@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
184 lines • 8.1 kB
JavaScript
"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var tf = require("../index");
var test_util_1 = require("../test_util");
var jasmine_util_1 = require("../jasmine_util");
jasmine_util_1.describeWithFlags('conv2d', test_util_1.ALL_ENVS, function () {
it('x=[2,2,1] f=[1,1,1,2] s=1 d=1 p=0', function () {
var inputDepth = 1;
var inputShape = [2, 2, inputDepth];
var outputDepth = 1;
var fSize = 1;
var pad = 0;
var stride = 1;
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
var w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
var result = tf.conv2d(x, w, stride, pad);
test_util_1.expectArraysClose(result, [2, 4, 6, 8]);
});
it('x=[2,2,2,1] f=[1,1,1,1] s=1 d=1 p=0', function () {
var inputDepth = 1;
var inShape = [2, 2, 2, inputDepth];
var outputDepth = 1;
var fSize = 1;
var pad = 0;
var stride = 1;
var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
var w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
var result = tf.conv2d(x, w, stride, pad);
expect(result.shape).toEqual([2, 2, 2, 1]);
var expected = [2, 4, 6, 8, 10, 12, 14, 16];
test_util_1.expectArraysClose(result, expected);
});
it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=0', function () {
var inputDepth = 1;
var inputShape = [2, 2, inputDepth];
var outputDepth = 1;
var fSize = 2;
var pad = 0;
var stride = 1;
var dataFormat = 'NHWC';
var dilation = 1;
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
var result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
test_util_1.expectArraysClose(result, [20]);
});
it('x=[4,4,1] f=[2,2,1,1] s=1 d=2 p=0', function () {
var inputDepth = 1;
var inputShape = [4, 4, inputDepth];
var outputDepth = 1;
var fSize = 2;
var fSizeDilated = 3;
var pad = 0;
var stride = 1;
var dataFormat = 'NHWC';
var dilation = 2;
var noDilation = 1;
var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
var w = tf.tensor4d([3, 1, 5, 2], [fSize, fSize, inputDepth, outputDepth]);
var wDilated = tf.tensor4d([3, 0, 1, 0, 0, 0, 5, 0, 2], [fSizeDilated, fSizeDilated, inputDepth, outputDepth]);
var result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
var expectedResult = tf.conv2d(x, wDilated, stride, pad, dataFormat, noDilation);
expect(result.shape).toEqual(expectedResult.shape);
test_util_1.expectArraysClose(result, expectedResult);
});
it('throws when x is not rank 3', function () {
var inputDepth = 1;
var outputDepth = 1;
var fSize = 2;
var pad = 0;
var stride = 1;
var x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
expect(function () { return tf.conv2d(x, w, stride, pad); }).toThrowError();
});
it('throws when weights is not rank 4', function () {
var inputDepth = 1;
var inputShape = [2, 2, inputDepth];
var pad = 0;
var stride = 1;
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
var w = tf.tensor3d([3, 1, 5, 0], [2, 2, 1]);
expect(function () { return tf.conv2d(x, w, stride, pad); }).toThrowError();
});
it('throws when x depth does not match weight depth', function () {
var inputDepth = 1;
var wrongInputDepth = 5;
var inputShape = [2, 2, inputDepth];
var outputDepth = 1;
var fSize = 2;
var pad = 0;
var stride = 1;
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
var w = tf.randomNormal([fSize, fSize, wrongInputDepth, outputDepth]);
expect(function () { return tf.conv2d(x, w, stride, pad); }).toThrowError();
});
it('throws when dimRoundingMode is set and pad is not a number', function () {
var inputDepth = 1;
var inputShape = [2, 2, inputDepth];
var outputDepth = 1;
var fSize = 2;
var pad = 'valid';
var stride = 1;
var dataFormat = 'NHWC';
var dilation = 1;
var dimRoundingMode = 'round';
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
var w = tf.randomNormal([fSize, fSize, inputDepth, outputDepth]);
expect(function () {
return tf.conv2d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode);
})
.toThrowError();
});
it('throws when both stride and dilation are greater than 1', function () {
var inputDepth = 1;
var inputShape = [2, 2, inputDepth];
var outputDepth = 1;
var fSize = 2;
var pad = 0;
var stride = [2, 1];
var dataFormat = 'NHWC';
var dilation = [1, 2];
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
expect(function () { return tf.conv2d(x, w, stride, pad, dataFormat, dilation); })
.toThrowError();
});
it('gradient input=[3,3,1] f=[2,2,1,1] s=1 p=0', function () {
var inputDepth = 1;
var outputDepth = 1;
var inputShape = [3, 3, inputDepth];
var filterSize = 2;
var stride = 1;
var pad = 0;
var filterShape = [filterSize, filterSize, inputDepth, outputDepth];
var filter = tf.ones(filterShape);
var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
var dy = tf.tensor3d([3, 1, 2, 0], [2, 2, 1]);
var grads = tf.grads(function (x, filter) { return x.conv2d(filter, stride, pad); });
var _a = grads([x, filter], dy), dx = _a[0], dfilter = _a[1];
expect(dx.shape).toEqual(x.shape);
test_util_1.expectArraysClose(dx, [3, 4, 1, 5, 6, 1, 2, 2, 0]);
expect(dfilter.shape).toEqual(filterShape);
test_util_1.expectArraysClose(dfilter, [13, 19, 31, 37]);
});
it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', function () {
var inputDepth = 1;
var outputDepth = 1;
var inputShape = [2, 3, 3, inputDepth];
var filterSize = 2;
var stride = 1;
var pad = 0;
var filterShape = [filterSize, filterSize, inputDepth, outputDepth];
var filter = tf.ones(filterShape);
var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
var dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
var grads = tf.grads(function (x, filter) { return x.conv2d(filter, stride, pad); });
var _a = grads([x, filter], dy), dx = _a[0], dfilter = _a[1];
expect(dx.shape).toEqual(x.shape);
test_util_1.expectArraysClose(dx, [3, 4, 1, 5, 6, 1, 2, 2, 0, 3, 4, 1, 5, 6, 1, 2, 2, 0]);
expect(dfilter.shape).toEqual(filterShape);
test_util_1.expectArraysClose(dfilter, [13 * 2, 19 * 2, 31 * 2, 37 * 2]);
});
it('throws when passed x as a non-tensor', function () {
var inputDepth = 1;
var outputDepth = 1;
var fSize = 1;
var pad = 0;
var stride = 1;
var w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
expect(function () { return tf.conv2d({}, w, stride, pad); })
.toThrowError(/Argument 'x' passed to 'conv2d' must be a Tensor/);
});
it('throws when passed filter as a non-tensor', function () {
var inputDepth = 1;
var inputShape = [2, 2, inputDepth];
var pad = 0;
var stride = 1;
var x = tf.tensor3d([1, 2, 3, 4], inputShape);
expect(function () { return tf.conv2d(x, {}, stride, pad); })
.toThrowError(/Argument 'filter' passed to 'conv2d' must be a Tensor/);
});
});
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