@tensorflow/tfjs-core
Version:
Hardware-accelerated JavaScript library for machine intelligence
533 lines • 28.3 kB
JavaScript
;
/**
* @license
* Copyright 2018 Google Inc. 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.
* =============================================================================
*/
var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
return new (P || (P = Promise))(function (resolve, reject) {
function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
step((generator = generator.apply(thisArg, _arguments || [])).next());
});
};
var __generator = (this && this.__generator) || function (thisArg, body) {
var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
function verb(n) { return function (v) { return step([n, v]); }; }
function step(op) {
if (f) throw new TypeError("Generator is already executing.");
while (_) try {
if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
if (y = 0, t) op = [op[0] & 2, t.value];
switch (op[0]) {
case 0: case 1: t = op; break;
case 4: _.label++; return { value: op[1], done: false };
case 5: _.label++; y = op[1]; op = [0]; continue;
case 7: op = _.ops.pop(); _.trys.pop(); continue;
default:
if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
if (t[2]) _.ops.pop();
_.trys.pop(); continue;
}
op = body.call(thisArg, _);
} catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
}
};
var _this = this;
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var jasmine_util_1 = require("../jasmine_util");
var test_util_1 = require("../test_util");
jasmine_util_1.describeWithFlags('separableConv2d', jasmine_util_1.ALL_ENVS, function () {
it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = 1;
chMul = 1;
inDepth = 1;
outDepth = 2;
x = tf.tensor4d([
0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
0.0741907, 0.409265, 0.351377
], [1, 3, 3, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
0.07788632, -0.15577264
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2 in tensor', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = 1;
chMul = 1;
inDepth = 1;
outDepth = 2;
x = tf.tensor4d([
0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
0.0741907, 0.409265, 0.351377
], [1, 3, 3, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
-0.13408193, 0.07788632, -0.15577264
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=2,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = 1;
chMul = 2;
inDepth = 1;
outDepth = 3;
x = tf.tensor4d([
0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
0.0741907, 0.409265, 0.351377
], [1, 3, 3, inDepth]);
depthwiseFilter = tf.tensor4d([
0.303873, 0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
-0.144333, -0.803373
], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2, -0.1, 0.2, 0.15, 0.15], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.00305368, 0.0140969, 0.00980358, -0.10853045, -0.06339455, -0.0699412,
0.11010849, 0.0347524, 0.05214475, 0.10307151, 0.02221644, 0.04224815
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2,3D input', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = 1;
chMul = 1;
inDepth = 1;
outDepth = 2;
x = tf.tensor3d([
0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
0.0741907, 0.409265, 0.351377
], [3, 3, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
-0.13408193, 0.07788632, -0.15577264
]]);
expect(result.shape).toEqual([2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = [2, 2];
chMul = 1;
inDepth = 1;
outDepth = 2;
x = tf.tensor4d([
0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
0.047613, 0.554795
], [1, 4, 4, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,
0.05555845, -0.11111691
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=2x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = [2, 2];
chMul = 1;
inDepth = 1;
outDepth = 2;
x = tf.tensor4d([
0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335,
0.804231, 0.329673, 0.924503, 0.728742, 0.180217, 0.210459,
0.133869, 0.650827, 0.047613, 0.554795, -0.675707, -0.758567,
-0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
-0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
-0.047613, -0.554795
], [2, 4, 4, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544, -0.19803089,
0.05555845, -0.11111691, -0.04919822, 0.09839644, -0.07275512, 0.14551024,
-0.09901544, 0.19803089, -0.05555845, 0.11111691
]]);
expect(result.shape).toEqual([2, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x4x4x2,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = [2, 2];
chMul = 1;
inDepth = 2;
outDepth = 2;
x = tf.tensor4d([
0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335,
0.804231, 0.329673, 0.924503, 0.728742, 0.180217, 0.210459,
0.133869, 0.650827, 0.047613, 0.554795, -0.675707, -0.758567,
-0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
-0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
-0.047613, -0.554795
], [1, 4, 4, inDepth]);
depthwiseFilter = tf.tensor4d([
0.303873, 0.229223, 0.144333, 0.803373, 0.98976838, 0.56597068,
0.42654137, 0.66445535
], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2, 0.05, -0.05], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.20072255, -0.32641545, 0.08474462, -0.11823604, -0.20072255, 0.32641545,
-0.08474462, 0.11823604
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x4x4x1,f=2,s=1,d=2,p=valid,chMul=1,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, dilationRate, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'valid';
stride = 1;
chMul = 1;
inDepth = 1;
outDepth = 2;
dilationRate = 2;
x = tf.tensor4d([
0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
0.047613, 0.554795
], [1, 4, 4, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad, dilationRate);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.05783373, -0.11566745, 0.07257301, -0.14514601, 0.03079498, -0.06158997,
0.06460048, -0.12920095
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('input=1x4x4x1,f=2,s=1,d=1,p=same,chMul=1,outDepth=2', function () { return __awaiter(_this, void 0, void 0, function () {
var fSize, pad, stride, chMul, inDepth, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
