/**
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* @license
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* Copyright 2019 Google LLC. 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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import { ENGINE } from '../../engine';
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import { customGrad } from '../../gradients';
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import { FusedConv2D } from '../../kernel_names';
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import { makeTypesMatch } from '../../tensor_util';
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import { convertToTensor } from '../../tensor_util_env';
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import * as util from '../../util';
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import { add } from '../add';
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import * as broadcast_util from '../broadcast_util';
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import { conv2d as unfusedConv2d } from '../conv2d';
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import { conv2DBackpropFilter } from '../conv2d_backprop_filter';
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import { conv2DBackpropInput } from '../conv2d_backprop_input';
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import * as conv_util from '../conv_util';
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import { applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse } from '../fused_util';
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import { op } from '../operation';
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import { reshape } from '../reshape';
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/**
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* Computes a 2D convolution over the input x, optionally fused with adding a
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* bias and applying an activation.
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*
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* ```js
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* const inputDepth = 2;
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* const inShape = [2, 2, 2, inputDepth];
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* const outputDepth = 2;
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* const fSize = 1;
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* const pad = 0;
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* const strides = 1;
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*
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* const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
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* 16], inShape);
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* const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,
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* outputDepth]);
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*
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* tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',
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* dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();
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* ```
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*
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* @param obj An object with the following properties:
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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_docs/python/tf/nn/convolution](
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* https://www.tensorflow.org/api_docs/python/tf/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]. Only "NHWC" is currently supported.
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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 A string from: 'ceil', 'round', 'floor'. If none is
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* provided, it will default to truncate.
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* @param bias Tensor to be added to the result.
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* @param activation Name of activation kernel (defaults to `linear`) to be
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* applied
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* after biasAdd.
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* @param preluActivationWeights Tensor of prelu weights to be applied as part
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* of a `prelu` activation, typically the same shape as `x`.
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* @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`
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* activation.
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*/
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function fusedConv2d_({ x, filter, strides, pad, dataFormat = 'NHWC', dilations = [1, 1], dimRoundingMode, bias, activation = 'linear', preluActivationWeights, leakyreluAlpha }) {
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activation = activation || 'linear';
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if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {
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// TODO: Transpose bias and preluActivationWeights properly for NCHW
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// format before computation.
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util.assert(dataFormat === 'NHWC', () => `Error in fused conv2d: got dataFormat of ${dataFormat} but ` +
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`only NHWC is currently supported for the case of gradient depth ` +
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`is 0 and the activation is not linear.`);
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let result = unfusedConv2d(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
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if (bias != null) {
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result = add(result, bias);
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}
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return applyActivation(result, activation, preluActivationWeights, leakyreluAlpha);
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}
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const $x = convertToTensor(x, 'x', 'conv2d', 'float32');
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const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');
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let x4D = $x;
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let reshapedTo4D = false;
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if ($x.rank === 3) {
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reshapedTo4D = true;
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x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);
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}
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util.assert(x4D.rank === 4, () => `Error in fused conv2d: input must be rank 4, but got rank ` +
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`${x4D.rank}.`);
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util.assert($filter.rank === 4, () => `Error in fused conv2d: filter must be rank 4, but got rank ` +
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`${$filter.rank}.`);
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conv_util.checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);
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const inputChannels = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];
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util.assert($filter.shape[2] === inputChannels, () => `Error in conv2d: depth of input (${inputChannels}) must match ` +
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`input depth for filter ${$filter.shape[2]}.`);
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util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), () => 'Error in conv2D: Either strides or dilations must be 1. ' +
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`Got strides ${strides} and dilations '${dilations}'`);
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const convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
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let $bias;
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if (bias != null) {
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$bias = convertToTensor(bias, 'bias', 'fused conv2d');
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[$bias] = makeTypesMatch($bias, $x);
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// According to TensorFlow, the bias is supposed be a 1-D tensor or a
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// scalar.
