def execute(self, pc1, pc2, reduction='mean', dims='BNC'):
assert dims in ['BNC', 'BCN']
if dims == 'BCN':
pc1, pc2 = pc1.permute(0, 2, 1), pc2.permute(0, 2, 1)
batch_size_1, N, _ = pc1.shape
batch_size_2, M, _ = pc2.shape
assert batch_size_1 == batch_size_2
batch_size = batch_size_1
temp = jt.zeros([batch_size, (N + M) * 2], pc1.dtype)
match = jt.code(
shape=[batch_size, M, N],
dtype=pc1.dtype,
inputs=[pc1, pc2, temp],
cuda_header=EMD_gpu_header,
cuda_src=approxmatch_gpu_src,
)
emd = jt.code(
shape=[batch_size],
dtype=pc1.dtype,
inputs=[pc1, pc2, match],
cuda_header=EMD_gpu_header,
cuda_src=matchcost_gpu_src,
)
self.saved_vars = (pc1, pc2, match, reduction)
if reduction is None:
return emd
elif reduction == 'sum':
return emd.sum()
elif reduction == 'mean':
return emd.mean()
def test_error_msg_trace_py_var(self):
a = jt.array([3, 2, 1])
b = jt.code(a.shape,
a.dtype, [a],
cpu_header="""
#include <algorithm>
@alias(a, in0)
@alias(b, out)
""",
cpu_src="""
for (int i=0; i<a_shape0; i++)
@b(i) = @a(i);
std::sort(&@b(0), &@b(in0_shape0));
throw std::runtime_error("???");
""")
msg = ""
try:
print(b)
except Exception as e:
msg = str(e)
print(msg)
assert "[Reason]: ???" in msg
assert "[Input]: int32[3,]" in msg
assert "[OP TYPE]: code" in msg
assert "[Async Backtrace]:" in msg
assert "test_error_msg.py:" in msg
def mask_prob_cuda(embed_pixel, embed_center, sigma_center, boxes, box_areas,
area_sum, mask_width):
assert embed_pixel.ndim == 2, "embed_pixel should be MxDim"
assert embed_center.ndim == 2, "embed_center should be NxDim"
assert sigma_center.ndim == 1, "sigma_center should be N"
assert embed_pixel.shape[1] == embed_center.shape[1], "Dim should the same"
assert embed_center.shape[0] == sigma_center.shape[
0], "center number should be the same"
assert embed_center.shape[0] == boxes.shape[
0], "center number and box number should be the same"
output_shape = (embed_pixel.shape[0], embed_center.shape[0])
if output_shape[0] * output_shape[1] == 0:
return jt.array([], embed_pixel.dtype)
output_type = embed_pixel.dtype
option = jt.empty((0, ))
option.compile_options = {
"area_sum": int(area_sum),
"mask_width": int(mask_width)
}
inputs = [
embed_pixel, embed_center, sigma_center, boxes, box_areas, option
]
output = jt.code(output_shape,
output_type,
inputs,
cuda_header=CUDA_HEADER,
cuda_src=CUDA_SRC)
return output
def execute(self, x):
'''
Parameters
----------
x: jt.Var, (B, N, 3)
Returns
-------
y: jt.Var, (B, n_samples, 3)
'''
batch_size, n_points, n_coords = x.shape
assert self.n_samples <= n_points
assert n_coords == 3
assert x.dtype == 'float32'
block_size = optimal_block(batch_size)
cuda_src = self.cuda_src.replace('#block_size', str(block_size))
idxs_shape = [batch_size, self.n_samples]
idxs = jt.code(idxs_shape, 'int32', [x,], cuda_src=cuda_src)
y = x.reindex([batch_size, self.n_samples, 3], [
'i0', # Batchid
'@e0(i0, i1)', # Nid
'i2'
], extras=[idxs])
return y
def grad(self, grad_x):
x = self.save_vars
return jt.code(x.shape, x.dtype, [x, grad_x],
cpu_src='''
for (int i=0; i<in0_shape0; i++)
@out(i) = @in1(i)*@in0(i)*4;
''')
def test_type(self):
import numpy as np
assert str(jt.NanoString(float)) == "float"
assert str(jt.NanoString(np.float)) == "float"
assert str(jt.NanoString(np.float32)) == "float32"
assert str(jt.NanoString(np.float64)) == "float64"
assert str(jt.NanoString(np.int8)) == "int8"
assert str(jt.NanoString(np.array([1, 2, 3]).dtype)) == "int64"
assert str(jt.NanoString(jt.float)) == "float"
assert str(jt.NanoString(jt.float32)) == "float32"
assert str(jt.NanoString(jt.float64)) == "float64"
assert str(jt.NanoString(jt.int8)) == "int8"
assert str(jt.NanoString(jt.array([1, 2, 3]).dtype)) == "int64"
assert str(jt.NanoString(jt.sum)) == "add"
def get_error_str(call):
es = ""
try:
call()
except Exception as e:
es = str(e)
return es
e = get_error_str(lambda: jt.code([
1,
], {}, [1], cpu_header=""))
assert "help(jt.ops.code)" in e
assert "cpu_header=str" in e
e = get_error_str(lambda: jt.NanoString([1, 2, 3], f**k=1))
assert "f**k=int" in str(e)
assert "(list, )" in str(e)
def execute(self, x):
self.save_vars = x
return jt.code(x.shape, x.dtype, [x],
cpu_src='''
for (int i=0; i<in0_shape0; i++)
@out(i) = @in0(i)*@in0(i)*2;
''')
def chamfer_loss(pc1, pc2, reduction='mean', sqrt=True):
'''
return the chamfer loss from pc1 to pc2.
