def setup_simple_classifier():
BATCH_SIZE = 1
NUM_TOKENS = 3
EMB_DIM = 300
VOCAB_SIZE = 10
NUM_CLASSES = 3
embedding = Embedding.from_pretrained(torch.zeros([VOCAB_SIZE, EMB_DIM]))
embedder = VanillaEmbedder(embedding_dim=EMB_DIM, embedding=embedding)
labels = torch.LongTensor([[1]])
encoder = BOW_Encoder(emb_dim=EMB_DIM,
embedder=embedder,
dropout_value=0,
aggregation_type="sum")
tokens = np.random.randint(0,
VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TOKENS))
tokens = torch.LongTensor(tokens)
simple_classifier = SimpleClassifier(
encoder=encoder,
encoding_dim=EMB_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=False,
)
iter_dict = {"tokens": tokens, "label": labels}
return iter_dict, simple_classifier, BATCH_SIZE, NUM_CLASSES
def setup_zero_embeddings(request):
EMB_DIM = 300
VOCAB_SIZE = 10
BATCH_SIZE = 10
aggregation_type = request.param[0]
embedding_type = request.param[1]
embedding = None
if embedding_type == "zeros":
embedding = torch.zeros([VOCAB_SIZE, EMB_DIM])
elif embedding_type == "ones":
embedding = torch.ones([VOCAB_SIZE, EMB_DIM])
embedding = nn.Embedding.from_pretrained(embedding)
embedder = VanillaEmbedder(embedding=embedding, embedding_dim=EMB_DIM)
encoder = BOW_Encoder(emb_dim=EMB_DIM,
embedder=embedder,
aggregation_type=aggregation_type)
tokens = np.random.randint(0, VOCAB_SIZE - 1, size=(BATCH_SIZE, EMB_DIM))
tokens = torch.LongTensor(tokens)
iter_dict = {"tokens": tokens}
options = {
"EMB_DIM": EMB_DIM,
"VOCAB_SIZE": VOCAB_SIZE,
"BATCH_SIZE": BATCH_SIZE,
"EMBEDDING_TYPE": embedding_type,
"AGGREGATION_TYPE": aggregation_type,
}
return encoder, iter_dict, options
def setup_lstm2seqencoder(request):
EMBEDDING_DIM = 100
VOCAB_SIZE = 1000
BATCH_SIZE = 2
HIDDEN_DIM = 1024
NUM_TIME_STEPS = 10
BIDIRECTIONAL = request.param[0]
COMBINE_STRATEGY = request.param[1]
NUM_LAYERS = request.param[2]
EMBEDDING = nn.Embedding.from_pretrained(
torch.zeros([VOCAB_SIZE, EMBEDDING_DIM]))
tokens = np.random.randint(0,
VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TIME_STEPS))
tokens = torch.LongTensor(tokens)
embedder = VanillaEmbedder(embedding=EMBEDDING,
embedding_dim=EMBEDDING_DIM)
encoder = Lstm2SeqEncoder(
emb_dim=EMBEDDING_DIM,
embedder=embedder,
dropout_value=0.0,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
rnn_bias=False,
num_layers=NUM_LAYERS,
)
return (
encoder,
{
"EMBEDDING_DIM":
EMBEDDING_DIM,
"VOCAB_SIZE":
VOCAB_SIZE,
"BATCH_SIZE":
BATCH_SIZE,
"HIDDEN_DIM":
HIDDEN_DIM,
"COMBINE_STRATEGY":
COMBINE_STRATEGY,
"BIDIRECTIONAL":
BIDIRECTIONAL,
"tokens":
tokens,
"EXPECTED_HIDDEN_DIM":
2 * HIDDEN_DIM
if COMBINE_STRATEGY == "concat" and BIDIRECTIONAL else HIDDEN_DIM,
"TIME_STEPS":
NUM_TIME_STEPS,
"NUM_LAYERS":
NUM_LAYERS,
},
)
def test_raises_error_on_wrong_combine_strategy(self,
setup_lstm2vecencoder):
with pytest.raises(AssertionError):
encoder = LSTM2VecEncoder(
emb_dim=300,
embedder=VanillaEmbedder(nn.Embedding(10, 1024),
embedding_dim=1024),
combine_strategy="add",
)
def setup_lstm2vecencoder(request):
emb_dim = 300
time_steps = 10
vocab_size = 100
batch_size = 32
embedding = nn.Embedding.from_pretrained(torch.zeros([vocab_size,
emb_dim]))
hidden_dimension = 1024
combine_strategy = request.param[1]
bidirectional = request.param[0]
tokens = np.random.randint(0,
vocab_size - 1,
size=(batch_size, time_steps))
tokens = torch.LongTensor(tokens)
iter_dict = {"tokens": tokens}
embedder = VanillaEmbedder(embedding=embedding, embedding_dim=emb_dim)
encoder = LSTM2VecEncoder(
emb_dim=emb_dim,
embedder=embedder,
dropout_value=0.0,
hidden_dim=hidden_dimension,
bidirectional=bidirectional,
combine_strategy=combine_strategy,
rnn_bias=False,
)
return (
encoder,
{
"emb_dim":
emb_dim,
"vocab_size":
vocab_size,
"hidden_dim":
2 * hidden_dimension if bidirectional
and combine_strategy == "concat" else hidden_dimension,
"bidirectional":
False,
"combine_strategy":
combine_strategy,
"tokens":
tokens,
"batch_size":
batch_size,
"iter_dict":
iter_dict,
},
)
def get_bow_lc_parsect_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
