@tensorflow/tfjs-converter
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Tensorflow model converter for javascript
59 lines • 13.7 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) => {
switch (node.op) {
case 'ResizeBilinear': {
const images = getParamValue('images', node, tensorMap, context);
const size = getParamValue('size', node, tensorMap, context);
const alignCorners = getParamValue('alignCorners', node, tensorMap, context);
const halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context);
return [tfOps.image.resizeBilinear(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
}
case 'ResizeNearestNeighbor': {
const images = getParamValue('images', node, tensorMap, context);
const size = getParamValue('size', node, tensorMap, context);
const alignCorners = getParamValue('alignCorners', node, tensorMap, context);
const halfPixelCenters = getParamValue('halfPixelCenters', node, tensorMap, context);
return [tfOps.image.resizeNearestNeighbor(images, [size[0], size[1]], alignCorners, halfPixelCenters)];
}
case 'CropAndResize': {
const image = getParamValue('image', node, tensorMap, context);
const boxes = getParamValue('boxes', node, tensorMap, context);
const boxInd = getParamValue('boxInd', node, tensorMap, context);
const cropSize = getParamValue('cropSize', node, tensorMap, context);
const method = getParamValue('method', node, tensorMap, context);
const extrapolationValue = getParamValue('extrapolationValue', node, tensorMap, context);
return [tfOps.image.cropAndResize(image, boxes, boxInd, cropSize, method, extrapolationValue)];
}
case 'ImageProjectiveTransformV3': {
const images = getParamValue('images', node, tensorMap, context);
const transforms = getParamValue('transforms', node, tensorMap, context);
const outputShape = getParamValue('outputShape', node, tensorMap, context);
const fillValue = getParamValue('fillValue', node, tensorMap, context);
const interpolation = getParamValue('interpolation', node, tensorMap, context);
const fillMode = getParamValue('fillMode', node, tensorMap, context);
return [tfOps.image.transform(images, transforms, interpolation.toLowerCase(), fillMode.toLowerCase(), fillValue, outputShape)];
}
default:
throw TypeError(`Node type ${node.op} is not implemented`);
}
};
export const CATEGORY = 'image';
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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 {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} 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,\n     context: ExecutionContext): Tensor[] => {\n      switch (node.op) {\n        case 'ResizeBilinear': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number[];\n          const alignCorners =\n              getParamValue('alignCorners', node, tensorMap, context) as\n              boolean;\n          const halfPixelCenters =\n              getParamValue('halfPixelCenters', node, tensorMap, context) as\n              boolean;\n          return [tfOps.image.resizeBilinear(\n              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,\n              halfPixelCenters)];\n        }\n        case 'ResizeNearestNeighbor': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const size =\n              getParamValue('size', node, tensorMap, context) as number[];\n          const alignCorners =\n              getParamValue('alignCorners', node, tensorMap, context) as\n              boolean;\n          const halfPixelCenters =\n              getParamValue('halfPixelCenters', node, tensorMap, context) as\n              boolean;\n          return [tfOps.image.resizeNearestNeighbor(\n              images as Tensor3D | Tensor4D, [size[0], size[1]], alignCorners,\n              halfPixelCenters)];\n        }\n        case 'CropAndResize': {\n          const image =\n              getParamValue('image', node, tensorMap, context) as Tensor;\n          const boxes =\n              getParamValue('boxes', node, tensorMap, context) as Tensor;\n          const boxInd =\n              getParamValue('boxInd', node, tensorMap, context) as Tensor;\n          const cropSize =\n              getParamValue('cropSize', node, tensorMap, context) as number[];\n          const method =\n              getParamValue('method', node, tensorMap, context) as string;\n          const extrapolationValue =\n              getParamValue('extrapolationValue', node, tensorMap, context) as\n              number;\n          return [tfOps.image.cropAndResize(\n              image as Tensor4D, boxes as Tensor2D, boxInd as Tensor1D,\n              cropSize as [number, number], method as 'bilinear' | 'nearest',\n              extrapolationValue)];\n        }\n        case 'ImageProjectiveTransformV3': {\n          const images =\n              getParamValue('images', node, tensorMap, context) as Tensor;\n          const transforms =\n              getParamValue('transforms', node, tensorMap, context) as Tensor;\n          const outputShape =\n              getParamValue('outputShape', node, tensorMap, context) as\n              number[];\n          const fillValue =\n              getParamValue('fillValue', node, tensorMap, context) as number;\n          const interpolation =\n              getParamValue('interpolation', node, tensorMap, context) as\n              string;\n          const fillMode =\n              getParamValue('fillMode', node, tensorMap, context) as string;\n          return [tfOps.image.transform(\n              images as Tensor4D,\n              transforms as Tensor2D,\n              interpolation.toLowerCase() as 'bilinear' | 'nearest',\n              fillMode.toLowerCase() as 'constant' | 'reflect' | 'wrap' | 'nearest',\n              fillValue,\n              outputShape as [number, number])];\n        }\n        default:\n          throw TypeError(`Node type ${node.op} is not implemented`);\n      }\n    };\n\nexport const CATEGORY = 'image';\n"]}