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build/scripts-2.7/run.py
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#!/u/parcollt/anaconda2/bin/python
# -*- coding: utf-8 -*-
# Contributors: Titouan Parcollet
# Authors: Olexa Bilaniuk
# Imports.
import sys; sys.path += [".", ".."]
import argparse as Ap
import logging as L
import numpy as np
import os, pdb, sys
import time
import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
__version__ = "0.0.0"
#
# Message Formatter
#
class MsgFormatter(L.Formatter):
"""Message Formatter
Formats messages with time format YYYY-MM-DD HH:MM:SS.mmm TZ
"""
def formatTime(self, record, datefmt):
t = record.created
timeFrac = abs(t-long(t))
timeStruct = time.localtime(record.created)
timeString = ""
timeString += time.strftime("%F %T", timeStruct)
timeString += "{:.3f} ".format(timeFrac)[1:]
timeString += time.strftime("%Z", timeStruct)
return timeString
#############################################################################################################
############################## Subcommands ##################################
#############################################################################################################
class Subcommand(object):
name = None
@classmethod
def addArgParser(cls, subp, *args, **kwargs):
argp = subp.add_parser(cls.name, usage=cls.__doc__, *args, **kwargs)
cls.addArgs(argp)
argp.set_defaults(__subcmdfn__=cls.run)
return argp
@classmethod
def addArgs(cls, argp):
pass
@classmethod
def run(cls, d):
pass
class Screw(Subcommand):
"""Screw around with me in Screw(Subcommand)."""
name = "screw"
@classmethod
def run(cls, d):
print(cls.__doc__)
class Train(Subcommand):
name = "train"
LOGLEVELS = {"none":L.NOTSET, "debug": L.DEBUG, "info": L.INFO,
"warn":L.WARN, "err": L.ERROR, "crit": L.CRITICAL}
@classmethod
def addArgs(cls, argp):
argp.add_argument("-d", "--datadir", default=".", type=str,
help="Path to datasets directory.")
argp.add_argument("-w", "--workdir", default=".", type=str,
help="Path to the workspace directory for this experiment.")
argp.add_argument("-l", "--loglevel", default="info", type=str,
choices=cls.LOGLEVELS.keys(),
help="Logging severity level.")
argp.add_argument("-s", "--seed", default=0xe4223644e98b8e64, type=long,
help="Seed for PRNGs.")
argp.add_argument("--summary", action="store_true",
help="""Print a summary of the network.""")
argp.add_argument("--batchnorm", default=0, type=int,
help="0 = No batchNorm; 1 = BatchNorm")
argp.add_argument("--dataset", default="cifar10", type=str,
choices=["timit","decoda","cifar10", "cifar100", "svhn"],
help="Dataset Selection.")
argp.add_argument("--model", default="real", type=str,
choices=["complex","quaternion", "real"],
help="Dataset Selection.")
argp.add_argument("--dropout", default=0, type=float,
help="Dropout probability.")
argp.add_argument("-n", "--num-epochs", default=1000, type=int,
help="Number of epochs")
argp.add_argument("-b", "--batch-size", default=64, type=int,
help="Batch Size")
argp.add_argument("--start-filter", "--sf", default=11, type=int,
help="Number of feature maps in starting stage")
argp.add_argument("--num-blocks", "--nb", default=10, type=int,
help="Number of filters in initial block")
argp.add_argument("--spectral-param", action="store_true",
help="""Use spectral parametrization.""")
argp.add_argument("--spectral-pool-gamma", default=0.50, type=float,
help="""Use spectral pooling, preserving a fraction gamma of frequencies""")
argp.add_argument("--spectral-pool-scheme", default="none", type=str,
choices=["none", "stagemiddle", "proj", "nodownsample"],
help="""Spectral pooling scheme""")
argp.add_argument("--act", default="relu", type=str,
choices=["relu"],
help="Activation.")
argp.add_argument("--aact", default="modrelu", type=str,
choices=["modrelu"],
help="Advanced Activation.")
argp.add_argument("--no-validation", action="store_true",
help="Do not create a separate validation set.")
argp.add_argument("--comp-init", default='complex', type=str,
help="Initializer for the complex kernel.")
argp.add_argument("--quat-init", default='quaternion', type=str,
help="Initializer for the quaternion kernel.")
optp = argp.add_argument_group("Optimizers", "Tunables for all optimizers")
optp.add_argument("--optimizer", "--opt", default="nag", type=str,
choices=["sgd", "nag", "adam", "rmsprop"],
help="Optimizer selection.")
