if config["data"]["embeddings"] == "wiki.en.vec":
word2idx, idx2word, weights = load_word_vectors_from_fasttext(
os.path.join(EMB_DIR, config["data"]["embeddings"]),
config["data"]["embeddings_dim"])
else:
word2idx, idx2word, weights = load_word_vectors(
os.path.join(EMB_DIR, config["data"]["embeddings"]),
config["data"]["embeddings_dim"])
checkpoint_name = "Psych_exp_baseline"
state = load_checkpoint(checkpoint_name)
# features, feat_length = load_features(config["data"]["features"])
test_set = ClfDataset(X_test, y_test, word2idx, name="psych_test")
test_lengths = [len(x) for x in test_set.data]
test_sampler = SortedSampler(test_lengths)
test_loader = DataLoader(test_set,
sampler=test_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate())
model = Classifier(ntokens=weights.shape[0], nclasses=7, **config["model"])
model.load_state_dict(state["model"])
posteriors_list = []
predicted_list = []
#####################################################################
# Load Trained Model
def sent_clf(dataset, config, opts, transfer=False):
from logger.experiment import Experiment
opts.name = config["name"]
X_train, y_train, _, X_val, y_val, _ = dataset
vocab = None
if transfer:
opts.transfer = config["pretrained_lm"]
checkpoint = load_checkpoint(opts.transfer)
config["vocab"].update(checkpoint["config"]["vocab"])
dict_pattern_rename(checkpoint["config"]["model"],
{"rnn_": "bottom_rnn_"})
config["model"].update(checkpoint["config"]["model"])
vocab = checkpoint["vocab"]
####################################################################
# Load Preprocessed Datasets
####################################################################
if config["preprocessor"] == "twitter":
preprocessor = twitter_preprocessor()
else:
preprocessor = None
print("Building training dataset...")
train_set = ClfDataset(X_train,
y_train,
vocab=vocab,
preprocess=preprocessor,
vocab_size=config["vocab"]["size"],
seq_len=config["data"]["seq_len"])
print("Building validation dataset...")
val_set = ClfDataset(X_val,
y_val,
seq_len=train_set.seq_len,
preprocess=preprocessor,
vocab=train_set.vocab)
src_lengths = [len(x) for x in train_set.data]
val_lengths = [len(x) for x in val_set.data]
# select sampler & dataloader
train_sampler = BucketBatchSampler(src_lengths, config["batch_size"], True)
val_sampler = SortedSampler(val_lengths)
val_sampler_train = SortedSampler(src_lengths)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=opts.cores,
collate_fn=ClfCollate())
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate())
val_loader_train_dataset = DataLoader(train_set,
sampler=val_sampler_train,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate())
####################################################################
# Model
####################################################################
ntokens = len(train_set.vocab)
model = Classifier(ntokens, len(set(train_set.labels)), **config["model"])
model.to(opts.device)
clf_criterion = nn.CrossEntropyLoss()
lm_criterion = nn.CrossEntropyLoss(ignore_index=0)
embed_parameters = filter(lambda p: p.requires_grad,
model.embed.parameters())
bottom_parameters = filter(
lambda p: p.requires_grad,
chain(model.bottom_rnn.parameters(), model.vocab.parameters()))
if config["model"]["has_att"]:
top_parameters = filter(
lambda p: p.requires_grad,
chain(model.top_rnn.parameters(), model.attention.parameters(),
model.classes.parameters()))
else:
top_parameters = filter(
lambda p: p.requires_grad,
chain(model.top_rnn.parameters(), model.classes.parameters()))
embed_optimizer = optim.ASGD(embed_parameters, lr=0.0001)
rnn_optimizer = optim.ASGD(bottom_parameters)
top_optimizer = Adam(top_parameters, lr=config["top_lr"])
####################################################################
# Training Pipeline
####################################################################
# Trainer: responsible for managing the training process
trainer = SentClfTrainer(model,
train_loader,
val_loader, (lm_criterion, clf_criterion),
[embed_optimizer, rnn_optimizer, top_optimizer],
config,
opts.device,
valid_loader_train_set=val_loader_train_dataset,
unfreeze_embed=config["unfreeze_embed"],
unfreeze_rnn=config["unfreeze_rnn"])
####################################################################
# Experiment: logging and visualizing the training process
####################################################################
# exp = Experiment(opts.name, config, src_dirs=opts.source,
# output_dir=EXP_DIR)
# exp.add_metric("ep_loss_lm", "line", "epoch loss lm",
# ["TRAIN", "VAL"])
# exp.add_metric("ep_loss_cls", "line", "epoch loss class",
# ["TRAIN", "VAL"])
# exp.add_metric("ep_f1", "line", "epoch f1", ["TRAIN", "VAL"])
# exp.add_metric("ep_acc", "line", "epoch accuracy", ["TRAIN", "VAL"])
#
# exp.add_value("epoch", title="epoch summary")
# exp.add_value("progress", title="training progress")
ep_loss_lm = [10000, 10000]
ep_loss_cls = [10000, 10000]
ep_f1 = [0, 0]
ep_acc = [0, 0]
e_log = 0
progress = 0
####################################################################
# Resume Training from a previous checkpoint
####################################################################
if transfer:
print("Transferring Encoder weights ...")
