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## @package add_bias
# Module caffe2.python.layers.add_bias
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import math
class AddBias(ModelLayer):
def __init__(self, model, input_record, bias_init=None,
bias_optim=None, name='add_bias'):
super(AddBias, self).__init__(model, name, input_record)
assert isinstance(input_record, schema.Scalar), "Incorrect input type"
assert len(input_record.field_type().shape) > 0, (
"AddBias expects limited dimensions of the input tensor")
input_dims = input_record.field_type().shape[0]
assert input_dims > 0, (
"AddBias expects input dimensions > 0, got {}".format(input_dims))
scale = math.sqrt(1.0 / input_dims)
bias_init = bias_init if bias_init else (
'UniformFill', {'min': -scale, 'max': scale})
self.b = self.create_param(
param_name='b',
shape=[input_dims, ],
initializer=bias_init,
optimizer=bias_optim,
)
self.output_schema = schema.Scalar(
(input_record.field_type().base, (input_dims, )),
self.get_next_blob_reference('output')
)
def add_ops(self, net):
net.Add(self.input_record.field_blobs() + [self.b],
self.output_schema.field_blobs(), broadcast=1)
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