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Summary: Add a helper function for parametric op ElementwiseLinear The typical syntax is model.ElementwiseLinear(input, output, dimension) Reviewed By: harouwu, akyrola Differential Revision: D5114152 fbshipit-source-id: 8e8c691f824f518ae510a72ab0c12de1b018f3b5
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
## @package elementwise_linear
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# Module caffe2.python.helpers.elementwise_linear
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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from caffe2.python import core
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from caffe2.python.modeling.parameter_info import ParameterTags
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def _elementwise_linear(
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model, op_call, blob_in, blob_out, dim,
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weight_init=None, bias_init=None, **kwargs
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):
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"""Elementwise_Linear"""
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weight_init = weight_init or ('ConstantFill', {'value': 1.0})
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bias_init = bias_init or ('ConstantFill', {'value': 0.0})
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blob_out = blob_out or model.net.NextName()
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if model.init_params:
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weight = model.param_init_net.__getattr__(weight_init[0])(
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[],
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blob_out + '_w',
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shape=[dim],
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**weight_init[1]
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)
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bias = model.param_init_net.__getattr__(bias_init[0])(
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[],
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blob_out + '_b',
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shape=[dim],
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**bias_init[1]
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)
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else:
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weight = core.ScopedBlobReference(
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blob_out + '_w', model.param_init_net)
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bias = core.ScopedBlobReference(
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blob_out + '_b', model.param_init_net)
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model.AddParameter(weight, ParameterTags.WEIGHT)
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model.AddParameter(bias, ParameterTags.BIAS)
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return op_call([blob_in, weight, bias], blob_out, **kwargs)
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def elementwise_linear(model, *args, **kwargs):
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return _elementwise_linear(
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model, model.net.ElementwiseLinear, *args, **kwargs)
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