def main():
args = parser.parse_args()
args.seed = None # temp moran
if args.seed is not None:
random.seed(args.seed)
torch.manual_seed(args.seed)
cudnn.deterministic = True
warnings.warn('You have chosen to seed training. '
'This will turn on the CUDNN deterministic setting, '
'which can slow down your training considerably! '
'You may see unexpected behavior when restarting '
'from checkpoints.')
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
with MLlogger(os.path.join(home, 'mxt-sim/mllog_runs'),
args.experiment,
args,
name_args=[
args.arch, args.dataset,
"W{}A{}".format(args.bit_weights, args.bit_act)
]) as ml_logger:
main_worker(args, ml_logger)
def main():
args = parser.parse_args()
if args.config_file is not None:
with open(args.config_file) as f:
config_dict = json.loads(f.read())
parser.set_defaults(**config_dict)
args = parser.parse_args()
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
# if args.evaluate:
# args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
args.save_path = path.join(args.results_dir, args.save)
if args.exp is '':
exp = str(args.model) + '_' + str(args.dataset)
else:
exp = args.exp
with MLlogger(path.join(args.results_dir, 'mlruns'),
exp,
args,
name_args=[args.model, args.dataset]) as ml_logger:
main_worker(args, ml_logger)
def main():
args = parser.parse_args()
args.post_relu = not args.pre_relu
if args.seed is not None:
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
with MLlogger(os.path.join(home, 'mxt-sim/mllog_runs'),
args.experiment,
args,
name_args=[
args.arch, args.dataset,
"W{}A{}".format(args.bit_weights, args.bit_act)
]) as ml_logger:
main_worker(args, ml_logger)
mq.set_clipping(scales, inf_model.device)
loss = inf_model.evaluate_calibration()
ml_logger.log_metric('Loss {}'.format(args.min_method), loss.item(), step='auto')
# evaluate
acc = inf_model.validate()
ml_logger.log_metric('Acc {}'.format(args.min_method), acc, step='auto')
data['powell'] = {'alpha': scales, 'loss': loss.item(), 'acc': acc}
# save scales
f_name = "scales_{}_W{}A{}.pkl".format(args.arch, args.bit_weights, args.bit_act)
f = open(os.path.join(proj_root_dir, 'data', f_name), 'wb')
pickle.dump(data, f)
f.close()
print("Data saved to {}".format(f_name))
if __name__ == '__main__':
args = parser.parse_args()
if args.cal_batch_size is None:
args.cal_batch_size = args.batch_size
if args.cal_batch_size > args.batch_size:
print("Changing cal_batch_size parameter from {} to {}".format(args.cal_batch_size, args.batch_size))
args.cal_batch_size = args.batch_size
if args.cal_set_size is None:
args.cal_set_size = args.batch_size
with MLlogger(os.path.join(home, 'mxt-sim/mllog_runs'), args.experiment, args,
name_args=[args.arch, args.dataset, "W{}A{}".format(args.bit_weights, args.bit_act)]) as ml_logger:
main(args, ml_logger)
'bcorr_weight': args.bias_corr_weight,
'vcorr_weight': args.var_corr_weight,
'logger': logger,
'measure_entropy': args.measure_entropy,
'mtd_quant': args.mid_thread_quant
},
'qmanager':{
'rho_act': args.rho_act,
'rho_weight': args.rho_weight
}
} # TODO: add params for bfloat
return qparams
if __name__ == '__main__':
if args.stats_mode != 'collect':
experiment = args.arch if args.mlf_experiment is None else args.mlf_experiment
with MLlogger(os.path.join(home, 'mlruns_mxt'), experiment, args,
name_args=[args.arch, "W{}A{}".format(args.qweight, args.qtype)]) as ml_logger:
with QM(args, get_params(ml_logger)):
im = InferenceModel(ml_logger)
im.run()
if args.measure_entropy:
for id in ml_logger.metters:
print("Average bit rate: {} - {}".format(id, ml_logger.metters[id].avg))
else:
with QM(args, get_params()):
im = InferenceModel()
im.run()
