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Summary: Previously, the code below would go out of bound. Reviewed By: xianjiec Differential Revision: D4968037 fbshipit-source-id: 3760e2cddc919c45d85ac644ac3fabf72dbaf666
66 lines
2.2 KiB
Python
66 lines
2.2 KiB
Python
## @package concat
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# Module caffe2.python.layers.concat
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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 (
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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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if axis == 0:
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self.output_schema = schema.from_blob_list(
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input_record[0],
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[model.net.NextScopedBlob(name + '_output')]
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)
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return
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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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