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Summary: Currently using two tower models with cosine distance results in bad calibration. Adding bias to the output of cosine term solves the problem. Reviewed By: xianjiec Differential Revision: D5132606 fbshipit-source-id: eb4fa75acf908db89954eeee67627b4a00572f61
55 lines
1.9 KiB
Python
55 lines
1.9 KiB
Python
## @package add_bias
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# Module caffe2.python.layers.add_bias
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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, schema
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from caffe2.python.layers.layers import (
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ModelLayer,
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LayerParameter
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)
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import math
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import numpy as np
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class AddBias(ModelLayer):
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def __init__(self, model, input_record, bias_init=None,
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bias_optim=None, name='add_bias'):
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super(AddBias, self).__init__(model, name, input_record)
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assert isinstance(input_record, schema.Scalar), "Incorrect input type"
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assert len(input_record.field_type().shape) > 0, (
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"AddBias expects limited dimensions of the input tensor")
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input_dims = input_record.field_type().shape[0]
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assert input_dims > 0, (
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"AddBias expects input dimensions > 0, got {}".format(input_dims))
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self.output_schema = schema.Scalar(
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(input_record.field_type().base, (input_dims, )),
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model.net.NextScopedBlob(name + '_output')
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)
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scale = math.sqrt(1.0 / input_dims)
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bias_init = bias_init if bias_init else (
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'UniformFill', {'min': -scale, 'max': scale})
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self.b = model.net.NextScopedBlob(name + "_b")
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self.params.append(
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LayerParameter(
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parameter=self.b,
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initializer=core.CreateOperator(bias_init[0],
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[],
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self.b,
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shape=[input_dims, ],
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**bias_init[1]
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),
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optimizer=bias_optim))
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def add_ops(self, net):
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net.Add(self.input_record.field_blobs() + [self.b],
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self.output_schema.field_blobs(), broadcast=1)
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