gx

chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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/**
 * @license
 * Copyright 2017 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 * as tf from '../index';
import { ALL_ENVS, describeWithFlags } from '../jasmine_util';
import { expectArraysClose } from '../test_util';
import { backend } from '../index';
describeWithFlags('reverse', ALL_ENVS, () => {
    it('throws when passed a non-tensor', () => {
        expect(() => tf.reverse({}))
            .toThrowError(/Argument 'x' passed to 'reverse' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const input = [1, 2, 3];
        const result = tf.reverse(input);
        expect(result.shape).toEqual([3]);
        expectArraysClose(await result.data(), [3, 2, 1]);
    });
    it('works with int32 input', async () => {
        if (backend() && backend().floatPrecision() === 32) {
            // TODO: Use skip() instead when it is implemented
            const input = tf.tensor1d([1, 2, 12345678], 'int32');
            const result = tf.reverse(input);
            expect(result.shape).toEqual([3]);
            expect(result.dtype).toEqual('int32');
            expectArraysClose(await result.data(), [12345678, 2, 1]);
        }
    });
    it('ensure no memory leak', async () => {
        const numTensorsBefore = tf.memory().numTensors;
        const numDataIdBefore = tf.engine().backend.numDataIds();
        const input = tf.tensor1d([1, 2, 3]);
        const result = tf.reverse(input);
        expect(result.shape).toEqual([3]);
        expectArraysClose(await result.data(), [3, 2, 1]);
        input.dispose();
        result.dispose();
        const numTensorsAfter = tf.memory().numTensors;
        const numDataIdAfter = tf.engine().backend.numDataIds();
        expect(numTensorsAfter).toBe(numTensorsBefore);
        expect(numDataIdAfter).toBe(numDataIdBefore);
    });
});
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