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58f7f2b441
Summary: Closes https://github.com/caffe2/caffe2/pull/226 Differential Revision: D4793550 Pulled By: JoelMarcey fbshipit-source-id: cc33e58186304fa8dcac2ee9115dcc271d785b1e
60 lines
1.8 KiB
Python
60 lines
1.8 KiB
Python
## @package batch_softmax_loss
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# Module caffe2.python.layers.batch_softmax_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 ModelLayer
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from caffe2.python.layers.tags import Tags
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import numpy as np
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class BatchSoftmaxLoss(ModelLayer):
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def __init__(
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self,
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model,
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input_record,
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name='batch_softmax_loss',
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**kwargs
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):
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super(BatchSoftmaxLoss, self).__init__(
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model, name, input_record, **kwargs)
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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.Struct(
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(
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'softmax', schema.Scalar(
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input_record.prediction.field_type(),
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model.net.NextScopedBlob(name + '_softmax')
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)
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),
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(
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'loss', schema.Scalar(
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np.float32, model.net.NextScopedBlob(name + '_loss')
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)
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),
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)
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def add_ops(self, net):
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label = self.input_record.label.field_blobs()
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if self.input_record.label.field_types()[0].base != np.int32:
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label = [
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net.Cast(label,
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net.NextScopedBlob('int32_label'),
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to=core.DataType.INT32)
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]
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net.SoftmaxWithLoss(
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self.input_record.prediction.field_blobs() + label,
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self.output_schema.field_blobs()
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)
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