@tensorflow/tfjs-converter
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Tensorflow model converter for javascript
63 lines • 12.7 kB
JavaScript
/**
* @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 { getParamValue } from './utils';
export const executeOp = (node, tensorMap, context, ops = tfOps) => {
switch (node.op) {
case 'BatchMatMul':
case 'BatchMatMulV2':
case 'MatMul':
return [ops.matMul(getParamValue('a', node, tensorMap, context), getParamValue('b', node, tensorMap, context), getParamValue('transposeA', node, tensorMap, context), getParamValue('transposeB', node, tensorMap, context))];
case 'Einsum':
return [ops.einsum(getParamValue('equation', node, tensorMap, context), ...getParamValue('tensors', node, tensorMap, context))];
case 'Transpose':
return [ops.transpose(getParamValue('x', node, tensorMap, context), getParamValue('perm', node, tensorMap, context))];
case '_FusedMatMul':
const [extraOp, activationFunc] = getParamValue('fusedOps', node, tensorMap, context);
const isBiasAdd = extraOp === 'biasadd';
const isPrelu = activationFunc === 'prelu';
const numArgs = getParamValue('numArgs', node, tensorMap, context);
const leakyreluAlpha = getParamValue('leakyreluAlpha', node, tensorMap, context);
if (isBiasAdd) {
if (isPrelu && numArgs !== 2) {
throw new Error('Fused MatMul with BiasAdd and Prelu must have two ' +
'extra arguments: bias and alpha.');
}
if (!isPrelu && numArgs !== 1) {
throw new Error('Fused MatMul with BiasAdd must have one extra argument: bias.');
}
}
const [biasArg, preluArg] = getParamValue('args', node, tensorMap, context);
return [ops.fused.matMul({
a: getParamValue('a', node, tensorMap, context),
b: getParamValue('b', node, tensorMap, context),
transposeA: getParamValue('transposeA', node, tensorMap, context),
transposeB: getParamValue('transposeB', node, tensorMap, context),
bias: biasArg,
activation: activationFunc,
preluActivationWeights: preluArg,
leakyreluAlpha
})];
case 'MatrixBandPart':
return [ops.linalg.bandPart(getParamValue('a', node, tensorMap, context), getParamValue('numLower', node, tensorMap, context), getParamValue('numUpper', node, tensorMap, context))];
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
export const CATEGORY = 'matrices';
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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 {Scalar, Tensor, Tensor2D} 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 {getParamValue} from './utils';\n\nexport const executeOp: InternalOpExecutor =\n    (node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,\n     ops = tfOps): Tensor[] => {\n      switch (node.op) {\n        case 'BatchMatMul':\n        case 'BatchMatMulV2':\n        case 'MatMul':\n          return [ops.matMul(\n              getParamValue('a', node, tensorMap, context) as Tensor2D,\n              getParamValue('b', node, tensorMap, context) as Tensor2D,\n              getParamValue('transposeA', node, tensorMap, context) as boolean,\n              getParamValue('transposeB', node, tensorMap, context) as\n                  boolean)];\n\n        case 'Einsum':\n          return [ops.einsum(\n              getParamValue('equation', node, tensorMap, context) as string,\n              ...getParamValue('tensors', node, tensorMap, context) as\n                  Tensor[])];\n\n        case 'Transpose':\n          return [ops.transpose(\n              getParamValue('x', node, tensorMap, context) as Tensor,\n              getParamValue('perm', node, tensorMap, context) as number[])];\n\n        case '_FusedMatMul':\n          const [extraOp, activationFunc] =\n              (getParamValue('fusedOps', node, tensorMap, context) as string[]);\n\n          const isBiasAdd = extraOp === 'biasadd';\n          const isPrelu = activationFunc === 'prelu';\n\n          const numArgs =\n              (getParamValue('numArgs', node, tensorMap, context) as number);\n          const leakyreluAlpha =\n              getParamValue('leakyreluAlpha', node, tensorMap, context) as\n              number;\n\n          if (isBiasAdd) {\n            if (isPrelu && numArgs !== 2) {\n              throw new Error(\n                  'Fused MatMul with BiasAdd and Prelu must have two ' +\n                  'extra arguments: bias and alpha.');\n            }\n            if (!isPrelu && numArgs !== 1) {\n              throw new Error(\n                  'Fused MatMul with BiasAdd must have one extra argument: bias.');\n            }\n          }\n          const [biasArg, preluArg] =\n              getParamValue('args', node, tensorMap, context) as Tensor[];\n          return [ops.fused.matMul({\n            a: getParamValue('a', node, tensorMap, context) as Tensor2D,\n            b: getParamValue('b', node, tensorMap, context) as Tensor2D,\n            transposeA: getParamValue('transposeA', node, tensorMap, context) as\n                boolean,\n            transposeB: getParamValue('transposeB', node, tensorMap, context) as\n                boolean,\n            bias: biasArg,\n            activation: activationFunc as tfOps.fused.Activation,\n            preluActivationWeights: preluArg,\n            leakyreluAlpha\n          })];\n\n        case 'MatrixBandPart':\n          return [ops.linalg.bandPart(\n              getParamValue('a', node, tensorMap, context) as Tensor2D,\n              getParamValue('numLower', node, tensorMap, context) as Scalar,\n              getParamValue('numUpper', node, tensorMap, context) as Scalar)];\n\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'matrices';\n"]}