Parameters:
===========
pc1: [B, N, xyz]
pc2: [B, N, xyz]
reduction: 'mean', 'sum', or None
'''
batch_size_1, n_samples_pc1, _ = pc1.shape
batch_size_2, n_samples_pc2, _ = pc2.shape
assert batch_size_1 == batch_size_2
batch_size = batch_size_1
idx = jt.code([batch_size, n_samples_pc1],
'int32', [pc1, pc2],
cpu_src=cpu_src,
cuda_src=cuda_src)
nearest_pts = select_vertices(pc2, idx)
if sqrt:
chamfer_distance = (((pc1 - nearest_pts)**2).sum(dim=-1)).sqrt()
else:
chamfer_distance = (((pc1 - nearest_pts)**2).sum(dim=-1))
if reduction is None:
return chamfer_distance
elif reduction == 'sum':
return jt.sum(chamfer_distance)
elif reduction == 'mean':
return jt.mean(chamfer_distance)
def test_cuda2(self):
a = jt.random((100,100))
b = jt.random((100,100))
c = jt.code(a.shape, a.dtype, [a,b],
cuda_src='''
__global__ static void kernel1(@ARGS_DEF) {
@PRECALC
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
@out(i,j) = @in0(i,j)*@in1(i,j);
}
kernel1<<<32, 32>>>(@ARGS);
''',
cuda_grad_src = ['''
__global__ static void kernel(@ARGS_DEF) {
@PRECALC
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
@out(i,j) = @dout(i,j)*@in1(i,j);
}
kernel<<<32, 32>>>(@ARGS);
''', '''
__global__ static void kernel(@ARGS_DEF) {
@PRECALC
@pout(0,0);
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
@out(i,j) = @dout(i,j)*@in0(i,j);
}
kernel<<<32, 32>>>(@ARGS);
'''])
da, db = jt.grad(c, [a, b])
assert np.allclose(c.data, a.data*b.data), (c.data, a.data*b.data)
assert np.allclose(da.data, b.data)
assert np.allclose(db.data, a.data)
def searchsorted(sorted, values, right=False):
"""
Find the indices from the innermost dimension of `sorted` for each `values`.
