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# ----------------------------------------------------------------------------
# - Open3D: www.open3d.org -
# ----------------------------------------------------------------------------
# The MIT License (MIT)
#
# Copyright (c) 2018-2021 www.open3d.org
#
# 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.
# ----------------------------------------------------------------------------
import os
import platform
import subprocess
import numpy as np
import tabulate
import nvidia_smi
def get_processor_name():
if platform.system() == "Windows":
return platform.processor()
elif platform.system() == "Darwin":
os.environ['PATH'] = os.environ['PATH'] + os.pathsep + '/usr/sbin'
command = "sysctl -n machdep.cpu.brand_string"
return subprocess.check_output(command).strip()
elif platform.system() == "Linux":
command = "cat /proc/cpuinfo | grep 'model name' -m 1"
name = subprocess.check_output(command, shell=True).strip()
return str(name, 'utf-8')
def run_command(command):
result = subprocess.run(command,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
universal_newlines=True,
shell=True)
return result.stdout.strip()
def print_system_info():
# print results
nvcc_version = run_command("nvcc --version")
os_version = run_command("cat /etc/os-release")
cpu_info = get_processor_name()
gpu_info = run_command("nvidia-smi")
print("======System Info======")
print("[CUDA]")
print(nvcc_version)
print("")
print("[OS]")
print(os_version)
print("")
print("[CPU]")
print(cpu_info)
print("")
print("[GPU]")
print(gpu_info)
print("======System Info [End]=")
def measure_time(fn, min_samples=10, max_samples=100, max_time_in_sec=10.0):
"""Measure time to run fn. Returns the elapsed time each run."""
from time import perf_counter_ns
t = []
for i in range(max_samples):
if sum(t) / 1e9 >= max_time_in_sec and i >= min_samples:
break
t.append(-perf_counter_ns())
try:
ans = fn()
except Exception as e:
print(e)
return np.array([np.nan])
t[-1] += perf_counter_ns()
del ans
print('.', end='')
return np.array(t) / 1e9
def measure_memory(fn, handle):
"""Measure memory to run fn. Returns the maximum allocated memory each run."""
try:
_ = fn()
except Exception as e:
print(e)
return np.nan
info = nvidia_smi.nvmlDeviceGetMemoryInfo(handle)
memory = info.used / 1000. / 1000. / 1000.
print('.', end='')
return memory
def print_table(methods, results):
headers = [''] + [f'{n}_setup' for n in methods
] + [f'{n}_search' for n in methods]
rows = []
for x in results[0]:
r = [x] + list(
map(np.median, [r[x]['setup'] for r in results] +
[r[x]['search'] for r in results]))
rows.append(r)
print(tabulate.tabulate(rows, headers=headers))
def print_table_simple(methods, results):
headers = [''] + [f'{n}_search' for n in methods]
rows = []
for x in results[0]:
r = [x] + list(map(np.median, [r[x]['search'] for r in results]))
rows.append(r)
print(tabulate.tabulate(rows, headers=headers))
def print_table_memory(methods, results):
headers = [''] + [f'{n}' for n in methods]
rows = []
for x in results[0]:
r = [x] + list(map(np.median, [r[x]['memory'] for r in results]))
rows.append(r)
print(tabulate.tabulate(rows, headers=headers))
def sample_points(points, num_sample):
if points.shape[0] < num_sample:
num_sample = points.shape[0]
idx = np.round(np.linspace(0, len(points) - 1, num_sample)).astype(int)
return points[idx]
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