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* @license\n * Copyright 2019 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, NODE_ENVS} from './jasmine_util';\nimport {expectArraysClose} from './test_util';\n\ndescribe('deprecation warnings', () => {\n beforeEach(() => {\n spyOn(console, 'warn').and.callFake((msg: string): void => null);\n });\n\n it('deprecationWarn warns', () => {\n // flags_test.ts verifies deprecation warnings are on by default.\n const deprecationVal = tf.env().get('DEPRECATION_WARNINGS_ENABLED');\n tf.env().set('DEPRECATION_WARNINGS_ENABLED', true);\n tf.deprecationWarn('xyz is deprecated.');\n tf.env().set('DEPRECATION_WARNINGS_ENABLED', deprecationVal);\n expect(console.warn).toHaveBeenCalledTimes(1);\n expect(console.warn)\n .toHaveBeenCalledWith(\n 'xyz is deprecated. You can disable deprecation warnings with ' +\n 'tf.disableDeprecationWarnings().');\n });\n\n it('disableDeprecationWarnings called, deprecationWarn doesnt warn', () => {\n tf.disableDeprecationWarnings();\n expect(console.warn).toHaveBeenCalledTimes(1);\n expect(console.warn)\n .toHaveBeenCalledWith(\n 'TensorFlow.js deprecation warnings have been disabled.');\n\n // deprecationWarn no longer warns.\n tf.deprecationWarn('xyz is deprecated.');\n expect(console.warn).toHaveBeenCalledTimes(1);\n });\n});\n\ndescribe('Flag flipping methods', () => {\n beforeEach(() => {\n tf.env().reset();\n });\n\n afterEach(() => {\n tf.env().reset();\n });\n\n it('tf.enableProdMode', () => {\n tf.enableProdMode();\n expect(tf.env().getBool('PROD')).toBe(true);\n });\n\n it('tf.enableDebugMode', () => {\n // Silence debug warnings.\n spyOn(console, 'warn');\n tf.enableDebugMode();\n expect(tf.env().getBool('DEBUG')).toBe(true);\n });\n});\n\ndescribeWithFlags('time cpu', NODE_ENVS, () => {\n it('simple upload', async () => {\n const a = tf.zeros([10, 10]);\n const time = await tf.time(() => a.square());\n expect(time.kernelMs > 0);\n expect(time.wallMs >= time.kernelMs);\n });\n});\n\ndescribeWithFlags('tidy', ALL_ENVS, () => {\n it('returns Tensor', async () => {\n tf.tidy(() => {\n const a = tf.tensor1d([1, 2, 3]);\n let b = tf.tensor1d([0, 0, 0]);\n\n expect(tf.memory().numTensors).toBe(2);\n tf.tidy(() => {\n const result = tf.tidy(() => {\n b = tf.add(a, b);\n b = tf.add(a, b);\n b = tf.add(a, b);\n return tf.add(a, b);\n });\n\n // result is new. All intermediates should be disposed.\n expect(tf.memory().numTensors).toBe(2 + 1);\n expect(result.shape).toEqual([3]);\n expect(result.isDisposed).toBe(false);\n });\n\n // a, b are still here, result should be disposed.\n expect(tf.memory().numTensors).toBe(2);\n });\n\n expect(tf.memory().numTensors).toBe(0);\n });\n\n it('multiple disposes does not affect num arrays', () => {\n expect(tf.memory().numTensors).toBe(0);\n const a = tf.tensor1d([1, 2, 3]);\n const b = tf.tensor1d([1, 2, 3]);\n expect(tf.memory().numTensors).toBe(2);\n a.dispose();\n a.dispose();\n expect(tf.memory().numTensors).toBe(1);\n b.dispose();\n expect(tf.memory().numTensors).toBe(0);\n });\n\n it('allows primitive types', () => {\n const a = tf.tidy(() => 5);\n expect(a).toBe(5);\n\n const b = tf.tidy(() => 'hello');\n expect(b).toBe('hello');\n });\n\n it('allows complex types', async () => {\n const res = tf.tidy(() => {\n return {a: tf.scalar(1), b: 'hello', c: [tf.scalar(2), 'world']};\n });\n expectArraysClose(await res.a.data(), [1]);\n expectArraysClose(await (res.c[0] as tf.Tensor).data(), [2]);\n });\n\n it('returns Tensor[]', async () => {\n const a = tf.tensor1d([1, 2, 3]);\n const b = tf.tensor1d([0, -1, 1]);\n expect(tf.memory().numTensors).toBe(2);\n\n tf.tidy(() => {\n const result = tf.tidy(() => {\n tf.add(a, b);\n return [tf.add(a, b), tf.sub(a, b)];\n });\n\n // the 2 results are new. All intermediates should be disposed.