"use strict";
|
/**
|
* @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");
|
var defaultValue;
|
jasmine_util_1.describeWithFlags('sparseToDense', jasmine_util_1.ALL_ENVS, function () {
|
beforeEach(function () { return defaultValue = tf.scalar(0, 'int32'); });
|
it('should work for scalar indices', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.scalar(2, 'int32');
|
values = tf.scalar(100, 'int32');
|
shape = [6];
|
result = tf.sparseToDense(indices, values, shape, defaultValue);
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual(values.dtype);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 0, 100, 0, 0, 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should work for vector', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.tensor1d([0, 2, 4], 'int32');
|
values = tf.tensor1d([100, 101, 102], 'int32');
|
shape = [6];
|
result = tf.sparseToDense(indices, values, shape, defaultValue);
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual(values.dtype);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [100, 0, 101, 0, 102, 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should work for scalar value', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.tensor1d([0, 2, 4], 'int32');
|
values = tf.scalar(10, 'int32');
|
shape = [6];
|
result = tf.sparseToDense(indices, values, shape, defaultValue);
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual(values.dtype);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [10, 0, 10, 0, 10, 0]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should work for matrix', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
|
values = tf.tensor1d([5, 6], 'float32');
|
shape = [2, 2];
|
result = tf.sparseToDense(indices, values, shape, defaultValue.toFloat());
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual(values.dtype);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 5, 0, 6]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should throw exception if default value does not match dtype', function () {
|
var indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
|
var values = tf.tensor1d([5, 6], 'float32');
|
var shape = [2, 2];
|
expect(function () { return tf.sparseToDense(indices, values, shape, tf.scalar(1, 'int32')); })
|
.toThrowError();
|
});
|
it('should allow setting default value', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
|
values = tf.tensor1d([5, 6], 'float32');
|
shape = [2, 2];
|
result = tf.sparseToDense(indices, values, shape, tf.scalar(1));
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual(values.dtype);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [1, 5, 1, 6]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('no default value passed', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
|
values = tf.tensor1d([5, 6], 'float32');
|
shape = [2, 2];
|
result = tf.sparseToDense(indices, values, shape);
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual(values.dtype);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 5, 0, 6]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should support TensorLike inputs', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, shape, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = [[0, 1], [1, 1]];
|
values = [5, 6];
|
shape = [2, 2];
|
result = tf.sparseToDense(indices, values, shape, defaultValue.toFloat());
|
expect(result.shape).toEqual(shape);
|
expect(result.dtype).toEqual('float32');
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [0, 5, 0, 6]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should work with 0-sized tensors', function () { return __awaiter(_this, void 0, void 0, function () {
|
var indices, values, defaultValue, result, _a;
|
return __generator(this, function (_b) {
|
switch (_b.label) {
|
case 0:
|
indices = tf.zeros([0], 'int32');
|
values = tf.zeros([0]);
|
defaultValue = tf.scalar(5);
|
result = tf.sparseToDense(indices, values, [3], defaultValue);
|
_a = test_util_1.expectArraysClose;
|
return [4 /*yield*/, result.data()];
|
case 1:
|
_a.apply(void 0, [_b.sent(), [5, 5, 5]]);
|
return [2 /*return*/];
|
}
|
});
|
}); });
|
it('should throw error when indices are not int32', function () {
|
var indices = tf.scalar(2, 'float32');
|
var values = tf.scalar(100, 'int32');
|
var shape = [6];
|
expect(function () { return tf.sparseToDense(indices, values, shape, defaultValue); })
|
.toThrow();
|
});
|
it('should throw error when indices rank > 2', function () {
|
var indices = tf.tensor3d([1], [1, 1, 1], 'int32');
|
var values = tf.tensor1d([100], 'float32');
|
var shape = [6];
|
expect(function () { return tf.sparseToDense(indices, values, shape, defaultValue); })
|
.toThrow();
|
});
|
it('should throw error when values has rank > 1', function () {
|
var indices = tf.tensor1d([0, 4, 2], 'int32');
|
var values = tf.tensor2d([1.0, 2.0, 3.0], [3, 1], 'float32');
|
var shape = [6];
|
expect(function () { return tf.sparseToDense(indices, values, shape, defaultValue); })
|
.toThrow();
|
});
|
it('should throw error when values has wrong size', function () {
|
var indices = tf.tensor1d([0, 4, 2], 'int32');
|
var values = tf.tensor1d([1.0, 2.0, 3.0, 4.0], 'float32');
|
var shape = [6];
|
expect(function () { return tf.sparseToDense(indices, values, shape, defaultValue); })
|
.toThrow();
|
});
|
});
|
//# sourceMappingURL=sparse_to_dense_test.js.map
|
