def test(args):
test_set = Dataset.from_bin_file(args.test_file)
assert args.load_model
print('load model from [%s]' % args.load_model, file=sys.stderr)
params = torch.load(args.load_model,
map_location=lambda storage, loc: storage)
transition_system = params['transition_system']
saved_args = params['args']
saved_args.cuda = args.cuda
# set the correct domain from saved arg
args.lang = saved_args.lang
parser_cls = Registrable.by_name(args.parser)
parser = parser_cls.load(model_path=args.load_model, cuda=args.cuda)
parser.eval()
evaluator = Registrable.by_name(args.evaluator)(transition_system,
args=args)
eval_results, decode_results = evaluation.evaluate(
test_set.examples,
parser,
evaluator,
args,
verbose=args.verbose,
return_decode_result=True)
print(eval_results, file=sys.stderr)
if args.save_decode_to:
pickle.dump(decode_results, open(args.save_decode_to, 'wb'))
def pl_debug(args):
test_set = Dataset.from_bin_file(args.test_file)
test_set.examples = [x for i,x in enumerate(test_set.examples) if i in debug_idx]
assert args.load_model
print('load model from [%s]' % args.load_model, file=sys.stderr)
params = torch.load(args.load_model, map_location=lambda storage, loc: storage)
transition_system = params['transition_system']
saved_args = params['args']
saved_args.cuda = args.cuda
# set the correct domain from saved arg
args.lang = saved_args.lang
parser_cls = Registrable.by_name(args.parser)
parser = parser_cls.load(model_path=args.load_model, cuda=args.cuda)
parser.eval()
evaluator = Registrable.by_name(args.evaluator)(transition_system, args=args)
# decode_results, turning_point = [before_decodes, after_decodes],
eval_results, decode_results, debug_info = evaluation.pl_evaluate(test_set.examples, parser, evaluator, args,
verbose=args.verbose, return_decode_result=True, debug=True)
print(eval_results, file=sys.stderr)
# if args.save_decode_to:
# pickle.dump(decode_results, open(args.save_decode_to, 'wb'))
# dump_debug_info
with open("debug_info.txt", "w") as f:
for idx, ex, pred_hyps, info in zip(debug_idx, test_set.examples, decode_results, debug_info):
predictions = [x.code.replace(" ", "") for x in pred_hyps]
f.write("----------------{}------------------\n".format(idx))
f.write("Src: {}\n".format(" ".join(ex.src_sent)))
f.write("Tgt: {}\n".format(ex.tgt_code.replace(" ", "")))
f.write("Predictions:\n")
pred_results = eval_streg_predictions(predictions, ex)
for p, r in zip(predictions, pred_results):
f.write("\t{} {}\n".format(r, p.replace(" ", "")))
if info is None:
f.write("\n")
continue
prev_beam, latter_beam = info
prev_beam.sort(key=lambda hyp: -hyp.score)
latter_beam.sort(key=lambda hyp: -hyp.score)
f.write("Beam {}:\n".format(prev_beam[0].t))
for p_hyp in prev_beam:
_, partial_ast = partial_asdl_ast_to_streg_ast(p_hyp.tree)
f.write("\t{:.2f} {}\n".format(p_hyp.score, partial_ast.debug_form()))
f.write("Beam {}:\n".format(latter_beam[0].t))
for p_hyp in latter_beam:
_, partial_ast = partial_asdl_ast_to_streg_ast(p_hyp.tree)
f.write("\t{:.2f} {}\n".format(p_hyp.score, partial_ast.debug_form()))
f.write("\n")
def __init__(self,
parser_name,
model_path,
example_processor_name,
beam_size=5,
cuda=False):
print('load parser from [%s]' % model_path, file=sys.stderr)
self.parser = parser = Registrable.by_name(parser_name).load(
model_path, cuda=cuda).eval()
self.example_processor = Registrable.by_name(example_processor_name)(
parser.transition_system)
self.beam_size = beam_size
def test(args):
assert args.load_model
print('load model from [%s]' % args.load_model, file=sys.stderr)
