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