def main(args):
try:
os.stat(args.save)
input("Save File Exists, OverWrite? <CTL-C> for no")
except:
os.mkdir(args.save)
ds = dataset(args)
args = dynArgs(args, ds)
m = model(args)
print(args.device)
m = m.to(args.device)
if args.ckpt:
#
# with open(args.save+"/commandLineArgs.txt") as f:
# clargs = f.read().strip().split("\n")
# argdif =[x for x in sys.argv[1:] if x not in clargs]
# assert(len(argdif)==2);
# assert([x for x in argdif if x[0]=='-']==['-ckpt'])
#
cpt = torch.load(args.ckpt)
m.load_state_dict(cpt)
starte = int(args.ckpt.split("/")[-1].split(".")[0]) + 1
args.lr = float(args.ckpt.split("-")[-1])
print('ckpt restored')
else:
with open(args.save + "/commandLineArgs.txt", 'w') as f:
f.write("\n".join(sys.argv[1:]))
starte = 0
o = torch.optim.SGD(m.parameters(), lr=args.lr, momentum=0.9)
# early stopping based on Val Loss
lastloss = 1000000
for e in range(starte, args.epochs):
print("epoch ", e, "lr", o.param_groups[0]['lr'])
train(m, o, ds, args)
vloss = evaluate(m, ds, args)
if args.lrwarm:
update_lr(o, args, e)
print("Saving model")
torch.save(
m.state_dict(), args.save + "/" + str(e) + ".vloss-" +
str(vloss)[:8] + ".lr-" + str(o.param_groups[0]['lr']))
if vloss > lastloss:
if args.lrdecay:
print("decay lr")
o.param_groups[0]['lr'] *= 0.5
lastloss = vloss
def main(args):
args.eval = True
ds = dataset(args)
args = dynArgs(args, ds)
m = model(args)
print(args.device)
m = m.to(args.device)
'''
with open(args.save+"/commandLineArgs.txt") as f:
clargs = f.read().strip().split("\n")
argdif =[x for x in sys.argv[1:] if x not in clargs]
assert(len(argdif)==2);
assert([x for x in argdif if x[0]=='-']==['-ckpt'])
'''
cpt = torch.load(args.ckpt)
m.load_state_dict(cpt)
starte = int(args.ckpt.split("/")[-1].split(".")[0]) + 1
args.lr = float(args.ckpt.split("-")[-1])
print('ckpt restored')
m.args = args
m.maxlen = args.max
m.starttok = ds.OUTP.vocab.stoi['<start>']
m.endtok = ds.OUTP.vocab.stoi['<eos>']
m.eostok = ds.OUTP.vocab.stoi['.']
args.vbsz = 1
preds, gold = test(args, ds, m)
sys.exit()
o = torch.optim.SGD(m.parameters(), lr=args.lr, momentum=0.9)
# early stopping based on Val Loss
lastloss = 1000000
for e in range(starte, starte + 1):
print("epoch ", e, "lr", o.param_groups[0]['lr'])
vloss = evaluate(m, ds, args)
m.train()
return preds,golds
'''
def metrics(preds,gold):
cands = {'generated_description'+str(i):x.strip() for i,x in enumerate(preds)}
refs = {'generated_description'+str(i):[x.strip()] for i,x in enumerate(gold)}
x = evalMetrics.Evaluate()
scores = x.evaluate(live=True, cand=cands, ref=refs)
return scores
'''
if __name__=="__main__":
args = pargs()
args.eval = True
ds = dataset(args)
args = dynArgs(args,ds)
m = model(args)
cpt = torch.load(args.save)
m.load_state_dict(cpt)
m = m.to(args.device)
m.args = args
m.maxlen = args.max
m.starttok = ds.OUTP.vocab.stoi['<start>']
m.endtok = ds.OUTP.vocab.stoi['<eos>']
m.eostok = ds.OUTP.vocab.stoi['.']
args.vbsz = 1
preds,gold = test(args,ds,m)
'''
scores = metrics(preds,gold)
for k,v in scores.items():