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Commit d42e2629 authored by rusty1s's avatar rusty1s
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clean up

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aten/THC/THCBasis.cu
View file @ d42e2629
#include "THCBasis.h"
#include "common.cuh"
#include "THCNumerics.cuh"
#define THC_TENSOR_BASIS_FORWARD(NAME, state, basis, weightIndex, pseudo, kernelSize, \
isOpenSpline) { \
THCAssertSameGPU( \
THCTensor_(checkGPU)(state, 5, basis, weightIndex, pseudo, kernelSize, isOpenSpline)); \
\
TensorInfo<real> basisInfo = THCTensor_(getTensorInfo)(state, basis); \
TensorInfo<int64_t> weightIndexInfo = THCudaLongTensor_getTensorInfo(state, weightIndex); \
TensorInfo<real> pseudoInfo = THCTensor_(getTensorInfo)(state, pseudo); \
int64_t *kernelSizeData = THCudaLongTensor_data(state, kernelSize); \
uint8_t *isOpenSplineData = THCudaByteTensor_data(state, isOpenSpline); \
\
KERNEL_REAL_RUN(NAME, THCTensor_(nElement)(state, basis), basisInfo, \
weightIndexInfo, pseudoInfo, kernelSizeData, isOpenSplineData); \
}
#define THC_TENSOR_BASIS_FORWARD_KERNEL(M, basis, weightIndex, pseudo, kernelSize, isOpenSpline, \
N, CODE) { \
KERNEL_LOOP(i, N) { \
ptrdiff_t e = i / basis.size[1], s = i % basis.size[1], d; \
int64_t k = s, kMod, wi = 0, wiOffset = 1; \
T b = ScalarConvert<int, T>::to(1), v; \
\
for (d = 0; d < pseudo.size[1]; d++) { \
kMod = k % (M + 1); \
k /= M + 1; \
\
v = pseudo.data[e * pseudo.stride[0] + d * pseudo.stride[1]]; \
v = THCNumerics<T>::mul(v, ScalarConvert<int64_t, T>::to(kernelSize[d] - M * isOpenSpline[d])); \
\
wi += ((ScalarConvert<T, int64_t>::to(v) + kMod) % kernelSize[d]) * wiOffset; \
wiOffset *= kernelSize[d]; \
\
v = THCNumerics<T>::sub(v, ScalarConvert<int64_t, T>::to(ScalarConvert<T, int64_t>::to(v))); \
CODE \
b = THCNumerics<T>::mul(b, v); \
} \
\
basis.data[e * basis.stride[0] + s * basis.stride[1]] = b; \
weightIndex.data[e * weightIndex.stride[0] + s * weightIndex.stride[1]] = wi; \
} \
}
template<typename T>
struct BasisForward {
static inline __device__ T linear(T v, int64_t kMod) {
// 1 - v - kMod + 2 * v * kMod
T tmp1 = THCNumerics<T>::sub(ScalarConvert<int, T>::to(1), v);
tmp1 = THCNumerics<T>::sub(tmp1, ScalarConvert<int64_t, T>::to(kMod));
T tmp2 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(2), v);
tmp2 = THCNumerics<T>::mul(tmp2, ScalarConvert<int64_t, T>::to(kMod));
return THCNumerics<T>::add(tmp1, tmp2);
}
static inline __device__ T quadratic(T v, int64_t kMod) {
if (kMod == 0) {
// 0.5 * v * v - v + 0.5
T tmp = THCNumerics<T>::mul(THCNumerics<T>::mul(ScalarConvert<float, T>::to(0.5), v), v);
return THCNumerics<T>::sub(tmp, THCNumerics<T>::add(v, ScalarConvert<float, T>::to(0.5)));
}
else if (kMod == 1) {
// -v * v + v + 0.5
T tmp = THCNumerics<T>::mul(THCNumerics<T>::neg(v), v);
return THCNumerics<T>::add(THCNumerics<T>::add(tmp, v), ScalarConvert<float, T>::to(0.5));
}
else {
// 0.5 * v * v
return THCNumerics<T>::mul(ScalarConvert<float, T>::to(0.5), THCNumerics<T>::mul(v, v));
