def main(args, logger):
random.seed(args.seed)
torch.manual_seed(args.seed)
device = torch.device('cuda' if args.cuda else 'cpu')
dat = TaggingDataset(args.data, args.batch_size, device)
dat.log(logger)
logger.log(str(args))
model = BiLSTMTagger(len(dat.word2x), len(dat.tag2y), len(dat.char2c),
args.wdim, args.cdim, args.hdim, args.dropout,
args.layers, args.nochar, args.loss,
args.init).to(device)
model.apply(get_init_weights(args.init))
optim = torch.optim.Adam(model.parameters(), lr=args.lr)
best_model = copy.deepcopy(model)
best_perf = float('-inf')
bad_epochs = 0
try:
for ep in range(1, args.epochs+1):
random.shuffle(dat.batches_train)
output = model.do_epoch(ep, dat.batches_train, args.clip, optim,
logger=logger,
check_interval=args.check_interval)
if math.isnan(output['loss']):
break
with torch.no_grad():
eval_result = model.evaluate(dat.batches_val, dat.tag2y)
perf = eval_result['acc'] if not 'O' in dat.tag2y else \
eval_result['f1_<all>']
logger.log('Epoch {:3d} | '.format(ep) +
' '.join(['{:s} {:8.3f} | '.format(key, output[key])
for key in output]) +
' val perf {:8.3f}'.format(perf), newline=False)
if perf > best_perf:
best_perf = perf
bad_epochs = 0
logger.log('\t*Updating best model*')
best_model.load_state_dict(model.state_dict())
else:
bad_epochs += 1
logger.log('\tBad epoch %d' % bad_epochs)
if bad_epochs >= args.max_bad_epochs:
break
except KeyboardInterrupt:
logger.log('-'*89)
logger.log('Exiting from training early')
return best_model, best_perf
def run_tests(args):
device = torch.device('cuda' if args.cuda else 'cpu')
dat = TaggingDataset(args.data, 8, device)
package = torch.load(args.model) if args.cuda else \
torch.load(args.model, map_location=torch.device('cpu'))
opt = package['opt']
model = BiLSTMTagger(len(dat.word2x), len(dat.tag2y), len(dat.char2c),
opt.wdim, opt.cdim, opt.hdim, opt.dropout,
opt.layers, opt.nochar, opt.loss, opt.init).to(device)
model.load_state_dict(package['sd'])
with torch.no_grad():
val_result = model.evaluate(dat.batches_val, dat.tag2y)
test_result = model.evaluate(dat.batches_test, dat.tag2y)
return val_result, test_result
