@tensorflow-models/coco-ssd
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Object detection model (coco-ssd) in TensorFlow.js
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JavaScript
"use strict";
/**
* @license
* Copyright 2018 Google Inc. 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.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
var environment_1 = require("../environment");
var tensor_util_env_1 = require("../tensor_util_env");
var util = require("../util");
var conv_util = require("./conv_util");
var operation_1 = require("./operation");
/**
* Computes a 1D convolution over the input x.
*
* @param x The input tensor, of rank 3 or rank 2, of shape
* `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.
* @param filter The filter, rank 3, of shape
* `[filterWidth, inDepth, outDepth]`.
* @param stride The number of entries by which the filter is moved right at
* each step.
* @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 "NWC", "NCW". Defaults to "NWC",
* the data is stored in the order of [batch, in_width, in_channels]. Only
* "NWC" is currently supported.
* @param dilation The dilation rate in which we sample input values in
* atrous convolution. Defaults to `1`. If it is greater than 1, then
* stride must be `1`.
* @param dimRoundingMode The rounding mode used when computing output
* dimensions if pad is a number. If none is provided, it will not round
* and error if the output is of fractional size.
*/
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
function conv1d_(x, filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
if (dataFormat === void 0) { dataFormat = 'NWC'; }
if (dilation === void 0) { dilation = 1; }
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv1d');
var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv1d');
var x3D = $x;
var reshapedTo3D = false;
if ($x.rank === 2) {
reshapedTo3D = true;
x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);
}
util.assert(x3D.rank === 3, "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + ".");
util.assert($filter.rank === 3, "Error in conv1d: filter must be rank 3, but got rank " +
($filter.rank + "."));
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), "Error in conv1d: pad must be an integer when using, " +
("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
}
util.assert(x3D.shape[2] === $filter.shape[1], "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " +
("input depth for filter " + $filter.shape[1] + "."));
util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), 'Error in conv1D: Either stride or dilation must be 1. ' +
("Got stride " + stride + " and dilation '" + dilation + "'"));
util.assert(dataFormat === 'NWC', "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported.");
var filter4D = $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);
var input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);
var strides = [1, stride];
var dilations = [1, dilation];
var conv2dDataFormat = 'NHWC';
var res = exports.conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
if (reshapedTo3D) {
return res.as2D(res.shape[2], res.shape[3]);
}
return res.as3D(res.shape[0], res.shape[2], res.shape[3]);
}
/**
* Computes a 2D convolution over the input x.
*
* @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, rank 4, of shape
* `[filterHeight, filterWidth, inDepth, outDepth]`.
* @param strides The strides of the convolution: `[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: "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 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 `dilations` is a single
* number, then `dilationHeight == dilationWidth`. If it is greater than
* 1, then all values of `strides` must be 1.
* @param dimRoundingMode The rounding mode used when computing output
* dimensions if pad is a number. If none is provided, it will not round
* and error if the output is of fractional size.
*/
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
if (dataFormat === void 0) { dataFormat = 'NHWC'; }
if (dilations === void 0) { dilations = [1, 1]; }
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2d');
var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2d');
var x4D = $x;
var reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
}
util.assert(x4D.rank === 4, "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + ".");
util.assert($filter.rank === 4, "Error in conv2d: filter must be rank 4, but got rank " +
($filter.rank + "."));
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), "Error in conv2d: pad must be an integer when using, " +
("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
}
util.assert(x4D.shape[3] === $filter.shape[2], "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " +
("input depth for filter " + $filter.shape[2] + "."));
util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in conv2D: Either strides or dilations must be 1. ' +
("Got strides " + strides + " and dilations '" + dilations + "'"));
util.assert(dataFormat === 'NHWC', "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported.");
var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
var grad = function (dy) {
util.assert(conv_util.tupleValuesAreOne(dilations), 'Error in gradient of conv2D: dilation rates greater than 1 are not' +
("yet supported in gradients. Got dilations '" + dilations + "'"));
return {
x: function () { return conv2dDerInput_(x4D.shape, dy, $filter, strides, pad); },
$filter: function () { return conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad); }
};
};
var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv2d(x4D, $filter, convInfo); }, { x: x4D, $filter: $filter }, grad);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
}
/**
* Computes the derivative of the input of a 2D convolution.
*
* @param xShape The shape of the input: [batch, height, width, inDepth].
* If length of 3, batch of 1 is assumed.
* @param dy The derivative of the output, of rank 4 or rank 3 of shape
* `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is
* assumed.
* @param filter The filter, rank 4, of shape
* `[filterHeight, filterWidth, inDepth, outDepth]`.
* @param strides The strides of the convolution: `[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.
* @param dimRoundingMode The rounding mode used when computing output
* dimensions if pad is a number. If none is provided, it will not round
* and error if the output is of fractional size.
