@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
139 lines • 28.8 kB
JavaScript
/**
* @license
* Copyright 2022 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('tensorScatterUpdate', ALL_ENVS, () => {
it('should work for 2d', async () => {
const tensor = tf.ones([5, 3], 'int32');
const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
const updates = tf.tensor2d([100, 101, 102, 777, 778, 779, 1000, 1001, 1002], [3, 3], 'int32');
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(tensor.shape);
expect(result.dtype).toEqual(tensor.dtype);
expectArraysClose(await result.data(), [100, 101, 102, 1, 1, 1, 1000, 1001, 1002, 1, 1, 1, 777, 778, 779]);
});
it('should work for simple 1d', async () => {
const shape = [5];
const tensor = tf.ones(shape);
const indices = tf.tensor2d([3], [1, 1], 'int32');
const updates = tf.tensor1d([101], 'float32');
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(shape);
expect(result.dtype).toEqual(updates.dtype);
expectArraysClose(await result.data(), [1, 1, 1, 101, 1]);
});
it('should work for multiple 1d', async () => {
const shape = [5];
const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
const updates = tf.tensor1d([100, 101, 102], 'float32');
const tensor = tf.ones(shape);
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(shape);
expect(result.dtype).toEqual(updates.dtype);
expectArraysClose(await result.data(), [100, 1, 102, 1, 101]);
});
it('should work for high rank updates', async () => {
const indices = tf.tensor2d([0, 2], [2, 1], 'int32');
const updates = tf.tensor3d([
5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
], [2, 4, 4], 'float32');
const shape = [4, 4, 4];
const tensor = tf.ones(shape);
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(shape);
expect(result.dtype).toEqual(updates.dtype);
expectArraysClose(await result.data(), [
5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
]);
});
it('should work for high rank indices', async () => {
const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
const updates = tf.tensor1d([10, 20], 'float32');
const shape = [3, 3];
const tensor = tf.ones(shape);
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(shape);
expect(result.dtype).toEqual(updates.dtype);
expectArraysClose(await result.data(), [1, 20, 10, 1, 1, 1, 1, 1, 1]);
});
it('should work for high rank indices and update', () => {
const indices = tf.tensor2d([1, 1, 1, 1, 1, 1], [3, 2], 'int32');
const updates = tf.ones([3, 256], 'float32');
const shape = [2, 2, 256];
const tensor = tf.ones(shape);
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(shape);
expect(result.dtype).toEqual(updates.dtype);
});
it('should work for tensorLike input', async () => {
const indices = [[0], [4], [2]];
const updates = [100, 101, 102];
const shape = [5];
const tensor = tf.ones(shape);
const result = tf.tensorScatterUpdate(tensor, indices, updates);
expect(result.shape).toEqual(shape);
expect(result.dtype).toEqual('float32');
expectArraysClose(await result.data(), [100, 1, 102, 1, 101]);
});
it('should throw error when indices type is not int32', () => {
const indices = tf.tensor2d([0, 2, 1, 1], [2, 2], 'float32');
const updates = tf.tensor1d([10, 20], 'float32');
const shape = [3, 3];
const tensor = tf.ones(shape);
expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
});
it('should throw error when tensor and updates type does not match', () => {
const indices = tf.tensor2d([0, 2, 1, 1], [2, 2], 'float32');
const updates = tf.tensor1d([10, 20], 'int32');
const shape = [3, 3];
const tensor = tf.ones(shape, 'float32');
expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
});
it('should throw error when indices and update mismatch', () => {
const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
const updates = tf.tensor2d([100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004], [3, 4], 'float32');
const shape = [5, 3];
const tensor = tf.ones(shape);
expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
});
it('should throw error when indices and update count mismatch', () => {
const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
const updates = tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
const shape = [5, 3];
const tensor = tf.ones(shape);
expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
});
it('should throw error when indices are scalar', () => {
const indices = tf.scalar(1, 'int32');
const updates = tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
const shape = [5, 3];
const tensor = tf.ones(shape);
expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
});
it('should throw error when update is scalar', () => {
const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
const updates = tf.scalar(1, 'float32');
const shape = [5, 3];
const tensor = tf.ones(shape);
expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();
});
});
