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train.py
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68 lines (58 loc) · 2.77 KB
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import argparse
import importlib
from utils.utils import *
import yaml
import logging
MODEL_DIR=None
DATA_DIR = '/data1/LibriSpeech_fscil/spk_segments'
#DATA_DIR = '/data1/nsynth/'
PROJECT='meta_sc'
def dict2namespace(dicts):
for i in dicts:
if isinstance(dicts[i], dict):
dicts[i] = dict2namespace(dicts[i])
ns = argparse.Namespace(**dicts)
return ns
if __name__ == '__main__':
parser = argparse.ArgumentParser()
# about dataset and network
parser.add_argument('-project', type=str, default=PROJECT)
parser.add_argument('-dataset', type=str, default='nsynth-100',
choices=['nsynth-100', 'nsynth-200', 'nsynth-300', 'nsynth-400', 'librispeech', 'esc', 'pqrs'])
parser.add_argument('-dataroot', type=str, default=DATA_DIR)
parser.add_argument('-save_path', type=str, default='')
parser.add_argument('-config', type=str, default="configs/default.yaml")
parser.add_argument('-debug', action='store_true')
parser.add_argument('-lamda_proto', type=float, default=1.0)
parser.add_argument('-way', type=int, default=5)
parser.add_argument('-shot', type=int, default=5)
parser.add_argument('-num_session', type=int, default=10)
parser.add_argument('-batch_size_base', type=int, default=100)
# about training
parser.add_argument('-gpu', default='0')
# print(parser.parse_args())
args, unknown = parser.parse_known_args()
args = parser.parse_args()
with open(args.config, 'r') as config:
cfg = yaml.safe_load(config)
cfg = cfg['train']
cfg.update(vars(args))
# args = argparse.Namespace(**cfg)
args = dict2namespace(cfg)
set_seed(args.seed)
pprint(vars(args))
args.num_gpu = set_gpu(args)
with open("per_cls_"+args.dataset+".txt", "a") as result_file:
result_file.write("Class\tAccuracy\n")
result_file.write("\t".join([str(cls_idx) for cls_idx in range(100)]) + "\n")
with open("per_cls_"+args.dataset+".txt", "a") as result_file:
result_file.write("Class\tAccuracy\n")
result_file.write("\t".join([str(cls_idx) for cls_idx in range(100)]) + "\n")
with open("per_cls_"+args.dataset+".txt", "a") as result_file:
result_file.write("Class\tAccuracy\n")
result_file.write("\t".join([str(cls_idx) for cls_idx in range(50)]) + "\n")
with open("per_cls_"+args.dataset+".txt", "a") as result_file:
result_file.write("Class\tAccuracy\n")
result_file.write("\t".join([str(cls_idx) for cls_idx in range(10)]) + "\n")
trainer = importlib.import_module('models.%s.fscil_trainer' % (args.project)).FSCILTrainer(args)
trainer.train()