diff options
Diffstat (limited to 'lib/THCUNN/generic/FeatureLPPooling.cu')
| -rw-r--r-- | lib/THCUNN/generic/FeatureLPPooling.cu | 267 |
1 files changed, 267 insertions, 0 deletions
diff --git a/lib/THCUNN/generic/FeatureLPPooling.cu b/lib/THCUNN/generic/FeatureLPPooling.cu new file mode 100644 index 0000000..9300450 --- /dev/null +++ b/lib/THCUNN/generic/FeatureLPPooling.cu @@ -0,0 +1,267 @@ +#ifndef THC_GENERIC_FILE +#define THC_GENERIC_FILE "generic/FeatureLPPooling.cu" +#else + +#include "../common.h" + +// non-batch mode: +// [feature dim] +// [feature dim][opt dim 1] +// [feature dim][opt dim 1][opt dim 2] +// +// batch mode: +// [batch dim][feature dim] +// [batch dim][feature dim][opt dim 1] +// [batch dim][feature dim][opt dim 1][opt dim 2] +THCDeviceTensor<real, 4> +THNN_(FeatureLPPooling_upcast)(THCState* state, THCTensor* t, bool batchMode) { + int inputDim = THCTensor_(nDimension)(state, t); + + if (inputDim == 1) { + // [feature dim] + return toDeviceTensor<real, 1>(state, t). + upcastOuter<2>().upcastInner<4>(); + } else if (inputDim == 2) { + if (batchMode) { + // [batch dim][feature dim] + return toDeviceTensor<real, 2>(state, t). + upcastInner<4>(); + } else { + // [feature dim][opt dim 1] + return toDeviceTensor<real, 2>(state, t). + upcastOuter<3>().upcastInner<4>(); + } + } else if (inputDim == 3) { + if (batchMode) { + // [batch dim][feature dim][opt dim 1] + return toDeviceTensor<real, 3>(state, t). + upcastInner<4>(); + } else { + // [feature dim][opt dim 1][opt dim 2] + return toDeviceTensor<real, 3>(state, t). + upcastOuter<4>(); + } + } else { + // inputDim == 4 + // [batch dim][feature dim][opt dim 1][opt dim 2] + THAssert(batchMode); + return toDeviceTensor<real, 4>(state, t); + } +} + +// Resizes `toResize` based on the output size for `src` as an input +// tensor +void +THNN_(FeatureLPPooling_resizeForOutput)(THCState* state, + THCTensor* toResize, + THCTensor* input, + bool batchMode, + int width, + int stride) { + int inputDim = THCTensor_(nDimension)(state, input); + THAssert(inputDim >= 1 && inputDim <= 4); + + long outSize = + lpPoolingOutputSize(THCTensor_(size)(state, input, 0), width, stride); + if (batchMode) { + THAssert(inputDim > 1); + outSize = + lpPoolingOutputSize(THCTensor_(size)(state, input, 1), width, stride); + } else { + THAssert(inputDim < 4); + } + + if (inputDim == 1) { + THCTensor_(resize1d)(state, toResize, outSize); + } else if (inputDim == 2) { + if (batchMode) { + THCTensor_(resize2d)( + state, toResize, THCTensor_(size)(state, input, 0), outSize); + } else { + THCTensor_(resize2d)( + state, toResize, outSize, THCTensor_(size)(state, input, 1)); + } + } else if (inputDim == 3) { + if (batchMode) { + THCTensor_(resize3d)( + state, + toResize, + THCTensor_(size)(state, input, 0), outSize, + THCTensor_(size)(state, input, 2)); + } else { + THCTensor_(resize3d)( + state, + toResize, + outSize, THCTensor_(size)(state, input, 1), + THCTensor_(size)(state, input, 2)); + } + } else if (inputDim == 4) { + THCTensor_(resize4d)( + state, + toResize, + THCTensor_(size)(state, input, 0), outSize, + THCTensor_(size)(state, input, 2), THCTensor_(size)(state, input, 3)); + } +} + +// Makes `toResize` the same size/dimensionality as `src` +void +THNN_(FeatureLPPooling_resize)(THCState* state, + THCTensor* toResize, + THCTensor* src) { + int inputDim = THCTensor_(nDimension)(state, src); + THAssert(inputDim >= 1 && inputDim <= 4); + + if (inputDim == 1) { + THCTensor_(resize1d)(state, + toResize, + THCTensor_(size)(state, src, 0)); + } else if (inputDim == 2) { + THCTensor_(resize2d)( + state, + toResize, + THCTensor_(size)(state, src, 0), + THCTensor_(size)(state, src, 1)); + } else if (inputDim == 3) { + THCTensor_(resize3d)( + state, + toResize, + THCTensor_(size)(state, src, 0), + THCTensor_(size)(state, src, 1), + THCTensor_(size)(state, src, 2)); + } else if (inputDim == 4) { + THCTensor_(resize4d)( + state, + toResize, + THCTensor_(size)(state, src, 0), + THCTensor_(size)(state, src, 