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* @license\n * Copyright 2022 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('tensorScatterUpdate', ALL_ENVS, () => {\n  it('should work for 2d', async () => {\n    const tensor = tf.ones([5, 3], 'int32');\n    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');\n    const updates = tf.tensor2d(\n        [100, 101, 102, 777, 778, 779, 1000, 1001, 1002], [3, 3], 'int32');\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(tensor.shape);\n    expect(result.dtype).toEqual(tensor.dtype);\n    expectArraysClose(\n        await result.data(),\n        [100, 101, 102, 1, 1, 1, 1000, 1001, 1002, 1, 1, 1, 777, 778, 779]);\n  });\n\n  it('should work for simple 1d', async () => {\n    const shape = [5];\n    const tensor = tf.ones(shape);\n    const indices = tf.tensor2d([3], [1, 1], 'int32');\n    const updates = tf.tensor1d([101], 'float32');\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(shape);\n    expect(result.dtype).toEqual(updates.dtype);\n    expectArraysClose(await result.data(), [1, 1, 1, 101, 1]);\n  });\n\n  it('should work for multiple 1d', async () => {\n    const shape = [5];\n    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');\n    const updates = tf.tensor1d([100, 101, 102], 'float32');\n    const tensor = tf.ones(shape);\n\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(shape);\n    expect(result.dtype).toEqual(updates.dtype);\n    expectArraysClose(await result.data(), [100, 1, 102, 1, 101]);\n  });\n\n  it('should work for high rank updates', async () => {\n    const indices = tf.tensor2d([0, 2], [2, 1], 'int32');\n    const updates = tf.tensor3d(\n        [\n          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,\n          5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8\n        ],\n        [2, 4, 4], 'float32');\n    const shape = [4, 4, 4];\n    const tensor = tf.ones(shape);\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(shape);\n    expect(result.dtype).toEqual(updates.dtype);\n    expectArraysClose(await result.data(), [\n      5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 1, 1, 1, 1, 1, 1,\n      1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,\n      8, 8, 8, 8, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1\n    ]);\n  });\n\n  it('should work for high rank indices', async () => {\n    const indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');\n    const updates = tf.tensor1d([10, 20], 'float32');\n    const shape = [3, 3];\n    const tensor = tf.ones(shape);\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(shape);\n    expect(result.dtype).toEqual(updates.dtype);\n    expectArraysClose(await result.data(), [1, 20, 10, 1, 1, 1, 1, 1, 1]);\n  });\n\n  it('should work for high rank indices and update', () => {\n    const indices = tf.tensor2d([1, 1, 1, 1, 1, 1], [3, 2], 'int32');\n    const updates = tf.ones([3, 256], 'float32');\n    const shape = [2, 2, 256];\n    const tensor = tf.ones(shape);\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(shape);\n    expect(result.dtype).toEqual(updates.dtype);\n  });\n\n  it('should work for tensorLike input', async () => {\n    const indices = [[0], [4], [2]];\n    const updates = [100, 101, 102];\n    const shape = [5];\n    const tensor = tf.ones(shape);\n    const result = tf.tensorScatterUpdate(tensor, indices, updates);\n    expect(result.shape).toEqual(shape);\n    expect(result.dtype).toEqual('float32');\n    expectArraysClose(await result.data(), [100, 1, 102, 1, 101]);\n  });\n\n  it('should throw error when indices type is not int32', () => {\n    const indices = tf.tensor2d([0, 2, 1, 1], [2, 2], 'float32');\n    const updates = tf.tensor1d([10, 20], 'float32');\n    const shape = [3, 3];\n    const tensor = tf.ones(shape);\n    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();\n  });\n\n  it('should throw error when tensor and updates type does not match', () => {\n    const indices = tf.tensor2d([0, 2, 1, 1], [2, 2], 'float32');\n    const updates = tf.tensor1d([10, 20], 'int32');\n    const shape = [3, 3];\n    const tensor = tf.ones(shape, 'float32');\n    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();\n  });\n\n  it('should throw error when indices and update mismatch', () => {\n    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');\n    const updates = tf.tensor2d(\n        [100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004],\n        [3, 4], 'float32');\n    const shape = [5, 3];\n    const tensor = tf.ones(shape);\n    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();\n  });\n\n  it('should throw error when indices and update count mismatch', () => {\n    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');\n    const updates =\n        tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');\n    const shape = [5, 3];\n    const tensor = tf.ones(shape);\n    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();\n  });\n\n  it('should throw error when indices are scalar', () => {\n    const indices = tf.scalar(1, 'int32');\n    const updates =\n        tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');\n    const shape = [5, 3];\n    const tensor = tf.ones(shape);\n    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();\n  });\n\n  it('should throw error when update is scalar', () => {\n    const indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');\n    const updates = tf.scalar(1, 'float32');\n    const shape = [5, 3];\n    const tensor = tf.ones(shape);\n    expect(() => tf.tensorScatterUpdate(tensor, indices, updates)).toThrow();\n  });\n});\n"]}