def run(self):
print("Running on", self.a.device)
self.set_device(self.a.device)
np.random.seed(self.a.seed)
torch.manual_seed(self.a.seed)
torch.backends.cudnn.benchmark = True
# create training set
if self.a.train:
log('loading corpus from %s' % self.a.train)
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])
IMAGEIDField = SparseField(sequential=False,
use_vocab=False,
batch_first=True)
SENTIDField = SparseField(sequential=False,
use_vocab=False,
batch_first=True)
# IMAGEField = SparseField(sequential=False, use_vocab=False, batch_first=True)
WordsField = Field(lower=True, include_lengths=True, batch_first=True)
PosTagsField = Field(lower=True, batch_first=True)
EntityLabelsField = MultiTokenField(lower=False, batch_first=True)
AdjMatrixField = SparseField(sequential=False,
use_vocab=False,
batch_first=True)
EntitiesField = EntityField(lower=False,
batch_first=True,
use_vocab=False)
if self.a.amr:
colcc = 'amr-colcc'
else:
colcc = 'stanford-colcc'
print(colcc)
train_set = GroundingDataset(
path=self.a.train,
img_dir=self.a.img_dir,
fields={
"id": ("IMAGEID", IMAGEIDField),
"sentence_id": ("SENTID", SENTIDField),
"words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"all-entities": ("ENTITIES", EntitiesField),
# "image": ("IMAGE", IMAGEField),
},
transform=transform,
amr=self.a.amr,
load_object=self.a.add_object,
object_ontology_file=self.a.object_class_map_file,
object_detection_pkl_file=self.a.object_detection_pkl_file,
object_detection_threshold=self.a.object_detection_threshold,
)
dev_set = GroundingDataset(
path=self.a.dev,
img_dir=self.a.img_dir,
fields={
"id": ("IMAGEID", IMAGEIDField),
"sentence_id": ("SENTID", SENTIDField),
"words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"all-entities": ("ENTITIES", EntitiesField),
# "image": ("IMAGE", IMAGEField),
},
transform=transform,
amr=self.a.amr,
load_object=self.a.add_object,
object_ontology_file=self.a.object_class_map_file,
object_detection_pkl_file=self.a.object_detection_pkl_file,
object_detection_threshold=self.a.object_detection_threshold,
)
test_set = GroundingDataset(
path=self.a.test,
img_dir=self.a.img_dir,
fields={
"id": ("IMAGEID", IMAGEIDField),
"sentence_id": ("SENTID", SENTIDField),
"words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"all-entities": ("ENTITIES", EntitiesField),
# "image": ("IMAGE", IMAGEField),
},
transform=transform,
amr=self.a.amr,
load_object=self.a.add_object,
object_ontology_file=self.a.object_class_map_file,
object_detection_pkl_file=self.a.object_detection_pkl_file,
object_detection_threshold=self.a.object_detection_threshold,
)
if self.a.webd:
pretrained_embedding = Vectors(self.a.webd,
".",
unk_init=partial(
torch.nn.init.uniform_,
a=-0.15,
b=0.15))
WordsField.build_vocab(train_set.WORDS,
dev_set.WORDS,
vectors=pretrained_embedding)
else:
WordsField.build_vocab(train_set.WORDS, dev_set.WORDS)
# WordsField.build_vocab(train_set.WORDS, dev_set.WORDS)
PosTagsField.build_vocab(train_set.POSTAGS, dev_set.POSTAGS)
EntityLabelsField.build_vocab(train_set.ENTITYLABELS,
dev_set.ENTITYLABELS)
# sr model initialization
self.a.sr_hps = eval(self.a.sr_hps)
vocab_noun = Vocab(os.path.join(self.a.vocab,
'vocab_situation_noun.pkl'),
load=True)
vocab_role = Vocab(os.path.join(self.a.vocab,
'vocab_situation_role.pkl'),
load=True)
vocab_verb = Vocab(os.path.join(self.a.vocab,
'vocab_situation_verb.pkl'),
load=True)
embeddingMatrix_noun = torch.FloatTensor(np.load(self.a.wnebd)).to(
self.device)
embeddingMatrix_verb = torch.FloatTensor(np.load(self.a.wvebd)).to(
self.device)
embeddingMatrix_role = torch.FloatTensor(np.load(self.a.wrebd)).to(
self.device)
if "wvemb_size" not in self.a.sr_hps:
self.a.sr_hps["wvemb_size"] = len(vocab_verb.id2word)