Example::
sorted = jt.array([[1, 3, 5, 7, 9], [2, 4, 6, 8, 10]])
values = jt.array([[3, 6, 9], [3, 6, 9]])
ret = jt.searchsorted(sorted, values)
assert (ret == [[1, 3, 4], [1, 2, 4]]).all(), ret
ret = jt.searchsorted(sorted, values, right=True)
assert (ret == [[2, 3, 5], [1, 3, 4]]).all(), ret
sorted_1d = jt.array([1, 3, 5, 7, 9])
ret = jt.searchsorted(sorted_1d, values)
assert (ret == [[1, 3, 4], [1, 3, 4]]).all(), ret
"""
_searchsorted_header = f"""
namespace jittor {{
@python.jittor.auto_parallel(2)
inline static void searchsorted(
int batch_num, int batch_id, int value_num, int value_id,
int sorted_num, int batch_stride,
{sorted.dtype}* __restrict__ sort_p, {values.dtype}* __restrict__ value_p,
int32* __restrict__ index_p) {{
int32 l = batch_id * batch_stride;
int32 r = l + sorted_num;
auto v = value_p[batch_id * value_num + value_id];
while (l<r) {{
int32 m = (l+r)/2;
if (sort_p[m] {"<=" if right else "<"} v)
l = m+1;
else
r = m;
}}
index_p[batch_id * value_num + value_id] = l - batch_id * batch_stride;
}}
}}
"""
_searchsorted_src = """
int value_num = in1->shape[in1->shape.size()-1];
int sorted_num = in0->shape[in0->shape.size()-1];
int32 batch_num = in0->num / sorted_num;
int32 batch_num2 = in1->num / value_num;
int32 batch_stride = batch_num == 1 ? 0 : sorted_num;
CHECK(batch_num == batch_num2 || batch_num == 1);
searchsorted(batch_num2, 0, value_num, 0, sorted_num, batch_stride, in0_p, in1_p, out0_p);
"""
return jt.code(values.shape,
"int32", [sorted, values],
cpu_header=_searchsorted_header,
cpu_src=_searchsorted_src,
cuda_header=_searchsorted_header,
cuda_src=_searchsorted_src)
def roi_pool(input,rois,output_size,spatial_scale):
output_size = _pair(output_size)
spatial_scale = jt.array([spatial_scale])
output_shapes = [(rois.shape[0], input.shape[1], output_size[0], output_size[1])]*2
inputs = [input,rois,spatial_scale]
output_types = [input.dtype,'int32']
output,arg_output = jt.code(output_shapes,output_types,inputs,cuda_header=CUDA_HEADER,cuda_src=CUDA_SRC,cuda_grad_src=CUDA_GRAD_SRC)
return output
def test_array_migrate(self):
with jt.flag_scope(use_cuda=1):
a = jt.array(np.float32([1,2,3]))
b = jt.code(a.shape, a.dtype, [a], cpu_src="""
for (int i=0; i<in0shape0; i++)
@out(i) = @in0(i)*@in0(i)*2;
""")
assert (b.data==[2,8,18]).all()
def test_parallel(self):
a = jt.code([4],
"int",
cpu_src="""
#pragma omp parallel num_threads(4)
@out(omp_get_thread_num()) = 456;
""",
cpu_header='#include <omp.h>').data
assert (a == [456] * 4).all(), a
def test_return_multi_output(self):
a = jt.array([3, 2, 1])
b = jt.array([1, 2])
c = jt.array([3, 4, 5, 6])
jt.code([a], [b, c],
cpu_src="""
@alias(a, in0)
@alias(b, out0)
@alias(c, out1)
for (int i=0; i<a_shape0; i++) {
if (i<b_shape0) @b(i) += @a(i);
if (i<c_shape0) @c(i) += @a(i);
}
""")
assert b.shape == [2]
assert c.shape == [4]
assert (b.data == [4, 4]).all()
assert (c.data[:3] == [6, 6, 6]).all()
def chamfer_loss(pc1, pc2, reduction='mean', dims='BNC', bidirectional=False):
''' return the chamfer loss from pc1 to pc2.
:param pc1: input point cloud
:type pc1: jittor array
:param pc2: input point cloud
:type pc2: jittor array
:param reduction: reduction method in batches, can be 'mean', 'sum', or None. Default: 'mean'.
:type reduction: str, optional
:param dims: a string that represents each dimension, can be
'[BNC]' ([batch, number of points, xyz]), or
'[BCN]' ([batch, xyz, number of points]). Default: 'BNC'.
