mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 12:21:27 +01:00
a8695178aa
Summary:
To achive this, I modified the blob name scheme defined in a layer.
Before it was scope/fc_w and scope/fc_w_auto_0 (if there is another fc
within the same scope).
Now I change it to scope/fc/w and scope/fc_auto_0/w.
That is, we rely on the uniqueness of the scoped layer name to define
names for blobs.
I also overwrote the create_param method in LayerModelHelper to let it
use the resolved name for blobs given the sharingparameter context.
There are some details such as making the initializer more structured
that I need to finalize.
Reviewed By: kennyhorror
Differential Revision: D5435132
fbshipit-source-id: a0525f5ea0977e255dd5ea765b38913f5951d455
72 lines
2.1 KiB
Python
72 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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self.get_next_blob_reference('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, self.get_next_blob_reference('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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