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## @package split
# Module caffe2.python.layers.split
from caffe2.python import schema
from caffe2.python.layers.layers import (
ModelLayer,
)
class Split(ModelLayer):
def __init__(self, model, input_record, num_splits=1, axis=1,
name='split', split=None, **kwargs):
super(Split, self).__init__(model, name, input_record, **kwargs)
self.axis = axis
# Assume that first dimension is batch, so actual axis in shape is
# axis - 1
axis -= 1
assert axis >= 0
assert isinstance(input_record, schema.Scalar),\
"Incorrect input type. Expected Scalar, but received: {0}".\
format(input_record)
input_shape = input_record.field_type().shape
assert len(input_shape) >= axis
if split is None:
assert input_shape[axis] % num_splits == 0
else:
num_splits = len(split)
assert input_shape[axis] == sum(split)
if split is None:
output_shape = list(input_shape)
output_shape[axis] = int(output_shape[axis] / num_splits)
else:
output_shape = []
for i in range(num_splits):
output_shape_i = list(input_shape)
output_shape_i[axis] = split[i]
output_shape.append(output_shape_i)
data_type = input_record.field_type().base
if split is None:
output_scalars = [
schema.Scalar(
(data_type, output_shape),
self.get_next_blob_reference('output_{}'.format(i)),
)
for i in range(num_splits)
]
else:
output_scalars = [
schema.Scalar(
(data_type, output_shape[i]),
self.get_next_blob_reference('output_{}'.format(i)),
)
for i in range(num_splits)
]
self.output_schema = schema.Tuple(*output_scalars)
self.split = split
def add_ops(self, net):
net.Split(
self.input_record.field_blobs(),
self.output_schema.field_blobs(),
split=self.split,
axis=self.axis,
)
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