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# @package constant_weight
# Module caffe2.fb.python.layers.constant_weight
from caffe2.python import schema
from caffe2.python.layers.layers import ModelLayer
import numpy as np
class ConstantWeight(ModelLayer):
def __init__(
self,
model,
input_record,
weights=None,
name='constant_weight',
**kwargs
):
super(ConstantWeight,
self).__init__(model, name, input_record, **kwargs)
self.output_schema = schema.Scalar(
np.float32, self.get_next_blob_reference('constant_weight')
)
self.data = self.input_record.field_blobs()
self.num = len(self.data)
weights = (
weights if weights is not None else
[1. / self.num for _ in range(self.num)]
)
assert len(weights) == self.num
self.weights = [
self.model.add_global_constant(
'%s_weight_%d' % (self.name, i), float(weights[i])
) for i in range(self.num)
]
def add_ops(self, net):
net.WeightedSum(
[b for x_w_pair in zip(self.data, self.weights) for b in x_w_pair],
self.output_schema()
)
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