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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 {expectPromiseToFail} from '../test_util';\n\ndescribeWithFlags('toPixels no canvas', ALL_ENVS, () => {\n it('draws a rank-2 float32 tensor', async () => {\n const x = tf.tensor2d([.15, .2], [2, 1], 'float32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([\n Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255), 255,\n Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255), 255\n ]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-2 int32 tensor', async () => {\n const x = tf.tensor2d([10, 20], [2, 1], 'int32');\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-3 float32 tensor, 1 channel', async () => {\n const x = tf.tensor3d([.15, .2], [2, 1, 1], 'float32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([\n Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255), 255,\n Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255), 255\n ]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-3 int32 tensor, 1 channel', async () => {\n const x = tf.tensor3d([10, 20], [2, 1, 1], 'int32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-3 float32 tensor, 3 channel', async () => {\n // 0.1 and 0.3 are changed to 0.1001 and 0.3001 to avoid boundary conditions\n // such as Math.round(~25.5) which on Mobile Safari gives 25 and Desktop\n // gives 26.\n const x =\n tf.tensor3d([.05, .1001, .15, .2, .25, .3001], [2, 1, 3], 'float32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([\n Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),\n 255, Math.round(.2 * 255), Math.round(.25 * 255), Math.round(.3001 * 255),\n 255\n ]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-3 int32 tensor, 3 channel', async () => {\n const x = tf.tensor3d([10, 20, 30, 40, 50, 60], [2, 1, 3], 'int32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([10, 20, 30, 255, 40, 50, 60, 255]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-3 float32 tensor, 4 channel', async () => {\n const x = tf.tensor3d(\n [.05, .1001, .15, .2, .25, .3001, .35, .4], [2, 1, 4], 'float32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([\n Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),\n Math.round(.20 * 255), Math.round(.25 * 255), Math.round(.3001 * 255),\n Math.round(.35 * 255), Math.round(.4 * 255)\n ]);\n expect(data).toEqual(expected);\n });\n\n it('draws a rank-3 int32 tensor, 4 channel', async () => {\n const x = tf.tensor3d([10, 20, 30, 40, 50, 60, 70, 80], [2, 1, 4], 'int32');\n\n const data = await tf.browser.toPixels(x);\n const expected = new Uint8ClampedArray([10, 20, 30, 40, 50, 60, 70, 80]);\n expect(data).toEqual(expected);\n });\n\n it('throws for scalars', done => {\n // tslint:disable-next-line:no-any\n expectPromiseToFail(() => tf.browser.toPixels(tf.scalar(1) as any), done);\n });\n\n it('throws for rank-1 tensors', done => {\n expectPromiseToFail(\n // tslint:disable-next-line:no-any\n () => tf.browser.toPixels(tf.tensor1d([1]) as any), done);\n });\n it('throws for rank-4 tensors', done => {\n expectPromiseToFail(\n // tslint:disable-next-line:no-any\n () => tf.browser.toPixels(tf.tensor4d([1], [1, 1, 1, 1]) as any), done);\n });\n it('throws for bool dtype', done => {\n expectPromiseToFail(\n () => tf.browser.toPixels(tf.tensor2d([1], [1, 1], 'bool')), done);\n });\n it('throws for rank-3 depth = 2', done => {\n expectPromiseToFail(\n () => tf.browser.toPixels(tf.tensor3d([1, 2], [1, 1, 2])), done);\n });\n it('throws for rank-3 depth = 5', done => {\n expectPromiseToFail(\n () => tf.browser.toPixels(tf.tensor3d([1, 2, 3, 4, 5], [1, 1, 5])),\n done);\n });\n it('throws for float32 tensor with values not in [0 - 1]', done => {\n expectPromiseToFail(\n () => tf.browser.toPixels(tf.tensor2d([-1, .5], [1, 2])), done);\n });\n it('throws for int32 tensor with values not in [0 - 255]', done => {\n expectPromiseToFail(\n () => tf.browser.toPixels(tf.tensor2d([-1, 100], [1, 2], 'int32')),\n done);\n });\n it('throws when passed a non-tensor', done => {\n // tslint:disable-next-line:no-any\n expectPromiseToFail(() => tf.browser.toPixels({} as any), done);\n });\n\n it('accepts a tensor-like object', async () => {\n const x = [[10], [20]]; // 2x1;\n const data = await tf.browser.toPixels(x);\n\n const expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);\n expect(data).toEqual(expected);\n });\n\n it('does not leak memory', async () => {\n const x = tf.tensor2d([[.1], [.2]], [2, 1]);\n const startNumTensors = tf.memory().numTensors;\n await tf.browser.toPixels(x);\n expect(tf.memory().numTensors).toEqual(startNumTensors);\n });\n\n it('does not leak memory given a tensor-like object', async () => {\n const x = [[10], [20]]; // 2x1;\n const startNumTensors = tf.memory().numTensors;\n await tf.browser.toPixels(x);\n expect(tf.memory().numTensors).toEqual(startNumTensors);\n });\n});\n"]} |