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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';\n\ndescribeWithFlags('pad 1d', ALL_ENVS, () => {\n it('Should pad 1D arrays', async () => {\n const a = tf.tensor1d([1, 2, 3, 4, 5, 6], 'int32');\n const b = tf.pad1d(a, [2, 3]);\n expectArraysClose(await b.data(), [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);\n });\n\n it('Should not pad 1D arrays with 0s', async () => {\n const a = tf.tensor1d([1, 2, 3, 4], 'int32');\n const b = tf.pad1d(a, [0, 0]);\n expectArraysClose(await b.data(), [1, 2, 3, 4]);\n });\n\n it('Should handle padding with custom value', async () => {\n let a = tf.tensor1d([1, 2, 3, 4], 'int32');\n let b = tf.pad1d(a, [2, 3], 9);\n expectArraysClose(await b.data(), [9, 9, 1, 2, 3, 4, 9, 9, 9]);\n\n a = tf.tensor1d([1, 2, 3, 4]);\n b = tf.pad1d(a, [2, 1], 1.1);\n expectArraysClose(await b.data(), [1.1, 1.1, 1, 2, 3, 4, 1.1]);\n\n a = tf.tensor1d([1, 2, 3, 4]);\n b = tf.pad1d(a, [2, 1], 1);\n expectArraysClose(await b.data(), [1, 1, 1, 2, 3, 4, 1]);\n\n a = tf.tensor1d([1, 2, 3, 4]);\n b = tf.pad1d(a, [2, 1], Number.NEGATIVE_INFINITY);\n expectArraysClose(await b.data(), [\n Number.NEGATIVE_INFINITY, Number.NEGATIVE_INFINITY, 1, 2, 3, 4,\n Number.NEGATIVE_INFINITY\n ]);\n\n a = tf.tensor1d([1, 2, 3, 4]);\n b = tf.pad1d(a, [2, 1], Number.POSITIVE_INFINITY);\n expectArraysClose(await b.data(), [\n Number.POSITIVE_INFINITY, Number.POSITIVE_INFINITY, 1, 2, 3, 4,\n Number.POSITIVE_INFINITY\n ]);\n });\n\n it('Should handle NaNs with 1D arrays', async () => {\n const a = tf.tensor1d([1, NaN, 2, NaN]);\n const b = tf.pad1d(a, [1, 1]);\n expectArraysClose(await b.data(), [0, 1, NaN, 2, NaN, 0]);\n });\n\n it('Should handle invalid paddings', () => {\n const a = tf.tensor1d([1, 2, 3, 4], 'int32');\n const f = () => {\n // tslint:disable-next-line:no-any\n tf.pad1d(a, [2, 2, 2] as any);\n };\n expect(f).toThrowError();\n });\n\n it('grad', async () => {\n const a = tf.tensor1d([1, 2, 3]);\n const dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);\n const da = tf.grad((a: tf.Tensor1D) => tf.pad1d(a, [2, 1]))(a, dy);\n expect(da.shape).toEqual([3]);\n expectArraysClose(await da.data(), [30, 40, 50]);\n });\n\n it('gradient with clones', async () => {\n const a = tf.tensor1d([1, 2, 3]);\n const dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);\n const da =\n tf.grad((a: tf.Tensor1D) => tf.pad1d(a.clone(), [2, 1]).clone())(a, dy);\n expect(da.shape).toEqual([3]);\n expectArraysClose(await da.data(), [30, 40, 50]);\n });\n\n it('accepts a tensor-like object', async () => {\n const a = [1, 2, 3, 4, 5, 6];\n const b = tf.pad1d(a, [2, 3]);\n expectArraysClose(await b.data(), [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);\n });\n});\n\ndescribeWithFlags('pad 2d', ALL_ENVS, () => {\n it('Should pad 2D arrays', async () => {\n let a = tf.tensor2d([[1], [2]], [2, 1], 'int32');\n let b = tf.pad2d(a, [[1, 1], [1, 1]]);\n // 0, 0, 0\n // 0, 1, 0\n // 0, 2, 0\n // 0, 0, 0\n expectArraysClose(await b.data(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);\n\n a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');\n b = tf.pad2d(a, [[2, 2], [1, 1]]);\n // 0, 0, 0, 0, 0\n // 0, 0, 0, 0, 0\n // 0, 1, 2, 3, 0\n // 0, 4, 5, 6, 0\n // 0, 0, 0, 0, 0\n // 0, 0, 0, 0, 0\n expectArraysClose(await b.data(), [\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,\n 0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n ]);\n });\n\n it('Should not pad 2D arrays with 0s', async () => {\n const a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');\n const b = tf.pad2d(a, [[0, 0], [0, 0]]);\n expectArraysClose(await b.data(), [1, 2, 3, 4, 5, 6]);\n });\n\n it('Should handle padding with custom value', async () => {\n let a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');\n let b = tf.pad2d(a, [[1, 1], [1, 1]], 10);\n expectArraysClose(await b.data(), [\n 10, 10, 10, 10, 10, 10, 1, 2, 3, 10,\n 10, 4, 5, 6, 10, 10, 10, 10, 10, 10\n ]);\n\n a = tf.tensor2d([[1], [1]], [2, 1]);\n b = tf.pad2d(a, [[1, 1], [1, 1]], -2.1);\n expectArraysClose(\n await b.data(),\n [-2.1, -2.1, -2.1, -2.1, 1, -2.1, -2.1, 1, -2.1, -2.1, -2.1, -2.1]);\n\n a = tf.tensor2d([[1], [1]], [2, 1]);\n b = tf.pad2d(a, [[1, 1], [1, 1]], -2);\n expectArraysClose(\n await b.data(), [-2, -2, -2, -2, 1, -2, -2, 1, -2, -2, -2, -2]);\n });\n\n it('Should handle NaNs with 2D arrays', async () => {\n const a = tf.tensor2d([[1, NaN], [1, NaN]], [2, 2]);\n const b = tf.pad2d(a, [[1, 1], [1, 1]]);\n // 0, 0, 0, 0\n // 0, 1, NaN, 0\n // 0, 1, NaN, 0\n // 0, 0, 0, 0\n expectArraysClose(\n await b.data(), [0, 0, 0, 0, 0, 1, NaN, 0, 0, 1, NaN, 0, 0, 0, 0, 0]);\n });\n\n it('Should handle invalid paddings', () => {\n const a = tf.tensor2d([[1], [2]], [2, 1], 'int32');\n const f = () => {\n // tslint:disable-next-line:no-any\n tf.pad2d(a, [[2, 2, 2], [1, 1, 1]] as any);\n };\n expect(f).toThrowError();\n });\n\n it('grad', async () => {\n const a = tf.tensor2d([[1, 2], [3, 4]]);\n const dy = tf.tensor2d([[0, 0, 0], [10, 20, 0], [30, 40, 0]], [3, 3]);\n const da =\n tf.grad((a: tf.Tensor2D) => tf.pad2d(a, [[1, 0], [0, 1]]))(a, dy);\n expect(da.shape).toEqual([2, 2]);\n expectArraysClose(await da.data(), [10, 20, 30, 40]);\n });\n\n it('accepts a tensor-like object', async () => {\n const a = [[1, 2, 3], [4, 5, 6]]; // 2x3\n const b = tf.pad2d(a, [[0, 0], [0, 0]]);\n expectArraysClose(await b.data(), [1, 2, 3, 4, 5, 6]);\n });\n});\n\ndescribeWithFlags('pad 3d', ALL_ENVS, () => {\n it('works with 3d tensor, float32', async () => {\n const a = tf.tensor3d([[[1]], [[2]]], [2, 1, 1], 'float32');\n const b = tf.pad3d(a, [[1, 1], [1, 1], [1, 1]]);\n // 0, 0, 0\n // 0, 0, 0\n // 0, 0, 0\n\n // 0, 0, 0\n // 0, 1, 0\n // 0, 0, 0\n\n // 0, 0, 0\n // 0, 2, 0\n // 0, 0, 0\n\n // 0, 0, 0\n // 0, 0, 0\n // 0, 0, 0\n expect(b.shape).toEqual([4, 3, 3]);\n expectArraysClose(await b.data(), [\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n ]);\n });\n});\n\ndescribeWithFlags('pad 4d', ALL_ENVS, () => {\n it('Should pad 4D arrays', async () => {\n const a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');\n const b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);\n const expected = tf.tensor4d(\n [[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1],\n 'int32');\n expectArraysClose(await b.data(), await expected.data());\n expect(b.dtype).toBe('int32');\n expect(b.shape).toEqual([1, 3, 3, 1]);\n });\n\n it('does not leak memory', () => {\n const a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');\n // The first call to pad may create and keeps internal singleton tensors.\n // Subsequent calls should always create exactly one new tensor.\n tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);\n // Count before real call.\n const numTensors = tf.memory().numTensors;\n tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);\n expect(tf.memory().numTensors).toEqual(numTensors + 1);\n });\n\n it('accepts a tensor-like object', async () => {\n const a = [[[[9]]]]; // 1x1x1x1\n const b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);\n const expected = tf.tensor4d(\n [[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1],\n 'float32');\n expectArraysClose(await b.data(), await expected.data());\n expect(b.dtype).toBe('float32');\n expect(b.shape).toEqual([1, 3, 3, 1]);\n });\n});\n\ndescribeWithFlags('pad', ALL_ENVS, () => {\n it('Pad tensor2d', async () => {\n let a = tf.tensor2d([[1], [2]], [2, 1], 'int32');\n let b = tf.pad(a, [[1, 1], [1, 1]]);\n // 0, 0, 0\n // 0, 1, 0\n // 0, 2, 0\n // 0, 0, 0\n expectArraysClose(await b.data(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);\n\n a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');\n b = tf.pad(a, [[2, 2], [1, 1]]);\n // 0, 0, 0, 0, 0\n // 0, 0, 0, 0, 0\n // 0, 1, 2, 3, 0\n // 0, 4, 5, 6, 0\n // 0, 0, 0, 0, 0\n // 0, 0, 0, 0, 0\n expectArraysClose(await b.data(), [\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,\n 0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0\n ]);\n });\n\n it('throws when passed a non-tensor', () => {\n expect(() => tf.pad({} as tf.Tensor, [[0, 0]]))\n .toThrowError(/Argument 'x' passed to 'pad' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const x = [[1], [2]];\n const res = tf.pad(x, [[1, 1], [1, 1]]);\n // 0, 0, 0\n // 0, 1, 0\n // 0, 2, 0\n // 0, 0, 0\n expectArraysClose(await res.data(), [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);\n });\n});\n"]} |