data2/LocalFaceRecognition.git

/**
 * @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';
describeWithFlags('conv1d', ALL_ENVS, () => {
    it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=explicit', async () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = [[0, 0], [0, 0], [0, 0], [0, 0]];
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [3, 6, 9, 12]);
    });
    it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=same', async () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [3, 6, 9, 12]);
    });
    it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=1,p=valid', async () => {
        const inputDepth = 1;
        const inputShape = [4, inputDepth];
        const outputDepth = 1;
        const fSize = 2;
        const pad = 'valid';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor2d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        expect(result.shape).toEqual([3, 1]);
        expectArraysClose(await result.data(), [4, 7, 10]);
    });
    it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=2,p=valid', async () => {
        const inputDepth = 1;
        const inputShape = [4, inputDepth];
        const outputDepth = 1;
        const fSize = 2;
        const fSizeDilated = 3;
        const pad = 'valid';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 2;
        const dilationWEffective = 1;
        const x = tf.tensor2d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);
        // adding a dilation rate is equivalent to using a filter
        // with 0s for the dilation rate
        const wDilated = tf.tensor3d([2, 0, 1], [fSizeDilated, inputDepth, outputDepth]);
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        const expectedResult = tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);
        expect(result.shape).toEqual(expectedResult.shape);
        expectArraysClose(await result.data(), await expectedResult.data());
    });
    it('conv1d input=14x1,d2=1,f=3x1x1,s=1,d=3,p=valid', async () => {
        const inputDepth = 1;
        const inputShape = [14, inputDepth];
        const outputDepth = 1;
        const fSize = 3;
        const fSizeDilated = 7;
        const pad = 'valid';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 3;
        const dilationWEffective = 1;
        const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);
        const w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);
        // adding a dilation rate is equivalent to using a filter
        // with 0s for the dilation rate
        const wDilated = tf.tensor3d([3, 0, 0, 2, 0, 0, 1], [fSizeDilated, inputDepth, outputDepth]);
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        const expectedResult = tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);
        expect(result.shape).toEqual(expectedResult.shape);
        expectArraysClose(await result.data(), await expectedResult.data());
    });
    it('throws when dimRoundingMode is set and pad is same', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const dimRoundingMode = 'round';
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
            .toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is valid', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = 'valid';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const dimRoundingMode = 'round';
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
            .toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is a non-integer number', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = 1.2;
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const dimRoundingMode = 'round';
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
            .toThrowError();
    });
    it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +
        'number', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]];
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const dimRoundingMode = 'round';
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))
            .toThrowError();
    });
    it('TensorLike', async () => {
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = [[[1], [2]], [[3], [4]]];
        const w = [[[3]]];
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [3, 6, 9, 12]);
    });
    it('TensorLike Chained', async () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = [[[3]]];
        const result = x.conv1d(w, stride, pad, dataFormat, dilation);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [3, 6, 9, 12]);
    });
    it('throws when x is not rank 3', () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const fSize = 2;
        const pad = 0;
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        // tslint:disable-next-line:no-any
        const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
        const w = tf.tensor3d([3, 1], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
            .toThrowError();
    });
    it('throws when weights is not rank 3', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const pad = 0;
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        // tslint:disable-next-line:no-any
        const w = tf.tensor4d([3, 1, 5, 0], [2, 2, 1, 1]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
