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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.
- # ============================================================================
- """ tests for quant """
- import mindspore.context as context
- from mindspore import nn
- from mindspore.nn.layer import combined
-
- context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
-
-
- class LeNet5(nn.Cell):
- """
- Lenet network
-
- Args:
- num_class (int): Num classes. Default: 10.
-
- Returns:
- Tensor, output tensor
- Examples:
- >>> LeNet(num_class=10)
-
- """
-
- def __init__(self, num_class=10):
- super(LeNet5, self).__init__()
- self.num_class = num_class
- self.conv1 = combined.Conv2d(
- 1, 6, kernel_size=5, batchnorm=True, activation='relu6')
- self.conv2 = combined.Conv2d(6, 16, kernel_size=5, activation='relu')
- self.fc1 = combined.Dense(16 * 5 * 5, 120, activation='relu')
- self.fc2 = combined.Dense(120, 84, activation='relu')
- self.fc3 = combined.Dense(84, self.num_class)
- self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
- self.flattern = nn.Flatten()
-
- def construct(self, x):
- x = self.conv1(x)
- x = self.bn(x)
- x = self.relu(x)
- x = self.max_pool2d(x)
- x = self.conv2(x)
- x = self.max_pool2d(x)
- x = self.flattern(x)
- x = self.fc1(x)
- x = self.fc2(x)
- x = self.fc3(x)
- return x
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