mirror of
https://github.com/zebrajr/pytorch.git
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e89e71c595
Summary: - Condensed operators in RFF layer - Adjusted RFF layer test; made test code more concise Reviewed By: chocjy Differential Revision: D5391436 fbshipit-source-id: 08748861cd6fb4a9e4cc9c8762996371492020a1
108 lines
3.9 KiB
Python
108 lines
3.9 KiB
Python
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 numpy as np
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class RandomFourierFeatures(ModelLayer):
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"""
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Implementation of random fourier feature map for feature processing.
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Applies sqrt(2 / output_dims) * cos(wx+b), where:
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output_dims is the output feature dimensions, and
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wx + b applies FC using randomized, fixed weight and bias parameters
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For more information, see the original paper:
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https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf
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Inputs:
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output_dims -- output feature dimensions
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sigma -- bandwidth for the Gaussian kernel estimator
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w_init -- initalization options for weight parameter
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b_init -- initalization options for bias parameter
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"""
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def __init__(
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self,
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model,
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input_record,
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output_dims,
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sigma, # bandwidth
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w_init=None,
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b_init=None,
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name='random_fourier_features',
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**kwargs):
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super(RandomFourierFeatures, self).__init__(model, name, input_record,
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**kwargs)
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assert isinstance(input_record, schema.Scalar), "Incorrect input type"
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input_dims = input_record.field_type().shape[0]
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assert input_dims >= 1, "Expected input dimensions >= 1, got %s" \
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% input_dims
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self.output_dims = output_dims
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assert self.output_dims >= 1, "Expected output dimensions >= 1, got %s" \
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% self.output_dims
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self.output_schema = schema.Scalar(
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(np.float32, (self.output_dims, )),
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model.net.NextScopedBlob(name + '_output')
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)
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assert sigma > 0.0, "Expected bandwidth > 0, got %s" % sigma
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# Initialize train_init_net parameters
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w_init = w_init if w_init else (
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'GaussianFill', {'mean': 0.0, 'std': 1.0 / sigma}
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)
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b_init = b_init if b_init else (
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'UniformFill', {'min': 0.0, 'max': 2 * np.pi}
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)
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self.w = model.net.NextScopedBlob(name + "_w")
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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.w,
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initializer=core.CreateOperator(w_init[0],
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[],
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self.w,
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shape=(self.output_dims, input_dims),
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**w_init[1]
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),
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optimizer=model.NoOptim))
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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(b_init[0],
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[],
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self.b,
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shape=[self.output_dims],
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**b_init[1]
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),
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optimizer=model.NoOptim))
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def add_ops(self, net):
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# Random features: wx + b
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cosine_arg = net.FC(self.input_record.field_blobs() + [self.w, self.b],
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net.NextScopedBlob("cosine_arg"))
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# Apply cosine to new vectors
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new_feature_vec = net.Cos([cosine_arg],
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net.NextScopedBlob('new_feature_vec'))
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# Multiply each element in vector by sqrt(2/D)
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scale = np.sqrt(2.0 / self.output_dims)
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net.Scale([new_feature_vec],
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self.output_schema.field_blobs(),
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scale=scale)
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