            .toThrowError();
    });
    it('throws when x depth does not match weight depth', () => {
        const inputDepth = 1;
        const wrongInputDepth = 5;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 2;
        const pad = 0;
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.randomNormal([fSize, wrongInputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
            .toThrowError();
    });
    it('throws when stride is less than or equal to 0', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = [[0, 0], [0, 0], [0, 0], [0, 0]];
        const stride = 0;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
            .toThrowError();
    });
    it('throws when dilation is less than or equal to 0', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = [[0, 0], [0, 0], [0, 0], [0, 0]];
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 0;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
            .toThrowError();
    });
    it('throws when both stride and dilation are greater than 1', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const pad = 'same';
        const stride = 2;
        const dataFormat = 'NWC';
        const dilation = 2;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))
            .toThrowError();
    });
    it('throws when passed x as a non-tensor', () => {
        const inputDepth = 1;
        const outputDepth = 1;
        const fSize = 1;
        const pad = 'same';
        const stride = 2;
        const dataFormat = 'NWC';
        const dilation = 2;
        const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
        expect(() => tf.conv1d({}, w, stride, pad, dataFormat, dilation))
            .toThrowError(/Argument 'x' passed to 'conv1d' must be a Tensor/);
    });
    it('throws when passed filter as a non-tensor', () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const pad = 'same';
        const stride = 2;
        const dataFormat = 'NWC';
        const dilation = 2;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        expect(() => tf.conv1d(x, {}, stride, pad, dataFormat, dilation))
            .toThrowError(/Argument 'filter' passed to 'conv1d' must be a Tensor/);
    });
    it('accepts a tensor-like object', async () => {
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = [[[1], [2]], [[3], [4]]]; // 2x2x1
        const w = [[[3]]]; // 1x1x1
        const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
        expect(result.shape).toEqual([2, 2, 1]);
        expectArraysClose(await result.data(), [3, 6, 9, 12]);
    });
    it('gradient with clones, input=2x2x1,d2=1,f=1,s=1,d=1,p=same', async () => {
        const inputDepth = 1;
        const inputShape = [2, 2, inputDepth];
        const outputDepth = 1;
        const fSize = 1;
        const filterShape = [fSize, inputDepth, outputDepth];
        const pad = 'same';
        const stride = 1;
        const dataFormat = 'NWC';
        const dilation = 1;
        const x = tf.tensor3d([1, 2, 3, 4], inputShape);
        const w = tf.tensor3d([3], filterShape);
        const dy = tf.tensor3d([3, 2, 1, 0], inputShape);
        const grads = tf.grads((x, w) => tf.conv1d(x.clone(), w.clone(), stride, pad, dataFormat, dilation)
            .clone());
        const [dx, dw] = grads([x, w], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [9, 6, 3, 0]);
        expect(dw.shape).toEqual(w.shape);
        expectArraysClose(await dw.data(), [10]);
    });
    it('conv1d gradients input=14x1,d2=1,f=3x1x1,s=1,p=valid', async () => {
        const inputDepth = 1;
        const inputShape = [14, inputDepth];
        const outputDepth = 1;
        const fSize = 3;
        const pad = 'valid';
        const stride = 1;
        const dataFormat = 'NWC';
        const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);
        const w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);
        const dy = tf.tensor2d([3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0], [12, inputDepth]);
        const grads = tf.grads((x, w) => tf.conv1d(x, w, stride, pad, dataFormat));
        const [dx, dw] = grads([x, w], dy);
        expect(dx.shape).toEqual(x.shape);
        expectArraysClose(await dx.data(), [9, 12, 10, 4, 10, 12, 10, 4, 10, 12, 10, 4, 1, 0]);
        expect(dw.shape).toEqual(w.shape);
        expectArraysClose(await dw.data(), [102, 120, 138]);
    });
});
