pad);
|
}
|
exports.conv1d = operation_1.op({ conv1d_: conv1d_ });
|
exports.conv2d = operation_1.op({ conv2d_: conv2d_ });
|
exports.conv3d = operation_1.op({ conv3d_: conv3d_ });
|
exports.conv2dDerFilter = operation_1.op({ conv2dDerFilter_: conv2dDerFilter_ });
|
exports.conv2dDerInput = operation_1.op({ conv2dDerInput_: conv2dDerInput_ });
|
exports.depthwiseConv2d = operation_1.op({ depthwiseConv2d_: depthwiseConv2d_ });
|
exports.depthwiseConv2dDerInput = operation_1.op({ depthwiseConv2dDerInput_: depthwiseConv2dDerInput_ });
|
exports.depthwiseConv2dDerFilter = operation_1.op({ depthwiseConv2dDerFilter_: depthwiseConv2dDerFilter_ });
|
exports.separableConv2d = operation_1.op({ separableConv2d_: separableConv2d_ });
|
exports.conv2dTranspose = operation_1.op({ conv2dTranspose_: conv2dTranspose_ });
|
exports.conv3dTranspose = operation_1.op({ conv3dTranspose_: conv3dTranspose_ });
|
//# sourceMappingURL=conv.js.map
|
