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https://github.com/zebrajr/pytorch.git
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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
49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
# Module caffe2.python.layers.dropout
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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 schema
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from caffe2.python.layers.layers import ModelLayer
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class Dropout(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='dropout',
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ratio=0.5,
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**kwargs):
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super(Dropout, self).__init__(model, name, input_record, **kwargs)
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assert isinstance(input_record, schema.Scalar), "Incorrect input type"
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assert (ratio >= 0 and ratio < 1.0), \
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"Expected 0 <= ratio < 1, but got ratio of %s" % ratio
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self.output_schema = input_record.clone_schema()
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self.output_schema.set_value(self.get_next_blob_reference('output'))
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self.ratio = ratio
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def _add_ops(self, net, is_test):
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input_blob = self.input_record.field_blobs()
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output_blobs = self.output_schema.field_blobs() \
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+ [net.NextScopedBlob('d_mask')]
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net.Dropout(input_blob,
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output_blobs,
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ratio=self.ratio,
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is_test=is_test)
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def add_train_ops(self, net):
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self._add_ops(net, is_test=False)
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def add_eval_ops(self, net):
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self._add_ops(net, is_test=True)
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def add_ops(self, net):
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self.add_eval_ops(net)
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