@tensorflow/tfjs-converter
Version:
Tensorflow model converter for javascript
106 lines • 18.9 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.
* =============================================================================
*/
import * as tfc from '@tensorflow/tfjs-core';
import { NodeValueImpl } from './custom_op/node_value_impl';
import { getRegisteredOp } from './custom_op/register';
import * as arithmetic from './executors/arithmetic_executor';
import * as basicMath from './executors/basic_math_executor';
import * as control from './executors/control_executor';
import * as convolution from './executors/convolution_executor';
import * as creation from './executors/creation_executor';
import * as dynamic from './executors/dynamic_executor';
import * as evaluation from './executors/evaluation_executor';
import * as graph from './executors/graph_executor';
import * as hashTable from './executors/hash_table_executor';
import * as image from './executors/image_executor';
import * as logical from './executors/logical_executor';
import * as matrices from './executors/matrices_executor';
import * as normalization from './executors/normalization_executor';
import * as reduction from './executors/reduction_executor';
import * as sliceJoin from './executors/slice_join_executor';
import * as sparse from './executors/sparse_executor';
import * as spectral from './executors/spectral_executor';
import * as string from './executors/string_executor';
import * as transformation from './executors/transformation_executor';
/**
* Executes the op defined by the node object.
* @param node
* @param tensorMap contains tensors for executed nodes and weights
* @param context contains tensors and information for running the current node.
* @param resourceManager Optional. Contains global resources of the model.
*/
export function executeOp(node, tensorMap, context, resourceManager) {
const value = ((node, tensorMap, context) => {
switch (node.category) {
case 'arithmetic':
return tfc.tidy(() => arithmetic.executeOp(node, tensorMap, context));
case 'basic_math':
return tfc.tidy(() => basicMath.executeOp(node, tensorMap, context));
case 'control':
return control.executeOp(node, tensorMap, context);
case 'convolution':
return tfc.tidy(() => convolution.executeOp(node, tensorMap, context));
case 'creation':
return tfc.tidy(() => creation.executeOp(node, tensorMap, context));
case 'dynamic':
return dynamic.executeOp(node, tensorMap, context);
case 'evaluation':
return tfc.tidy(() => evaluation.executeOp(node, tensorMap, context));
case 'image':
return tfc.tidy(() => image.executeOp(node, tensorMap, context));
case 'graph':
return tfc.tidy(() => graph.executeOp(node, tensorMap, context));
case 'logical':
return tfc.tidy(() => logical.executeOp(node, tensorMap, context));
case 'matrices':
return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));
case 'normalization':
return tfc.tidy(() => normalization.executeOp(node, tensorMap, context));
case 'reduction':
return tfc.tidy(() => reduction.executeOp(node, tensorMap, context));
case 'slice_join':
return tfc.tidy(() => sliceJoin.executeOp(node, tensorMap, context));
case 'sparse':
return tfc.tidy(() => sparse.executeOp(node, tensorMap, context));
case 'spectral':
return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));
case 'string':
return tfc.tidy(() => string.executeOp(node, tensorMap, context));
case 'transformation':
return tfc.tidy(() => transformation.executeOp(node, tensorMap, context));
case 'hash_table':
return hashTable.executeOp(node, tensorMap, context, resourceManager);
case 'custom':
const opMapper = getRegisteredOp(node.op);
if (opMapper && opMapper.customExecutor) {
return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));
}
else {
throw TypeError(`Custom op ${node.op} is not registered.`);
}
default:
throw TypeError(`Unknown op '${node.op}'. File an issue at ` +
`https://github.com/tensorflow/tfjs/issues so we can add it` +
`, or register a custom execution with tf.registerOp()`);
}
})(node, tensorMap, context);
if (tfc.util.isPromise(value)) {
return value.then((data) => [].concat(data));
}
return [].concat(value);
}