fSize = 2;
pad = 'same';
stride = 1;
chMul = 1;
inDepth = 1;
outDepth = 2;
x = tf.tensor4d([
0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
0.047613, 0.554795
], [1, 4, 4, inDepth]);
depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.04919822, -0.09839644, 0.09860218, -0.19720435, 0.07275512, -0.14551024,
0.03405062, -0.06810125, 0.08081452, -0.16162904, 0.04651042, -0.09302084,
0.05150411, -0.10300821, 0.01305549, -0.02611098, 0.09901544, -0.19803089,
0.03949417, -0.07898834, 0.05555845, -0.11111691, 0.0144028, -0.02880561,
0.01898637, -0.03797274, 0.02086828, -0.04173655, 0.01416401, -0.02832802,
0.01685872, -0.03371745
]]);
expect(result.shape).toEqual([1, 4, 4, outDepth]);
return [2 /*return*/];
}
});
}); });
it('TensorLike', function () { return __awaiter(_this, void 0, void 0, function () {
var pad, stride, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
pad = 'valid';
stride = 1;
outDepth = 2;
x = [[
[[0.230664], [0.987388], [0.0685208]],
[[0.419224], [0.887861], [0.731641]],
[[0.0741907], [0.409265], [0.351377]]
]];
depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
pointwiseFilter = [[[[0.1, -0.2]]]];
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
0.07788632, -0.15577264
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('TensorLike Chained', function () { return __awaiter(_this, void 0, void 0, function () {
var pad, stride, outDepth, inDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
pad = 'valid';
stride = 1;
outDepth = 2;
inDepth = 1;
x = tf.tensor4d([
0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
0.0741907, 0.409265, 0.351377
], [1, 3, 3, inDepth]);
depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
pointwiseFilter = [[[[0.1, -0.2]]]];
result = x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
0.07788632, -0.15577264
]]);
expect(result.shape).toEqual([1, 2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
it('Incorrect input rank raises error', function () {
// tslint:disable-next-line:no-any
var x = tf.zeros([4, 4]);
var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
var pointwiseFilter = tf.zeros([1, 1, 2, 4]);
expect(function () {
return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
})
.toThrowError(/rank 4/);
});
it('Incorrect depthwise filter rank raises error', function () {
var x = tf.zeros([1, 4, 4, 1]);
// tslint:disable-next-line:no-any
var depthwiseFilter = tf.zeros([2, 2, 1]);
var pointwiseFilter = tf.zeros([1, 1, 2, 4]);
expect(function () {
return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
})
.toThrowError(/rank 4/);
});
it('Incorrect depthwise filter rank raises error', function () {
var x = tf.zeros([1, 4, 4, 1]);
var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
// tslint:disable-next-line:no-any
var pointwiseFilter = tf.zeros([1, 1, 2]);
expect(function () {
return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
})
.toThrowError(/rank 4/);
});
it('Incorrect point filter 1st dimension raises error', function () {
var x = tf.zeros([1, 4, 4, 1]);
var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
var pointwiseFilter = tf.zeros([2, 1, 3, 6]);
expect(function () {
return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
})
.toThrowError(/must be 1, but got 2/);
});
it('Incorrect point filter 2nd dimension raises error', function () {
var x = tf.zeros([1, 4, 4, 1]);
var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
var pointwiseFilter = tf.zeros([1, 5, 3, 6]);
expect(function () {
return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
})
.toThrowError(/must be 1, but got 5/);
});
it('Incorrect pointwise filter 3rd dimension raises error', function () {
var x = tf.zeros([1, 4, 4, 1]);
var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
var pointwiseFilter = tf.zeros([1, 1, 4, 6]);
expect(function () {
return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
})
.toThrowError(/must be 3, but got 4/);
});
it('throws when passed x as a non-tensor', function () {
var fSize = 2;
var pad = 'valid';
var stride = 1;
var chMul = 1;
var inDepth = 1;
var outDepth = 2;
var depthwiseFilter = tf.zeros([fSize, fSize, inDepth, chMul]);
var pointwiseFilter = tf.zeros([1, 1, inDepth * chMul, outDepth]);
var e = /Argument 'x' passed to 'separableConv2d' must be a Tensor/;
expect(function () { return tf.separableConv2d({}, depthwiseFilter, pointwiseFilter, stride, pad); })
.toThrowError(e);
});
it('throws when passed depthwiseFilter as a non-tensor', function () {
var pad = 'valid';
var stride = 1;
var chMul = 1;
var inDepth = 1;
var outDepth = 2;
var x = tf.zeros([1, 3, 3, inDepth]);
var pointwiseFilter = tf.zeros([1, 1, inDepth * chMul, outDepth]);
var e = new RegExp('Argument \'depthwiseFilter\' passed to \'separableConv2d\'' +
' must be a Tensor');
expect(function () { return tf.separableConv2d(x, {}, pointwiseFilter, stride, pad); })
.toThrowError(e);
});
it('throws when passed pointwiseFilter as a non-tensor', function () {
var fSize = 2;
var pad = 'valid';
var stride = 1;
var chMul = 1;
var inDepth = 1;
var x = tf.zeros([1, 3, 3, inDepth]);
var depthwiseFilter = tf.zeros([fSize, fSize, inDepth, chMul]);
var e = new RegExp('Argument \'pointwiseFilter\' passed to \'separableConv2d\'' +
' must be a Tensor');
expect(function () { return tf.separableConv2d(x, depthwiseFilter, {}, stride, pad); })
.toThrowError(e);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var pad, stride, outDepth, x, depthwiseFilter, pointwiseFilter, result, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
pad = 'valid';
stride = 1;
outDepth = 2;
x = [
[[0.230664], [0.987388], [0.0685208]],
[[0.419224], [0.887861], [0.731641]],
[[0.0741907], [0.409265], [0.351377]]
];
depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
pointwiseFilter = [[[[0.1, -0.2]]]];
result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), [
0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096, -0.13408193,
0.07788632, -0.15577264
]]);
expect(result.shape).toEqual([2, 2, outDepth]);
return [2 /*return*/];
}
});
}); });
});
//# sourceMappingURL=conv2d_separable_test.js.map