if "wremb_size" not in self.a.sr_hps:
self.a.sr_hps["wremb_size"] = len(vocab_role.id2word)
if "wnemb_size" not in self.a.sr_hps:
self.a.sr_hps["wnemb_size"] = len(vocab_noun.id2word)
if "ae_oc" not in self.a.sr_hps:
self.a.sr_hps["ae_oc"] = len(vocab_role.id2word)
self.a.ee_hps = eval(self.a.ee_hps)
if "wemb_size" not in self.a.ee_hps:
self.a.ee_hps["wemb_size"] = len(WordsField.vocab.itos)
if "pemb_size" not in self.a.ee_hps:
self.a.ee_hps["pemb_size"] = len(PosTagsField.vocab.itos)
if "psemb_size" not in self.a.ee_hps:
self.a.ee_hps["psemb_size"] = max(
[train_set.longest(),
dev_set.longest(),
test_set.longest()]) + 2
if "eemb_size" not in self.a.ee_hps:
self.a.ee_hps["eemb_size"] = len(EntityLabelsField.vocab.itos)
if "oc" not in self.a.ee_hps:
self.a.ee_hps["oc"] = 36 #???
if "ae_oc" not in self.a.ee_hps:
self.a.ee_hps["ae_oc"] = 20 #???
tester = self.get_tester()
if self.a.finetune_sr:
log('init sr model from ' + self.a.finetune_sr)
sr_model = load_sr_model(self.a.sr_hps, embeddingMatrix_noun,
embeddingMatrix_verb,
embeddingMatrix_role, self.a.finetune_sr,
self.device)
log('sr model loaded, there are %i sets of params' %
len(sr_model.parameters_requires_grads()))
else:
sr_model = load_sr_model(self.a.sr_hps, embeddingMatrix_noun,
embeddingMatrix_verb,
embeddingMatrix_role, None, self.device)
log('sr model created from scratch, there are %i sets of params' %
len(sr_model.parameters_requires_grads()))
if self.a.finetune_ee:
log('init model from ' + self.a.finetune_ee)
ee_model = load_ee_model(self.a.ee_hps, self.a.finetune_ee,
WordsField.vocab.vectors, self.device)
log('model loaded, there are %i sets of params' %
len(ee_model.parameters_requires_grads()))
else:
ee_model = load_ee_model(self.a.ee_hps, None,
WordsField.vocab.vectors, self.device)
log('model created from scratch, there are %i sets of params' %
len(ee_model.parameters_requires_grads()))
model = GroundingModel(ee_model, sr_model, self.get_device())
if self.a.optimizer == "adadelta":
optimizer_constructor = partial(
torch.optim.Adadelta,
params=model.parameters_requires_grads(),
weight_decay=self.a.l2decay)
elif self.a.optimizer == "adam":
optimizer_constructor = partial(
torch.optim.Adam,
params=model.parameters_requires_grads(),
weight_decay=self.a.l2decay)
else:
optimizer_constructor = partial(
torch.optim.SGD,
params=model.parameters_requires_grads(),
weight_decay=self.a.l2decay,
momentum=0.9)
log('optimizer in use: %s' % str(self.a.optimizer))
if not os.path.exists(self.a.out):
os.mkdir(self.a.out)
with open(os.path.join(self.a.out, "word.vec"), "wb") as f:
pickle.dump(WordsField.vocab, f)
with open(os.path.join(self.a.out, "pos.vec"), "wb") as f:
pickle.dump(PosTagsField.vocab.stoi, f)
with open(os.path.join(self.a.out, "entity.vec"), "wb") as f:
pickle.dump(EntityLabelsField.vocab.stoi, f)
with open(os.path.join(self.a.out, "ee_hyps.json"), "w") as f:
json.dump(self.a.ee_hps, f)
with open(os.path.join(self.a.out, "sr_hyps.json"), "w") as f:
json.dump(self.a.sr_hps, f)
log('init complete\n')
self.a.word_i2s = vocab_noun.id2word
self.a.label_i2s = vocab_verb.id2word # LabelField.vocab.itos
self.a.role_i2s = vocab_role.id2word
self.a.word_i2s = WordsField.vocab.itos
# self.a.label_i2s = LabelField.vocab.itos
# self.a.role_i2s = EventsField.vocab.itos
writer = SummaryWriter(os.path.join(self.a.out, "exp"))
self.a.writer = writer
grounding_train(
model=model,
train_set=train_set,
dev_set=dev_set,
test_set=test_set,
optimizer_constructor=optimizer_constructor,
epochs=self.a.epochs,
tester=tester,
parser=self.a,
other_testsets={
# "dev 1/1": dev_set1,
# "test 1/1": test_set1,
},
transform=transform,
vocab_objlabel=vocab_noun.word2id)
log('Done!')