:type dims: str, optional
Example:
>>> import jittor as jt
>>> from jittor.loss3d import chamfer_loss
>>> jt.flags.use_cuda = True
>>> pc1 = jt.rand([10, 100, 3], dtype=jt.float32)
>>> pc2 = jt.rand([10, 100, 3], dtype=jt.float32)
>>> cf = chamfer_loss(pc1, pc2, dims='BNC', bidirectional=True)
>>> print('chamfer loss =', cf.item())
'''
if bidirectional:
return chamfer_loss(pc1, pc2, reduction, dims) + chamfer_loss(
pc2, pc1, reduction, dims)
assert dims in ['BNC', 'BCN']
if dims == 'BCN':
pc1, pc2 = pc1.permute(0, 2, 1), pc2.permute(0, 2, 1)
batch_size_1, N, _ = pc1.shape
batch_size_2, M, _ = pc2.shape
assert batch_size_1 == batch_size_2
batch_size = batch_size_1
idx = jt.code([batch_size, N],
'int32', [pc1, pc2],
cpu_src=cpu_src,
cuda_src=cuda_src)
nearest_pts = pc2.reindex([batch_size, idx.shape[1], 3],
['i0', '@e0(i0, i1)', 'i2'],
extras=[idx])
chamfer_distance = (((pc1 - nearest_pts)**2).sum(dim=-1)).sqrt()
if reduction is None:
return chamfer_distance
elif reduction == 'sum':
return jt.sum(chamfer_distance)
elif reduction == 'mean':
return jt.mean(chamfer_distance)
def test_device_allocator(self):
a = jt.array([1, 2, 3, 4, 5])
b = a + 1
c = jt.code(a.shape,
a.dtype, [b],
cpu_src="""
for (int i=0; i<in0_shape0; i++)
@out(i) = @in0(i)*@in0(i)*2;
""")
assert (c.data == [8, 18, 32, 50, 72]).all()
def test_header(self):
a = jt.array([3, 2, 1])
b = jt.code(a.shape,
a.dtype, [a],
header='#include <algorithm>',
cpu_src="""
for (int i=0; i<in0shape0; i++)
@out(i) = @in0(i);
std::sort(&@out(0), &@out(in0shape0));
""")
assert (b.data == [1, 2, 3]).all()
def execute(self, a, b):
self.save_vars = a, b
return jt.code(a.shape, a.dtype, [a,b],
cuda_src='''
__global__ static void kernel1(@ARGS_DEF) {
@PRECALC
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x)
@out(i,j) = @in0(i,j)*@in1(i,j);
}
kernel1<<<32, 32>>>(@ARGS);
''')
def simple_presum(x):
src = '''
__inline_static__
@python.jittor.auto_parallel(1)
void kernel(int n0, int i0, in0_type* x, in0_type* out, int nl) {
out[i0*(nl+1)] = 0;
for (int i=0; i<nl; i++)
out[i0*(nl+1)+i+1] = out[i0*(nl+1)+i] + x[i0*(nl+1)+i];
}
kernel(in0->num/in0->shape[in0->shape.size()-1], 0, in0_p, out0_p, in0->num);
'''
return jt.code(x.shape[:-1]+(x.shape[-1]+1,), x.dtype, [x],
cpu_src=src, cuda_src=src)
def execute(self, x_q, x_r): # n_points, c_dim
batch_size, c_dim, q_points = x_q.shape
batch_size, c_dim, r_points = x_r.shape
out_idx_shapes = [batch_size, self.k, q_points]
tmp_dist = jt.empty((batch_size, r_points, q_points), "float32")
idxs, = jt.code(
[out_idx_shapes],
['int32'],
[x_r, x_q, tmp_dist], # in0 r point in1 q point
cuda_src=self.cuda_src,
cuda_header=self.cuda_inc,
)
return idxs
def grad(self, grad):
x, numangle, numrho = self.save_vars
cuda_src_backward = csb.replace('#numangle', str(numangle))
cuda_src_backward = cuda_src_backward.replace('#numrho', str(numrho))
irho = int((h * h + w * w)**0.5 + 1) / float((numrho - 1))
itheta = 3.14159265358979323846 / numangle
angle = jt.arange(numangle) * itheta
tabCos = angle.cos() / irho
tabSin = angle.sin() / irho
return jt.code([x.shape], [x.dtype], [x, grad, tabCos, tabSin],
cuda_src=cuda_src_backward)
def grad(self, grad):
a, b = self.save_vars
return jt.code([a.shape, b.shape], [a.dtype, b.dtype], [a, b, grad],
cuda_src='''
__global__ static void kernel2(@ARGS_DEF) {
@PRECALC
for (int i=blockIdx.x; i<in0_shape0; i+=gridDim.x)
for (int j=threadIdx.x; j<in0_shape1; j+=blockDim.x) {
@out0(i,j) = @in2(i,j)*@in1(i,j);
@out1(i,j) = @in2(i,j)*@in0(i,j);
}
}
kernel2<<<32, 32>>>(@ARGS);
''')
def grad(self, grad):
pc1, pc2, match, reduction = self.saved_vars
if reduction == 'sum':
grad = jt.ones([pc1.shape[0]]) * grad
elif reduction == 'mean':
grad = jt.ones([pc1.shape[0]]) * grad / pc1.shape[0]
grad_pc1 = jt.code(
shape=pc1.shape,
dtype=pc1.dtype,
inputs=[grad, pc1, pc2, match],
cuda_src=matchcost_grad1_gpu_src,
)
grad_pc2 = jt.code(
shape=pc2.shape,
dtype=pc2.dtype,
inputs=[grad, pc1, pc2, match],
cuda_src=matchcost_grad2_gpu_src,
)
return grad_pc1, grad_pc2
def test_header(self):
a = jt.array([3, 2, 1])
b = jt.code(a.shape,
a.dtype, [a],
cpu_header="""
#include <algorithm>
@alias(a, in0)
@alias(b, out)
""",
cpu_src="""
for (int i=0; i<a_shape0; i++)
@b(i) = @a(i);
std::sort(&@b(0), &@b(in0_shape0));
""")
assert (b.data == [1, 2, 3]).all()
def execute(self,featuremap, boxes, box_ind):
"""
RoIAlign based on crop_and_resize.
