gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @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 { ENGINE } from '../engine';
import { isWebGLData, isWebGPUData } from '../types';
import { assert, assertNonNegativeIntegerDimensions, flatten, inferDtype, isTypedArray, sizeFromShape, toTypedArray } from '../util';
/** This is shared code across all tensor creation methods. */
export function makeTensor(values, shape, inferredShape, dtype) {
    if (dtype == null) {
        dtype = inferDtype(values);
    }
    else if (dtype === 'complex64') {
        throw new Error(`Cannot construct a complex64 tensor directly. ` +
            `Please use tf.complex(real, imag).`);
    }
    if (isWebGPUData(values) || isWebGLData(values)) {
        if (dtype !== 'float32' && dtype !== 'int32') {
            throw new Error(`Creating tensor from GPU data only supports ` +
                `'float32'|'int32' dtype, while the dtype is ${dtype}.`);
        }
        return ENGINE.backend.createTensorFromGPUData(values, shape || inferredShape, dtype);
    }
    if (!isTypedArray(values) && !Array.isArray(values) &&
        typeof values !== 'number' && typeof values !== 'boolean' &&
        typeof values !== 'string') {
        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +
            'an array of numbers/booleans/strings, or a TypedArray');
    }
    // Verify that the shape matches the inferred shape.
    if (shape != null) {
        assertNonNegativeIntegerDimensions(shape);
        const providedSize = sizeFromShape(shape);
        const inferredSize = sizeFromShape(inferredShape);
        assert(providedSize === inferredSize, () => `Based on the provided shape, [${shape}], the tensor should have ` +
            `${providedSize} values but has ${inferredSize}`);
        for (let i = 0; i < inferredShape.length; ++i) {
            const inferred = inferredShape[i];
            const flatDimsDontMatch = i === inferredShape.length - 1 ?
                inferred !== sizeFromShape(shape.slice(i)) :
                true;
            assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, () => `Error creating a new Tensor. Inferred shape ` +
                `(${inferredShape}) does not match the provided ` +
                `shape (${shape}). `);
        }
    }
    if (!isTypedArray(values) && !Array.isArray(values)) {
        values = [values];
    }
    shape = shape || inferredShape;
    values = dtype !== 'string' ?
        toTypedArray(values, dtype) :
        flatten(values, [], true);
    return ENGINE.makeTensor(values, shape, dtype);
}
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