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egs/wsj/s5/utils/nnet/make_cnn_proto.py
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#!/usr/bin/env python
# Copyright 2014 Brno University of Technology (author: Katerina Zmolikova, Karel Vesely)
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# THIS CODE IS PROVIDED *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
# WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
# MERCHANTABLITY OR NON-INFRINGEMENT.
# See the Apache 2 License for the specific language governing permissions and
# limitations under the License.
# Generated Nnet prototype, to be initialized by 'nnet-initialize'.
from __future__ import division
from __future__ import print_function
import math, random, sys
from optparse import OptionParser
###
### Parse options
###
usage="%prog [options] <feat-dim> <num-leaves> <num-hidden-layers> <num-hidden-neurons> >nnet-proto-file"
parser = OptionParser(usage)
parser.add_option('--activation-type', dest='activation_type',
help='Select type of activation function : (<Sigmoid>|<Tanh>) [default: %default]',
default='<Sigmoid>', type='string');
parser.add_option('--num-filters1', dest='num_filters1',
help='Number of filters in first convolutional layer [default: %default]',
default=128, type='int')
parser.add_option('--num-filters2', dest='num_filters2',
help='Number of filters in second convolutional layer [default: %default]',
default=256, type='int')
parser.add_option('--pool-size', dest='pool_size',
help='Size of pooling [default: %default]',
default=3, type='int')
parser.add_option('--pool-step', dest='pool_step',
help='Step of pooling [default: %default]',
default=3, type='int')
parser.add_option('--pool-type', dest='pool_type',
help='Type of pooling (Max || Average) [default: %default]',
default='Max', type='string')
parser.add_option('--pitch-dim', dest='pitch_dim',
help='Number of features representing pitch [default: %default]',
default=0, type='int')
parser.add_option('--delta-order', dest='delta_order',
help='Order of delta features [default: %default]',
default=2, type='int')
parser.add_option('--splice', dest='splice',
help='Length of splice [default: %default]',
default=5,type='int')
parser.add_option('--patch-step1', dest='patch_step1',
help='Patch step of first convolutional layer [default: %default]',
default=1, type='int')
parser.add_option('--patch-dim1', dest='patch_dim1',
help='Dim of convolutional kernel in 1st layer (freq. axis) [default: %default]',
default=8, type='int')
parser.add_option('--patch-dim2', dest='patch_dim2',
help='Dim of convolutional kernel in 2nd layer (freq. axis) [default: %default]',
default=4, type='int')
parser.add_option('--dir', dest='protodir',
help='Directory, where network prototypes will be saved [default: %default]',
default='.', type='string')
parser.add_option('--num-pitch-neurons', dest='num_pitch_neurons',
help='Number of neurons in layers processing pitch features [default: %default]',
default='200', type='int')
(o,args) = parser.parse_args()
if len(args) != 1 :
parser.print_help()
sys.exit(1)
feat_dim = int(args[0]);
### End parse options
feat_raw_dim = feat_dim / (o.delta_order+1) / (o.splice*2+1) - o.pitch_dim # we need number of feats without deltas and splice and pitch
# Check
assert(feat_dim > 0)
assert(o.pool_type == 'Max' or o.pool_type == 'Average')
###
### Print prototype of the network
###
# Begin the prototype
print("<NnetProto>")
# Convolutional part of network
num_patch1 = 1 + (feat_raw_dim - o.patch_dim1) / o.patch_step1
num_pool = 1 + (num_patch1 - o.pool_size) / o.pool_step
patch_dim2 = o.patch_dim2
