mirror of
https://github.com/zebrajr/pytorch.git
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ef2701a57e
Summary: A layer that takes raw ids as inputs and outputs the indices which can be used as labels. The mapping will be stored with the model. Reviewed By: kittipatv Differential Revision: D4902556 fbshipit-source-id: 647db47b0362142cdba997effa2ef7a5294c84ee
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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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 LayerParameter, 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 = model.net.NextScopedBlob(name + "_handler")
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self.params.append(
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LayerParameter(
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parameter=self.handler,
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initializer=core.CreateOperator("LongIndexCreate",
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[],
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self.handler,
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max_elements=self.max_index,
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),
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optimizer=model.NoOptim,
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)
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)
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self.output_schema = schema.Scalar(
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np.int64, model.net.NextScopedBlob(name + "_indices")
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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"),
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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())
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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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