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# Copyright (c) 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 argparse
import numpy as np
from urllib.request import urlretrieve
try:
import open3d as o3d
except ImportError:
raise ImportError("Please install open3d with `pip install open3d`.")
import torch
import MinkowskiEngine as ME
from MinkowskiCommon import convert_to_int_list
from examples.common import Timer
# Check if the weights and file exist and download
if not os.path.isfile("1.ply"):
print("Downloading a room ply file...")
urlretrieve("http://cvgl.stanford.edu/data2/minkowskiengine/1.ply", "1.ply")
parser = argparse.ArgumentParser()
parser.add_argument("--file_name", type=str, default="1.ply")
parser.add_argument("--voxel_size", type=float, default=0.02)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--max_kernel_size", type=int, default=7)
def quantize(coordinates):
D = coordinates.size(1) - 1
coordinate_manager = ME.CoordinateManager(
D=D, coordinate_map_type=ME.CoordinateMapType.CPU
)
coordinate_map_key = ME.CoordinateMapKey(convert_to_int_list(1, D), "")
key, (unique_map, inverse_map) = coordinate_manager.insert_and_map(
coordinates, *coordinate_map_key.get_key()
)
return unique_map, inverse_map
def load_file(file_name, voxel_size):
pcd = o3d.io.read_point_cloud(file_name)
coords = torch.from_numpy(np.array(pcd.points))
feats = torch.from_numpy(np.array(pcd.colors)).float()
quantized_coords = torch.floor(coords / voxel_size).int()
inds, inverse_inds = quantize(quantized_coords)
return quantized_coords[inds], feats[inds], pcd
def generate_input_sparse_tensor(file_name, voxel_size=0.05, batch_size=1):
# Create a batch, this process is done in a data loader during training in parallel.
batch = [load_file(file_name, voxel_size),] * batch_size
coordinates_, featrues_, pcds = list(zip(*batch))
coordinates, features = ME.utils.sparse_collate(coordinates_, featrues_)
# Normalize features and create a sparse tensor
return features, coordinates
if __name__ == "__main__":
config = parser.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Define a model and load the weights
all_convs = {}
for k in range(3, config.max_kernel_size + 1, 2):
for in_ch in [3, 8, 16, 32, 64, 128]:
for out_ch in [16, 32, 64, 128, 256]:
all_convs[(k, in_ch, out_ch)] = ME.MinkowskiConvolution(
in_channels=in_ch,
out_channels=out_ch,
kernel_size=k,
stride=2,
dimension=3,
).to(device)
# Measure time
print("Initialization time")
features, coordinates = generate_input_sparse_tensor(
config.file_name, voxel_size=config.voxel_size, batch_size=config.batch_size
)
timer = Timer()
for i in range(20):
timer.tic()
sinput = ME.SparseTensor(
features.to(device), coordinates=coordinates.to(device)
)
timer.toc()
print(f"{timer.min_time:.12f} for initialization of {len(sinput)} voxels")
print("Forward")
for k, conv in all_convs.items():
timer = Timer()
features = torch.rand(len(coordinates), k[1]).to(device)
# Feed-forward pass and get the prediction
for i in range(20):
sinput = ME.SparseTensor(
features.to(device), coordinates=coordinates.to(device)
)
timer.tic()
soutput = conv(sinput)
timer.toc()
print(
f"{timer.min_time:.12f} for {k} strided convolution with {len(sinput)} voxel"
)
print("Backward")
sinput = ME.SparseTensor(
features.to(device), coordinates=coordinates.to(device)
)
for k, conv in all_convs.items():
timer = Timer()
sinput._F = torch.rand(len(sinput), k[1]).to(device)
soutput = conv(sinput)
loss = soutput.F.sum()
# Feed-forward pass and get the prediction
for i in range(20):
timer.tic()
loss.backward()
timer.toc()
print(
f"{timer.min_time:.12f} for {k} strided convolution with {len(sinput)} voxel"
)
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