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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
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import numpy as np
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from caffe2.python import core, schema
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from caffe2.python.layers.layers import ModelLayer
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class MapToRange(ModelLayer):
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"""
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This layer aims to build a mapping from raw keys to indices within [0, max_index).
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The mapping is continuously built during training. The mapping will be frozen during
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evaluation and prediction. Unseen keys will be assigned to index 0.
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"""
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def __init__(
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self, model,
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input_record,
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max_index,
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name='map_to_range',
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**kwargs
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):
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super(MapToRange, self).__init__(model, name, input_record, **kwargs)
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assert max_index > 0
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assert isinstance(input_record, schema.Scalar)
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self.max_index = max_index
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self.handler = self.create_param(
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param_name='handler',
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shape=[],
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initializer=('LongIndexCreate', {'max_elements': self.max_index}),
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optimizer=model.NoOptim
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)
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self.output_schema = schema.Struct(
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('indices', schema.Scalar(
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np.int64, self.get_next_blob_reference("indices")
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)),
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('handler', schema.Scalar(
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np.void, self.handler
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)),
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)
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def add_train_ops(self, net):
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if self.input_record.field_type().base != np.int64:
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keys = net.Cast(
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self.input_record(),
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net.NextScopedBlob("indices_before_mapping"),
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to=core.DataType.INT64
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)
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else:
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keys = self.input_record()
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# Load keys into indices
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indices = net.IndexGet([self.handler, keys],
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self.output_schema.indices())
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net.StopGradient(indices, indices)
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def add_eval_ops(self, net):
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net.IndexFreeze(self.handler, self.handler)
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self.add_train_ops(net)
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def add_ops(self, net):
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self.add_eval_ops(net)
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