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https://github.com/saymrwulf/onnxruntime.git
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59f42cccb8
### Description <!-- Describe your changes. --> refactor tensor type in onnxruntime-common. ### Motivation and Context There major motivation is that I am doing a local change to address the API part of #15312. And I am doing a refactoring of onnxruntime-common anyway (#15772). The `tensor.ts` and `tensor-impl.ts` are too large, so I split contents into multiple files to make the type declarations clearer. The original target of this change is for API only ( ie. do not refactor any implementation.). However, there are a few type/implementation inconsistencies so I also made minimal changes to fix them. ### Changes - extract `TensorUtils` for non-template interfaces - extract `TensorFactory` for all overloads of `Tensor.fromImage()` - refactor options type that used for `Tensor.fromImage()` - fix JSDoc comments to make option descriptions consistent with actual type declarations - fix an inconsistency for `options.format` and `options.bitmapFormat`; change all `bitmapFormat` to `format` - extract `ConversionUtils` for `tensor.toDataURL()` and `tensor.toImageData()` - put implementations into multiple files from `tensor-impl.ts` - fix a bug that cause unittest fail. put comments for future fix.
30 lines
850 B
TypeScript
30 lines
850 B
TypeScript
// Copyright (c) Microsoft Corporation. All rights reserved.
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// Licensed under the MIT License.
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import {Tensor} from './tensor';
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/**
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* calculate size from dims.
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*
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* @param dims the dims array. May be an illegal input.
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*/
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export const calculateSize = (dims: readonly unknown[]): number => {
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let size = 1;
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for (let i = 0; i < dims.length; i++) {
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const dim = dims[i];
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if (typeof dim !== 'number' || !Number.isSafeInteger(dim)) {
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throw new TypeError(`dims[${i}] must be an integer, got: ${dim}`);
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}
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if (dim < 0) {
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throw new RangeError(`dims[${i}] must be a non-negative integer, got: ${dim}`);
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}
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size *= dim;
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}
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return size;
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};
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/**
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* implementation of Tensor.reshape()
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*/
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export const tensorReshape = (tensor: Tensor, dims: readonly number[]): Tensor =>
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new Tensor(tensor.type, tensor.data, dims);
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