86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
# Copyright 2022 Huawei Technologies Co., Ltd
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# ============================================================================
|
|
import numpy as np
|
|
import pytest
|
|
|
|
import mindspore.nn as nn
|
|
from mindspore import Tensor
|
|
import mindspore.context as context
|
|
from mindspore.ops import functional as F
|
|
from mindspore.ops.operations import _inner_ops as inner
|
|
|
|
|
|
class Net(nn.Cell):
|
|
def __init__(self, seed=-1):
|
|
super(Net, self).__init__()
|
|
self.bernoulli = F.bernoulli
|
|
|
|
def construct(self, x, p):
|
|
return self.bernoulli(x, p)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_bernoulli():
|
|
"""
|
|
Feature: bernoulli function
|
|
Description: test cases for Bernoulli
|
|
Expectation: the result matches scipy
|
|
"""
|
|
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
|
x_shape = [32, 16, 2, 5]
|
|
x = np.ones(x_shape).astype(np.float32)
|
|
bernoulli = Net()
|
|
tx = Tensor(x)
|
|
output = bernoulli(tx, 0.5)
|
|
# check output
|
|
output_np = output.asnumpy()
|
|
nonzero_count = np.count_nonzero(output_np)
|
|
elem_count = x.size
|
|
assert elem_count * 0.4 < nonzero_count < elem_count * 0.6
|
|
|
|
|
|
class BernoulliDynamic(nn.Cell):
|
|
def __init__(self, seed=-1):
|
|
super(BernoulliDynamic, self).__init__()
|
|
self.test_dynamic = inner.GpuConvertToDynamicShape()
|
|
self.bernoulli = F.bernoulli
|
|
|
|
def construct(self, x, p):
|
|
x = self.test_dynamic(x)
|
|
p = self.test_dynamic(p)
|
|
return self.bernoulli(x, p)
|
|
|
|
|
|
@pytest.mark.level1
|
|
@pytest.mark.platform_x86_gpu_training
|
|
@pytest.mark.env_onecard
|
|
def test_bernoulli_dynamic():
|
|
"""
|
|
Feature: bernoulli function
|
|
Description: test cases for Bernoulli
|
|
Expectation: the result matches scipy
|
|
"""
|
|
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
|
x = np.ones([32, 16, 2, 5]).astype(np.float32)
|
|
p = np.ones([5]).astype(np.float32) * 0.5
|
|
net = BernoulliDynamic()
|
|
|
|
output = net(Tensor(x), Tensor(p))
|
|
nonzero_count = np.count_nonzero(output.asnumpy())
|
|
elem_count = x.size
|
|
assert elem_count * 0.4 < nonzero_count < elem_count * 0.6
|