forked from mindspore-Ecosystem/mindspore
64 lines
2.0 KiB
Python
64 lines
2.0 KiB
Python
# 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 nn pad """
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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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from mindspore.common.api import ms_function
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from mindspore.ops.composite import GradOperation
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class Net(nn.Cell):
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def __init__(self, raw_paddings, mode):
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super(Net, self).__init__()
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self.pad = nn.Pad(raw_paddings, mode=mode)
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@ms_function
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def construct(self, x):
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return self.pad(x)
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, self).__init__()
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self.grad = GradOperation(get_all=True, sens_param=True)
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self.network = network
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@ms_function
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def construct(self, x, grads):
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return self.grad(self.network)(x, grads)
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def test_pad_train():
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mode = 'CONSTANT'
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x = np.random.random(size=(2, 3)).astype(np.float32)
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raw_paddings = ((1, 1), (2, 2))
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grads = np.random.random(size=(4, 7)).astype(np.float32)
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grad = Grad(Net(raw_paddings, mode))
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output = grad(Tensor(x), Tensor(grads))
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print("=================output====================")
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print(output)
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def test_pad_infer():
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mode = 'CONSTANT'
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x = np.random.random(size=(2, 3)).astype(np.float32)
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raw_paddings = ((1, 1), (2, 2))
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net = Net(raw_paddings, mode)
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output = net(Tensor(x))
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print("=================output====================")
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print(output)
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