Java Code Examples for android.renderscript.Element#F32
The following examples show how to use
android.renderscript.Element#F32 .
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Example 1
Source File: Layer.java From rscnn with MIT License | 6 votes |
protected void allocFeatureMap() { Type.Builder outputType = new Type.Builder(renderScript, Element.F32(renderScript)); outputType.setZ(outputShape[0]); outputType.setY(outputShape[1] * outputShape[2]); outputType.setX(getOutputChannelAligned()); Allocation outAllocation = Allocation.createTyped(renderScript, outputType.create()); FeatureMap output = new FeatureMap(); output.setFeatureMap(outAllocation); output.setN(outputShape[0]); output.setH(outputShape[1]); output.setW(outputShape[2]); output.setC(outputShape[3]); output.setPad4(true); if(this.featureMapOutput!=null){ ((FeatureMap)featureMapOutput).getFeatureMap().destroy(); } this.featureMapOutput = output; }
Example 2
Source File: Layer.java From rscnn with MIT License | 6 votes |
protected void allocFeatureMapNoPad() { Type.Builder outputType = new Type.Builder(renderScript, Element.F32(renderScript)); outputType.setZ(outputShape[0]); outputType.setY(outputShape[1] * outputShape[2]); outputType.setX(outputShape[3]); Allocation outAllocation = Allocation.createTyped(renderScript, outputType.create()); FeatureMap output = new FeatureMap(); output.setFeatureMap(outAllocation); output.setN(outputShape[0]); output.setH(outputShape[1]); output.setW(outputShape[2]); output.setC(outputShape[3]); output.setPad4(false); if(this.featureMapOutput!=null){ ((FeatureMap)featureMapOutput).getFeatureMap().destroy(); } this.featureMapOutput = output; }
Example 3
Source File: Deconvolution.java From rscnn with MIT License | 4 votes |
private void initKernel() { int inputChannel = inputShape[0][3]; int kernelHeight = kernelH; int kernelWidth = kernelW; int kernelSize = kernelHeight * kernelWidth; int inputChannelAligned = getInputChannelAligned(); int totalKernelSize = inputChannelAligned * kernelSize; Allocation kernelAllocation; Allocation biasAllocation; Type.Builder kernelType = new Type.Builder(renderScript, Element.F32(renderScript)); kernelType.setX(inputChannelAligned); kernelType.setY(kernelH * kernelW); Type biasType = Type.createX(renderScript, Element.F32(renderScript), inputChannelAligned); kernelAllocation = Allocation.createTyped(renderScript, kernelType.create(), Allocation.MipmapControl.MIPMAP_NONE, Allocation.USAGE_GRAPHICS_TEXTURE | Allocation.USAGE_SCRIPT); biasAllocation = Allocation.createTyped(renderScript, biasType, Allocation.MipmapControl.MIPMAP_NONE, Allocation.USAGE_GRAPHICS_TEXTURE | Allocation.USAGE_SCRIPT); if(weightBuffer==null) { float[] kernelMatrix = new float[totalKernelSize]; float[] biasArray = new float[inputChannelAligned]; int count = 0; for (int j = 0; j < kernelHeight; j++) { for (int k = 0; k < kernelWidth; k++) { for (int i = 0; i < inputChannelAligned; i++) { if (i >= inputChannel) { kernelMatrix[count++] = 0; } else { kernelMatrix[count++] = weight[i][0][j][k]; } } } } for (int i = 0; i < inputChannelAligned; i++) { if (i >= inputChannel) { biasArray[i] = 0; } else { biasArray[i] = bias[i]; } } kernelAllocation.copyFrom(kernelMatrix); biasAllocation.copyFrom(biasArray); } else { kernelAllocation.copyFromUnchecked(weightBuffer); biasAllocation.copyFromUnchecked(biasBuffer); } scriptDeconvolution = new ScriptC_Deconvolution(renderScript); scriptDeconvolution.set_BiasData(biasAllocation); scriptDeconvolution.set_KernelData(kernelAllocation); scriptDeconvolution.set_channelAligned(inputChannelAligned); scriptDeconvolution.set_padH(padH); scriptDeconvolution.set_padW(padW); scriptDeconvolution.set_strideH(strideH); scriptDeconvolution.set_strideW(strideW); scriptDeconvolution.set_kernelH(kernelH); scriptDeconvolution.set_kernelW(kernelW); }