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//
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// 3-D or 4-D bias is not disabled for NHWC format, because they are
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// currently being used in some cases. For examplem in our code base,
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// https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/fused_conv2d_test.ts#L1972.
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if (dataFormat === 'NHWC') {
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broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);
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}
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else {
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util.assert($bias.shape.length <= 1, () => `Error in fused conv2d: only supports scalar or 1-D Tensor ` +
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`bias for NCHW format but got the bias of ` +
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`rank-${$bias.shape.length}.`);
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util.assert($bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels ||
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$bias.shape[0] === 1, () => `Error in fused conv2d: bias shape (${$bias.shape}) is not ` +
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`compatible with the number of output channels ` +
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`(${convInfo.outChannels})`);
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}
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}
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let $preluActivationWeights;
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if (preluActivationWeights != null) {
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// PReLU's activation weights could be a scalar, a 1-D tensor or a 3-D
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// tensor.
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const alphaShape = preluActivationWeights.shape;
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util.assert(alphaShape.length <= 1 || alphaShape.length === 3, () => `Error in fused conv2d: only supports scalar, 1-D Tensor or ` +
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`3-D Tensor PReLU activation weights but got a tensor of ` +
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`rank-${alphaShape.length}.`);
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if (alphaShape.length === 1) {
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// Whether the data format is NCHW or NHWC, the 1-D PReLU activation
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// weights tensor should be aligned with the output channels of conv2d
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// result.
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util.assert(alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels, () => `Error in fused conv2d: PReLU activation weights ` +
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`(${alphaShape}) is not compatible with the number of output ` +
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`channels (${convInfo.outChannels}).`);
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}
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else if (alphaShape.length === 3) {
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// Whether the data format is NCHW or NHWC, the PReLU activation weights
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// tensor should has the compatible shape with the result of conv2d.
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try {
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broadcast_util.assertAndGetBroadcastShape(alphaShape, convInfo.outShape);
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}
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catch (e) {
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const errMsg = `Error in fused conv2d: PReLU activation weights (${alphaShape}) ` +
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`is not compatible with the output shape of the conv2d ` +
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`(${convInfo.outShape}).`;
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throw Error(errMsg);
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}
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}
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$preluActivationWeights = convertToTensor(preluActivationWeights, 'prelu weights', 'fused conv2d');
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}
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const grad = (dy, saved) => {
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util.assert(dataFormat === 'NHWC', () => `Error in gradient of fused conv2D: got dataFormat of ${dataFormat} but only NHWC is currently supported.`);
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const [$filter, x4D, y, $bias] = saved;
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const dyActivation = getFusedDyActivation(dy, y, activation);
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util.assert(conv_util.tupleValuesAreOne(dilations), () => 'Error in gradient of fused conv2D: ' +
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`dilation rates greater than 1 ` +
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`are not yet supported in gradients. Got dilations '${dilations}'`);
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const xDer = conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);
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const filterDer = conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);
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const der = [xDer, filterDer];
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if ($bias != null) {
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const biasDer = getFusedBiasGradient($bias, dyActivation);
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der.push(biasDer);
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}
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return der;
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};
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const inputs = {
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x: x4D,
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filter: $filter,
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bias: $bias,
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preluActivationWeights: $preluActivationWeights
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};
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const attrs = {
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strides,
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pad,
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dataFormat,
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dilations,
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dimRoundingMode,
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activation,
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leakyreluAlpha
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};
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// Depending on the the params passed in we will have different number of
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// inputs and thus a a different number of elements in the gradient.