optp.add_argument("--clipnorm", "--cn", default=1.0, type=float,
help="The norm of the gradient will be clipped at this magnitude.")
optp.add_argument("--clipval", "--cv", default=1.0, type=float,
help="The values of the gradients will be individually clipped at this magnitude.")
optp.add_argument("--l1", default=0, type=float,
help="L1 penalty.")
optp.add_argument("--l2", default=0, type=float,
help="L2 penalty.")
optp.add_argument("--lr", default=1e-4, type=float,
help="Master learning rate for optimizers.")
optp.add_argument("--momentum", "--mom", default=0.9, type=float,
help="Momentum for optimizers supporting momentum.")
optp.add_argument("--decay", default=0, type=float,
help="Learning rate decay for optimizers.")
optp.add_argument("--schedule", default="default", type=str,
help="Learning rate schedule")
optp = argp.add_argument_group("Adam", "Tunables for Adam optimizer")
optp.add_argument("--beta1", default=0.9, type=float,
help="Beta1 for Adam.")
optp.add_argument("--beta2", default=0.999, type=float,
help="Beta2 for Adam.")
optp.add_argument("--device", default="0", type=str,
help="CUDA Device, starting at 0.")
optp.add_argument("--gpus", default=1, type=int,
help="Number of GPUs to be used, starting at 1")
optp.add_argument("--memory", default=1.0, type=float,
help="Memory to be allocated on the selected device, only for tensorflow backend, from 0 to 1")
optp.add_argument("--save-prefix", default="", type=str,
help="Save prefix for resuming and saving best model")
optp.add_argument("--seg", default="chiheb", type=str,
choices=["chiheb", "parcollet"], help="Segmentation to be use on quaternions, \
following NIPS Deep Complex Networks or SLT Quaternion Neural Networks")
optp.add_argument("--output-type", default="real", type=str,
choices=["quaternion", "real"],
help="Type of the dense output layer")
@classmethod
def run(cls, d):
if not os.path.isdir(d.workdir):
os.mkdir(d.workdir)
logDir = os.path.join(d.workdir, "logs")
if not os.path.isdir(logDir):
os.mkdir(logDir)
logFormatter = MsgFormatter ("[%(asctime)s ~~ %(levelname)-8s] %(message)s")
stdoutLogSHandler = L.StreamHandler(sys.stdout)
stdoutLogSHandler .setLevel (cls.LOGLEVELS[d.loglevel])
stdoutLogSHandler .setFormatter (logFormatter)
defltLogger = L.getLogger ()
defltLogger .setLevel (cls.LOGLEVELS[d.loglevel])
defltLogger .addHandler (stdoutLogSHandler)
trainLogFilename = os.path.join(d.workdir, "logs", "train.txt")
trainLogFHandler = L.FileHandler (trainLogFilename, "a", "UTF-8", delay=True)
trainLogFHandler .setLevel (cls.LOGLEVELS[d.loglevel])
trainLogFHandler .setFormatter (logFormatter)
trainLogger = L.getLogger ("train")
trainLogger .setLevel (cls.LOGLEVELS[d.loglevel])
trainLogger .addHandler (trainLogFHandler)
entryLogFilename = os.path.join(d.workdir, "logs", "entry.txt")
entryLogFHandler = L.FileHandler (entryLogFilename, "a", "UTF-8", delay=True)
entryLogFHandler .setLevel (cls.LOGLEVELS[d.loglevel])
entryLogFHandler .setFormatter (logFormatter)
entryLogger = L.getLogger ("entry")
entryLogger .setLevel (cls.LOGLEVELS[d.loglevel])
entryLogger .addHandler (entryLogFHandler)
np.random.seed(d.seed % 2**32)
import training;training.train(d)
#############################################################################################################
############################## Argument Parsers #################################
#############################################################################################################
def getArgParser(prog):
argp = Ap.ArgumentParser(prog = prog,
usage = None,
description = None,
epilog = None,
version = __version__)
subp = argp.add_subparsers()
argp.set_defaults(argp=argp)
argp.set_defaults(subp=subp)
# Add global args to argp here?
# ...
# Add subcommands
for v in globals().itervalues():
if(isinstance(v, type) and
issubclass(v, Subcommand) and
v != Subcommand):
v.addArgParser(subp)
# Return argument parser.
return argp
#############################################################################################################
############################## Main ##################################
#############################################################################################################
def main(argv):
sys.setrecursionlimit(10000)
d = getArgParser(argv[0]).parse_args(argv[1:])
return d.__subcmdfn__(d)
if __name__ == "__main__":
main(sys.argv)
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