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79 lines
2.7 KiB
79 lines
2.7 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_training """
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import numpy as np
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.common.tensor import Tensor
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from mindspore.nn import WithGradCell, WithLossCell
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from mindspore.nn.optim import Momentum
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from mindspore.ops import operations as P
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from mindspore.train.model import Model
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from ..ut_filter import non_graph_engine
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def setup_module(module):
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context.set_context(mode=context.PYNATIVE_MODE)
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class LeNet5(nn.Cell):
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""" LeNet5 definition """
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def __init__(self):
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super(LeNet5, self).__init__()
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self.conv1 = nn.Conv2d(1, 6, 5, pad_mode='valid')
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self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
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self.fc1 = nn.Dense(16 * 5 * 5, 120)
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self.fc2 = nn.Dense(120, 84)
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self.fc3 = nn.Dense(84, 10)
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self.relu = nn.ReLU()
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self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
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self.flatten = P.Flatten()
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def construct(self, x):
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x = self.max_pool2d(self.relu(self.conv1(x)))
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x = self.max_pool2d(self.relu(self.conv2(x)))
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x = self.flatten(x)
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x = self.relu(self.fc1(x))
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x = self.relu(self.fc2(x))
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x = self.fc3(x)
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return x
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@non_graph_engine
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def test_loss_cell_wrapper():
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""" test_loss_cell_wrapper """
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data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([1, 10]).astype(np.float32))
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net = LeNet5()
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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loss_net = WithLossCell(net, loss_fn)
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loss_out = loss_net(data, label)
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assert loss_out.asnumpy().dtype == 'float32' or loss_out.asnumpy().dtype == 'float64'
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@non_graph_engine
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def test_grad_cell_wrapper():
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""" test_grad_cell_wrapper """
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data = Tensor(np.ones([1, 1, 32, 32]).astype(np.float32) * 0.01)
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label = Tensor(np.ones([1, 10]).astype(np.float32))
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dout = Tensor(np.ones([1]).astype(np.float32))
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net = LeNet5()
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loss_fn = nn.SoftmaxCrossEntropyWithLogits()
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grad_net = WithGradCell(net, loss_fn, dout)
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gradients = grad_net(data, label)
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assert isinstance(gradients[0].asnumpy()[0][0][0][0], (np.float32, np.float64))
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