@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @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.
* =============================================================================
*/
import { ENGINE } from '../../engine';
import { customGrad } from '../../gradients';
import { FusedConv2D } from '../../kernel_names';
import { makeTypesMatch } from '../../tensor_util';
import { convertToTensor } from '../../tensor_util_env';
import * as util from '../../util';
import { add } from '../add';
import * as broadcast_util from '../broadcast_util';
import { conv2d as unfusedConv2d } from '../conv2d';
import { conv2DBackpropFilter } from '../conv2d_backprop_filter';
import { conv2DBackpropInput } from '../conv2d_backprop_input';
import * as conv_util from '../conv_util';
import { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';
import { op } from '../operation';
import { reshape } from '../reshape';
/**
* Computes a 2D convolution over the input x, optionally fused with adding a
* bias and applying an activation.
*
* ```js
* const inputDepth = 2;
* const inShape = [2, 2, 2, inputDepth];
* const outputDepth = 2;
* const fSize = 1;
* const pad = 0;
* const strides = 1;
*
* const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
* 16], inShape);
* const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
* outputDepth]);
*
* tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
* dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
* ```
*
* @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, 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 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`) to be
* applied
* after biasAdd.
* @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.
*/
function fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {
activation = activation || 'linear';
if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
if (bias != null) {
result = add(result, bias);
}
return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
}
const $x = convertToTensor(x, 'x', 'conv2d');
const $filter = convertToTensor(filter, 'filter', 'conv2d');
let x4D = $x;
let reshapedTo4D = false;
if ($x.rank === 3) {
reshapedTo4D = true;
x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
}
util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +
`${x4D.rank}.`);
util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +
`${$filter.rank}.`);
if (dimRoundingMode != null) {
util.assert(util.isInt(pad), () => `Error in fused 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.`);
const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
let $bias;
if (bias != null) {
$bias = convertToTensor(bias, 'bias', 'fused conv2d');
[$bias] = makeTypesMatch($bias, $x);
broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
}
let $preluActivationWeights;
if (preluActivationWeights != null) {
$preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');
}
const grad = (dy, saved) => {
const [$filter, x4D, y, $bias] = saved;
const dyActivation = getFusedDyActivation(dy, y, activation);
util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +
`dilation rates greater than 1 ` +
`are not yet supported in gradients. Got dilations '${dilations}'`);
const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);
const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);
const der = [xDer, filterDer];
if ($bias != null) {
const biasDer = getFusedBiasGradient($bias, dyActivation);
der.push(biasDer);
}
return der;
};
const inputs = {
x: x4D,
filter: $filter,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs = {
strides,
pad,
dataFormat,
dilations,
dimRoundingMode,
activation,
leakyreluAlpha
};
// Depending on the the params passed in we will have different number of
// inputs and thus a a different number of elements in the gradient.
if (bias == null) {
const customOp = customGrad((x4D, filter, save) => {
let res =
// tslint:disable-next-line: no-unnecessary-type-assertion
ENGINE.runKernel(FusedConv2D, inputs, attrs);
save([filter, x4D, res]);
if (reshapedTo4D) {
// tslint:disable-next-line: no-unnecessary-type-assertion
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return { value: res, gradFunc: grad };
});
return customOp(x4D, $filter);
}
else {
const customOpWithBias = customGrad((x4D, filter, bias, save) => {
let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
save([filter, x4D, res, bias]);
if (reshapedTo4D) {
// tslint:disable-next-line: no-unnecessary-type-assertion
res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
}
return { value: res, gradFunc: grad };
});
return customOpWithBias(x4D, $filter, $bias);
}
}
export const conv2d = op({ fusedConv2d_ });
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