EMBEDDING_DIMENSION = config["EMBEDDING_DIMENSION"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
VOCAB_SIZE = config["VOCAB_SIZE"]
NUM_CLASSES = config["NUM_CLASSES"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIMENSION)
embedder = VanillaEmbedder(embedding_dim=EMBEDDING_DIMENSION,
embedding=embedding)
encoder = BOW_Encoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=0.0,
aggregation_type="sum",
)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMBEDDING_DIMENSION,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
dataset.print_stats()
parsect_inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return parsect_inference
def embedder():
batch_size = 32
time_steps = 10
vocab_size = 3000
embedding_dim = 300
embedding = nn.Embedding.from_pretrained(embeddings=torch.rand(
vocab_size, embedding_dim),
freeze=False)
tokens = torch.LongTensor(
torch.randint(0, vocab_size, size=(batch_size, time_steps)))
options = {
"tokens": tokens,
"embedding_size": embedding_dim,
"batch_size": batch_size,
"time_steps": time_steps,
}
embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=embedding_dim)
return embedder, options
idx - 8: classname
for idx, classname in idx2classnames.items() if idx in range(8, 16)
}
material_idx2classnames = {
idx - 16: classname
for idx, classname in idx2classnames.items() if idx in range(16, 24)
}
task_constraints = allowed_transitions(constraint_type="BIOUL",
labels=task_idx2classnames)
process_constraints = allowed_transitions(constraint_type="BIOUL",
labels=process_idx2classnames)
material_constraints = allowed_transitions(constraint_type="BIOUL",
labels=material_idx2classnames)
embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=EMBEDDING_DIMENSION)
if USE_CHAR_ENCODER:
char_embedder = VanillaEmbedder(embedding=char_embedding,
embedding_dim=CHAR_EMBEDDING_DIMENSION)
char_encoder = CharLSTMEncoder(
char_emb_dim=CHAR_EMBEDDING_DIMENSION,
char_embedder=char_embedder,
bidirectional=True,
hidden_dim=CHAR_ENCODER_HIDDEN_DIM,
combine_strategy="concat",
device=torch.device(DEVICE),
)
embedder = ConcatEmbedders([embedder, char_encoder])
EMBEDDING_DIMENSION += 2 * CHAR_ENCODER_HIDDEN_DIM
def get_elmo_bilstm_lc_infer(dirname: str):
exp_dirpath = pathlib.Path(dirname)
hyperparam_config_filepath = exp_dirpath.joinpath("config.json")
test_dataset_params = exp_dirpath.joinpath("test_dataset_params.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
with open(test_dataset_params, "r") as fp:
test_dataset_args = json.load(fp)
DEVICE = config["DEVICE"]
EMBEDDING_DIM = config["EMBEDDING_DIMENSION"]
VOCAB_SIZE = config["VOCAB_SIZE"]
HIDDEN_DIM = config["HIDDEN_DIMENSION"]
BIDIRECTIONAL = config["BIDIRECTIONAL"]
COMBINE_STRATEGY = config["COMBINE_STRATEGY"]
NUM_CLASSES = config["NUM_CLASSES"]
MODEL_SAVE_DIR = config["MODEL_SAVE_DIR"]
model_filepath = os.path.join(MODEL_SAVE_DIR, "best_model.pt")
embedding = nn.Embedding(VOCAB_SIZE, EMBEDDING_DIM)
elmo_embedder = BowElmoEmbedder(
layer_aggregation="sum",
cuda_device_id=-1 if DEVICE == "cpu" else int(
DEVICE.split("cuda:")[1]),
)
vanilla_embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=EMBEDDING_DIM)
embedders = ConcatEmbedders([vanilla_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIM + 1024,
embedder=embedders,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIM if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIM)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=encoding_dim,
num_classes=NUM_CLASSES,
classification_layer_bias=True,
)
dataset = SectLabelDataset(**test_dataset_args)
inference = ClassificationInference(model=model,
model_filepath=model_filepath,
dataset=dataset)
return inference
def setup_engine_once(
config_dict: Dict[str, str],
experiment_name: str,
train_data_filepath: pathlib.Path,
test_data_filepath: pathlib.Path,
):
DEBUG = config_dict["DEBUG"]
DEBUG_DATASET_PROPORTION = config_dict["DEBUG_DATASET_PROPORTION"]
BATCH_SIZE = config_dict["BATCH_SIZE"]
LEARNING_RATE = config_dict["LEARNING_RATE"]