1), + THCTensor_(size)(state, src, 2), + THCTensor_(size)(state, src, 3)); + } +} + +void THNN_(FeatureLPPooling_updateOutput)(THCState* state, + THCTensor* inputTH, + THCTensor* outputTH, + accreal power, + int width, + int stride, + bool batchMode) { + THCUNN_assertSameGPU(state, 2, inputTH, outputTH); + + int inputDim = THCTensor_(nDimension)(state, inputTH); + + if (batchMode) { + THArgCheck(inputDim >= 2 && inputDim <= 4, 2, + "input must be 2-4 dimensions for batch mode"); + } else { + THArgCheck(inputDim >= 1 && inputDim <= 3, 2, + "input must be 1-3 dimensions for non-batch mode"); + } + + THArgCheck(TensorUtils<THCTensor>::canUse32BitIndexMath(state, inputTH), 2, + "input tensor must fit into 32-bit index math"); + + THCDeviceTensor<TensorUtils<THCTensor>::DataType, 4> input; + THCDeviceTensor<TensorUtils<THCTensor>::DataType, 4> output; + + input = THNN_(FeatureLPPooling_upcast)(state, inputTH, batchMode); + + // Make sure the feature dimension is properly sized + THArgCheck(input.getSize(1) >= width, 2, + "input: feature dimension must be >= width"); + + // Make sure that width and stride are within range + THArgCheck(width >= 2 && width <= 16, 5, + "width must be between 2 - 16"); + + THArgCheck(stride >= 1 && stride <= 4, 6, + "stride must be between 1 - 4"); + + THNN_(FeatureLPPooling_resizeForOutput)( + state, outputTH, inputTH, batchMode, width, stride); + + output = THNN_(FeatureLPPooling_upcast)(state, outputTH, batchMode); + + bool found = runFeatureLPPoolingUpdateOutput(state, + input, + output, + power, + width, + stride); + THAssert(found); +} + +void THNN_(FeatureLPPooling_updateGradInput)(THCState* state, + THCTensor* gradOutputTH, + THCTensor* inputTH, + THCTensor* outputTH, + THCTensor* gradInputTH, + accreal power, + int width, + int stride, + bool batchMode) { + THArgCheck(TensorUtils<THCTensor>::canUse32BitIndexMath(state, gradOutputTH), 2, + "output gradient tensor must fit into 32-bit index math"); + THArgCheck(TensorUtils<THCTensor>::canUse32BitIndexMath(state, inputTH), 3, + "input tensor must fit into 32-bit index math"); + THCUNN_assertSameGPU(state, 4, gradOutputTH, inputTH, outputTH, gradInputTH); + + int inputDim = THCTensor_(nDimension)(state, inputTH); + + if (batchMode) { + THArgCheck(inputDim >= 2 && inputDim <= 4, 2, + "input must be 2-4 dimensions for batch mode"); + } else { + THArgCheck(inputDim >= 1 && inputDim <= 3, 2, + "input must be 1-3 dimensions for non-batch mode"); + } + + THCDeviceTensor<TensorUtils<THCTensor>::DataType, 4> gradOutput; + THCDeviceTensor<TensorUtils<THCTensor>::DataType, 4> input; + THCDeviceTensor<TensorUtils<THCTensor>::DataType, 4> output; + THCDeviceTensor<TensorUtils<THCTensor>::DataType, 4> gradInput; + + input = THNN_(FeatureLPPooling_upcast)(state, inputTH, batchMode); + + // Make sure the feature dimension is properly sized + THArgCheck(input.getSize(1) >= width, 3, + "input: feature dimension must be >= width"); + + // Make sure that width and stride are within range + THArgCheck(width >= 2 && width <= 16, 7, + "width must be between 2 - 16"); + + THArgCheck(stride >= 1 && stride <= 4, 8, + "stride must be between 1 - 4"); + + gradOutput = THNN_(FeatureLPPooling_upcast)(state, gradOutputTH, batchMode); + output = THNN_(FeatureLPPooling_upcast)(state, outputTH, batchMode); + + for (int i = 0; i < 4; ++i) { + THAssertMsg(output.getSize(i) == gradOutput.getSize(i), + "output and gradOutput sizes do not match"); + } + + // Make sure that the input sizes produce the output sizes + THArgCheck(lpPoolingOutputSize(input.getSize(1), width, stride) == + output.getSize(1), 3, + "input and output sizes do not match with respect to " + "width and stride"); + + // Resize `gradInput` based on `input` + THNN_(FeatureLPPooling_resize)(state, gradInputTH, inputTH); + gradInput = THNN_(FeatureLPPooling_upcast)(state, gradInputTH, batchMode); + + bool found = runFeatureLPPoolingUpdateGradInput(state, + gradOutput, + input, + output, + gradInput, + power, + width, + stride); + THAssert(found); +} + +#endif |