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* @license\n * Copyright 2017 Google LLC. 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 * as tf from '../index';\nimport {ALL_ENVS, describeWithFlags} from '../jasmine_util';\nimport {expectArraysClose} from '../test_util';\nimport {Rank} from '../types';\n\ndescribeWithFlags('conv1d', ALL_ENVS, () => {\n  it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=explicit', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad =\n        [[0, 0], [0, 0], [0, 0], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 6, 9, 12]);\n  });\n\n  it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=same', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 6, 9, 12]);\n  });\n\n  it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=1,p=valid', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number] = [4, inputDepth];\n    const outputDepth = 1;\n    const fSize = 2;\n    const pad = 'valid';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor2d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n\n    expect(result.shape).toEqual([3, 1]);\n    expectArraysClose(await result.data(), [4, 7, 10]);\n  });\n\n  it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=2,p=valid', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number] = [4, inputDepth];\n    const outputDepth = 1;\n    const fSize = 2;\n    const fSizeDilated = 3;\n    const pad = 'valid';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 2;\n    const dilationWEffective = 1;\n\n    const x = tf.tensor2d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);\n    // adding a dilation rate is equivalent to using a filter\n    // with 0s for the dilation rate\n    const wDilated =\n        tf.tensor3d([2, 0, 1], [fSizeDilated, inputDepth, outputDepth]);\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n    const expectedResult =\n        tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);\n\n    expect(result.shape).toEqual(expectedResult.shape);\n    expectArraysClose(await result.data(), await expectedResult.data());\n  });\n\n  it('conv1d input=14x1,d2=1,f=3x1x1,s=1,d=3,p=valid', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number] = [14, inputDepth];\n    const outputDepth = 1;\n    const fSize = 3;\n    const fSizeDilated = 7;\n    const pad = 'valid';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 3;\n    const dilationWEffective = 1;\n\n    const x = tf.tensor2d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);\n    const w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);\n    // adding a dilation rate is equivalent to using a filter\n    // with 0s for the dilation rate\n    const wDilated = tf.tensor3d(\n        [3, 0, 0, 2, 0, 0, 1], [fSizeDilated, inputDepth, outputDepth]);\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n    const expectedResult =\n        tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);\n\n    expect(result.shape).toEqual(expectedResult.shape);\n    expectArraysClose(await result.data(), await expectedResult.data());\n  });\n\n  it('throws when dimRoundingMode is set and pad is same', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n    const dimRoundingMode = 'round';\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    expect(\n        () =>\n            tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is valid', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad = 'valid';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n    const dimRoundingMode = 'round';\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    expect(\n        () =>\n            tf.conv1d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode))\n        .toThrowError();\n  });\n\n  it('throws when dimRoundingMode is set and pad is a non-integer number',\n     () => {\n       const inputDepth = 1;\n       const inputShape: [number, number, number] = [2, 2, inputDepth];\n       const outputDepth = 1;\n       const fSize = 1;\n       const pad = 1.2;\n       const stride = 1;\n       const dataFormat = 'NWC';\n       const dilation = 1;\n       const dimRoundingMode = 'round';\n\n       const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n       const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n       expect(\n           () => tf.conv1d(\n               x, w, stride, pad, dataFormat, dilation, dimRoundingMode))\n           .toThrowError();\n     });\n\n  it('throws when dimRoundingMode is set and pad is explicit by non-integer ' +\n         'number',\n     () => {\n       const inputDepth = 1;\n       const inputShape: [number, number, number] = [2, 2, inputDepth];\n       const outputDepth = 1;\n       const fSize = 1;\n       const pad = [[0, 0], [0, 2.1], [1, 1], [0, 0]] as\n           tf.backend_util.ExplicitPadding;\n       const stride = 1;\n       const dataFormat = 'NWC';\n       const dilation = 1;\n       const dimRoundingMode = 'round';\n\n       const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n       const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n       expect(\n           () => tf.conv1d(\n               x, w, stride, pad, dataFormat, dilation, dimRoundingMode))\n           .toThrowError();\n     });\n\n  it('TensorLike', async () => {\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = [[[1], [2]], [[3], [4]]];\n    const w = [[[3]]];\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 6, 9, 12]);\n  });\n  it('TensorLike Chained', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = [[[3]]];\n\n    const result = x.conv1d(w, stride, pad, dataFormat, dilation);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 6, 9, 12]);\n  });\n\n  it('throws when x is not rank 3', () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const fSize = 2;\n    const pad = 0;\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    // tslint:disable-next-line:no-any\n    const x: any = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n    const w = tf.tensor3d([3, 