@tensorflow/tfjs-converter/dist/operations/executors/convolution_executor.js

/**
 * @license
 * Copyright 2018 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.
 * =============================================================================
 */
// tslint:disable-next-line: no-imports-from-dist
import * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';
import { getPadding, getParamValue } from './utils';
function fusedConvAndDepthWiseParams(node, tensorMap, context) {
    const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);
    const isBiasAdd = extraOp === 'biasadd';
    const noBiasAdd = !isBiasAdd;
    const isPrelu = activationFunc === 'prelu';
    const isBatchNorm = extraOp === 'fusedbatchnorm';
    const numArgs = getParamValue('numArgs', node, tensorMap, context);
    if (isBiasAdd) {
        if (isPrelu && numArgs !== 2) {
            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +
                'must have two extra arguments: bias and alpha.');
        }
        if (!isPrelu && isBiasAdd && numArgs !== 1) {
            throw new Error('FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +
                'one extra argument: bias.');
        }
    }
    if (isBatchNorm) {
        throw new Error('FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');
    }
    const stride = getParamValue('strides', node, tensorMap, context);
    const pad = getPadding(node, tensorMap, context);
    const dataFormat = getParamValue('dataFormat', node, tensorMap, context)
        .toUpperCase();
    const dilations = getParamValue('dilations', node, tensorMap, context);
    let [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);
    if (noBiasAdd) {
        preluArg = biasArg;
        biasArg = undefined;
    }
    const leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context);
    return {
        stride,
        pad,
        dataFormat,
        dilations,
        biasArg,
        preluArg,
        activationFunc,
        leakyreluAlpha
    };
}
export const executeOp = (node, tensorMap, context, ops = tfOps) => {
    switch (node.op) {
        case 'Conv1D': {
            const stride = getParamValue('stride', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)
                .toUpperCase();
            const dilation = getParamValue('dilation', node, tensorMap, context);
            return [ops.conv1d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), stride, pad, dataFormat, dilation)];
        }
        case 'Conv2D': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getPadding(node, tensorMap, context);
            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)
                .toUpperCase();
            const dilations = getParamValue('dilations', node, tensorMap, context);
            return [ops.conv2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];
        }
        case '_FusedConv2D': {
            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha } = fusedConvAndDepthWiseParams(node, tensorMap, context);
            return [ops.fused.conv2d({
                    x: getParamValue('x', node, tensorMap, context),
                    filter: getParamValue('filter', node, tensorMap, context),
                    strides: [stride[1], stride[2]],
                    pad: pad,
                    dataFormat: dataFormat,
                    dilations: [dilations[1], dilations[2]],
                    bias: biasArg,
                    activation: activationFunc,
                    preluActivationWeights: preluArg,
                    leakyreluAlpha
                })];
        }
        case 'FusedDepthwiseConv2dNative': {
            const { stride, pad, dataFormat, dilations, biasArg, preluArg, activationFunc, leakyreluAlpha, } = fusedConvAndDepthWiseParams(node, tensorMap, context);
            return [ops.fused.depthwiseConv2d({
                    x: getParamValue('x', node, tensorMap, context),
                    filter: getParamValue('filter', node, tensorMap, context),
                    strides: [stride[1], stride[2]],
                    pad: pad,
                    dataFormat: dataFormat,
                    dilations: [dilations[1], dilations[2]],
                    bias: biasArg,
                    activation: activationFunc,
                    preluActivationWeights: preluArg,
                    leakyreluAlpha
                })];
        }
        case 'Conv2DBackpropInput':
        case 'Conv2dTranspose': {
            const shape = getParamValue('outputShape', node, tensorMap, context);
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getPadding(node, tensorMap, context);
            return [ops.conv2dTranspose(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), shape, [stride[1], stride[2]], pad)];
        }
        case 'DepthwiseConv2dNative':
        case 'DepthwiseConv2d': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getPadding(node, tensorMap, context);
            const dilations = getParamValue('dilations', node, tensorMap, context);
            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)
                .toUpperCase();
            return [ops.depthwiseConv2d(getParamValue('input', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2]], pad, dataFormat, [dilations[1], dilations[2]])];
        }
        case 'Conv3D': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const dataFormat = getParamValue('dataFormat', node, tensorMap, context)
                .toUpperCase();
            const dilations = getParamValue('dilations', node, tensorMap, context);
            return [ops.conv3d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [stride[1], stride[2], stride[3]], pad, dataFormat, [dilations[1], dilations[2], dilations[3]])];
        }
        case 'AvgPool': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);
            return [ops.avgPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];
        }
        case 'MaxPool': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);
            return [ops.maxPool(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad)];
        }
        case 'MaxPoolWithArgmax': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);
            const includeBatchInIndex = getParamValue('includeBatchInIndex', node, tensorMap, context);
            const { result, indexes } = ops.maxPoolWithArgmax(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2]], [stride[1], stride[2]], pad, includeBatchInIndex);
            return [result, indexes];
        }
        case 'AvgPool3D': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);
            return [ops.avgPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];
        }
        case 'MaxPool3D': {
            const stride = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const kernelSize = getParamValue('kernelSize', node, tensorMap, context);
            return [ops.maxPool3d(getParamValue('x', node, tensorMap, context), [kernelSize[1], kernelSize[2], kernelSize[3]], [stride[1], stride[2], stride[3]], pad)];
        }
        case 'Dilation2D': {
            const strides = getParamValue('strides', node, tensorMap, context);
            const pad = getParamValue('pad', node, tensorMap, context);
            const dilations = getParamValue('dilations', node, tensorMap, context);
            // strides: [1, stride_height, stride_width, 1].
