mirror of
https://github.com/zebrajr/pytorch.git
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a458aa4b2a
Summary: Cleaning up the tagging system. Introducing tags EXCLUDE_FROM_{CONTEXT}.
Reviewed By: kennyhorror
Differential Revision: D4974842
fbshipit-source-id: b0fa6772299bb70afa2192c39e45191c9f41336a
75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
## @package batch_lr_loss
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# Module caffe2.python.layers.batch_lr_loss
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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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from caffe2.python import core, schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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)
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from caffe2.python.layers.tags import (
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Tags
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)
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import numpy as np
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class BatchLRLoss(ModelLayer):
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def __init__(self, model, input_record, name='batch_lr_loss',
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average_loss=True, **kwargs):
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super(BatchLRLoss, self).__init__(model, name, input_record, **kwargs)
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self.average_loss = average_loss
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assert schema.is_schema_subset(
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schema.Struct(
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('label', schema.Scalar()),
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('prediction', schema.Scalar())
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),
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input_record
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)
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self.tags.update(Tags.TRAIN_ONLY)
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self.output_schema = schema.Scalar(
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np.float32,
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model.net.NextScopedBlob(name + '_output'))
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# This should be a bit more complicated than it is right now
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def add_ops(self, net):
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class_probabilities = net.MakeTwoClass(
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self.input_record.prediction.field_blobs(),
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net.NextScopedBlob('two_class_predictions')
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)
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label = self.input_record.label.field_blobs()
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if self.input_record.label.field_type().base != np.int32:
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label = [net.Cast(
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label,
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net.NextScopedBlob('int32_label'),
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to=core.DataType.INT32)]
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# LabelCrossEntropyGraidentOp does not output gradient for the label
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xent = net.LabelCrossEntropy(
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[class_probabilities] + label,
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net.NextScopedBlob('cross_entropy'),
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)
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if 'weight' in self.input_record.fields:
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weight_blob = self.input_record.weight()
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if self.input_record.weight.field_type().base != np.float32:
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weight_blob = net.Cast(
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weight_blob,
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weight_blob + '_float32',
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to=core.DataType.FLOAT
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)
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weight_blob = net.StopGradient([weight_blob], [weight_blob])
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xent = net.Mul(
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[xent, weight_blob],
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net.NextScopedBlob('weighted_cross_entropy'),
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)
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if self.average_loss:
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net.AveragedLoss(xent, self.output_schema.field_blobs())
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else:
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net.ReduceFrontSum(xent, self.output_schema.field_blobs())
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