dict_pattern_rename(checkpoint["model"], {
"encoder": "bottom_rnn",
"decoder": "vocab"
})
load_state_dict_subset(model, checkpoint["model"])
print(model)
####################################################################
# Training Loop
####################################################################
best_loss = None
early_stopping = EarlyStopping("min", config["patience"])
for epoch in range(0, config["epochs"]):
train_loss = trainer.train_epoch()
val_loss, y, y_pred = trainer.eval_epoch(val_set=True)
_, y_train, y_pred_train = trainer.eval_epoch(train_set=True)
# exp.update_metric("ep_loss_lm", train_loss[0], "TRAIN")
ep_loss_lm[0] = train_loss[0]
# exp.update_metric("ep_loss_lm", val_loss[0], "VAL")
ep_loss_lm[1] = val_loss[0]
# exp.update_metric("ep_loss_cls", train_loss[1], "TRAIN")
# exp.update_metric("ep_loss_cls", val_loss[1], "VAL")
ep_loss_cls[0] = train_loss[1]
ep_loss_cls[1] = val_loss[1]
# exp.update_metric("ep_f1", f1_macro(y_train, y_pred_train),
# "TRAIN")
ep_f1[0] = f1_macro(y_train, y_pred_train)
# exp.update_metric("ep_f1", f1_macro(y, y_pred), "VAL")
ep_f1[1] = f1_macro(y, y_pred)
# exp.update_metric("ep_acc", acc(y_train, y_pred_train), "TRAIN")
# exp.update_metric("ep_acc", acc(y, y_pred), "VAL")
ep_acc[0] = acc(y_train, y_pred_train)
ep_acc[1] = acc(y, y_pred)
# print('Train lm Loss : {}\nVal lm Loss : {}\nTrain cls Loss : {}\nVal cls Loss : {}\n Train f1 : {}\nVal f1 : {}\nTrain acc : {}\n Val acc : {}'.format(
# ep_loss_lm[0], ep_loss_lm[1], ep_loss_cls[0], ep_loss_cls[1], ep_f1[0], ep_f1[1], ep_acc[0], ep_acc[1]
# ))
# epoch_log = exp.log_metrics(["ep_loss_lm", "ep_loss_cls","ep_f1", "ep_acc"])
epoch_log = 'Train lm Loss : {}\nVal lm Loss : {}\nTrain cls Loss : {}\nVal cls Loss : {}\n Train f1 : {}\nVal f1 : {}\nTrain acc : {}\n Val acc : {}'.format(
ep_loss_lm[0], ep_loss_lm[1], ep_loss_cls[0], ep_loss_cls[1],
ep_f1[0], ep_f1[1], ep_acc[0], ep_acc[1])
print(epoch_log)
