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path: root/lib/THCUNN/generic/SpatialFullConvolution.cu
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Diffstat (limited to 'lib/THCUNN/generic/SpatialFullConvolution.cu')
-rw-r--r--lib/THCUNN/generic/SpatialFullConvolution.cu421
1 files changed, 10 insertions, 411 deletions
diff --git a/lib/THCUNN/generic/SpatialFullConvolution.cu b/lib/THCUNN/generic/SpatialFullConvolution.cu
index 76abb90..af9a473 100644
--- a/lib/THCUNN/generic/SpatialFullConvolution.cu
+++ b/lib/THCUNN/generic/SpatialFullConvolution.cu
@@ -2,65 +2,6 @@
#define THC_GENERIC_FILE "generic/SpatialFullConvolution.cu"
#else
-static inline void THNN_(SpatialFullConvolution_shapeCheck)(
- THCState *state,
- THCTensor *input, THCTensor *gradOutput,
- THCTensor *weight, THCTensor *bias,
- int kH, int kW, int dH, int dW, int padH, int padW,
- int adjH, int adjW) {
- THArgCheck(kW > 0 && kH > 0, 9,
- "kernel size should be greater than zero, but got kH: %d kW: %d", kH, kW);
- THArgCheck(dW > 0 && dH > 0, 11,
- "stride should be greater than zero, but got dH: %d dW: %d", dH, dW);
- THArgCheck(adjW < dW && adjH < dH, 15,
- "output adjustment must be smaller than stride, but got adjH: %d adjW: %d dH: %d dW: %d",
- adjH, adjW, dH, dW);
- THArgCheck(THCTensor_(isContiguous)(state, weight), 4,
- "weight tensor has to be contiguous");
- THArgCheck(!bias || THCTensor_(isContiguous)(state, bias), 5,
- "bias tensor has to be contiguous");
- THCUNN_argCheck(state, weight->nDimension == 2 || weight->nDimension == 4, 5, weight,
- "2D or 4D weight tensor expected, but got: %s");
-
- if (bias != NULL) {
- THCUNN_check_dim_size(state, bias, 1, 0, weight->size[1]);
- }
-
- int ndim = input->nDimension;
- int dimf = 0;
- int dimh = 1;
- int dimw = 2;
-
- if (ndim == 4) {
- dimf++;
- dimh++;
- dimw++;
- }
-
- THCUNN_argCheck(state, ndim == 3 || ndim == 4, 2, input,
- "3D or 4D input tensor expected but got: %s");
-
- long nInputPlane = weight->size[0];
- long inputHeight = input->size[dimh];
- long inputWidth = input->size[dimw];
- long nOutputPlane = weight->size[1];
- long outputHeight = (inputHeight - 1) * dH - 2*padH + kH + adjH;
- long outputWidth = (inputWidth - 1) * dW - 2*padW + kW + adjW;
-
- if (outputWidth < 1 || outputHeight < 1)
- THError("Given input size: (%d x %d x %d). "
- "Calculated output size: (%d x %d x %d). Output size is too small",
- nInputPlane,inputHeight,inputWidth,nOutputPlane,outputHeight,outputWidth);
-
- THCUNN_check_dim_size(state, input, ndim, dimf, nInputPlane);
-
- if (gradOutput != NULL) {
- THCUNN_check_dim_size(state, gradOutput, ndim, dimf, nOutputPlane);
- THCUNN_check_dim_size(state, gradOutput, ndim, dimh, outputHeight);
- THCUNN_check_dim_size(state, gradOutput, ndim, dimw, outputWidth);
- }
-}
-
void THNN_(SpatialFullConvolution_updateOutput)(
THCState *state,
THCTensor *input,
@@ -74,133 +15,9 @@ void THNN_(SpatialFullConvolution_updateOutput)(
int padW, int padH,
int adjW, int adjH)
{
-
- int nInputPlane = THCTensor_(size)(state, weight, 0);
- int nOutputPlane = THCTensor_(size)(state, weight, 1);
-
- THCUNN_assertSameGPU(state, 6, input, output, weight,
- bias, columns, ones);
- THNN_(SpatialFullConvolution_shapeCheck)
- (state, input, NULL, weight, bias, kH, kW, dH, dW, padH, padW, adjH, adjW);
-
- input = THCTensor_(newContiguous)(state, input);
- weight = THCTensor_(newContiguous)(state, weight);
- bias = bias ? THCTensor_(newContiguous)(state, bias) : bias;
-
- int batch = 1;
- if (input->nDimension == 3) {
- // Force batch
- batch = 0;
- THCTensor_(resize4d)(state, input, 1, input->size[0], input->size[1], input->size[2]);
- }
-
- long inputWidth = input->size[3];
- long inputHeight = input->size[2];
- long outputWidth = (inputWidth - 1) * dW - 2*padW + kW + adjW;
- long outputHeight = (inputHeight - 1) * dH - 2*padH + kH + adjH;
-
- // Batch size + input planes
- long batchSize = input->size[0];
-
- // Resize output
- THCTensor_(resize4d)(state, output, batchSize, nOutputPlane, outputHeight, outputWidth);
-
- // Resize temporary columns
- THCTensor_(resize2d)(state, columns, nOutputPlane*kW*kH, inputHeight*inputWidth);
-
- // Define a buffer of ones, for bias accumulation
- // Note: this buffer can be shared with other modules, it only ever gets increased,
- // and always contains ones.
