def multi_model_valid(ensemble_model, model_lst, dataloader, criterion,
device):
ensemble_model.eval()
epoch_start = time.time()
running_loss = 0.0
running_accuracy = 0.0
with torch.no_grad():
for batch in dataloader:
premises = batch["premise"].to(device)
premises_lengths = batch["premise_length"].to(device)
hypotheses = batch["hypothesis"].to(device)
hypotheses_lengths = batch["hypothesis_length"].to(device)
labels = batch["label"].to(device)
logits_probs_lst = [
model(premises, premises_lengths, hypotheses,
hypotheses_lengths) for model in model_lst
]
logits_lst = [i[0].unsqueeze(1) for i in logits_probs_lst]
probs_lst = [i[1].unsqueeze(1) for i in logits_probs_lst]
logits, probs = ensemble_model(logits_lst, probs_lst)
loss = criterion(logits, labels)
running_loss += loss.item()
running_accuracy += correct_predictions(probs, labels)
epoch_time = time.time() - epoch_start
epoch_loss = running_loss / len(dataloader)
epoch_accuracy = running_accuracy / (len(dataloader.dataset))
return epoch_time, epoch_loss, epoch_accuracy
def multi_model_train(args,
epoch,
ensemble_model,
model_lst,
dataloader,
optimizer,
criterion,
max_gradient_norm,
device):
ensemble_model.train()
epoch_start = time.time()
batch_time_avg = 0.0
running_loss = 0.0
correct_preds = 0
tqdm_batch_iterator = tqdm(dataloader)
num_labels = 0
for batch_index, batch in enumerate(tqdm_batch_iterator):
if args.local_rank != -1:
dataloader.sampler.set_epoch(epoch)
batch_start = time.time()
premises = batch["premise"].to(device)
premises_lengths = batch["premise_length"].to(device)
hypotheses = batch["hypothesis"].to(device)
hypotheses_lengths = batch["hypothesis_length"].to(device)
labels = batch["label"].to(device)
num_labels += len(labels)
optimizer.zero_grad()
logits_probs_lst = [model(premises,
premises_lengths,
hypotheses,
hypotheses_lengths) for model in model_lst]
logits_lst = [i[0].unsqueeze(1) for i in logits_probs_lst]
probs_lst = [i[1].unsqueeze(1) for i in logits_probs_lst]
logits, probs = ensemble_model(logits_lst, probs_lst)
loss = criterion(logits, labels)
loss.backward()
nn.utils.clip_grad_norm_(ensemble_model.parameters(), max_gradient_norm)
optimizer.step()
batch_time_avg += time.time() - batch_start
running_loss += loss.item()
correct_preds += correct_predictions(probs, labels)
description = "Avg. batch proc. time: {:.4f}s, loss: {:.4f}"\
.format(batch_time_avg/(batch_index+1),
running_loss/(batch_index+1))
tqdm_batch_iterator.set_description(description)
epoch_time = time.time() - epoch_start
epoch_loss = running_loss / len(dataloader)
epoch_accuracy = correct_preds / num_labels
return epoch_time, epoch_loss, epoch_accuracy
def test(model, dataloader):
"""
Test the accuracy of a model on some labelled test dataset.
Args:
model: The torch module on which testing must be performed.
dataloader: A DataLoader object to iterate over some dataset.
Returns:
batch_time: The average time to predict the classes of a batch.
total_time: The total time to process the whole dataset.
accuracy: The accuracy of the model on the input data.
