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adapt the CUDA functions with PyTorch 0.4.1 API #21

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adapt the CUDA functions with PyTorch 0.4.1 API #21

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130 changes: 65 additions & 65 deletions src/deform_conv_cuda.c
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Original file line number Diff line number Diff line change
Expand Up @@ -9,10 +9,10 @@ void shape_check(THCState *state, THCudaTensor *input, THCudaTensor *offset,
int dH, int dW, int padH, int padW, int dilationH,
int dilationW, int deformable_group) {

THArgCheck(weight->nDimension == 4, 5,
THArgCheck(THCudaTensor_nDimension(state, weight) == 4, 5,
"4D weight tensor (nOutputPlane,nInputPlane,kH,kW) expected, "
"but got: %s",
weight->nDimension);
THCudaTensor_nDimension(state, weight));

THArgCheck(THCudaTensor_isContiguous(state, weight), 5,
"weight tensor has to be contiguous");
Expand All @@ -21,10 +21,10 @@ void shape_check(THCState *state, THCudaTensor *input, THCudaTensor *offset,
"kernel size should be greater than zero, but got kH: %d kW: %d",
kH, kW);

THArgCheck((weight->size[2] == kH && weight->size[3] == kW), 9,
THArgCheck((THCudaTensor_size(state, weight, 2) == kH && THCudaTensor_size(state, weight, 3) == kW), 9,
"kernel size should be consistent with weight, ",
"but got kH: %d kW: %d weight.size(2): %d, weight.size(3): %d", kH,
kW, weight->size[2], weight->size[3]);
kW, THCudaTensor_size(state, weight, 2), THCudaTensor_size(state, weight, 3));

THArgCheck(dW > 0 && dH > 0, 11,
"stride should be greater than zero, but got dH: %d dW: %d", dH,
Expand All @@ -35,7 +35,7 @@ void shape_check(THCState *state, THCudaTensor *input, THCudaTensor *offset,
"dilation should be greater than 0, but got dilationH: %d dilationW: %d",
dilationH, dilationW);

int ndim = input->nDimension;
int ndim = THCudaTensor_nDimension(state, input);
int dimf = 0;
int dimh = 1;
int dimw = 2;
Expand All @@ -49,10 +49,10 @@ void shape_check(THCState *state, THCudaTensor *input, THCudaTensor *offset,
THArgCheck(ndim == 3 || ndim == 4, 2,
"3D or 4D input tensor expected but got: %s", ndim);

long nInputPlane = weight->size[1];
long inputHeight = input->size[dimh];
long inputWidth = input->size[dimw];
long nOutputPlane = weight->size[0];
long nInputPlane = THCudaTensor_size(state, weight, 1);
long inputHeight = THCudaTensor_size(state, input, dimh);
long inputWidth = THCudaTensor_size(state, input, dimw);
long nOutputPlane = THCudaTensor_size(state, weight, 0);
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;
long outputWidth =
Expand All @@ -68,30 +68,30 @@ void shape_check(THCState *state, THCudaTensor *input, THCudaTensor *offset,
nInputPlane, inputHeight, inputWidth, nOutputPlane, outputHeight,
outputWidth);

THArgCheck(input->size[1] == nInputPlane, 2,
THArgCheck(THCudaTensor_size(state, input, 1) == nInputPlane, 2,
"invalid number of input planes, expected: %d, but got: %d",
nInputPlane, input->size[1]);
nInputPlane, THCudaTensor_size(state, input, 1));

THArgCheck((inputHeight >= kH && inputWidth >= kW), 2,
"input image is smaller than kernel");

THArgCheck(
(offset->size[2] == outputHeight && offset->size[3] == outputWidth), 3,
(THCudaTensor_size(state, offset, 2) == outputHeight && THCudaTensor_size(state, offset, 3) == outputWidth), 3,
"invalid spatial size of offset, expected height: %d width: %d, but got height: %d width: %d", outputHeight, outputWidth,
offset->size[2], offset->size[3]);
THCudaTensor_size(state, offset, 2), THCudaTensor_size(state, offset, 3));

THArgCheck((offset->size[1] == deformable_group * 2 * kH * kW), 3,
THArgCheck((THCudaTensor_size(state, offset, 1) == deformable_group * 2 * kH * kW), 3,
"invalid number of channels of offset");

if (gradOutput != NULL) {
THArgCheck(gradOutput->size[dimf] == nOutputPlane, 4,
THArgCheck(THCudaTensor_size(state, gradOutput, dimf) == nOutputPlane, 4,
"invalid number of gradOutput planes, expected: %d, but got: %d",
nOutputPlane, gradOutput->size[dimf]);
nOutputPlane, THCudaTensor_size(state, gradOutput, dimf));

