Xun Deng
5 years ago
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600704ddde
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# Copyright 2020 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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"""
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Test util functions used in distribution classes.
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"""
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import numpy as np
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import pytest
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from mindspore.nn.cell import Cell
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from mindspore import context
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from mindspore import dtype
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from mindspore import Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.nn.probability.distribution._utils.utils import set_param_type, \
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cast_to_tensor, CheckTuple, CheckTensor
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def test_set_param_type():
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"""
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Test set_param_type function.
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"""
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tensor_fp16 = Tensor(0.1, dtype=dtype.float16)
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tensor_fp32 = Tensor(0.1, dtype=dtype.float32)
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tensor_fp64 = Tensor(0.1, dtype=dtype.float64)
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tensor_int32 = Tensor(0.1, dtype=dtype.int32)
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array_fp32 = np.array(1.0).astype(np.float32)
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array_fp64 = np.array(1.0).astype(np.float64)
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array_int32 = np.array(1.0).astype(np.int32)
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dict1 = {'a': tensor_fp32, 'b': 1.0, 'c': tensor_fp32}
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dict2 = {'a': tensor_fp32, 'b': 1.0, 'c': tensor_fp64}
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dict3 = {'a': tensor_int32, 'b': 1.0, 'c': tensor_int32}
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dict4 = {'a': array_fp32, 'b': 1.0, 'c': tensor_fp32}
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dict5 = {'a': array_fp32, 'b': 1.0, 'c': array_fp64}
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dict6 = {'a': array_fp32, 'b': 1.0, 'c': array_int32}
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dict7 = {'a': 1.0}
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dict8 = {'a': 1.0, 'b': 1.0, 'c': 1.0}
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dict9 = {'a': tensor_fp16, 'b': tensor_fp16, 'c': tensor_fp16}
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dict10 = {'a': tensor_fp64, 'b': tensor_fp64, 'c': tensor_fp64}
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dict11 = {'a': array_fp64, 'b': array_fp64, 'c': tensor_fp64}
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ans1 = set_param_type(dict1, dtype.float16)
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assert ans1 == dtype.float32
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with pytest.raises(TypeError):
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set_param_type(dict2, dtype.float32)
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ans3 = set_param_type(dict3, dtype.float16)
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assert ans3 == dtype.float32
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ans4 = set_param_type(dict4, dtype.float16)
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assert ans4 == dtype.float32
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with pytest.raises(TypeError):
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set_param_type(dict5, dtype.float32)
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with pytest.raises(TypeError):
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set_param_type(dict6, dtype.float32)
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ans7 = set_param_type(dict7, dtype.float32)
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assert ans7 == dtype.float32
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ans8 = set_param_type(dict8, dtype.float32)
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assert ans8 == dtype.float32
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ans9 = set_param_type(dict9, dtype.float32)
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assert ans9 == dtype.float16
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ans10 = set_param_type(dict10, dtype.float32)
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assert ans10 == dtype.float32
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ans11 = set_param_type(dict11, dtype.float32)
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assert ans11 == dtype.float32
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def test_cast_to_tensor():
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"""
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Test cast_to_tensor.
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"""
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with pytest.raises(ValueError):
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cast_to_tensor(None, dtype.float32)
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with pytest.raises(TypeError):
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cast_to_tensor(True, dtype.float32)
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with pytest.raises(TypeError):
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cast_to_tensor({'a': 1, 'b': 2}, dtype.float32)
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with pytest.raises(TypeError):
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cast_to_tensor('tensor', dtype.float32)
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ans1 = cast_to_tensor(Parameter(Tensor(0.1, dtype=dtype.float32), 'param'))
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assert isinstance(ans1, Parameter)
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ans2 = cast_to_tensor(np.array(1.0).astype(np.float32))
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assert isinstance(ans2, Tensor)
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ans3 = cast_to_tensor([1.0, 2.0])
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assert isinstance(ans3, Tensor)
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ans4 = cast_to_tensor(Tensor(0.1, dtype=dtype.float32), dtype.float32)
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assert isinstance(ans4, Tensor)
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ans5 = cast_to_tensor(0.1, dtype.float32)
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assert isinstance(ans5, Tensor)
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ans6 = cast_to_tensor(1, dtype.float32)
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assert isinstance(ans6, Tensor)
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class Net(Cell):
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"""
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Test class: CheckTuple.
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"""
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def __init__(self, value):
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super(Net, self).__init__()
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self.checktuple = CheckTuple()
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self.value = value
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def construct(self, value=None):
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if value is None:
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return self.checktuple(self.value, 'input')
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return self.checktuple(value, 'input')
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def test_check_tuple():
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"""
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Test CheckTuple.
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"""
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net1 = Net((1, 2, 3))
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ans1 = net1()
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assert isinstance(ans1, tuple)
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with pytest.raises(TypeError):
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net2 = Net('tuple')
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net2()
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context.set_context(mode=context.GRAPH_MODE)
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net3 = Net((1, 2, 3))
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ans3 = net3()
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assert isinstance(ans3, tuple)
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with pytest.raises(TypeError):
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net4 = Net('tuple')
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net4()
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class Net1(Cell):
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"""
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Test class: CheckTensor.
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"""
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def __init__(self, value):
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super(Net1, self).__init__()
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self.checktensor = CheckTensor()
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self.value = value
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self.context = context.get_context('mode')
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def construct(self, value=None):
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value = self.value if value is None else value
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if self.context == 0:
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self.checktensor(value, 'input')
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return value
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return self.checktensor(value, 'input')
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def test_check_tensor():
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"""
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Test CheckTensor.
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"""
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value = Tensor(0.1, dtype=dtype.float32)
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net1 = Net1(value)
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ans1 = net1()
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assert isinstance(ans1, Tensor)
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ans1 = net1(value)
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assert isinstance(ans1, Tensor)
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with pytest.raises(TypeError):
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net2 = Net1('tuple')
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net2()
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context.set_context(mode=context.GRAPH_MODE)
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net3 = Net1(value)
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ans3 = net3()
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assert isinstance(ans3, Tensor)
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ans3 = net3(value)
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assert isinstance(ans3, Tensor)
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with pytest.raises(TypeError):
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net4 = Net1('tuple')
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net4()
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