mirror of
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
57 lines
2.0 KiB
Python
57 lines
2.0 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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import numpy as np
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class Concat(ModelLayer):
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def __init__(self, model, input_record, axis=1,
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name='concat', **kwargs):
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super(Concat, self).__init__(model, name, input_record, **kwargs)
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self.axis = axis
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assert isinstance(input_record, schema.Struct),\
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"Incorrect input type. Excpected Struct, but received: {0}".\
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format(input_record)
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shapes = []
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for field_name, field_type in input_record.fields.items():
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assert isinstance(field_type, schema.Scalar),\
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"Incorrect input type for {}. Excpected Scalar, but got: {}".\
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format(field_name, field_type)
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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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assert len(field_type.field_type().shape) >= axis,\
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"Concat expects that limited dimensions of the input tensor"
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shapes.append(list(field_type.field_type().shape))
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concat_dim = 0
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for shape in shapes:
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concat_dim += shape[axis - 1]
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shape[axis - 1] = 0
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assert shape == shapes[0],\
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"Shapes {0} and {1} are not compatible for Concat".\
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format(shape, shapes[0])
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output_dims = shapes[0]
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output_dims[axis - 1] = concat_dim
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self.output_schema = schema.Scalar(
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(np.float32, output_dims),
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model.net.NextScopedBlob(name + '_output'))
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def add_ops(self, net):
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net.Concat(
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self.input_record.field_blobs(),
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[
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self.output_schema.field_blobs()[0],
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("_" + self.output_schema.field_blobs()[0] + "_concat_dims")
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],
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axis=self.axis,
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)
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