}
}
static inline __device__ T cubic(T v, int64_t kMod) {
if (kMod == 0) {
// (1 - v) * (1 -v) * (1 - v) / 6
T tmp = THCNumerics<T>::sub(ScalarConvert<int, T>::to(1), v);
tmp = THCNumerics<T>::mul(THCNumerics<T>::mul(tmp, tmp), tmp);
return THCNumerics<T>::div(tmp, ScalarConvert<int, T>::to(6));
}
else if (kMod == 1) {
// (3 * v * v * v - 6 * v * v + 4) / 6
T tmp1 = THCNumerics<T>::mul(THCNumerics<T>::mul(v, v), v);
tmp1 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(3), tmp1);
T tmp2 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(6), THCNumerics<T>::mul(v, v));
tmp1 = THCNumerics<T>::add(THCNumerics<T>::sub(tmp1, tmp2), ScalarConvert<int, T>::to(4));
return THCNumerics<T>::div(tmp1, ScalarConvert<int, T>::to(6));
}
else if (kMod == 2) {
// (-3 * v * v * v + 3 * v * v + 3 * v + 1) / 6
T tmp1 = THCNumerics<T>::mul(THCNumerics<T>::mul(v, v), v);
tmp1 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(-3), tmp1);
T tmp2 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(3), THCNumerics<T>::mul(v, v));
T tmp3 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(3), v);
tmp1 = THCNumerics<T>::add(THCNumerics<T>::add(tmp1, tmp2), tmp3);
tmp1 = THCNumerics<T>::add(tmp1, ScalarConvert<int, T>::to(1));
return THCNumerics<T>::div(tmp1, ScalarConvert<int, T>::to(6));
}
else {
// v * v * v / 6
T tmp = THCNumerics<T>::mul(THCNumerics<T>::mul(v, v), v);
return THCNumerics<T>::div(tmp, ScalarConvert<int, T>::to(6));
}
}
};
#include "THCBasisForward.cuh"
template<typename T>
__global__ void linearBasisForwardKernel(TensorInfo<T> basis, TensorInfo<int64_t>weightIndex,
......
aten/THC/THCBasisForward.cuh 0 → 100644
View file @ d42e2629
#ifndef THC_BASIS_FORWARD_INC
#define THC_BASIS_FORWARD_INC
#include "common.cuh"
#include "THCNumerics.cuh"
#define THC_TENSOR_BASIS_FORWARD(NAME, state, basis, weightIndex, pseudo, kernelSize, \
isOpenSpline) { \
THCAssertSameGPU( \
THCTensor_(checkGPU)(state, 5, basis, weightIndex, pseudo, kernelSize, isOpenSpline)); \
\
TensorInfo<real> basisInfo = THCTensor_(getTensorInfo)(state, basis); \
TensorInfo<int64_t> weightIndexInfo = THCudaLongTensor_getTensorInfo(state, weightIndex); \
TensorInfo<real> pseudoInfo = THCTensor_(getTensorInfo)(state, pseudo); \
int64_t *kernelSizeData = THCudaLongTensor_data(state, kernelSize); \
uint8_t *isOpenSplineData = THCudaByteTensor_data(state, isOpenSpline); \
\
KERNEL_REAL_RUN(NAME, THCTensor_(nElement)(state, basis), basisInfo, \
weightIndexInfo, pseudoInfo, kernelSizeData, isOpenSplineData); \
}
#define THC_TENSOR_BASIS_FORWARD_KERNEL(M, basis, weightIndex, pseudo, kernelSize, isOpenSpline, \
N, CODE) { \
KERNEL_LOOP(i, N) { \
ptrdiff_t e = i / basis.size[1], s = i % basis.size[1], d; \
int64_t k = s, kMod, wi = 0, wiOffset = 1; \
T b = ScalarConvert<int, T>::to(1), v; \
\
for (d = 0; d < pseudo.size[1]; d++) { \
kMod = k % (M + 1); \
k /= M + 1; \
\
v = pseudo.data[e * pseudo.stride[0] + d * pseudo.stride[1]]; \
