def test(self):
IC, OC = 3, 5
coordinates = torch.IntTensor([[0, 1, -1], [0, 2, 1]])
in_features = torch.rand(len(coordinates), IC)
manager = _C.CoordinateMapManager()
in_key, unique_inverse_map = manager.insert_and_map(coordinates, [1, 1], "")
kernel_size = [3, 3]
kernel_stride = [2, 2]
kernel_dilation = [1, 1]
out_key = _C.CoordinateMapKey(3)
# size, in, out
kernel = torch.rand(9, IC, OC)
out_features = _C.ConvolutionForwardCPU(
in_features,
kernel,
kernel_size,
kernel_stride,
kernel_dilation,
_C.RegionType.HYPER_CUBE,
torch.IntTensor(),
in_key,
out_key,
manager,
)
print(in_features, out_features)
def test_pcd2(self):
IC, OC = 128, 128
coords, colors, pcd = load_file("1.ply")
kernel_size = [3, 3, 3]
kernel_stride = [2, 2, 2]
kernel_dilation = [1, 1, 1]
for IC in [3, 8, 16, 32, 64, 128]:
for OC in [16, 32, 64, 128, 256]:
# size, in, out
kernel = torch.rand(np.prod(kernel_size), IC, OC).to(0)
for batch_size in [1]:
for voxel_size in [0.02]:
min_time = 100000
dcoords = torch.from_numpy(
np.floor(coords / voxel_size)).int()
bcoords = batched_coordinates(
[dcoords for i in range(batch_size)])
for i in range(10):
manager = _C.CoordinateMapManager()
# batch insert
in_key, (unique_map,
inverse_map) = manager.insert_and_map(
bcoords.to(0), [1, 1, 1], "")
in_feats = torch.rand(manager.size(in_key),
IC).to(0)
out_key = _C.CoordinateMapKey(4)
stime = time.time()
out_features = _C.ConvolutionForwardGPU(
in_feats,
kernel,
kernel_size,
kernel_stride,
kernel_dilation,
_C.RegionType.HYPER_CUBE,
torch.IntTensor(),
in_key,
out_key,
manager,
)
min_time = min(time.time() - stime, min_time)
print(
f"{batch_size}\t{manager.size(in_key)}\t{manager.size(out_key)}\t{IC}\t{OC}\t{min_time}"
)