forked from TensorLayer/tensorlayer3
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import unittest
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorflow as tf
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import tensorlayer as tl
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from tests.utils import CustomTestCase
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class Layer_Super_Resolution_Test(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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t_signal = tf.placeholder('float32', [10, 100, 4], name='x')
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n = tl.layers.InputLayer(t_signal, name='in')
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n = tl.layers.Conv1d(n, n_filter=32, filter_size=3, stride=1, padding='SAME', name='conv1d')
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net1 = tl.layers.SubpixelConv1d(n, scale=2, name='subpixel')
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net1.print_layers()
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net1.print_params(False)
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cls.net1_shape = net1.outputs.get_shape().as_list()
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cls.net1_layers = net1.all_layers
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cls.net1_params = net1.all_params
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cls.net1_n_params = net1.count_params()
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## 2D
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x = tf.placeholder('float32', [10, 100, 100, 3], name='x')
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n = tl.layers.InputLayer(x, name='in')
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n = tl.layers.Conv2d(n, n_filter=32, filter_size=(3, 2), strides=(1, 1), padding='SAME', name='conv2d')
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net2 = tl.layers.SubpixelConv2d(n, scale=2, name='subpixel2d')
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net2.print_layers()
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net2.print_params(False)
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cls.net2_shape = net2.outputs.get_shape().as_list()
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cls.net2_layers = net2.all_layers
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cls.net2_params = net2.all_params
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cls.net2_n_params = net2.count_params()
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@classmethod
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def tearDownClass(cls):
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tf.reset_default_graph()
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def test_net1_shape(self):
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self.assertEqual(self.net1_shape, [10, 200, 16])
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self.assertEqual(len(self.net1_layers), 3)
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self.assertEqual(len(self.net1_params), 2)
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self.assertEqual(self.net1_n_params, 416)
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def test_net2_shape(self):
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self.assertEqual(self.net2_shape, [10, 200, 200, 8])
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self.assertEqual(len(self.net2_layers), 3)
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self.assertEqual(len(self.net2_params), 2)
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self.assertEqual(self.net2_n_params, 608)
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if __name__ == '__main__':
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tf.logging.set_verbosity(tf.logging.DEBUG)
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tl.logging.set_verbosity(tl.logging.DEBUG)
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unittest.main()
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