NUM_EPOCHS = config_dict["NUM_EPOCHS"]
SAVE_EVERY = config_dict["SAVE_EVERY"]
LOG_TRAIN_METRICS_EVERY = config_dict["LOG_TRAIN_METRICS_EVERY"]
EMBEDDING_DIMENSION = config_dict["EMBEDDING_DIMENSION"]
CHAR_EMBEDDING_DIMENSION = config_dict["CHAR_EMBEDDING_DIMENSION"]
EMBEDDING_TYPE = config_dict["EMBEDDING_TYPE"]
MAX_NUM_WORDS = config_dict["MAX_NUM_WORDS"]
MAX_LENGTH = config_dict["MAX_LENGTH"]
DEVICE = config_dict["DEVICE"]
HIDDEN_DIM = config_dict["HIDDEN_DIM"]
BIDIRECTIONAL = config_dict["BIDIRECTIONAL"]
COMBINE_STRATEGY = config_dict["COMBINE_STRATEGY"]
MAX_CHAR_LENGTH = config_dict["MAX_CHAR_LENGTH"]
USE_CHAR_ENCODER = config_dict["USE_CHAR_ENCODER"]
CHAR_ENCODER_HIDDEN_DIM = config_dict["CHAR_ENCODER_HIDDEN_DIM"]
DROPOUT = config_dict["DROPOUT"]
EXP_NAME = experiment_name
EXP_DIR_PATH = os.path.join(OUTPUT_DIR, EXP_NAME)
if not os.path.isdir(EXP_DIR_PATH):
os.mkdir(EXP_DIR_PATH)
MODEL_SAVE_DIR = os.path.join(EXP_DIR_PATH, "checkpoints")
if not os.path.isdir(MODEL_SAVE_DIR):
os.mkdir(MODEL_SAVE_DIR)
VOCAB_STORE_LOCATION = os.path.join(EXP_DIR_PATH, "vocab.json")
CHAR_VOCAB_STORE_LOCATION = os.path.join(EXP_DIR_PATH,
"char_vocab.json")
CAPITALIZATION_VOCAB_STORE_LOCATION = os.path.join(
EXP_DIR_PATH, "capitalization_vocab.json")
CAPITALIZATION_EMBEDDING_DIMENSION = 10
TENSORBOARD_LOGDIR = os.path.join(".", "runs", EXP_NAME)
train_dataset = ParscitDataset(
filename=str(train_data_filepath),
dataset_type="train",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
max_char_length=MAX_CHAR_LENGTH,
word_vocab_store_location=VOCAB_STORE_LOCATION,
char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,
captialization_vocab_store_location=
CAPITALIZATION_VOCAB_STORE_LOCATION,
capitalization_emb_dim=CAPITALIZATION_EMBEDDING_DIMENSION,
debug=DEBUG,
debug_dataset_proportion=DEBUG_DATASET_PROPORTION,
word_embedding_type=EMBEDDING_TYPE,
word_embedding_dimension=EMBEDDING_DIMENSION,
char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,
word_start_token="<SOS>",
word_end_token="<EOS>",
word_pad_token="<PAD>",
word_unk_token="<UNK>",
word_add_start_end_token=False,
)
validation_dataset = ParscitDataset(
filename=str(test_data_filepath),
dataset_type="valid",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
max_char_length=MAX_CHAR_LENGTH,
word_vocab_store_location=VOCAB_STORE_LOCATION,
char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,
captialization_vocab_store_location=
CAPITALIZATION_VOCAB_STORE_LOCATION,
capitalization_emb_dim=CAPITALIZATION_EMBEDDING_DIMENSION,
debug=DEBUG,
debug_dataset_proportion=DEBUG_DATASET_PROPORTION,
word_embedding_type=EMBEDDING_TYPE,
word_embedding_dimension=EMBEDDING_DIMENSION,
char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,
word_start_token="<SOS>",
word_end_token="<EOS>",
word_pad_token="<PAD>",
word_unk_token="<UNK>",
word_add_start_end_token=False,
)
test_dataset = ParscitDataset(
filename=str(test_data_filepath),
dataset_type="test",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
max_char_length=MAX_CHAR_LENGTH,
word_vocab_store_location=VOCAB_STORE_LOCATION,
char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,
captialization_vocab_store_location=
CAPITALIZATION_VOCAB_STORE_LOCATION,
capitalization_emb_dim=CAPITALIZATION_EMBEDDING_DIMENSION,
debug=DEBUG,
debug_dataset_proportion=DEBUG_DATASET_PROPORTION,
word_embedding_type=EMBEDDING_TYPE,
word_embedding_dimension=EMBEDDING_DIMENSION,
char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,
word_start_token="<SOS>",
word_end_token="<EOS>",
word_pad_token="<PAD>",
word_unk_token="<UNK>",
word_add_start_end_token=False,
)
train_dataset.print_stats()
validation_dataset.print_stats()
test_dataset.print_stats()
VOCAB_SIZE = train_dataset.word_vocab.get_vocab_len()
NUM_CLASSES = train_dataset.get_num_classes()
embedding = train_dataset.word_vocab.load_embedding()
embedding = nn.Embedding.from_pretrained(embedding, freeze=False)
char_embedding = train_dataset.char_vocab.load_embedding()