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if (bias == null) {
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const customOp = customGrad((x4D, filter, save) => {
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let res =
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// tslint:disable-next-line: no-unnecessary-type-assertion
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ENGINE.runKernel(FusedConv2D, inputs, attrs);
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save([filter, x4D, res]);
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if (reshapedTo4D) {
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// tslint:disable-next-line: no-unnecessary-type-assertion
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res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
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}
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return { value: res, gradFunc: grad };
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});
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return customOp(x4D, $filter);
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}
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else {
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const customOpWithBias = customGrad((x4D, filter, bias, save) => {
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let res = ENGINE.runKernel(FusedConv2D, inputs, attrs);
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save([filter, x4D, res, bias]);
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if (reshapedTo4D) {
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// tslint:disable-next-line: no-unnecessary-type-assertion
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res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]);
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}
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return { value: res, gradFunc: grad };
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});
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return customOpWithBias(x4D, $filter, $bias);
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}
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}
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export const conv2d = /* @__PURE__ */ op({ fusedConv2d_ });
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* @license\n * Copyright 2019 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENGINE} from '../../engine';\nimport {customGrad} from '../../gradients';\nimport {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs} from '../../kernel_names';\nimport {NamedAttrMap} from '../../kernel_registry';\nimport {Tensor, Tensor3D, Tensor4D} from '../../tensor';\nimport {GradSaveFunc, NamedTensorMap} from '../../tensor_types';\nimport {makeTypesMatch} from '../../tensor_util';\nimport {convertToTensor} from '../../tensor_util_env';\nimport {TensorLike} from '../../types';\nimport * as util from '../../util';\nimport {add} from '../add';\nimport * as broadcast_util from '../broadcast_util';\nimport {conv2d as unfusedConv2d} from '../conv2d';\nimport {conv2DBackpropFilter} from '../conv2d_backprop_filter';\nimport {conv2DBackpropInput} from '../conv2d_backprop_input';\nimport * as conv_util from '../conv_util';\nimport {Activation} from '../fused_types';\nimport {applyActivation, getFusedBiasGradient, getFusedDyActivation, shouldFuse} from '../fused_util';\nimport {op} from '../operation';\nimport {reshape} from '../reshape';\n\n/**\n * Computes a 2D convolution over the input x, optionally fused with adding a\n * bias and applying an activation.\n *\n * ```js\n * const inputDepth = 2;\n * const inShape = [2, 2, 2, inputDepth];\n * const outputDepth = 2;\n * const fSize = 1;\n * const pad = 0;\n * const strides = 1;\n *\n * const x = tf.tensor4d( [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,\n * 16], inShape);\n * const w = tf.tensor4d([-1, 1, -2, 0.5], [fSize, fSize, inputDepth,\n * outputDepth]);\n *\n * tf.fused.conv2d({ x, filter: w, strides, pad, dataFormat: 'NHWC',\n * dilations: [1, 1], bias: tf.scalar(5), activation: 'relu' }).print();\n * ```\n *\n * @param obj An object with the following properties:\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid` output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_docs/python/tf/nn/convolution](\n *          https://www.tensorflow.org/api_docs/python/tf/nn/convolution)\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. If none is\n *     provided, it will default to truncate.