forked from TensorLayer/tensorlayer3
125 lines
4.1 KiB
Python
125 lines
4.1 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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import numpy as np
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os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
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import tensorlayer as tl
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from tests.utils import CustomTestCase
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class Layer_Shape_Test(CustomTestCase):
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@classmethod
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def setUpClass(cls):
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cls.data = tl.layers.Input(shape=(8, 4, 3), init=tl.initializers.random_normal())
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cls.imgdata = tl.layers.Input(shape=(2, 16, 16, 8), init=tl.initializers.random_normal())
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@classmethod
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def tearDownClass(cls):
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pass
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def test_flatten(self):
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class CustomizeModel(tl.layers.Module):
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def __init__(self):
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super(CustomizeModel, self).__init__()
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self.flatten = tl.layers.Flatten()
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def forward(self, x):
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return self.flatten(x)
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model = CustomizeModel()
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print(model.flatten)
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model.set_train()
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out = model(self.data)
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self.assertEqual(out.get_shape().as_list(), [8, 12])
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def test_reshape(self):
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class CustomizeModel(tl.layers.Module):
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def __init__(self):
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super(CustomizeModel, self).__init__()
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self.reshape1 = tl.layers.Reshape(shape=(8, 12))
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self.reshape2 = tl.layers.Reshape(shape=(-1, 12))
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self.reshape3 = tl.layers.Reshape(shape=())
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def forward(self, x):
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return self.reshape1(x), self.reshape2(x), self.reshape3(x[0][0][0])
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model = CustomizeModel()
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print(model.reshape1)
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print(model.reshape2)
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print(model.reshape3)
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model.set_train()
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out1, out2, out3 = model(self.data)
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self.assertEqual(out1.get_shape().as_list(), [8, 12])
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self.assertEqual(out2.get_shape().as_list(), [8, 12])
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self.assertEqual(out3.get_shape().as_list(), [])
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def test_transpose(self):
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class CustomizeModel(tl.layers.Module):
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def __init__(self):
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super(CustomizeModel, self).__init__()
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self.transpose1 = tl.layers.Transpose()
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self.transpose2 = tl.layers.Transpose([2, 1, 0])
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self.transpose3 = tl.layers.Transpose([0, 2, 1])
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self.transpose4 = tl.layers.Transpose(conjugate=True)
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def forward(self, x):
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return self.transpose1(x), self.transpose2(x), self.transpose3(x), self.transpose4(x)
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real = tl.layers.Input(shape=(8, 4, 3), init=tl.initializers.random_normal())
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comp = tl.layers.Input(shape=(8, 4, 3), init=tl.initializers.random_normal())
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import tensorflow as tf
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complex_data = tf.dtypes.complex(real, comp)
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model = CustomizeModel()
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print(model.transpose1)
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print(model.transpose2)
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print(model.transpose3)
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print(model.transpose4)
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model.set_train()
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out1, out2, out3, out4 = model(self.data)
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self.assertEqual(out1.get_shape().as_list(), [3, 4, 8])
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self.assertEqual(out2.get_shape().as_list(), [3, 4, 8])
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self.assertEqual(out3.get_shape().as_list(), [8, 3, 4])
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self.assertEqual(out4.get_shape().as_list(), [3, 4, 8])
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self.assertTrue(np.array_equal(out1.numpy(), out4.numpy()))
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out1, out2, out3, out4 = model(complex_data)
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self.assertEqual(out1.get_shape().as_list(), [3, 4, 8])
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self.assertEqual(out2.get_shape().as_list(), [3, 4, 8])
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self.assertEqual(out3.get_shape().as_list(), [8, 3, 4])
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self.assertEqual(out4.get_shape().as_list(), [3, 4, 8])
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self.assertTrue(np.array_equal(np.conj(out1.numpy()), out4.numpy()))
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def test_shuffle(self):
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class CustomizeModel(tl.layers.Module):
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def __init__(self, x):
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super(CustomizeModel, self).__init__()
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self.shuffle = tl.layers.Shuffle(x)
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def forward(self, x):
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return self.shuffle(x)
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model = CustomizeModel(2)
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print(model.shuffle)
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model.set_train()
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out = model(self.imgdata)
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self.assertEqual(out.get_shape().as_list(), [2, 16, 16, 8])
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if __name__ == '__main__':
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unittest.main()
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