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.test_ee:
log('testing event extraction corpus from %s' % self.a.test_ee)
if self.a.test_ee:
log('testing event extraction corpus from %s' % self.a.test_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)
print('self.a.train_ee', self.a.train_ee)
LabelField.build_vocab(train_ee_set.LABEL, dev_ee_set.LABEL)
print('LabelField.vocab.stoi', LabelField.vocab.stoi)
EventsField.build_vocab(train_ee_set.EVENT, dev_ee_set.EVENT)
print('EventsField.vocab.stoi', EventsField.vocab.stoi)
print('len(EventsField.vocab.itos)', len(EventsField.vocab.itos))
print('len(EventsField.vocab.stoi)', len(EventsField.vocab.stoi))
#################### 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)
# only need get_role_mask() and sr_mapping()
train_sr_set = ImSituDataset(self.a.image_dir, vocab_noun, vocab_role, vocab_verb,
EventsField.vocab.stoi, LabelField.vocab.stoi,
self.a.imsitu_ontology_file,
self.a.train_sr, self.a.verb_mapping_file,
None, None,
0,
transform, filter_irrelevant_verbs=self.a.filter_irrelevant_verbs,
load_object=False, 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=None,
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)
dev_grounding_set = GroundingDataset(path=self.a.dev_grounding,
img_dir=None,
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)
# test_grounding_set = GroundingDataset(path=self.a.test_grounding,
# img_dir=None,
# 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)
#################### 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)
# if "ae_oc" not in self.a.sr_hps:
# self.a.sr_hps["ae_oc"] = len(vocab_role.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
# if not self.a.ee_hps_path:
# 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)
ace_classifier = ACEClassifier(self.a.sr_hps["wemb_dim"], self.a.sr_hps["oc"], self.a.sr_hps["ae_oc"],
self.device)
ee_model = None
# # if self.a.score_ee:
# 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.score_sr:
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)
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)
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)
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
# self.a.ee_role_mask = None
# if self.a.apply_ee_role_mask:
# self.a.ee_role_mask = event_role_mask(self.a.train_ee, self.a.dev_ee, LabelField.vocab.stoi, EventsField.vocab.stoi, self.device)
# 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
self.a.role_masks = train_sr_set.get_role_mask().to_dense().to(self.device)
writer = SummaryWriter(os.path.join(self.a.out, "exp"))
self.a.writer = writer
# loading testing data
# voa_text = self.a.test_voa_text
voa_image_dir = self.a.test_voa_image
gt_voa_image = self.a.gt_voa_image
gt_voa_text = self.a.gt_voa_text
gt_voa_align =self.a.gt_voa_align
sr_verb_mapping, sr_role_mapping = train_sr_set.get_sr_mapping()
test_m2e2_set = M2E2Dataset(path=gt_voa_text,
img_dir=voa_image_dir,
fields={"image": ("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),
"golden-event-mentions": ("LABEL", LabelField),
"all-events": ("EVENT", EventsField),
},
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,
keep_events=self.a.keep_events,
)
object_results, object_label, object_detection_threshold = test_m2e2_set.get_object_results()
# build batch on cpu
test_m2e2_iter = BucketIterator(test_m2e2_set, batch_size=1, train=False,
shuffle=False, device=-1,
sort_key=lambda x: len(x.POSTAGS))
# scores = 0.0
# now_bad = 0
# restart_used = 0
print("\nStarting testing...\n")
# lr = parser.lr
# optimizer = optimizer_constructor(lr=lr)
# 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)
# if self.a.visual_voa_ee_path is not None:
# ee_visualizer = EDVisualizer(self.a.gt_voa_text)
# else:
# ee_visualizer = None
image_gt = json.load(open(gt_voa_image))
# all_y = []
# all_y_ = []
# all_events = []
# all_events_ = []
vision_result = dict()