# exp.update_value("epoch", epoch_log)
e_log = epoch_log
# print('')
# Save the model if the val loss is the best we've seen so far.
# if not best_loss or val_loss[1] < best_loss:
# best_loss = val_loss[1]
# trainer.best_acc = acc(y, y_pred)
# trainer.best_f1 = f1_macro(y, y_pred)
# trainer.checkpoint(name=opts.name, timestamp=True)
best_loss = val_loss[1]
trainer.best_acc = acc(y, y_pred)
trainer.best_f1 = f1_macro(y, y_pred)
trainer.checkpoint(name=opts.name, tags=str(epoch))
# if early_stopping.stop(val_loss[1]):
# print("Early Stopping (according to classification loss)....")
# break
print("\n" * 2)
return best_loss, trainer.best_acc, trainer.best_f1
def sent_clf_test(dataset,
config,
opts,
transfer=False,
output_dir=None,
checkpoint_name='scv2_aux_ft_gu_last'):
opts.name = config["name"]
X_test, y_test, _, pids, human_summaries = dataset
vocab = None
if transfer:
opts.transfer = config["pretrained_lm"]
checkpoint = load_checkpoint(opts.transfer)
config["vocab"].update(checkpoint["config"]["vocab"])
dict_pattern_rename(checkpoint["config"]["model"],
{"rnn_": "bottom_rnn_"})
config["model"].update(checkpoint["config"]["model"])
vocab = checkpoint["vocab"]
####################################################################
# Load Preprocessed Datasets
####################################################################
if config["preprocessor"] == "twitter":
preprocessor = twitter_preprocessor()
else:
preprocessor = None
####################################################################
# Model
####################################################################
ntokens = 70004
model = Classifier(ntokens, len(set([0, 1])), **config["model"])
model.to(opts.device)
clf_criterion = nn.CrossEntropyLoss()
lm_criterion = nn.CrossEntropyLoss(ignore_index=0)
embed_parameters = filter(lambda p: p.requires_grad,
model.embed.parameters())
bottom_parameters = filter(
lambda p: p.requires_grad,
chain(model.bottom_rnn.parameters(), model.vocab.parameters()))
if config["model"]["has_att"]:
top_parameters = filter(
lambda p: p.requires_grad,
chain(model.top_rnn.parameters(), model.attention.parameters(),
model.classes.parameters()))
else:
top_parameters = filter(
lambda p: p.requires_grad,
chain(model.top_rnn.parameters(), model.classes.parameters()))
embed_optimizer = optim.ASGD(embed_parameters, lr=0.0001)
rnn_optimizer = optim.ASGD(bottom_parameters)
top_optimizer = Adam(top_parameters, lr=config["top_lr"])
####################################################################
# Training Pipeline
####################################################################
# Trainer: responsible for managing the training process
trainer = SentClfTrainer(model,
None,
None, (lm_criterion, clf_criterion),
[embed_optimizer, rnn_optimizer, top_optimizer],
config,
opts.device,
valid_loader_train_set=None,
unfreeze_embed=config["unfreeze_embed"],
unfreeze_rnn=config["unfreeze_rnn"],
test_loader=None)
####################################################################
# Resume Training from a previous checkpoint
####################################################################
if transfer:
print("Transferring Encoder weights ...")