- if (ones->nDimension != 2 || ones->size[0]*ones->size[1] < outputHeight*outputWidth) {
- // Resize plane and fill with ones...
- THCTensor_(resize2d)(state, ones, outputHeight, outputWidth);
- THCTensor_(fill)(state, ones, ScalarConvert<int, real>::to(1));
- }
-
- // Helpers
- THCTensor *input_n = THCTensor_(new)(state);
- THCTensor *output_n = THCTensor_(new)(state);
-
- // For each elt in batch, do:
- for (int elt = 0; elt < batchSize; elt ++) {
- // Matrix mulitply per output:
- THCTensor_(select)(state, input_n, input, 0, elt);
- THCTensor_(select)(state, output_n, output, 0, elt);
-
- // M,N,K are dims of matrix A and B
- // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm)
- long m = weight->size[1] * weight->size[2] * weight->size[3];
- long n = columns->size[1];
- long k = weight->size[0];
-
- // Do GEMM (note: this is a bit confusing because gemm assumes column-major matrices)
- #ifdef THC_REAL_IS_FLOAT
- THCudaBlas_Sgemm(
- #elif defined(THC_REAL_IS_HALF)
- THCudaBlas_Hgemm(
- #elif defined(THC_REAL_IS_DOUBLE)
- THCudaBlas_Dgemm(
- #endif
- state,
- 'n', 't',
- n, m, k,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, input_n), n,
- THCTensor_(data)(state, weight), m,
- ScalarConvert<int, real>::to(0),
- THCTensor_(data)(state, columns), n
- );
-
- // Unpack columns back into input:
- col2im<real, accreal>(
- THCState_getCurrentStream(state),
- THCTensor_(data)(state, columns),
- nOutputPlane, outputHeight, outputWidth, kH, kW, padH, padW, dH, dW,
- 1, 1, THCTensor_(data)(state, output_n)
- );
-
- // Do Bias after:
- // M,N,K are dims of matrix A and B
- // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm)
- long m_ = nOutputPlane;
- long n_ = outputHeight * outputWidth;
- long k_ = 1;
-
- // Do GEMM (note: this is a bit confusing because gemm assumes column-major matrices)
- if (bias) {
- #ifdef THC_REAL_IS_FLOAT
- THCudaBlas_Sgemm(
- #elif defined(THC_REAL_IS_HALF)
- THCudaBlas_Hgemm(
- #elif defined(THC_REAL_IS_DOUBLE)
- THCudaBlas_Dgemm(
- #endif
- state,
- 't', 'n',
- n_, m_, k_,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, ones), k_,
- THCTensor_(data)(state, bias), k_,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, output_n), n_
- );
- }
- }
-
- // Free
- THCTensor_(free)(state, input_n);
- THCTensor_(free)(state, output_n);
-
- // Resize output
- if (batch == 0) {
- THCTensor_(resize3d)(state, output, nOutputPlane, outputHeight, outputWidth);
- THCTensor_(resize3d)(state, input, nInputPlane, inputHeight, inputWidth);
- }
-
- THCTensor_(free)(state, input);
- THCTensor_(free)(state, weight);
- if (bias) THCTensor_(free)(state, bias);
-
+ THNN_(SpatialFullDilatedConvolution_updateOutput)(
+ state, input, output, weight, bias, columns, ones,
+ kW, kH, dW, dH, padW, padH, 1, 1, adjW, adjH);
}
void THNN_(SpatialFullConvolution_updateGradInput)(
@@ -215,98 +32,9 @@ void THNN_(SpatialFullConvolution_updateGradInput)(
int padW, int padH,
int adjW, int adjH)
{
- int nInputPlane = THCTensor_(size)(state, weight, 0);
- int nOutputPlane = THCTensor_(size)(state, weight, 1);
-
- THCUNN_assertSameGPU(state, 5, input, gradOutput, weight,
- gradColumns, gradInput);
- THNN_(SpatialFullConvolution_shapeCheck)
- (state, input, gradOutput, weight, NULL, kH, kW, dH, dW, padH, padW, adjH, adjW);
-
- input = THCTensor_(newContiguous)(state, input);
- gradOutput = THCTensor_(newContiguous)(state, gradOutput);
- weight = THCTensor_(newContiguous)(state, weight);
- int batch = 1;
- if (input->nDimension == 3) {
- // Force batch
- batch = 0;
- THCTensor_(resize4d)(state, input, 1, input->size[0], input->size[1], input->size[2]);
- THCTensor_(resize4d)(state, gradOutput, 1, gradOutput->size[0], gradOutput->size[1], gradOutput->size[2]);
- }
-
- long inputWidth = input->size[3];
- long inputHeight = input->size[2];
- long outputWidth = (inputWidth - 1) * dW - 2*padW + kW + adjW;
