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# Copyright (c) 2020 NVIDIA CORPORATION.
# Copyright (c) 2018-2020 Chris Choy (chrischoy@ai.stanford.edu).
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.
#
# Please cite "4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural
# Networks", CVPR'19 (https://arxiv.org/abs/1904.08755) if you use any part
# of the code.
import os
import numpy as np
import collections
from urllib.request import urlretrieve
import torch
try:
import open3d as o3d
except ImportError:
raise ImportError("Please install open3d with `pip install open3d`.")
if not os.path.isfile("1.ply"):
urlretrieve("https://bit.ly/3c2iLhg", "1.ply")
def load_file(file_name):
pcd = o3d.io.read_point_cloud(file_name)
coords = np.array(pcd.points)
colors = np.array(pcd.colors)
return coords, colors, pcd
def batched_coordinates(coords, dtype=torch.int32, device=None):
r"""Create a `ME.SparseTensor` coordinates from a sequence of coordinates
Given a list of either numpy or pytorch tensor coordinates, return the
batched coordinates suitable for `ME.SparseTensor`.
Args:
:attr:`coords` (a sequence of `torch.Tensor` or `numpy.ndarray`): a
list of coordinates.
:attr:`dtype`: torch data type of the return tensor. torch.int32 by default.
Returns:
:attr:`batched_coordindates` (`torch.Tensor`): a batched coordinates.
.. warning::
From v0.4, the batch index will be prepended before all coordinates.
"""
assert isinstance(
coords, collections.abc.Sequence
), "The coordinates must be a sequence."
assert np.array(
[cs.ndim == 2 for cs in coords]
).all(), "All coordinates must be in a 2D array."
D = np.unique(np.array([cs.shape[1] for cs in coords]))
assert len(D) == 1, f"Dimension of the array mismatch. All dimensions: {D}"
D = D[0]
if device is None:
if isinstance(coords, torch.Tensor):
device = coords[0].device
else:
device = "cpu"
assert dtype in [
torch.int32,
torch.float32,
], "Only torch.int32, torch.float32 supported for coordinates."
# Create a batched coordinates
N = np.array([len(cs) for cs in coords]).sum()
bcoords = torch.zeros((N, D + 1), dtype=dtype, device=device) # uninitialized
s = 0
for b, cs in enumerate(coords):
if dtype == torch.int32:
if isinstance(cs, np.ndarray):
cs = torch.from_numpy(np.floor(cs))
elif not (
isinstance(cs, torch.IntTensor) or isinstance(cs, torch.LongTensor)
):
cs = cs.floor()
cs = cs.int()
else:
if isinstance(cs, np.ndarray):
cs = torch.from_numpy(cs)
cn = len(cs)
# BATCH_FIRST:
bcoords[s : s + cn, 1:] = cs
bcoords[s : s + cn, 0] = b
s += cn
return bcoords |