def run(self):
print("Running on", self.a.device)
self.set_device(self.a.device)
np.random.seed(self.a.seed)
torch.manual_seed(self.a.seed)
torch.backends.cudnn.benchmark = True
#################### loading event extraction dataset ####################
if self.a.train_ee:
log('loading event extraction corpus from %s' % self.a.train_ee)
# both for grounding and ee
WordsField = Field(lower=True, include_lengths=True, batch_first=True)
PosTagsField = Field(lower=True, batch_first=True)
EntityLabelsField = MultiTokenField(lower=False, batch_first=True)
AdjMatrixField = SparseField(sequential=False, use_vocab=False, batch_first=True)
EntitiesField = EntityField(lower=False, batch_first=True, use_vocab=False)
# only for ee
LabelField = Field(lower=False, batch_first=True, pad_token='0', unk_token=None)
EventsField = EventField(lower=False, batch_first=True)
SENTIDField = SparseField(sequential=False, use_vocab=False, batch_first=True)
if self.a.amr:
colcc = 'simple-parsing'
else:
colcc = 'combined-parsing'
print(colcc)
train_ee_set = ACE2005Dataset(path=self.a.train_ee,
fields={"sentence_id": ("SENTID", SENTIDField), "words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"golden-event-mentions": ("LABEL", LabelField),
"all-events": ("EVENT", EventsField),
"all-entities": ("ENTITIES", EntitiesField)},
amr=self.a.amr, keep_events=1)
dev_ee_set = ACE2005Dataset(path=self.a.dev_ee,
fields={"sentence_id": ("SENTID", SENTIDField), "words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"golden-event-mentions": ("LABEL", LabelField),
"all-events": ("EVENT", EventsField),
"all-entities": ("ENTITIES", EntitiesField)},
amr=self.a.amr, keep_events=0)
test_ee_set = ACE2005Dataset(path=self.a.test_ee,
fields={"sentence_id": ("SENTID", SENTIDField), "words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"golden-event-mentions": ("LABEL", LabelField),
"all-events": ("EVENT", EventsField),
"all-entities": ("ENTITIES", EntitiesField)},
amr=self.a.amr, keep_events=0)
if self.a.webd:
pretrained_embedding = Vectors(self.a.webd, ".", unk_init=partial(torch.nn.init.uniform_, a=-0.15, b=0.15))
LabelField.build_vocab(train_ee_set.LABEL, dev_ee_set.LABEL, vectors=pretrained_embedding)
EventsField.build_vocab(train_ee_set.EVENT, dev_ee_set.EVENT, vectors=pretrained_embedding)
else:
LabelField.build_vocab(train_ee_set.LABEL, dev_ee_set.LABEL)
EventsField.build_vocab(train_ee_set.EVENT, dev_ee_set.EVENT)
# add role mask
self.a.role_mask = event_role_mask(self.a.train_ee, self.a.dev_ee, LabelField.vocab.stoi,
EventsField.vocab.stoi, self.device)
#################### loading SR dataset ####################
# both for grounding and sr
if self.a.train_sr:
log('loading corpus from %s' % self.a.train_sr)
transform = transforms.Compose([
transforms.Resize(256),
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.485, 0.456, 0.406),
(0.229, 0.224, 0.225))])
vocab_noun = Vocab(os.path.join(self.a.vocab, 'vocab_situation_noun.pkl'), load=True)
vocab_role = Vocab(os.path.join(self.a.vocab, 'vocab_situation_role.pkl'), load=True)
