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* @license\n * Copyright 2020 Google Inc. 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('gather', ALL_ENVS, () => {\n it('1D (gather), scalar indices', async () => {\n const t = tf.tensor1d([1, 2, 3]);\n\n const t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);\n\n expect(t2.shape).toEqual([]);\n expectArraysClose(await t2.data(), [2]);\n });\n\n it('1D (gather), 1D indices', async () => {\n const t = tf.tensor1d([1, 2, 3]);\n\n const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n expect(t2.shape).toEqual([4]);\n expectArraysClose(await t2.data(), [1, 3, 1, 2]);\n });\n\n it('1D (gather), 2D indices', async () => {\n const t = tf.tensor1d([1, 2, 3]);\n\n const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [1, 4], 'int32'), 0);\n\n expect(t2.shape).toEqual([1, 4]);\n expectArraysClose(await t2.data(), [1, 3, 1, 2]);\n });\n\n it('2D (gather), scalar indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n let t2 = tf.gather(t, tf.scalar(1, 'int32'), 0);\n expect(t2.shape).toEqual([2]);\n expectArraysClose(await t2.data(), [2, 22]);\n\n t2 = tf.gather(t, tf.scalar(1, 'int32'), 1);\n expect(t2.shape).toEqual([2]);\n expectArraysClose(await t2.data(), [11, 22]);\n });\n\n it('2D (gather), 1D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n let t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 0);\n expect(t2.shape).toEqual([4, 2]);\n expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);\n\n t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 1);\n expect(t2.shape).toEqual([2, 4]);\n expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);\n });\n\n it('2D (gather), 2D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n let t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 0);\n expect(t2.shape).toEqual([2, 2, 2]);\n expectArraysClose(await t2.data(), [2, 22, 1, 11, 1, 11, 2, 22]);\n\n t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1);\n expect(t2.shape).toEqual([2, 2, 2]);\n expectArraysClose(await t2.data(), [11, 1, 1, 11, 22, 2, 2, 22]);\n });\n\n it('2D (gather), 2D indices, non-zero batchDims', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, 1);\n expect(t2.shape).toEqual([2, 2]);\n expectArraysClose(await t2.data(), [11, 1, 2, 22]);\n });\n\n it('2D (gather), 2D indices, negative batchDims', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 1, -1);\n expect(t2.shape).toEqual([2, 2]);\n expectArraysClose(await t2.data(), [11, 1, 2, 22]);\n });\n\n it('3D (gather), 1D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n const t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 2);\n\n expect(t2.shape).toEqual([2, 2, 4]);\n expectArraysClose(\n await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);\n });\n\n it('3D (gather), 2D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n const t2 = tf.gather(t, tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32'), 2);\n\n expect(t2.shape).toEqual([2, 2, 2, 2]);\n expectArraysClose(\n await t2.data(), [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);\n });\n\n it('3D (gather), 2D indices, non-zero batchDims', async () => {\n const t = tf.tensor3d([1, 2, 3, 4], [1, 2, 2]);\n\n const t2 = tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1);\n\n expect(t2.shape).toEqual([1, 2, 3]);\n expectArraysClose(await t2.data(), [2, 1, 2, 4, 3, 4]);\n });\n\n it('throws when batch dims greater than axis', () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n expect(() => tf.gather(t, tf.tensor3d([1, 0, 1], [1, 1, 3], 'int32'), 2, 3))\n .toThrowError(/must be less than or equal to axis/);\n });\n\n it('throws when batch dims greater than indices rank', () => {\n const t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);\n\n expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 3))\n .toThrowError(/Expect batchDims in the range of /);\n });\n\n it('throws when batch dims do not match', () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);\n\n expect(() => tf.gather(t, tf.tensor2d([1, 0, 1], [1, 3], 'int32'), 2, 1))\n .toThrowError(/should be equal to indices.shape/);\n });\n\n it('bool (gather), 1D indices', async () => {\n const t = tf.tensor1d([true, false, true], 'bool');\n\n const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n expect(t2.shape).toEqual([4]);\n expect(t2.dtype).toBe('bool');\n expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));\n });\n\n it('bool (gather), 2D indices', async () => {\n const t = tf.tensor1d([true, false, true], 'bool');\n\n const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);\n\n expect(t2.shape).toEqual([2, 2]);\n expect(t2.dtype).toBe('bool');\n expect(await t2.data()).toEqual(new Uint8Array([1, 1, 1, 0]));\n });\n\n it('int32 (gather), 1D indices', async () => {\n const t = tf.tensor1d([1, 2, 5], 'int32');\n\n const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n