THArgCheck((gradOutput->size[dimh] == outputHeight &&
gradOutput->size[dimw] == outputWidth),
THArgCheck((THCudaTensor_size(state, gradOutput, dimh) == outputHeight &&
THCudaTensor_size(state, gradOutput, dimw) == outputWidth),
4, "invalid size of gradOutput, expected height: %d width: %d , but got height: %d width: %d", outputHeight, outputWidth,
gradOutput->size[dimh], gradOutput->size[dimw]);
THCudaTensor_size(state, gradOutput, dimh), THCudaTensor_size(state, gradOutput, dimw));
}
}

Expand All @@ -113,28 +113,28 @@ int deform_conv_forward_cuda(THCudaTensor *input, THCudaTensor *weight,
weight = THCudaTensor_newContiguous(state, weight);

int batch = 1;
if (input->nDimension == 3) {
if (THCudaTensor_nDimension(state, input) == 3) {
// Force batch
batch = 0;
THCudaTensor_resize4d(state, input, 1, input->size[0], input->size[1],
input->size[2]);
THCudaTensor_resize4d(state, offset, 1, offset->size[0], offset->size[1],
offset->size[2]);
THCudaTensor_resize4d(state, input, 1, THCudaTensor_size(state, input, 0), THCudaTensor_size(state, input, 1),
THCudaTensor_size(state, input, 2));
THCudaTensor_resize4d(state, offset, 1, THCudaTensor_size(state, offset, 0), THCudaTensor_size(state, offset, 1),
THCudaTensor_size(state, offset, 2));
}

long batchSize = input->size[0];
long nInputPlane = input->size[1];
long inputHeight = input->size[2];
long inputWidth = input->size[3];
long batchSize = THCudaTensor_size(state, input, 0);
long nInputPlane = THCudaTensor_size(state, input, 1);
long inputHeight = THCudaTensor_size(state, input, 2);
long inputWidth = THCudaTensor_size(state, input, 3);

long nOutputPlane = weight->size[0];
long nOutputPlane = THCudaTensor_size(state, weight, 0);

long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;

THArgCheck((offset->size[0] == batchSize), 3, "invalid batch size of offset");
THArgCheck((THCudaTensor_size(state, offset, 0) == batchSize), 3, "invalid batch size of offset");

// bias = bias ? THCudaTensor_newContiguous(state, bias) : bias;

Expand All @@ -144,8 +144,8 @@ int deform_conv_forward_cuda(THCudaTensor *input, THCudaTensor *weight,
THCudaTensor_resize2d(state, columns, nInputPlane * kW * kH,
outputHeight * outputWidth);

if (ones->nDimension != 2 ||
ones->size[0] * ones->size[1] < outputHeight * outputWidth) {
if (THCudaTensor_nDimension(state, ones) != 2 ||
THCudaTensor_size(state, ones, 0) * THCudaTensor_size(state, ones, 1) < outputHeight * outputWidth) {
THCudaTensor_resize2d(state, ones, outputHeight, outputWidth);
THCudaTensor_fill(state, ones, 1);
}
Expand Down Expand Up @@ -183,7 +183,7 @@ int deform_conv_forward_cuda(THCudaTensor *input, THCudaTensor *weight,
deformable_group, THCudaTensor_data(state, columns));

long m = nOutputPlane;
long n = columns->size[1];
long n = THCudaTensor_size(state, columns, 1);
long k = nInputPlane * kH * kW;

THCudaBlas_Sgemm(state, 'n', 'n', n, m, k, 1.0f,
Expand All @@ -200,8 +200,8 @@ int deform_conv_forward_cuda(THCudaTensor *input, THCudaTensor *weight,
THCudaTensor_resize3d(state, output, nOutputPlane, outputHeight,
outputWidth);
THCudaTensor_resize3d(state, input, nInputPlane, inputHeight, inputWidth);
THCudaTensor_resize3d(state, offset, offset->size[1], offset->size[2],
offset->size[3]);
THCudaTensor_resize3d(state, offset, THCudaTensor_size(state, offset, 1), THCudaTensor_size(state, offset, 2),
THCudaTensor_size(state, offset, 3));
}

THCudaTensor_free(state, input);
Expand Down Expand Up @@ -230,30 +230,30 @@ int deform_conv_backward_input_cuda(
weight = THCudaTensor_newContiguous(state, weight);

int batch = 1;
if (input->nDimension == 3) {
if (THCudaTensor_nDimension(state, input) == 3) {
// Force batch
batch = 0;
THCudaTensor_resize4d(state, input, 1, input->size[0], input->size[1],
input->size[2]);
THCudaTensor_resize4d(state, offset, 1, offset->size[0], offset->size[1],
offset->size[2]);
THCudaTensor_resize4d(state, gradOutput, 1, gradOutput->size[0],
gradOutput->size[1], gradOutput->size[2]);
THCudaTensor_resize4d(state, input, 1, THCudaTensor_size(state, input, 0), THCudaTensor_size(state, input, 1),
THCudaTensor_size(state, input, 2));
THCudaTensor_resize4d(state, offset, 1, THCudaTensor_size(state, offset, 0), THCudaTensor_size(state, offset, 1),
THCudaTensor_size(state, offset, 2));
THCudaTensor_resize4d(state, gradOutput, 1, THCudaTensor_size(state, gradOutput, 0),
THCudaTensor_size(state, gradOutput, 1), THCudaTensor_size(state, gradOutput, 2));
}

