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!9778 fix the example in doc has something error (mindspore.compression) 

From: @xiaoyisd
Reviewed-by: @zhoufeng54,@guoqi1024
Signed-off-by: @guoqi1024
This commit is contained in:
mindspore-ci-bot 2020-12-10 19:31:41 +08:00 committed by Gitee
parent 995e973fec 10062603a0
commit ae301eff0f
1 changed files with 27 additions and 27 deletions
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54
mindspore/compression/quant/qat.py
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@ -142,33 +142,33 @@ class QuantizationAwareTraining(Quantizer):
Examples:
>>> class LeNet5(nn.Cell):
>>> def __init__(self, num_class=10, channel=1):
>>> super(LeNet5, self).__init__()
>>> self.type = "fusion"
>>> self.num_class = num_class
>>>
>>> # change `nn.Conv2d` to `nn.Conv2dBnAct`
>>> self.conv1 = nn.Conv2dBnAct(channel, 6, 5, pad_mode='valid', activation='relu')
>>> self.conv2 = nn.Conv2dBnAct(6, 16, 5, pad_mode='valid', activation='relu')
>>> # change `nn.Dense` to `nn.DenseBnAct`
>>> self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
>>> self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
>>> self.fc3 = nn.DenseBnAct(84, self.num_class)
>>>
>>> self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
>>> self.flatten = nn.Flatten()
>>>
>>> def construct(self, x):
>>> x = self.conv1(x)
>>> x = self.max_pool2d(x)
>>> x = self.conv2(x)
>>> x = self.max_pool2d(x)
>>> x = self.flatten(x)
>>> x = self.fc1(x)
>>> x = self.fc2(x)
>>> x = self.fc3(x)
>>> return x
>>>
... def __init__(self, num_class=10, channel=1):
... super(LeNet5, self).__init__()
... self.type = "fusion"
... self.num_class = num_class
...
... # change `nn.Conv2d` to `nn.Conv2dBnAct`
... self.conv1 = nn.Conv2dBnAct(channel, 6, 5, pad_mode='valid', activation='relu')
... self.conv2 = nn.Conv2dBnAct(6, 16, 5, pad_mode='valid', activation='relu')
... # change `nn.Dense` to `nn.DenseBnAct`
... self.fc1 = nn.DenseBnAct(16 * 5 * 5, 120, activation='relu')
... self.fc2 = nn.DenseBnAct(120, 84, activation='relu')
... self.fc3 = nn.DenseBnAct(84, self.num_class)
...
... self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
... self.flatten = nn.Flatten()
...
... def construct(self, x):
... x = self.conv1(x)
... x = self.max_pool2d(x)
... x = self.conv2(x)
... x = self.max_pool2d(x)
... x = self.flatten(x)
... x = self.fc1(x)
... x = self.fc2(x)
... x = self.fc3(x)
... return x
...
>>> net = LeNet5()
>>> quantizer = QuantizationAwareTraining(bn_fold=False, per_channel=[True, False], symmetric=[True, False])
>>> net_qat = quantizer.quantize(net)