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114 lines
4.5 KiB
114 lines
4.5 KiB
# Copyright (c) 2020 PaddlePaddle Authors. 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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import unittest
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import numpy as np
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import paddle.fluid as fluid
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from paddle.static import InputSpec
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from paddle.fluid.framework import core, convert_np_dtype_to_dtype_
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class TestInputSpec(unittest.TestCase):
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def test_default(self):
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tensor_spec = InputSpec([3, 4])
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self.assertEqual(tensor_spec.dtype,
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convert_np_dtype_to_dtype_('float32'))
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self.assertEqual(tensor_spec.name, None)
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def test_from_tensor(self):
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x_bool = fluid.layers.fill_constant(shape=[1], dtype='bool', value=True)
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bool_spec = InputSpec.from_tensor(x_bool)
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self.assertEqual(bool_spec.dtype, x_bool.dtype)
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self.assertEqual(bool_spec.shape, x_bool.shape)
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self.assertEqual(bool_spec.name, x_bool.name)
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bool_spec2 = InputSpec.from_tensor(x_bool, name='bool_spec')
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self.assertEqual(bool_spec2.name, bool_spec2.name)
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def test_from_numpy(self):
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x_numpy = np.ones([10, 12])
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x_np_spec = InputSpec.from_numpy(x_numpy)
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self.assertEqual(x_np_spec.dtype,
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convert_np_dtype_to_dtype_(x_numpy.dtype))
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self.assertEqual(x_np_spec.shape, x_numpy.shape)
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self.assertEqual(x_np_spec.name, None)
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x_numpy2 = np.array([1, 2, 3, 4]).astype('int64')
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x_np_spec2 = InputSpec.from_numpy(x_numpy2, name='x_np_int64')
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self.assertEqual(x_np_spec2.dtype,
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convert_np_dtype_to_dtype_(x_numpy2.dtype))
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self.assertEqual(x_np_spec2.shape, x_numpy2.shape)
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self.assertEqual(x_np_spec2.name, 'x_np_int64')
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def test_shape_with_none(self):
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tensor_spec = InputSpec([None, 4, None], dtype='int8', name='x_spec')
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self.assertEqual(tensor_spec.dtype, convert_np_dtype_to_dtype_('int8'))
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self.assertEqual(tensor_spec.name, 'x_spec')
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self.assertEqual(tensor_spec.shape, (-1, 4, -1))
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def test_shape_raise_error(self):
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# 1. shape should only contain int and None.
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with self.assertRaises(ValueError):
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tensor_spec = InputSpec(['None', 4, None], dtype='int8')
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# 2. shape should be type `list` or `tuple`
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with self.assertRaises(TypeError):
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tensor_spec = InputSpec(4, dtype='int8')
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# 3. len(shape) should be greater than 0.
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with self.assertRaises(ValueError):
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tensor_spec = InputSpec([], dtype='int8')
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def test_batch_and_unbatch(self):
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tensor_spec = InputSpec([10])
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# insert batch_size
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batch_tensor_spec = tensor_spec.batch(16)
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self.assertEqual(batch_tensor_spec.shape, (16, 10))
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# unbatch
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unbatch_spec = batch_tensor_spec.unbatch()
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self.assertEqual(unbatch_spec.shape, (10, ))
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# 1. `unbatch` requires len(shape) > 1
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with self.assertRaises(ValueError):
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unbatch_spec.unbatch()
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# 2. `batch` requires len(batch_size) == 1
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with self.assertRaises(ValueError):
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tensor_spec.batch([16, 12])
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# 3. `batch` requires type(batch_size) == int
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with self.assertRaises(TypeError):
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tensor_spec.batch('16')
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def test_eq_and_hash(self):
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tensor_spec_1 = InputSpec([10, 16], dtype='float32')
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tensor_spec_2 = InputSpec([10, 16], dtype='float32')
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tensor_spec_3 = InputSpec([10, 16], dtype='float32', name='x')
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tensor_spec_4 = InputSpec([16], dtype='float32', name='x')
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# override ``__eq__`` according to [shape, dtype, name]
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self.assertTrue(tensor_spec_1 == tensor_spec_2)
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self.assertTrue(tensor_spec_1 != tensor_spec_3) # different name
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self.assertTrue(tensor_spec_3 != tensor_spec_4) # different shape
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# override ``__hash__`` according to [shape, dtype]
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self.assertTrue(hash(tensor_spec_1) == hash(tensor_spec_2))
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self.assertTrue(hash(tensor_spec_1) == hash(tensor_spec_3))
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self.assertTrue(hash(tensor_spec_3) != hash(tensor_spec_4))
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if __name__ == '__main__':
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unittest.main()
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