vocab_verb = Vocab(os.path.join(self.a.vocab, 'vocab_situation_verb.pkl'), load=True)
# train_sr_loader = imsitu_loader(self.a.image_dir, self.vocab_noun, self.vocab_role, self.vocab_verb, self.a.imsitu_ontology_file,
# self.a.train_sr, self.a.verb_mapping_file, self.a.role_mapping_file,
# self.a.object_class_map_file, self.a.object_detection_pkl_file,
# self.a.object_detection_threshold,
# transform, self.a.batch, shuffle=self.a.shuffle, num_workers=1) #self.a.shuffle
# dev_sr_loader = imsitu_loader(self.a.image_dir, self.vocab_noun, self.vocab_role, self.vocab_verb, self.a.imsitu_ontology_file,
# self.a.dev_sr, self.a.verb_mapping_file, self.a.role_mapping_file,
# self.a.object_class_map_file, self.a.object_detection_pkl_file,
# self.a.object_detection_threshold,
# transform, self.a.batch, shuffle=self.a.shuffle, num_workers=1)
# test_sr_loader = imsitu_loader(self.a.image_dir, self.vocab_noun, self.vocab_role, self.vocab_verb, self.a.imsitu_ontology_file,
# self.a.test_sr, self.a.verb_mapping_file, self.a.role_mapping_file,
# self.a.object_class_map_file, self.a.object_detection_pkl_file,
# self.a.object_detection_threshold,
# transform, self.a.batch, shuffle=self.a.shuffle, num_workers=1)
train_sr_set = ImSituDataset(self.a.image_dir, vocab_noun, vocab_role, vocab_verb,
LabelField.vocab.stoi, EventsField.vocab.stoi,
self.a.imsitu_ontology_file,
self.a.train_sr, self.a.verb_mapping_file,
self.a.object_class_map_file, self.a.object_detection_pkl_file,
self.a.object_detection_threshold,
transform, filter_irrelevant_verbs=self.a.filter_irrelevant_verbs,
load_object=self.a.add_object, filter_place=self.a.filter_place)
dev_sr_set = ImSituDataset(self.a.image_dir, vocab_noun, vocab_role, vocab_verb,
LabelField.vocab.stoi, EventsField.vocab.stoi,
self.a.imsitu_ontology_file,
self.a.dev_sr, self.a.verb_mapping_file,
self.a.object_class_map_file, self.a.object_detection_pkl_file,
self.a.object_detection_threshold,
transform, filter_irrelevant_verbs=self.a.filter_irrelevant_verbs,
load_object=self.a.add_object, filter_place=self.a.filter_place)
test_sr_set = ImSituDataset(self.a.image_dir, vocab_noun, vocab_role, vocab_verb,
LabelField.vocab.stoi, EventsField.vocab.stoi,
self.a.imsitu_ontology_file,
self.a.test_sr, self.a.verb_mapping_file,
self.a.object_class_map_file, self.a.object_detection_pkl_file,
self.a.object_detection_threshold,
transform, filter_irrelevant_verbs=self.a.filter_irrelevant_verbs,
load_object=self.a.add_object, filter_place=self.a.filter_place)
#################### loading grounding dataset ####################
if self.a.train_grounding:
log('loading grounding corpus from %s' % self.a.train_grounding)
# only for grounding
IMAGEIDField = SparseField(sequential=False, use_vocab=False, batch_first=True)
SENTIDField = SparseField(sequential=False, use_vocab=False, batch_first=True)
# IMAGEField = SparseField(sequential=False, use_vocab=False, batch_first=True)
train_grounding_set = GroundingDataset(path=self.a.train_grounding,
img_dir=self.a.img_dir_grounding,
fields={"id": ("IMAGEID", IMAGEIDField),
"sentence_id": ("SENTID", SENTIDField),
