def main():
logger.info('Launching the SAN')
opt = vars(args)
logger.info('Loading data')
version = 'v1'
gold_version = 'v1.1'
dev_path = gen_name(args.data_dir, args.dev_data, version)
dev_gold_path = gen_gold_name(args.data_dir, args.dev_gold, gold_version)
test_path = gen_name(args.data_dir, args.test_data, version)
test_gold_path = gen_gold_name(args.data_dir, args.test_gold, gold_version)
if args.v2_on:
version = 'v2'
gold_version = 'v2.0'
dev_labels = load_squad_v2_label(args.dev_gold)
embedding, opt = load_meta(
opt, gen_name(args.data_dir, args.meta, version, suffix='pick'))
# train_data = BatchGen(gen_name(args.data_dir, args.train_data, version),
# batch_size=args.batch_size,
# gpu=args.cuda,
# with_label=args.v2_on,
# elmo_on=args.elmo_on)
# import pdb; pdb.set_trace()
dev_data = BatchGen(gen_name(args.data_dir, args.dev_data, version),
batch_size=args.batch_size,
gpu=args.cuda,
is_train=False,
elmo_on=args.elmo_on)
test_data = None
test_gold = None
# if os.path.exists(test_path):
# test_data = BatchGen(test_path,
# batch_size=args.batch_size,
# gpu=args.cuda, is_train=False, elmo_on=args.elmo_on)
# load golden standard
dev_gold = load_squad(dev_gold_path)
if os.path.exists(test_gold_path):
test_gold = load_squad(test_gold_path)
model = DocReaderModel(opt, embedding) ### model = your_model()
# model meta str
headline = '############# Model Arch of SAN #############'
# print network
logger.info('\n{}\n{}\n'.format(headline, model.network))
model.setup_eval_embed(embedding)
logger.info("Total number of params: {}".format(model.total_param))
if args.cuda:
model.cuda()
best_em_score, best_f1_score = 0.0, 0.0
# test epoch value..
epoch = 2
# for epoch in range(0, args.epoches):
# logger.warning('At epoch {}'.format(epoch))
# train_data.reset()
# start = datetime.now()
# for i, batch in enumerate(train_data):
# model.update(batch)
# if (model.updates) % args.log_per_updates == 0 or i == 0:
# logger.info('#updates[{0:6}] train loss[{1:.5f}] remaining[{2}]'.format(
# model.updates, model.train_loss.avg,
# str((datetime.now() - start) / (i + 1) * (len(train_data) - i - 1)).split('.')[0]))
# dev eval
# load the best model from disk...
# import pdb;pdb.set_trace()
f'loading the model from disk........'
path1 = '/demo-mount/san_mrc/checkpoint/checkpoint_v1_epoch_0_full_model.pt'
# model = torch.load('/home/ofsdms/san_mrc/checkpoint/best_v1_checkpoint.pt', map_location='cpu')
# checkpoint_test = torch.load('/home/ofsdms/san_mrc/checkpoint/best_v1_checkpoint.pt', map_location='cpu')
model = torch.load(path1)
results, labels = predict_squad(model, dev_data, v2_on=args.v2_on)
if args.v2_on:
metric = evaluate_v2(dev_gold,
results,
na_prob_thresh=args.classifier_threshold)
em, f1 = metric['exact'], metric['f1']
acc = compute_acc(labels, dev_labels)
else:
metric = evaluate(dev_gold, results)
em, f1 = metric['exact_match'], metric['f1']
output_path = os.path.join(model_dir, 'dev_output_{}.json'.format(epoch))