Example 4
Source File: Convolution.java From rscnn with MIT License | 4 votes |
@Override public void computeFeatureMap() { int outputHeight = outputShape[1]; int outputWidth = outputShape[2]; int inputChannel = inputShape[0][3]; int outputChannel = outputShape[3]; int outputChannelAligned = getOutputChannelAligned(); int inputChannelAligned = getInputChannelAligned(); FeatureMap input = (FeatureMap) featureMapInput[0]; FeatureMap output = (FeatureMap) featureMapOutput; Allocation inputFeatureMap = input.getFeatureMap(); Allocation outputFeatureMap = output.getFeatureMap(); scriptConvolution.set_InputData(inputFeatureMap); scriptConvolution.set_OutputData(outputFeatureMap); ScriptC.LaunchOptions option = new Script.LaunchOptions(); boolean useIntrinsicBlas = false; if(conv1x1UserIntrinsic && kernelH==1 && kernelW==1){ useIntrinsicBlas = true; } if(convnxnUseIntrinsic && kernelH!=1 && kernelW!=1){ conv1x1UserIntrinsic = true; } if(useIntrinsicBlas){ if(kernelH==1 && kernelW==1){ scriptIntrinsicBLAS.SGEMM(ScriptIntrinsicBLAS.NO_TRANSPOSE, ScriptIntrinsicBLAS.TRANSPOSE, 1.f, inputFeatureMap, kernelAllocation, 0.f, outputFeatureMap); } else if (inputChannel == group) { option.setX(0, getInputChannelAligned() / 4).setY(0, outputHeight * outputWidth); scriptConvolution.forEach_conv_dw4(option); return; } else { Type.Builder colType = new Type.Builder(renderScript, Element.F32(renderScript)); colType.setX(kernelH * kernelW * inputChannelAligned).setY(outputHeight * outputWidth); Allocation colAllocation = Allocation.createTyped(renderScript, colType.create()); scriptConvolution.set_ColData(colAllocation); option.setX(0, kernelH * kernelW).setY(0, outputHeight * outputWidth); scriptConvolution.forEach_conv_im2col2(option); scriptIntrinsicBLAS.SGEMM(ScriptIntrinsicBLAS.NO_TRANSPOSE, ScriptIntrinsicBLAS.TRANSPOSE, 1.f, colAllocation, kernelAllocation, 0.f, outputFeatureMap); colAllocation.destroy(); } if(nextRelu && biasTerm){ scriptConvolution.forEach_conv_bias_relu(outputFeatureMap, outputFeatureMap); } else if(biasTerm){ scriptConvolution.forEach_conv_bias(outputFeatureMap, outputFeatureMap); } else if(nextRelu){ scriptConvolution.forEach_conv_relu(outputFeatureMap, outputFeatureMap); } } else { if(kernelH==1 && kernelW==1){ option.setX(0, getOutputChannelAligned() / 4).setY(0, outputHeight * outputWidth); scriptConvolution.forEach_conv1x1(option); } else if (inputChannel == group) { option.setX(0, getInputChannelAligned() / 4).setY(0, outputHeight * outputWidth); scriptConvolution.forEach_conv_dw4(option); } else { int blockSize = 4; int[] blockSizeList = {256, 128, 96, 64, 48, 32, 16, 8}; for (int blk : blockSizeList) { if (outputChannelAligned % blk == 0) { blockSize = blk; break; } } scriptConvolution.set_nblock(blockSize); option.setX(0, outputChannelAligned / blockSize).setY(0, outputHeight * outputWidth); scriptConvolution.forEach_conv4n(option); } } }