- long outputHeight = (inputHeight - 1) * dH - 2*padH + kH + adjH;
-
- // Batch size + input planes
- long batchSize = input->size[0];
-
- // Resize output
- THCTensor_(resize4d)(state, gradInput, batchSize, nInputPlane, inputHeight, inputWidth);
-
- // Resize temporary columns
- THCTensor_(resize2d)(state, gradColumns, nOutputPlane*kW*kH, inputHeight*inputWidth);
-
- // Helpers
- THCTensor *gradInput_n = THCTensor_(new)(state);
- THCTensor *gradOutput_n = THCTensor_(new)(state);
-
- // For each elt in batch, do:
- for (int elt = 0; elt < batchSize; elt ++) {
- // Matrix mulitply per sample:
- THCTensor_(select)(state, gradInput_n, gradInput, 0, elt);
- THCTensor_(select)(state, gradOutput_n, gradOutput, 0, elt);
-
- // Extract columns:
- im2col(
- THCState_getCurrentStream(state),
- THCTensor_(data)(state, gradOutput_n),
- nOutputPlane, outputHeight, outputWidth, kH, kW, padH, padW, dH, dW,
- 1, 1, THCTensor_(data)(state, gradColumns)
- );
-
-
- // M,N,K are dims of matrix A and B
- // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm)
- long m = weight->size[0];
- long n = gradColumns->size[1];
- long k = weight->size[1] * weight->size[2] * weight->size[3];
-
- // Do GEMM (note: this is a bit confusing because gemm assumes column-major matrices)
- #ifdef THC_REAL_IS_FLOAT
- THCudaBlas_Sgemm(
- #elif defined(THC_REAL_IS_HALF)
- THCudaBlas_Hgemm(
- #elif defined(THC_REAL_IS_DOUBLE)
- THCudaBlas_Dgemm(
- #endif
- state,
- 'n', 'n',
- n, m, k,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, gradColumns), n,
- THCTensor_(data)(state, weight), k,
- ScalarConvert<int, real>::to(0),
- THCTensor_(data)(state, gradInput_n), n
- );
- }
-
-
- // Free
- THCTensor_(free)(state, gradInput_n);
- THCTensor_(free)(state, gradOutput_n);
-
- // Resize output
- if (batch == 0) {
- THCTensor_(resize3d)(state, gradOutput, nOutputPlane, outputHeight, outputWidth);
- THCTensor_(resize3d)(state, input, nInputPlane, inputHeight, inputWidth);
- THCTensor_(resize3d)(state, gradInput, nInputPlane, inputHeight, inputWidth);
- }
-
- THCTensor_(free)(state, input);
- THCTensor_(free)(state, gradOutput);
- THCTensor_(free)(state, weight);
+ THNN_(SpatialFullDilatedConvolution_updateGradInput)(
+ state, input, gradOutput, gradInput, weight, gradColumns,
+ kW, kH, dW, dH, padW, padH, 1, 1, adjW, adjH);
}
@@ -324,139 +52,10 @@ void THNN_(SpatialFullConvolution_accGradParameters)(
int adjW, int adjH,
accreal scale_)
{
- real scale = ScalarConvert<accreal, real>::to(scale_);
- int nInputPlane = THCTensor_(size)(state, gradWeight, 0);
- int nOutputPlane = THCTensor_(size)(state, gradWeight, 1);
-
- THCUNN_assertSameGPU(state, 6, input, gradOutput, gradWeight,
- gradBias, columns, ones);
- THNN_(SpatialFullConvolution_shapeCheck)
- (state, input, gradOutput, gradWeight, gradBias, kH, kW, dH, dW, padH, padW, adjH, adjW);
-
- THArgCheck(THCTensor_(isContiguous)(state, gradWeight), 4, "gradWeight needs to be contiguous");
- if (gradBias)
- THArgCheck(THCTensor_(isContiguous)(state, gradBias), 5, "gradBias needs to be contiguous");
- input = THCTensor_(newContiguous)(state, input);
- gradOutput = THCTensor_(newContiguous)(state, gradOutput);
- int batch = 1;
- if (input->nDimension == 3) {
- // Force batch
- batch = 0;
- THCTensor_(resize4d)(state, input, 1, input->size[0], input->size[1], input->size[2]);
- THCTensor_(resize4d)(state, gradOutput, 1, gradOutput->size[0], gradOutput->size[1], gradOutput->size[2]);
- }
-
- long inputWidth = input->size[3];
- long inputHeight = input->size[2];
- long outputWidth = (inputWidth - 1) * dW - 2*padW + kW + adjW;
- long outputHeight = (inputHeight - 1) * dH - 2*padH + kH + adjH;
-
- // Batch size + input planes
- long batchSize = input->size[0];
-
- // Define a buffer of ones, for bias accumulation
- if (ones->nDimension != 2 || ones->size[0]*ones->size[1] < outputHeight*outputWidth) {
- // Resize plane and fill with ones...