"""
# Switch the model to eval mode.
model.eval()
device = model.device
time_start = time.time()
batch_time = 0.0
accuracy = 0.0
# Deactivate autograd for evaluation.
with torch.no_grad():
for batch in dataloader:
batch_start = time.time()
# Move input and output data to the GPU if one is used.
q1 = batch["q1"].to(device)
q1_lengths = batch["q1_length"].to(device)
q2 = batch["q2"].to(device)
q2_lengths = batch["q2_length"].to(device)
labels = batch["label"].to(device)
_, probs = model(q1, q1_lengths, q2, q2_lengths)
accuracy += correct_predictions(probs, labels)
batch_time += time.time() - batch_start
batch_time /= len(dataloader)
total_time = time.time() - time_start
accuracy /= (len(dataloader.dataset))
return batch_time, total_time, accuracy
def validate(model, dataloader, criterion, device):
model.eval()
epoch_start = time.time()
running_loss = 0.0
running_accuracy = 0.0
with torch.no_grad():
for batch in dataloader:
premises = batch["premise"].to(device)
premises_lengths = batch["premise_length"].to(device)
hypotheses = batch["hypothesis"].to(device)
hypotheses_lengths = batch["hypothesis_length"].to(device)
labels = batch["label"].to(device)
logits, probs = model(premises, premises_lengths, hypotheses,
hypotheses_lengths)
loss = criterion(logits, labels)
running_loss += loss.item()
running_accuracy += correct_predictions(probs, labels)
epoch_time = time.time() - epoch_start
epoch_loss = running_loss / len(dataloader)
epoch_accuracy = running_accuracy / (len(dataloader.dataset))
return epoch_time, epoch_loss, epoch_accuracy
def output_predictions(model,
dataset='test',
filename='svhn_classification_{dataset}.txt'):
correct = 0
total = 0
with open(filename.format(dataset=dataset), "w",
encoding="utf-8") as out_file:
# TODO: Predict the digits and their bounding boxes on the test set.
dataset = getattr(SVHN(), dataset).map(SVHN.parse)
mapped_dataset = dataset.map(scale_input(model.args.image_size))
for x, xorig in zip(mapped_dataset.batch(1), dataset):
predicted_bboxes, scores, predicted_classes, valid = model.predict_on_batch(
x)
num_valid = valid[0].numpy()
predicted_bboxes = predicted_bboxes[0, :num_valid, ...].numpy()
predicted_classes = predicted_classes[0, :num_valid, ...].numpy()
scores = scores[0, :num_valid, ...].numpy()
predicted_bboxes = predicted_bboxes[
scores > model.args.score_threshold]
predicted_classes = predicted_classes[
scores > model.args.score_threshold]
transformed_bboxes = []
output = []
for label, bbox in zip(predicted_classes, predicted_bboxes):
output.append(label.astype(np.int32))
bbox_transformed = tf.cast(
tf.shape(xorig['image'])[1],
tf.float32).numpy() * bbox / float(model.args.image_size)
transformed_bboxes.append(bbox_transformed.astype(np.int32))
output.extend(bbox_transformed.astype(np.int32))
print(*output, file=out_file)
correct += utils.correct_predictions(
xorig["classes"].numpy(), xorig["bboxes"].numpy(),
predicted_classes.astype(np.int32), transformed_bboxes)
total += 1
return correct / total
def test(args, model, dataloader, device):
model.eval()
time_start = time.time()
batch_time = 0.0
accuracy = 0.0
first = True
batch_i = 0
n_high_overlap = 0
n_reg_overlap = 0
n_low_overlap = 0
n_long_sentence = 0
n_reg_sentence = 0
n_short_sentence = 0
n_negation = 0
n_quantifier = 0
n_belief = 0
n_total_high_overlap = 0
n_total_reg_overlap = 0
n_total_low_overlap = 0
n_total_long_sentence = 0
n_total_reg_sentence = 0
n_total_short_sentence = 0
n_total_negation = 0
n_total_quantifier = 0
n_total_belief = 0
n_total_entailment = 0
n_total_neutral = 0
n_total_contradiction = 0
n_correct_entailment = 0
n_correct_neutral = 0
n_correct_contradiction = 0
stat_file = args.test_statistics
with open(stat_file, "rb") as pkl:
test_statistics = pickle.load(pkl)
set_idx_high_overlap = set(test_statistics["high_overlap"])
set_idx_reg_overlap = set(test_statistics["reg_overlap"])
set_idx_low_overlap = set(test_statistics["low_overlap"])
set_idx_long_sentence = set(test_statistics["long_sentence"])
set_idx_reg_sentence = set(test_statistics["reg_sentence"])
set_idx_short_sentence = set(test_statistics["short_sentence"])
set_idx_negation = set(test_statistics["negation"])
set_idx_quantifier = set(test_statistics["quantifier"])
set_idx_belief = set(test_statistics["belief"])