v = THCNumerics<T>::mul(v, ScalarConvert<int64_t, T>::to(kernelSize[d] - M * isOpenSpline[d])); \
\
wi += ((ScalarConvert<T, int64_t>::to(v) + kMod) % kernelSize[d]) * wiOffset; \
wiOffset *= kernelSize[d]; \
\
v = THCNumerics<T>::sub(v, ScalarConvert<int64_t, T>::to(ScalarConvert<T, int64_t>::to(v))); \
CODE \
b = THCNumerics<T>::mul(b, v); \
} \
\
basis.data[e * basis.stride[0] + s * basis.stride[1]] = b; \
weightIndex.data[e * weightIndex.stride[0] + s * weightIndex.stride[1]] = wi; \
} \
}
template<typename T>
struct BasisForward {
static inline __device__ T linear(T v, int64_t kMod) {
// 1 - v - kMod + 2 * v * kMod
T tmp1 = THCNumerics<T>::sub(ScalarConvert<int, T>::to(1), v);
tmp1 = THCNumerics<T>::sub(tmp1, ScalarConvert<int64_t, T>::to(kMod));
T tmp2 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(2), v);
tmp2 = THCNumerics<T>::mul(tmp2, ScalarConvert<int64_t, T>::to(kMod));
return THCNumerics<T>::add(tmp1, tmp2);
}
static inline __device__ T quadratic(T v, int64_t kMod) {
if (kMod == 0) {
// 0.5 * v * v - v + 0.5
T tmp = THCNumerics<T>::mul(THCNumerics<T>::mul(ScalarConvert<float, T>::to(0.5), v), v);
return THCNumerics<T>::add(THCNumerics<T>::sub(tmp, v), ScalarConvert<float, T>::to(0.5));
}
else if (kMod == 1) {
// -v * v + v + 0.5
T tmp = THCNumerics<T>::mul(THCNumerics<T>::neg(v), v);
return THCNumerics<T>::add(THCNumerics<T>::add(tmp, v), ScalarConvert<float, T>::to(0.5));
}
else {
// 0.5 * v * v
return THCNumerics<T>::mul(ScalarConvert<float, T>::to(0.5), THCNumerics<T>::mul(v, v));
}
}
static inline __device__ T cubic(T v, int64_t kMod) {
if (kMod == 0) {
// (1 - v) * (1 -v) * (1 - v) / 6
T tmp = THCNumerics<T>::sub(ScalarConvert<int, T>::to(1), v);
tmp = THCNumerics<T>::mul(THCNumerics<T>::mul(tmp, tmp), tmp);
return THCNumerics<T>::div(tmp, ScalarConvert<int, T>::to(6));
}
else if (kMod == 1) {
// (3 * v * v * v - 6 * v * v + 4) / 6
T tmp1 = THCNumerics<T>::mul(THCNumerics<T>::mul(v, v), v);
tmp1 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(3), tmp1);
T tmp2 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(6), THCNumerics<T>::mul(v, v));
tmp1 = THCNumerics<T>::add(THCNumerics<T>::sub(tmp1, tmp2), ScalarConvert<int, T>::to(4));
return THCNumerics<T>::div(tmp1, ScalarConvert<int, T>::to(6));
}
else if (kMod == 2) {
// (-3 * v * v * v + 3 * v * v + 3 * v + 1) / 6
T tmp1 = THCNumerics<T>::mul(THCNumerics<T>::mul(v, v), v);
tmp1 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(-3), tmp1);
T tmp2 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(3), THCNumerics<T>::mul(v, v));
T tmp3 = THCNumerics<T>::mul(ScalarConvert<int, T>::to(3), v);
tmp1 = THCNumerics<T>::add(THCNumerics<T>::add(tmp1, tmp2), tmp3);
tmp1 = THCNumerics<T>::add(tmp1, ScalarConvert<int, T>::to(1));
return THCNumerics<T>::div(tmp1, ScalarConvert<int, T>::to(6));
}
else {
// v * v * v / 6
T tmp = THCNumerics<T>::mul(THCNumerics<T>::mul(v, v), v);
return THCNumerics<T>::div(tmp, ScalarConvert<int, T>::to(6));
}
}
};
