/
utils.py
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/
utils.py
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import argparse
import logging
import os
import random
from pathlib import Path
import numpy as np
import torch
def create_dir(directory):
if not os.path.exists(directory):
os.makedirs(directory)
def str2bool(v):
if isinstance(v, bool):
return v
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def get_downstream_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--down_stream_task', default="iemocap", type=str,
help='''down_stream task name one of
birdsong_freefield1010 , birdsong_warblr ,
speech_commands_v1 , speech_commands_v2
libri_100 , musical_instruments , iemocap , tut_urban , voxceleb1 , musan
''')
parser.add_argument('--batch_size', default=32, type=int,
help='batch size ')
parser.add_argument('--epochs', default=30, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--resume', default = False, type=str2bool,
help='number of total epochs to run')
parser.add_argument('--pretrain_path', default=None, type=Path,
help='Path to Pretrain weights')
parser.add_argument('--freeze_effnet', default=True, type=str2bool,
help='Path to Pretrain weights')
parser.add_argument('--final_pooling_type', default='Avg', type=str,
help='valid final pooling types are Avg,Max')
parser.add_argument('--load_only_efficientNet',default = True,type =str2bool)
parser.add_argument('--tag',default = "pretrain_big",type =str)
parser.add_argument('--exp-dir',default='./exp/',type=Path,help="experiment root directory")
parser.add_argument('--lr',default=0.001,type=float,help="experiment root directory")
return parser
def freeze_effnet(model):
logger=logging.getLogger("__main__")
logger.info("freezing effnet weights")
for param in model.model_efficient.parameters():
param.requires_grad = False
def load_pretrain(path,model,
load_only_effnet=False,freeze_effnet=False):
logger=logging.getLogger("__main__")
logger.info("loading from checkpoint only weights : "+ str(path))
checkpoint = torch.load(path)
if load_only_effnet :
for key in checkpoint['state_dict'].copy():
if not 'model_efficient' in key:
del checkpoint['state_dict'][key]
mod_missing_keys,mod_unexpected_keys = model.load_state_dict(checkpoint['state_dict'],strict=False)
logger.info("Model missing keys")
logger.info(mod_missing_keys)
print(mod_missing_keys)
logger.info("Model unexpected keys")
logger.info(mod_unexpected_keys)
print(mod_unexpected_keys)
if freeze_effnet :
logger.info("freezing effnet weights")
for param in model.model_efficient.parameters():
param.requires_grad = False
logger.info("done loading")
return model
def resume_from_checkpoint(path,model,optimizer):
logger = logging.getLogger("__main__")
logger.info("loading from checkpoint : "+path)
checkpoint = torch.load(path)
start_epoch = checkpoint['epoch']
logger.info("Task :: {}".format(checkpoint['down_stream_task']))
mod_missing_keys,mod_unexpected_keys = model.load_state_dict(checkpoint['state_dict'],strict=False)
opt_missing_keys,opt_unexpected_keys = optimizer.load_state_dict(checkpoint['optimizer'])
logger.info("Model missing keys",mod_missing_keys)
logger.info("Model unexpected keys",mod_unexpected_keys)
logger.info("Opt missing keys",opt_missing_keys)
logger.info("Opt unexpected keys",opt_unexpected_keys)
logger.info("=> loaded checkpoint (epoch {})".format(checkpoint['epoch']))
return start_epoch , model, optimizer
def save_to_checkpoint(down_stream_task,dir,epoch,model,opt):
torch.save({
'down_stream_task': down_stream_task,
'epoch': epoch,
'state_dict': model.state_dict(),
'optimizer' : opt.state_dict()
},
os.path.join('.',dir,'models', 'checkpoint_' + str(epoch) + "_" + '.pth.tar')
)
def set_seed(seed = 31):
random.seed(seed)
torch.manual_seed(seed)
np.random.seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def move_to_gpu(*args):
if torch.cuda.is_available():
for item in args:
item.cuda()
class Metric(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val):
if isinstance(val, (torch.Tensor)):
val = val.numpy()
self.val = val
self.sum += np.sum(val)
self.count += np.size(val)
self.avg = self.sum / self.count
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count