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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
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
## @package batch_sigmoid_cross_entropy_loss
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# Module caffe2.python.layers.batch_sigmoid_cross_entropy_loss
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from caffe2.python import schema
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from caffe2.python.layers.layers import ModelLayer
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from caffe2.python.layers.tags import Tags
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import numpy as np
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class BatchSigmoidCrossEntropyLoss(ModelLayer):
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def __init__(
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self,
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model,
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input_record,
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name='batch_sigmoid_cross_entropy_loss',
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**kwargs
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):
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super().__init__(model, name, input_record, **kwargs)
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assert schema.is_schema_subset(
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schema.Struct(
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('label', schema.Scalar(np.float32)),
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('prediction', schema.Scalar(np.float32)),
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),
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input_record
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)
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assert input_record.prediction.field_type().shape == \
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input_record.label.field_type().shape, \
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"prediction and label must have the same shape"
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self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
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self.output_schema = schema.Scalar(
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(np.float32, tuple()), self.get_next_blob_reference('loss')
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)
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def add_ops(self, net):
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sigmoid_cross_entropy = net.SigmoidCrossEntropyWithLogits(
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[self.input_record.prediction(), self.input_record.label()],
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net.NextScopedBlob('sigmoid_cross_entropy')
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)
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net.AveragedLoss(
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sigmoid_cross_entropy, self.output_schema.field_blobs())
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