| 1 | 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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 * as tensorflow from '../data/compiled_api';\n\nimport * as arithmetic from './op_list/arithmetic';\nimport * as basicMath from './op_list/basic_math';\nimport * as control from './op_list/control';\nimport * as convolution from './op_list/convolution';\nimport * as creation from './op_list/creation';\nimport * as dynamic from './op_list/dynamic';\nimport * as evaluation from './op_list/evaluation';\nimport * as graph from './op_list/graph';\nimport * as hashTable from './op_list/hash_table';\nimport * as image from './op_list/image';\nimport * as logical from './op_list/logical';\nimport * as matrices from './op_list/matrices';\nimport * as normalization from './op_list/normalization';\nimport * as reduction from './op_list/reduction';\nimport * as sliceJoin from './op_list/slice_join';\nimport * as sparse from './op_list/sparse';\nimport * as spectral from './op_list/spectral';\nimport * as string from './op_list/string';\nimport * as transformation from './op_list/transformation';\nimport {OperationMapper} from './operation_mapper';\nimport {Graph} from './types';\n\nconst ops = [\n arithmetic, basicMath, control, convolution, creation, dynamic, evaluation,\n graph, hashTable, image, logical, matrices, normalization, reduction,\n sliceJoin, sparse, spectral, string, transformation\n];\nconst mapper: OperationMapper = OperationMapper.Instance;\nlet convertedGraph: Graph;\n\nconst SIMPLE_MODEL: tensorflow.IGraphDef = {\n node: [\n {\n name: 'image_placeholder',\n op: 'Placeholder',\n attr: {\n dtype: {\n type: tensorflow.DataType.DT_FLOAT,\n },\n shape: {shape: {dim: [{size: '3'}, {size: 3}, {size: '3'}, {size: 1}]}}\n }\n },\n {\n name: 'Const',\n op: 'Const',\n attr: {\n dtype: {type: tensorflow.DataType.DT_INT32},\n value: {\n tensor: {\n dtype: tensorflow.DataType.DT_INT32,\n tensorShape: {dim: [{size: 3}, {size: 3}, {size: 1}, {size: 1}]},\n intVal: [0, 0, 0, 0, 1, 0, 0, 0, 0]\n }\n }\n }\n },\n {\n name: 'Shape',\n op: 'Const',\n attr: {\n dtype: {type: tensorflow.DataType.DT_INT32},\n value: {\n tensor: {\n dtype: tensorflow.DataType.DT_INT32,\n tensorShape: {dim: [{size: 3}, {size: 1}, {size: 1}, {size: 1}]},\n intVal: [1, 1, 1]\n }\n }\n }\n },\n {\n name: 'Value',\n op: 'Const',\n attr: {dtype: {type: tensorflow.DataType.DT_INT32}, value: {i: 1}}\n },\n {name: 'Fill', op: 'Fill', input: ['Shape', 'Value'], attr: {}},\n {\n name: 'Conv2D',\n op: 'Conv2D',\n input: ['image_placeholder', 'Const'],\n attr: {\n T: {type: tensorflow.DataType.DT_FLOAT},\n dataFormat: {s: 'TkhXQw=='},\n padding: {s: 'U0FNRQ=='},\n strides: {list: {f: [], i: [1, 2, 2, 1]}},\n useCudnnOnGpu: {b: true}\n }\n },\n {\n name: 'BiasAdd',\n op: 'BiasAdd',\n input: ['Conv2D', 'Shape'],\n attr:\n {T: {type: tensorflow.DataType.DT_FLOAT}, dataFormat: {s: 'TkhXQw=='}}\n },\n {\n name: 'Cast',\n op: 'Cast',\n input: ['BiasAdd'],\n attr: {DstT: {type: tensorflow.DataType.DT_INT64}}\n },\n {\n name: 'Squeeze',\n op: 'Squeeze',\n input: ['Cast'],\n attr: {squeeze_dims: {list: {i: ['1', '2']}}}\n },\n {\n name: 'Squeeze2',\n op: 'Squeeze',\n input: ['BiasAdd'],\n attr: {squeeze_dims: {list: {}}}\n },\n {\n name: 'Split',\n op: 'Split',\n input: ['image_placeholder'],\n attr: {num_split: {i: 3} as tensorflow.IAttrValue}\n },\n {name: 'LogicalNot', op: 'LogicalNot', input: ['image_placeholder']},\n {\n name: 'FusedBatchNorm',\n op: 'FusedBatchNorm',\n input: ['image_placeholder'],\n attr: {epsilon: {f: 0.0001} as tensorflow.IAttrValue}\n },\n {\n name: 'Cast2',\n op: 'Cast',\n input: ['BiasAdd'],\n attr: {DstT: {type: tensorflow.DataType.DT_UINT8}}\n },\n ],\n library: {\n function: [\n {\n signature: {\n name: '__inference_while_cond_10_49_frozen',\n inputArg: [\n {name: 'while_loop_counter', type: tensorflow.DataType.DT_INT32}, {\n name: 'while_maximum_iterations',\n type: tensorflow.DataType.DT_INT32\n },\n {name: 'placeholder', type: tensorflow.DataType.DT_INT32},\n {name: 'less_y', type: tensorflow.DataType.DT_INT32}, {\n name: 'while_cond_10___redundant_placeholder0',\n type: tensorflow.DataType.DT_INT32\n }\n ],\n outputArg: [{name: 'identity', type: tensorflow.DataType.DT_BOOL}]\n },\n nodeDef: [{\n name: 'Less',\n op: 'Less',\n input: ['placeholder', 'less_y'],\n attr: {T: {type: tensorflow.DataType.DT_INT32}}\n }],\n ret: {identity: 'Less:z:0'}\n },\n {\n signature: {\n name: '__inference_while_body_11_40_frozen',\n inputArg: [\n {name: 'while_loop_counter', type: tensorflow.DataType.DT_INT32}, {\n name: 'while_maximum_iterations',\n type: tensorflow.DataType.DT_INT32\n },\n {name: 'placeholder', type: tensorflow.DataType.DT_INT32},\n {name: 'y_0', type: tensorflow.DataType.DT_INT32},\n {name: 'add_1_z_0', type: tensorflow.DataType.DT_INT32}\n ],\n outputArg: [\n {name: 'identity', type: tensorflow.DataType.DT_INT32},\n {name: 'identity_1', type: tensorflow.DataType.DT_INT32},\n {name: 'identity_2', type: tensorflow.DataType.DT_INT32},\n {name: 'y', type: tensorflow.DataType.DT_INT32},\n {name: 'add_1_z', type: tensorflow.DataType.DT_INT32}\n ]\n },\n nodeDef: [\n {\n name: 'add_2/y',\n op: 'Const',\n attr: {\n dtype: {type: tensorflow.DataType.DT_INT32},\n value: {\n tensor: {\n dtype: tensorflow.DataType.DT_INT32,\n tensorShape: {},\n intVal: [1]\n }\n }\n }\n },\n {\n name: 'add',\n op: 'AddV2',\n input: ['placeholder', 'y_0'],\n attr: {T: {type: tensorflow.DataType.DT_INT32}}\n },\n {\n name: 'add_2',\n op: 'AddV2',\n input: ['add_2/y:output:0', 'while_loop_counter'],\n attr: {T: {type: tensorflow.DataType.DT_INT32}}\n },\n {\n name: 'add_1',\n op: 'AddV2',\n input: ['add:z:0', 'add_1_z_0'],\n attr: {T: {type: tensorflow.DataType.DT_INT32}}\n }\n ],\n ret: {\n identity_1: 'while_maximum_iterations',\n identity_2: 'add_1:z:0',\n y: 'y_0',\n identity: 'add_2:z:0',\n add_1_z: 'add_1_z_0'\n }\n }\n ]\n },\n versions: {producer: 1.0}\n};\n\nconst SIGNATURE: tensorflow.ISignatureDef = {\n inputs: {\n image: {\n name: 'image_placeholder',\n dtype: tensorflow.DataType.DT_INT32,\n tensorShape: {\n\n }\n }\n },\n outputs: {\n squeeze:\n {name: 'Squeeze', dtype: tensorflow.DataType.DT_FLOAT, tensorShape: {}}\n }\n};\n\ndescribe('completeness check', () => {\n it('should convert all op categories', () => {\n ops.forEach(op => {\n op.json.forEach(tfOp => {\n const graph = {\n node: [{name: tfOp.tfOpName, op: tfOp.tfOpName, attr: {}}]\n };\n convertedGraph = mapper.transformGraph(graph);\n expect(Object.keys(convertedGraph.nodes)).toEqual([tfOp.tfOpName]);\n expect(convertedGraph.nodes[tfOp.tfOpName].op).toEqual(tfOp.tfOpName);\n });\n });\n });\n it('should convert op with outputs field', () => {\n const name = 'string split';\n const op = 'StringSplit';\n const graph = {node: [{name, op: 'StringSplit', attr: {}}]};\n convertedGraph = mapper.transformGraph(graph);\n expect(Object.keys(convertedGraph.nodes)).toEqual([name]);\n expect(convertedGraph.nodes[name].op).toEqual(op);\n expect(convertedGraph.nodes[name].outputs).toEqual([\n 'indices', 'values', 'shape'\n ]);\n });\n});\ndescribe('operationMapper without signature', () => {\n beforeEach(() => {\n convertedGraph = mapper.transformGraph(SIMPLE_MODEL);\n });\n afterEach(() => {});\n\n describe('transform graph', () => {\n describe('graph level', () => {\n it('should find the graph input nodes', () => {\n expect(convertedGraph.inputs.map(node => node.name)).toEqual([\n 'image_placeholder'\n ]);\n });\n\n it('should find the graph output