char_embedding = nn.Embedding.from_pretrained(char_embedding,
freeze=False)
embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=EMBEDDING_DIMENSION)
if USE_CHAR_ENCODER:
char_embedder = VanillaEmbedder(
embedding=char_embedding,
embedding_dim=CHAR_EMBEDDING_DIMENSION)
char_encoder = CharLSTMEncoder(
char_emb_dim=CHAR_EMBEDDING_DIMENSION,
char_embedder=char_embedder,
bidirectional=True,
hidden_dim=CHAR_ENCODER_HIDDEN_DIM,
combine_strategy="concat",
device=torch.device(DEVICE),
)
embedder = ConcatEmbedders([embedder, char_encoder])
EMBEDDING_DIMENSION += 2 * CHAR_ENCODER_HIDDEN_DIM
lstm2seqencoder = Lstm2SeqEncoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=DROPOUT,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
rnn_bias=True,
device=torch.device(DEVICE),
)
model = ParscitTagger(
rnn2seqencoder=lstm2seqencoder,
num_classes=NUM_CLASSES,
hid_dim=2 * HIDDEN_DIM
if BIDIRECTIONAL and COMBINE_STRATEGY == "concat" else HIDDEN_DIM,
)
optimizer = optim.Adam(params=model.parameters(), lr=LEARNING_RATE)
metric = TokenClassificationAccuracy(
idx2labelname_mapping=train_dataset.idx2classname)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer=optimizer, mode="max", factor=0.1, patience=2)
engine = Engine(
model=model,
train_dataset=train_dataset,
validation_dataset=validation_dataset,
test_dataset=test_dataset,
optimizer=optimizer,
batch_size=BATCH_SIZE,
save_dir=MODEL_SAVE_DIR,
num_epochs=NUM_EPOCHS,
save_every=SAVE_EVERY,
log_train_metrics_every=LOG_TRAIN_METRICS_EVERY,
tensorboard_logdir=TENSORBOARD_LOGDIR,
device=torch.device(DEVICE),
metric=metric,
track_for_best="macro_fscore",
lr_scheduler=scheduler,
)
config_dict["VOCAB_STORE_LOCATION"] = VOCAB_STORE_LOCATION
config_dict["CHAR_VOCAB_STORE_LOCATION"] = CHAR_VOCAB_STORE_LOCATION
config_dict["MODEL_SAVE_DIR"] = MODEL_SAVE_DIR
config_dict["VOCAB_SIZE"] = VOCAB_SIZE
config_dict["NUM_CLASSES"] = NUM_CLASSES
with open(os.path.join(f"{EXP_DIR_PATH}", "config.json"), "w") as fp:
json.dump(config_dict, fp)
return engine
def get_science_ie_infer(dirname: str):
model_folder = pathlib.Path(dirname)
hyperparam_config_filename = model_folder.joinpath("config.json")
with open(hyperparam_config_filename, "r") as fp:
config = json.load(fp)
MAX_NUM_WORDS = config.get("MAX_NUM_WORDS", None)
MAX_LENGTH = config.get("MAX_LENGTH", None)
MAX_CHAR_LENGTH = config.get("MAX_CHAR_LENGTH", None)
VOCAB_STORE_LOCATION = config.get("VOCAB_STORE_LOCATION", None)
DEBUG = config.get("DEBUG", None)
DEBUG_DATASET_PROPORTION = config.get("DEBUG_DATASET_PROPORTION", None)
EMBEDDING_TYPE = config.get("EMBEDDING_TYPE", None)
EMBEDDING_DIMENSION = config.get("EMBEDDING_DIMENSION", None)
HIDDEN_DIMENSION = config.get("HIDDEN_DIM", None)
BIDIRECTIONAL = config.get("BIDIRECTIONAL", None)
COMBINE_STRATEGY = config.get("COMBINE_STRATEGY", None)
DEVICE = config.get("DEVICE", "cpu")
NUM_CLASSES = config.get("NUM_CLASSES", None)
MODEL_SAVE_DIR = config.get("MODEL_SAVE_DIR", None)
model_filepath = pathlib.Path(MODEL_SAVE_DIR, "best_model.pt")
CHAR_VOCAB_STORE_LOCATION = config.get("CHAR_VOCAB_STORE_LOCATION", None)
CHAR_EMBEDDING_DIMENSION = config.get("CHAR_EMBEDDING_DIMENSION", None)
USE_CHAR_ENCODER = config.get("USE_CHAR_ENCODER", None)
CHAR_ENCODER_HIDDEN_DIM = config.get("CHAR_ENCODER_HIDDEN_DIM", None)
NUM_LAYERS = config.get("NUM_LAYERS", 1)
DROPOUT = config.get("DROPOUT", 0.0)
print(f"NUM_LAYERS", NUM_LAYERS)
test_science_ie_conll_filepath = pathlib.Path(DATA_DIR, "dev_science_ie_conll.txt")
test_dataset = ScienceIEDataset(
filename=test_science_ie_conll_filepath,
dataset_type="test",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
max_char_length=MAX_CHAR_LENGTH,
word_vocab_store_location=VOCAB_STORE_LOCATION,
char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,
debug=DEBUG,
debug_dataset_proportion=DEBUG_DATASET_PROPORTION,
word_embedding_type=EMBEDDING_TYPE,
word_embedding_dimension=EMBEDDING_DIMENSION,
char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,
word_start_token="<SOS>",
