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https://github.com/zebrajr/pytorch.git
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d0621a2449
Summary: Remove the use of `NextName` in layer model helper, so that the same function return `model_helper` that should construct identical `Net`, when under the same NameScope. The `NextScopedBlob` should only take effect when there is real name conflicting, otherwise it returns ScopedBlobReference. This is critical for parameter blobs. In long run, we need to be able to specify parameter blobs more explicitly. (kennyhorror is working on this). This solution works in short term for e.g., two tower sparse nn models. Reviewed By: kennyhorror Differential Revision: D4555423 fbshipit-source-id: 2c4b99a61392e5d51aa878f7346466a8f14be187
51 lines
1.5 KiB
Python
51 lines
1.5 KiB
Python
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 (
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ModelLayer,
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)
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class Split(ModelLayer):
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def __init__(self, model, input_record, num_splits, axis=1,
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name='split', **kwargs):
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super(Split, self).__init__(model, name, input_record, **kwargs)
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self.axis = axis
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# Assume that first dimension is batch, so actual axis in shape is
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# axis - 1
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axis -= 1
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assert axis >= 0
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assert isinstance(input_record, schema.Scalar),\
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"Incorrect input type. Excpected Scalar, but received: {0}".\
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format(input_record)
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input_shape = input_record.field_type().shape
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assert len(input_shape) >= axis
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assert input_shape[axis] % num_splits == 0
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output_shape = list(input_shape)
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output_shape[axis] = int(output_shape[axis] / num_splits)
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data_type = input_record.field_type().base
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output_scalars = [
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schema.Scalar(
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(data_type, output_shape),
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model.net.NextScopedBlob(name + '_output_{}'.format(i)),
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)
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for i in range(num_splits)
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]
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self.output_schema = schema.Tuple(*output_scalars)
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def add_ops(self, net):
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net.Split(
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self.input_record.field_blobs(),
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self.output_schema.field_blobs(),
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axis=self.axis,
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)
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