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Summary: weighted batch_softmax_loss when weight exists in input_record Reviewed By: kittipatv Differential Revision: D5291646 fbshipit-source-id: f1bcd386ad1fc0e95e0a0315ec1c36531c792495
71 lines
2.1 KiB
Python
71 lines
2.1 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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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.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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softmax_input = self.input_record.prediction.field_blobs() + label
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if 'weight' in self.input_record:
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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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softmax_input += [weight_blob]
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net.SoftmaxWithLoss(
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softmax_input,
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self.output_schema.field_blobs()
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)
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