mirror of
https://github.com/wassname/Flex-Convolution.git
synced 2026-08-21 11:10:02 +08:00
134 lines
5.5 KiB
Python
134 lines
5.5 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_convolution_transpose
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case = FakePointCloud(B=2, N=32, K=4, Din=2, Dout=6, Dp=3)
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class FlexConvTest(VerboseTestCase):
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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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case.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_transpose(case.features_op,
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case.position_op,
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case.neighborhood_op,
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case.theta_op, case.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-4)
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def _backward_features(self, use_gpu=False, dtype=np.float32, numdiff=True):
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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 = flex_convolution_transpose(case.features_op,
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case.position_op,
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case.neighborhood_op,
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case.theta_op, case.bias_op)
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if numdiff:
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return tf.test.compute_gradient(
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[case.features_op], [case.features.shape], actual_op,
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case.expected_output_shape())[0]
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else:
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return sess.run(tf.gradients(actual_op, [case.features_op]))[0]
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def _backward_bias(self, use_gpu=False, dtype=np.float32, numdiff=True):
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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 = flex_convolution_transpose(case.features_op,
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case.position_op,
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case.neighborhood_op,
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case.theta_op, case.bias_op)
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if numdiff:
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return tf.test.compute_gradient(
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[case.bias_op], [case.bias.shape], actual_op,
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case.expected_output_shape())[0]
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else:
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return sess.run(tf.gradients(actual_op, [case.bias_op]))[0]
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def _backward_theta(self, use_gpu=False, dtype=np.float32, numdiff=True):
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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 = flex_convolution_transpose(case.features_op,
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case.position_op,
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case.neighborhood_op,
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case.theta_op, case.bias_op)
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if numdiff:
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return tf.test.compute_gradient(
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[case.theta_op], [case.theta.shape], actual_op,
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case.expected_output_shape())[0]
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else:
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return sess.run(tf.gradients(actual_op, [case.theta_op]))[0]
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def test_backward_features_cpu_float64(self):
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actual, expected = self._backward_features(use_gpu=False, dtype=np.float64)
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self.assertAllClose(actual, expected)
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def test_backward_bias_cpu_float64(self):
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actual, expected = self._backward_bias(use_gpu=False, dtype=np.float64)
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self.assertAllClose(actual, expected)
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def test_backward_theta_cpu_float64(self):
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actual, expected = self._backward_theta(use_gpu=False, dtype=np.float64)
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self.assertAllClose(actual, expected)
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def test_backward_features_gpu_float64(self):
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actual, expected = self._backward_features(use_gpu=True, dtype=np.float64)
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self.assertAllClose(actual, expected)
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def test_backward_bias_gpu_float64(self):
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actual, expected = self._backward_bias(use_gpu=True, dtype=np.float64)
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self.assertAllClose(actual, expected)
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def test_backward_theta_gpu_float64(self):
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actual, expected = self._backward_theta(use_gpu=True, dtype=np.float64)
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self.assertAllClose(actual, expected)
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def test_backward_features_gpu_float32(self, dtype=np.float32):
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cpu = self._backward_features(use_gpu=False, dtype=dtype, numdiff=False)
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gpu = self._backward_features(use_gpu=True, dtype=dtype, numdiff=False)
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self.assertAllClose(cpu, gpu)
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def test_backward_bias_gpu_float32(self, dtype=np.float32):
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cpu = self._backward_bias(use_gpu=False, dtype=dtype, numdiff=False)
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gpu = self._backward_bias(use_gpu=True, dtype=dtype, numdiff=False)
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self.assertAllClose(cpu, gpu, 1e-5)
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def test_backward_theta_gpu_float32(self, dtype=np.float32):
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cpu = self._backward_theta(use_gpu=False, dtype=dtype, numdiff=False)
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gpu = self._backward_theta(use_gpu=True, dtype=dtype, numdiff=False)
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self.assertAllClose(cpu, gpu)
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if __name__ == '__main__':
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tf.test.main()
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