# if self.a.visual_voa_g_path is not None and not os.path.exists(self.a.visual_voa_g_path):
# os.makedirs(self.a.visual_voa_g_path, exist_ok=True)
# if self.a.visual_voa_ee_path is not None and not os.path.exists(self.a.visual_voa_ee_path):
# os.makedirs(self.a.visual_voa_ee_path, exist_ok=True)
if self.a.visual_voa_sr_path is not None and not os.path.exists(self.a.visual_voa_sr_path):
os.makedirs(self.a.visual_voa_sr_path, exist_ok=True)
# grounding_writer = open(self.a.visual_voa_g_path, 'w')
doc_done = set()
with torch.no_grad():
model.eval()
for batch in test_m2e2_iter:
vision_result = joint_test_batch(
model_g=model,
batch_g=batch,
device=self.device,
transform=transform,
img_dir=voa_image_dir,
# ee_hyps=self.a.ee_hps,
# ee_word_i2s=self.a.ee_word_i2s,
# ee_label_i2s=self.a.ee_label_i2s,
# ee_role_i2s=self.a.ee_role_i2s,
# ee_tester=ee_tester,
# ee_visualizer=ee_visualizer,
sr_noun_i2s=self.a.sr_word_i2s,
sr_verb_i2s=self.a.sr_label_i2s,
sr_role_i2s=self.a.sr_role_i2s,
# sr_tester=sr_tester,
role_masks=self.a.role_masks,
# ee_role_mask=self.a.ee_role_mask,
# j_tester=j_tester,
image_gt=image_gt,
verb2type=sr_verb_mapping,
role2role=sr_role_mapping,
vision_result=vision_result,
# all_y=all_y,
# all_y_=all_y_,
# all_events=all_events,
# all_events_=all_events_,
# visual_g_path=self.a.visual_voa_g_path,
# visual_ee_path=self.a.visual_voa_ee_path,
load_object=self.a.add_object,
object_results=object_results,
object_label=object_label,
object_detection_threshold=object_detection_threshold,
vocab_objlabel=vocab_noun.word2id,
# apply_ee_role_mask=self.a.apply_ee_role_mask
keep_events_sr=self.a.keep_events_sr,
doc_done=doc_done,
)
print('vision_result size', len(vision_result))
# pickle.dump(vision_result, open(os.path.join(self.a.out, 'vision_result.pkl'), 'w'))
# ep, er, ef = ee_tester.calculate_report(all_y, all_y_, transform=True)
# ap, ar, af = ee_tester.calculate_sets(all_events, all_events_)
# if self.a.visual_voa_ee_path is not None:
# ee_visualizer.rewrite_brat(self.a.visual_voa_ee_path, self.a.visual_voa_ee_gt_ann)
#
# print('text ep, er, ef', ep, er, ef)
# print('text ap, ar, af', ap, ar, af)
evt_p, evt_r, evt_f1, role_scores = j_tester.calculate_report(
vision_result, voa_image_dir, self.a.visual_voa_sr_path, self.a.add_object,
keep_events_sr=self.a.keep_events_sr
)#consts.O_LABEL, consts.ROLE_O_LABEL)
print('image event ep, er, ef \n', evt_p, '\n', evt_r, '\n', evt_f1)
# if not self.a.add_object:
# print('image att_iou ap, ar, af', role_scores['role_att_iou_p'], role_scores['role_att_iou_r'],
# role_scores['role_att_iou_f1'])
# print('image att_hit ap, ar, af', role_scores['role_att_hit_p'], role_scores['role_att_hit_r'],
# role_scores['role_att_hit_f1'])
# print('image att_cor ap, ar, af', role_scores['role_att_cor_p'], role_scores['role_att_cor_r'],
# role_scores['role_att_cor_f1'])
# else:
# print('image obj_iou ap, ar, af', role_scores['role_obj_iou_p'], role_scores['role_obj_iou_r'],
# role_scores['role_obj_iou_f1'])
# print('image obj_iou_union ap, ar, af', role_scores['role_obj_iou_union_p'], role_scores['role_obj_iou_union_r'],
# role_scores['role_obj_iou_union_f1'])
for key in role_scores:
print(key)
for key_ in role_scores[key]:
print(key_, role_scores[key][key_])
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.test_ee:
log('loading event extraction corpus from %s' % self.a.test_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)
LabelField = Field(lower=False,
batch_first=True,
pad_token='0',
unk_token=None)
EventsField = EventField(lower=False, batch_first=True)
EntitiesField = EntityField(lower=False,
batch_first=True,
use_vocab=False)
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.load_grounding:
#################### 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)
# IMAGEField = SparseField(sequential=False, use_vocab=False, batch_first=True)
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))
