redesigned bijector classes, changed bacth_shape calculation in transformed distribution and upgraded dtype logic of bijector class
Xun Deng
6 years ago
parent
05643cb20c
commit
fb0263f869
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# Copyright 2019 Huawei Technologies Co., Ltd
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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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# ============================================================================
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"""test cases for exp"""
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import numpy as np
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import pytest
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import mindspore.nn as nn
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import mindspore.nn.probability.bijector as msb
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from mindspore import Tensor
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from mindspore import dtype
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class MyBijector(msb.Bijector):
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"""
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Customized bijector class with dtype not specified.
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"""
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def __init__(self, param1, param2):
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param = dict(locals())
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param['param_dict'] = {'param1': param1, 'param2': param2}
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super(MyBijector, self).__init__(name='MyBijector', dtype=None, param=param)
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self._param1 = self._add_parameter(param1, 'param1')
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self._param2 = self._add_parameter(param2, 'param2')
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@property
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def param1(self):
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return self._param1
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@property
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def param2(self):
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return self._param2
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def _forward(self, value):
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value = self._check_value_dtype(value)
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param1_local = self.cast_param_by_value(value, self.param1)
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param2_local = self.cast_param_by_value(value, self.param2)
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return value * param1_local + param2_local
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class MySecondBijector(msb.Bijector):
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"""
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Customized bijector class with dtype specified.
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"""
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def __init__(self, param1, param2):
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param = dict(locals())
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param['param_dict'] = {'param1': param1, 'param2': param2}
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super(MySecondBijector, self).__init__(name='MySecondBijector', dtype=dtype.float32, param=param)
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self._param1 = self._add_parameter(param1, 'param1')
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self._param2 = self._add_parameter(param2, 'param2')
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@property
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def param1(self):
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return self._param1
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@property
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def param2(self):
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return self._param2
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def _forward(self, value):
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value = self._check_value_dtype(value)
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param1_local = self.cast_param_by_value(value, self.param1)
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param2_local = self.cast_param_by_value(value, self.param2)
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return value * param1_local + param2_local
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def test_arguments_same_type():
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"""
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Test bijector initializations.
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"""
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param1_1 = np.array(1.0).astype(np.float16)
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param2_1 = np.array(2.0).astype(np.float32)
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with pytest.raises(TypeError):
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MyBijector(param1_1, param2_1)
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param1_2 = Tensor(1.0, dtype=dtype.float16)
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param2_2 = Tensor(2.0, dtype=dtype.float32)
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with pytest.raises(TypeError):
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MyBijector(param1_2, param2_2)
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with pytest.raises(TypeError):
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MyBijector(True, param2_2)
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with pytest.raises(TypeError):
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MyBijector(None, param2_2)
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param1_3 = Tensor(1.0, dtype=dtype.float32)
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param2_3 = Tensor(2.0, dtype=dtype.float32)
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bijector = MyBijector(param1_3, param2_3)
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assert isinstance(bijector, msb.Bijector)
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param1_4 = np.array([1.0, 2.0]).astype(np.float16)
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param2_4 = np.array([1.0, 2.0]).astype(np.float16)
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bijector = MyBijector(param1_4, param2_4)
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assert isinstance(bijector, msb.Bijector)
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bijector = MyBijector(1.0, 2.0)
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assert isinstance(bijector, msb.Bijector)
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def test_arguments_with_dtype_specified():
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"""
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Customized bijector class with dtype not specified.
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"""
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param1_1 = np.array(1.0).astype(np.float16)
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param2_1 = np.array(2.0).astype(np.float16)
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with pytest.raises(TypeError):
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MySecondBijector(param1_1, param2_1)
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param1_2 = Tensor(1.0, dtype=dtype.float16)
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param2_2 = Tensor(2.0, dtype=dtype.float32)
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with pytest.raises(TypeError):
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MySecondBijector(param1_2, param2_2)
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with pytest.raises(TypeError):
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MySecondBijector(True, param2_2)
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with pytest.raises(TypeError):
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MySecondBijector(None, param2_2)
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param1_3 = Tensor(1.0, dtype=dtype.float32)
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param2_3 = Tensor(2.0, dtype=dtype.float32)
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bijector = MyBijector(param1_3, param2_3)
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assert isinstance(bijector, msb.Bijector)
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param1_4 = np.array(2.0).astype(np.float32)
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param2_4 = np.array(1.0).astype(np.float32)
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bijector = MyBijector(param1_4, param2_4)
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assert isinstance(bijector, msb.Bijector)
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class Net1(nn.Cell):
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"""
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Test input value when bijector's dtype is not specified.
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"""
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def __init__(self):
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super(Net1, self).__init__()
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self.bijector = MyBijector(np.array(1.0).astype(np.float32), np.array(2.0).astype(np.float32))
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def construct(self, value):
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return self.bijector.forward(value)
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class Net2(nn.Cell):
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"""
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Test input value when bijector's dtype is specified.
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"""
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def __init__(self):
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super(Net2, self).__init__()
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self.bijector = MySecondBijector(np.array(1.0).astype(np.float32), np.array(2.0).astype(np.float32))
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def construct(self, value):
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return self.bijector.forward(value)
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def test_input_value():
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"""
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Test validity of input value.
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"""
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net = Net1()
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value = None
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with pytest.raises(TypeError):
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ans = net(value)
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value = 1.0
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with pytest.raises(TypeError):
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ans = net(value)
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value = Tensor(1.0, dtype=dtype.int32)
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with pytest.raises(TypeError):
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ans = net(value)
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value = Tensor(1.0, dtype=dtype.float32)
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ans = net(value)
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assert ans.dtype == dtype.float32
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value = Tensor(1.0, dtype=dtype.float16)
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ans = net(value)
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assert ans.dtype == dtype.float16
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def test_input_value2():
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"""
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Test validity of input value.
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"""
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net = Net2()
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value = None
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with pytest.raises(TypeError):
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ans = net(value)
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value = 1.0
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with pytest.raises(TypeError):
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ans = net(value)
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value = Tensor(1.0, dtype=dtype.int32)
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with pytest.raises(TypeError):
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ans = net(value)
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value = Tensor(1.0, dtype=dtype.float16)
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with pytest.raises(TypeError):
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ans = net(value)
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value = Tensor(1.0, dtype=dtype.float32)
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ans = net(value)
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assert ans.dtype == dtype.float32
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