# Deactivate autograd for evaluation.
with torch.no_grad():
for batch in dataloader:
batch_start = time.time()
# Move input and output data to the GPU if one is used.
premises = batch["premise"].to(device)
premises_lengths = batch["premise_length"].to(device)
hypotheses = batch["hypothesis"].to(device)
hypotheses_lengths = batch["hypothesis_length"].to(device)
labels = batch["label"].to(device)
_, probs = model(premises, premises_lengths, hypotheses,
hypotheses_lengths)
accuracy += correct_predictions(probs, labels)
batch_time += time.time() - batch_start
_, out_classes = probs.max(dim=1)
# if first:
# print ('Predictions for the first 5 sentences:')
# print ('Predictions:')
# print (out_classes[:5])
# print ('Labels:')
# print (labels[:5])
# print ('0 = entailment, 1 = neutral, 2 = contradiction')
# first = False
# statistics
for i in range(len(out_classes)):
line_i = batch_i * 32 + i
if labels[i] == 0:
n_total_entailment += 1
elif labels[i] == 1:
n_total_neutral += 1
elif labels[i] == 2:
n_total_contradiction += 1
if line_i in set_idx_high_overlap:
n_total_high_overlap += 1
if line_i in set_idx_reg_overlap:
n_total_reg_overlap += 1
if line_i in set_idx_low_overlap:
n_total_low_overlap += 1
if line_i in set_idx_long_sentence:
n_total_long_sentence += 1
if line_i in set_idx_reg_sentence:
n_total_reg_sentence += 1
if line_i in set_idx_short_sentence:
n_total_short_sentence += 1
if line_i in set_idx_negation:
n_total_negation += 1
if line_i in set_idx_quantifier:
n_total_quantifier += 1
if line_i in set_idx_belief:
n_total_belief += 1
if out_classes[i] == labels[i]:
if labels[i] == 0:
n_correct_entailment += 1
elif labels[i] == 1:
n_correct_neutral += 1
elif labels[i] == 2:
n_correct_contradiction += 1
if line_i in set_idx_high_overlap:
n_high_overlap += 1
if line_i in set_idx_reg_overlap:
n_reg_overlap += 1
if line_i in set_idx_low_overlap:
n_low_overlap += 1
if line_i in set_idx_long_sentence:
n_long_sentence += 1
if line_i in set_idx_reg_sentence:
n_reg_sentence += 1
if line_i in set_idx_short_sentence:
n_short_sentence += 1
if line_i in set_idx_negation:
n_negation += 1
if line_i in set_idx_quantifier:
n_quantifier += 1
if line_i in set_idx_belief:
n_belief += 1
batch_i += 1
# print ('Total Entailment:' + str(n_total_entailment))
# print ('Correct Entailment:' + str(n_correct_entailment))
# print ('Accuracy:' + str(float(n_correct_entailment) / n_total_entailment))
# print ('Total Neutral:' + str(n_total_neutral))
# print ('Correct Neutral:' + str(n_correct_neutral))
# print ('Accuracy:' + str(float(n_correct_neutral) / n_total_neutral))
#
# print ('Total Contradiction:' + str(n_total_contradiction))
# print ('Correct Contradiction:' + str(n_correct_contradiction))
# print ('Accuracy:' + str(float(n_correct_contradiction) / n_total_contradiction))
# print ('Total high overlap sentence:' + str(n_total_high_overlap))
# print ('Correct high overlap sentence:' + str(n_high_overlap))
# print ('Accuracy:' + str(float(n_high_overlap) / n_total_high_overlap))
# print ('Total regular overlap sentence:' + str(n_total_reg_overlap))
# print ('Correct regular overlap sentence:' + str(n_reg_overlap))
# print ('Accuracy:' + str(float(n_reg_overlap) / n_total_reg_overlap))
# print ('Total low overlap sentence:' + str(n_total_low_overlap))
# print ('Correct low overlap sentence:' + str(n_low_overlap))
# print ('Accuracy:' + str(float(n_low_overlap) / n_total_low_overlap))
# print ('Total long sentence:' + str(n_total_long_sentence))
# print ('Correct long sentence:' + str(n_long_sentence))
# print ('Accuracy:' + str(float(n_long_sentence) / n_total_long_sentence))
# print ('Total regular sentence:' + str(n_total_reg_sentence))
# print ('Correct regular sentence:' + str(n_reg_sentence))
# print ('Accuracy:' + str(float(n_reg_sentence) / n_total_reg_sentence))
# print ('Total short sentence:' + str(n_total_short_sentence))
# print ('Correct short sentence:' + str(n_short_sentence))
# print ('Accuracy:' + str(float(n_short_sentence) / n_total_short_sentence))
# print ('Total sentence with negation:' + str(n_total_negation))
# print ('Correct sentence with negation:' + str(n_negation))
# print ('Accuracy:' + str(float(n_negation) / n_total_negation))
# print ('Total sentence with quantifier:' + str(n_total_quantifier))
# print ('Correct sentence with quantifier:' + str(n_quantifier))
# print ('Accuracy:' + str(float(n_quantifier) / n_total_quantifier))
# print ('Total sentence with belief:' + str(n_total_belief))
# print ('Correct sentence with belief:' + str(n_belief))
# print ('Accuracy:' + str(float(n_belief) / n_total_belief))
batch_time /= len(dataloader)
total_time = time.time() - time_start
accuracy /= (len(dataloader.dataset))
return batch_time, total_time, accuracy
def main(args, logger):
stat_file = args.test_statistics
device = args.local_rank if args.local_rank != -1 else (torch.device(
'cuda:0') if torch.cuda.is_available() else torch.device('cpu'))
if args.local_rank != -1:
torch.cuda.set_device(args.local_rank)
load_path = args.load_path
test_file = args.test_data
embedding_file = args.embeddings
batch_size = args.batch_size
info = "\t* Loading testing data..."
rank_logger_info(logger, args.local_rank, info)
with open(test_file, "rb") as pkl:
test_data = NLIDataset(pickle.load(pkl))
test_loader = DataLoader(test_data, shuffle=False, batch_size=batch_size)
with open(embedding_file, "rb") as pkl:
embeddings = torch.tensor(pickle.load(pkl),
dtype=torch.float).to(device)
with open(stat_file, "rb") as pkl:
test_statistics = pickle.load(pkl)
info = "\t* Loading pretrained models..."
rank_logger_info(logger, args.local_rank, info)
model_path_lst = os.listdir(load_path)
checkpoint_lst = [
torch.load(os.path.join(load_path, pretrained_file),
map_location=torch.device(device))
for pretrained_file in model_path_lst
]
model_state_dict_lst = [
checkpoint["model_state_dict"] for checkpoint in checkpoint_lst
]
best_score_lst = [
checkpoint["best_score"] for checkpoint in checkpoint_lst
]
epochs_count = [
checkpoint["epochs_count"] for checkpoint in checkpoint_lst
]
model_n = len(model_state_dict_lst)
info = "\t* Loading done : {}".format(model_n)
rank_logger_info(logger, args.local_rank, info)
model_lst = [
DRLSTM(embeddings.shape[0],
embeddings.shape[1],
hidden_size=args.hidden_size,
embeddings=embeddings,
padding_idx=0,
dropout=args.dropout,
num_classes=args.num_classes,
device=device,
pooling_method_lst=args.pooling_method,
embedding_dropout=args.embedding_dropout)
for i in range(model_n)
]
for idx, model in enumerate(model_lst):
model.load_state_dict(model_state_dict_lst[idx])
model.to(device)
for params in model.parameters():
params.requires_grad = False
if args.ensemble_mode == 1 or args.ensemble_mode == 2:
info = "\t* training..."
