@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
"use strict";
/**
* @license
* Copyright 2017 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('conv2dTranspose', jasmine_util_1.ALL_ENVS, function () {
it('input=2x2x1,d2=1,f=2,s=1,p=0', function () { return __awaiter(_this, void 0, void 0, function () {
var origInputDepth, origOutputDepth, inputShape, fSize, origPad, origStride, x, w, result, expected, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
origInputDepth = 1;
origOutputDepth = 1;
inputShape = [1, 1, origOutputDepth];
fSize = 2;
origPad = 0;
origStride = 1;
x = tf.tensor3d([2], inputShape);
w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
expected = [6, 2, 10, 0];
expect(result.shape).toEqual([2, 2, 1]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), expected]);
return [2 /*return*/];
}
});
}); });
it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', function () { return __awaiter(_this, void 0, void 0, function () {
var origInputDepth, origOutputDepth, inputShape, fSize, origPad, origStride, x, w, result, expected, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
origInputDepth = 1;
origOutputDepth = 1;
inputShape = [2, 1, 1, origOutputDepth];
fSize = 2;
origPad = 0;
origStride = 1;
x = tf.tensor4d([2, 3], inputShape);
w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
expected = [6, 2, 10, 0, 9, 3, 15, 0];
expect(result.shape).toEqual([2, 2, 2, 1]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), expected]);
return [2 /*return*/];
}
});
}); });
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([[
// [[-0.14656299], [0.32942239], [-1.90302866]],
// [[-0.06487813], [-2.02637842], [-1.83669377]],
// [[0.82650784], [-0.89249092], [0.01207666]]
// ]]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
// [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2])
// print(y)
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % x_grad)
// print("filt_grad = %s" % filt_grad)
// ```
it('gradient with clones input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid', function () { return __awaiter(_this, void 0, void 0, function () {
var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, expectedXGrad, _b, _c, expectedFiltGrad, _d, _e;
return __generator(this, function (_f) {
switch (_f.label) {
case 0:
inputDepth = 1;
outputDepth = 2;
inputShape = [1, 3, 3, inputDepth];
filterSize = 2;
stride = 1;
pad = 'valid';
filterShape = [filterSize, filterSize, outputDepth, inputDepth];
x = tf.tensor4d([[
[[-0.14656299], [0.32942239], [-1.90302866]],
[[-0.06487813], [-2.02637842], [-1.83669377]],
[[0.82650784], [-0.89249092], [0.01207666]]
]], inputShape);
filt = tf.tensor4d([
[[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
[[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
], filterShape);
grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x.clone(), filter.clone(), [1, 4, 4, outputDepth], stride, pad)
.clone();
});
dy = tf.ones([1, 4, 4, outputDepth]);
_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
expectedXGrad = tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947));
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, xGrad.data()];
case 1:
_c = [_f.sent()];
return [4 /*yield*/, expectedXGrad.data()];
case 2:
_b.apply(void 0, _c.concat([_f.sent()]));
expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599));
_d = test_util_1.expectArraysClose;
return [4 /*yield*/, filtGrad.data()];
case 3:
_e = [_f.sent()];
return [4 /*yield*/, expectedFiltGrad.data()];
case 4:
_d.apply(void 0, _e.concat([_f.sent()]));
return [2 /*return*/];
}
});
}); });
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([
// [[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]
// ]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
// [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(x, filt, [1, 4, 4, 2], strides=(2,
// 2)) print(y)
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % -x_grad)
// print("filt_grad = %s" % -filt_grad)
// ```
it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', function () { return __awaiter(_this, void 0, void 0, function () {
