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