"""
params = torch.load(args.load_model, map_location=lambda storage, loc: storage)
transition_system = params['transition_system']
saved_args = params['args']
saved_args.cuda = args.cuda
args.lang = saved_args.lang
"""
parser_cls = Registrable.by_name(args.parser)
model = parser_cls.load(model_path=args.load_model, cuda=args.cuda)
decode_results = []
count = 0
hyps = model.parse(beam_size)
decoded_hyps = []
for hyp_id, hyp in enumerate(hyps):
try:
hyp.code = model.transition_system.ast_to_surface_code(hyp.tree)
print(hyp.code)
decoded_hyps.append(hyp)
except:
pass
"""
def test(args):
tmpvocab={'null':0}
tmpvocab1={'null':0,'unk':1}
tmpvocab2={'null':0,'unk':1}
tmpvocab3={'null':0,'unk':1}
train_set = Dataset.from_bin_file(args.train_file)
from dependency import sentencetoadj,sentencetoextra_message
valid=0
for example in tqdm.tqdm(train_set.examples):
# print(example.src_sent)
example.mainnode,example.adj,example.edge,_,isv=sentencetoadj(example.src_sent,tmpvocab)
example.contains,example.pos,example.ner,example.types,example.tins,example.F1=sentencetoextra_message(example.src_sent,[item.tokens for item in example.table.header],[item.type for item in example.table.header],tmpvocab1,tmpvocab2,tmpvocab3,True)
valid+=isv
# print(example.adj)
# a=input('gh')
print('bukey',valid)
# if args.dev_file:
# dev_set = Dataset.from_bin_file(args.dev_file)
# else: dev_set = Dataset(examples=[])
test_set = Dataset.from_bin_file(args.test_file)
for example in tqdm.tqdm(test_set.examples):
# print(example.src_sent)
example.mainnode,example.adj,example.edge,_,_=sentencetoadj(example.src_sent,tmpvocab)
example.contains,example.pos,example.ner,example.types,example.tins,example.F1=sentencetoextra_message(example.src_sent,[item.tokens for item in example.table.header],[item.type for item in example.table.header],tmpvocab1,tmpvocab2,tmpvocab3,False)
bertmodels=BertModel.from_pretrained('./pretrained_models/base-uncased/')
tokenizer=BertTokenizer.from_pretrained('./pretrained_models/bert-base-uncased-vocab.txt')
assert args.load_model
print('load model from [%s]' % args.load_model, file=sys.stderr)
params = torch.load(args.load_model, map_location=lambda storage, loc: storage)
transition_system = params['transition_system']
saved_args = params['args']
saved_args.cuda = args.cuda
# set the correct domain from saved arg
args.lang = saved_args.lang
parser_cls = Registrable.by_name(args.parser)
parser = parser_cls.load(model_path=args.load_model, cuda=args.cuda,bert=bertmodels,tmpv=tmpvocab,v1=tmpvocab1,v2=tmpvocab2,v3=tmpvocab3)
parser.tokenizer=tokenizer
parser.eval()
evaluator = Registrable.by_name(args.evaluator)(transition_system, args=args)
eval_results, decode_results = evaluation.evaluate(test_set.examples, parser, evaluator, args,
verbose=args.verbose, return_decode_result=True)
print(eval_results, file=sys.stderr)
if args.save_decode_to:
pickle.dump(decode_results, open(args.save_decode_to, 'wb'))
def test(cfg: argparse.Namespace):
cli_logger.info("=== Testing ===")
experiment_base_dir = prologue(cfg)
test_set = Dataset.from_bin_file(cfg.test_file)
cli_logger.info(f"Loaded test file from [{cfg.test_file}]")
params = torch.load(cfg.load_model, map_location=lambda storage, loc: storage)
cli_logger.info(f"Loaded model from [{cfg.load_model}]")
transition_system = params['transition_system']
cli_logger.info(f"Loaded transition system [{type(transition_system)}]")
saved_args: argparse.Namespace = params['args']
saved_args.cuda = cfg.cuda
