mirror of
https://github.com/wassname/Flex-Convolution.git
synced 2026-08-21 11:10:02 +08:00
69 lines
2.5 KiB
Python
69 lines
2.5 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
|
|
|
|
|
|
"""
|
|
Demonstration of using FlexConvolution, FlexPooling Layer.
|
|
"""
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from tabulate import tabulate
|
|
from layers import flex_convolution, flex_convolution_transpose, flex_pooling
|
|
|
|
B, Din, Dout, Dout2, Dp, N, N2, K, K2 = 1, 2, 4, 8, 3, 10, 5, 5, 3
|
|
|
|
features = np.random.randn(B, Din, N).astype(np.float32)
|
|
positions = np.random.randn(B, Dp, N).astype(np.float32)
|
|
neighbors = np.random.randint(0, N, [B, K, N]).astype(np.int32)
|
|
neighbors2 = np.random.randint(0, N, [B, K2, N2]).astype(np.int32)
|
|
|
|
features = tf.convert_to_tensor(features, name='features')
|
|
positions = tf.convert_to_tensor(positions, name='positions')
|
|
neighbors = tf.convert_to_tensor(neighbors, name='neighbors')
|
|
neighbors2 = tf.convert_to_tensor(neighbors2, name='neighbors2')
|
|
|
|
net = [features]
|
|
# use our FlexConv similar to a traditional convolution layer
|
|
net.append(flex_convolution(net[-1], positions, neighbors, Dout))
|
|
# pool and sub-sampling are different operations
|
|
net.append(flex_pooling(net[-1], neighbors))
|
|
|
|
# when ordering the points beforehand sub-sampling is simply
|
|
features = net[-1][:, :, :N2]
|
|
positions = positions[:, :, :N2]
|
|
|
|
net.append(features)
|
|
# we didn't notice any improvements using the transposed version vs. pooling
|
|
net.append(flex_convolution_transpose(net[-1], positions, neighbors2, Dout2))
|
|
# of course any commonly used arguments work here as well
|
|
net.append(flex_convolution(net[-1], positions,
|
|
neighbors2, Dout2, trainable=False))
|
|
|
|
|
|
|
|
with tf.Session() as sess:
|
|
sess.run(tf.global_variables_initializer())
|
|
sess.run(net[-1])
|
|
|
|
print(tabulate([[v.name, v.shape] for v in tf.trainable_variables()],
|
|
headers=["Name", "Shape"]))
|
|
|
|
print(tabulate([[n.name, n.shape] for n in net], headers=["Name", "Shape"]))
|