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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('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 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"]} |