rank_logger_info(logger, args.local_rank, info)
if args.ensemble_mode == 1:
ensemble_model = Ensemble_model1(model_n)
else:
ensemble_model = Ensemble_model2(model_n)
ensemble_model.to(device)
with open(args.valid_data, "rb") as pkl:
valid_data = NLIDataset(pickle.load(pkl))
valid_loader = DataLoader(valid_data,
shuffle=False,
batch_size=args.batch_size)
criterion = nn.CrossEntropyLoss()
if args.optim == "adam":
optimizer = torch.optim.Adam(ensemble_model.parameters(),
lr=args.lr)
elif args.optim == "rmsprop":
optimizer = torch.optim.RMSprop(ensemble_model.parameters(),
lr=args.lr)
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer,
mode="max",
factor=0.5,
patience=0)
best_score = 0.0
start_epoch = 1
# Data for loss curves plot.
epochs_count = []
train_losses = []
valid_losses = []
train_accuracy = []
valid_accuracy = []
info = "\n" + 20 * "=" + "Training model on device: {}".format(
device) + 20 * "="
rank_logger_info(logger, args.local_rank, info)
patience_counter = 0
for epoch in range(start_epoch, args.epochs + 1):
epochs_count.append(epoch)
info = "* Training epoch {}:".format(epoch)
rank_logger_info(logger, local_rank, info)
epoch_time, epoch_loss, epoch_accuracy = multi_model_train(
args, epoch, ensemble_model, model_lst, valid_loader,
optimizer, criterion, args.max_gradient_norm, device)
train_losses.append(epoch_loss)
train_accuracy.append(epoch_accuracy)
info = "Training epoch: {}, time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n".format(
epoch, epoch_time, epoch_loss, (epoch_accuracy * 100))
rank_logger_info(logger, args.local_rank, info)
weight_lst = ensemble_model.weight_layer.weight.data.cpu().numpy(
).tolist()[0]
rank_logger_info(logger, args.local_rank, weight_lst)
info = "* Validation for epoch {}:".format(epoch)
rank_logger_info(logger, local_rank, info)
epoch_time, epoch_loss, epoch_accuracy = multi_model_valid(
ensemble_model, model_lst, test_loader, criterion, device)
valid_losses.append(epoch_loss)
valid_accuracy.append(epoch_accuracy)
info = "Validing epoch: {}, time: {:.4f}s, loss: {:.4f}, accuracy: {:.4f}%\n".format(
epoch, epoch_time, epoch_loss, (epoch_accuracy * 100))
rank_logger_info(logger, args.local_rank, info)
scheduler.step(epoch_accuracy)
if epoch_accuracy <= best_score:
patience_counter += 1
else:
best_score = epoch_accuracy
best_model = ensemble_model
patience_counter = 0
if args.local_rank in [-1, 0]:
torch.save(
{
"epoch": epoch,
"model_state_dict": best_model.state_dict(),
"best_score": best_score,
"epochs_count": epochs_count,
"train_losses": train_losses,
"valid_losses": valid_losses
}, os.path.join(args.save_path, "best.pth.tar"))
if patience_counter >= args.patience:
info = "-> Early stopping: patience limit reached, stopping..."
rank_logger_info(logger, args.local_rank, info)
break
if args.local_rank in [-1, 0]:
report_result(epochs_count, train_losses, valid_losses,
train_accuracy, valid_accuracy, args.save_path)
else: # 模式3 4
info = "\t* testing..."