*/
function conv2dDerInput_(xShape, dy, filter, strides, pad, dimRoundingMode) {
util.assert(xShape.length === dy.rank, "Length of inShape " +
("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"));
var xShape4D = xShape;
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]);
xShape4D = [1, xShape[0], xShape[1], xShape[2]];
}
var inDepth = xShape4D[3];
var outDepth = dy4D.shape[3];
util.assert(xShape4D.length === 4, "Error in conv2dDerInput: inShape must be length 4, but got length " +
(xShape4D.length + "."));
util.assert(dy4D.rank === 4, "Error in conv2dDerInput: dy must be rank 4, but got " +
("rank " + dy4D.rank));
util.assert(filter.rank === 4, "Error in conv2dDerInput: filter must be rank 4, but got " +
("rank " + filter.rank));
util.assert(inDepth === filter.shape[2], "Error in conv2dDerInput: depth of input (" + inDepth + ") must " +
("match input depth for filter " + filter.shape[2] + "."));
util.assert(outDepth === filter.shape[3], "Error in conv2dDerInput: depth of output (" + outDepth + ") must " +
("match output depth for filter " + filter.shape[3] + "."));
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), "Error in conv2dDerInput: pad must be an integer when using, " +
("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
}
var dilations = 1;
var grad = function (ddx) {
var dataFormat = 'NHWC';
return {
dy4D: function () { return exports.conv2d(ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode); },
filter: function () { return exports.conv2dDerFilter(ddx, dy4D, filter.shape, strides, pad, dimRoundingMode); }
};
};
var convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);
var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv2dDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D, filter: filter }, grad);
if (reshapedTo4D) {
return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
}
return res;
}
/**
* Computes the derivative of the filter of a 2D convolution.
*
* @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 dy The dy image, of rank 4 or rank 3, of shape
* [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.
* @param filterShape The shape of the filter, length 4,
* [filterHeight, filterWidth, inDepth, outDepth].
* @param strides The strides of the convolution: [strideHeight,
* strideWidth].
* @param pad A string from: 'same', 'valid'. The type of padding algorithm
* used in the forward prop of the op.
* @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The
* rounding mode used when computing output dimensions if pad is a
* number. If none is provided, it will not round and error if the output
* is of fractional size.
*/
function conv2dDerFilter_(x, dy, filterShape, strides, pad, dimRoundingMode) {
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]);
}
util.assert(x4D.rank === 4, "Error in conv2dDerFilter: input must be rank 4, but got shape " +
(x4D.shape + "."));
util.assert(dy4D.rank === 4, "Error in conv2dDerFilter: dy must be rank 4, but got shape " +
(dy4D.shape + "."));
util.assert(filterShape.length === 4, "Error in conv2dDerFilter: filterShape must be length 4, but got " +
(filterShape + "."));
util.assert(x4D.shape[3] === filterShape[2], "Error in conv2dDerFilter: depth of input " + x4D.shape[3] + ") must " +
("match input depth in filter (" + filterShape[2] + "."));
util.assert(dy4D.shape[3] === filterShape[3], "Error in conv2dDerFilter: depth of dy (" + dy4D.shape[3] + ") must " +
("match output depth for filter (" + filterShape[3] + ")."));
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), "Error in conv2dDerFilter: pad must be an integer when using, " +
("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
}
var dilations = 1;
var convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);
return environment_1.ENV.engine.runKernel(function (backend) { return backend.conv2dDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
}
/**
* Computes the transposed 2D convolution of an image, also known as a
* deconvolution.
*
* @param x The input image, of rank 4 or rank 3, of shape
* `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.
* @param filter The filter, rank 4, of shape
* `[filterHeight, filterWidth, outDepth, inDepth]`.
* `inDepth` must match `inDepth` in `x`.
* @param outputShape Output shape, of rank 4 or rank 3:
* `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.
* @param strides The strides of the original convolution:
* `[strideHeight, strideWidth]`.
* @param pad The type of padding algorithm used in the non-transpose version
* of the op.
* @param dimRoundingMode The rounding mode used when computing output
* dimensions if pad is a number. If none is provided, it will not round
* and error if the output is of fractional size.
*/
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2dTranspose');
var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2dTranspose');
return conv2dDerInput_(outputShape, $x, $filter, strides, pad, dimRoundingMode);
}
/**
* Depthwise 2D convolution.
*
* 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 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_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.
* @param dimRoundingMode The rounding mode used when computing output
* dimensions if pad is a number. If none is provided, it will not round
* and error if the output is of fractional size.