# FIXME ?? set the correct domain from saved arg
cfg.lang = saved_args.lang
dump_cfg(experiment_base_dir + "/was_trained_with.txt", cfg=saved_args.__dict__)
parser_cls = Registrable.by_name(cfg.parser)
parser = parser_cls.load(model_path=cfg.load_model, cuda=cfg.cuda)
parser.eval()
cli_logger.info(f"Loaded parser model [{cfg.parser}]")
evaluator = Registrable.by_name(cfg.evaluator)(transition_system, args=cfg)
cli_logger.info(f"Loaded evaluator [{cfg.evaluator}]")
# Do the evaluation
eval_results, decoded_results = evaluation.evaluate(
examples=test_set.examples, model=parser, evaluator=evaluator, args=cfg, verbose=cfg.verbose, return_decoded_result=True
)
cli_logger.info(eval_results)
if cfg.save_decode_to:
pickle.dump(decoded_results, open(cfg.save_decode_to, 'wb'))
cli_logger.info(f"Saved decoded results to [{cfg.save_decode_to}]")
epilogue(cfg)
cli_logger.info("=== Done ===")
def train(args):
"""Maximum Likelihood Estimation"""
# load in train/dev set
train_set = Dataset.from_bin_file(args.train_file)
vocab = pickle.load(open(args.vocab, 'rb'))
grammar = ASDLGrammar.from_text(open(args.asdl_file).read())
transition_system = Registrable.by_name(args.transition_system)(grammar)
parser_cls = Registrable.by_name(args.parser) # TODO: add arg
model = parser_cls(args, vocab, transition_system)
model.train()
if args.cuda: model.cuda()
optimizer_cls = eval('torch.optim.%s' %
args.optimizer) # FIXME: this is evil!
optimizer = optimizer_cls(model.parameters(), lr=args.lr)
nn_utils.glorot_init(model.parameters())
print('begin training, %d training examples' % len(train_set),
file=sys.stderr)
print('vocab: %s' % repr(vocab), file=sys.stderr)
epoch = train_iter = 0
report_loss = report_examples = 0.
while True:
epoch += 1
epoch_begin = time.time()
for batch_examples in train_set.batch_iter(batch_size=args.batch_size,
shuffle=True):
batch_examples = [e for e in batch_examples if len(e.tgt_actions)]
train_iter += 1
optimizer.zero_grad()
ret_val = model.score(batch_examples)
loss = -ret_val
# print(loss.data)
loss_val = torch.sum(loss).data
report_loss += loss_val
report_examples += len(batch_examples)
loss = torch.mean(loss)
loss.backward()
# clip gradient
if args.clip_grad > 0.:
grad_norm = torch.nn.utils.clip_grad_norm_(
model.parameters(), args.clip_grad)
optimizer.step()
if train_iter % args.log_every == 0:
log_str = '[Iter %d] loss=%.5f' % (train_iter, report_loss /
report_examples)
print(log_str, file=sys.stderr)
report_loss = report_examples = 0.
print('[Epoch %d] epoch elapsed %ds' %
(epoch, time.time() - epoch_begin),
file=sys.stderr)
model_file = args.save_to + '.iter%d.bin' % train_iter
print('save model to [%s]' % model_file, file=sys.stderr)
model.save(model_file)
if epoch == args.max_epoch:
print('reached max epoch, stop!', file=sys.stderr)
exit(0)
def train(args):
"""Maximum Likelihood Estimation"""
# load in train/dev set
train_set = Dataset.from_bin_file(args.train_file)
if args.dev_file:
dev_set = Dataset.from_bin_file(args.dev_file)
else: dev_set = Dataset(examples=[])
vocab = pickle.load(open(args.vocab, 'rb'))
grammar = ASDLGrammar.from_text(open(args.asdl_file).read())
transition_system = Registrable.by_name(args.transition_system)(grammar)
parser_cls = Registrable.by_name(args.parser) # TODO: add arg
model = parser_cls(args, vocab, transition_system)
model.train()
evaluator = Registrable.by_name(args.evaluator)(transition_system, args=args)
if args.cuda: model.cuda()
optimizer_cls = eval('torch.optim.%s' % args.optimizer) # FIXME: this is evil!