rank_logger_info(logger, args.local_rank, info)
if args.ensemble_mode == 3:
ensemble_model = Ensemble_model3(model_n)
else:
ensemble_model = Ensemble_model4(model_n)
ensemble_model.to(device)
ensemble_model.eval()
time_start = time.time()
batch_time = 0.0
accuracy = 0.0
with torch.no_grad():
for batch in test_loader:
batch_start = time.time()
premises = batch["premise"].to(device)
premises_lengths = batch["premise_length"].to(device)
hypotheses = batch["hypothesis"].to(device)
hypotheses_lengths = batch["hypothesis_length"].to(device)
labels = batch["label"].to(device)
logits_probs_lst = [
model(premises, premises_lengths, hypotheses,
hypotheses_lengths) for model in model_lst
]
logits_lst = [i[0].unsqueeze(1) for i in logits_probs_lst]
probs_lst = [i[1].unsqueeze(1) for i in logits_probs_lst]
_, probs = ensemble_model(logits_lst, probs_lst)
accuracy += correct_predictions(probs, labels)
batch_time += time.time() - batch_start
_, out_classes = probs.max(dim=1)
batch_time /= len(test_loader)
total_time = time.time() - time_start
accuracy /= (len(test_loader.dataset))
info = "-> Average batch processing time: {:.4f}s, total test time:\
{:.4f}s, accuracy: {:.4f}%".format(batch_time, total_time,
(accuracy * 100))
rank_logger_info(logger, args.local_rank, info)
def fit(self):
self._start_wandb()
dataset = self.dataset.shuffle(3000) \
.batch(args.batch_size) \
.prefetch(4)
val_dataset = self.val_dataset \
.batch(args.batch_size) \
.prefetch(4)
for epoch in range(self.args.epochs):
self._epoch.assign(epoch)
log_append = dict()
# Reset metrics
for m in self.metrics.values():
m.reset_states()
# Train on train dataset
for epoch_step, x in enumerate(dataset):
self._epoch_step.assign(epoch_step)
loss, regression_loss, class_loss = self.train_on_batch(x)
self.metrics['loss'].update_state(loss)
self.metrics['regression_loss'].update_state(regression_loss)
self.metrics['class_loss'].update_state(class_loss)
# Run validation
for x in val_dataset:
loss, regression_loss, class_loss = self.evaluate_on_batch(x)
self.metrics['val_loss'].update_state(loss)
self.metrics['val_regression_loss'].update_state(
regression_loss)
self.metrics['val_class_loss'].update_state(class_loss)
# Compute straka's metric
# TODO: vectorize Straka's metric
predictions = self.predict(self.val_dataset)
for (boxes, classes, scores), gold in zip(predictions,
self.val_dataset):
gold_classes, gold_boxes = gold['gt-class'].numpy(
), gold['gt-bbox'].numpy()
num_gt = gold['gt-length'].numpy()
gold_classes, gold_boxes = gold_classes[:
num_gt], gold_boxes[:
num_gt]
boxes, classes = boxes[
scores > self.args.score_threshold], classes[
scores > self.args.score_threshold]
self.metrics['val_score'].update_state(
utils.correct_predictions(gold_classes, gold_boxes,
classes, boxes))
# mAP metric should be implemented here. Note, that predictions
# That are generated use transformed bb, i.e., the bb is scaled to args.image_size
# the original dataset has different image sizes and this needs to be taken care of
# in the metric
predictions = self.predict(self.val_dataset)
self.coco_metric.evaluate(predictions)
# Save model every 20 epochs
if (epoch + 1) % 20 == 0:
self.save()
val_acc = output_predictions(self, 'dev')
output_predictions(self, 'test')
if hasattr(self, '_wandb'):
self._wandb.save('svhn_classification_dev.txt')
self._wandb.save('svhn_classification_test.txt')
print('model saved')
print(f'validation score: {val_acc * 100:.2f}')
log_append = dict(saved_val_score=val_acc, **log_append)
# Log current values
self.log(**log_append)
