コード例 #1
0
from titan_runtime.parse_logs import get_latest_log
from titan_runtime.parse_logs import get_current_accuracy_googlenet
import titan_runtime.conf_rt as conf
import os

if __name__ == "__main__":

    jobs = os.listdir(conf.net_dir)
    jobs = [j for j in jobs if 'googlenet' in j]
    jobs = sorted(jobs)
    for logdir in jobs:
        logF = get_latest_log(conf.net_dir + '/' + logdir)
        if logF is None:
            continue
        accuracy_dict = get_current_accuracy_googlenet(logF)
        print '  net: %s, accuracy: %0.1f, at iter %d' % (
            logdir, accuracy_dict['accuracy'] * 100, accuracy_dict['iter'])
コード例 #2
0
if __name__ == "__main__":

  '''
  #TODO: 100-iteration solver
  cp 100 iteration solver to directory to evaluate
  run 100-iterations solver (for various numbers of GPUs)
  parse results

  '''

  #train_dir = '/lustre/atlas/scratch/forresti/csc103/dnn_exploration/nets_nov2015_done/FireNet_8_fireLayers_base_r_64_64_incr_r_64_64_CEratio_0.125_freq_2'
  train_dir = '/lustre/atlas/scratch/forresti/csc103/dnn_exploration/nets_nov2015_done/FireNet_8_fireLayers_base_64_64_64_incr_64_64_64_freq_2/'
  n_gpu = 32
  gen_solver_prototxt(train_dir, n_gpu)

  training_cmd = './do_training.sh %s %d' %(train_dir, n_gpu)
  os.system(training_cmd)

  #TODO: parse results.
  latest_log = get_latest_log(train_dir, for_timing=True)
  time_stats = get_time_per_iter(latest_log)


  '''
  log_fname = '/lustre/atlas/scratch/forresti/csc103/dnn_exploration/nets_nov2015_done/FireNet_8_fireLayers_base_64_64_64_incr_64_64_64_freq_2/train_Mon_2015_12_14__16_07_30.log'

  time_stats = get_time_per_iter(log_fname)
  print time_stats
  '''
コード例 #3
0
    f.write(out_st)
    f.close()


if __name__ == "__main__":
    '''
  #TODO: 100-iteration solver
  cp 100 iteration solver to directory to evaluate
  run 100-iterations solver (for various numbers of GPUs)
  parse results

  '''

    #train_dir = '/lustre/atlas/scratch/forresti/csc103/dnn_exploration/nets_nov2015_done/FireNet_8_fireLayers_base_r_64_64_incr_r_64_64_CEratio_0.125_freq_2'
    train_dir = '/lustre/atlas/scratch/forresti/csc103/dnn_exploration/nets_nov2015_done/FireNet_8_fireLayers_base_64_64_64_incr_64_64_64_freq_2/'
    n_gpu = 32
    gen_solver_prototxt(train_dir, n_gpu)

    training_cmd = './do_training.sh %s %d' % (train_dir, n_gpu)
    os.system(training_cmd)

    #TODO: parse results.
    latest_log = get_latest_log(train_dir, for_timing=True)
    time_stats = get_time_per_iter(latest_log)
    '''
  log_fname = '/lustre/atlas/scratch/forresti/csc103/dnn_exploration/nets_nov2015_done/FireNet_8_fireLayers_base_64_64_64_incr_64_64_64_freq_2/train_Mon_2015_12_14__16_07_30.log'

  time_stats = get_time_per_iter(log_fname)
  print time_stats
  '''
コード例 #4
0
ファイル: parse_logs_NiN.py プロジェクト: forresti/DeepDSE
from titan_runtime.parse_logs import get_latest_log
from titan_runtime.parse_logs import get_current_accuracy
import titan_runtime.conf_rt as conf
import os

if __name__ == "__main__":

  jobs = os.listdir(conf.net_dir)
  #jobs = [j for j in jobs if 'NiN' in j]
  #jobs = [j for j in jobs if not j.startswith('googlenet')]
  jobs = [j for j in jobs if not 'googlenet' in j]
  jobs = sorted(jobs)
  for logdir in jobs:
    logF = get_latest_log(conf.net_dir + '/' + logdir)
    if logF is None:
      continue
    accuracy_dict = get_current_accuracy(logF)
    if accuracy_dict is not 'error':
      if 'accuracy_top5' in accuracy_dict.keys():
        print '  net: %s, top1: %0.1f, top5: %0.1f, at iter %d' %(logdir, accuracy_dict['accuracy']*100, accuracy_dict['accuracy_top5']*100, accuracy_dict['iter'])
      else:
        print '  net: %s, top1: %0.1f, at iter %d' %(logdir, accuracy_dict['accuracy']*100, accuracy_dict['iter'])