word_end_token="<EOS>",
word_pad_token="<PAD>",
word_unk_token="<UNK>",
word_add_start_end_token=False,
)
embedding = test_dataset.word_vocab.load_embedding()
embedding = nn.Embedding.from_pretrained(embedding)
char_embedding = test_dataset.char_vocab.load_embedding()
char_embedding = nn.Embedding.from_pretrained(char_embedding)
classnames2idx = ScienceIEDataset.get_classname2idx()
idx2classnames = {idx: classname for classname, idx in classnames2idx.items()}
task_idx2classnames = {
idx: classname
for idx, classname in idx2classnames.items()
if idx in range(0, 8)
}
process_idx2classnames = {
idx - 8: classname
for idx, classname in idx2classnames.items()
if idx in range(8, 16)
}
material_idx2classnames = {
idx - 16: classname
for idx, classname in idx2classnames.items()
if idx in range(16, 24)
}
task_constraints = allowed_transitions(
constraint_type="BIOUL", labels=task_idx2classnames
)
process_constraints = allowed_transitions(
constraint_type="BIOUL", labels=process_idx2classnames
)
material_constraints = allowed_transitions(
constraint_type="BIOUL", labels=material_idx2classnames
)
embedder = VanillaEmbedder(embedding=embedding, embedding_dim=EMBEDDING_DIMENSION)
if USE_CHAR_ENCODER:
char_embedder = VanillaEmbedder(
embedding=char_embedding, embedding_dim=CHAR_EMBEDDING_DIMENSION
)
char_encoder = CharLSTMEncoder(
char_embedder=char_embedder,
char_emb_dim=CHAR_EMBEDDING_DIMENSION,
bidirectional=True,
hidden_dim=CHAR_ENCODER_HIDDEN_DIM,
combine_strategy="concat",
device=torch.device(DEVICE),
)
embedder = ConcatEmbedders([embedder, char_encoder])
EMBEDDING_DIMENSION += 2 * CHAR_ENCODER_HIDDEN_DIM
lstm2seqencoder = Lstm2SeqEncoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=DROPOUT,
hidden_dim=HIDDEN_DIMENSION,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
num_layers=NUM_LAYERS,
rnn_bias=True,
device=torch.device(DEVICE),
)
model = ScienceIETagger(
rnn2seqencoder=lstm2seqencoder,
num_classes=NUM_CLASSES,
hid_dim=2 * HIDDEN_DIMENSION
if BIDIRECTIONAL and COMBINE_STRATEGY == "concat"
else HIDDEN_DIMENSION,
task_constraints=task_constraints,
process_constraints=process_constraints,
material_constraints=material_constraints,
device=torch.device(DEVICE),
)
inference_client = ScienceIEInference(
model=model, model_filepath=str(model_filepath), dataset=test_dataset
)
return inference_client
def setup_science_ie_tagger(request):
EMBEDDING_DIM = 100
CHARACTER_EMBEDDING_DIM = 25
VOCAB_SIZE = 1000
BATCH_SIZE = 2
HIDDEN_DIM = 1024
CHARACTER_ENCODER_HIDDEN_DIM = 100
NUM_TIME_STEPS = 10
MAX_CHAR_LENGTH = 25
CHAR_VOCAB_SIZE = 100
BIDIRECTIONAL = request.param[0]
COMBINE_STRATEGY = request.param[1]
HAVE_CHARACTER_ENCODER = request.param[2]
DEVICE = torch.device("cpu")
NUM_CLASSES = 8
EMBEDDING = nn.Embedding.from_pretrained(
torch.zeros([VOCAB_SIZE, EMBEDDING_DIM]))
CHARACTER_EMBEDDING = nn.Embedding.from_pretrained(
torch.zeros([CHAR_VOCAB_SIZE, CHARACTER_EMBEDDING_DIM]))
tokens = np.random.randint(0,
VOCAB_SIZE,
size=(BATCH_SIZE, NUM_TIME_STEPS))
task_labels = np.random.randint(0, 8, size=(BATCH_SIZE, NUM_TIME_STEPS))
process_labels = np.random.randint(8,
16,
size=(BATCH_SIZE, NUM_TIME_STEPS))
material_labels = np.random.randint(16,
24,
size=(BATCH_SIZE, NUM_TIME_STEPS))
task_labels = torch.LongTensor(task_labels)
process_labels = torch.LongTensor(process_labels)
material_labels = torch.LongTensor(material_labels)
labels = torch.cat([task_labels, process_labels, material_labels], dim=1)
char_tokens = np.random.randint(0,
CHAR_VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TIME_STEPS,
MAX_CHAR_LENGTH))
tokens = torch.LongTensor(tokens)
labels = torch.LongTensor(labels)
char_tokens = torch.LongTensor(char_tokens)
classnames2idx = ScienceIEDataset.get_classname2idx()
idx2classnames = {
idx: classname
for classname, idx in classnames2idx.items()
}
task_idx2classnames = {
idx: classname
for idx, classname in idx2classnames.items() if idx in range(0, 8)
}
process_idx2classnames = {
idx - 8: classname
