forked from mindspore-Ecosystem/mindspore
76 lines
2.2 KiB
Python
76 lines
2.2 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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"""LeNet test."""
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import numpy as np
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from lenet import LeNet5
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import mindspore.nn as nn
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import mindspore.ops.composite as C
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import _executor
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context.set_context(mode=context.GRAPH_MODE)
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grad_all_with_sens = C.GradOperation(get_all=True, sens_param=True)
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batch_size = 1
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channel = 1
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height = 32
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weight = 32
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num_class = 10
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class LeNetGrad(nn.Cell):
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"""Backward of LeNet"""
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def __init__(self, network):
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super(LeNetGrad, self).__init__()
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self.grad_op = grad_all_with_sens
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self.network = network
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def construct(self, x, sens):
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grad_op = self.grad_op(self.network)(x, sens)
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return grad_op
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def test_compile():
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"""Compile forward graph"""
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net = LeNet(num_class=num_class)
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np.random.seed(7)
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inp = Tensor(np.array(np.random.randn(batch_size,
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channel,
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height,
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weight) * 3, np.float32))
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_executor.compile(net, inp)
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def test_compile_grad():
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"""Compile forward and backward graph"""
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net = LeNet5(num_class=num_class)
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inp = Tensor(np.array(np.random.randn(batch_size,
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channel,
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height,
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weight) * 3, np.float32))
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sens = Tensor(np.ones([batch_size, num_class]).astype(np.float32))
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grad_op = LeNetGrad(net)
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_executor.compile(grad_op, inp, sens)
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