See more details on https://github.com/longcw/RoIAlign.pytorch
:param featuremap: NxCxHxW
:param boxes: Mx4 float box with (x1, y1, x2, y2) **without normalization**
:param box_ind: M
:return: MxCxoHxoW
"""
x1, y1, x2, y2 = [boxes.reindex([boxes.shape[0],1], ["i0", str(i)]) for i in range(4)]
image_height, image_width = featuremap.shape[2:4]
if self.transform_fpcoor:
spacing_w = (x2 - x1) / float(self.crop_width)
spacing_h = (y2 - y1) / float(self.crop_height)
nx0 = (x1 + spacing_w / 2 - 0.5) / float(image_width - 1)
ny0 = (y1 + spacing_h / 2 - 0.5) / float(image_height - 1)
nw = spacing_w * float(self.crop_width - 1) / float(image_width - 1)
nh = spacing_h * float(self.crop_height - 1) / float(image_height - 1)
boxes = jt.contrib.concat((ny0, nx0, ny0 + nh, nx0 + nw), 1)
else:
x1 = x1 / float(image_width - 1)
x2 = x2 / float(image_width - 1)
y1 = y1 / float(image_height - 1)
y2 = y2 / float(image_height - 1)
boxes = jt.contrib.concat((y1, x1, y2, x2), 1)
num_boxes = boxes.shape[0]
depth = featuremap.shape[1]
output_shapes = (num_boxes, depth, self.crop_height, self.crop_width)
output_types = featuremap.dtype
extrapolation_value = jt.array([self.extrapolation_value])
inputs = [featuremap,boxes,box_ind,extrapolation_value]
cpu_header = ROIALIGN_CPU_HEADER
cpu_src =ROIALIGN_CPU_SRC
cpu_grad_src = ROIALIGN_CPU_GRAD_SRC
cuda_header = ROIALIGN_CUDA_HEADER
cuda_src= ROIALIGN_CUDA_SRC
cuda_grad_src= ROIALIGN_CUDA_GRAD_SRC
output = jt.code(output_shapes,output_types,inputs,cpu_header = cpu_header,
cpu_src=cpu_src,cpu_grad_src=cpu_grad_src,cuda_header=cuda_header,cuda_src=cuda_src,cuda_grad_src=cuda_grad_src)
return output
def roi_align(input, rois, output_size, spatial_scale, sampling_ratio):
output_size = _pair(output_size)
options = jt.array([spatial_scale, sampling_ratio])
output_shapes = (rois.shape[0], input.shape[1], output_size[0],
output_size[1])
inputs = [input, rois, options]
output_types = input.dtype
if rois.shape[0] == 0:
return jt.zeros(output_shapes, input.dtype)
output = jt.code(output_shapes,
output_types,
inputs,
cuda_header=CUDA_HEADER,
cuda_src=CUDA_SRC,
cuda_grad_src=CUDA_GRAD_SRC)
return output
def test_cuda(self):
a = jt.random([100000])
b = jt.random([100000])
c = jt.code(a.shape,
a.dtype, [a, b],
cuda_header='''
namespace jittor {
__global__ static void kernel1(@ARGS_DEF) {
@PRECALC
int i = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i=0; i<in0shape0; i++)
@out(i) = @in0(i)*@in1(i);
}
__global__ static void kernel2(@ARGS_DEF) {
@PRECALC
int i = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i=0; i<in0shape0; i++)
@out(i) = @dout(i)*@in1(i);
}
__global__ static void kernel3(@ARGS_DEF) {
@PRECALC
int i = threadIdx.x + blockIdx.x * blockDim.x;
int stride = blockDim.x * gridDim.x;