for idx, classname in idx2classnames.items() if idx in range(8, 16)
}
material_idx2classnames = {
idx - 16: classname
for idx, classname in idx2classnames.items() if idx in range(16, 24)
}
task_constraints: List[(int, int)] = allowed_transitions(
constraint_type="BIOUL", labels=task_idx2classnames)
process_constraints: List[(int, int)] = allowed_transitions(
constraint_type="BIOUL", labels=process_idx2classnames)
material_constraints: List[(int, int)] = allowed_transitions(
constraint_type="BIOUL", labels=material_idx2classnames)
embedder = VanillaEmbedder(embedding=EMBEDDING,
embedding_dim=EMBEDDING_DIM)
if HAVE_CHARACTER_ENCODER:
char_embedder = VanillaEmbedder(embedding=CHARACTER_EMBEDDING,
embedding_dim=CHARACTER_EMBEDDING_DIM)
char_encoder = CharLSTMEncoder(
char_embedder=char_embedder,
char_emb_dim=CHARACTER_EMBEDDING_DIM,
hidden_dim=CHARACTER_ENCODER_HIDDEN_DIM,
bidirectional=True,
combine_strategy="concat",
)
embedder = ConcatEmbedders([embedder, char_encoder])
EMBEDDING_DIM += 2 * CHARACTER_ENCODER_HIDDEN_DIM
encoder = Lstm2SeqEncoder(
emb_dim=EMBEDDING_DIM,
embedder=embedder,
dropout_value=0.0,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
rnn_bias=False,
)
tagger = ScienceIETagger(
rnn2seqencoder=encoder,
hid_dim=2 * HIDDEN_DIM
if BIDIRECTIONAL and COMBINE_STRATEGY == "concat" else HIDDEN_DIM,
num_classes=NUM_CLASSES,
task_constraints=task_constraints,
process_constraints=process_constraints,
material_constraints=material_constraints,
device=DEVICE,
)
return (
tagger,
{
"EMBEDDING_DIM":
EMBEDDING_DIM,
"VOCAB_SIZE":
VOCAB_SIZE,
"BATCH_SIZE":
BATCH_SIZE,
"HIDDEN_DIM":
2 * HIDDEN_DIM
if BIDIRECTIONAL and COMBINE_STRATEGY == "concat" else HIDDEN_DIM,
"COMBINE_STRATEGY":
COMBINE_STRATEGY,
"BIDIRECTIONAL":
BIDIRECTIONAL,
"tokens":
tokens,
"labels":
labels,
"EXPECTED_HIDDEN_DIM":
2 * HIDDEN_DIM
if COMBINE_STRATEGY == "concat" and BIDIRECTIONAL else HIDDEN_DIM,
"TIME_STEPS":
NUM_TIME_STEPS,
"NUM_CLASSES":
NUM_CLASSES,
"HAVE_CHAR_ENCODER":
HAVE_CHARACTER_ENCODER,
"char_tokens":
char_tokens,
},
)
def vanilla_embedder():
embedding = nn.Embedding(1000, 100)
embedder = VanillaEmbedder(embedding=embedding, embedding_dim=100)
return embedder, {"EMBEDDING_DIM": 100, "VOCAB_SIZE": 1000}
def setup_parscit_tagger(request):
EMBEDDING_DIM = 100
CHARACTER_EMBEDDING_DIM = 25
VOCAB_SIZE = 1000
BATCH_SIZE = 2
HIDDEN_DIM = 1024
CHARACTER_ENCODER_HIDDEN_DIM = 100
NUM_TIME_STEPS = 10
MAX_CHAR_LENGTH = 25
CHAR_VOCAB_SIZE = 100
BIDIRECTIONAL = request.param[0]
COMBINE_STRATEGY = request.param[1]
HAVE_CHARACTER_ENCODER = request.param[2]
NUM_CLASSES = 5
EMBEDDING = nn.Embedding.from_pretrained(
torch.zeros([VOCAB_SIZE, EMBEDDING_DIM]))
CHARACTER_EMBEDDING = nn.Embedding.from_pretrained(
torch.zeros([CHAR_VOCAB_SIZE, CHARACTER_EMBEDDING_DIM]))
tokens = np.random.randint(0,
VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TIME_STEPS))
labels = np.random.randint(0,
NUM_CLASSES - 1,
size=(BATCH_SIZE, NUM_TIME_STEPS))
char_tokens = np.random.randint(0,
CHAR_VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TIME_STEPS,
MAX_CHAR_LENGTH))
tokens = torch.LongTensor(tokens)
labels = torch.LongTensor(labels)
char_tokens = torch.LongTensor(char_tokens)
embedder = VanillaEmbedder(embedding=EMBEDDING,
embedding_dim=EMBEDDING_DIM)
if HAVE_CHARACTER_ENCODER:
char_embedder = VanillaEmbedder(embedding=CHARACTER_EMBEDDING,
embedding_dim=CHARACTER_EMBEDDING_DIM)
char_encoder = CharLSTMEncoder(
char_embedder=char_embedder,
char_emb_dim=CHARACTER_EMBEDDING_DIM,
hidden_dim=CHARACTER_ENCODER_HIDDEN_DIM,
bidirectional=True,
combine_strategy="concat",
)
embedder = ConcatEmbedders([embedder, char_encoder])
EMBEDDING_DIM = EMBEDDING_DIM + (2 * CHARACTER_ENCODER_HIDDEN_DIM)
encoder = Lstm2SeqEncoder(
emb_dim=EMBEDDING_DIM,
embedder=embedder,
dropout_value=0.0,
hidden_dim=HIDDEN_DIM,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
rnn_bias=False,
)
tagger = ParscitTagger(
rnn2seqencoder=encoder,
hid_dim=2 * HIDDEN_DIM
if BIDIRECTIONAL and COMBINE_STRATEGY == "concat" else HIDDEN_DIM,