optimizer = optimizer_cls(model.parameters(), lr=args.lr)
if args.uniform_init:
print('uniformly initialize parameters [-%f, +%f]' % (args.uniform_init, args.uniform_init), file=sys.stderr)
nn_utils.uniform_init(-args.uniform_init, args.uniform_init, model.parameters())
elif args.glorot_init:
print('use glorot initialization', file=sys.stderr)
nn_utils.glorot_init(model.parameters())
# load pre-trained word embedding (optional)
if args.glove_embed_path:
print('load glove embedding from: %s' % args.glove_embed_path, file=sys.stderr)
glove_embedding = GloveHelper(args.glove_embed_path)
glove_embedding.load_to(model.src_embed, vocab.source)
print('begin training, %d training examples, %d dev examples' % (len(train_set), len(dev_set)), file=sys.stderr)
print('vocab: %s' % repr(vocab), file=sys.stderr)
epoch = train_iter = 0
report_loss = report_examples = report_sup_att_loss = 0.
history_dev_scores = []
num_trial = patience = 0
while True:
epoch += 1
epoch_begin = time.time()
for batch_examples in train_set.batch_iter(batch_size=args.batch_size, shuffle=True):
batch_examples = [e for e in batch_examples if len(e.tgt_actions) <= args.decode_max_time_step]
train_iter += 1
optimizer.zero_grad()
ret_val = model.score(batch_examples)
loss = -ret_val[0]
# print(loss.data)
loss_val = torch.sum(loss).data[0]
report_loss += loss_val
report_examples += len(batch_examples)
loss = torch.mean(loss)
if args.sup_attention:
att_probs = ret_val[1]
if att_probs:
sup_att_loss = -torch.log(torch.cat(att_probs)).mean()
sup_att_loss_val = sup_att_loss.data[0]
report_sup_att_loss += sup_att_loss_val
loss += sup_att_loss
loss.backward()
# clip gradient
if args.clip_grad > 0.:
grad_norm = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)
optimizer.step()
if train_iter % args.log_every == 0:
log_str = '[Iter %d] encoder loss=%.5f' % (train_iter, report_loss / report_examples)
if args.sup_attention:
log_str += ' supervised attention loss=%.5f' % (report_sup_att_loss / report_examples)
report_sup_att_loss = 0.
print(log_str, file=sys.stderr)
report_loss = report_examples = 0.
print('[Epoch %d] epoch elapsed %ds' % (epoch, time.time() - epoch_begin), file=sys.stderr)
if args.save_all_models:
model_file = args.save_to + '.iter%d.bin' % train_iter
print('save model to [%s]' % model_file, file=sys.stderr)
model.save(model_file)
# perform validation
if args.dev_file:
if epoch % args.valid_every_epoch == 0:
print('[Epoch %d] begin validation' % epoch, file=sys.stderr)
eval_start = time.time()
eval_results = evaluation.evaluate(dev_set.examples, model, evaluator, args,
verbose=True, eval_top_pred_only=args.eval_top_pred_only)
dev_score = eval_results[evaluator.default_metric]
print('[Epoch %d] evaluate details: %s, dev %s: %.5f (took %ds)' % (
epoch, eval_results,
evaluator.default_metric,
dev_score,
time.time() - eval_start), file=sys.stderr)
is_better = history_dev_scores == [] or dev_score > max(history_dev_scores)
history_dev_scores.append(dev_score)
else:
is_better = True
if args.decay_lr_every_epoch and epoch > args.lr_decay_after_epoch:
lr = optimizer.param_groups[0]['lr'] * args.lr_decay
print('decay learning rate to %f' % lr, file=sys.stderr)
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if is_better:
patience = 0
model_file = args.save_to + '.bin'
print('save the current model ..', file=sys.stderr)
print('save model to [%s]' % model_file, file=sys.stderr)
model.save(model_file)
# also save the optimizers' state
torch.save(optimizer.state_dict(), args.save_to + '.optim.bin')
elif patience < args.patience and epoch >= args.lr_decay_after_epoch:
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if epoch == args.max_epoch:
print('reached max epoch, stop!', file=sys.stderr)
exit(0)
if patience >= args.patience and epoch >= args.lr_decay_after_epoch:
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == args.max_num_trial:
