mirror of
https://github.com/wassname/Flex-Convolution.git
synced 2026-08-21 11:10:02 +08:00
75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
#!/usr/bin/env python
|
|
# -*- coding: utf-8 -*-
|
|
|
|
# Copyright 2017 ComputerGraphics Tuebingen. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ==============================================================================
|
|
# Authors: Fabian Groh, Patrick Wieschollek, Hendrik P.A. Lensch
|
|
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
|
|
from misc import FakePointCloud
|
|
from __init__ import flex_convolution
|
|
|
|
|
|
"""
|
|
export LD_LIBRARY_PATH=/graphics/opt/opt_Ubuntu16.04/cuda/toolkit_9.0/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
|
|
"""
|
|
|
|
|
|
np.random.seed(42)
|
|
tf.set_random_seed(42)
|
|
|
|
|
|
case = FakePointCloud(B=8, N=4096, K=8, Din=64, Dout=64, Dp=3)
|
|
case.init_ops(dtype=np.float32)
|
|
|
|
forward_op = flex_convolution(case.features_op,
|
|
case.position_op,
|
|
case.neighborhood_op,
|
|
case.theta_op,
|
|
case.bias_op)
|
|
|
|
builder = tf.profiler.ProfileOptionBuilder
|
|
opts = builder(builder.time_and_memory()).order_by('micros').build()
|
|
|
|
with tf.contrib.tfprof.ProfileContext('./.profiling_outputs/flex_convolution') as pctx:
|
|
|
|
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
|
|
sess.run(tf.global_variables_initializer())
|
|
|
|
back_prop = tf.gradients(
|
|
forward_op, [case.theta_op, case.bias_op, case.features_op])
|
|
back_prop2 = tf.gradients(forward_op, [case.theta_op, case.bias_op,
|
|
case.features_op, case.position_op])
|
|
|
|
# warmup
|
|
for i in range(2):
|
|
actual = sess.run([forward_op, back_prop])
|
|
|
|
# benchmark
|
|
for i in range(10):
|
|
pctx.trace_next_step()
|
|
pctx.dump_next_step()
|
|
_ = sess.run([forward_op])
|
|
pctx.profiler.profile_operations(options=opts)
|
|
|
|
for i in range(10):
|
|
pctx.trace_next_step()
|
|
pctx.dump_next_step()
|
|
_ = sess.run([back_prop])
|
|
pctx.profiler.profile_operations(options=opts)
|