forked from mindspore-Ecosystem/mindspore
64 lines
2.3 KiB
Python
64 lines
2.3 KiB
Python
# Copyright 2021 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 categorical distribution"""
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import pytest
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import numpy as np
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import mindspore.context as context
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import mindspore.nn as nn
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import mindspore.nn.probability.distribution as msd
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from mindspore import Tensor
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from mindspore import dtype as ms
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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def generate_probs(seed, shape):
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np.random.seed(seed)
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probs = np.random.dirichlet(np.ones(shape[3]), size=1)
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for _ in range(shape[0] - 1):
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for _ in range(shape[1] - 1):
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for _ in range(shape[2] - 1):
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probs = np.vstack(((np.random.dirichlet(np.ones(shape[3]), size=1)), probs))
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probs = np.array([probs, probs])
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probs = np.array([probs, probs])
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return probs
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class CategoricalProb(nn.Cell):
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def __init__(self, probs, seed=10, dtype=ms.int32, name='Categorical'):
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super().__init__()
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self.b = msd.Categorical(probs, seed, dtype, name)
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def construct(self, value, probs=None):
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out1 = self.b.prob(value, probs)
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out2 = self.b.log_prob(value, probs)
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out3 = self.b.cdf(value, probs)
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out4 = self.b.log_cdf(value, probs)
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out5 = self.b.survival_function(value, probs)
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out6 = self.b.log_survival(value, probs)
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return out1, out2, out3, out4, out5, out6
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@pytest.mark.level1
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_probability_categorical_prob_cdf_probs_none():
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probs = None
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probs1 = generate_probs(3, shape=(2, 2, 1, 64))
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value = np.random.randint(0, 63, size=(64)).astype(np.float32)
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net = CategoricalProb(probs)
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net(Tensor(value), Tensor(probs1))
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