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64 lines
2.2 KiB
64 lines
2.2 KiB
# Copyright 2020 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_conv """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import Tensor
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weight = Tensor(np.ones([2, 2]))
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in_channels = 3
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out_channels = 64
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kernel_size = 3
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def test_check_conv2d_1():
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m = nn.Conv2d(3, 64, 3, bias_init='zeros')
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output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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def test_check_conv2d_2():
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Tensor(np.ones([2, 2]))
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m = nn.Conv2d(3, 64, 4, has_bias=False, weight_init='normal')
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output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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def test_check_conv2d_3():
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Tensor(np.ones([2, 2]))
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m = nn.Conv2d(3, 64, (3, 3))
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output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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def test_check_conv2d_4():
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Tensor(np.ones([2, 2]))
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m = nn.Conv2d(3, 64, (3, 3), stride=2, pad_mode='pad', padding=4)
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output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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def test_check_conv2d_bias():
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m = nn.Conv2d(3, 64, 3, bias_init='zeros')
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output = m.construct(Tensor(np.ones([1, 3, 16, 50], dtype=np.float32)))
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output_np = output.asnumpy()
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assert isinstance(output_np[0][0][0][0], (np.float32, np.float64))
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