def start_training(train_arguments, folder_index):
rcnn = RCNN(train_arguments.pos_loss_method, train_arguments.loss_weight_lambda,
train_arguments.prevent_overfitting_method).cuda()
rcnn.train() # train mode could use dropout.
npz_path = train_arguments.get_train_data_path(folder_index)
npz = np.load(npz_path)
print("\n\n\nload from: ", npz_path)
train_arguments.train_sentences = npz['train_sentences']
train_arguments.train_sentence_info = npz['train_sentence_info']
train_arguments.train_roi = npz['train_roi']
train_arguments.train_cls = npz['train_cls']
if train_arguments.normalize:
if train_arguments.dx_compute_method == "left_boundary":
train_arguments.train_tbbox = npz["train_norm_lb_tbbox"]
else:
train_arguments.train_tbbox = npz["train_norm_tbbox"]
else:
train_arguments.train_tbbox = npz['train_tbbox']
train_arguments.train_sentences = t.Tensor(train_arguments.train_sentences)
train_arguments.train_set = np.random.permutation(train_arguments.train_sentences.size(0)) # like shuffle
if train_arguments.prevent_overfitting_method.lower() == "l2 regu":
if train_arguments.partial_l2_penalty:
optimizer = optim.Adam([
{"params": rcnn.conv1.parameters(), "weight_decay": 0},
{"params": rcnn.cls_fc1.parameters(), "weight_decay": train_arguments.l2_beta},
{"params": rcnn.cls_score.parameters(), "weight_decay": train_arguments.l2_beta},
{"params": rcnn.bbox_fc1.parameters(), "weight_decay": train_arguments.l2_beta},
{"params": rcnn.bbox.parameters(), "weight_decay": train_arguments.l2_beta}
], lr=train_arguments.learning_rate)
else:
optimizer = optim.Adam(rcnn.parameters(), lr=train_arguments.learning_rate,
weight_decay=train_arguments.l2_beta)
else: # dropout optimizer
optimizer = optim.Adam(rcnn.parameters(), lr=train_arguments.learning_rate)
rcnn.optimizer = optimizer
for epoch_time in range(train_arguments.max_iter_epoch):
print('===========================================')
print('[Training Epoch {}]'.format(epoch_time + 1))
train_epoch(train_arguments, rcnn)
if epoch_time >= train_arguments.start_save_epoch:
save_directory = train_arguments.get_save_directory(folder_index)
save_path = save_directory + "model_epoch" + str(epoch_time + 1) + ".pth"
t.save(rcnn.state_dict(), save_path)
print("Model save in ", save_path)
def main(args):
model = RCNN(vocab_size=args.vocab_size,
embedding_dim=args.embedding_dim,
hidden_size=args.hidden_size,
hidden_size_linear=args.hidden_size_linear,
class_num=args.class_num,
dropout=args.dropout).to(args.device)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=0)
train_texts, train_labels = read_file(args.train_file_path)
word2idx = build_dictionary(train_texts, vocab_size=args.vocab_size)
logger.info('Dictionary Finished!')
full_dataset = CustomTextDataset(train_texts, train_labels, word2idx)
num_train_data = len(full_dataset) - args.num_val_data
train_dataset, val_dataset = random_split(
full_dataset, [num_train_data, args.num_val_data])
train_dataloader = DataLoader(dataset=train_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
valid_dataloader = DataLoader(dataset=val_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
train(model, optimizer, train_dataloader, valid_dataloader, args)
logger.info('******************** Train Finished ********************')
# Test
if args.test_set:
test_texts, test_labels = read_file(args.test_file_path)
test_dataset = CustomTextDataset(test_texts, test_labels, word2idx)
test_dataloader = DataLoader(dataset=test_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
model.load_state_dict(
torch.load(os.path.join(args.model_save_path, "best.pt")))
_, accuracy, precision, recall, f1, cm = evaluate(
model, test_dataloader, args)
logger.info('-' * 50)
logger.info(
f'|* TEST SET *| |ACC| {accuracy:>.4f} |PRECISION| {precision:>.4f} |RECALL| {recall:>.4f} |F1| {f1:>.4f}'
)
logger.info('-' * 50)
logger.info('---------------- CONFUSION MATRIX ----------------')
for i in range(len(cm)):
logger.info(cm[i])
logger.info('--------------------------------------------------')
def main(args):
acc_list = []
f1_score_list = []
prec_list = []
recall_list = []
for i in range(10):
setup_data()
model = RCNN(vocab_size=args.vocab_size,
embedding_dim=args.embedding_dim,
hidden_size=args.hidden_size,
hidden_size_linear=args.hidden_size_linear,
class_num=args.class_num,
dropout=args.dropout).to(args.device)
if args.n_gpu > 1:
model = torch.nn.DataParallel(model, dim=0)
train_texts, train_labels = read_file(args.train_file_path)
word2idx, embedding = build_dictionary(train_texts, args.vocab_size,
args.lexical, args.syntactic,
args.semantic)
logger.info('Dictionary Finished!')
