mirror of
https://github.com/wassname/Flex-Convolution.git
synced 2026-08-31 11:40:55 +08:00
48 lines
1.6 KiB
Python
48 lines
1.6 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 Layer.
|
|
"""
|
|
|
|
import numpy as np
|
|
import tensorflow as tf
|
|
from layers import flex_convolution
|
|
|
|
B, Din, Dout, Dp, N, K = 1, 2, 4, 3, 10, 5
|
|
|
|
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)
|
|
|
|
features = tf.convert_to_tensor(features)
|
|
positions = tf.convert_to_tensor(positions)
|
|
neighbors = tf.convert_to_tensor(neighbors)
|
|
|
|
features2 = flex_convolution(features, positions, neighbors, Dout)
|
|
features3 = flex_convolution(features2, positions, neighbors, Dout, trainable=False)
|
|
|
|
with tf.Session() as sess:
|
|
sess.run(tf.global_variables_initializer())
|
|
sess.run(features2)
|
|
sess.run(features3)
|
|
|
|
print(tf.trainable_variables())
|