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1# Copyright 2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15"""test cases for categorical distribution"""
16
17import pytest
18import numpy as np
19import mindspore.context as context
20import mindspore.nn as nn
21import mindspore.nn.probability.distribution as msd
22from mindspore import Tensor
23from mindspore import dtype as ms
24
25context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
26
27def generate_probs(seed, shape):
28    np.random.seed(seed)
29    probs = np.random.dirichlet(np.ones(shape[3]), size=1)
30    for _ in range(shape[0] - 1):
31        for _ in range(shape[1] - 1):
32            for _ in range(shape[2] - 1):
33                probs = np.vstack(((np.random.dirichlet(np.ones(shape[3]), size=1)), probs))
34            probs = np.array([probs, probs])
35        probs = np.array([probs, probs])
36    return probs
37
38
39class CategoricalProb(nn.Cell):
40    def __init__(self, probs, seed=10, dtype=ms.int32, name='Categorical'):
41        super().__init__()
42        self.b = msd.Categorical(probs, seed, dtype, name)
43
44    def construct(self, value, probs=None):
45        out1 = self.b.prob(value, probs)
46        out2 = self.b.log_prob(value, probs)
47        out3 = self.b.cdf(value, probs)
48        out4 = self.b.log_cdf(value, probs)
49        out5 = self.b.survival_function(value, probs)
50        out6 = self.b.log_survival(value, probs)
51        return out1, out2, out3, out4, out5, out6
52
53
54
55@pytest.mark.level1
56@pytest.mark.platform_x86_gpu_training
57@pytest.mark.env_onecard
58def test_probability_categorical_prob_cdf_probs_none():
59    probs = None
60    probs1 = generate_probs(3, shape=(2, 2, 1, 64))
61    value = np.random.randint(0, 63, size=(64)).astype(np.float32)
62    net = CategoricalProb(probs)
63    net(Tensor(value), Tensor(probs1))
64