[Dy2stat] Support InputSpec and Return callable class instance in @declarative (#25960)
* add InputSpec * add unittest for tensorSpec and SimpleNetrevert-26856-strategy_example2
Aurelius84
5 years ago
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# 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 paddle
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from paddle.static import InputSpec
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from paddle.fluid.dygraph.dygraph_to_static.function_spec import FunctionSpec
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from test_declarative import foo_func
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import unittest
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class TestFunctionSpec(unittest.TestCase):
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def test_constructor(self):
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foo_spec = FunctionSpec(foo_func)
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args_name = foo_spec.args_name
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self.assertListEqual(args_name, ['a', 'b', 'c', 'd'])
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self.assertTrue(foo_spec.dygraph_function == foo_func)
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self.assertTrue(foo_spec.input_spec is None)
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def test_verify_input_spec(self):
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a_spec = InputSpec([None, 10], name='a')
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b_spec = InputSpec([10], name='b')
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# type(input_spec) should be list or tuple
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with self.assertRaises(TypeError):
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foo_spec = FunctionSpec(foo_func, input_spec=a_spec)
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# each element of input_spec should be `InputSpec`
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with self.assertRaises(ValueError):
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foo_spec = FunctionSpec(foo_func, input_spec=[a_spec, 10])
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foo_spec = FunctionSpec(foo_func, input_spec=[a_spec, b_spec])
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self.assertTrue(len(foo_spec.flat_input_spec) == 2)
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def test_unified_args_and_kwargs(self):
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foo_spec = FunctionSpec(foo_func)
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# case 1: foo(10, 20, c=4)
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args, kwargs = foo_spec.unified_args_and_kwargs([10, 20], {'c': 4})
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self.assertTupleEqual(args, (10, 20, 4, 2))
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self.assertTrue(len(kwargs) == 0)
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# case 2: foo(a=10, b=20, d=4)
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args, kwargs = foo_spec.unified_args_and_kwargs(
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[], {'a': 10,
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'b': 20,
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'd': 4})
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self.assertTupleEqual(args, (10, 20, 1, 4))
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self.assertTrue(len(kwargs) == 0)
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# case 3: foo(10, b=20)
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args, kwargs = foo_spec.unified_args_and_kwargs([10], {'b': 20})
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self.assertTupleEqual(args, (10, 20, 1, 2))
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self.assertTrue(len(kwargs) == 0)
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# assert len(self._arg_names) >= len(args)
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with self.assertRaises(ValueError):
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foo_spec.unified_args_and_kwargs([10, 20, 30, 40, 50], {'c': 4})
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# assert arg_name should be in kwargs
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with self.assertRaises(ValueError):
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foo_spec.unified_args_and_kwargs([10], {'c': 4})
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def test_args_to_input_spec(self):
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a_spec = InputSpec([None, 10], name='a')
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b_spec = InputSpec([10], name='b')
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a_tensor = paddle.static.data(name='a_var', shape=[4, 10])
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b_tensor = paddle.static.data(name='b_var', shape=[4, 10])
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kwargs = {'c': 1, 'd': 2}
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# case 1
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foo_spec = FunctionSpec(foo_func, input_spec=[a_spec, b_spec])
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input_with_spec = foo_spec.args_to_input_spec(
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(a_tensor, b_tensor, 1, 2), {})
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self.assertTrue(len(input_with_spec) == 4)
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self.assertTrue(input_with_spec[0] == a_spec) # a
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self.assertTrue(input_with_spec[1] == b_spec) # b
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self.assertTrue(input_with_spec[2] == 1) # c
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self.assertTrue(input_with_spec[3] == 2) # d
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# case 2
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foo_spec = FunctionSpec(foo_func, input_spec=[a_spec])
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input_with_spec = foo_spec.args_to_input_spec((a_tensor, b_tensor), {})
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self.assertTrue(len(input_with_spec) == 2)
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self.assertTrue(input_with_spec[0] == a_spec) # a
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self.assertTupleEqual(input_with_spec[1].shape, (4, 10)) # b.shape
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self.assertEqual(input_with_spec[1].name, 'b_var') # b.name
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# case 3
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# assert kwargs is None if set `input_spec`
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foo_spec = FunctionSpec(foo_func, input_spec=[a_spec])
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with self.assertRaises(ValueError):
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input_with_spec = foo_spec.args_to_input_spec((a_tensor, b_tensor),
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{'c': 4})
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# case 4
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# assert len(args) >= len(self._input_spec)
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foo_spec = FunctionSpec(foo_func, input_spec=[a_spec, b_spec])
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with self.assertRaises(ValueError):
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input_with_spec = foo_spec.args_to_input_spec((a_tensor, ), {})
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,113 @@
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# 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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