mirror of
https://github.com/wassname/Flex-Convolution.git
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103 lines
3.6 KiB
Python
103 lines
3.6 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 misc import FakePointCloud, VerboseTestCase
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import tensorflow as tf
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import numpy as np
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from __init__ import flex_pooling
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case = FakePointCloud(B=2, N=32, K=4, Din=2, Dout=6, Dp=3)
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class FlexPoolTest(VerboseTestCase):
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def __init__(self, methodName="runTest"):
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super(FlexPoolTest, self).__init__(methodName)
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def _forward(self, use_gpu=False, dtype=np.float32):
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case.init_ops(dtype=dtype)
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
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actual_op, winner_op = flex_pooling(
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case.features_op, case.neighborhood_op)
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actual = sess.run(actual_op)
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return actual
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def test_forward_same_float32(self):
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cpu = self._forward(use_gpu=False, dtype=np.float32)
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gpu = self._forward(use_gpu=True, dtype=np.float32)
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self.assertAllClose(cpu, gpu)
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def test_forward_same_float64(self):
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cpu = self._forward(use_gpu=False, dtype=np.float64)
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gpu = self._forward(use_gpu=True, dtype=np.float64)
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self.assertAllClose(cpu, gpu)
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def test_backward_same_float32(self):
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cpu, winner_cpu = self._backward(use_gpu=False, dtype=np.float32)
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gpu, winner_gpu = self._backward(use_gpu=True, dtype=np.float32)
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self.assertAllClose(winner_cpu, winner_gpu)
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self.assertAllClose(cpu, gpu)
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def test_backward_same_float64(self):
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cpu, winner_cpu = self._backward(use_gpu=False, dtype=np.float64)
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gpu, winner_gpu = self._backward(use_gpu=True, dtype=np.float64)
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self.assertAllClose(winner_cpu, winner_gpu)
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self.assertAllClose(cpu, gpu)
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def _backward(self, use_gpu=False, dtype=np.float32):
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case.init_ops(dtype=dtype)
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
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actual_op, winner_op = flex_pooling(
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case.features_op,
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case.neighborhood_op)
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graph_features_grad = tf.gradients(actual_op, [case.features_op])[0]
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dx, winner = sess.run([graph_features_grad, winner_op])
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return dx, winner
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def _simple_backward(self, use_gpu=False):
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# BN
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x = np.array([[[1], [2], [5], [3]]]).transpose(0, 2, 1)
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n = np.array([[[0, 1, 2, 3], [1, 2, 3, 0], [2, 3, 0, 1],
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[3, 0, 1, 2, ]]]).transpose(0, 2, 1)
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x = tf.convert_to_tensor(x.astype(np.float32))
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n = tf.convert_to_tensor(n.astype(np.int32))
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with self.test_session(use_gpu=use_gpu, force_gpu=use_gpu) as sess:
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actual_op, winner_op = flex_pooling(x, n)
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graph_features_grad = tf.gradients(actual_op, [x])[0]
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return sess.run(graph_features_grad)
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def test_backward_simple_cpu(self):
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cpu = self._simple_backward(use_gpu=False)
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cpu[0, 0, 2] -= 4
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self.assertEqual(cpu.sum(), 0)
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def test_backward_simple_gpu(self):
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gpu = self._simple_backward(use_gpu=True)
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gpu[0, 0, 2] -= 4
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self.assertEqual(gpu.sum(), 0)
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if __name__ == '__main__':
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tf.test.main()
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