"words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"all-entities": ("ENTITIES", EntitiesField),
# "image": ("IMAGE", IMAGEField),
},
transform=transform,
amr=self.a.amr,
load_object=self.a.add_object,
object_ontology_file=self.a.object_class_map_file,
object_detection_pkl_file=self.a.object_detection_pkl_file_g,
object_detection_threshold=self.a.object_detection_threshold,
)
dev_grounding_set = GroundingDataset(path=self.a.dev_grounding,
img_dir=self.a.img_dir_grounding,
fields={"id": ("IMAGEID", IMAGEIDField),
"sentence_id": ("SENTID", SENTIDField),
"words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"all-entities": ("ENTITIES", EntitiesField),
# "image": ("IMAGE", IMAGEField),
},
transform=transform,
amr=self.a.amr,
load_object=self.a.add_object,
object_ontology_file=self.a.object_class_map_file,
object_detection_pkl_file=self.a.object_detection_pkl_file_g,
object_detection_threshold=self.a.object_detection_threshold,
)
test_grounding_set = GroundingDataset(path=self.a.test_grounding,
img_dir=self.a.img_dir_grounding,
fields={"id": ("IMAGEID", IMAGEIDField),
"sentence_id": ("SENTID", SENTIDField),
"words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"all-entities": ("ENTITIES", EntitiesField),
# "image": ("IMAGE", IMAGEField),
},
transform=transform,
amr=self.a.amr,
load_object=self.a.add_object,
object_ontology_file=self.a.object_class_map_file,
object_detection_pkl_file=self.a.object_detection_pkl_file_g,
object_detection_threshold=self.a.object_detection_threshold,
)
#################### build vocabulary ####################
if self.a.webd:
pretrained_embedding = Vectors(self.a.webd, ".", unk_init=partial(torch.nn.init.uniform_, a=-0.15, b=0.15))
WordsField.build_vocab(train_ee_set.WORDS, dev_ee_set.WORDS, train_grounding_set.WORDS, dev_grounding_set.WORDS, vectors=pretrained_embedding)
else:
WordsField.build_vocab(train_ee_set.WORDS, dev_ee_set.WORDS, train_grounding_set.WORDS, dev_grounding_set.WORDS)
PosTagsField.build_vocab(train_ee_set.POSTAGS, dev_ee_set.POSTAGS, train_grounding_set.POSTAGS, dev_grounding_set.POSTAGS)
EntityLabelsField.build_vocab(train_ee_set.ENTITYLABELS, dev_ee_set.ENTITYLABELS, train_grounding_set.ENTITYLABELS, dev_grounding_set.ENTITYLABELS)
consts.O_LABEL = LabelField.vocab.stoi[consts.O_LABEL_NAME]
# print("O label is", consts.O_LABEL)
consts.ROLE_O_LABEL = EventsField.vocab.stoi[consts.ROLE_O_LABEL_NAME]
# print("O label for AE is", consts.ROLE_O_LABEL)
dev_ee_set1 = ACE2005Dataset(path=self.a.dev_ee,
fields={"sentence_id": ("SENTID", SENTIDField), "words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"golden-event-mentions": ("LABEL", LabelField),
"all-events": ("EVENT", EventsField),
"all-entities": ("ENTITIES", EntitiesField)},
amr=self.a.amr, keep_events=1, only_keep=True)
test_ee_set1 = ACE2005Dataset(path=self.a.test_ee,
fields={"sentence_id": ("SENTID", SENTIDField), "words": ("WORDS", WordsField),
"pos-tags": ("POSTAGS", PosTagsField),
"golden-entity-mentions": ("ENTITYLABELS", EntityLabelsField),
colcc: ("ADJM", AdjMatrixField),
"golden-event-mentions": ("LABEL", LabelField),
