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8d45f555d7
Rewrite Python built-in class `super()` calls. Only non-semantic changes should be applied. - #94587 - #94588 - #94592 Also, methods with only a `super()` call are removed: ```diff class MyModule(nn.Module): - def __init__(self): - super().__init__() - def forward(self, ...): ... ``` Some cases that change the semantics should be kept unchanged. E.g.:f152a79be9/caffe2/python/net_printer.py (L184-L190)f152a79be9/test/test_jit_fuser_te.py (L2628-L2635)Pull Request resolved: https://github.com/pytorch/pytorch/pull/94587 Approved by: https://github.com/ezyang
45 lines
1.3 KiB
Python
45 lines
1.3 KiB
Python
# @package optimizer
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# Module caffe2.python.normalizer
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class Normalizer:
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def __init__(self):
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pass
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"""
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Adds normalization to train_net for given parameter. Its factor ahead of
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regularization is given when initialization.
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The param should be a BlobReference.
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"""
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def __call__(self, net, param):
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return self._run(net, param)
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def _run(self, net, param):
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raise Exception("Not Impelemented")
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class BatchNormalizer(Normalizer):
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def __init__(self, momentum, scale_init_value=1.0):
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super().__init__()
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self._momentum = float(momentum)
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self._scale_init_value = float(scale_init_value)
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def _run(self, layer_model, param):
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return layer_model.BatchNormalization(
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param, momentum=self._momentum, scale_init_value=self._scale_init_value
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)
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class LayerNormalizer(Normalizer):
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def __init__(self, epsilon, use_layer_norm_op=True, scale_init_value=1.0):
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super().__init__()
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self._epsilon = float(epsilon)
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self._use_layer_norm_op = use_layer_norm_op
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self._scale_init_value = float(scale_init_value)
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def _run(self, layer_model, param):
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return layer_model.LayerNormalization(
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param, epsilon=self._epsilon, use_layer_norm_op=self._use_layer_norm_op, scale_init_value=self._scale_init_value
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)
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