dict_pattern_rename(checkpoint["model"], {
"encoder": "bottom_rnn",
"decoder": "vocab"
})
load_state_dict_subset(model, checkpoint["model"])
print(model)
_vocab = trainer.load_checkpoint(name=checkpoint_name, path=None)
test_set = ClfDataset(X_test,
y_test,
seq_len=config['data']['seq_len'],
preprocess=preprocessor,
vocab=_vocab)
test_lengths = [len(x) for x in test_set.data]
# test_sampler = SequentialSampler(test_lengths)
test_sampler = SortedSampler(test_lengths)
test_loader = DataLoader(test_set,
sampler=test_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate())
trainer.test_loader = test_loader
_, labels_array, predicted = trainer.test_epoch()
# str_to_print = ''
# for x, y in zip(labels_array, predicted):
# str_to_print += '{}\t{}\n'.format(x, y)
# print(str_to_print)
pids_dic = {}
if human_summaries is None:
for x, y, sent, z in zip(y_test, predicted, X_test, pids):
if z in pids_dic:
pids_dic[z].append([x, y, sent])
else:
pids_dic[z] = [[x, y, sent]]
else:
for x, y, sent, z, h_summary in zip(y_test, predicted, X_test, pids,
human_summaries):
if z in pids_dic:
pids_dic[z].append([x, y, sent, h_summary])
else:
pids_dic[z] = [[x, y, sent, h_summary]]
import os
if not os.path.exists('{}/ref_abs'.format(output_dir)):
os.mkdir('{}/ref_abs'.format(output_dir))
if not os.path.exists('{}/dec'.format(output_dir)):
os.mkdir('{}/dec'.format(output_dir))
file_index = 0
for elem_key in pids_dic:
write_ref = open('{}/ref_abs/{}.ref'.format(output_dir, file_index),
'w')
write_pred = open('{}/dec/{}.dec'.format(output_dir, file_index), 'w')
if human_summaries is None:
for pair in pids_dic[elem_key]:
if pair[0] == 1:
write_ref.write(pair[2] + '\n')
if pair[1] == 1:
write_pred.write(pair[2] + '\n')
else:
write_ref.write(pids_dic[elem_key][0][3] + '\n')
for pair in pids_dic[elem_key]:
if pair[1] == 1:
write_pred.write(pair[2] + '\n')
write_ref.close()
write_pred.close()
file_index += 1
def clf_features_baseline_runner(yaml,
word2idx,
idx2word,
weights,
cluster=False):
if cluster is False:
from logger.experiment import Experiment
# torch.manual_seed(0)
# torch.backends.cudnn.deterministic = True
# torch.backends.cudnn.benchmark = False
opts, config = train_options(yaml)
####################################################################
# Data Loading and Preprocessing
####################################################################
X_train, y_train, X_val, y_val, X_test, y_test = load_dataset(
config["data"]["dataset"])
# load word embeddings
# if config["data"]["embeddings"] == "wiki.en.vec":
# word2idx, idx2word, weights = load_word_vectors_from_fasttext(
# os.path.join(EMB_DIR, config["data"]["embeddings"]),
# config["data"]["embeddings_dim"])
# else:
# word2idx, idx2word, weights = load_word_vectors(
# os.path.join(EMB_DIR, config["data"]["embeddings"]),
# config["data"]["embeddings_dim"])
####################################################################
# Linguistic Features Loading and Selection
####################################################################
# Any features/lexicon should be in the form of a dictionary
# For example: lex = {'word':[0., 1., ..., 0.]}
# load affect features
print("Loading linguistic features...")
# todo: streamline feature loading pipeline
features, feat_length = load_features(config["data"]["features"])
# assert ... same len
# build dataset
print("Building training dataset...")
train_set = ClfDataset(X_train,
y_train,
word2idx,
feat_length=feat_length,
features_dict=features)
print("Building validation dataset...")
val_set = ClfDataset(X_test,
y_test,
word2idx,
features_dict=features,
feat_length=feat_length)
test_set = ClfDataset(X_test,
y_test,
word2idx,
features_dict=features,
feat_length=feat_length)
train_set.truncate(500)
val_set.truncate(100)
src_lengths = [len(x) for x in train_set.data]
val_lengths = [len(x) for x in val_set.data]
test_lengths = [len(x) for x in test_set.data]
# select sampler & dataloader
train_sampler = BucketBatchSampler(src_lengths, config["batch_size"], True)
val_sampler = SortedSampler(val_lengths)
val_sampler_train = SortedSampler(src_lengths)
test_sampler = SortedSampler(test_lengths)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
val_loader_train_dataset = DataLoader(train_set,
sampler=val_sampler_train,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
test_loader = DataLoader(test_set,
sampler=test_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate_withFeatures())
####################################################################