print('early stop!', file=sys.stderr)
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * args.lr_decay
print('load previously best model and decay learning rate to %f' % lr, file=sys.stderr)
# load model
params = torch.load(args.save_to + '.bin', map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
if args.cuda: model = model.cuda()
# load optimizers
if args.reset_optimizer:
print('reset optimizer', file=sys.stderr)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
else:
print('restore parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(torch.load(args.save_to + '.optim.bin'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
def train(args):
"""Maximum Likelihood Estimation"""
tokenizer=BertTokenizer.from_pretrained('./pretrained_models/bert-base-uncased-vocab.txt')
bertmodels=BertModel.from_pretrained('./pretrained_models/base-uncased/')
print(len(tokenizer.vocab))
# load in train/dev set
tmpvocab={'null':0}
tmpvocab1={'null':0,'unk':1}
tmpvocab2={'null':0,'unk':1}
tmpvocab3={'null':0,'unk':1}
train_set = Dataset.from_bin_file(args.train_file)
from dependency import sentencetoadj,sentencetoextra_message
valid=0
# for example in tqdm.tqdm(train_set.examples):
## print(example.src_sent)
## example.mainnode,example.adj,example.edge,_,isv=sentencetoadj(example.src_sent,tmpvocab)
# example.contains,example.pos,example.ner,example.types,example.tins,example.F1=sentencetoextra_message(example.src_sent,[item.tokens for item in example.table.header],[item.type for item in example.table.header],tmpvocab1,tmpvocab2,tmpvocab3,True)
## valid+=isv
## print(example.src_sent)
### print( example.contains,example.pos,example.ner,example.types)
## a=input('gh')
# print('bukey',valid)
if args.dev_file:
dev_set = Dataset.from_bin_file(args.dev_file)
else: dev_set = Dataset(examples=[])
# for example in tqdm.tqdm(dev_set.examples):
## print(example.src_sent)
## example.mainnode,example.adj,example.edge,_,_=sentencetoadj(example.src_sent,tmpvocab)
# example.contains,example.pos,example.ner,example.types,example.tins,example.F1=sentencetoextra_message(example.src_sent,[item.tokens for item in example.table.header],[item.type for item in example.table.header],tmpvocab1,tmpvocab2,tmpvocab3,False)
vocab = pickle.load(open(args.vocab, 'rb'))
print(len(vocab.source))
vocab.source.copyandmerge(tokenizer)
print(len(vocab.source))
# tokenizer.update(vocab.source.word2id)
# print(len(tokenizer.vocab))
# print(tokenizer.vocab['metodiev'])
# bertmodels.resize_token_embeddings(len(vocab.source))
grammar = ASDLGrammar.from_text(open(args.asdl_file).read())
transition_system = Registrable.by_name(args.transition_system)(grammar)
parser_cls = Registrable.by_name(args.parser) # TODO: add arg
model = parser_cls(args, vocab, transition_system,tmpvocab,tmpvocab1,tmpvocab2,tmpvocab3)
model.train()
model.tokenizer=tokenizer
evaluator = Registrable.by_name(args.evaluator)(transition_system, args=args)
if args.cuda: model.cuda()
if args.uniform_init:
print('uniformly initialize parameters [-%f, +%f]' % (args.uniform_init, args.uniform_init), file=sys.stderr)
nn_utils.uniform_init(-args.uniform_init, args.uniform_init, model.parameters())
elif args.glorot_init:
print('use glorot initialization', file=sys.stderr)
nn_utils.glorot_init(model.parameters())
# load pre-trained word embedding (optional)
if args.glove_embed_path:
print('load glove embedding from: %s' % args.glove_embed_path, file=sys.stderr)
glove_embedding = GloveHelper(args.glove_embed_path)
glove_embedding.load_to(model.src_embed, vocab.source)
print([name for name,_ in model.named_parameters()])
model.bert_model=bertmodels
# print([name for name,_ in model.named_parameters()])
model.train()
if args.cuda: model.cuda()
# return 0
# a=input('haha')
optimizer_cls = eval('torch.optim.%s' % args.optimizer) # FIXME: this is evil!