            const strideHeight = strides[1];
            const strideWidth = strides[2];
            // dilations: [1, dilation_height, dilation_width, 1].
            const dilationHeight = dilations[1];
            const dilationWidth = dilations[2];
            return [ops.dilation2d(getParamValue('x', node, tensorMap, context), getParamValue('filter', node, tensorMap, context), [strideHeight, strideWidth], pad, [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];
        }
        default:
            throw TypeError(`Node type ${node.op} is not implemented`);
    }
};
export const CATEGORY = 'convolution';
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* @license\n * Copyright 2018 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 {Rank, Tensor, Tensor3D, Tensor4D, Tensor5D} from '@tensorflow/tfjs-core';\n// tslint:disable-next-line: no-imports-from-dist\nimport * as tfOps from '@tensorflow/tfjs-core/dist/ops/ops_for_converter';\n\nimport {NamedTensorsMap} from '../../data/types';\nimport {ExecutionContext} from '../../executor/execution_context';\nimport {InternalOpExecutor, Node} from '../types';\n\nimport {getPadding, getParamValue} from './utils';\n\nfunction fusedConvAndDepthWiseParams(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) {\n  const [extraOp, activationFunc] =\n      (getParamValue('fusedOps', node, tensorMap, context) as string[]);\n\n  const isBiasAdd = extraOp === 'biasadd';\n  const noBiasAdd = !isBiasAdd;\n  const isPrelu = activationFunc === 'prelu';\n  const isBatchNorm = extraOp === 'fusedbatchnorm';\n\n  const numArgs =\n      (getParamValue('numArgs', node, tensorMap, context) as number);\n  if (isBiasAdd) {\n    if (isPrelu && numArgs !== 2) {\n      throw new Error(\n          'FusedConv2d and DepthwiseConv2d with BiasAdd and Prelu ' +\n          'must have two extra arguments: bias and alpha.');\n    }\n    if (!isPrelu && isBiasAdd && numArgs !== 1) {\n      throw new Error(\n          'FusedConv2d and DepthwiseConv2d with BiasAdd must have ' +\n          'one extra argument: bias.');\n    }\n  }\n  if (isBatchNorm) {\n    throw new Error(\n        'FusedConv2d and DepthwiseConv2d with FusedBatchNorm is not supported');\n  }\n  const stride = getParamValue('strides', node, tensorMap, context) as number[];\n  const pad = getPadding(node, tensorMap, context);\n  const dataFormat =\n      (getParamValue('dataFormat', node, tensorMap, context) as string)\n          .toUpperCase();\n  const dilations =\n      getParamValue('dilations', node, tensorMap, context) as number[];\n  let [biasArg, preluArg] =\n      getParamValue('args', node, tensorMap, context) as Tensor[];\n  if (noBiasAdd) {\n    preluArg = biasArg;\n    biasArg = undefined;\n  }\n  const leakyreluAlpha =\n      getParamValue('leakyreluAlpha', node, tensorMap, context) as number;\n\n  return {\n    stride,\n    pad,\n    dataFormat,\n    dilations,\n    biasArg,\n    preluArg,\n    activationFunc,\n    leakyreluAlpha\n  };\n}\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap,\n     context: ExecutionContext, ops = tfOps): Tensor[] => {\n      switch (node.op) {\n        case 'Conv1D': {\n          const stride =\n              getParamValue('stride', node, tensorMap, context) as number;\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilation =\n              getParamValue('dilation', node, tensorMap, context) as number;\n          return [ops.conv1d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D,\n              getParamValue('filter', node, tensorMap, context) as Tensor3D,\n              stride, pad as 'valid' | 'same', dataFormat as 'NWC' | 'NCW',\n              dilation)];\n        }\n        case 'Conv2D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          return [ops.conv2d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              [stride[1], stride[2]], pad as 'valid' | 'same',\n              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];\n        }\n        case '_FusedConv2D': {\n          const {\n            stride,\n            pad,\n            dataFormat,\n            dilations,\n            biasArg,\n            preluArg,\n            activationFunc,\n            leakyreluAlpha\n          } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n\n          return [ops.fused.conv2d({\n            x: getParamValue('x', node, tensorMap, context) as Tensor3D |\n                Tensor4D,\n            filter: getParamValue('filter', node, tensorMap, context) as\n                Tensor4D,\n            strides: [stride[1], stride[2]],\n            pad: pad as 'valid' | 'same',\n            dataFormat: dataFormat as 'NHWC' | 'NCHW',\n            dilations: [dilations[1], dilations[2]],\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n        }\n\n        case 'FusedDepthwiseConv2dNative': {\n          const {\n            stride,\n            pad,\n            dataFormat,\n            dilations,\n            biasArg,\n            preluArg,\n            activationFunc,\n            