config.py

#coding=utf-8

import warnings
import torch as t

class DefaultConfig(object):
	# visdom
	vis = False 
	env = 'default'
	vis_port = 8097

	# model
	model = 'RSHNet'
	load_model_path = None
	checkpoint = './' + model + '_checkpoint'
	model_kwargs = {
		"num_bins":257,
		"hidden_size":600,
		"bidirectional":True,
		"num_layers":2,
		"rnn":"lstm",
		"act_func":"sigmoid"
	}

	# train args
	alpha = 1.
	beta = 1.
	greedy = True

	# dataset path ...
	train_data_path = '../data/{num}speakers/tr'  # 训练集存放路径
	cv_data_path = '../data/{num}speakers/cv'
	test_data_path = '../data/{num}speakers/ts'  # 测试集存放路径

	# data preprocess args...
	window = 'blackman'
	window_size = 512
	window_shift = 128

	# train args
	speaker_nums = [2, 3, 4]
	optimizer = 'adam'
	batch_size = 16  # batch size
	use_gpu = False	# user GPU or not
	num_workers = 4  # how many workers for loading data
	print_freq = 20  # print info every N batch
	
	# test args
	evaluations = ['Acc', 'SDR', ]

	max_epoch = 15
	lr = 0.001  # learning rate
	weight_decay = 1e-5

	def _parse(self, kwargs):
		"""
		根据字典kwargs 更新 config参数
		"""
		for k, v in kwargs.items():
			if not hasattr(self, k):
				warnings.warn("Warning: opt has not attribut %s" % k)
			setattr(self, k, v)
		
		opt.device = t.device('cuda') if opt.use_gpu else t.device('cpu')


		print('user config:')
		for k, v in self.__class__.__dict__.items():
			if not k.startswith('_'):
				print(k, getattr(self, k))

opt = DefaultConfig()