@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
221 lines • 10.4 kB
JavaScript
"use strict";
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', test_util_1.ALL_ENVS, function () {
it('input=2x2x1,d2=1,f=2,s=1,p=0', 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);
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
var result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
var expected = [6, 2, 10, 0];
expect(result.shape).toEqual([2, 2, 1]);
test_util_1.expectArraysClose(result, expected);
});
it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', function () {
var origInputDepth = 1;
var origOutputDepth = 1;
var inputShape = [2, 1, 1, origOutputDepth];
var fSize = 2;
var origPad = 0;
var origStride = 1;
var x = tf.tensor4d([2, 3], inputShape);
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
var result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
var expected = [6, 2, 10, 0, 9, 3, 15, 0];
expect(result.shape).toEqual([2, 2, 2, 1]);
test_util_1.expectArraysClose(result, expected);
});
it('gradient input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid', function () {
var inputDepth = 1;
var outputDepth = 2;
var inputShape = [1, 3, 3, inputDepth];
var filterSize = 2;
var stride = 1;
var pad = 'valid';
var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
var x = tf.tensor4d([[
[[-0.14656299], [0.32942239], [-1.90302866]],
[[-0.06487813], [-2.02637842], [-1.83669377]],
[[0.82650784], [-0.89249092], [0.01207666]]
]], inputShape);
var filt = tf.tensor4d([
[[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
[[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
], filterShape);
var grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad);
});
var dy = tf.ones([1, 4, 4, outputDepth]);
var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
test_util_1.expectArraysClose(xGrad, tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947)));
test_util_1.expectArraysClose(filtGrad, tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599)));
});
it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', function () {
var inputDepth = 1;
var outputDepth = 2;
var inputShape = [1, 2, 2, inputDepth];
var filterSize = 2;
var stride = [2, 2];
var pad = 'valid';
var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
var x = tf.tensor4d([[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]], inputShape);
var filt = tf.tensor4d([
[[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
[[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
], filterShape);
var grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad);
});
var dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
test_util_1.expectArraysClose(xGrad, tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196)));
test_util_1.expectArraysClose(filtGrad, tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618)));
});
it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', function () {
var inputDepth = 1;
var outputDepth = 2;
var inputShape = [1, 3, 3, inputDepth];
var filterSize = 2;
var stride = [1, 1];
var pad = 'same';
var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
var x = tf.tensor4d([[
[[1.52433065], [-0.77053435], [-0.64562341]],
[[0.77962889], [1.58413887], [-0.25581856]],
[[-0.58966221], [0.05411662], [0.70749138]]
]], inputShape);
var filt = tf.tensor4d([
[[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
[[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
], filterShape);
var grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
});
var dy = tf.ones([1, 3, 3, outputDepth]);
var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
test_util_1.expectArraysClose(xGrad, tf.tensor4d([[
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.30709858], [1.30709858], [-0.92814366]],
[[1.19666437], [1.19666437], [-0.85476589]]
]]));
test_util_1.expectArraysClose(filtGrad, tf.tensor4d([
[[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
[[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
]));
});
it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', function () {
var inputDepth = 2;
var outputDepth = 2;
var inputShape = [1, 2, 2, inputDepth];
var filterSize = 2;
var stride = [2, 2];
var pad = 'same';
var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
var x = tf.tensor4d([[
[[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
[[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
]], inputShape);
var 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);
var grads = tf.grads(function (x, filter) {
return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
});
var dy = tf.ones([1, 3, 3, outputDepth]);
var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
test_util_1.expectArraysClose(xGrad, tf.tensor4d([[
[[1.54219678, -2.19204008], [2.70032732, -2.84470257]],
[[0.66744391, -0.94274245], [0.89843743, -0.85675972]]
]]));
test_util_1.expectArraysClose(filtGrad, tf.tensor4d([
[
[[-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]]
]
]));
});
it('throws when x is not rank 3', function () {
var origInputDepth = 1;
var origOutputDepth = 1;
var fSize = 2;
var origPad = 0;
var origStride = 1;
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);
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 () {
var origPad = 0;
var origStride = 1;
var x = [[[2]]];
var w = [[[[3]], [[1]]], [[[5]], [[0]]]];
var result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
var expected = [6, 2, 10, 0];
expect(result.shape).toEqual([2, 2, 1]);
test_util_1.expectArraysClose(result, expected);
});
});
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