with open(output_path, 'w') as f:
json.dump(results, f)
if test_data is not None:
test_results, test_labels = predict_squad(model,
test_data,
v2_on=args.v2_on)
test_output_path = os.path.join(
model_dir, 'test_output_{}.json'.format(epoch))
with open(test_output_path, 'w') as f:
json.dump(test_results, f)
if (test_gold is not None):
if args.v2_on:
test_metric = evaluate_v2(
test_gold,
test_results,
na_prob_thresh=args.classifier_threshold)
test_em, test_f1 = test_metric['exact'], test_metric['f1']
test_acc = compute_acc(labels, test_labels)
else:
test_metric = evaluate(test_gold, test_results)
test_em, test_f1 = test_metric['exact_match'], test_metric[
'f1']
# setting up scheduler
if model.scheduler is not None:
logger.info('scheduler_type {}'.format(opt['scheduler_type']))
if opt['scheduler_type'] == 'rop':
model.scheduler.step(f1, epoch=epoch)
else:
model.scheduler.step()
# save
model_file = os.path.join(
model_dir, 'checkpoint_{}_epoch_{}.pt'.format(version, epoch))
model.save(model_file, epoch)
if em + f1 > best_em_score + best_f1_score:
copyfile(
os.path.join(model_dir, model_file),
os.path.join(model_dir,
'best_{}_checkpoint.pt'.format(version)))
best_em_score, best_f1_score = em, f1
logger.info('Saved the new best model and prediction')
logger.warning(
"Epoch {0} - dev EM: {1:.3f} F1: {2:.3f} (best EM: {3:.3f} F1: {4:.3f})"
.format(epoch, em, f1, best_em_score, best_f1_score))
if args.v2_on:
logger.warning("Epoch {0} - ACC: {1:.4f}".format(epoch, acc))
if metric is not None:
logger.warning("Detailed Metric at Epoch {0}: {1}".format(
epoch, metric))
if (test_data is not None) and (test_gold is not None):
logger.warning("Epoch {0} - test EM: {1:.3f} F1: {2:.3f}".format(
epoch, test_em, test_f1))
if args.v2_on:
logger.warning("Epoch {0} - test ACC: {1:.4f}".format(
epoch, test_acc))
def main():
logger.info('Launching the SAN')
opt = vars(args)
logger.info('Loading data')
version = 'v2' if args.v2_on else 'v1'
gold_version = 'v2.0' if args.v2_on else 'v1.1'
dev_path = gen_name(args.data_dir, args.dev_data, version)
dev_gold_path = gen_gold_name(args.data_dir, args.dev_gold, gold_version)
dev_labels = load_squad_v2_label(dev_gold_path)
embedding, opt = load_meta(opt, gen_name(args.data_dir, args.meta, version, suffix='pick'))
dev_data = BatchGen(dev_path,
batch_size=args.batch_size,
gpu=args.cuda, is_train=False, elmo_on=args.elmo_on)
dev_gold = load_squad(dev_gold_path)
checkpoint_path = args.checkpoint_path
logger.info(f'path to given checkpoint is {checkpoint_path}')
checkpoint = torch.load(checkpoint_path) if args.cuda else torch.load(checkpoint_path, map_location='cpu')
state_dict = checkpoint['state_dict']
# Set up the model
logger.info('Loading model ...')
model = DocReaderModel(opt, embedding,state_dict)
model.setup_eval_embed(embedding)
logger.info('done')
if args.cuda:
model.cuda()
# dev eval
logger.info('Predicting ...')