#endif // THC_BASIS_FORWARD_INC
test/basis.json deleted 100644 → 0
View file @ c99021ca
[
{
"name": "Linear and open B-splines",
"degree": 1,
"pseudo": [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1],
"kernel_size": [5],
"is_open_spline": [1],
"expected_basis": [[1, 0], [0.8, 0.2], [1, 0], [1, 0], [1, 0], [0.2, 0.8], [1, 0]],
"expected_index": [[0, 1], [0, 1], [1, 2], [2, 3], [3, 4], [3, 4], [4, 0]]
},
{
"name": "Linear and closed B-splines",
"degree": 1,
"pseudo": [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1],
"kernel_size": [4],
"is_open_spline": [0],
"expected_basis": [[1, 0], [0.8, 0.2], [1, 0], [1, 0], [1, 0], [0.2, 0.8], [1, 0]],
"expected_index": [[0, 1], [0, 1], [1, 2], [2, 3], [3, 0], [3, 0], [0, 1]]
},
{
"name": "Quadratic and open B-splines",
"degree": 2,
"pseudo": [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1],
"kernel_size": [6],
"is_open_spline": [1],
"expected_basis": [[0.5, 0.5, 0], [0.32, 0.66, 0.02], [0.5, 0.5, 0], [0.5, 0.5, 0], [0.5, 0.5, 0], [0.02, 0.66, 0.32], [0.5, 0.5, 0]],
"expected_index": [[0, 1, 2], [0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [3, 4, 5], [4, 5, 0]]
},
{
"name": "Quadratic and closed B-splines",
"degree": 2,
"pseudo": [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1],
"kernel_size": [4],
"is_open_spline": [0],
"expected_basis": [[0.5, 0.5, 0], [0.32, 0.66, 0.02], [0.5, 0.5, 0], [0.5, 0.5, 0], [0.5, 0.5, 0], [0.02, 0.66, 0.32], [0.5, 0.5, 0]],
"expected_index": [[0, 1, 2], [0, 1, 2], [1, 2, 3], [2, 3, 0], [3, 0, 1], [3, 0, 1], [0, 1, 2]]
},
{
"name": "Cubic and open B-splines",
"degree": 3,
"pseudo": [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1],
"kernel_size": [7],
"is_open_spline": [1],
"expected_basis": [[0.16667, 0.6667, 0.1667, 0], [0.0853, 0.6307, 0.2827, 0.00133], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.00133, 0.2827, 0.6307, 0.0853], [0.1667, 0.6667, 0.1667, 0]],
"expected_index": [[0, 1, 2, 3], [0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], [3, 4, 5, 6], [4, 5, 6, 0]]
},
{
"name": "Cubic and closed B-splines",
"degree": 3,
"pseudo": [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1],
"kernel_size": [4],
"is_open_spline": [0],
"expected_basis": [[0.16667, 0.6667, 0.1667, 0], [0.0853, 0.6307, 0.2827, 0.00133], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.1667, 0.6667, 0.1667, 0], [0.00133, 0.2827, 0.6307, 0.0853], [0.1667, 0.6667, 0.1667, 0]],
"expected_index": [[0, 1, 2, 3], [0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 0, 1], [3, 0, 1, 2], [3, 0, 1, 2], [0, 1, 2, 3]]
},
{
"name": "Two-dimensional pseudo-coordinates",
"degree": 1,
"pseudo": [[0.125, 0.5], [0.5, 0.5], [0.75, 0.125]],
"kernel_size": [5, 5],
"is_open_spline": [1, 1],
"expected_basis": [[0.5, 0.5, 0, 0], [1, 0, 0, 0], [0.5, 0, 0.5, 0]],
"expected_index": [[2, 7, 3, 8], [12, 17, 13, 18], [15, 20, 16, 21]]
}
]
test/test_basis.py
View file @ d42e2629
......@@ -41,52 +41,14 @@ def test_basis_forward_cpu(tensor, i):
