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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
51 lines
1.4 KiB
Python
51 lines
1.4 KiB
Python
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from caffe2.python import schema
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from caffe2.python.layers.layers import (
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get_categorical_limit,
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ModelLayer,
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IdList
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)
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import numpy as np
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class MergeIdLists(ModelLayer):
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"""Merge multiple ID_LISTs into a single ID_LIST
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Args:
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model: A layer model instance
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input_record: Tuple (Struct) of ID_LIST features to be
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merged
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Returns:
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the merged ID_LIST feature
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"""
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def __init__(self, model, input_record, name='merged'):
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super().__init__(model, name, input_record)
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assert all(schema.equal_schemas(x, IdList) for x in input_record), \
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"Inputs to MergeIdLists should all be IdLists."
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assert all(record.items.metadata is not None
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for record in self.input_record), \
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"Features without metadata are not supported"
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merge_dim = max(get_categorical_limit(record)
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for record in self.input_record)
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assert merge_dim is not None, "Unbounded features are not supported"
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self.output_schema = schema.NewRecord(
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model.net, schema.List(
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schema.Scalar(
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np.int64,
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blob=model.net.NextBlob(name),
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metadata=schema.Metadata(categorical_limit=merge_dim)
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)))
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def add_ops(self, net):
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return net.MergeIdLists(self.input_record.field_blobs(),
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self.output_schema.field_blobs())
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