num_classes=NUM_CLASSES,
)
return (
tagger,
{
"EMBEDDING_DIM":
EMBEDDING_DIM,
"VOCAB_SIZE":
VOCAB_SIZE,
"BATCH_SIZE":
BATCH_SIZE,
"HIDDEN_DIM":
2 * HIDDEN_DIM
if BIDIRECTIONAL and COMBINE_STRATEGY == "concat" else HIDDEN_DIM,
"COMBINE_STRATEGY":
COMBINE_STRATEGY,
"BIDIRECTIONAL":
BIDIRECTIONAL,
"tokens":
tokens,
"labels":
labels,
"EXPECTED_HIDDEN_DIM":
2 * HIDDEN_DIM
if COMBINE_STRATEGY == "concat" and BIDIRECTIONAL else HIDDEN_DIM,
"TIME_STEPS":
NUM_TIME_STEPS,
"NUM_CLASSES":
NUM_CLASSES,
"HAVE_CHAR_ENCODER":
HAVE_CHARACTER_ENCODER,
"char_tokens":
char_tokens,
},
)
def get_bilstm_crf_infer(dirname: str):
hyperparam_config_filepath = pathlib.Path(dirname, "config.json")
with open(hyperparam_config_filepath, "r") as fp:
config = json.load(fp)
MAX_NUM_WORDS = config.get("MAX_NUM_WORDS", None)
MAX_LENGTH = config.get("MAX_LENGTH", None)
MAX_CHAR_LENGTH = config.get("MAX_CHAR_LENGTH", None)
VOCAB_STORE_LOCATION = config.get("VOCAB_STORE_LOCATION", None)
DEBUG = config.get("DEBUG", None)
DEBUG_DATASET_PROPORTION = config.get("DEBUG_DATASET_PROPORTION", None)
EMBEDDING_TYPE = config.get("EMBEDDING_TYPE", None)
EMBEDDING_DIMENSION = config.get("EMBEDDING_DIMENSION", None)
HIDDEN_DIMENSION = config.get("HIDDEN_DIM", None)
BIDIRECTIONAL = config.get("BIDIRECTIONAL", None)
COMBINE_STRATEGY = config.get("COMBINE_STRATEGY", None)
DEVICE = config.get("DEVICE", "cpu")
NUM_CLASSES = config.get("NUM_CLASSES", None)
MODEL_SAVE_DIR = config.get("MODEL_SAVE_DIR", None)
model_filepath = pathlib.Path(MODEL_SAVE_DIR, "best_model.pt")
CHAR_VOCAB_STORE_LOCATION = config.get("CHAR_VOCAB_STORE_LOCATION", None)
CHAR_EMBEDDING_DIMENSION = config.get("CHAR_EMBEDDING_DIMENSION", None)
USE_CHAR_ENCODER = config.get("USE_CHAR_ENCODER", None)
CHAR_ENCODER_HIDDEN_DIM = config.get("CHAR_ENCODER_HIDDEN_DIM", None)
DROPOUT = config.get("DROPOUT", 0.0)
test_conll_filepath = pathlib.Path(DATA_DIR, "cora_conll.txt")
test_dataset = ParscitDataset(
filename=test_conll_filepath,
dataset_type="test",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
max_char_length=MAX_CHAR_LENGTH,
word_vocab_store_location=VOCAB_STORE_LOCATION,
char_vocab_store_location=CHAR_VOCAB_STORE_LOCATION,
debug=DEBUG,
debug_dataset_proportion=DEBUG_DATASET_PROPORTION,
word_embedding_type=EMBEDDING_TYPE,
word_embedding_dimension=EMBEDDING_DIMENSION,
char_embedding_dimension=CHAR_EMBEDDING_DIMENSION,
word_start_token="<SOS>",
word_end_token="<EOS>",
word_pad_token="<PAD>",
word_unk_token="<UNK>",
word_add_start_end_token=False,
)
embedding = test_dataset.word_vocab.load_embedding()
embedding = nn.Embedding.from_pretrained(embedding)
embedder = VanillaEmbedder(embedding=embedding,
embedding_dim=EMBEDDING_DIMENSION)
char_embedding = test_dataset.char_vocab.load_embedding()
char_embedding = nn.Embedding.from_pretrained(char_embedding)
if USE_CHAR_ENCODER:
char_embedder = VanillaEmbedder(embedding=char_embedding,
embedding_dim=CHAR_EMBEDDING_DIMENSION)
char_encoder = CharLSTMEncoder(
char_embedder=char_embedder,
char_emb_dim=CHAR_EMBEDDING_DIMENSION,
hidden_dim=CHAR_ENCODER_HIDDEN_DIM,
bidirectional=True,
combine_strategy="concat",
)
embedder = ConcatEmbedders([embedder, char_encoder])
EMBEDDING_DIMENSION += 2 * CHAR_ENCODER_HIDDEN_DIM
lstm2seqencoder = Lstm2SeqEncoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=DROPOUT,
hidden_dim=HIDDEN_DIMENSION,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
rnn_bias=True,
device=torch.device(DEVICE),
)
model = ParscitTagger(
rnn2seqencoder=lstm2seqencoder,
num_classes=NUM_CLASSES,
hid_dim=2 * HIDDEN_DIMENSION if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIMENSION,
)
inference_client = ParscitInference(model=model,
model_filepath=str(model_filepath),
dataset=test_dataset)
return inference_client
def setup_engine_test_with_simple_classifier(request, tmpdir_factory):
MAX_NUM_WORDS = 1000
MAX_LENGTH = 50
vocab_store_location = tmpdir_factory.mktemp("tempdir").join("vocab.json")
DEBUG = True
BATCH_SIZE = 1
NUM_TOKENS = 3
EMB_DIM = 300