*/
/** @doc {heading: 'Operations', subheading: 'Convolution'} */
function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
if (dataFormat === void 0) { dataFormat = 'NHWC'; }
if (dilations === void 0) { dilations = [1, 1]; }
var $x = tensor_util_env_1.convertToTensor(x, 'x', 'depthwiseConv2d');
var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'depthwiseConv2d');
var x4D = $x;
var reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
}
util.assert(x4D.rank === 4, "Error in depthwiseConv2d: input must be rank 4, but got " +
("rank " + x4D.rank + "."));
util.assert($filter.rank === 4, "Error in depthwiseConv2d: filter must be rank 4, but got rank " +
($filter.rank + "."));
util.assert(x4D.shape[3] === $filter.shape[2], "Error in depthwiseConv2d: number of input channels " +
("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
("filter " + $filter.shape[2] + "."));
if (dilations == null) {
dilations = [1, 1];
}
util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in 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 + "."));
}
var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true /* depthwise */);
var grad = function (dy) {
util.assert(conv_util.tupleValuesAreOne(dilations), 'Error in gradient of depthwiseConv2d: dilation rates greater than ' +
("1 are not yet supported. Got dilations '" + dilations + "'"));
return {
x: function () { return depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo); },
$filter: function () { return depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo); },
};
};
var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2D(x4D, $filter, convInfo); }, { x: x4D, $filter: $filter }, grad);
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], $x.shape[2]);
}
if (dataFormat === 'NCHW') {
throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +
'NHWC is supported');
}
util.assert(x4D.rank === 4, "Error in separableConv2d: input must be rank 4, but got " +
("rank " + x4D.rank + "."));
util.assert($depthwiseFilter.rank === 4, "Error in separableConv2d: depthwise filter must be rank 4, but got " +
("rank " + $depthwiseFilter.rank + "."));
util.assert($pointwiseFilter.rank === 4, "Error in separableConv2d: pointwise filter must be rank 4, but got " +
("rank " + $depthwiseFilter.rank + "."));
util.assert($pointwiseFilter.shape[0] === 1, "Error in separableConv2d: the first dimension of pointwise filter " +
(" must be 1, but got " + $pointwiseFilter.shape[0] + "."));
util.assert($pointwiseFilter.shape[1] === 1, "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, "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 = environment_1.ENV.engine.runKernel(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 environment_1.ENV.engine.runKernel(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: "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,
* depth, height, width, channels]. Only "NHWC" 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 = 'NHWC'; }
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], $x.shape[3]);
}
util.assert(x5D.rank === 5, "Error in conv3d: input must be rank 5, but got rank " + x5D.rank + ".");
util.assert($filter.rank === 5, "Error in conv3d: filter must be rank 5, but got rank " +
($filter.rank + "."));
util.assert(x5D.shape[4] === $filter.shape[3], "Error in conv3d: depth of input (" + x5D.shape[4] + ") must match " +
("input depth for filter " + $filter.shape[3] + "."));
util.assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in conv3D: Either strides or dilations must be 1. ' +
("Got strides " + strides + " and dilations '" + dilations + "'"));
util.assert(dataFormat === 'NHWC', "Error in conv3d: got dataFormat of " + dataFormat + " but only NHWC is currently supported.");
var convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);
var grad = function (dy) {
util.assert(tupleValuesAreOne(dilations), 'Error in gradient of conv3D: dilation rates greater than 1 are not' +
("yet supported in gradients. Got dilations '" + dilations + "'"));
return {
x: function () { return conv3dDerInput_(x5D.shape, dy, $filter, strides, pad); },
$filter: function () { return conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad); }
};
};
var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv3d(x5D, $filter, convInfo); }, { 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, "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, "Error in conv3dDerInput: inShape must be length 5, but got length " +
(xShape5D.length + "."));
util.assert(dy5D.rank === 5, "Error in conv3dDerInput: dy must be rank 5, but got " +
("rank " + dy5D.rank));
util.assert(filter.rank === 5, "Error in conv3dDerInput: filter must be rank 5, but got " +
("rank " + filter.rank));
util.assert(inDepth === filter.shape[3], "Error in conv3dDerInput: depth of input (" + inDepth + ") must " +
("match input depth for filter " + filter.shape[3] + "."));
util.assert(outDepth === filter.shape[4], "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 = environment_1.ENV.engine.runKernel(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, "Error in conv3dDerFilter: input must be rank 5, but got shape " +
(x5D.shape + "."));
util.assert(dy5D.rank === 5, "Error in conv3dDerFilter: dy must be rank 5, but got shape " +
(dy5D.shape + "."));
util.assert(filterShape.length === 5, "Error in conv3dDerFilter: filterShape must be length 5, but got " +
(filterShape + "."));
util.assert(x5D.shape[4] === filterShape[3], "Error in conv3dDerFilter: depth of input " + x5D.shape[4] + ") must " +
("match input depth in filter (" + filterShape[3] + "."));
util.assert(dy5D.shape[4] === filterShape[4], "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 environment_1.ENV.engine.runKernel(function (backend) { return backend.conv3dDerFilter(x5D, dy5D, convInfo); }, { x5D: x5D, dy5D: dy5D });
}
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.depthwiseConv2d = operation_1.op({ depthwiseConv2d_: depthwiseConv2d_ });
exports.separableConv2d = operation_1.op({ separableConv2d_: separableConv2d_ });
exports.conv2dTranspose = operation_1.op({ conv2dTranspose_: conv2dTranspose_ });
//# sourceMappingURL=conv.js.map