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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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"use strict"; Object.defineProperty(exports, "__esModule", { value: true }); var Conv2DDerFilterProgram = (function () { function Conv2DDerFilterProgram(convInfo) { this.variableNames = ['x', 'dy']; this.outputShape = convInfo.filterShape; var strideHeight = convInfo.strideHeight; var strideWidth = convInfo.strideWidth; var padTop = convInfo.padInfo.top; var padLeft = convInfo.padInfo.left; this.userCode = "\n void main() {\n ivec4 coords = getOutputCoords();\n int wR = coords.x;\n int wC = coords.y;\n int d1 = coords.z;\n int d2 = coords.w;\n\n // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n\n for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n continue;\n }\n\n for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n continue;\n }\n\n float dyValue = getDy(b, yR, yC, d2);\n float xValue = getX(b, xR, xC, d1);\n dotProd += (xValue * dyValue);\n }\n }\n }\n setOutput(dotProd);\n }\n "; } return Conv2DDerFilterProgram; }()); exports.Conv2DDerFilterProgram = Conv2DDerFilterProgram; var Conv2DDerInputProgram = (function () { function Conv2DDerInputProgram(convInfo) { this.variableNames = ['dy', 'W']; this.outputShape = convInfo.inShape; var filterHeight = convInfo.filterHeight; var filterWidth = convInfo.filterWidth; var strideHeight = convInfo.strideHeight; var strideWidth = convInfo.strideWidth; var padTop = filterHeight - 1 - convInfo.padInfo.top; var padLeft = filterWidth - 1 - convInfo.padInfo.left; this.userCode = "\n const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n void main() {\n ivec4 coords = getOutputCoords();\n int batch = coords[0];\n int d1 = coords[3];\n\n ivec2 dyCorner = coords.yz - pads;\n int dyRCorner = dyCorner.x;\n int dyCCorner = dyCorner.y;\n\n // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n // ? = to be determined. : = across all values in that axis.\n float dotProd = 0.0;\n for (int wR = 0; wR < " + filterHeight + "; wR++) {\n float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n continue;\n }\n int idyR = int(dyR);\n\n int wRPerm = " + filterHeight + " - 1 - wR;\n\n for (int wC = 0; wC < " + filterWidth + "; wC++) {\n float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n fract(dyC) > 0.0) {\n continue;\n }\n int idyC = int(dyC);\n\n int wCPerm = " + filterWidth + " - 1 - wC;\n\n for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n float xValue = getDy(batch, idyR, idyC, d2);\n float wValue = getW(wRPerm, wCPerm, d1, d2);\n dotProd += xValue * wValue;\n }\n }\n }\n setOutput(dotProd);\n }\n "; } return Conv2DDerInputProgram; }()); exports.Conv2DDerInputProgram = Conv2DDerInputProgram; //# sourceMappingURL=conv_backprop_gpu.js.map