"use strict";
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/**
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* @license
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* Copyright 2019 Google LLC All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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Object.defineProperty(exports, "__esModule", { value: true });
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var LRNPackedProgram = /** @class */ (function () {
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function LRNPackedProgram(xShape, radius, bias, alpha, beta) {
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this.variableNames = ['x'];
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this.outputShape = [];
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this.packedInputs = true;
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this.packedOutput = true;
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var rad = radius;
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var maxD = xShape[3] - 1;
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this.outputShape = xShape;
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// optimize pow(bias + alpha * sum, -beta)
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// src: https://github.com/tensorflow/tensorflow/..
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// blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..
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// tensorflow/core/kernels/mkl_lrn_op.cc#L320
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var powOperator;
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var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
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if (beta === 0.5) {
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powOperator = "inversesqrt(" + basis + ")";
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}
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else if (beta === 1.0) {
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powOperator = "1.0/(" + basis + ")";
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}
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else {
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powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
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}
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this.userCode = "\n void main() {\n ivec4 coords = getOutputCoords();\n int b = coords.x;\n int r = coords.y;\n int c = coords.z;\n int d = coords.w;\n\n bool hasNextCol = d < " + this.outputShape[3] + ";\n bool hasNextRow = c < " + this.outputShape[2] + ";\n\n vec4 sum = vec4(0.);\n vec4 xFragAtOutputCoords = getX(b, r, c, d);\n\n vec4 xAtOutputCoords = vec4(\n getChannel(xFragAtOutputCoords, vec2(c, d)),\n hasNextCol ?\n getChannel(xFragAtOutputCoords, vec2(c, d + 1)) : 0.0,\n hasNextRow ?\n getChannel(xFragAtOutputCoords , vec2(c + 1, d)) : 0.0,\n (hasNextRow && hasNextCol) ?\n getChannel(xFragAtOutputCoords, vec2(c + 1, d + 1)) : 0.0\n );\n\n int firstChannel = d - " + rad + ";\n vec2 cache = vec2(0.);\n if(firstChannel >= 0){\n vec4 firstChannelFrag = getX(b, r, c, firstChannel);\n cache.x = getChannel(firstChannelFrag, vec2(c, firstChannel));\n if(hasNextRow){\n cache.y = getChannel(firstChannelFrag, vec2(c + 1, firstChannel));\n }\n }\n\n ivec2 depth = ivec2(d, d + 1);\n for (int j = - " + rad + "; j <= " + rad + "; j++) {\n ivec2 idx = depth + j;\n bvec2 aboveLowerBound = greaterThanEqual(idx, ivec2(0));\n bvec2 belowUpperBound = lessThanEqual(idx, ivec2(" + maxD + "));\n\n bool depthInRange = aboveLowerBound.x && belowUpperBound.x;\n bool depthPlusOneInRange = aboveLowerBound.y && belowUpperBound.y;\n\n if(depthInRange || depthPlusOneInRange){\n vec4 z = vec4(0.);\n vec4 xFragAtCurrentDepth;\n z.xz = cache.xy;\n if(depthPlusOneInRange && hasNextCol){\n xFragAtCurrentDepth = idx.y != d ?\n getX(b, r, c, idx.y) : xFragAtOutputCoords;\n z.y = getChannel(xFragAtCurrentDepth, vec2(c, idx.y));\n if(hasNextRow){\n z.w = getChannel(xFragAtCurrentDepth, vec2(c + 1, idx.y));\n }\n }\n cache.xy = z.yw;\n sum += z * z;\n }\n }\n vec4 result = xAtOutputCoords * " + powOperator + ";\n setOutput(result);\n }\n ";
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}
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return LRNPackedProgram;
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}());
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exports.LRNPackedProgram = LRNPackedProgram;
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//# sourceMappingURL=lrn_packed_gpu.js.map
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