var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, expectedXGrad, _b, _c, expectedFiltGrad, _d, _e;
return __generator(this, function (_f) {
switch (_f.label) {
case 0:
inputDepth = 1;
outputDepth = 2;
inputShape = [1, 2, 2, inputDepth];
filterSize = 2;
stride = [2, 2];
pad = 'valid';
filterShape = [filterSize, filterSize, outputDepth, inputDepth];
x = tf.tensor4d([[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]], inputShape);
filt = tf.tensor4d([
[[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
[[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
], filterShape);
grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad);
});
dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
expectedXGrad = tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196));
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, xGrad.data()];
case 1:
_c = [_f.sent()];
return [4 /*yield*/, expectedXGrad.data()];
case 2:
_b.apply(void 0, _c.concat([_f.sent()]));
expect(xGrad.shape).toEqual([1, 2, 2, 1]);
expectedFiltGrad = tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618));
_d = test_util_1.expectArraysClose;
return [4 /*yield*/, filtGrad.data()];
case 3:
_e = [_f.sent()];
return [4 /*yield*/, expectedFiltGrad.data()];
case 4:
_d.apply(void 0, _e.concat([_f.sent()]));
expect(filtGrad.shape).toEqual([2, 2, 2, 1]);
return [2 /*return*/];
}
});
}); });
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([[
// [[1.52433065], [-0.77053435], [-0.64562341]],
// [[0.77962889], [1.58413887], [-0.25581856]],
// [[-0.58966221], [0.05411662], [0.70749138]]
// ]]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
// [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(
// x, filt, [1, 3, 3, 2], strides=(1, 1), padding='same')
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % x_grad)
// print("filt_grad = %s" % filt_grad)
// ```
it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', function () { return __awaiter(_this, void 0, void 0, function () {
var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
inputDepth = 1;
outputDepth = 2;
inputShape = [1, 3, 3, inputDepth];
filterSize = 2;
stride = [1, 1];
pad = 'same';
filterShape = [filterSize, filterSize, outputDepth, inputDepth];
x = tf.tensor4d([[
[[1.52433065], [-0.77053435], [-0.64562341]],
[[0.77962889], [1.58413887], [-0.25581856]],
[[-0.58966221], [0.05411662], [0.70749138]]
]], inputShape);
filt = tf.tensor4d([
[[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
[[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
], filterShape);
grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
});
dy = tf.ones([1, 3, 3, outputDepth]);
_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, xGrad.array()];
case 1:
_b.apply(void 0, [_d.sent(), [[
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.19666437], [1.19666437], [-0.85476589]]
]]]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, filtGrad.array()];
case 2:
_c.apply(void 0, [_d.sent(), [
[[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
[[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
]]);
return [2 /*return*/];
}
});
}); });
// Reference (Python) TensorFlow code:
//
// ```py
// import numpy as np
// import tensorflow as tf
//
// tf.enable_eager_execution()
//
// x = tf.constant(np.array([[
// [[1.52433065], [-0.77053435]], [[0.77962889], [1.58413887]],
// ]]).astype(np.float32))
// filt = tf.constant(np.array([
// [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
// [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
// ]).astype(np.float32))
//
// with tf.GradientTape() as g:
// g.watch(x)
// g.watch(filt)
// y = tf.keras.backend.conv2d_transpose(
// x, filt, [1, 3, 3, 2], strides=(2, 2), padding='same')
// print(y.shape)
// (x_grad, filt_grad) = g.gradient(y, [x, filt])
//
// print("x_grad = %s" % x_grad)
// print("filt_grad = %s" % filt_grad)
// ```
it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', function () { return __awaiter(_this, void 0, void 0, function () {