- THCTensor_(resize2d)(state, ones, outputHeight, outputWidth);
- THCTensor_(fill)(state, ones, ScalarConvert<int, real>::to(1));
- }
-
- // Resize temporary columns
- THCTensor_(resize2d)(state, columns, nOutputPlane*kW*kH, inputHeight*inputWidth);
-
- // Helpers
- THCTensor *input_n = THCTensor_(new)(state);
- THCTensor *gradOutput_n = THCTensor_(new)(state);
-
- // For each elt in batch, do:
- for (int elt = 0; elt < batchSize; elt ++) {
- // Matrix mulitply per output:
- THCTensor_(select)(state, input_n, input, 0, elt);
- THCTensor_(select)(state, gradOutput_n, gradOutput, 0, elt);
-
- // Extract columns:
- im2col(
- THCState_getCurrentStream(state),
- THCTensor_(data)(state, gradOutput_n),
- nOutputPlane, outputHeight, outputWidth, kH, kW, padH, padW, dH, dW,
- 1, 1, THCTensor_(data)(state, columns)
- );
-
- // M,N,K are dims of matrix A and B
- // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm)
- long n = columns->size[0]; // nOutputPlane * kh * kw
- long m = input_n->size[0]; // nInputPlane
- long k = columns->size[1]; // inputHeight * inputWidth
-
- // Do GEMM (note: this is a bit confusing because gemm assumes column-major matrices)
- #ifdef THC_REAL_IS_FLOAT
- THCudaBlas_Sgemm(
- #elif defined(THC_REAL_IS_HALF)
- THCudaBlas_Hgemm(
- #elif defined(THC_REAL_IS_DOUBLE)
- THCudaBlas_Dgemm(
- #endif
- state,
- 't', 'n',
- n, m, k,
- scale,
- THCTensor_(data)(state, columns), k,
- THCTensor_(data)(state, input_n), k,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, gradWeight), n
- );
-
- // Do Bias:
- // M,N,K are dims of matrix A and B
- // (see http://docs.nvidia.com/cuda/cublas/#cublas-lt-t-gt-gemm)
- long m_ = nOutputPlane;
- long k_ = outputHeight * outputWidth;
-
- // Do GEMV (note: this is a bit confusing because gemv assumes column-major matrices)
- if (gradBias) {
- #if defined(THC_REAL_IS_FLOAT) || defined(THC_REAL_IS_DOUBLE)
- #ifdef THC_REAL_IS_FLOAT
- THCudaBlas_Sgemv(
- #elif defined(THC_REAL_IS_DOUBLE)
- THCudaBlas_Dgemv(
- #endif
- state,
- 't',
- k_, m_,
- scale,
- THCTensor_(data)(state, gradOutput_n), k_,
- THCTensor_(data)(state, ones), 1,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, gradBias), 1
- );
- #endif
- #ifdef THC_REAL_IS_HALF
- THCudaBlas_Hgemm(
- state,
- 't', 'n',
- m_, 1, k_,
- scale,
- THCTensor_(data)(state, gradOutput_n), k_,
- THCTensor_(data)(state, ones), k_,
- ScalarConvert<int, real>::to(1),
- THCTensor_(data)(state, gradBias), m_
- );
- #endif
- }
- }
-
- // Free
- THCTensor_(free)(state, input_n);
- THCTensor_(free)(state, gradOutput_n);
-
- // Resize
- if (batch == 0) {
- THCTensor_(resize3d)(state, gradOutput, nOutputPlane, outputHeight, outputWidth);
- THCTensor_(resize3d)(state, input, nInputPlane, inputHeight, inputWidth);
- }
-
- THCTensor_(free)(state, input);
- THCTensor_(free)(state, gradOutput);
+ THNN_(SpatialFullDilatedConvolution_accGradParameters)(
+ state, input, gradOutput, gradWeight, gradBias,
+ columns, ones,
+ kW, kH, dW, dH, padW, padH, 1, 1, adjW, adjH, scale_);
}
#endif
generated by cgit v1.2.3 (git 2.25.1) at 2024-06-11 18:39:37 +0300