for (int i=0; i<in0shape0; i++)
@out(i) = @dout(i)*@in0(i);
}
}
''',
cuda_src='''
kernel1<<<(in0shape0-1)/1024+1, 1024>>>(@ARGS);
''',
cuda_grad_src=[
'''
kernel2<<<(in0shape0-1)/1024+1, 1024>>>(@ARGS);
''', '''
kernel3<<<(in0shape0-1)/1024+1, 1024>>>(@ARGS);
'''
])
da, db = jt.grad(c, [a, b])
assert np.allclose(c.data, a.data * b.data), (c.data, a.data * b.data)
assert np.allclose(da.data, b.data)
assert np.allclose(db.data, a.data)
def argmax_pool(x, size, stride, padding=0):
y_shape = list(x.shape)
y_shape[2] = (x.shape[2] + padding * 2 - size) // stride + 1
y_shape[3] = (x.shape[3] + padding * 2 - size) // stride + 1
y = jt.code(y_shape,
x.dtype, [x],
cpu_src=f'''
for (int i=0; i<out_shape0; i++)
for (int j=0; j<out_shape1; j++)
for (int k=0; k<out_shape2; k++)
for (int l=0; l<out_shape3; l++) {{
int kx=k*{stride}+{size}/2-{padding};
int ky=l*{stride}+{size}/2-{padding};
@out(i,j,k,l) = @in0(i,j,kx,ky);
for (int p=kx-{size}/2;p<=kx+{size}/2;p++)
for (int q=ky-{size}/2;q<=ky+{size}/2;q++)
if (p>=0 && q>=0 && p<in0_shape2 && q<in0_shape3)
if (@out(i,j,k,l) < @in0(i,j,p,q))
@out(i,j,k,l) = @in0(i,j,p,q);
}}
''',
cpu_grad_src=[
f'''
for (int i=0; i<out_shape0; i++)
for (int j=0; j<out_shape1; j++)
for (int k=0; k<out_shape2; k++)
for (int l=0; l<out_shape3; l++) @out(i,j,k,l) = 0;
for (int i=0; i<pout_shape0; i++)
for (int j=0; j<pout_shape1; j++)
for (int k=0; k<pout_shape2; k++)
for (int l=0; l<pout_shape3; l++) {{
int kx=k*{stride}+{size}/2-{padding};
int ky=l*{stride}+{size}/2-{padding};
int bo=1;
for (int p=kx-{size}/2;p<=kx+{size}/2 && bo;p++)
for (int q=ky-{size}/2;q<=ky+{size}/2 && bo;q++)
if (p>=0 && q>=0 && p<in0_shape2 && q<in0_shape3)
if (@pout(i,j,k,l) == @in0(i,j,p,q)) {{
@out(i,j,p,q) += @dout(i,j,k,l);
bo=0;
}}
}}
'''
])
return y
def execute(self, x, numangle, numrho):
n, c, h, w = x.shape
cuda_src_forward = csf.replace('#numangle', str(numangle))
cuda_src_forward = cuda_src_forward.replace('#numrho', str(numrho))
irho = int((h * h + w * w)**0.5 + 1) / float((numrho - 1))
itheta = 3.14159265358979323846 / numangle
angle = jt.arange(numangle) * itheta
tabCos = angle.cos() / irho
tabSin = angle.sin() / irho
output = jt.code([n, c, numangle, numrho],
x.dtype, [x, tabCos, tabSin],
cuda_src=cuda_src_forward)
self.save_vars = x, numangle, numrho
return output
def test_multi_output(self):
a = jt.array([3, 2, 1])
b, c = jt.code([[2], [4]], ["float32", "float64"], [a],
cpu_src="""
@alias(a, in0)
@alias(b, out0)
@alias(c, out1)
for (int i=0; i<a_shape0; i++) {
if (i<b_shape0) @b(i) = @a(i);
if (i<c_shape0) @c(i) = @a(i);
}
""")
assert b.shape == [2]
assert c.shape == [4]
assert b.dtype == "float32"
assert c.dtype == "float64"
assert (b.data == [3, 2]).all()
assert (c.data[:3] == [3, 2, 1]).all()
