forked from mindspore-Ecosystem/mindspore
新增深度概率用例
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rainyhorse
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# 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 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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import pytest
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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.level0
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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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# 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 cauchy distribution"""
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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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import pytest
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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class CauchyMean(nn.Cell):
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def __init__(self, loc, scale, seed=10, dtype=ms.float32, name='Cauchy'):
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super().__init__()
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self.b = msd.Cauchy(loc, scale, seed, dtype, name)
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def construct(self):
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out4 = self.b.entropy()
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return out4
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_probability_cauchy_mean_loc_scale_rand_2_ndarray():
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loc = np.random.randn(1024, 512, 7, 7).astype(np.float32)
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scale = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
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net = CauchyMean(loc, scale)
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net()
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class CauchyProb(nn.Cell):
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def __init__(self, loc, scale, seed=10, dtype=ms.float32, name='Cauchy'):
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super().__init__()
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self.b = msd.Cauchy(loc, scale, seed, dtype, name)
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def construct(self, value):
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out1 = self.b.prob(value)
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out2 = self.b.log_prob(value)
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out3 = self.b.cdf(value)
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out4 = self.b.log_cdf(value)
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out5 = self.b.survival_function(value)
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out6 = self.b.log_survival(value)
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return out1, out2, out3, out4, out5, out6
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_probability_cauchy_prob_cdf_loc_scale_rand_4_ndarray():
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loc = np.random.randn(1024, 512, 7, 7).astype(np.float32)
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scale = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
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value = np.random.randn(1024, 512, 7, 7).astype(np.float32)
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net = CauchyProb(loc, scale)
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net(Tensor(value))
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# 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 gamma distribution"""
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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 dtype as ms
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import pytest
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context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
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class GammaMean(nn.Cell):
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def __init__(self, concentration, rate, seed=10, dtype=ms.float32, name='Gamma'):
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super().__init__()
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self.b = msd.Gamma(concentration, rate, seed, dtype, name)
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def construct(self):
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out1 = self.b.mean()
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out2 = self.b.mode()
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out3 = self.b.var()
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out4 = self.b.entropy()
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out5 = self.b.sd()
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return out1, out2, out3, out4, out5
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@pytest.mark.level0
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_onecard
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def test_probability_gamma_mean_cdoncentration_rate_rand_2_ndarray():
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concentration = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
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rate = np.random.uniform(0.0001, 100, size=(1024, 512, 7, 7)).astype(np.float32)
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net = GammaMean(concentration, rate)
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net()
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