@pytest.mark.skipif(not torch.cuda.is_available(), reason='no CUDA')
def test_basis_forward_gpu(): # pragma: no cover
pseudo = torch.cuda.FloatTensor([0, 0.0625, 0.25, 0.75, 0.9375, 1])
kernel_size = torch.cuda.LongTensor([5])
is_open_spline = torch.cuda.ByteTensor([1])
basis, weight_index = basis_forward(1, pseudo, kernel_size, is_open_spline)
print(basis.cpu().tolist())
print(weight_index.cpu().tolist())
# 'basis': [[1, 0], [0.75, 0.25], [1, 0], [1, 0], [0.25, 0.75], [1, 0]],
# 'weight_index': [[0, 1], [0, 1], [1, 2], [3, 4], [3, 4], [4, 0]],
# @pytest.mark.parametrize('tensor,i', product(tensors, range(len(data))))
# def test_spline_basis_cpu(tensor, i):
# degree = data[i].get('degree')
# pseudo = Tensor(tensor, data[i]['pseudo'])
# pseudo = pseudo.unsqueeze(-1) if pseudo.dim() == 1 else pseudo
# kernel_size = torch.LongTensor(data[i]['kernel_size'])
# is_open_spline = torch.ByteTensor(data[i]['is_open_spline'])
# K = kernel_size.prod()
# expected_basis = Tensor(tensor, data[i]['expected_basis'])
# expected_index = torch.LongTensor(data[i]['expected_index'])
# basis, index = spline_basis_forward(degree, pseudo, kernel_size,
# is_open_spline, K)
# basis = [pytest.approx(b, 0.01) for b in basis.view(-1).tolist()]
# assert basis == expected_basis.view(-1).tolist()
# assert index.tolist() == expected_index.tolist()
# @pytest.mark.skipif(not torch.cuda.is_available(), reason='no CUDA')
# @pytest.mark.parametrize('tensor,i', product(tensors, range(len(data))))
# def test_spline_basis_gpu(tensor, i): # pragma: no cover
# degree = data[i].get('degree')
# pseudo = Tensor(tensor, data[i]['pseudo']).cuda()
# pseudo = pseudo.unsqueeze(-1) if pseudo.dim() == 1 else pseudo
# kernel_size = torch.cuda.LongTensor(data[i]['kernel_size'])
# is_open_spline = torch.cuda.ByteTensor(data[i]['is_open_spline'])
# K = kernel_size.prod()
# expected_basis = Tensor(tensor, data[i]['expected_basis'])
# expected_index = torch.LongTensor(data[i]['expected_index'])
@pytest.mark.parametrize('tensor,i', product(tensors, range(len(tests))))
def test_basis_forward_gpu(tensor, i): # pragma: no cover
data = tests[i]
# basis, index = spline_basis_forward(degree, pseudo, kernel_size,
# is_open_spline, K)
# basis, index = basis.cpu(), index.cpu()
# basis = [pytest.approx(b, 0.01) for b in basis.view(-1).tolist()]
pseudo = getattr(torch.cuda, tensor)(data['pseudo'])
kernel_size = torch.cuda.LongTensor(data['kernel_size'])
is_open_spline = torch.cuda.ByteTensor(data['is_open_spline'])
# assert basis == expected_basis.view(-1).tolist()
# assert index.tolist() == expected_index.tolist()
basis, weight_index = basis_forward(1, pseudo, kernel_size, is_open_spline)
assert basis.cpu().tolist() == data['basis']
assert weight_index.cpu().tolist() == data['weight_index']
test/utils.py deleted 100644 → 0
View file @ c99021ca
import torch
tensors = ['FloatTensor', 'DoubleTensor']
def Tensor(str, x):
tensor = getattr(torch, str)
return tensor(x)
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