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提交 ab5ddd68 authored 作者: Frederic Bastien's avatar Frederic Bastien
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implemented mode wrap_centered on gpu for Images2Neibs

上级 b3203222
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正在显示 包含 118 行增加 和 34 行删除
theano/sandbox/neighbours.py
浏览文件 @ ab5ddd68
......@@ -52,8 +52,8 @@ class Images2Neibs(Op):
fail = sub['fail']
mode=self.mode
return """
int grid_c; //number of patch in height
int grid_d; //number of patch in width
int grid_c = -1; //number of patch in height
int grid_d = -1; //number of patch in width
{
if (%(ten4)s->nd != 4)
{
......@@ -230,8 +230,8 @@ def neibs2images(neibs, neib_shape, original_shape):
# This is work in progress
class GpuImages2Neibs(Images2Neibs):
def __init__(self, mode='valid'):
if mode not in ['valid']:
raise NotImplementedError("Only the mode valid have been implemented for the op GpuImages2Neibs")
if mode not in ['valid', 'wrap_centered']:
raise NotImplementedError("Only the mode valid and wrap_centered have been implemented for the op GpuImages2Neibs")
self.mode = mode
def make_node(self, ten4, neib_shape, neib_step):
......@@ -253,7 +253,8 @@ class GpuImages2Neibs(Images2Neibs):
return (2,)
def c_support_code_apply(self, node, nodename):
return """
if self.mode=="valid":
return """
static __global__ void k_multi_warp_%(nodename)s(
const int nb_batch,
const int nb_stack,
......@@ -302,11 +303,74 @@ class GpuImages2Neibs(Images2Neibs):
}
""" % locals()
if self.mode=="wrap_centered":
return """
static __global__ void k_multi_warp_%(nodename)s(
const int nb_batch,
const int nb_stack,
const int height,
const int width,
const int c,
const int d,
const int grid_c,
const int grid_d,
const int stride0, const int stride1, const int stride2, const int stride3,
float * global_ten4,
float * global_out
)
{
const int wrap_centered_idx_shift_x = c/2;
const int wrap_centered_idx_shift_y = d/2;
for(int tblock = blockIdx.x;tblock<nb_batch*nb_stack*grid_c*grid_d;tblock+=gridDim.x){
const int b = tblock%%grid_d;
int left = tblock/grid_d;
const int a = left%%grid_c;
left = left/grid_c;
const int s = left%%nb_stack;
left = left/nb_stack;
const int n = left;
if(n>nb_batch)continue;
if(s>nb_stack)continue;
if(a>grid_c)continue;
if(b>grid_d)continue;
int z_row = b + grid_d*(a + grid_c*(s + nb_stack*n));
for (int i = 0; i < c; i++) // loop over c
{
int ten4_2 = i + a * c;
ten4_2 -= wrap_centered_idx_shift_x;
if ( ten4_2 < 0 ) ten4_2 += height;
else if (ten4_2 >= height) ten4_2 -= height;
for (int j = threadIdx.x; j < d; j+=blockDim.x) // loop over d
{
int ten4_3 = j + b * d;
ten4_3 -= wrap_centered_idx_shift_y;
if ( ten4_3 < 0 ) ten4_3 += width;
else if (ten4_3 >= width) ten4_3 -= width;
//int ten4_idx = ten4_3 + width*(ten4_2 + height*(s +nb_stack*n));
//int ten4_idx = stride3*ten4_3 + stride2*(ten4_2 + stride1*(s + stride0*n));
int ten4_idx = stride3*ten4_3 + stride2*ten4_2 + stride1*s + stride0*n;
int z_col = j + d * i;
int z_idx = z_col + c*d*z_row;
global_out[z_idx] = global_ten4[ten4_idx];
}
}
}
}
""" % locals()
def c_code(self, node, name, (ten4, neib_shape), (z,), sub):
fail = sub['fail']
mode = self.mode
return """
int grid_c = -1;
int grid_d = -1;
{
if (%(ten4)s->nd != 4)
{
......@@ -339,26 +403,46 @@ class GpuImages2Neibs(Images2Neibs):
const int c = *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 0);
const int d = *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 1);
//const float * cd = CudaNdarray_DEV_DATA(%(neib_shape)s);
//const int c = (int) cd[0];
//const int d = (int) cd[1];
if ( CudaNdarray_HOST_DIMS(%(ten4)s)[2] %% c != 0)
{
PyErr_Format(PyExc_TypeError, "neib_shape[0] must divide ten4.shape[2]");
%(fail)s;
}
if ( CudaNdarray_HOST_DIMS(%(ten4)s)[3] %% d != 0)
{
PyErr_Format(PyExc_TypeError, "neib_shape[1] must divide ten4.shape[3]");
%(fail)s;
const int step_x = c;//will change when we implement neib_step