\n * @param bias Tensor to be added to the result.\n * @param activation Name of activation kernel (defaults to `linear`) to be\n *     applied\n *      after biasAdd.\n * @param preluActivationWeights Tensor of prelu weights to be applied as part\n *     of a `prelu` activation, typically the same shape as `x`.\n * @param leakyreluAlpha Optional. Alpha to be applied as part of a `leakyrelu`\n *     activation.\n */\nfunction fusedConv2d_<T extends Tensor3D|Tensor4D>({\n  x,\n  filter,\n  strides,\n  pad,\n  dataFormat = 'NHWC',\n  dilations = [1, 1],\n  dimRoundingMode,\n  bias,\n  activation = 'linear',\n  preluActivationWeights,\n  leakyreluAlpha\n}: {\n  x: T|TensorLike,\n  filter: Tensor4D|TensorLike,\n  strides: [number, number]|number,\n  pad: 'valid'|'same'|number|conv_util.ExplicitPadding,\n  dataFormat?: 'NHWC'|'NCHW',\n  dilations?: [number, number]|number,\n  dimRoundingMode?: 'floor'|'round'|'ceil',\n  bias?: Tensor|TensorLike,\n  activation?: Activation,\n  preluActivationWeights?: Tensor,\n  leakyreluAlpha?: number\n}): T {\n  activation = activation || 'linear';\n\n  if (shouldFuse(ENGINE.state.gradientDepth, activation) === false) {\n    // TODO: Transpose bias and preluActivationWeights properly for NCHW\n    // format before computation.\n    util.assert(\n        dataFormat === 'NHWC',\n        () => `Error in fused conv2d: got dataFormat of ${dataFormat} but ` +\n            `only NHWC is currently supported for the case of gradient depth ` +\n            `is 0 and the activation is not linear.`);\n\n    let result = unfusedConv2d(\n        x, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n    if (bias != null) {\n      result = add(result, bias);\n    }\n\n    return applyActivation(\n               result, activation, preluActivationWeights, leakyreluAlpha) as T;\n  }\n\n  const $x = convertToTensor(x, 'x', 'conv2d', 'float32');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d', 'float32');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = reshape($x, [1, $x.shape[0], $x.shape[1], $x.shape[2]]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      () => `Error in fused conv2d: input must be rank 4, but got rank ` +\n          `${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      () => `Error in fused conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  conv_util.checkPadOnDimRoundingMode('fused conv2d', pad, dimRoundingMode);\n  const inputChannels = dataFormat === 'NHWC' ? x4D.shape[3] : x4D.shape[1];\n  util.assert(\n      $filter.shape[2] === inputChannels,\n      () => `Error in conv2d: depth of input (${inputChannels}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      () => 'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let $bias: Tensor;\n  if (bias != null) {\n    $bias = convertToTensor(bias, 'bias', 'fused conv2d');\n    [$bias] = makeTypesMatch($bias, $x);\n\n    // According to TensorFlow, the bias is supposed be a 1-D tensor or a\n    // scalar.\n    //\n    // 3-D or 4-D bias is not disabled for NHWC format, because they are\n    // currently being used in some cases. For examplem in our code base,\n    // https://github.com/tensorflow/tfjs/blob/b53bd47e880367ae57493f0ea628abaf08db2d5d/tfjs-core/src/ops/fused/fused_conv2d_test.ts#L1972.\n    if (dataFormat === 'NHWC') {\n      broadcast_util.assertAndGetBroadcastShape(convInfo.outShape, $bias.shape);\n    } else {\n      util.assert(\n          $bias.shape.length <= 1,\n          () => `Error in fused conv2d: only supports scalar or 1-D Tensor ` +\n              `bias for NCHW format but got the bias of ` +\n              `rank-${$bias.shape.length}.`);\n\n      util.assert(\n          $bias.shape.length === 0 || $bias.shape[0] === convInfo.outChannels ||\n              $bias.shape[0] === 1,\n          () => `Error in fused conv2d: bias shape (${$bias.shape}) is not ` +\n              `compatible with the number of output channels ` +\n              `(${convInfo.outChannels})`);\n    }\n  }\n\n  let $preluActivationWeights: Tensor;\n  if (preluActivationWeights != null) {\n    // PReLU's activation weights could be a scalar, a 1-D tensor or a 3-D\n    // tensor.