var inputDepth, outputDepth, inputShape, filterSize, stride, pad, filterShape, x, filt, grads, dy, _a, xGrad, filtGrad, _b, _c;
return __generator(this, function (_d) {
switch (_d.label) {
case 0:
inputDepth = 2;
outputDepth = 2;
inputShape = [1, 2, 2, inputDepth];
filterSize = 2;
stride = [2, 2];
pad = 'same';
filterShape = [filterSize, filterSize, outputDepth, inputDepth];
x = tf.tensor4d([[
[[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
[[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
]], inputShape);
filt = tf.tensor4d([
[
[[0.19529686, -0.79594708], [0.70314057, -0.06081263]],
[[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]
],
[
[[0.51872197, -1.17569193], [1.28316791, -0.81225092]],
[[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]
]
], filterShape);
grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
});
dy = tf.ones([1, 3, 3, outputDepth]);
_a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
_b = test_util_1.expectArraysClose;
return [4 /*yield*/, xGrad.data()];
case 1:
_b.apply(void 0, [_d.sent(), [
1.54219678, -2.19204008, 2.70032732, -2.84470257, 0.66744391, -0.94274245,
0.89843743, -0.85675972
]]);
expect(xGrad.shape).toEqual([1, 2, 2, 2]);
_c = test_util_1.expectArraysClose;
return [4 /*yield*/, filtGrad.data()];
case 2:
_c.apply(void 0, [_d.sent(), [
-1.00204261, 0.27276259, -1.00204261, 0.27276259, -2.99976385, 0.66119574,
-2.99976385, 0.66119574, -1.86705711, 1.27211472, -1.86705711, 1.27211472,
-1.81506593, 1.00900095, -1.81506593, 1.00900095
]]);
expect(filtGrad.shape).toEqual([2, 2, 2, 2]);
return [2 /*return*/];
}
});
}); });
it('throws when x is not rank 3', function () {
var origInputDepth = 1;
var origOutputDepth = 1;
var fSize = 2;
var origPad = 0;
var origStride = 1;
// tslint:disable-next-line:no-any
var x = tf.tensor2d([2, 2], [2, 1]);
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad); })
.toThrowError();
});
it('throws when weights is not rank 4', function () {
var origInputDepth = 1;
var origOutputDepth = 1;
var inputShape = [1, 1, origOutputDepth];
var fSize = 2;
var origPad = 0;
var origStride = 1;
var x = tf.tensor3d([2], inputShape);
// tslint:disable-next-line:no-any
var w = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);
expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad); })
.toThrowError();
});
it('throws when x depth does not match weights original output depth', function () {
var origInputDepth = 1;
var origOutputDepth = 2;
var wrongOrigOutputDepth = 3;
var inputShape = [1, 1, origOutputDepth];
var fSize = 2;
var origPad = 0;
var origStride = 1;
var x = tf.tensor3d([2, 2], inputShape);
var w = tf.randomNormal([fSize, fSize, origInputDepth, wrongOrigOutputDepth]);
expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad); })
.toThrowError();
});
it('throws when passed x as a non-tensor', function () {
var origInputDepth = 1;
var origOutputDepth = 1;
var fSize = 2;
var origPad = 0;
var origStride = 1;
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
expect(function () { return tf.conv2dTranspose({}, w, [2, 2, 1], origStride, origPad); })
.toThrowError(/Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);
});
it('throws when passed filter as a non-tensor', function () {
var origOutputDepth = 1;
var inputShape = [1, 1, origOutputDepth];
var origPad = 0;
var origStride = 1;
var x = tf.tensor3d([2], inputShape);
expect(function () { return tf.conv2dTranspose(x, {}, [2, 2, 1], origStride, origPad); })
.toThrowError(/Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);
});
it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
var origPad, origStride, x, w, result, expected, _a;
return __generator(this, function (_b) {
switch (_b.label) {
case 0:
origPad = 0;
origStride = 1;
x = [[[2]]];
w = [[[[3]], [[1]]], [[[5]], [[0]]]];
result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
expected = [6, 2, 10, 0];
expect(result.shape).toEqual([2, 2, 1]);
_a = test_util_1.expectArraysClose;
return [4 /*yield*/, result.data()];
case 1:
_a.apply(void 0, [_b.sent(), expected]);
return [2 /*return*/];
}
});
}); });
});
//# sourceMappingURL=conv2d_transpose_test.js.map