"all-events": ("EVENT", EventsField),
"all-entities": ("ENTITIES", EntitiesField)},
amr=self.a.amr, keep_events=1, only_keep=True)
print("train set length", len(train_ee_set))
print("dev set length", len(dev_ee_set))
print("dev set 1/1 length", len(dev_ee_set1))
print("test set length", len(test_ee_set))
print("test set 1/1 length", len(test_ee_set1))
# sr model initialization
if not self.a.sr_hps_path:
self.a.sr_hps = eval(self.a.sr_hps)
embeddingMatrix_noun = torch.FloatTensor(np.load(self.a.wnebd)).to(self.device)
embeddingMatrix_verb = torch.FloatTensor(np.load(self.a.wvebd)).to(self.device)
embeddingMatrix_role = torch.FloatTensor(np.load(self.a.wrebd)).to(self.device)
if "wvemb_size" not in self.a.sr_hps:
self.a.sr_hps["wvemb_size"] = len(vocab_verb.id2word)
if "wremb_size" not in self.a.sr_hps:
self.a.sr_hps["wremb_size"] = len(vocab_role.id2word)
if "wnemb_size" not in self.a.sr_hps:
self.a.sr_hps["wnemb_size"] = len(vocab_noun.id2word)
self.a.ee_label_weight = torch.ones([len(LabelField.vocab.itos)]) * 5
self.a.ee_label_weight[consts.O_LABEL] = 1.0
self.a.ee_arg_weight = torch.ones([len(EventsField.vocab.itos)]) * 5
self.a.ee_hps = eval(self.a.ee_hps)
if "wemb_size" not in self.a.ee_hps:
self.a.ee_hps["wemb_size"] = len(WordsField.vocab.itos)
if "pemb_size" not in self.a.ee_hps:
self.a.ee_hps["pemb_size"] = len(PosTagsField.vocab.itos)
if "psemb_size" not in self.a.ee_hps:
# self.a.ee_hps["psemb_size"] = max([train_grounding_set.longest(), dev_grounding_set.longest(), test_grounding_set.longest()]) + 2
self.a.ee_hps["psemb_size"] = max([train_ee_set.longest(), dev_ee_set.longest(), test_ee_set.longest(), train_grounding_set.longest(), dev_grounding_set.longest(), test_grounding_set.longest()]) + 2
if "eemb_size" not in self.a.ee_hps:
self.a.ee_hps["eemb_size"] = len(EntityLabelsField.vocab.itos)
if "oc" not in self.a.ee_hps:
self.a.ee_hps["oc"] = len(LabelField.vocab.itos)
if "ae_oc" not in self.a.ee_hps:
self.a.ee_hps["ae_oc"] = len(EventsField.vocab.itos)
if "oc" not in self.a.sr_hps:
self.a.sr_hps["oc"] = len(LabelField.vocab.itos)
if "ae_oc" not in self.a.sr_hps:
self.a.sr_hps["ae_oc"] = len(EventsField.vocab.itos)
ee_tester = EDTester(LabelField.vocab.itos, EventsField.vocab.itos, self.a.ignore_time_test)
sr_tester = SRTester()
g_tester = GroundingTester()
j_tester = JointTester(self.a.ignore_place_sr_test, self.a.ignore_time_test)
ace_classifier = ACEClassifier(2 * self.a.ee_hps["lstm_dim"], self.a.ee_hps["oc"], self.a.ee_hps["ae_oc"], self.device)
if self.a.finetune_ee:
log('init ee model from ' + self.a.finetune_ee)
ee_model = load_ee_model(self.a.ee_hps, self.a.finetune_ee, WordsField.vocab.vectors, self.device, ace_classifier)
log('ee model loaded, there are %i sets of params' % len(ee_model.parameters_requires_grads()))
else:
ee_model = load_ee_model(self.a.ee_hps, None, WordsField.vocab.vectors, self.device, ace_classifier)
log('ee model created from scratch, there are %i sets of params' % len(ee_model.parameters_requires_grads()))
if self.a.finetune_sr:
log('init sr model from ' + self.a.finetune_sr)
