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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
72 lines
2.2 KiB
Python
72 lines
2.2 KiB
Python
## @package sampling_train
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# Module caffe2.python.layers.sampling_train
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from caffe2.python import schema
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from caffe2.python.layers.layers import ModelLayer, get_layer_class
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from caffe2.python.layers.sampling_trainable_mixin import SamplingTrainableMixin
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class SamplingTrain(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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prediction_layer,
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output_dims,
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subtract_log_odd=True,
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name='sampling_train',
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**kwargs
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):
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super(SamplingTrain, self).__init__(
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model, name, input_record, **kwargs
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)
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layer_class = get_layer_class(prediction_layer)
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assert issubclass(layer_class, SamplingTrainableMixin)
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assert 'indices' in input_record
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assert isinstance(input_record.indices, schema.Scalar),\
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"input_record.indices is expected to be a schema.Scalar"
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assert 'input' in input_record
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self.subtract_log_odd = subtract_log_odd
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if self.subtract_log_odd:
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assert 'sampling_prob' in input_record
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self._prediction_layer = layer_class(
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model,
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input_record.input,
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output_dims=output_dims,
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**kwargs
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)
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self._prediction_layer.train_param_blobs = [
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model.net.NextBlob(str(blob) + '_sampled')
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for blob in self._prediction_layer.param_blobs
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]
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self.params = self._prediction_layer.params
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self.output_schema = self._prediction_layer.output_schema
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def add_ops(self, net):
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self._prediction_layer.add_ops(net)
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def add_train_ops(self, net):
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for full_blob, sampled_blob in zip(
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self._prediction_layer.param_blobs,
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self._prediction_layer.train_param_blobs
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):
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net.Gather([full_blob, self.input_record.indices()], sampled_blob)
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self._prediction_layer.add_train_ops(net)
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if not self.subtract_log_odd:
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return
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log_q = net.Log(self.input_record.sampling_prob(),
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net.NextScopedBlob("log_q"))
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net.Sub([self.output_schema(), log_q], self.output_schema(),
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broadcast=1, use_grad_hack=1)
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