const int step_y = d;//will change when we implement neib_step
if ( "%(mode)s" == "wrap_centered") {
if (c%%2!=1 || d%%2!=1){
PyErr_Format(PyExc_TypeError, "Images2Neibs: in mode wrap_centered need patch with odd shapes");
%(fail)s;
}
if ( CudaNdarray_HOST_DIMS(%(ten4)s)[2] < c || CudaNdarray_HOST_DIMS(%(ten4)s)[3] < d)
{
PyErr_Format(PyExc_TypeError, "Images2Neibs: in wrap_centered mode, don't support image shapes smaller then the patch shapes: neib_shape=(%%d,%%d), ten4[2:]=[%%d,%%d]",
c, d, CudaNdarray_HOST_DIMS(%(ten4)s)[2], CudaNdarray_HOST_DIMS(%(ten4)s)[3]);
%(fail)s;
}
//grid_c = CEIL_INTDIV(((CudaNdarray_HOST_DIMS(%(ten4)s))[2]),step_x)
//grid_d = CEIL_INTDIV(((CudaNdarray_HOST_DIMS(%(ten4)s))[3]),step_y)
grid_c = ((CudaNdarray_HOST_DIMS(%(ten4)s))[2])/step_x + ((((CudaNdarray_HOST_DIMS(%(ten4)s))[2])%%step_x)? 1:0);
grid_d = ((CudaNdarray_HOST_DIMS(%(ten4)s))[3])/step_y + ((((CudaNdarray_HOST_DIMS(%(ten4)s))[3])%%step_y)? 1:0);
}else if ( "%(mode)s" == "valid") {
if ( CudaNdarray_HOST_DIMS(%(ten4)s)[2] %% c != 0)
{
PyErr_Format(PyExc_TypeError, "neib_shape[0] must divide ten4.shape[2]");
%(fail)s;
}
if ( CudaNdarray_HOST_DIMS(%(ten4)s)[3] %% d != 0)
{
PyErr_Format(PyExc_TypeError, "neib_shape[1] must divide ten4.shape[3]");
%(fail)s;
}
grid_c = 1+(((CudaNdarray_HOST_DIMS(%(ten4)s))[2]-c)/step_x); //number of patch in height
grid_d = 1+(((CudaNdarray_HOST_DIMS(%(ten4)s))[3]-d)/step_y); //number of patch in width
}else{
PyErr_Format(PyExc_TypeError, "Images2Neibs: unknow mode '%(mode)s'");
%(fail)s;
}
// new dimensions for z
const int z_dim1 = c * d;
const int z_dim0 = CudaNdarray_HOST_DIMS(%(ten4)s)[2] / c
* CudaNdarray_HOST_DIMS(%(ten4)s)[3] / d
const int z_dim0 = grid_c
* grid_d
* CudaNdarray_HOST_DIMS(%(ten4)s)[1]
* CudaNdarray_HOST_DIMS(%(ten4)s)[0];
......@@ -400,9 +484,6 @@ class GpuImages2Neibs(Images2Neibs):
const int c = *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 0);
const int d = *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 1);
const int grid_c = height/c;
const int grid_d = width/d;
int nb_block;
if (nb_batch %% 32 == 0)
nb_block = nb_batch/32;
......
theano/sandbox/test_neighbours.py
浏览文件 @ ab5ddd68
......@@ -200,17 +200,19 @@ def test_neibs_wrap_centered_step_manual():
images = shared(numpy.asarray(numpy.arange(numpy.prod(shape)).reshape(shape),dtype='float32'))
neib_shape = T.as_tensor_variable((3,3))
neib_step = T.as_tensor_variable((2,2))
neib_step = neib_shape
modes = [mode_without_gpu]
if cuda.cuda_available:
modes.append(mode_with_gpu)
for mode in modes:
for mode_idx,mode in enumerate(modes):
f = function([], images2neibs(images, neib_shape, neib_step, mode="wrap_centered"), mode=mode)
neibs = f()
print repr(neibs)
print neibs.shape
print images.value
expected = numpy.asarray([[24, 20, 21, 4, 0, 1, 9, 5, 6],
expected1 = numpy.asarray([[24, 20, 21, 4, 0, 1, 9, 5, 6],
[21, 22, 23, 1, 2, 3, 6, 7, 8],
[23, 24, 20, 3, 4, 0, 8, 9, 5],
[ 9, 5, 6, 14, 10, 11, 19, 15, 16],
......@@ -219,13 +221,14 @@ def test_neibs_wrap_centered_step_manual():
[19, 15, 16, 24, 20, 21, 4, 0, 1],
[16, 17, 18, 21, 22, 23, 1, 2, 3],
[18, 19, 15, 23, 24, 20, 3, 4, 0]])
assert numpy.allclose(neibs[0:9,:],expected)
assert numpy.allclose(neibs[9:18,:],expected+25)
assert numpy.allclose(neibs[18:27,:],expected+50)
assert numpy.allclose(neibs[27:36,:],expected+75)
assert numpy.allclose(neibs[36:45,:],expected+100)
assert numpy.allclose(neibs[45:,:],expected+125)
expected2 = numpy.asarray([[ 24., 20., 21., 4., 0., 1., 9., 5., 6.],
[ 22., 23., 24., 2., 3., 4., 7., 8., 9.],
[ 14., 10., 11., 19., 15., 16., 24., 20., 21.],
[ 12., 13., 14., 17., 18., 19., 22., 23., 24.]])
expected = expected2
for i in range(shape[0]*shape[1]):
assert numpy.allclose(neibs[i*expected.shape[0]:(i+1)*expected.shape[0],:],expected+25*i), mode_idx
#g = function([], neibs2images(neibs, neib_shape, images.shape), mode=mode_without_gpu)
......
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