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FusedConv2D.ts
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FusedConv2D.ts
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/**
* @license
* Copyright 2020 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 {FusedConv2D, FusedConv2DAttrs, FusedConv2DInputs, KernelConfig, KernelFunc, TensorInfo} from '@tensorflow/tfjs-core';
import {MathBackendCPU} from '../backend_cpu';
import {applyActivation} from '../utils/fused_utils';
import {add} from './Add';
import {conv2D} from './Conv2D';
import {reshape} from './Reshape';
export function fusedConv2D(args: {
inputs: FusedConv2DInputs,
backend: MathBackendCPU,
attrs: FusedConv2DAttrs
}): TensorInfo {
const {inputs, backend, attrs} = args;
const {x, filter, bias, preluActivationWeights} = inputs;
const {
strides,
pad,
dataFormat,
dilations,
dimRoundingMode,
activation,
leakyreluAlpha
} = attrs;
let result = conv2D({
inputs: {x, filter},
backend,
attrs: {strides, pad, dataFormat, dilations, dimRoundingMode}
});
if (bias) {
const resultOld = result;
// For NCHW format, if bias is a 1-D tensor, it is supposed to be aligned
// to the channel of the conv2d's result; if the bias is a scalar, the
// bias_add is computed as if the bias was broadcasted to the shape of the
// conv2d's result.
if (dataFormat === 'NCHW' && bias.shape.length === 1 &&
bias.shape[0] !== 1) {
const reshapedBias = reshape(
{inputs: {x: bias}, backend, attrs: {shape: [bias.shape[0], 1, 1]}});
result =
add({inputs: {a: result, b: reshapedBias}, backend}) as TensorInfo;
backend.disposeIntermediateTensorInfo(reshapedBias);
} else {
// This condition handles NHWC and NCHW (scalar case). The only other case
// for NCHW (1D case) is handled above.
result = add({inputs: {a: result, b: bias}, backend}) as TensorInfo;
}
backend.disposeIntermediateTensorInfo(resultOld);
}
if (activation) {
const resultOld = result;
// For NCHW format, if PReLu activation weights is a 1-D tensor, it is
// supposed to be aligned with the channel of the conv2d's result. For other
// cases, whether NCHW or NHWC data format, the conv2d result is
// already aligned with the activation weights.
if (dataFormat === 'NCHW' && activation === 'prelu' &&
preluActivationWeights.shape.length === 1 &&
preluActivationWeights.shape[0] !== 1) {
const reshapedAlpha = reshape({
inputs: {x: preluActivationWeights},
backend,
attrs: {shape: [preluActivationWeights.shape[0], 1, 1]}
});
result = applyActivation(
backend, result, activation, reshapedAlpha, leakyreluAlpha);
backend.disposeIntermediateTensorInfo(reshapedAlpha);
} else {
result = applyActivation(
backend, result, activation, preluActivationWeights, leakyreluAlpha);
}
backend.disposeIntermediateTensorInfo(resultOld);
}
return result;
}
export const fusedConv2DConfig: KernelConfig = {
kernelName: FusedConv2D,
backendName: 'cpu',
kernelFunc: fusedConv2D as unknown as KernelFunc
};