def main():
args = parseParams()
try:
os.stat(args.savestr)
except:
os.mkdir(args.savestr)
DS = torch.load(args.datafile)
DS.mkbatches(args.bsz)
args.vsz = DS.vsz
args.evsz = len(DS.itos)
print("Vocab Size: ", args.vsz)
print("E Vocab Size: ", args.evsz)
start = 0
if args.resume:
M, optimizer = torch.load(args.resume)
start = int(args.resume.split("/")[-1].split("_")[0]) + 1
M.enc.flatten_parameters()
M.dec.flatten_parameters()
else:
M = model(args).cuda()
optimizer = torch.optim.Adam(M.parameters(), lr=args.lr)
print(M)
for epoch in range(start, args.epochs):
args.epoch = str(epoch)
trainloss = train(M, DS, args, optimizer)
print("train loss epoch", epoch, trainloss)
b = validate(M, DS, args)
print("valid bleu ", b)
torch.save((M, optimizer),
args.savestr + args.epoch + "_bleu-" + str(b))
def main():
args = parseParams()
DS = torch.load(args.datafile)
if args.debug:
args.bsz = 2
DS.train = DS.train[:2]
DS.valid = DS.valid[:2]
args.vsz = DS.vsz
args.svsz = DS.svsz
if args.resume:
M, optimizer = torch.load(args.resume)
M.enc.flatten_parameters()
M.dec.flatten_parameters()
e = args.resume.split("/")[-1] if "/" in args.resume else args.resume
e = e.split('_')[0]
e = int(e) + 1
else:
optimizer = torch.optim.Adam(M.parameters(), lr=args.lr)
M = model(args).cuda()
print(M)
for epoch in range(e, args.epochs):
args.epoch = str(epoch)
trainloss = train(M, DS, args, optimizer)
print("train loss epoch", epoch, trainloss)
b = validate(M, DS, args)
print("valid bleu ", b)
torch.save((M, optimizer),
args.savestr + args.epoch + "_bleu-" + str(b))
def main():
args = parseParams()
args.savestr = "saved_models/extra/"
try:
os.stat(args.savestr)
except:
os.mkdir(args.savestr)
if args.debug:
args.bsz = 2
args.datafile = "data/multiref_debug.pt"
DS = torch.load(args.datafile)
DS.mkbatches(args.bsz)
args.vsz = DS.vsz
M = model(args).cuda()
init_vocab(M, DS, args)
print(M)
optimizer = torch.optim.Adam(M.parameters(), lr=args.lr)
for epoch in range(args.epochs):
args.epoch = str(epoch)
trainloss = train(M, DS, args, optimizer)
print("train loss epoch", epoch, trainloss)
b = validate(M, DS, args)
print("valid bleu ", b)
torch.save((M, optimizer),
args.savestr + args.epoch + "_bleu-" + str(b))
args.temperature *= 0.5
def main():
args = parseParams()
DS = torch.load(args.datafile)
DS.args = args
models = [x for x in os.listdir(args.savestr) if x[0].isdigit()]
for m in models:
args.epoch = m
I, S, _, _ = torch.load(args.savestr + m)
if not args.cuda:
print('move to cpu')
S = S.cpu()
I = I.cpu()
S.dec.flatten_parameters()
I.enc.flatten_parameters()
I.vdec.flatten_parameters()
S.args = args
I.args = args
S.endtok = DS.vocab.index("<eos>")
S.punct = [DS.vocab.index(t) for t in ['.', '!', '?']]
validate(I, S, DS, args, m)
def main():
args = parseParams()
try:
os.stat(args.savestr)
except:
os.mkdir(args.savestr)
DS = torch.load(args.datafile)
DS.mkbatches(args.bsz)
args.vsz = DS.vsz
print("Vocab Size: ",args.vsz)
M = model(args).cuda()
print(M)
optimizer = torch.optim.Adam(M.parameters(), lr=args.lr)
for epoch in range(args.epochs):
args.epoch = str(epoch)
trainloss = train(M,DS,args,optimizer)
print("train loss epoch",epoch,trainloss)
b = validate(M,DS,args)
print("valid bleu ",b)
torch.save((M,optimizer),args.savestr+args.epoch+"_bleu-"+str(b))
def main():
args = parseParams()
DS = torch.load(args.datafile)
if args.debug:
args.bsz=2
DS.train = DS.train[:2]
DS.valid= DS.valid[:2]
args.vsz = DS.vsz
args.svsz = DS.svsz
args.catsz = len(DS.vcats)+1
M = model(args).cuda()
print(M)
optimizer = torch.optim.Adam(M.parameters(), lr=args.lr)
for epoch in range(args.epochs):
args.epoch = str(epoch)
trainloss = train(M,DS,args,optimizer)
print("train loss epoch",epoch,trainloss)
b = validate(M,DS,args)
print("valid bleu ",b)
torch.save((M,optimizer),args.savestr+args.epoch+"_bleu-"+str(b))
from preprocess import *
from arguments import s2s1cats as parseParams
if __name__ == "__main__":
args = parseParams()
DS = load_data(args)
torch.save(DS, args.datafile)