])
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)
else:
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,
vectors=pretrained_embedding)
else:
WordsField.build_vocab(train_ee_set.WORDS, dev_ee_set.WORDS)
PosTagsField.build_vocab(train_ee_set.POSTAGS, dev_ee_set.POSTAGS)
EntityLabelsField.build_vocab(train_ee_set.ENTITYLABELS,
dev_ee_set.ENTITYLABELS)
LabelField.build_vocab(train_ee_set.LABEL, dev_ee_set.LABEL)
EventsField.build_vocab(train_ee_set.EVENT, dev_ee_set.EVENT)
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)
self.a.label_weight = torch.ones([len(LabelField.vocab.itos)]) * 5
self.a.label_weight[consts.O_LABEL] = 1.0
self.a.arg_weight = torch.ones([len(EventsField.vocab.itos)]) * 5
# 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)
# print('self.a.hps', self.a.hps)
if not self.a.hps_path:
self.a.hps = eval(self.a.hps)
if "wemb_size" not in self.a.hps:
self.a.hps["wemb_size"] = len(WordsField.vocab.itos)
if "pemb_size" not in self.a.hps:
self.a.hps["pemb_size"] = len(PosTagsField.vocab.itos)
if "psemb_size" not in self.a.hps:
self.a.hps["psemb_size"] = max([
train_ee_set.longest(),
dev_ee_set.longest(),
test_ee_set.longest()
]) + 2
if "eemb_size" not in self.a.hps:
self.a.hps["eemb_size"] = len(EntityLabelsField.vocab.itos)
if "oc" not in self.a.hps:
self.a.hps["oc"] = len(LabelField.vocab.itos)
if "ae_oc" not in self.a.hps:
self.a.hps["ae_oc"] = len(EventsField.vocab.itos)
tester = self.get_tester(LabelField.vocab.itos, EventsField.vocab.itos)
visualizer = EDVisualizer(self.a.gt_voa_text)
if self.a.finetune:
log('init model from ' + self.a.finetune)
model = load_ee_model(self.a.hps, self.a.finetune,
WordsField.vocab.vectors, self.device)
log('model loaded, there are %i sets of params' %
len(model.parameters_requires_grads()))
else:
model = load_ee_model(self.a.hps, None, WordsField.vocab.vectors,
self.device)
log('model created from scratch, there are %i sets of params' %
len(model.parameters_requires_grads()))
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
# train_iter = BucketIterator(train_ee_set, batch_size=self.a.batch,
# train=True, shuffle=False, device=-1,
# sort_key=lambda x: len(x.POSTAGS))
# dev_iter = BucketIterator(dev_ee_set, batch_size=self.a.batch, train=False,
# shuffle=False, device=-1,
# sort_key=lambda x: len(x.POSTAGS))
# test_iter = BucketIterator(test_ee_set, batch_size=self.a.batch, train=False,
# shuffle=False, device=-1,
# sort_key=lambda x: len(x.POSTAGS))
test_m2e2_set = ACE2005Dataset(
path=self.a.gt_voa_text,
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=self.a.keep_events)
# test_m2e2_set = M2E2Dataset(path=gt_voa_text,
# img_dir=voa_image_dir,
# fields={"image": ("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),
# "golden-event-mentions": ("LABEL", LabelField),
# "all-events": ("EVENT", EventsField),
# },
# 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_m2e2_iter = BucketIterator(test_m2e2_set,
batch_size=1,
train=False,
shuffle=False,
device=-1,
sort_key=lambda x: len(x.POSTAGS))
print("\nStarting testing ...\n")
# Testing Phrase
test_loss, test_ed_p, test_ed_r, test_ed_f1, \
test_ae_p, test_ae_r, test_ae_f1 = run_over_data(data_iter=test_m2e2_iter,
optimizer=None,
model=model,
need_backward=False,
MAX_STEP=len(test_m2e2_iter),
tester=tester,
visualizer=visualizer,
hyps=model.hyperparams,
device=model.device,
maxnorm=self.a.maxnorm,
word_i2s=self.a.word_i2s,
label_i2s=self.a.label_i2s,
role_i2s=self.a.role_i2s,
weight=self.a.label_weight,
arg_weight=self.a.arg_weight,
save_output=os.path.join(
self.a.out,
"test_final.txt"),
role_mask=self.a.role_mask)
print("\nFinally test loss: ", test_loss, "\ntest ed p: ", test_ed_p,
" test ed r: ", test_ed_r, " test ed f1: ", test_ed_f1,
"\ntest ae p: ", test_ae_p, " test ae r: ", test_ae_r,
" test ae f1: ", test_ae_f1)
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!')