# Model
####################################################################
model = BaselineConcClassifier(ntokens=weights.shape[0],
nclasses=len(set(train_set.labels)),
feat_size=feat_length,
**config["model"])
model.word_embedding.embedding.weight = nn.Parameter(
torch.from_numpy(weights), requires_grad=False)
model.to(opts.device)
print(model)
####################################################################
# Count total parameters of model
####################################################################
total_params = sum(p.numel() for p in model.parameters())
total_trainable_params = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print("Total Params:", number_h(total_params))
print("Total Trainable Params:", number_h(total_trainable_params))
if config["class_weights"]:
class_weights = class_weigths(train_set.labels, to_pytorch=True)
class_weights = class_weights.to(opts.device)
criterion = nn.CrossEntropyLoss(weight=class_weights)
else:
criterion = nn.CrossEntropyLoss()
clf_parameters = filter(lambda p: p.requires_grad, model.parameters())
clf_optimizer = Adam(clf_parameters, weight_decay=1e-5)
####################################################################
# Training Pipeline
####################################################################
_outputs = []
def batch_callback(i_batch, losses, batch_outputs):
_outputs.append(batch_outputs)
if trainer.step % config["log_interval"] == 0:
outputs = list(zip(*_outputs))
_losses = numpy.array(losses[-config["log_interval"]:]).mean(0)
exp.update_metric("clf-loss", _losses)
_y_hat = torch.cat(outputs[0]).max(-1)[1].cpu().data.numpy()
_y = torch.cat(outputs[1]).cpu().data.numpy()
f1 = f1_score(_y, _y_hat, average='macro')
exp.update_metric("f1-train", f1)
losses_log = exp.log_metrics(["clf-loss", 'f1-train'])
exp.update_value("progress",
trainer.progress_log + "\n" + losses_log)
# clean lines and move cursor back up N lines
print("\n\033[K" + losses_log)
print("\033[F" * (len(losses_log.split("\n")) + 2))
_outputs.clear()
# Trainer: responsible for managing the training process
trainer = ClfTrainer_withFeatures(
model=model,
train_loader=train_loader,
valid_loader=val_loader,
valid_loader_train_set=val_loader_train_dataset,
test_loader=test_loader,
criterion=criterion,
optimizers=clf_optimizer,
config=config,
device=opts.device,
batch_end_callbacks=None)
####################################################################
# Experiment: logging and visualizing the training process
####################################################################
if cluster is False:
exp = Experiment(opts.name,
config,
src_dirs=opts.source,
output_dir=EXP_DIR)
exp.add_metric("ep_loss", "line", "epoch loss", ["TRAIN", "VAL"])
exp.add_metric("ep_f1", "line", "epoch f1", ["TRAIN", "VAL"])
exp.add_metric("ep_acc", "line", "epoch accuracy", ["TRAIN", "VAL"])
exp.add_metric("ep_pre", "line", "epoch precision", ["TRAIN", "VAL"])
exp.add_metric("ep_rec", "line", "epoch recall", ["TRAIN", "VAL"])
exp.add_value("epoch", title="epoch summary", vis_type="text")
exp.add_value("progress", title="training progress", vis_type="text")
####################################################################
# Training Loop
####################################################################
best_loss = None
early_stopping = Early_stopping("min", config["patience"])
for epoch in range(config["epochs"]):
train_loss = trainer.train_epoch()
val_loss, y, y_pred = trainer.eval_epoch(val_set=True)
_, y_train, y_pred_train = trainer.eval_epoch(train_set=True)
# Calculate accuracy and f1-macro on the evaluation set
if cluster is False:
exp.update_metric("ep_loss", train_loss.item(), "TRAIN")
exp.update_metric("ep_loss", val_loss.item(), "VAL")
exp.update_metric("ep_f1", f1_macro(y_train, y_pred_train),
"TRAIN")
exp.update_metric("ep_f1", f1_macro(y, y_pred), "VAL")
exp.update_metric("ep_acc", acc(y_train, y_pred_train), "TRAIN")
exp.update_metric("ep_acc", acc(y, y_pred), "VAL")
exp.update_metric("ep_pre", precision_macro(y_train, y_pred_train),
"TRAIN")
exp.update_metric("ep_pre", precision_macro(y, y_pred), "VAL")
exp.update_metric("ep_rec", recall_macro(y_train, y_pred_train),
"TRAIN")
exp.update_metric("ep_rec", recall_macro(y, y_pred), "VAL")
print()
epoch_log = exp.log_metrics(
["ep_loss", "ep_f1", "ep_acc", "ep_pre", "ep_rec"])
print(epoch_log)
exp.update_value("epoch", epoch_log)
exp.save()
else:
print("epoch: {}, train loss: {}, val loss: {}, f1: {}".format(
epoch, train_loss.item(), val_loss.item(), f1_macro(y,
y_pred)))
