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- #!/usr/bin/env python
- # -*- coding: utf-8 -*-
-
- import os
- import unittest
-
- os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
-
- import tensorlayer as tl
- from tensorlayer.layers import *
-
- from tests.utils import CustomTestCase
-
-
- class Layer_Pooling_Test(CustomTestCase):
-
- @classmethod
- def setUpClass(cls):
-
- ## 1D ========================================================================
-
- x_1_input_shape = [None, 100, 1]
- nin_1 = Input(x_1_input_shape, name='test_in1')
-
- n1 = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, name='test_conv1d')(nin_1)
- n2 = tl.layers.MaxPool1d(filter_size=3, strides=2, padding='SAME', name='test_maxpool1d')(n1)
- n3 = tl.layers.MeanPool1d(filter_size=3, strides=2, padding='SAME', name='test_meanpool1d')(n1)
- n4 = tl.layers.GlobalMaxPool1d(name='test_maxpool1d')(n1)
- n5 = tl.layers.GlobalMeanPool1d(name='test_meanpool1d')(n1)
- n16 = tl.layers.MaxPool1d(filter_size=3, strides=1, padding='VALID', name='test_maxpool1d')(n1)
- n17 = tl.layers.MeanPool1d(filter_size=3, strides=1, padding='VALID',
- name='test_meanpool1d')(n1)
- n19 = tl.layers.AdaptiveMeanPool1d(output_size=44, name='test_adaptivemeanpool1d')(n1)
- n20 = tl.layers.AdaptiveMaxPool1d(output_size=44, name='test_adaptivemaxpool1d')(n1)
-
- cls.n1_shape = tl.get_tensor_shape(n1)
- cls.n2_shape = tl.get_tensor_shape(n2)
- cls.n3_shape = tl.get_tensor_shape(n3)
- cls.n4_shape = tl.get_tensor_shape(n4)
- cls.n5_shape = tl.get_tensor_shape(n5)
- cls.n16_shape = tl.get_tensor_shape(n16)
- cls.n17_shape = tl.get_tensor_shape(n17)
- cls.n19_shape = tl.get_tensor_shape(n19)
- cls.n20_shape = tl.get_tensor_shape(n20)
-
- ## 2D ========================================================================
-
- x_2_input_shape = [None, 100, 100, 3]
- nin_2 = Input(x_2_input_shape, name='test_in2')
-
- n6 = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='test_conv2d')(nin_2)
- n7 = tl.layers.MaxPool2d(filter_size=(3, 3), strides=(2, 2), padding='SAME', name='test_maxpool2d')(n6)
- n8 = tl.layers.MeanPool2d(filter_size=(3, 3), strides=(2, 2), padding='SAME', name='test_meanpool2d')(n6)
- n9 = tl.layers.GlobalMaxPool2d(name='test_maxpool2d')(n6)
- n10 = tl.layers.GlobalMeanPool2d(name='test_meanpool2d')(n6)
- n15 = tl.layers.PoolLayer(name='test_pool2d')(n6)
- # n18 = tl.layers.CornerPool2d('TopLeft', name='test_cornerpool2d')(n6)
- n21 = tl.layers.AdaptiveMeanPool2d(output_size=(45, 32), name='test_adaptivemeanpool2d')(n6)
- n22 = tl.layers.AdaptiveMaxPool2d(output_size=(45, 32), name='test_adaptivemaxpool2d')(n6)
-
- cls.n6_shape = tl.get_tensor_shape(n6)
- cls.n7_shape = tl.get_tensor_shape(n7)
- cls.n8_shape = tl.get_tensor_shape(n8)
- cls.n9_shape = tl.get_tensor_shape(n9)
- cls.n10_shape = tl.get_tensor_shape(n10)
- cls.n15_shape = tl.get_tensor_shape(n15)
- cls.n21_shape = tl.get_tensor_shape(n21)
- cls.n22_shape = tl.get_tensor_shape(n22)
-
- ## 3D ========================================================================
-
- x_3_input_shape = [None, 100, 100, 100, 3]
