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- # Copyright 2021 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.context as context
- import mindspore.nn as nn
- from mindspore import Tensor
- from mindspore.ops import operations as P
- from mindspore.ops.composite import GradOperation
-
-
- class Grad(nn.Cell):
- def __init__(self, network):
- super(Grad, self).__init__()
- self.grad = GradOperation(get_all=True, sens_param=True)
- self.network = network
-
- def construct(self, input_x, dout):
- return self.grad(self.network)(input_x, dout)
-
-
- class Net(nn.Cell):
- def __init__(self):
- super(Net, self).__init__()
- self.hswish = P.HSwish()
-
- def construct(self, x):
- return self.hswish(x)
-
-
- def expect_hswish_forward_result(x):
- return np.where(x <= -3, 0, np.where(x >= 3, x, x * (x + 3) / 6))
-
-
- def expect_hswish_backward_result(x, dout):
- return np.where(x <= -3, 0, np.where(x >= 3, 1, x / 3 + 0.5)) * dout
-
-
- def judge_result_correct(result, expect):
- assert result.dtype == expect.dtype
- assert result.shape == expect.shape
- assert np.allclose(result, expect)
-
-
- def generate_test_cases(np_type, mode):
- context.set_context(mode=mode, device_target="GPU")
- x = np.array([-1, -2, 0, 4, 5]).astype(np_type)
- net = Net()
- output = net(Tensor(x))
- expect = expect_hswish_forward_result(x)
- judge_result_correct(output.asnumpy(), expect)
-
- sens = np.array([-1.45, 0.63, 0.34, 6.43, 34.6]).astype(np_type)
- backward_net = Grad(Net())
- output = backward_net(Tensor(x), Tensor(sens))
- expect = expect_hswish_backward_result(x, sens)
- judge_result_correct(output[0].asnumpy(), expect)
-
-
- @pytest.mark.level0
- @pytest.mark.platform_x86_gpu_training
- @pytest.mark.env_onecard
- def test_hswish_forward_and_backward():
- modes = (context.GRAPH_MODE, context.PYNATIVE_MODE)
- dtypes = (np.float32, np.float16)
- for mode in modes:
- for dtype in dtypes:
- generate_test_cases(dtype, mode)
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