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## @package elementwise_linear
# Module caffe2.python.helpers.elementwise_linear
from caffe2.python import core
from caffe2.python.modeling.parameter_info import ParameterTags
def _elementwise_linear(
model, op_call, blob_in, blob_out, dim,
weight_init=None, bias_init=None, **kwargs
):
"""Elementwise_Linear"""
weight_init = weight_init or ('ConstantFill', {'value': 1.0})
bias_init = bias_init or ('ConstantFill', {'value': 0.0})
blob_out = blob_out or model.net.NextName()
if model.init_params:
weight = model.param_init_net.__getattr__(weight_init[0])(
[],
blob_out + '_w',
shape=[dim],
**weight_init[1]
)
bias = model.param_init_net.__getattr__(bias_init[0])(
[],
blob_out + '_b',
shape=[dim],
**bias_init[1]
)
else:
weight = core.ScopedBlobReference(
blob_out + '_w', model.param_init_net)
bias = core.ScopedBlobReference(
blob_out + '_b', model.param_init_net)
model.AddParameter(weight, ParameterTags.WEIGHT)
model.AddParameter(bias, ParameterTags.BIAS)
return op_call([blob_in, weight, bias], blob_out, **kwargs)
def elementwise_linear(model, *args, **kwargs):
return _elementwise_linear(
model, model.net.ElementwiseLinear, *args, **kwargs)
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