def main():
parser = argparse.ArgumentParser()
parser.add_argument("--out",required=True,help="The name of the yaml file produced")
parser.add_argument("--template",required=True,help="YAML template")
parser.add_argument("--hparams",required=True,help="Hyper-parameters configuration")
parser.add_argument("--force",action='store_true',help="Force to overwrite the old yaml file produced")
parser.add_argument("--range",nargs=2,type=int,help="Subrange of files to execute")
options = parser.parse_args()
out = options.out
template = options.template
hparams = options.hparams
force = options.force
print options
# Generates a list of hyper-parameter names and a list of
# hyper-parameter values
hpnames, hpvalues = generate_params(hparamfile=hparams,
generate="uniform",
search_mode="fix-grid-search")
# Writes template with each hyper-parameter settings in
# succesive files and returns the name of the files
files = write_files(template="".join(open(template,"r")),hpnames=hpnames,
hpvalues=hpvalues,save_path=out,force=force)
if options.range:
if options.range[1]==-1:
options.range[1] = len(files)
assert options.range[0] < options.range[1]
iterator = xrange(*options.range)
else:
iterator = xrange(0,len(files))
print list(iterator)
print_error_message("errors\n",out,"w")
from pylearn2.utils import serial
for i in iterator:#xrange(0,len(files)):
f = files[i]
print f
try:
serial.load_train_file(f).main_loop()
except BaseException as e:
print traceback.format_exc()
print e
print_error_message("%s : %s\n" % (f,str(e)),out)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--out",
required=True,
help="The name of the yaml file produced")
parser.add_argument("--template", required=True, help="YAML template")
parser.add_argument("--hparams",
required=True,
help="Hyper-parameters configuration")
parser.add_argument("--force",
action='store_true',
help="Force to overwrite the old yaml file produced")
options = parser.parse_args()
out = options.out
template = options.template
hparams = options.hparams
force = options.force
print options
# Generates a list of hyper-parameter names and a list of
# hyper-parameter values
hpnames, hpvalues = generate_params(hparamfile=hparams,
generate="uniform",
search_mode="fix-grid-search")
# Writes template with each hyper-parameter settings in
# succesive files and returns the name of the files
files = write_files(template="".join(open(template, "r")),
hpnames=hpnames,
hpvalues=hpvalues,
save_path=out,
force=force)
print_error_message("errors\n", out, "w")
from pylearn2.utils import serial
for i in xrange(0, len(files)):
f = files[i]
try:
serial.load_train_file(f).main_loop()
except BaseException as e:
print traceback.format_exc()
print e
print_error_message("%s : %s\n" % (f, str(e)), out)
OUT = "/yaml/test.yaml"
TEMPLATE = DIR+"template.yaml"
HPARAMS = "hparams.conf"
if __name__ == "__main__":
# for transformation in ['translate','scale','rotate','flip','gaussian','sharpen','denoize','occlusion','halfface']:
# for transformation in ['scale','rotate','flip','gaussian','sharpen','denoize','occlusion','halfface']:
for transformation in ['denoize','sharpen']:
out = DIR+transformation+OUT
t_template = "".join(open(DIR+transformation+"/"+transformation+".yaml",'r'))
# Generates a list of hyper-parameter names and a list of
# hyper-parameter values
hpnames, hpvalues = generate_params(hparamfile=DIR+transformation+"/"+transformation+".conf",
generate="log-uniform",
search_mode="fix-grid-search")
template = "".join(open(TEMPLATE,'r')) % {'transformations': t_template,'save_path':'%(save_path)s'}
# Writes template with each hyper-parameter settings in
# succesive files and returns the name of the files
files = write_files(template=template,hpnames=hpnames,
hpvalues=hpvalues,save_path=out,force=True)
# files = write_files(template="".join(open(TEMPLATE),'r'),hpnames=hpnames,
# hpvalues=hpvalues,save_path=OUT)
for f in files:
serial.load_train_file(f).main_loop()
import sys
import os
DIR = "/home/xavier/ift6266kaggle/conv/exp3/"
OUT = DIR+"yaml/test.yaml"
TEMPLATE = DIR+"template.yaml"
HPARAMS = DIR+"hparams.conf"
if __name__ == "__main__":
# Generates a list of hyper-parameter names and a list of
# hyper-parameter values
hpnames, hpvalues = generate_params(hparamfile=HPARAMS,
generate="uniform",
search_mode="fix-grid-search")
force = len(sys.argv)>1 and sys.argv[1]=="--force"
# Writes template with each hyper-parameter settings in
# succesive files and returns the name of the files
files = write_files(template="".join(open(TEMPLATE,"r")),hpnames=hpnames,
hpvalues=hpvalues,save_path=OUT,force=force)
for f in files:
serial.load_train_file(f).main_loop()
# for i in range(46-24):
# f = DIR+"yaml/second%d.yaml" % (i+24)
# print i+24,"on",46,"done"
# serial.load_train_file(f).main_loop()
from pylearn2.utils.shell import run_shell_command
from gen_yaml import generate_params, write_files
import os
DIR = "/data/lisatmp/ift6266h13/bouthilx/"
OUT = DIR+"yaml/test.yaml"
TEMPLATE = DIR+"gen_yaml/template.yaml"
HPARAMS = DIR+"gen_yaml/hparams.conf"
if __name__ == "__main__":
# Generates a list of hyper-parameter names and a list of
# hyper-parameter values
hpnames, hpvalues = generate_params(hparamfile=HPARAMS,
generate="log-uniform",
search_mode="fix-grid-search")
# Writes template with each hyper-parameter settings in
# succesive files and returns the name of the files
files = write_files(template=TEMPLATE,hpnames=hpnames,
hpvalues=hpvalues,save_path=OUT)
command = """jobdispatch --condor --env=THEANO_FLAGS=device=gpu,floatX=float32,force_device=True --duree=48:00:00 --whitespace --gpu bash %(dir)strain.py \"{{%(files)s\"}}""" % {"dir":DIR,"files":files[0]}
output, rc = run_shell_command(command)