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utils.py
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# -*- coding: utf-8 -*-
import torch
import torch.nn.functional as F
from torchtext import data
from torchtext import datasets
from torchtext.vocab import Vectors, GloVe, CharNGram, FastText
import numpy as np
from functools import wraps
import time
import sys
import logging
import os,configparser,re
def log_time_delta(func):
@wraps(func)
def _deco(*args, **kwargs):
start = time.time()
ret = func(*args, **kwargs)
end = time.time()
delta = end - start
print( "%s runed %.2f seconds"% (func.__name__,delta))
return ret
return _deco
def clip_gradient(optimizer, grad_clip):
for group in optimizer.param_groups:
for param in group['params']:
if param.grad is not None and param.requires_grad:
param.grad.data.clamp_(-grad_clip, grad_clip)
def loadData(opt):
if not opt.from_torchtext:
import dataHelper as helper
return helper.loadData(opt)
device = 0 if torch.cuda.is_available() else -1
TEXT = data.Field(lower=True, include_lengths=True, batch_first=True,fix_length=opt.max_seq_len)
LABEL = data.Field(sequential=False)
if opt.dataset=="imdb":
train, test = datasets.IMDB.splits(TEXT, LABEL)
elif opt.dataset=="sst":
train, val, test = datasets.SST.splits( TEXT, LABEL, fine_grained=True, train_subtrees=True,
filter_pred=lambda ex: ex.label != 'neutral')
elif opt.dataset=="trec":
train, test = datasets.TREC.splits(TEXT, LABEL, fine_grained=True)
else:
print("does not support this datset")
TEXT.build_vocab(train, vectors=GloVe(name='6B', dim=300))
LABEL.build_vocab(train)
# print vocab information
print('len(TEXT.vocab)', len(TEXT.vocab))
print('TEXT.vocab.vectors.size()', TEXT.vocab.vectors.size())
train_iter, test_iter = data.BucketIterator.splits((train, test), batch_size=opt.batch_size,device=device,repeat=False,shuffle=True)
opt.label_size= len(LABEL.vocab)
opt.vocab_size = len(TEXT.vocab)
opt.embedding_dim= TEXT.vocab.vectors.size()[1]
opt.embeddings = TEXT.vocab.vectors
return train_iter, test_iter
def evaluation(model,test_iter,from_torchtext=True):
model.eval()
accuracy=[]
# batch= next(iter(test_iter))
for index,batch in enumerate( test_iter):
text = batch.text[0] if from_torchtext else batch.text
predicted = model(text)
prob, idx = torch.max(predicted, 1)
percision=(idx== batch.label).float().mean()
if torch.cuda.is_available():
accuracy.append(percision.data.item() )
else:
accuracy.append(percision.data.numpy()[0] )
model.train()
return np.mean(accuracy)
def getOptimizer(params,name="adam",lr=1,momentum=None,scheduler=None):
name = name.lower().strip()
if name=="adadelta":
optimizer=torch.optim.Adadelta(params, lr=1.0*lr, rho=0.9, eps=1e-06, weight_decay=0).param_groups()
elif name == "adagrad":
optimizer=torch.optim.Adagrad(params, lr=0.01*lr, lr_decay=0, weight_decay=0)
elif name == "sparseadam":
optimizer=torch.optim.SparseAdam(params, lr=0.001*lr, betas=(0.9, 0.999), eps=1e-08)
elif name =="adamax":
optimizer=torch.optim.Adamax(params, lr=0.002*lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
elif name =="asgd":
optimizer=torch.optim.ASGD(params, lr=0.01*lr, lambd=0.0001, alpha=0.75, t0=1000000.0, weight_decay=0)
elif name == "lbfgs":
optimizer=torch.optim.LBFGS(params, lr=1*lr, max_iter=20, max_eval=None, tolerance_grad=1e-05, tolerance_change=1e-09, history_size=100, line_search_fn=None)
elif name == "rmsprop":
optimizer=torch.optim.RMSprop(params, lr=0.01*lr, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False)
elif name =="rprop":
optimizer=torch.optim.Rprop(params, lr=0.01*lr, etas=(0.5, 1.2), step_sizes=(1e-06, 50))
elif name =="sgd":
optimizer=torch.optim.SGD(params, lr=0.1*lr, momentum=0, dampening=0, weight_decay=0, nesterov=False)
elif name =="adam":
optimizer=torch.optim.Adam(params, lr=0.1*lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
else:
print("undefined optimizer, use adam in default")
optimizer=torch.optim.Adam(params, lr=0.1*lr, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
if scheduler is not None:
if scheduler == "lambdalr":
lambda1 = lambda epoch: epoch // 30
lambda2 = lambda epoch: 0.95 ** epoch
return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda1, lambda2])
elif scheduler=="steplr":
return torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1)
elif scheduler =="multisteplr":
return torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[30,80], gamma=0.1)
elif scheduler =="reducelronplateau":
return torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
else:
pass
else:
return optimizer
return
def get_lr_scheduler(name):
# todo
return None
def getLogger():
import random
random_str = str(random.randint(1,10000))
now = int(time.time())
timeArray = time.localtime(now)
timeStamp = time.strftime("%Y%m%d%H%M%S", timeArray)
log_filename = "log/" +time.strftime("%Y%m%d", timeArray)
program = os.path.basename(sys.argv[0])
logger = logging.getLogger(program)
if not os.path.exists("log"):
os.mkdir("log")
if not os.path.exists(log_filename):
os.mkdir(log_filename)
logging.basicConfig(format='%(asctime)s: %(levelname)s: %(message)s',datefmt='%a, %d %b %Y %H:%M:%S',filename=log_filename+'/qa'+timeStamp+"_"+ random_str+'.log',filemode='w')
logging.root.setLevel(level=logging.INFO)
logger.info("running %s" % ' '.join(sys.argv))
return logger
def parse_grid_parameters(file_path):
config = configparser.ConfigParser()
config.read(file_path)
config_common = config['COMMON']
dictionary = {}
for key,value in config_common.items():
array = value.split(';')
is_numberic = re.compile(r'^[-+]?[0-9.]+$')
new_array = []
for value in array:
value = value.strip()
result = is_numberic.match(value)
if result:
if type(eval(value)) == int:
value= int(value)
else :
value= float(value)
new_array.append(value)
dictionary[key] = new_array
return dictionary
def is_writeable(path, check_parent=False):
'''
Check if a given path is writeable by the current user.
:param path: The path to check
:param check_parent: If the path to check does not exist, check for the
ability to write to the parent directory instead
:returns: True or False
'''
if os.access(path, os.F_OK) and os.access(path, os.W_OK):
# The path exists and is writeable
return True
if os.access(path, os.F_OK) and not os.access(path, os.W_OK):
# The path exists and is not writeable
return False
# The path does not exists or is not writeable
if check_parent is False:
# We're not allowed to check the parent directory of the provided path
return False
# Lets get the parent directory of the provided path
parent_dir = os.path.dirname(path)
if not os.access(parent_dir, os.F_OK):
# Parent directory does not exit
return False
# Finally, return if we're allowed to write in the parent directory of the
# provided path
return os.access(parent_dir, os.W_OK)
def is_readable(path):
'''
Check if a given path is readable by the current user.
:param path: The path to check
:returns: True or False
'''
if os.access(path, os.F_OK) and os.access(path, os.R_OK):
# The path exists and is readable
return True
# The path does not exist
return False