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* @license\n * Copyright 2018 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('batchToSpaceND', ALL_ENVS, () => {\n it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', async () => {\n const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([1, 2, 2, 1]);\n expectArraysClose(await res.data(), [1, 2, 3, 4]);\n });\n\n it('tensor4d, input shape=[4, 1, 1, 3], blockShape=[2, 2]', async () => {\n const t =\n tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 1, 1, 3]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([1, 2, 2, 3]);\n expectArraysClose(\n await res.data(), [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);\n });\n\n it('tensor4d, input shape=[4, 2, 2, 1], blockShape=[2, 2]', async () => {\n const t = tf.tensor4d(\n [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16], [4, 2, 2, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([1, 4, 4, 1]);\n expectArraysClose(\n await res.data(),\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);\n });\n\n it('tensor4d, input shape=[8, 1, 3, 1], blockShape=[2, 2]', async () => {\n const t = tf.tensor4d(\n [\n 0, 1, 3, 0, 9, 11, 0, 2, 4, 0, 10, 12,\n 0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16\n ],\n [8, 1, 3, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [2, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([2, 2, 4, 1]);\n expectArraysClose(\n await res.data(),\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);\n });\n\n it('tensor2d, blockShape [1]', async () => {\n const t = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n const blockShape = [2];\n const crops = [[0, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([1, 4]);\n expectArraysClose(await res.data(), [1, 3, 2, 4]);\n });\n\n it('tensor3d, blockSHape [1]', async () => {\n const t = tf.tensor(\n [\n -61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96,\n 44, -55, -64, -88, -94, 65, -32, -96, -73, -2, -77,\n -14, 47, 33, 15, 70, 20, 75, 28, 84, -13\n ],\n [8, 2, 2]);\n const blockShape = [2];\n const crops = [[0, 2]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([4, 2, 2]);\n expectArraysClose(\n await res.data(),\n [-61, 37, 65, -32, 31, 62, -2, -77, 28, 54, 33, 15, -55, -64, 75, 28]);\n });\n\n it('tensor3d, blockShape [2]', async () => {\n const t = tf.tensor(\n [\n -61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96,\n 44, -55, -64, -88, -94, 65, -32, -96, -73, -2, -77,\n -14, 47, 33, 15, 70, 20, 75, 28, 84, -13\n ],\n [8, 2, 2]);\n const blockShape = [2, 2];\n const crops = [[2, 0], [2, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([2, 2, 2]);\n expectArraysClose(await res.data(), [72, 44, -73, 20, -13, -94, 47, -13]);\n });\n\n it('throws when blockShape equal to input rank', () => {\n const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n const blockShape = [2, 2, 2, 2];\n const crops = [[0, 0], [0, 0], [0, 0], [0, 0]];\n\n expect(() => tf.batchToSpaceND(t, blockShape, crops))\n .toThrowError(\n `input rank is ${t.rank} but should be > than blockShape.length ${\n blockShape.length}`);\n });\n\n it('throws when crops row dimension not equal to blockshape', () => {\n const t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0]];\n\n expect(() => tf.batchToSpaceND(t, blockShape, crops))\n .toThrowError(`crops.length is ${\n crops.length} but should be equal to blockShape.length ${\n blockShape.length}`);\n });\n\n it('throws when input tensor batch not divisible by prod(blockShape)', () => {\n const t = tf.tensor4d([1, 2, 3, 4, 5], [5, 1, 1, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n const prod = blockShape.reduce((a, b) => a * b);\n\n expect(() => tf.batchToSpaceND(t, blockShape, crops))\n .toThrowError(\n `input tensor batch is ${t.shape[0]} but is not divisible by the ` +\n `product of the elements of blockShape ${\n blockShape.join(' * ')} === ${prod}`);\n });\n\n it('accepts a tensor-like object', async () => {\n const t = [[[[1]]], [[[2]]], [[[3]]], [[[4]]]];\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n\n const res = tf.batchToSpaceND(t, blockShape, crops);\n expect(res.shape).toEqual([1, 2, 2, 1]);\n expectArraysClose(await res.data(), [1, 2, 3, 4]);\n });\n\n it('gradients, input shape=[4, 2, 2], block shape=[2]', async () => {\n const t = tf.tensor(\n [-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94],\n [4, 2, 2]);\n const blockShape = [2];\n const crops = [[0, 2]];\n const dy = tf.tensor([.01, .02, .03, .04, .05, .06, .07, .08], [2, 2, 2]);\n\n const gradient =\n tf.grad(t => tf.batchToSpaceND(t, blockShape, crops))(t, dy);\n expect(gradient.shape).toEqual([4, 2, 2]);\n expectArraysClose(await gradient.data(), [\n 0.01, 0.02, 0, 0, 0.05, 0.06, 0, 0, 0.03, 0.04, 0, 0, 0.07, 0.08, 0, 0\n ]);\n });\n\n it('gradients, input shape=[4, 2, 2, 1], block shape=[2, 2]', async () => {\n const t = tf.tensor4d(\n [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16], [4, 2, 2, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n const dy = tf.tensor(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4, 1]);\n\n const gradient =\n tf.grad(t => tf.batchToSpaceND(t, blockShape, crops))(t, dy);\n expect(gradient.shape).toEqual([4, 2, 2, 1]);\n expectArraysClose(\n await gradient.data(),\n [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);\n });\n\n it('gradient with clones, input=[4, 2, 2, 1], block shape=[2, 2]',\n async () => {\n const t = tf.tensor4d(\n [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16],\n [4, 2, 2, 1]);\n const blockShape = [2, 2];\n const crops = [[0, 0], [0, 0]];\n const dy = tf.tensor(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16],\n [1, 4, 4, 1]);\n\n const gradient = tf.grad(\n t => tf.batchToSpaceND(t.clone(), blockShape, crops).clone())(t, dy);\n expect(gradient.shape).toEqual([4, 2, 2, 1]);\n expectArraysClose(\n await gradient.data(),\n [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);\n });\n});\n"]} |