# parameters=[p for name,p in model.named_parameters() if 'bert_model' not in name or 'embeddings' in name]
parameters=[p for name,p in model.named_parameters() if 'bert_model' not in name]
parameters1=[p for name,p in model.named_parameters() if 'bert_model' in name]
optimizer = optimizer_cls(parameters, lr=args.lr)
optimizer1 = optimizer_cls(parameters1, lr=0.00001)
print('begin training, %d training examples, %d dev examples' % (len(train_set), len(dev_set)), file=sys.stderr)
print('vocab: %s' % repr(vocab), file=sys.stderr)
is_better = False
epoch = train_iter = 0
report_loss = report_examples = report_sup_att_loss = 0.
report_loss1=0
history_dev_scores = []
num_trial = patience = 0
while True:
if epoch>40:break
epoch += 1
epoch_begin = time.time()
model.train()
for batch_examples in tqdm.tqdm(train_set.batch_iter(batch_size=args.batch_size, shuffle=True)):
def process(header,src_sent,tokenizer):
length1=len(header)
flat_src=[]
for item in src_sent:
flat_src.extend(tokenizer._tokenize(item))
flat=[token for item in header for token in item.tokens]
flat_head=[]
for item in flat:
flat_head.extend(tokenizer._tokenize(item))
# length2=len(flat)+length1+len(src_sent)
length2=len(flat_head)+length1+len(flat_src)
print(src_sent)
print([item.tokens for item in header])
print(flat_src)
print(flat)
a=input('hahaha')
return length2<130
batch_examples = [e for e in batch_examples if len(e.tgt_actions) <= args.decode_max_time_step and process(e.table.header,e.src_sent,tokenizer)]
train_iter += 1
optimizer.zero_grad()
optimizer1.zero_grad()
# params1=model.named_parameters()
# print([param for param,_ in params1])
# params=model.rnns.named_parameters()
# print([param for param,_ in params])
# print([type(param.grad) for _,param in model.rnns.named_parameters()])
# a=input('ghh;')
ret_val,_ = model.score(batch_examples)
loss = -ret_val[0]
loss1=ret_val[1]
# print(loss.data)
loss_val = torch.sum(loss).data.item()
report_loss += loss_val
report_loss1 += 1.0*torch.sum(ret_val[2])
report_examples += len(batch_examples)
loss = torch.mean(loss)+0*loss1+0*torch.mean(ret_val[2])
if args.sup_attention:
att_probs = ret_val[1]
if att_probs:
sup_att_loss = -torch.log(torch.cat(att_probs)).mean()
sup_att_loss_val = sup_att_loss.data.item()
report_sup_att_loss += sup_att_loss_val
loss += sup_att_loss
loss.backward()
# print([type(param.grad) for _,param in model.rnns.named_parameters()])
#
# print([type(param.grad) for param in model.parameters()])
# a=input('ghh;')
# clip gradient
if args.clip_grad > 0.:
grad_norm = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)
optimizer.step()
optimizer1.step()
loss=None
if train_iter % args.log_every == 0:
log_str = '[Iter %d] encoder loss=%.5f,coverage loss=%.5f' % (train_iter, report_loss / report_examples,report_loss1 / report_examples)
if args.sup_attention:
log_str += ' supervised attention loss=%.5f' % (report_sup_att_loss / report_examples)
report_sup_att_loss = 0.
print(log_str, file=sys.stderr)
report_loss = report_examples = 0.