1], [fSize, inputDepth, outputDepth]);\n\n    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))\n        .toThrowError();\n  });\n\n  it('throws when weights is not rank 3', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const pad = 0;\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    // tslint:disable-next-line:no-any\n    const w: any = tf.tensor4d([3, 1, 5, 0], [2, 2, 1, 1]);\n\n    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))\n        .toThrowError();\n  });\n\n  it('throws when x depth does not match weight depth', () => {\n    const inputDepth = 1;\n    const wrongInputDepth = 5;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 2;\n    const pad = 0;\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.randomNormal<Rank.R3>([fSize, wrongInputDepth, outputDepth]);\n\n    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))\n        .toThrowError();\n  });\n\n  it('throws when stride is less than or equal to 0', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad =\n        [[0, 0], [0, 0], [0, 0], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const stride = 0;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))\n        .toThrowError();\n  });\n\n  it('throws when dilation is less than or equal to 0', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad =\n        [[0, 0], [0, 0], [0, 0], [0, 0]] as tf.backend_util.ExplicitPadding;\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 0;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))\n        .toThrowError();\n  });\n\n  it('throws when both stride and dilation are greater than 1', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 2;\n    const dataFormat = 'NWC';\n    const dilation = 2;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    expect(() => tf.conv1d(x, w, stride, pad, dataFormat, dilation))\n        .toThrowError();\n  });\n\n  it('throws when passed x as a non-tensor', () => {\n    const inputDepth = 1;\n    const outputDepth = 1;\n    const fSize = 1;\n    const pad = 'same';\n    const stride = 2;\n    const dataFormat = 'NWC';\n    const dilation = 2;\n\n    const w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);\n\n    expect(\n        () =>\n            tf.conv1d({} as tf.Tensor3D, w, stride, pad, dataFormat, dilation))\n        .toThrowError(/Argument 'x' passed to 'conv1d' must be a Tensor/);\n  });\n\n  it('throws when passed filter as a non-tensor', () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const pad = 'same';\n    const stride = 2;\n    const dataFormat = 'NWC';\n    const dilation = 2;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n\n    expect(\n        () =>\n            tf.conv1d(x, {} as tf.Tensor3D, stride, pad, dataFormat, dilation))\n        .toThrowError(/Argument 'filter' passed to 'conv1d' must be a Tensor/);\n  });\n\n  it('accepts a tensor-like object', async () => {\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n    const x = [[[1], [2]], [[3], [4]]];  // 2x2x1\n    const w = [[[3]]];                   // 1x1x1\n\n    const result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);\n\n    expect(result.shape).toEqual([2, 2, 1]);\n    expectArraysClose(await result.data(), [3, 6, 9, 12]);\n  });\n\n  it('gradient with clones, input=2x2x1,d2=1,f=1,s=1,d=1,p=same', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number, number] = [2, 2, inputDepth];\n    const outputDepth = 1;\n    const fSize = 1;\n    const filterShape: [number, number, number] =\n        [fSize, inputDepth, outputDepth];\n    const pad = 'same';\n    const stride = 1;\n    const dataFormat = 'NWC';\n    const dilation = 1;\n\n    const x = tf.tensor3d([1, 2, 3, 4], inputShape);\n    const w = tf.tensor3d([3], filterShape);\n\n    const dy = tf.tensor3d([3, 2, 1, 0], inputShape);\n\n    const grads = tf.grads(\n        (x: tf.Tensor3D, w: tf.Tensor3D) =>\n            tf.conv1d(x.clone(), w.clone(), stride, pad, dataFormat, dilation)\n                .clone());\n    const [dx, dw] = grads([x, w], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(await dx.data(), [9, 6, 3, 0]);\n\n    expect(dw.shape).toEqual(w.shape);\n    expectArraysClose(await dw.data(), [10]);\n  });\n\n  it('conv1d gradients input=14x1,d2=1,f=3x1x1,s=1,p=valid', async () => {\n    const inputDepth = 1;\n    const inputShape: [number, number] = [14, inputDepth];\n\n    const outputDepth = 1;\n    const fSize = 3;\n    const pad = 'valid';\n    const stride = 1;\n    const dataFormat = 'NWC';\n\n    const x = tf.tensor2d(\n        [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);\n    const w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);\n\n    const dy =\n        tf.tensor2d([3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0], [12, inputDepth]);\n\n    const grads = tf.grads(\n        (x: tf.Tensor2D, w: tf.Tensor3D) =>\n            tf.conv1d(x, w, stride, pad, dataFormat));\n    const [dx, dw] = grads([x, w], dy);\n\n    expect(dx.shape).toEqual(x.shape);\n    expectArraysClose(\n        await dx.data(), [9, 12, 10, 4, 10, 12, 10, 4, 10, 12, 10, 4, 1, 0]);\n\n    expect(dw.shape).toEqual(w.shape);\n    expectArraysClose(await dw.data(), [102, 120, 138]);\n  });\n});\n"]}