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## @package random_neg_rank_loss
# Module caffe2.python.layers.random_neg_rank_loss
from caffe2.python import schema, core
from caffe2.python.layers.layers import (
ModelLayer,
)
from caffe2.python.layers.tags import (
Tags
)
import numpy as np
class MarginRankLoss(ModelLayer):
def __init__(self, model, input_record, name='margin_rank_loss',
margin=0.1, average_loss=False, **kwargs):
super(MarginRankLoss, self).__init__(model, name, input_record, **kwargs)
assert margin >= 0, ('For hinge loss, margin should be no less than 0')
self._margin = margin
self._average_loss = average_loss
assert schema.is_schema_subset(
schema.Struct(
('pos_prediction', schema.Scalar()),
('neg_prediction', schema.List(np.float32)),
),
input_record
)
self.tags.update([Tags.EXCLUDE_FROM_PREDICTION])
self.output_schema = schema.Scalar(
np.float32,
self.get_next_blob_reference('output'))
def add_ops(self, net):
neg_score = self.input_record.neg_prediction['values']()
pos_score = net.LengthsTile(
[
self.input_record.pos_prediction(),
self.input_record.neg_prediction['lengths']()
],
net.NextScopedBlob('pos_score_repeated')
)
const_1 = net.ConstantFill(
neg_score,
net.NextScopedBlob('const_1'),
value=1,
dtype=core.DataType.INT32
)
rank_loss = net.MarginRankingCriterion(
[pos_score, neg_score, const_1],
net.NextScopedBlob('rank_loss'),
margin=self._margin,
)
if self._average_loss:
net.AveragedLoss(rank_loss, self.output_schema.field_blobs())
else:
net.ReduceFrontSum(rank_loss, self.output_schema.field_blobs())
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