full_dataset = CustomTextDataset(train_texts, train_labels, word2idx,
args)
num_train_data = len(full_dataset) - args.num_val_data
train_dataset, val_dataset = random_split(
full_dataset, [num_train_data, args.num_val_data])
train_dataloader = DataLoader(dataset=train_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
valid_dataloader = DataLoader(dataset=val_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
train(model, optimizer, train_dataloader, valid_dataloader, embedding,
args)
logger.info('******************** Train Finished ********************')
# Test
if args.test_set:
test_texts, test_labels = read_file(args.test_file_path)
test_dataset = CustomTextDataset(test_texts, test_labels, word2idx,
args)
test_dataloader = DataLoader(
dataset=test_dataset,
collate_fn=lambda x: collate_fn(x, args),
batch_size=args.batch_size,
shuffle=True)
model.load_state_dict(
torch.load(os.path.join(args.model_save_path, "best.pt")))
_, accuracy, precision, recall, f1, cm = evaluate(
model, test_dataloader, embedding, args)
logger.info('-' * 50)
logger.info(
f'|* TEST SET *| |ACC| {accuracy:>.4f} |PRECISION| {precision:>.4f} |RECALL| {recall:>.4f} |F1| {f1:>.4f}'
)
logger.info('-' * 50)
logger.info('---------------- CONFUSION MATRIX ----------------')
for i in range(len(cm)):
logger.info(cm[i])
logger.info('--------------------------------------------------')
acc_list.append(accuracy / 100)
prec_list.append(precision)
recall_list.append(recall)
f1_score_list.append(f1)
avg_acc = sum(acc_list) / len(acc_list)
avg_prec = sum(prec_list) / len(prec_list)
avg_recall = sum(recall_list) / len(recall_list)
avg_f1_score = sum(f1_score_list) / len(f1_score_list)
logger.info('--------------------------------------------------')
logger.info(
f'|* TEST SET *| |Avg ACC| {avg_acc:>.4f} |Avg PRECISION| {avg_prec:>.4f} |Avg RECALL| {avg_recall:>.4f} |Avg F1| {avg_f1_score:>.4f}'
)
logger.info('--------------------------------------------------')
plot_df = pd.DataFrame({
'x_values': range(10),
'avg_acc': acc_list,
'avg_prec': prec_list,
'avg_recall': recall_list,
'avg_f1_score': f1_score_list
})
plt.plot('x_values',
'avg_acc',
data=plot_df,
marker='o',
markerfacecolor='blue',
markersize=12,
color='skyblue',
linewidth=4)
plt.plot('x_values',
'avg_prec',
data=plot_df,
marker='',
color='olive',
linewidth=2)
plt.plot('x_values',
'avg_recall',
data=plot_df,
marker='',
color='olive',
linewidth=2,
linestyle='dashed')
plt.plot('x_values',
'avg_f1_score',
data=plot_df,
marker='',
color='olive',
linewidth=2,
linestyle='dashed')
plt.legend()
fname = 'lexical-semantic-syntactic.png' if args.lexical and args.semantic and args.syntactic \
else 'semantic-syntactic.png' if args.semantic and args.syntactic \
else 'lexical-semantic.png' if args.lexical and args.semantic \
else 'lexical-syntactic.png'if args.lexical and args.syntactic \
else 'lexical.png' if args.lexical \
else 'syntactic.png' if args.syntactic \
else 'semantic.png' if args.semantic \
else 'plain.png'
if not (path.exists('./images')):
mkdir('./images')
plt.savefig(path.join('./images', fname))
model.train()
return score
if __name__ == "__main__":
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
train_data = pickle.load(open(os.path.join(data_path, train_name), "rb"))
dev_data = pickle.load(open(os.path.join(data_path, dev_name), "rb"))
vocabulary = pickle.load(open(os.path.join(data_path, vocabulary_name), "rb"))
print('dataset', len(train_data), len(dev_data))
# load w2v data
weight = pickle.load(open(os.path.join(data_path, weight_name), "rb"))
# model
train_device = torch.device(device if torch.cuda.is_available() else "cpu")
model = RCNN(vocab_size=len(vocabulary), embed_dim=embed_dim,
output_dim=class_num, hidden_dim=hidden_dim,
num_layers=num_layers, dropout=dropout, weight=weight)
model.to(train_device)
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# train
writer = SummaryWriter(log_dir=log_path)
train()
writer.close()
import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from model import RCNN from dataloader_mnist import dataloader,batch_size,test_dataset_len,train_dataset_len n_classes = 10 net = RCNN(n_classes=n_classes) learning_rate = 1e-3 epoch = 30 criterion = nn.CrossEntropyLoss() # optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.9) optimizer = optim.Adam(net.parameters(), lr=learning_rate) # scheduler = optim.lr_scheduler.ReduceLROnPlateau( # optimizer, 'min' , # factor=0.1 , # patience=(train_dataset_len/batch_size)*3, # verbose=True) use_gpu = torch.cuda.is_available() if use_gpu: net = net.cuda() loss_trend = []