sr_model = load_sr_model(self.a.sr_hps, embeddingMatrix_noun, embeddingMatrix_verb, embeddingMatrix_role, self.a.finetune_sr, self.device, ace_classifier, add_object=self.a.add_object, load_partial=True)
log('sr model loaded, there are %i sets of params' % len(sr_model.parameters_requires_grads()))
else:
sr_model = load_sr_model(self.a.sr_hps, embeddingMatrix_noun, embeddingMatrix_verb, embeddingMatrix_role, None, self.device, ace_classifier, add_object=self.a.add_object, load_partial=True)
log('sr model created from scratch, there are %i sets of params' % len(sr_model.parameters_requires_grads()))
model = GroundingModel(ee_model, sr_model, self.get_device())
# ee_model = torch.nn.DataParallel(ee_model)
# sr_model = torch.nn.DataParallel(sr_model)
# model = torch.nn.DataParallel(model)
if self.a.optimizer == "adadelta":
optimizer_constructor = partial(torch.optim.Adadelta, params=model.parameters_requires_grads(),
weight_decay=self.a.l2decay)
elif self.a.optimizer == "adam":
optimizer_constructor = partial(torch.optim.Adam, params=model.parameters_requires_grads(),
weight_decay=self.a.l2decay)
else:
optimizer_constructor = partial(torch.optim.SGD, params=model.parameters_requires_grads(),
weight_decay=self.a.l2decay,
momentum=0.9)
log('optimizer in use: %s' % str(self.a.optimizer))
if not os.path.exists(self.a.out):
os.mkdir(self.a.out)
with open(os.path.join(self.a.out, "word.vec"), "wb") as f:
pickle.dump(WordsField.vocab, f)
with open(os.path.join(self.a.out, "pos.vec"), "wb") as f:
pickle.dump(PosTagsField.vocab.stoi, f)
with open(os.path.join(self.a.out, "entity.vec"), "wb") as f:
pickle.dump(EntityLabelsField.vocab.stoi, f)
with open(os.path.join(self.a.out, "label.vec"), "wb") as f:
pickle.dump(LabelField.vocab.stoi, f)
with open(os.path.join(self.a.out, "role.vec"), "wb") as f:
pickle.dump(EventsField.vocab.stoi, f)
with open(os.path.join(self.a.out, "ee_hyps.json"), "w") as f:
json.dump(self.a.ee_hps, f)
with open(os.path.join(self.a.out, "sr_hyps.json"), "w") as f:
json.dump(self.a.sr_hps, f)
log('init complete\n')
# ee mappings
self.a.ee_word_i2s = WordsField.vocab.itos
self.a.ee_label_i2s = LabelField.vocab.itos
self.a.ee_role_i2s = EventsField.vocab.itos
# sr mappings
self.a.sr_word_i2s = vocab_noun.id2word
self.a.sr_label_i2s = vocab_verb.id2word # LabelField.vocab.itos
self.a.sr_role_i2s = vocab_role.id2word
writer = SummaryWriter(os.path.join(self.a.out, "exp"))
self.a.writer = writer
joint_train(
model_ee=ee_model,
model_sr=sr_model,
model_g=model,
train_set_g=train_grounding_set,
dev_set_g=dev_grounding_set,
test_set_g=test_grounding_set,
train_set_ee=train_ee_set,
dev_set_ee=dev_ee_set,
test_set_ee=test_ee_set,
train_set_sr=train_sr_set,
dev_set_sr=dev_sr_set,
test_set_sr=test_sr_set,
optimizer_constructor=optimizer_constructor,
epochs=self.a.epochs,
ee_tester=ee_tester,
sr_tester=sr_tester,
g_tester=g_tester,
j_tester=j_tester,
parser=self.a,
other_testsets={
"dev ee 1/1": dev_ee_set1,
"test ee 1/1": test_ee_set1,
},
transform=transform,
vocab_objlabel=vocab_noun.word2id
)
log('Done!')