report_loss1=0
print('[Epoch %d] epoch elapsed %ds' % (epoch, time.time() - epoch_begin), file=sys.stderr)
if args.save_all_models:
model_file = args.save_to + '.iter%d.bin' % train_iter
print('save model to [%s]' % model_file, file=sys.stderr)
model.save(model_file)
# perform validation
if args.dev_file and epoch>=6:
# a=input('gh')
if epoch % args.valid_every_epoch == 0:
print('[Epoch %d] begin validation' % epoch, file=sys.stderr)
eval_start = time.time()
eval_results = evaluation.evaluate(dev_set.examples, model, evaluator, args,
verbose=True, eval_top_pred_only=args.eval_top_pred_only)
dev_score = eval_results[evaluator.default_metric]
print('[Epoch %d] evaluate details: %s, dev %s: %.5f (took %ds)' % (
epoch, eval_results,
evaluator.default_metric,
dev_score,
time.time() - eval_start), file=sys.stderr)
is_better = history_dev_scores == [] or dev_score > max(history_dev_scores)
history_dev_scores.append(dev_score)
print('[Epoch %d] begin validation2' % epoch, file=sys.stderr)
# eval_start = time.time()
# eval_results = evaluation.evaluate(dev_set.examples[:2000], model, evaluator, args,
# verbose=True, eval_top_pred_only=args.eval_top_pred_only)
# dev_score = eval_results[evaluator.default_metric]
#
# print('[Epoch %d] evaluate details: %s, dev %s: %.5f (took %ds)' % (
# epoch, eval_results,
# evaluator.default_metric,
# dev_score,
# time.time() - eval_start), file=sys.stderr)
# is_better = history_dev_scores == [] or dev_score > max(history_dev_scores)
# history_dev_scores.append(dev_score)
else:
is_better = True
if args.decay_lr_every_epoch and epoch > args.lr_decay_after_epoch:
lr = optimizer.param_groups[0]['lr'] * args.lr_decay
print('decay learning rate to %f' % lr, file=sys.stderr)
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
if is_better:
patience = 0
model_file = args.save_to + '.bin'
print('save the current model ..', file=sys.stderr)
print('save model to [%s]' % model_file, file=sys.stderr)
model.save(model_file)
# also save the optimizers' state
torch.save(optimizer.state_dict(), args.save_to + '.optim.bin')
elif patience < args.patience and epoch >= args.lr_decay_after_epoch:
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if epoch == args.max_epoch:
print('reached max epoch, stop!', file=sys.stderr)
exit(0)
if patience >= args.patience and epoch >= args.lr_decay_after_epoch:
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == args.max_num_trial:
print('early stop!', file=sys.stderr)
a=input('hj')
exit(0)
# decay lr, and restore from previously best checkpoint
lr = optimizer.param_groups[0]['lr'] * args.lr_decay
print('load previously best model and decay learning rate to %f' % lr, file=sys.stderr)
# load model
params = torch.load(args.save_to + '.bin', map_location=lambda storage, loc: storage)
model.load_state_dict(params['state_dict'])
if args.cuda: model = model.cuda()
# load optimizers
if args.reset_optimizer:
print('reset optimizer', file=sys.stderr)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
else:
print('restore parameters of the optimizers', file=sys.stderr)
optimizer.load_state_dict(torch.load(args.save_to + '.optim.bin'))
# set new lr
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# reset patience
patience = 0
def train_rl(args):
test_set = Dataset.from_bin_file(args.test_file)
assert args.load_model
train_set = Dataset.from_bin_file(args.train_file)
print('load model from [%s]' % args.load_model, file=sys.stderr)
params = torch.load(args.load_model, map_location=lambda storage, loc: storage)
transition_system = params['transition_system']
saved_args = params['args']
saved_args.cuda = args.cuda
# set the correct domain from saved arg
args.lang = saved_args.lang
def getnew(model,e,shows):
example=copy.deepcopy(e)
hyps,extra = model.sample(example.src_sent, context=example.table,show=shows)
if(len(hyps)==0):
return e
actions=hyps[0].actions
example.tgt_actions = get_action_infos(example.src_sent, actions, force_copy=True)
example.actions=actions
example.extra=extra
if(shows):print('haha')
return example
parser_cls = Registrable.by_name(args.parser)
parser = parser_cls.load(model_path=args.load_model, cuda=args.cuda)
parser.train()
model=parser
evaluator = Registrable.by_name(args.evaluator)(transition_system, args=args)
if args.cuda: model.cuda()
optimizer_cls = eval('torch.optim.%s' % args.optimizer) # FIXME: this is evil!