\n    const alphaShape = preluActivationWeights.shape;\n    util.assert(\n        alphaShape.length <= 1 || alphaShape.length === 3,\n        () => `Error in fused conv2d: only supports scalar, 1-D Tensor or ` +\n            `3-D Tensor PReLU activation weights but got a tensor of ` +\n            `rank-${alphaShape.length}.`);\n\n    if (alphaShape.length === 1) {\n      // Whether the data format is NCHW or NHWC, the 1-D PReLU activation\n      // weights tensor should be aligned with the output channels of conv2d\n      // result.\n      util.assert(\n          alphaShape[0] === 1 || alphaShape[0] === convInfo.outChannels,\n          () => `Error in fused conv2d: PReLU activation weights ` +\n              `(${alphaShape}) is not compatible with the number of output ` +\n              `channels (${convInfo.outChannels}).`);\n    } else if (alphaShape.length === 3) {\n      // Whether the data format is NCHW or NHWC, the PReLU activation weights\n      // tensor should has the compatible shape with the result of conv2d.\n      try {\n        broadcast_util.assertAndGetBroadcastShape(\n            alphaShape, convInfo.outShape);\n      } catch (e) {\n        const errMsg =\n            `Error in fused conv2d: PReLU activation weights (${alphaShape}) ` +\n            `is not compatible with the output shape of the conv2d ` +\n            `(${convInfo.outShape}).`;\n        throw Error(errMsg);\n      }\n    }\n\n    $preluActivationWeights = convertToTensor(\n        preluActivationWeights, 'prelu weights', 'fused conv2d');\n  }\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    util.assert(\n        dataFormat === 'NHWC',\n        () => `Error in gradient of fused conv2D: got dataFormat of ${\n            dataFormat} but only NHWC is currently supported.`);\n\n    const [$filter, x4D, y, $bias] =\n        saved as [Tensor4D, Tensor4D, Tensor4D, Tensor];\n\n    const dyActivation = getFusedDyActivation(dy, y, activation) as Tensor4D;\n\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        () => 'Error in gradient of fused conv2D: ' +\n            `dilation rates greater than 1 ` +\n            `are not yet supported in gradients. Got dilations '${dilations}'`);\n\n    const xDer =\n        conv2DBackpropInput(x4D.shape, dyActivation, $filter, strides, pad);\n    const filterDer =\n        conv2DBackpropFilter(x4D, dyActivation, $filter.shape, strides, pad);\n    const der: Tensor[] = [xDer, filterDer];\n\n    if ($bias != null) {\n      const biasDer = getFusedBiasGradient($bias, dyActivation);\n      der.push(biasDer);\n    }\n    return der;\n  };\n\n  const inputs: FusedConv2DInputs = {\n    x: x4D,\n    filter: $filter,\n    bias: $bias,\n    preluActivationWeights: $preluActivationWeights\n  };\n\n  const attrs: FusedConv2DAttrs = {\n    strides,\n    pad,\n    dataFormat,\n    dilations,\n    dimRoundingMode,\n    activation,\n    leakyreluAlpha\n  };\n\n  // Depending on the the params passed in we will have different number of\n  // inputs and thus a a different number of elements in the gradient.\n  if (bias == null) {\n    const customOp =\n        customGrad((x4D: Tensor4D, filter: Tensor4D, save: GradSaveFunc) => {\n          let res: Tensor4D|Tensor3D =\n              // tslint:disable-next-line: no-unnecessary-type-assertion\n              ENGINE.runKernel(\n                  FusedConv2D, inputs as unknown as NamedTensorMap,\n                  attrs as unknown as NamedAttrMap);\n\n          save([filter, x4D, res]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n    return customOp(x4D, $filter) as T;\n  } else {\n    const customOpWithBias = customGrad(\n        (x4D: Tensor4D, filter: Tensor4D, bias: Tensor, save: GradSaveFunc) => {\n          let res: Tensor4D|Tensor3D = ENGINE.runKernel(\n              FusedConv2D, inputs as unknown as NamedTensorMap,\n              attrs as unknown as NamedAttrMap);\n\n          save([filter, x4D, res, bias]);\n\n          if (reshapedTo4D) {\n            // tslint:disable-next-line: no-unnecessary-type-assertion\n            res = reshape(res, [res.shape[1], res.shape[2], res.shape[3]]) as\n                Tensor3D;\n          }\n\n          return {value: res, gradFunc: grad};\n        });\n\n    return customOpWithBias(x4D, $filter, $bias) as T;\n  }\n}\nexport const conv2d = /* @__PURE__ */ op({fusedConv2d_});\n"]}
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