# Save the model if the validation loss is the best we've seen so far.
if not best_loss or val_loss < best_loss:
best_loss = val_loss
trainer.best_val_loss = best_loss
trainer.acc = acc(y, y_pred)
trainer.f1 = f1_macro(y, y_pred)
trainer.precision = precision_macro(y, y_pred)
trainer.recall = recall_macro(y, y_pred)
trainer.checkpoint(name=opts.name, verbose=False)
if early_stopping.stop(val_loss):
print("Early Stopping...")
break
print("\n")
# return trainer.best_val_loss, trainer.acc, trainer.f1, trainer.precision, trainer.recall
#################
# Test
#################
_, y_test_, y_test_predicted = trainer.eval_epoch(test_set=True)
f1_test = f1_macro(y_test_, y_test_predicted)
acc_test = acc(y_test_, y_test_predicted)
print("#" * 33)
print("F1 for test set: {}".format(f1_test))
print("Accuracy for test set: {}".format(acc_test))
print("#" * 33)
return trainer.best_val_loss, trainer.acc, trainer.f1, trainer.precision, trainer.recall, f1_test, acc_test
os.path.join(EMB_DIR, config["data"]["embeddings"]),
config["data"]["embeddings_dim"])
else:
word2idx, idx2word, weights = load_word_vectors(
os.path.join(EMB_DIR, config["data"]["embeddings"]),
config["data"]["embeddings_dim"])
checkpoint_name = "Psych_exp_concat"
state = load_checkpoint(checkpoint_name)
features, feat_length = load_features(config["data"]["features"])
test_set = ClfDataset(X_test, y_test, word2idx,
features_dict=features,
feat_length=feat_length,
name="psych_test")
test_lengths = [len(x) for x in test_set.data]
test_sampler = SortedSampler(test_lengths)
test_loader = DataLoader(test_set, sampler=test_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores, collate_fn=ClfCollate_withFeatures())
model = AffectiveAttention(ntokens=weights.shape[0],
nclasses=7,
feature_size=feat_length,
**config["model"])
model.load_state_dict(state["model"])
def sent_clf_no_aux(dataset, config, opts, transfer=False):
from logger.experiment import Experiment
opts.name = config["name"]
X_train, y_train, X_val, y_val = dataset
vocab = None
if transfer:
opts.transfer = config["pretrained_lm"]
checkpoint = load_checkpoint(opts.transfer)
config["vocab"].update(checkpoint["config"]["vocab"])
dict_pattern_rename(checkpoint["config"]["model"],
{"rnn_": "bottom_rnn_"})
config["model"].update(checkpoint["config"]["model"])
vocab = checkpoint["vocab"]
####################################################################
# Data Loading and Preprocessing
####################################################################
if config["preprocessor"] == "twitter":
preprocessor = twitter_preprocessor()
else:
preprocessor = None
print("Building training dataset...")
train_set = ClfDataset(X_train,
y_train,
vocab=vocab,
preprocess=preprocessor,
vocab_size=config["vocab"]["size"],
seq_len=config["data"]["seq_len"])
print("Building validation dataset...")