results, labels = predict_squad(model, dev_data, v2_on=args.v2_on)
logger.info('done')
# get actual and predicted labels (as lists)
actual_labels = []
predicted_labels = []
dropped = 0
for key in dev_labels.keys(): # convert from dictionaries to lists
try:
actual_labels.append(dev_labels[key])
predicted_labels.append(labels[key])
except:
dropped += 1
print(f'dropped: {dropped}')
actual_labels = np.array(actual_labels)
predicted_labels = np.array(predicted_labels)
# convert from continuous to discrete
actual_labels = (actual_labels > args.classifier_threshold).astype(np.int32)
predicted_labels = (predicted_labels > args.classifier_threshold).astype(np.int32)
# Print all metrics
print('accuracy', 100 - 100 * sum(abs(predicted_labels-actual_labels)) / len(actual_labels), '%')
print('confusion matrix', confusion_matrix(predicted_labels, actual_labels))
precision, recall, f1, _ = precision_recall_fscore_support(actual_labels, predicted_labels, average='binary')
print(f'Precision: {precision} recall: {recall} f1-score: {f1}')
def main():
opt = vars(args)
logger.info('Loading Squad')
version = 'v1'
gold_version = 'v1.1'
if args.v2_on:
version = 'v2'
gold_version = 'v2.0'
dev_labels = load_squad_v2_label(args.dev_gold)
logger.info('Loading Meta')
embedding, opt = load_meta(
opt, gen_name(args.data_dir, args.meta, version, suffix='pick'))
logger.info('Loading Train Batcher')
if args.elmo_on:
logger.info('ELMO ON')
dev_path = gen_name(args.data_dir, args.dev_data, version)
dev_gold_path = gen_gold_name(args.data_dir, args.dev_gold, gold_version)
train_data = BatchGen(gen_name(args.data_dir, args.train_data, version),
batch_size=args.batch_size,
gpu=args.cuda,
with_label=args.v2_on,
elmo_on=args.elmo_on)
logger.info('Loading Dev Batcher')
dev_data = BatchGen(dev_path,
batch_size=args.batch_size,
gpu=args.cuda,
is_train=False,
elmo_on=args.elmo_on)
logger.info('Loading Golden Standards')
# load golden standard
dev_gold = load_squad(args.dev_gold)
if len(args.resume) > 0:
logger.info('Loading resumed model')
model = DocReaderModel.load(args.resume, embedding, gpu=args.cuda)
resumeSplit = args.resume.split('_')
best_f1_score = float(resumeSplit[6].replace('.pt', ''))
best_em_score = float(resumeSplit[4])
resumed_epoch = int(resumeSplit[2]) + 1
#step scheduler
for i in range(resumed_epoch):
model.scheduler.step()
logger.info(
"RESUMING MODEL TRAINING. BEST epoch {} EM {} F1 {} ".format(
str(resumed_epoch), str(best_em_score), str(best_f1_score)))
else:
model = DocReaderModel(opt, embedding)
best_em_score, best_f1_score = 0.0, 0.0
resumed_epoch = 0
# model meta str
# headline = '############# Model Arch of SAN #############'
# print network
# logger.info('\n{}\n{}\n'.format(headline, model.network))
model.setup_eval_embed(embedding)
logger.info("Total number of params: {}".format(model.total_param))
logger.info("Batch Size {}".format(args.batch_size))
if args.cuda:
model.cuda()
else:
model.cpu()
for epoch in range(resumed_epoch, args.epoches):
logger.warning('At epoch {}'.format(epoch))
#shuffle training batch
train_data.reset()
start = datetime.now()
for i, batch in enumerate(train_data):
model.update(batch)
if (model.updates) % args.log_per_updates == 0 or i == 0:
logger.info(
'#updates[{0:6}] train loss[{1:.5f}] remaining[{2}]'.
format(
model.updates, model.train_loss.avg,
str((datetime.now() - start) / (i + 1) *
(len(train_data) - i - 1)).split('.')[0]))
# dev eval
results, labels = predict_squad(model, dev_data, v2_on=args.v2_on)
if args.v2_on:
metric = evaluate_v2(dev_gold,
results,
na_prob_thresh=args.classifier_threshold)
em, f1 = metric['exact'], metric['f1']
acc = compute_acc(labels, dev_labels)
else:
metric = evaluate(dev_gold, results)
em, f1 = metric['exact_match'], metric['f1']
output_path = os.path.join(model_dir,
'dev_output_{}.json'.format(epoch))
with open(output_path, 'w') as f:
json.dump(results, f)
# setting up scheduler
if model.scheduler is not None:
logger.info('scheduler_type {}'.format(opt['scheduler_type']))
if opt['scheduler_type'] == 'rop':
model.scheduler.step(f1, epoch=epoch)