long batchSize = input->size[0];
long nInputPlane = input->size[1];
long inputHeight = input->size[2];
long inputWidth = input->size[3];
long batchSize = THCudaTensor_size(state, input, 0);
long nInputPlane = THCudaTensor_size(state, input, 1);
long inputHeight = THCudaTensor_size(state, input, 2);
long inputWidth = THCudaTensor_size(state, input, 3);

long nOutputPlane = weight->size[0];
long nOutputPlane = THCudaTensor_size(state, weight, 0);

long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;

THArgCheck((offset->size[0] == batchSize), 3, "invalid batch size of offset");
THArgCheck((THCudaTensor_size(state, offset, 0) == batchSize), 3, "invalid batch size of offset");

THCudaTensor_resize4d(state, gradInput, batchSize, nInputPlane, inputHeight,
inputWidth);
Expand All @@ -275,7 +275,7 @@ int deform_conv_backward_input_cuda(
THCudaTensor_select(state, gradOutput_n, gradOutput, 0, elt);

long m = nInputPlane * kW * kH;
long n = columns->size[1];
long n = THCudaTensor_size(state, columns, 1);
long k = nOutputPlane;

THCudaBlas_Sgemm(state, 'n', 't', n, m, k, 1.0f,
Expand Down Expand Up @@ -309,10 +309,10 @@ int deform_conv_backward_input_cuda(
THCudaTensor_resize3d(state, input, nInputPlane, inputHeight, inputWidth);
THCudaTensor_resize3d(state, gradInput, nInputPlane, inputHeight,
inputWidth);
THCudaTensor_resize3d(state, offset, offset->size[1], offset->size[2],
offset->size[3]);
THCudaTensor_resize3d(state, gradOffset, offset->size[1], offset->size[2],
offset->size[3]);
THCudaTensor_resize3d(state, offset, THCudaTensor_size(state, offset, 1), THCudaTensor_size(state, offset, 2),
THCudaTensor_size(state, offset, 3));
THCudaTensor_resize3d(state, gradOffset, THCudaTensor_size(state, offset, 1), THCudaTensor_size(state, offset, 2),
THCudaTensor_size(state, offset, 3));
}

THCudaTensor_free(state, input);
Expand Down Expand Up @@ -341,28 +341,28 @@ int deform_conv_backward_parameters_cuda(
gradOutput = THCudaTensor_newContiguous(state, gradOutput);

int batch = 1;
if (input->nDimension == 3) {
if (THCudaTensor_nDimension(state, input) == 3) {
// Force batch
batch = 0;
THCudaTensor_resize4d(state, input, 1, input->size[0], input->size[1],
input->size[2]);
THCudaTensor_resize4d(state, gradOutput, 1, gradOutput->size[0],
gradOutput->size[1], gradOutput->size[2]);
THCudaTensor_resize4d(state, input, 1, THCudaTensor_size(state, input, 0), THCudaTensor_size(state, input, 1),
THCudaTensor_size(state, input, 2));
THCudaTensor_resize4d(state, gradOutput, 1, THCudaTensor_size(state, gradOutput, 0),
THCudaTensor_size(state, gradOutput, 1), THCudaTensor_size(state, gradOutput, 2));
}

long batchSize = input->size[0];
long nInputPlane = input->size[1];
long inputHeight = input->size[2];
long inputWidth = input->size[3];
long batchSize = THCudaTensor_size(state, input, 0);
long nInputPlane = THCudaTensor_size(state, input, 1);
long inputHeight = THCudaTensor_size(state, input, 2);
long inputWidth = THCudaTensor_size(state, input, 3);

long nOutputPlane = gradWeight->size[0];
long nOutputPlane = THCudaTensor_size(state, gradWeight, 0);

long outputWidth =
(inputWidth + 2 * padW - (dilationW * (kW - 1) + 1)) / dW + 1;
long outputHeight =
(inputHeight + 2 * padH - (dilationH * (kH - 1) + 1)) / dH + 1;

THArgCheck((offset->size[0] == batchSize), 3, "invalid batch size of offset");
THArgCheck((THCudaTensor_size(state, offset, 0) == batchSize), 3, "invalid batch size of offset");

THCudaTensor_resize2d(state, columns, nInputPlane * kW * kH,
outputHeight * outputWidth);
Expand All @@ -384,7 +384,7 @@ int deform_conv_backward_parameters_cuda(

long m = nOutputPlane;
long n = nInputPlane * kW * kH;
long k = columns->size[1];
long k = THCudaTensor_size(state, columns, 1);

THCudaBlas_Sgemm(state, 't', 'n', n, m, k, scale,
THCudaTensor_data(state, columns), k,
Expand Down