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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 * as tfc from '@tensorflow/tfjs-core';\n\nimport {NamedTensorsMap} from '../data/types';\nimport {ExecutionContext} from '../executor/execution_context';\nimport {ResourceManager} from '../executor/resource_manager';\n\nimport {NodeValueImpl} from './custom_op/node_value_impl';\nimport {getRegisteredOp} from './custom_op/register';\nimport * as arithmetic from './executors/arithmetic_executor';\nimport * as basicMath from './executors/basic_math_executor';\nimport * as control from './executors/control_executor';\nimport * as convolution from './executors/convolution_executor';\nimport * as creation from './executors/creation_executor';\nimport * as dynamic from './executors/dynamic_executor';\nimport * as evaluation from './executors/evaluation_executor';\nimport * as graph from './executors/graph_executor';\nimport * as hashTable from './executors/hash_table_executor';\nimport * as image from './executors/image_executor';\nimport * as logical from './executors/logical_executor';\nimport * as matrices from './executors/matrices_executor';\nimport * as normalization from './executors/normalization_executor';\nimport * as reduction from './executors/reduction_executor';\nimport * as sliceJoin from './executors/slice_join_executor';\nimport * as sparse from './executors/sparse_executor';\nimport * as spectral from './executors/spectral_executor';\nimport * as string from './executors/string_executor';\nimport * as transformation from './executors/transformation_executor';\nimport {Node} from './types';\n\n/**\n * Executes the op defined by the node object.\n * @param node\n * @param tensorMap contains tensors for executed nodes and weights\n * @param context contains tensors and information for running the current node.\n * @param resourceManager Optional. Contains global resources of the model.\n */\nexport function executeOp(\n    node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext,\n    resourceManager?: ResourceManager): tfc.Tensor[]|Promise<tfc.Tensor[]> {\n  const value =\n      ((node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) => {\n        switch (node.category) {\n          case 'arithmetic':\n            return tfc.tidy(\n                () => arithmetic.executeOp(node, tensorMap, context));\n          case 'basic_math':\n            return tfc.tidy(\n                () => basicMath.executeOp(node, tensorMap, context));\n          case 'control':\n            return control.executeOp(node, tensorMap, context);\n          case 'convolution':\n            return tfc.tidy(\n                () => convolution.executeOp(node, tensorMap, context));\n          case 'creation':\n            return tfc.tidy(() => creation.executeOp(node, tensorMap, context));\n          case 'dynamic':\n            return dynamic.executeOp(node, tensorMap, context);\n          case 'evaluation':\n            return tfc.tidy(\n                () => evaluation.executeOp(node, tensorMap, context));\n          case 'image':\n            return tfc.tidy(() => image.executeOp(node, tensorMap, context));\n          case 'graph':\n            return tfc.tidy(() => graph.executeOp(node, tensorMap, context));\n          case 'logical':\n            return tfc.tidy(() => logical.executeOp(node, tensorMap, context));\n          case 'matrices':\n            return tfc.tidy(() => matrices.executeOp(node, tensorMap, context));\n          case 'normalization':\n            return tfc.tidy(\n                () => normalization.executeOp(node, tensorMap, context));\n          case 'reduction':\n            return tfc.tidy(\n                () => reduction.executeOp(node, tensorMap, context));\n          case 'slice_join':\n            return tfc.tidy(\n                () => sliceJoin.executeOp(node, tensorMap, context));\n          case 'sparse':\n            return tfc.tidy(() => sparse.executeOp(node, tensorMap, context));\n          case 'spectral':\n            return tfc.tidy(() => spectral.executeOp(node, tensorMap, context));\n          case 'string':\n            return tfc.tidy(() => string.executeOp(node, tensorMap, context));\n          case 'transformation':\n            return tfc.tidy(\n                () => transformation.executeOp(node, tensorMap, context));\n          case 'hash_table':\n            return hashTable.executeOp(\n                node, tensorMap, context, resourceManager);\n          case 'custom':\n            const opMapper = getRegisteredOp(node.op);\n            if (opMapper && opMapper.customExecutor) {\n              return opMapper.customExecutor(\n                  new NodeValueImpl(node, tensorMap, context));\n            } else {\n              throw TypeError(`Custom op ${node.op} is not registered.`);\n            }\n          default:\n            throw TypeError(\n                `Unknown op '${node.op}'. File an issue at ` +\n                `https://github.com/tensorflow/tfjs/issues so we can add it` +\n                `, or register a custom execution with tf.registerOp()`);\n        }\n      })(node, tensorMap, context);\n  if (tfc.util.isPromise(value)) {\n    return (value as Promise<tfc.Tensor>).then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n"]}