else:
model.scheduler.step()
# save
model_file = os.path.join(
model_dir,
'cp_epoch_{}_em_{}_f1_{}.pt'.format(epoch, int(em), int(f1)))
model.save(model_file, epoch)
if em + f1 > best_em_score + best_f1_score:
copyfile(
os.path.join(model_dir, model_file),
os.path.join(
model_dir, 'best_epoch_{}_em_{}_f1_{}.pt'.format(
epoch, int(em), int(f1))))
best_em_score, best_f1_score = em, f1
logger.info('Saved the new best model and prediction')
logger.warning(
"Epoch {0} - dev EM: {1:.3f} F1: {2:.3f} (best EM: {3:.3f} F1: {4:.3f})"
.format(epoch, em, f1, best_em_score, best_f1_score))
if args.v2_on:
logger.warning("Epoch {0} - ACC: {1:.4f}".format(epoch, acc))
if metric is not None:
logger.warning("Detailed Metric at Epoch {0}: {1}".format(
epoch, metric))
def main():
logger.info('Launching the SAN')
opt = vars(args)
logger.info('Loading data')
version = 'v2' if args.v2_on else 'v1'
gold_version = 'v2.0' if args.v2_on else 'v1.1'
train_path = gen_name(args.data_dir, args.train_data, version)
train_gold_path = gen_gold_name(args.data_dir, 'train', gold_version)
dev_path = gen_name(args.data_dir, args.dev_data, version)
dev_gold_path = gen_gold_name(args.data_dir, args.dev_gold, gold_version)
test_path = gen_name(args.data_dir, args.test_data, version)
test_gold_path = gen_gold_name(args.data_dir, args.test_gold, gold_version)
train_labels = load_squad_v2_label(train_gold_path)
dev_labels = load_squad_v2_label(dev_gold_path)
#train_labels = load_squad_v2_label(train_gold_path)
embedding, opt = load_meta(
opt, gen_name(args.data_dir, args.meta, version, suffix='pick'))
train_data = BatchGen(train_path,
batch_size=args.batch_size,
gpu=args.cuda,
with_label=args.v2_on,
elmo_on=args.elmo_on)
dev_data = BatchGen(dev_path,
batch_size=args.batch_size,
gpu=args.cuda,
is_train=False,
elmo_on=args.elmo_on)
test_data = None
test_gold = None
if os.path.exists(test_path):
test_data = BatchGen(test_path,
batch_size=args.batch_size,
gpu=args.cuda,
is_train=False,
elmo_on=args.elmo_on)
# load golden standard
train_gold = load_squad(train_gold_path)
dev_gold = load_squad(dev_gold_path)
#train_gold = load_squad(train_gold_path)
if os.path.exists(test_gold_path):
test_gold = load_squad(test_gold_path)
#define csv path
csv_head = [
'epoch', 'train_loss', 'train_loss_san', 'train_loss_class', 'dev_em',
'dev_f1', 'dev_acc', 'train_em', 'train_f1', 'train_acc'
]
csvfile = 'results_{}.csv'.format(args.classifier_gamma)
csv_path = os.path.join(args.data_dir, csvfile)
result_params = []
#load previous checkpoint
start_epoch = 0
state_dict = None
if (args.load_checkpoint != 0):
start_epoch = args.load_checkpoint + 1
checkpoint_file = 'checkpoint_{}_epoch_{}.pt'.format(
version, args.load_checkpoint)
checkpoint_path = os.path.join(args.model_dir, checkpoint_file)
logger.info('path to prev checkpoint is {}'.format(checkpoint_path))
checkpoint = torch.load(checkpoint_path)
state_dict = checkpoint['state_dict']
opt = checkpoint['config']
#logger.warning('the checkpoint is {}'.format(checkpoint))
#load previous metrics
with open(csv_path, 'r') as csvfile:
csvreader = csv.reader(csvfile)
dummy = next(csvreader)
for row in csvreader:
result_params.append(row)
logger.info('Previous metrics loaded')
model = DocReaderModel(opt, embedding, state_dict)
# model meta str
#headline = '############# Model Arch of SAN #############'
# print network
#logger.info('\n{}\n{}\n'.format(headline, model.network))
model.setup_eval_embed(embedding)
logger.info("Total number of params: {}".format(model.total_param))
if args.cuda:
model.cuda()
best_em_score, best_f1_score = 0.0, 0.0
for epoch in range(start_epoch, args.epoches):
logger.warning('At epoch {}'.format(epoch))
loss, loss_san, loss_class = 0.0, 0.0, 0.0
train_data.reset()
start = datetime.now()
for i, batch in enumerate(train_data):
losses = model.update(batch)
loss += losses[0].item()
loss_san += losses[1].item()
if losses[2]:
loss_class += losses[2].item()
if (model.updates) % args.log_per_updates == 0 or i == 0:
logger.info(
'#updates[{0:6}] train loss[{1:.5f}] remaining[{2}]'.