expect(t2.shape).toEqual([4]);\n expect(t2.dtype).toBe('int32');\n expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));\n });\n\n it('int32 (gather), 2D indices', async () => {\n const t = tf.tensor1d([1, 2, 5], 'int32');\n\n const t2 = tf.gather(t, tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32'), 0);\n\n expect(t2.shape).toEqual([2, 2]);\n expect(t2.dtype).toBe('int32');\n expect(await t2.data()).toEqual(new Int32Array([1, 5, 1, 2]));\n });\n\n it('propagates NaNs', async () => {\n const t = tf.tensor1d([1, 2, NaN]);\n\n const t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);\n\n expect(t2.shape).toEqual([4]);\n expectArraysClose(await t2.data(), [1, NaN, 1, 2]);\n });\n\n it('chaining, axis=1', () => {\n const x = tf.zeros([2, 4, 6]);\n // [0, 2, 4]\n const indices = tf.range(0, 6, 2, 'int32');\n const axis = 2;\n expect(x.gather(indices, axis).shape).toEqual([2, 4, 3]);\n });\n\n it('indices not int32 throws error', () => {\n const x = tf.zeros([2, 4, 6]);\n // [0, 2, 4]\n const indices = tf.range(0, 6, 2);\n const axis = 2;\n expect(() => x.gather(indices, axis)).toThrowError();\n });\n\n it('throws when passed x as a non-tensor', () => {\n expect(() => tf.gather({} as tf.Tensor, tf.tensor1d([1])))\n .toThrowError(/Argument 'x' passed to 'gather' must be a Tensor/);\n });\n\n it('throws when passed indices as a non-tensor', () => {\n // tslint:disable-next-line:no-any\n expect(() => tf.gather(tf.tensor1d([1]), {} as any))\n .toThrowError(/Argument 'indices' passed to 'gather' must be a Tensor/);\n });\n\n it('accepts a tensor-like object', async () => {\n const res = tf.gather([1, 2, 3], [0, 2, 0, 1], 0);\n expect(res.shape).toEqual([4]);\n expectArraysClose(await res.data(), [1, 3, 1, 2]);\n });\n\n it('gradient 1D (gather), 1D indices', async () => {\n const t = tf.tensor1d([1, 2, 3]);\n const indices = tf.tensor1d([0, 2, 0, 1], 'int32');\n const dy = tf.tensor([3, 4, 5, 6]);\n\n const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [8, 6, 4]);\n });\n\n it('gradient with clones', () => {\n const t = tf.tensor1d([1, 2, 3]);\n const indices = tf.tensor1d([0, 2, 0, 1], 'int32');\n const gradF = tf.grad(t => tf.gather(t.clone(), indices.clone()).clone());\n const dt = gradF(t);\n expect(dt.shape).toEqual(t.shape);\n });\n\n it('gradient 1D (gather), 2D indices', async () => {\n const t = tf.tensor1d([1, 2, 3]);\n const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');\n const dy = tf.tensor2d([3, 4, 5, 6], [2, 2]);\n\n const gradients = tf.grad(t => tf.gather(t, indices))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [8, 6, 4]);\n });\n\n it('gradient 2D (gather) axis=0 shape=[2, 2] 1D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [4, 2]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [12, 14, 12, 14]);\n });\n\n it('gradient 2D (gather) axis=0 shape=[2, 2] 2D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [12, 14, 12, 14]);\n });\n\n it('gradient 2D (gather) axis=0 shape=[4, 1] 1D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n const dy = tf.tensor([23, 7, 19, 13], [4, 1]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [26, 36, 0, 0]);\n });\n\n it('gradient 2D (gather) axis=0 shape=[4, 1] 2D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n const dy = tf.tensor([23, 7, 19, 13], [2, 2, 1]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [26, 36, 0, 0]);\n });\n\n it('gradient 2D (gather) axis=1 shape=[2, 2] 1D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 4]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [9, 9, 17, 17]);\n });\n\n it('gradient 2D (gather) axis=1 shape=[2, 2] 2D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [2, 2]);\n const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n const dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 2, 2]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [9, 9, 17, 17]);\n });\n\n it('gradient 2D (gather) axis=1 shape=[4, 1] 1D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n const indices = tf.tensor1d([0, 0, 0, 0], 'int32');\n const dy = tf.tensor(\n [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 4]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [18, 34, 50, 66]);\n });\n\n it('gradient 2D (gather) axis=1 shape=[4, 1] 2D indices', async () => {\n const t = tf.tensor2d([1, 11, 2, 22], [4, 1]);\n const indices = tf.tensor2d([0, 0, 0, 0], [2, 2], 'int32');\n const dy = tf.tensor(\n [3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 2, 2]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(await gradients.data(), [18, 34, 50, 66]);\n });\n\n it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 1D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n const indices = tf.tensor1d([1, 0, 0, 1], 'int32');\n const dy = tf.tensor(\n [\n 2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,\n 