train_dataset = SectLabelDataset(
filename=SECT_LABEL_FILE,
dataset_type="train",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
word_vocab_store_location=vocab_store_location,
debug=DEBUG,
word_embedding_type="random",
word_embedding_dimension=EMB_DIM,
)
validation_dataset = SectLabelDataset(
filename=SECT_LABEL_FILE,
dataset_type="valid",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
word_vocab_store_location=vocab_store_location,
debug=DEBUG,
word_embedding_type="random",
word_embedding_dimension=EMB_DIM,
)
test_dataset = SectLabelDataset(
filename=SECT_LABEL_FILE,
dataset_type="test",
max_num_words=MAX_NUM_WORDS,
max_instance_length=MAX_LENGTH,
word_vocab_store_location=vocab_store_location,
debug=DEBUG,
word_embedding_type="random",
word_embedding_dimension=EMB_DIM,
)
VOCAB_SIZE = MAX_NUM_WORDS + len(train_dataset.word_vocab.special_vocab)
NUM_CLASSES = train_dataset.get_num_classes()
NUM_EPOCHS = 1
embedding = Embedding.from_pretrained(torch.zeros([VOCAB_SIZE, EMB_DIM]))
labels = torch.LongTensor([1])
metric = PrecisionRecallFMeasure(
idx2labelname_mapping=train_dataset.idx2classname)
embedder = VanillaEmbedder(embedding_dim=EMB_DIM, embedding=embedding)
encoder = BOW_Encoder(emb_dim=EMB_DIM,
embedder=embedder,
dropout_value=0,
aggregation_type="sum")
tokens = np.random.randint(0,
VOCAB_SIZE - 1,
size=(BATCH_SIZE, NUM_TOKENS))
tokens = torch.LongTensor(tokens)
model = SimpleClassifier(
encoder=encoder,
encoding_dim=EMB_DIM,
num_classes=NUM_CLASSES,
classification_layer_bias=False,
)
optimizer = optim.SGD(model.parameters(), lr=0.01)
engine = Engine(
model,
train_dataset,
validation_dataset,
test_dataset,
optimizer=optimizer,
batch_size=BATCH_SIZE,
save_dir=tmpdir_factory.mktemp("model_save"),
num_epochs=NUM_EPOCHS,
save_every=1,
log_train_metrics_every=10,
metric=metric,
track_for_best=request.param,
)
options = {
"MAX_NUM_WORDS": MAX_NUM_WORDS,
"MAX_LENGTH": MAX_LENGTH,
"BATCH_SIZE": BATCH_SIZE,
"NUM_TOKENS": NUM_TOKENS,
"EMB_DIM": EMB_DIM,
"VOCAB_SIZE": VOCAB_SIZE,
"NUM_CLASSES": NUM_CLASSES,
"NUM_EPOCHS": NUM_EPOCHS,
}
return engine, tokens, labels, options
with open(os.path.join(EXP_DIR_PATH, "config.json"), "w") as fp:
json.dump(config, fp)
# load the word embeddings
embeddings = train_dataset.word_vocab.load_embedding()
embeddings = nn.Embedding.from_pretrained(embeddings, freeze=False)
# instantiate the elmo embedder
elmo_embedder = BowElmoEmbedder(
layer_aggregation="sum",
cuda_device_id=-1 if DEVICE == "cpu" else int(
DEVICE.split("cuda:")[1]),
)
# instantiate the vanilla embedder
vanilla_embedder = VanillaEmbedder(embedding=embeddings,
embedding_dim=EMBEDDING_DIMENSION)
# concat the embeddings
embedder = ConcatEmbedders([vanilla_embedder, elmo_embedder])
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIMENSION + 1024,
embedder=embedder,
hidden_dim=HIDDEN_DIMENSION,
bidirectional=BIDIRECTIONAL,
combine_strategy=COMBINE_STRATEGY,
device=torch.device(DEVICE),
)
encoding_dim = (2 * HIDDEN_DIMENSION if BIDIRECTIONAL
and COMBINE_STRATEGY == "concat" else HIDDEN_DIMENSION)
NUM_CLASSES = train_dataset.get_num_classes()
config["VOCAB_SIZE"] = VOCAB_SIZE
config["NUM_CLASSES"] = NUM_CLASSES
with open(os.path.join(EXP_DIR_PATH, "config.json"), "w") as fp:
json.dump(config, fp)
with open(os.path.join(EXP_DIR_PATH, "test_dataset_params.json"), "w") as fp:
json.dump(test_dataset_params, fp)
random_embeddings = train_dataset.word_vocab.load_embedding()
random_embeddings = nn.Embedding.from_pretrained(random_embeddings, freeze=False)
embedder = VanillaEmbedder(
embedding_dim=EMBEDDING_DIMENSION, embedding=random_embeddings
)
encoder = LSTM2VecEncoder(
emb_dim=EMBEDDING_DIMENSION,
embedder=embedder,
dropout_value=0.0,
hidden_dim=HIDDEN_DIMENSION,
combine_strategy=COMBINE_STRATEGY,
bidirectional=BIDIRECTIONAL,
device=torch.device(DEVICE),
)
classiier_encoding_dim = 2 * HIDDEN_DIMENSION if BIDIRECTIONAL else HIDDEN_DIMENSION
model = SimpleClassifier(
encoder=encoder,
encoding_dim=classiier_encoding_dim,
