/**
 * @license
 * Copyright 2022 Google LLC.
 * 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 { expectArraysClose } from '../test_util';
/**
 * Test utility for testing AvgPool, MaxPool, etc where kernel size is 1x1,
 * effectively making them act as the identity function except where strides
 * affect the output.
 */
export function identityPoolTest(pool) {
    it('1x1 pool size (identity)', async () => {
        // tslint:disable-next-line: no-unnecessary-type-assertion
        const a = tf.range(0, 10).reshape([1, 1, 1, 10]);
        const result = pool(a, [1, 1], [1, 1], 'valid');
        expectArraysClose(await result.data(), await a.data());
    });
    it('1x1 pool size with strides', async () => {
        // tslint:disable-next-line: no-unnecessary-type-assertion
        const a = tf.range(0, 150).reshape([1, 10, 15, 1]);
        const result = pool(a, [1, 1], [3, 4], 'valid');
        expectArraysClose(await result.data(), [
            0, 4, 8, 12,
            45, 49, 53, 57,
            90, 94, 98, 102,
            135, 139, 143, 147,
        ]);
    });
    it('1x1 pool size batched', async () => {
        // 7 batches of 3 x 4
        const shape = [7, 3, 4, 1];
        const size = shape.reduce((a, b) => a * b, 1);
        // tslint:disable-next-line: no-unnecessary-type-assertion
        const a = tf.range(0, size).reshape(shape);
        const result = pool(a, [1, 1], [1, 1], 'valid');
        expectArraysClose(await result.data(), await a.data());
    });
    it('1x1 pool size batched with strides', async () => {
        // tslint:disable-next-line: no-unnecessary-type-assertion
        const a = tf.range(0, 300).reshape([2, 10, 15, 1]);
        const result = pool(a, [1, 1], [3, 4], 'valid');
        expectArraysClose(await result.data(), [
            // Batch 0
            0, 4, 8, 12,
            45, 49, 53, 57,
            90, 94, 98, 102,
            135, 139, 143, 147,
            // Batch 1
            150, 154, 158, 162,
            195, 199, 203, 207,
            240, 244, 248, 252,
            285, 289, 293, 297,
        ]);
    });
    it('1x1 pool size batched with strides and channels', async () => {
        // tslint:disable-next-line: no-unnecessary-type-assertion
        const a = tf.range(0, 900).reshape([2, 10, 15, 3]);
        const result = pool(a, [1, 1], [3, 4], 'valid');
        expectArraysClose(await result.data(), [
            // Batch 0
            0, 1, 2, 12, 13, 14, 24, 25, 26, 36, 37, 38,
            135, 136, 137, 147, 148, 149, 159, 160, 161, 171, 172, 173,
            270, 271, 272, 282, 283, 284, 294, 295, 296, 306, 307, 308,
            405, 406, 407, 417, 418, 419, 429, 430, 431, 441, 442, 443,
            // Batch 1
            450, 451, 452, 462, 463, 464, 474, 475, 476, 486, 487, 488,
            585, 586, 587, 597, 598, 599, 609, 610, 611, 621, 622, 623,
            720, 721, 722, 732, 733, 734, 744, 745, 746, 756, 757, 758,
            855, 856, 857, 867, 868, 869, 879, 880, 881, 891, 892, 893,
        ]);
    });
}
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* @license\n * Copyright 2022 Google LLC.\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 tf from '../index';\nimport {expectArraysClose} from '../test_util';\n\n/**\n * Test utility for testing AvgPool, MaxPool, etc where kernel size is 1x1,\n * effectively making them act as the identity function except where strides\n * affect the output.\n */\nexport function identityPoolTest(pool: typeof tf.avgPool) {\n  it('1x1 pool size (identity)', async () => {\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const a = tf.range(0, 10).reshape([1, 1, 1, 10]) as tf.Tensor4D;\n    const result = pool(a, [1, 1], [1, 1], 'valid');\n    expectArraysClose(await result.data(), await a.data());\n  });\n\n  it('1x1 pool size with strides', async () => {\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const a = tf.range(0, 150).reshape([1, 10, 15, 1]) as tf.Tensor4D;\n    const result = pool(a, [1, 1], [3, 4], 'valid');\n    expectArraysClose(await result.data(), [\n      0, 4, 8, 12,\n      45, 49, 53, 57,\n      90, 94, 98, 102,\n      135, 139, 143, 147,\n    ]);\n  });\n\n  it('1x1 pool size batched', async () => {\n    // 7 batches of 3 x 4\n    const shape = [7, 3, 4, 1];\n    const size = shape.reduce((a, b) => a * b, 1);\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const a = tf.range(0, size).reshape(shape) as tf.Tensor4D;\n    const result = pool(a, [1, 1], [1, 1], 'valid');\n    expectArraysClose(await result.data(), await a.data());\n  });\n\n  it('1x1 pool size batched with strides', async () => {\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const a = tf.range(0, 300).reshape([2, 10, 15, 1]) as tf.Tensor4D;\n    const result = pool(a, [1, 1], [3, 4], 'valid');\n    expectArraysClose(await result.data(), [\n      // Batch 0\n      0, 4, 8, 12,\n      45, 49, 53, 57,\n      90, 94, 98, 102,\n      135, 139, 143, 147,\n      // Batch 1\n      150, 154, 158, 162,\n      195, 199, 203, 207,\n      240, 244, 248, 252,\n      285, 289, 293, 297,\n    ]);\n  });\n\n  it('1x1 pool size batched with strides and channels', async () => {\n    // tslint:disable-next-line: no-unnecessary-type-assertion\n    const a = tf.range(0, 900).reshape([2, 10, 15, 3]) as tf.Tensor4D;\n    const result = pool(a, [1, 1], [3, 4], 'valid');\n    expectArraysClose(await result.data(), [\n      // Batch 0\n      0, 1, 2, 12, 13, 14, 24, 25, 26, 36, 37, 38,\n      135, 136, 137, 147, 148, 149, 159, 160, 161, 171, 172, 173,\n      270, 271, 272, 282, 283, 284, 294, 295, 296, 306, 307, 308,\n      405, 406, 407, 417, 418, 419, 429, 430, 431, 441, 442, 443,\n      // Batch 1\n      450, 451, 452, 462, 463, 464, 474, 475, 476, 486, 487, 488,\n      585, 586, 587, 597, 598, 599, 609, 610, 611, 621, 622, 623,\n      720, 721, 722, 732, 733, 734, 744, 745, 746, 756, 757, 758,\n      855, 856, 857, 867, 868, 869, 879, 880, 881, 891, 892, 893,\n    ]);\n  });\n}\n"]}