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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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