nodes', () => {\n expect(convertedGraph.outputs.map(node => node.name)).toEqual([\n 'Fill', 'Squeeze', 'Squeeze2', 'Split', 'LogicalNot',\n 'FusedBatchNorm', 'Cast2'\n ]);\n });\n\n it('should find the graph weight nodes', () => {\n expect(convertedGraph.weights.map(node => node.name)).toEqual([\n 'Const', 'Shape', 'Value'\n ]);\n });\n\n it('should convert nodes', () => {\n expect(Object.keys(convertedGraph.nodes)).toEqual([\n 'image_placeholder', 'Const', 'Shape', 'Value', 'Fill', 'Conv2D',\n 'BiasAdd', 'Cast', 'Squeeze', 'Squeeze2', 'Split', 'LogicalNot',\n 'FusedBatchNorm', 'Cast2'\n ]);\n });\n });\n\n describe('function level', () => {\n it('should convert the functions', () => {\n expect(Object.keys(convertedGraph.functions)).toEqual([\n '__inference_while_cond_10_49_frozen',\n '__inference_while_body_11_40_frozen'\n ]);\n });\n it('should find the graph input nodes', () => {\n expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']\n .inputs.map(node => node.name))\n .toEqual([\n 'while_loop_counter', 'while_maximum_iterations', 'placeholder',\n 'less_y', 'while_cond_10___redundant_placeholder0'\n ]);\n });\n\n it('should find the graph output nodes', () => {\n expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']\n .outputs.map(node => node.name))\n .toEqual(['Less']);\n });\n\n it('should find the graph weight nodes', () => {\n expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']\n .weights.map(node => node.name))\n .toEqual([]);\n });\n\n it('should convert nodes', () => {\n expect(Object.keys(convertedGraph\n .functions['__inference_while_cond_10_49_frozen']\n .nodes))\n .toEqual([\n 'Less', 'while_loop_counter', 'while_maximum_iterations',\n 'placeholder', 'less_y', 'while_cond_10___redundant_placeholder0'\n ]);\n });\n it('should convert signature', () => {\n expect(convertedGraph.functions['__inference_while_cond_10_49_frozen']\n .signature)\n .toEqual({\n methodName: '__inference_while_cond_10_49_frozen',\n inputs: {\n while_loop_counter: {\n name: 'while_loop_counter',\n dtype: tensorflow.DataType.DT_INT32\n },\n while_maximum_iterations: {\n name: 'while_maximum_iterations',\n dtype: tensorflow.DataType.DT_INT32\n },\n placeholder:\n {name: 'placeholder', dtype: tensorflow.DataType.DT_INT32},\n less_y: {name: 'less_y', dtype: tensorflow.DataType.DT_INT32},\n while_cond_10___redundant_placeholder0: {\n name: 'while_cond_10___redundant_placeholder0',\n dtype: tensorflow.DataType.DT_INT32\n }\n },\n outputs: {\n identity: {name: 'Less:z:0', dtype: tensorflow.DataType.DT_BOOL}\n }\n });\n });\n });\n describe('node level', () => {\n it('should find the input nodes', () => {\n expect(convertedGraph.nodes['Fill'].inputs.map(node => node.name))\n .toEqual(['Shape', 'Value']);\n });\n it('should find the children nodes', () => {\n expect(convertedGraph.nodes['image_placeholder'].children.map(\n node => node.name))\n .toEqual(['Conv2D', 'Split', 'LogicalNot', 'FusedBatchNorm']);\n });\n\n it('should map the input params', () => {\n expect(\n convertedGraph.nodes['Fill'].inputParams['shape'].inputIndexStart)\n .toEqual(0);\n expect(\n convertedGraph.nodes['Fill'].inputParams['value'].inputIndexStart)\n .toEqual(1);\n });\n\n it('should map the attribute params', () => {\n expect(convertedGraph.nodes['Conv2D'].attrParams['strides'].value)\n .toEqual([1, 2, 2, 1]);\n expect(convertedGraph.nodes['Conv2D'].attrParams['pad'].value)\n .toEqual('same');\n expect(convertedGraph.nodes['Conv2D'].attrParams['useCudnnOnGpu'].value)\n .toEqual(true);\n expect(\n convertedGraph.nodes['Split'].attrParams['numOrSizeSplits'].value)\n .toEqual(3);\n expect(\n convertedGraph.nodes['FusedBatchNorm'].attrParams['epsilon'].value)\n .toEqual(0.0001);\n expect(convertedGraph.nodes['Squeeze2'].attrParams['axis'].value)\n .toEqual([]);\n });\n\n it('should map the placeholder attribute params', () => {\n expect(\n convertedGraph.nodes['image_placeholder'].attrParams['shape'].value)\n .toEqual([3, 3, 3, 1]);\n expect(\n convertedGraph.nodes['image_placeholder'].attrParams['dtype'].value)\n .toEqual('float32');\n });\n it('should map params with deprecated name', () => {\n expect(convertedGraph.nodes['Squeeze'].attrParams['axis'].value)\n .toEqual([1, 2]);\n });\n it('should map params with int64 dtype', () => {\n expect(convertedGraph.nodes['Cast'].attrParams['dtype'].value)\n .toEqual('int32');\n });\n });\n });\n});\ndescribe('operationMapper with signature', () => {\n beforeEach(() => {\n convertedGraph = mapper.transformGraph(SIMPLE_MODEL, SIGNATURE);\n });\n afterEach(() => {});\n\n describe('transform graph', () => {\n describe('graph level', () => {\n it('should find the graph input nodes', () => {\n expect(convertedGraph.inputs.map(node => node.name)).toEqual([\n 'image_placeholder'\n ]);\n expect(convertedGraph.inputs.map(node => node.signatureKey)).toEqual([\n 'image'\n ]);\n });\n\n it('should find the graph output nodes', () => {\n expect(convertedGraph.outputs.map(node => node.name)).toEqual([\n 'Squeeze'\n ]);\n expect(convertedGraph.outputs.map(node => node.signatureKey)).toEqual([\n 'squeeze'\n ]);\n });\n\n it('should find the graph weight nodes', () => {\n expect(convertedGraph.weights.map(node => node.name)).toEqual([\n 'Const', 'Shape', 'Value'\n ]);\n });\n\n it('should convert nodes', () => {\n expect(Object.keys(convertedGraph.nodes)).toEqual([\n 'image_placeholder', 'Const', 'Shape', 'Value', 'Fill', 'Conv2D',\n 'BiasAdd', 'Cast', 'Squeeze', 'Squeeze2', 'Split', 'LogicalNot',\n 'FusedBatchNorm', 'Cast2'\n ]);\n });\n });\n\n describe('node level', () => {\n it('should find the input nodes', () => {\n expect(convertedGraph.nodes['Fill'].inputs.map(node => node.name))\n .toEqual(['Shape', 'Value']);\n });\n it('should find the children nodes', () => {\n expect(convertedGraph.nodes['image_placeholder'].children.map(\n node => node.name))\n .toEqual(['Conv2D', 'Split', 'LogicalNot', 'FusedBatchNorm']);\n });\n\n it('should map the input params', () => {\n expect(\n convertedGraph.nodes['Fill'].inputParams['shape'].inputIndexStart)\n .toEqual(0);\n expect(\n convertedGraph.nodes['Fill'].inputParams['value'].inputIndexStart)\n .toEqual(1);\n });\n\n it('should map the attribute params', () => {\n expect(convertedGraph.nodes['Conv2D'].attrParams['strides'].value)\n .toEqual([1, 2, 2, 1]);\n expect(convertedGraph.nodes['Conv2D'].attrParams['pad'].value)\n .toEqual('same');\n expect(convertedGraph.nodes['Conv2D'].attrParams['useCudnnOnGpu'].value)\n .toEqual(true);\n expect(\n convertedGraph.nodes['Split'].attrParams['numOrSizeSplits'].value)\n .toEqual(3);\n expect(\n convertedGraph.nodes['FusedBatchNorm'].attrParams['epsilon'].value)\n .toEqual(0.0001);\n expect(convertedGraph.nodes['Squeeze2'].attrParams['axis'].value)\n .toEqual([]);\n });\n\n it('should map the placeholder attribute params', () => {\n expect(\n convertedGraph.nodes['image_placeholder'].attrParams['shape'].value)\n .toEqual([3, 3, 3, 1]);\n expect(\n convertedGraph.nodes['image_placeholder'].attrParams['dtype'].value)\n .toEqual('float32');\n });\n it('should map params with deprecated name', () => {\n expect(convertedGraph.nodes['Squeeze'].attrParams['axis'].value)\n .toEqual([1, 2]);\n });\n it('should map params with int64 dtype', () => {\n expect(convertedGraph.nodes['Cast'].attrParams['dtype'].value)\n .toEqual('int32');\n });\n it('should map params with uint8 dtype', () => {\n expect(convertedGraph.nodes['Cast2'].attrParams['dtype'].value)\n .toEqual('int32');\n });\n });\n });\n});\n"]} |