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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.
- # ============================================================================
- """ test_conv """
- import numpy as np
-
- import mindspore.nn as nn
- from mindspore import Tensor
-
- weight = Tensor(np.ones([2, 2]))
- in_channels = 3
- out_channels = 64
- kernel_size = 3
-
-
- def test_check_conv2d_1():
- m = nn.Conv2d(3, 64, 3, bias_init='zeros')
- output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
- output_np = output.asnumpy()
- assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
-
-
- def test_check_conv2d_2():
- Tensor(np.ones([2, 2]))
- m = nn.Conv2d(3, 64, 4, has_bias=False, weight_init='normal')
- output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
- output_np = output.asnumpy()
- assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
-
-
- def test_check_conv2d_3():
- Tensor(np.ones([2, 2]))
- m = nn.Conv2d(3, 64, (3, 3))
- output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
- output_np = output.asnumpy()
- assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
-
-
- def test_check_conv2d_4():
- Tensor(np.ones([2, 2]))
- m = nn.Conv2d(3, 64, (3, 3), stride=2, pad_mode='pad', padding=4)
- output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
- output_np = output.asnumpy()
- assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
-
-
- def test_check_conv2d_bias():
- m = nn.Conv2d(3, 64, 3, bias_init='zeros')
- output = m(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
- output_np = output.asnumpy()
- assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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