optimizer = optimizer_cls(model.parameters(), lr=args.lr)
for e in train_set.examples:
e.actions=[item.action for item in e.tgt_actions]
train_iter=1
if(True):
parser.eval()
# print('[Epoch %d] begin validation' % epoch, file=sys.stderr)
eval_start = time.time()
eval_results = evaluation.evaluate(test_set.examples, model, evaluator, args,
verbose=True, eval_top_pred_only=args.eval_top_pred_only)
dev_score = eval_results[evaluator.default_metric]
print('[Epoch %d] evaluate details: %s, dev %s: %.5f (took %ds)' % (
train_iter, eval_results,
evaluator.default_metric,
dev_score,
time.time() - eval_start), file=sys.stderr)
a=input('df')
print(len(train_set.examples)/64)
bestscore=0
for _ in range(100):
model.train()
show=False
for batch_examples in tqdm.tqdm(list(train_set.batch_iter(batch_size=args.batch_size, shuffle=False))[0:]):
model.eval()
# if(train_iter>=23):
# show=True
batch_examples = [e for e in batch_examples if len(e.tgt_actions) <= args.decode_max_time_step]
newbatch_examples=[getnew(parser,e,show) for e in batch_examples]
# print(newbatch_examples[0].extra)
# model.train()
means=[]
model.eval()
for example in batch_examples:
hyps = model.parse(example.src_sent, context=example.table, beam_size=args.beam_size)
if(len(hyps)==0):
means.append(None)
else:means.append(hyps[0])
import asdl
weight=asdl.transition_system.get_scores(newbatch_examples,batch_examples,means)
# if(train_iter>1000):
# for i in range(len(batch_examples)):
# print('true'+str(batch_examples[i].actions))
# print('chou'+str(newbatch_examples[i].actions))
# print('max'+str(means[i].actions))
# print(weight[i])
# a=input('hj')
# if(train_iter>=22):
# print(weight)
## a=input('jj')
train_iter += 1
model.train()
optimizer.zero_grad()
ret_val,extra = model.score(batch_examples,weights=weight)
ret_val1,extra1 = model.score(batch_examples)
# print([torch.exp(item) for item in extra[0]])
# a=input('gh')
loss = -ret_val[0]
loss1= -ret_val1[0]
# print(loss.data)
loss_val = torch.sum(loss).data.item()
# report_loss += loss_val
# report_examples += len(batch_examples)
loss = torch.mean(loss)+torch.mean(loss1)*0.0
# if args.sup_attention:
# att_probs = ret_val[1]
# if att_probs:
# sup_att_loss = -torch.log(torch.cat(att_probs)).mean()
# sup_att_loss_val = sup_att_loss.data.item()
## report_sup_att_loss += sup_att_loss_val
#
# loss += sup_att_loss
loss.backward()
#
# # clip gradient
if args.clip_grad > 0.:
grad_norm = torch.nn.utils.clip_grad_norm(model.parameters(), args.clip_grad)
#
optimizer.step()
# print(train_iter)
if train_iter % 200 == 0:
parser.eval()
# print('[Epoch %d] begin validation' % epoch, file=sys.stderr)
eval_start = time.time()
eval_results = evaluation.evaluate(test_set.examples[:2000], model, evaluator, args,
verbose=True, eval_top_pred_only=args.eval_top_pred_only)
dev_score = eval_results[evaluator.default_metric]
if bestscore < dev_score:
bestscore =dev_score
print('[Epoch %d] evaluate details: %s, dev %s: %.5f (took %ds)' % (
train_iter, eval_results,
evaluator.default_metric,
dev_score,
time.time() - eval_start), file=sys.stderr)
print('hhah'+str(bestscore))
evaluator = Registrable.by_name(args.evaluator)(transition_system, args=args)
eval_results, decode_results = evaluation.evaluate(test_set.examples, parser, evaluator, args,
verbose=args.verbose, return_decode_result=True)
print(eval_results, file=sys.stderr)
if args.save_decode_to:
pickle.dump(decode_results, open(args.save_decode_to, 'wb'))