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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. Excpected Scalar, but received: {0}".\
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format(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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core.BlobReference(model.net.NextName(self.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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net.NextName(str("_" + self.output_schema.field_blobs()[0] +
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"_concat_dims"))],
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axis=self.axis,
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)
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