patch_step2 = o.patch_step1
patch_stride2 = num_pool # same as layer1 outputs
num_patch2 = 1 + (num_pool - patch_dim2) / patch_step2
inputdim_of_cnn = feat_dim
outputdim_of_cnn = o.num_filters2*num_patch2
convolution_proto = ''
convolution_proto += "<ConvolutionalComponent> <InputDim> %d <OutputDim> %d <PatchDim> %d <PatchStep> %d <PatchStride> %d <BiasMean> %f <BiasRange> %f <ParamStddev> %f <MaxNorm> %f
" % \
(feat_raw_dim * (o.delta_order+1) * (o.splice*2+1), o.num_filters1 * num_patch1, o.patch_dim1, o.patch_step1, feat_raw_dim, -1.0, 2.0, 0.02, 30) #~8x11x3 = 264 inputs
convolution_proto += "<%sPoolingComponent> <InputDim> %d <OutputDim> %d <PoolSize> %d <PoolStep> %d <PoolStride> %d
" % \
(o.pool_type, o.num_filters1*num_patch1, o.num_filters1*num_pool, o.pool_size, o.pool_step, o.num_filters1)
convolution_proto += "<Rescale> <InputDim> %d <OutputDim> %d <InitParam> %f
" % \
(o.num_filters1*num_pool, o.num_filters1*num_pool, 1)
convolution_proto += "<AddShift> <InputDim> %d <OutputDim> %d <InitParam> %f
" % \
(o.num_filters1*num_pool, o.num_filters1*num_pool, 0)
convolution_proto += "%s <InputDim> %d <OutputDim> %d
" % \
(o.activation_type, o.num_filters1*num_pool, o.num_filters1*num_pool)
convolution_proto += "<ConvolutionalComponent> <InputDim> %d <OutputDim> %d <PatchDim> %d <PatchStep> %d <PatchStride> %d <BiasMean> %f <BiasRange> %f <ParamStddev> %f <MaxNorm> %f
" % \
(o.num_filters1*num_pool, outputdim_of_cnn, patch_dim2, patch_step2, patch_stride2, -2.0, 4.0, 0.1, 50) #~4x128 = 512 inputs
convolution_proto += "<Rescale> <InputDim> %d <OutputDim> %d <InitParam> %f
" % \
(outputdim_of_cnn, outputdim_of_cnn, 1)
convolution_proto += "<AddShift> <InputDim> %d <OutputDim> %d <InitParam> %f
" % \
(outputdim_of_cnn, outputdim_of_cnn, 0)
convolution_proto += "%s <InputDim> %d <OutputDim> %d
" % \
(o.activation_type, outputdim_of_cnn, outputdim_of_cnn)
if (o.pitch_dim > 0):
# convolutional part
f_conv = open('%s/nnet.proto.convolution' % o.protodir, 'w')
f_conv.write('<NnetProto>
')
f_conv.write(convolution_proto)
f_conv.write('</NnetProto>
')
f_conv.close()
# pitch part
f_pitch = open('%s/nnet.proto.pitch' % o.protodir, 'w')
f_pitch.write('<NnetProto>
')
f_pitch.write('<AffineTransform> <InputDim> %d <OutputDim> %d <BiasMean> %f <BiasRange> %f <ParamStddev> %f
' % \
((o.pitch_dim * (o.delta_order+1) * (o.splice*2+1)), o.num_pitch_neurons, -2, 4, 0.02))
f_pitch.write('%s <InputDim> %d <OutputDim> %d
' % \
(o.activation_type, o.num_pitch_neurons, o.num_pitch_neurons))
f_pitch.write('<AffineTransform> <InputDim> %d <OutputDim> %d <BiasMean> %f <BiasRange> %f <ParamStddev> %f
' % \
(o.num_pitch_neurons, o.num_pitch_neurons, -2, 4, 0.1))
f_pitch.write('%s <InputDim> %d <OutputDim> %d
' % \
(o.activation_type, o.num_pitch_neurons, o.num_pitch_neurons))
f_pitch.write('</NnetProto>
')
f_pitch.close()
# paralell part
vector = ''
for i in range(1, inputdim_of_cnn, feat_raw_dim + o.pitch_dim):
vector += '%d:1:%d ' % (i, i + feat_raw_dim - 1)
for i in range(feat_raw_dim+1, inputdim_of_cnn + 1, feat_raw_dim + o.pitch_dim):
vector += '%d:1:%d ' % (i, i + o.pitch_dim - 1)
print('<Copy> <InputDim> %d <OutputDim> %d <BuildVector> %s </BuildVector>' % \
(inputdim_of_cnn, inputdim_of_cnn, vector))
print('<ParallelComponent> <InputDim> %d <OutputDim> %d <NestedNnetProto> %s %s </NestedNnetProto>' % \
(inputdim_of_cnn, o.num_pitch_neurons + outputdim_of_cnn, '%s/nnet.proto.convolution' % o.protodir, '%s/nnet.proto.pitch' % o.protodir))
else: # no pitch
print(convolution_proto)
# We are done!
sys.exit(0)
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