@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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/**
* @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 {FusedDepthwiseConv2D, FusedDepthwiseConv2DAttrs, FusedDepthwiseConv2DInputs} from '../../kernel_names';
import {NamedAttrMap} from '../../kernel_registry';
import {Tensor, Tensor3D, Tensor4D} from '../../tensor';
import {GradSaveFunc, NamedTensorMap} from '../../tensor_types';
import {makeTypesMatch} from '../../tensor_util';
import {convertToTensor} from '../../tensor_util_env';
import {TensorLike} from '../../types';
import * as util from '../../util';
import {add} from '../add';
import * as broadcast_util from '../broadcast_util';
import * as conv_util from '../conv_util';
import {depthwiseConv2d as unfusedDepthwiseConv2d} from '../depthwise_conv2d';
import {depthwiseConv2dNativeBackpropFilter} from '../depthwise_conv2d_native_backprop_filter';
import {depthwiseConv2dNativeBackpropInput} from '../depthwise_conv2d_native_backprop_input';
import {Activation} from '../fused_types';
import {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';
import {op} from '../operation';
import {reshape} from '../reshape';
/**
* Computes depthwise 2D convolution, optionally fused with adding a
* bias and applying an activation.
*
* Given a 4D `input` array and a `filter` array of shape
* `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing
* `inChannels` convolutional filters of depth 1, this op applies a
* different filter to each input channel (expanding from 1 channel to
* `channelMultiplier` channels for each), then concatenates the results
* together. The output has `inChannels * channelMultiplier` channels.
*
* See
* [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](
* https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)
* for more details.
*
* @param obj An object with the following properties:
* @param x The input tensor, of rank 4 or rank 3, of shape
* `[batch, height, width, inChannels]`. If rank 3, batch of 1 is
* assumed.
* @param filter The filter tensor, rank 4, of shape
* `[filterHeight, filterWidth, inChannels, channelMultiplier]`.
* @param strides The strides of the convolution: `[strideHeight,
* strideWidth]`. If strides is a single number, then `strideHeight ==
* strideWidth`.
* @param pad The type of padding algorithm.
* - `same` and stride 1: output will be of same size as input,
* regardless of filter size.
* - `valid`: output will be smaller than input if filter is larger
* than 1x1.
* - For more info, see this guide:
* [https://www.tensorflow.org/api_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 A string from: 'ceil', 'round', 'floor'. If none is
* provided, it will default to truncate.
* @param bias Tensor to be added to the result.
* @param activation Name of activation kernel (defaults to `linear`).
* @param preluActivationWeights Tensor of prelu weights to be applied as part
* of a `prelu` activation, typically the same shape as `x`.
* @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
* activation.
*/
function fusedDepthwiseConv2d_<T extends Tensor3D|Tensor4D>({
x,
filter,
strides,
pad,
dataFormat = 'NHWC',
dilations = [1, 1],
dimRoundingMode,
bias,
activation = 'linear',
preluActivationWeights,
leakyreluAlpha
}: {
x: T|TensorLike,
filter: Tensor4D|TensorLike,
strides: [number, number]|number,
pad: 'valid'|'same'|number,
dataFormat?: 'NHWC'|'NCHW',
dilations?: [number, number]|number,
dimRoundingMode?: 'floor'|'round'|'ceil',
bias?: Tensor|TensorLike,
activation?: Activation,
preluActivationWeights?: Tensor,
leakyreluAlpha?: number
}): T {
if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
let result = unfusedDepthwiseConv2d(
x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
if (bias != null) {
result = add(result, bias);
}
return applyActivation(
result, activation, preluActivationWeights, leakyreluAlpha) as T;
}
const $x = convertToTensor(x, 'x', 'depthwiseConv2d');
const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');
let x4D = $x as Tensor4D;
let reshapedTo4D = false;
if ($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 depthwiseConv2d: input must be rank 4, but got ` +
`rank ${x4D.rank}.`);
util.assert(
$filter.rank === 4,
() => `Error in fused depthwiseConv2d: filter must be rank 4, ` +
`but got rank ${$filter.rank}.`);
util.assert(
x4D.shape[3] === $filter.shape[2],
() => `Error in fused depthwiseConv2d: number of input channels ` +
`(${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 fused depthwiseConv2d: Either strides or dilations must ' +
`be 1. Got strides ${strides} and dilations '${dilations}'`);
if (dimRoundingMode != null) {
util.assert(
util.isInt(pad as number),
() => `Error in fused depthwiseConv2d: pad must be an integer when ` +
`using dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);
}
const convInfo = conv_util.computeConv2DInfo(
x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,
true /* depthwise */);
let $bias: Tensor;
if (bias != null) {
$bias = convertToTensor(bias, 'bias', 'fused conv2d');
[$bias] = makeTypesMatch($bias, $x);
broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
}
let $preluActivationWeights: Tensor;
if (preluActivationWeights != null) {
$preluActivationWeights = convertToTensor(
preluActivationWeights, 'prelu weights', 'fused depthwiseConv2d');
}
const grad = (dy: Tensor4D, saved: Tensor[]) => {
util.assert(
conv_util.tupleValuesAreOne(dilations),
() => 'Error in gradient of fused depthwiseConv2d: dilation rates ' +
`greater than 1 are not yet supported. Got dilations ` +
`'${dilations}'`);
const [$filter, x4D, y, bias] = saved;
const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;
const xDer = depthwiseConv2dNativeBackpropInput(
(x4D as Tensor4D).shape, dyActivation, $filter as Tensor4D, strides,
pad, dilations, dimRoundingMode);
const filterDer = depthwiseConv2dNativeBackpropFilter(
x4D as Tensor4D, dyActivation, ($filter as Tensor4D).shape, strides,
pad, dilations, dimRoundingMode);
if (bias != null) {
const biasDer = getFusedBiasGradient($bias, dyActivation);
return [xDer, filterDer, biasDer];
}
return [xDer, filterDer];
};
const inputs: FusedDepthwiseConv2DInputs = {
x: x4D,
filter: $filter,
bias: $bias,
preluActivationWeights: $preluActivationWeights
};
const attrs: FusedDepthwiseConv2DAttrs = {
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: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {
// tslint:disable-next-line: no-unnecessary-type-assertion
let res: Tensor4D|Tensor3D = ENGINE.runKernel(
FusedDepthwiseConv2D, inputs as {} as NamedTensorMap,
attrs as {} as NamedAttrMap);
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]]) as
Tensor3D;
}
return {value: res, gradFunc: grad};
});
return customOp(x4D, $filter) as T;
} else {
const customOpWithBias = customGrad(
(x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {
// tslint:disable-next-line: no-unnecessary-type-assertion
let res: Tensor4D|Tensor3D = ENGINE.runKernel(
FusedDepthwiseConv2D, inputs as {} as NamedTensorMap,
attrs as {} as NamedAttrMap);
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]]) as
Tensor3D;
}
return {value: res, gradFunc: grad};
});
return customOpWithBias(x4D, $filter, $bias) as T;
}
}
export const depthwiseConv2d = op({fusedDepthwiseConv2d_});