mirror of
https://github.com/wassname/Flex-Convolution.git
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110 lines
4.3 KiB
Python
110 lines
4.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# Copyright 2017 ComputerGraphics Tuebingen. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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# Authors: Fabian Groh, Patrick Wieschollek, Hendrik P.A. Lensch
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from PointTestCase import TPC, PointTestCase, summary
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import tensorflow as tf
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from __init__ import flex_convolution
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class FlexConvTest(PointTestCase):
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def __init__(self, methodName="runTest"):
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super(FlexConvTest, self).__init__(methodName)
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def _forward(self, use_gpu=False, force_gpu=False):
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self.init_ops()
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
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actual_op = flex_convolution(self.features_op,
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self.position_op, self.neighborhood_op,
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self.theta_op, self.bias_op)
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actual = sess.run(actual_op)
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return actual
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def test_forward(self):
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cpu = self._forward(use_gpu=False)
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gpu = self._forward(use_gpu=True)
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self.assertAllClose(cpu, gpu, 1e-5, 1e-5)
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def _backward_features(self, use_gpu=False):
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self.init_ops()
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu):
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actual_op = flex_convolution(self.features_op,
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self.position_op, self.neighborhood_op,
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self.theta_op, self.bias_op)
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graph_features_grad, num_features_grad = tf.test.compute_gradient(
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[self.features_op], [self.features.shape], actual_op,
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TPC.expected_output_shape())[0]
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summary(num_features_grad, graph_features_grad, 'self.features')
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err = tf.test.compute_gradient_error([self.features_op],
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[self.features.shape],
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actual_op, TPC.expected_output_shape())
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self.assertLess(err, 1e-2)
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def _backward_bias(self, use_gpu=False):
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self.init_ops()
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu):
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actual_op = flex_convolution(self.features_op,
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self.position_op, self.neighborhood_op,
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self.theta_op, self.bias_op)
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graph_bias_grad, num_bias_grad = tf.test.compute_gradient(
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[self.bias_op], [self.bias.shape], actual_op,
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TPC.expected_output_shape())[0]
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summary(num_bias_grad, graph_bias_grad, 'self.bias')
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err = tf.test.compute_gradient_error([self.bias_op],
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[self.bias.shape], actual_op,
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TPC.expected_output_shape())
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self.assertLess(err, 1e-2)
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def _backward_theta(self, use_gpu=False):
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self.init_ops()
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu):
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actual_op = flex_convolution(self.features_op,
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self.position_op, self.neighborhood_op,
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self.theta_op, self.bias_op)
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graph_theta_grad, num_theta_grad = tf.test.compute_gradient(
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[self.theta_op], [self.theta.shape], actual_op,
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TPC.expected_output_shape())[0]
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summary(num_theta_grad, graph_theta_grad, 'self.theta')
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err = tf.test.compute_gradient_error([self.theta_op],
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[self.theta.shape], actual_op,
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TPC.expected_output_shape())
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self.assertLess(err, 1e-2)
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def test_backward_features(self):
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self._backward_features(use_gpu=False)
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self._backward_features(use_gpu=True)
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def test_backward_bias(self):
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self._backward_bias(use_gpu=False)
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self._backward_bias(use_gpu=True)
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def test_backward_theta(self):
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self._backward_theta(use_gpu=False)
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self._backward_theta(use_gpu=True)
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if __name__ == '__main__':
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tf.test.main()
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