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- ## @package position_weighted
- # Module caffe2.python.layers.position_weighted
- import logging
- import numpy as np
- from caffe2.python import schema
- from caffe2.python.layers.layers import (
- get_categorical_limit,
- ModelLayer,
- )
- from caffe2.python.layers.tags import Tags
- logger = logging.getLogger(__name__)
- class PositionWeighted(ModelLayer):
- def __init__(self, model, input_record, weight_optim=None,
- name="position_weights"):
- super(PositionWeighted, self).__init__(model, name, input_record)
- assert isinstance(input_record, schema.List), "Incorrect input type"
- length_metadata = input_record.lengths.metadata
- max_length = (length_metadata.categorical_limit if length_metadata is
- not None else None)
- if max_length is not None:
- self.shape = max_length
- else:
- self.shape = get_categorical_limit(input_record)
- logger.warning(
- '{}: categorical_limit of lengths is not available, using '
- 'categorical_limit of the keys: {}'.format(
- str(input_record.lengths()), self.shape))
- self.pos_w = self.create_param(param_name='pos_w',
- shape=[self.shape, ],
- initializer=('ConstantFill', {'value': 1.0}),
- optimizer=weight_optim)
- self.output_schema = schema.Struct(
- ('position_weights',
- schema.Scalar((np.float32, self.shape),
- self.get_next_blob_reference("pos_w_gather")))
- )
- self.tags.update({Tags.HANDLE_AS_SPARSE_LAYER})
- def get_memory_usage(self):
- return self.shape
- def add_ops(self, net):
- inc_seq = net.LengthsRangeFill(
- [self.input_record.lengths()],
- self.input_record.lengths() + '_pos_w_seq'
- )
- net.Gather(
- [self.pos_w, inc_seq],
- self.output_schema.position_weights.field_blobs())
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