val_set = ClfDataset(X_val,
y_val,
seq_len=train_set.seq_len,
preprocess=preprocessor,
vocab=train_set.vocab)
src_lengths = [len(x) for x in train_set.data]
val_lengths = [len(x) for x in val_set.data]
# select sampler & dataloader
train_sampler = BucketBatchSampler(src_lengths, config["batch_size"], True)
val_sampler = SortedSampler(val_lengths)
val_sampler_train = SortedSampler(src_lengths)
train_loader = DataLoader(train_set,
batch_sampler=train_sampler,
num_workers=opts.cores,
collate_fn=ClfCollate())
val_loader = DataLoader(val_set,
sampler=val_sampler,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate())
val_loader_train_dataset = DataLoader(train_set,
sampler=val_sampler_train,
batch_size=config["batch_size"],
num_workers=opts.cores,
collate_fn=ClfCollate())
####################################################################
# Model
####################################################################
ntokens = len(train_set.vocab)
model = NaiveClassifier(ntokens,
len(set(train_set.labels)),
attention=config["model"]["has_att"],
**config["model"])
model.to(opts.device)
criterion = nn.CrossEntropyLoss()
if config["gu"]:
embed_parameters = filter(lambda p: p.requires_grad,
model.embed.parameters())
bottom_parameters = filter(lambda p: p.requires_grad,
chain(model.bottom_rnn.parameters()))
if config["model"]["has_att"]:
top_parameters = filter(
lambda p: p.requires_grad,
chain(model.attention.parameters(),
model.classes.parameters()))
else:
top_parameters = filter(lambda p: p.requires_grad,
model.classes.parameters())
embed_optimizer = Adam(embed_parameters)
rnn_optimizer = Adam(bottom_parameters)
top_optimizer = Adam(top_parameters)
# Trainer: responsible for managing the training process
trainer = SentClfNoAuxTrainer(
model,
train_loader,
val_loader,
criterion, [embed_optimizer, rnn_optimizer, top_optimizer],
config,
opts.device,
valid_loader_train_set=val_loader_train_dataset,
unfreeze_embed=config["unfreeze_embed"],
unfreeze_rnn=config["unfreeze_rnn"])
else:
parameters = filter(lambda p: p.requires_grad, model.parameters())
optimizer = optim.Adam(parameters, lr=config["top_lr"])
# Trainer: responsible for managing the training process
trainer = SentClfNoAuxTrainer(
model,
train_loader,
val_loader,
criterion, [optimizer],
config,
opts.device,
valid_loader_train_set=val_loader_train_dataset)
####################################################################
# Experiment: logging and visualizing the training process
####################################################################
exp = Experiment(opts.name,
config,
src_dirs=opts.source,
output_dir=EXP_DIR)
exp.add_metric("ep_loss", "line", "epoch loss class", ["TRAIN", "VAL"])
exp.add_metric("ep_f1", "line", "epoch f1", ["TRAIN", "VAL"])
exp.add_metric("ep_acc", "line", "epoch accuracy", ["TRAIN", "VAL"])
exp.add_value("epoch", title="epoch summary")
exp.add_value("progress", title="training progress")
####################################################################
# Resume Training from a previous checkpoint
####################################################################
if transfer:
print("Transferring Encoder weights ...")
dict_pattern_rename(checkpoint["model"], {"encoder": "bottom_rnn"})
load_state_dict_subset(model, checkpoint["model"])
print(model)
####################################################################
# Training Loop
####################################################################
best_loss = None
early_stopping = EarlyStopping("min", config["patience"])
for epoch in range(1, config["epochs"] + 1):
train_loss = trainer.train_epoch()
val_loss, y, y_pred = trainer.eval_epoch(val_set=True)
_, y_train, y_pred_train = trainer.eval_epoch(train_set=True)
# Calculate accuracy and f1-macro on the evaluation set
exp.update_metric("ep_loss", train_loss.item(), "TRAIN")
exp.update_metric("ep_loss", val_loss.item(), "VAL")
exp.update_metric("ep_f1", f1_macro(y_train, y_pred_train), "TRAIN")
exp.update_metric("ep_f1", f1_macro(y, y_pred), "VAL")
exp.update_metric("ep_acc", acc(y_train, y_pred_train), "TRAIN")
exp.update_metric("ep_acc", acc(y, y_pred), "VAL")
print()
epoch_log = exp.log_metrics(["ep_loss", "ep_f1", "ep_acc"])
print(epoch_log)
exp.update_value("epoch", epoch_log)
###############################################################
# Unfreezing the model after X epochs
###############################################################
# Save the model if the val loss is the best we've seen so far.
if not best_loss or val_loss < best_loss:
best_loss = val_loss
trainer.best_acc = acc(y, y_pred)
trainer.best_f1 = f1_macro(y, y_pred)
trainer.checkpoint(name=opts.name)
if early_stopping.stop(val_loss):
print("Early Stopping (according to cls loss)....")
break
print("\n" * 2)
return best_loss, trainer.best_acc, trainer.best_f1