4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25\n ],\n [4, 3, 2]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);\n });\n\n it('gradient 3D (gather) axis=0 shape=[2, 3, 2] 2D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n const indices = tf.tensor2d([1, 0, 0, 1], [2, 2], 'int32');\n const dy = tf.tensor(\n [\n 2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,\n 4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25\n ],\n [2, 2, 3, 2]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);\n });\n\n it('gradient 3D (gather) axis=0 shape=[1, 4, 4]', async () => {\n const t = tf.tensor3d(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n const indices = tf.tensor1d([0, 0], 'int32');\n const dy = tf.tensor(\n [\n 2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,\n 18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4\n ],\n [2, 4, 4]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);\n });\n\n it('gradient 3D (gather) axis=0 shape=[1, 4, 4] 1D indices', async () => {\n const t = tf.tensor3d(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n const indices = tf.tensor1d([0, 0], 'int32');\n const dy = tf.tensor(\n [\n 2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,\n 18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4\n ],\n [2, 4, 4]);\n const axis = 0;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);\n });\n\n it('gradient 3D (gather) axis=1 shape=[2, 3, 2] 2D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');\n const dy = tf.tensor(\n [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],\n [2, 2, 2, 2]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [0, 0, 3, 15, 10, 15.7, 0, 0, 12.01, -7, 16, 28]);\n });\n\n it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 1D indices', async () => {\n const t = tf.tensor3d(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n const indices = tf.tensor1d([1, 2, 2, 1], 'int32');\n const dy = tf.tensor(\n [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],\n [1, 4, 4]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);\n });\n\n it('gradient 3D (gather) axis=1 shape=[1, 4, 4] 2D indices', async () => {\n const t = tf.tensor3d(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);\n const indices = tf.tensor2d([1, 2, 2, 1], [2, 2], 'int32');\n const dy = tf.tensor(\n [2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7],\n [1, 2, 2, 4]);\n const axis = 1;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);\n });\n\n it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 1D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n const indices = tf.tensor1d([1, 0, 1, 0], 'int32');\n const dy = tf.tensor(\n [\n 2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,\n 4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25\n ],\n [2, 3, 4]);\n const axis = 2;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);\n });\n\n it('gradient 3D (gather) axis=2 shape=[2, 3, 2] 2D indices', async () => {\n const t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);\n const indices = tf.tensor2d([1, 0, 1, 0], [2, 2], 'int32');\n const dy = tf.tensor(\n [\n 2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,\n 4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25\n ],\n [2, 3, 2, 2]);\n const axis = 2;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);\n });\n\n it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 1D indices', async () => {\n const t = tf.tensor3d(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);\n const indices = tf.tensor1d([1, 3, 1], 'int32');\n const dy =\n tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 3]);\n const axis = 2;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);\n });\n\n it('gradient 3D (gather) axis=2 shape=[4, 1, 4] 2D indices', async () => {\n const t = tf.tensor3d(\n [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);\n const indices = tf.tensor2d([1, 3, 1], [1, 3], 'int32');\n const dy =\n tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 1, 3]);\n const axis = 2;\n\n const gradients = tf.grad(t => tf.gather(t, indices, axis))(t, dy);\n\n expect(gradients.shape).toEqual(t.shape);\n expectArraysClose(\n await gradients.data(),\n [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);\n });\n\n it('ensure no memory leak', async () => {\n const numTensorsBefore = tf.memory().numTensors;\n const numDataIdBefore = tf.engine().backend.numDataIds();\n const t = tf.tensor1d([1, 2, 3]);\n const t1 = tf.scalar(1, 'int32');\n const t2 = tf.gather(t, t1, 0);\n\n expect(t2.shape).toEqual([]);\n expectArraysClose(await t2.data(), [2]);\n\n t.dispose();\n t1.dispose();\n t2.dispose();\n\n const numTensorsAfter = tf.memory().numTensors;\n const numDataIdAfter = tf.engine().backend.numDataIds();\n expect(numTensorsAfter).toBe(numTensorsBefore);\n expect(numDataIdAfter).toBe(numDataIdBefore);\n });\n});\n"]} |