@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js

Hardware-accelerated JavaScript library for machine intelligence

"use strict";
Object.defineProperty(exports, "__esModule", { value: true });
var broadcast_util = require("../../ops/broadcast_util");
var BatchNormPackedProgram = (function () {
    function BatchNormPackedProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
        this.usesPackedTextures = true;
        this.variableNames = ['x', 'mean', 'variance'];
        broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);
        broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);
        var offsetSnippet = 'vec4(0.0)';
        if (offsetShape != null) {
            broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);
            this.variableNames.push('offset');
            offsetSnippet = 'getOffsetAtOutCoords()';
        }
        var scaleSnippet = 'vec4(1.0)';
        if (scaleShape != null) {
            broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);
            this.variableNames.push('scale');
            scaleSnippet = 'getScaleAtOutCoords()';
        }
        this.outputShape = xShape;
        this.userCode = "\n      void main() {\n        vec4 offset = " + offsetSnippet + ";\n        vec4 scale = " + scaleSnippet + ";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(" + varianceEpsilon + "));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";
    }
    return BatchNormPackedProgram;
}());
exports.BatchNormPackedProgram = BatchNormPackedProgram;
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