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- # Copyright 2020 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
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
-
-
- class DynamicGRUV2(nn.Cell):
- def __init__(self):
- super(DynamicGRUV2, self).__init__()
- self.dynamic_gru = P.DynamicGRUV2()
-
- def construct(self, x, weight_i, weight_h, bias_i, bias_h, init_h):
- return self.dynamic_gru(x, weight_i, weight_h, bias_i, bias_h, None, init_h)
-
-
- @pytest.mark.level1
- @pytest.mark.env_onecard
- @pytest.mark.platform_arm_ascend_training
- @pytest.mark.platform_x86_ascend_training
- def test_dynamic_gru_v2():
- x = Tensor(np.random.rand(2, 8, 64).astype(np.float16))
- weight_i = Tensor(np.random.rand(64, 48).astype(np.float16))
- weight_h = Tensor(np.random.rand(16, 48).astype(np.float16))
- bias_i = Tensor(np.random.rand(48).astype(np.float16))
- bias_h = Tensor(np.random.rand(48).astype(np.float16))
- init_h = Tensor(np.random.rand(8, 16).astype(np.float16))
- gru_net = DynamicGRUV2()
- output = gru_net(x, weight_i, weight_h, bias_i, bias_h, init_h)
- assert output[0].shape == (2, 8, 16)
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