format(
model.updates, model.train_loss.avg,
str((datetime.now() - start) / (i + 1) *
(len(train_data) - i - 1)).split('.')[0]))
# train eval
tr_results, tr_labels = predict_squad(model,
train_data,
v2_on=args.v2_on)
if args.v2_on and args.classifier_on:
train_metric = evaluate_v2(
train_gold,
tr_results,
na_prob_thresh=args.classifier_threshold)
train_em, train_f1 = train_metric['exact'], train_metric['f1']
train_acc = compute_acc(tr_labels, train_labels)
else:
train_metric = evaluate(train_gold, tr_results)
train_em, train_f1 = train_metric['exact_match'], train_metric[
'f1']
train_acc = -1
# dev eval
results, labels = predict_squad(model, dev_data, v2_on=args.v2_on)
if args.v2_on and args.classifier_on:
metric = evaluate_v2(dev_gold,
results,
na_prob_thresh=args.classifier_threshold)
em, f1 = metric['exact'], metric['f1']
acc = compute_acc(labels, dev_labels)
else:
metric = evaluate(dev_gold, results)
em, f1 = metric['exact_match'], metric['f1']
acc = -1
output_path = os.path.join(model_dir,
'dev_output_{}.json'.format(epoch))
with open(output_path, 'w') as f:
json.dump(results, f)
if test_data is not None:
test_results, test_labels = predict_squad(model,
test_data,
v2_on=args.v2_on)
test_output_path = os.path.join(
model_dir, 'test_output_{}.json'.format(epoch))
with open(test_output_path, 'w') as f:
json.dump(test_results, f)
if (test_gold is not None):
if args.v2_on:
test_metric = evaluate_v2(
test_gold,
test_results,
na_prob_thresh=args.classifier_threshold)
test_em, test_f1 = test_metric['exact'], test_metric['f1']
test_acc = compute_acc(
labels, test_labels
) #?? should be test_labels,test_gold_labels
else:
test_metric = evaluate(test_gold, test_results)
test_em, test_f1 = test_metric['exact_match'], test_metric[
'f1']
# setting up scheduler
# halves learning rate every 10 epochs
if model.scheduler is not None:
logger.info('scheduler_type {}'.format(opt['scheduler_type']))
if opt['scheduler_type'] == 'rop':
model.scheduler.step(f1, epoch=epoch)
else:
model.scheduler.step()
# save
model_file = os.path.join(
model_dir, 'checkpoint_{}_epoch_{}.pt'.format(version, epoch))
model.save(model_file, epoch)
if em + f1 > best_em_score + best_f1_score:
copyfile(
os.path.join(model_dir, model_file),
os.path.join(model_dir,
'best_{}_checkpoint.pt'.format(version)))
best_em_score, best_f1_score = em, f1
logger.info('Saved the new best model and prediction')
approx = lambda x: round(x, 3)
logger.warning(f""" Epoch {str(epoch).zfill(2)} ---
Train | acc: {approx(train_acc)} EM: {approx(train_em)} F1: {approx(train_f1)} loss ({approx(loss)}) = {approx(loss_san)} + {approx(loss_class)}
Dev | acc: {approx(acc)} EM: {approx(em)} F1: {approx(f1)}
--------------------------------
""")
#writing in CSV
result_params.append([
epoch, loss, loss_san, loss_class, em, f1, acc, train_em, train_f1,
train_acc
])
logger.info('Writing in {} the values {}'.format(
csv_path, result_params))
with open(csv_path, 'w') as csvfile:
csvwriter = csv.writer(csvfile)
csvwriter.writerow(csv_head)
csvwriter.writerows(result_params)