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1/**
2 * @license
3 * Copyright 2018 Google LLC. All Rights Reserved.
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 * =============================================================================
16 */
17import * as tfc from '@tensorflow/tfjs-core';
18import { NodeValueImpl } from './custom_op/node_value_impl';
19import { getRegisteredOp } from './custom_op/register';
20import * as arithmetic from './executors/arithmetic_executor';
21import * as basicMath from './executors/basic_math_executor';
22import * as control from './executors/control_executor';
23import * as convolution from './executors/convolution_executor';
24import * as creation from './executors/creation_executor';
25import * as dynamic from './executors/dynamic_executor';
26import * as evaluation from './executors/evaluation_executor';
27import * as graph from './executors/graph_executor';
28import * as hashTable from './executors/hash_table_executor';
29import * as image from './executors/image_executor';
30import * as logical from './executors/logical_executor';
31import * as matrices from './executors/matrices_executor';
32import * as normalization from './executors/normalization_executor';
33import * as ragged from './executors/ragged_executor';
34import * as reduction from './executors/reduction_executor';
35import * as sliceJoin from './executors/slice_join_executor';
36import * as sparse from './executors/sparse_executor';
37import * as spectral from './executors/spectral_executor';
38import * as string from './executors/string_executor';
39import * as transformation from './executors/transformation_executor';
40/**
41 * Executes the op defined by the node object.
42 * @param node
43 * @param tensorMap contains tensors for executed nodes and weights
44 * @param context contains tensors and information for running the current node.
45 * @param resourceManager Optional. Contains global resources of the model.
46 */
47export function executeOp(node, tensorMap, context, resourceManager, tidy = tfc.tidy) {
48 const value = ((node, tensorMap, context) => {
49 switch (node.category) {
50 case 'arithmetic':
51 return tidy(() => arithmetic.executeOp(node, tensorMap, context));
52 case 'basic_math':
53 return tidy(() => basicMath.executeOp(node, tensorMap, context));
54 case 'control':
55 return control.executeOp(node, tensorMap, context);
56 case 'convolution':
57 return tidy(() => convolution.executeOp(node, tensorMap, context));
58 case 'creation':
59 return tidy(() => creation.executeOp(node, tensorMap, context));
60 case 'dynamic':
61 return dynamic.executeOp(node, tensorMap, context);
62 case 'evaluation':
63 return tidy(() => evaluation.executeOp(node, tensorMap, context));
64 case 'image':
65 return tidy(() => image.executeOp(node, tensorMap, context));
66 case 'graph':
67 return tidy(() => graph.executeOp(node, tensorMap, context));
68 case 'logical':
69 return tidy(() => logical.executeOp(node, tensorMap, context));
70 case 'matrices':
71 return tidy(() => matrices.executeOp(node, tensorMap, context));
72 case 'normalization':
73 return tidy(() => normalization.executeOp(node, tensorMap, context));
74 case 'ragged':
75 return tidy(() => ragged.executeOp(node, tensorMap, context));
76 case 'reduction':
77 return tidy(() => reduction.executeOp(node, tensorMap, context));
78 case 'slice_join':
79 return tidy(() => sliceJoin.executeOp(node, tensorMap, context));
80 case 'sparse':
81 return tidy(() => sparse.executeOp(node, tensorMap, context));
82 case 'spectral':
83 return tidy(() => spectral.executeOp(node, tensorMap, context));
84 case 'string':
85 return tidy(() => string.executeOp(node, tensorMap, context));
86 case 'transformation':
87 return tidy(() => transformation.executeOp(node, tensorMap, context));
88 case 'hash_table':
89 return hashTable.executeOp(node, tensorMap, context, resourceManager);
90 case 'custom':
91 const opMapper = getRegisteredOp(node.op);
92 if (opMapper && opMapper.customExecutor) {
93 return opMapper.customExecutor(new NodeValueImpl(node, tensorMap, context));
94 }
95 else {
96 throw TypeError(`Custom op ${node.op} is not registered.`);
97 }
98 default:
99 throw TypeError(`Unknown op '${node.op}'. File an issue at ` +
100 `https://github.com/tensorflow/tfjs/issues so we can add it` +
101 `, or register a custom execution with tf.registerOp()`);
102 }
103 })(node, tensorMap, context);
104 if (tfc.util.isPromise(value)) {
105 return value.then((data) => [].concat(data));
106 }
107 return [].concat(value);
108}
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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 ragged from './executors/ragged_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, tidy = tfc.tidy): tfc.Tensor[]|\n    Promise<tfc.Tensor[]> {\n  const value =\n      ((node: Node, tensorMap: NamedTensorsMap, context: ExecutionContext) => {\n        switch (node.category) {\n          case 'arithmetic':\n            return tidy(() => arithmetic.executeOp(node, tensorMap, context));\n          case 'basic_math':\n            return tidy(() => basicMath.executeOp(node, tensorMap, context));\n          case 'control':\n            return control.executeOp(node, tensorMap, context);\n          case 'convolution':\n            return tidy(() => convolution.executeOp(node, tensorMap, context));\n          case 'creation':\n            return tidy(() => creation.executeOp(node, tensorMap, context));\n          case 'dynamic':\n            return dynamic.executeOp(node, tensorMap, context);\n          case 'evaluation':\n            return tidy(() => evaluation.executeOp(node, tensorMap, context));\n          case 'image':\n            return tidy(() => image.executeOp(node, tensorMap, context));\n          case 'graph':\n            return tidy(() => graph.executeOp(node, tensorMap, context));\n          case 'logical':\n            return tidy(() => logical.executeOp(node, tensorMap, context));\n          case 'matrices':\n            return tidy(() => matrices.executeOp(node, tensorMap, context));\n          case 'normalization':\n            return tidy(\n                () => normalization.executeOp(node, tensorMap, context));\n          case 'ragged':\n            return tidy(() => ragged.executeOp(node, tensorMap, context));\n          case 'reduction':\n            return tidy(() => reduction.executeOp(node, tensorMap, context));\n          case 'slice_join':\n            return tidy(() => sliceJoin.executeOp(node, tensorMap, context));\n          case 'sparse':\n            return tidy(() => sparse.executeOp(node, tensorMap, context));\n          case 'spectral':\n            return tidy(() => spectral.executeOp(node, tensorMap, context));\n          case 'string':\n            return tidy(() => string.executeOp(node, tensorMap, context));\n          case 'transformation':\n            return 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.then((data) => [].concat(data));\n  }\n  return [].concat(value);\n}\n"]}
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