\n expect(tf.memory().numTensors).toBe(4);\n expect(result[0].isDisposed).toBe(false);\n expect(result[0].shape).toEqual([3]);\n expect(result[1].isDisposed).toBe(false);\n expect(result[1].shape).toEqual([3]);\n expect(tf.memory().numTensors).toBe(4);\n });\n\n // the 2 results should be disposed.\n expect(tf.memory().numTensors).toBe(2);\n a.dispose();\n b.dispose();\n expect(tf.memory().numTensors).toBe(0);\n });\n\n it('basic usage without return', () => {\n const a = tf.tensor1d([1, 2, 3]);\n let b = tf.tensor1d([0, 0, 0]);\n\n expect(tf.memory().numTensors).toBe(2);\n\n tf.tidy(() => {\n b = tf.add(a, b);\n b = tf.add(a, b);\n b = tf.add(a, b);\n tf.add(a, b);\n });\n\n // all intermediates should be disposed.\n expect(tf.memory().numTensors).toBe(2);\n });\n\n it('nested usage', async () => {\n const a = tf.tensor1d([1, 2, 3]);\n let b = tf.tensor1d([0, 0, 0]);\n\n expect(tf.memory().numTensors).toBe(2);\n\n tf.tidy(() => {\n const result = tf.tidy(() => {\n b = tf.add(a, b);\n b = tf.tidy(() => {\n b = tf.tidy(() => {\n return tf.add(a, b);\n });\n // original a, b, and two intermediates.\n expect(tf.memory().numTensors).toBe(4);\n\n tf.tidy(() => {\n tf.add(a, b);\n });\n // All the intermediates should be cleaned up.\n expect(tf.memory().numTensors).toBe(4);\n\n return tf.add(a, b);\n });\n expect(tf.memory().numTensors).toBe(4);\n\n return tf.add(a, b);\n });\n\n expect(tf.memory().numTensors).toBe(3);\n expect(result.isDisposed).toBe(false);\n expect(result.shape).toEqual([3]);\n });\n expect(tf.memory().numTensors).toBe(2);\n });\n\n it('nested usage returns tensor created from outside scope', () => {\n const x = tf.scalar(1);\n\n tf.tidy(() => {\n tf.tidy(() => {\n return x;\n });\n });\n\n expect(x.isDisposed).toBe(false);\n });\n\n it('nested usage with keep works', () => {\n let b: tf.Tensor;\n tf.tidy(() => {\n const a = tf.scalar(1);\n tf.tidy(() => {\n b = tf.keep(a);\n });\n });\n\n expect(b.isDisposed).toBe(false);\n b.dispose();\n });\n\n it('single argument', () => {\n let hasRan = false;\n tf.tidy(() => {\n hasRan = true;\n });\n expect(hasRan).toBe(true);\n });\n\n it('single argument, but not a function throws error', () => {\n expect(() => {\n tf.tidy('asdf');\n }).toThrowError();\n });\n\n it('2 arguments, first is string', () => {\n let hasRan = false;\n tf.tidy('name', () => {\n hasRan = true;\n });\n expect(hasRan).toBe(true);\n });\n\n it('2 arguments, but first is not string throws error', () => {\n expect(() => {\n // tslint:disable-next-line:no-any\n tf.tidy(4 as any, () => {});\n }).toThrowError();\n });\n\n it('2 arguments, but second is not a function throws error', () => {\n expect(() => {\n // tslint:disable-next-line:no-any\n tf.tidy('name', 'another name' as any);\n }).toThrowError();\n });\n\n it('works with arbitrary depth of result', async () => {\n tf.tidy(() => {\n const res = tf.tidy(() => {\n return [tf.scalar(1), [[tf.scalar(2)]], {list: [tf.scalar(3)]}];\n });\n expect((res[0] as tf.Tensor).isDisposed).toBe(false);\n // tslint:disable-next-line:no-any\n expect((res[1] as any)[0][0].isDisposed).toBe(false);\n // tslint:disable-next-line:no-any\n expect((res[2] as any).list[0].isDisposed).toBe(false);\n expect(tf.memory().numTensors).toBe(3);\n return res[0];\n });\n // Everything but scalar(1) got disposed.\n expect(tf.memory().numTensors).toBe(1);\n });\n});\n"]} |