- nin_3 = Input(x_3_input_shape, name='test_in3')
-
- n11 = tl.layers.MeanPool3d(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME',
- name='test_meanpool3d')(nin_3)
- n12 = tl.layers.GlobalMaxPool3d(name='test_maxpool3d')(nin_3)
- n13 = tl.layers.GlobalMeanPool3d(name='test_meanpool3d')(nin_3)
- n14 = tl.layers.MaxPool3d(filter_size=(3, 3, 3), strides=(2, 2, 2), padding='SAME',
- name='test_maxpool3d')(nin_3)
-
- n23 = tl.layers.AdaptiveMeanPool3d(output_size=(45, 32, 55), name='test_adaptivemeanpool3d')(nin_3)
- n24 = tl.layers.AdaptiveMaxPool3d(output_size=(45, 32, 55), name='test_adaptivemaxpool3d')(nin_3)
-
- cls.n11_shape = n11.get_shape().as_list()
- cls.n12_shape = n12.get_shape().as_list()
- cls.n13_shape = n13.get_shape().as_list()
- cls.n14_shape = n14.get_shape().as_list()
- cls.n21_shape = n21.get_shape().as_list()
- cls.n22_shape = n22.get_shape().as_list()
- cls.n23_shape = n23.get_shape().as_list()
- cls.n24_shape = n24.get_shape().as_list()
-
- @classmethod
- def tearDownClass(cls):
- pass
- # tf.reset_default_graph()
-
- def test_n1_shape(self):
- self.assertEqual(self.n1_shape[1:3], [50, 32])
-
- def test_n2_shape(self):
- self.assertEqual(self.n2_shape[1:3], [25, 32])
-
- def test_n3_shape(self):
- self.assertEqual(self.n3_shape[1:3], [25, 32])
-
- def test_n4_shape(self):
- self.assertEqual(self.n4_shape[-1], 32)
-
- def test_n5_shape(self):
- self.assertEqual(self.n5_shape[-1], 32)
-
- def test_n6_shape(self):
- self.assertEqual(self.n6_shape[1:4], [50, 50, 32])
-
- def test_n7_shape(self):
- self.assertEqual(self.n7_shape[1:4], [25, 25, 32])
-
- def test_n8_shape(self):
- self.assertEqual(self.n8_shape[1:4], [25, 25, 32])
-
- def test_n9_shape(self):
- self.assertEqual(self.n9_shape[-1], 32)
-
- def test_n10_shape(self):
- self.assertEqual(self.n10_shape[-1], 32)
-
- def test_n11_shape(self):
- self.assertEqual(self.n11_shape[1:5], [50, 50, 50, 3])
-
- def test_n12_shape(self):
- self.assertEqual(self.n12_shape[-1], 3)
-
- def test_n13_shape(self):
- self.assertEqual(self.n13_shape[-1], 3)
-
- def test_n14_shape(self):
- self.assertEqual(self.n14_shape[1:5], [50, 50, 50, 3])
-
- def test_n15_shape(self):
- self.assertEqual(self.n15_shape[1:4], [25, 25, 32])
-
- def test_n16_shape(self):
- self.assertEqual(self.n16_shape[1:4], [48, 32])
-
- def test_n17_shape(self):
- self.assertEqual(self.n17_shape[1:4], [48, 32])
-
- def test_n19_shape(self):
- self.assertEqual(self.n19_shape[1:3], [44, 32])
-
- def test_n20_shape(self):
- self.assertEqual(self.n20_shape[1:3], [44, 32])
-
- def test_n21_shape(self):
- self.assertEqual(self.n21_shape[1:4], [45, 32, 32])
-
- def test_n22_shape(self):
- self.assertEqual(self.n22_shape[1:4], [45, 32, 32])
-
- def test_n23_shape(self):
- self.assertEqual(self.n23_shape[1:5], [45, 32, 55, 3])
-
- def test_n24_shape(self):
- self.assertEqual(self.n24_shape[1:5], [45, 32, 55, 3])
-
- # def test_n18_shape(self):
- # self.assertEqual(self.n18_shape[1:], [50, 50, 32])
-
-
- if __name__ == '__main__':
-
- tl.logging.set_verbosity(tl.logging.DEBUG)
-
- unittest.main()
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