leakyreluAlpha,\n          } = fusedConvAndDepthWiseParams(node, tensorMap, context);\n\n          return [ops.fused.depthwiseConv2d({\n            x: getParamValue('x', node, tensorMap, context) as Tensor3D |\n                Tensor4D,\n            filter: getParamValue('filter', node, tensorMap, context) as\n                Tensor4D,\n            strides: [stride[1], stride[2]],\n            pad: pad as 'valid' | 'same',\n            dataFormat: dataFormat as 'NHWC' | 'NCHW',\n            dilations: [dilations[1], dilations[2]],\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n        }\n        case 'Conv2DBackpropInput':\n        case 'Conv2dTranspose': {\n          const shape = getParamValue(\n                            'outputShape', node, tensorMap,\n                            context) as [number, number, number] |\n              [number, number, number, number];\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          return [ops.conv2dTranspose(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              shape, [stride[1], stride[2]], pad as 'valid' | 'same')];\n        }\n        case 'DepthwiseConv2dNative':\n        case 'DepthwiseConv2d': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getPadding(node, tensorMap, context);\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n\n          return [ops.depthwiseConv2d(\n              getParamValue('input', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor4D,\n              [stride[1], stride[2]], pad as 'valid' | 'same',\n              dataFormat as 'NHWC' | 'NCHW', [dilations[1], dilations[2]])];\n        }\n        case 'Conv3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dataFormat =\n              (getParamValue('dataFormat', node, tensorMap, context) as string)\n                  .toUpperCase();\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n          return [ops.conv3d(\n              getParamValue('x', node, tensorMap, context) as Tensor4D |\n                  Tensor<Rank.R5>,\n              getParamValue('filter', node, tensorMap, context) as\n                  Tensor<Rank.R5>,\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same',\n              dataFormat as 'NDHWC' | 'NCDHW',\n              [dilations[1], dilations[2], dilations[3]])];\n        }\n        case 'AvgPool': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [ops.avgPool(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same')];\n        }\n        case 'MaxPool': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [ops.maxPool(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same')];\n        }\n        case 'MaxPoolWithArgmax': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n          const includeBatchInIndex =\n              getParamValue('includeBatchInIndex', node, tensorMap, context) as\n              boolean;\n          const {result, indexes} = ops.maxPoolWithArgmax(\n              getParamValue('x', node, tensorMap, context) as Tensor4D,\n              [kernelSize[1], kernelSize[2]], [stride[1], stride[2]],\n              pad as 'valid' | 'same', includeBatchInIndex);\n          return [result, indexes];\n        }\n        case 'AvgPool3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [ops.avgPool3d(\n              getParamValue('x', node, tensorMap, context) as Tensor5D,\n              [kernelSize[1], kernelSize[2], kernelSize[3]],\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];\n        }\n\n        case 'MaxPool3D': {\n          const stride =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const kernelSize =\n              getParamValue('kernelSize', node, tensorMap, context) as number[];\n\n          return [ops.maxPool3d(\n              getParamValue('x', node, tensorMap, context) as Tensor5D,\n              [kernelSize[1], kernelSize[2], kernelSize[3]],\n              [stride[1], stride[2], stride[3]], pad as 'valid' | 'same')];\n        }\n\n        case 'Dilation2D': {\n          const strides =\n              getParamValue('strides', node, tensorMap, context) as number[];\n          const pad = getParamValue('pad', node, tensorMap, context);\n          const dilations =\n              getParamValue('dilations', node, tensorMap, context) as number[];\n\n          // strides: [1, stride_height, stride_width, 1].\n          const strideHeight = strides[1];\n          const strideWidth = strides[2];\n\n          // dilations: [1, dilation_height, dilation_width, 1].\n          const dilationHeight = dilations[1];\n          const dilationWidth = dilations[2];\n\n          return [ops.dilation2d(\n              getParamValue('x', node, tensorMap, context) as Tensor3D |\n                  Tensor4D,\n              getParamValue('filter', node, tensorMap, context) as Tensor3D,\n              [strideHeight, strideWidth], pad as 'valid' | 'same',\n              [dilationHeight, dilationWidth], 'NHWC' /* dataFormat */)];\n        }\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'convolution';\n"]}