def train(self, trainX, trainY, verbose=False):
trainY = np.squeeze(trainY)
self.forwardPropagation(trainX)
self.backPropagation(trainY)
self.updateParameters()
if verbose:
loss = Metrics.crossEntropyLoss(trainY,
self.outputLayer.predictions)
accuracy = Metrics.accuracy(trainY,
self.outputLayer.predictedLabels)
return [loss, accuracy]
def main():
global args
args = parse_args()
# argument validation
args.cuda = args.cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
print(args)
train_dir = glob.glob(os.path.join(args.data, 'train/holistic/*.pt'))
dev_dir = glob.glob(os.path.join(args.data, 'val/holistic/*.pt'))
test_dir = glob.glob(os.path.join(args.data, 'test/holistic/*.pt'))
train_dataset = Dataset(os.path.join(args.data, 'train'), train_dir)
dev_dataset = Dataset(os.path.join(args.data, 'val'), dev_dir)
test_dataset = Dataset(os.path.join(args.data, 'test'), test_dir)
print('==> Size of train data : %d ' % len(train_dataset))
print('==> Size of val data : %d ' % len(dev_dataset))
print('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
if args.pretrained_model == 'vgg16':
pretrained_vgg16 = models.vgg16(pretrained=True)
# Freeze training for all layers
for child in pretrained_vgg16.children():
for param in child.parameters():
param.requires_grad = False
if args.pretrained_holistic == 0:
model = model_vgg16.DocClassificationHolistic(
args, pretrained_vgg16)
elif args.pretrained_holistic == 1:
pretrained_orig_vgg16 = model_vgg16.DocClassificationHolistic(
args, pretrained_vgg16)
pretrained_holistic = model_vgg16.DocClassificationHolistic(
args, pretrained_orig_vgg16.pretrained_model)
checkpoint = torch.load('./checkpoints/vgg16.pt')
pretrained_holistic.load_state_dict(checkpoint['model'])
model = model_vgg16.DocClassificationRest(args,
pretrained_orig_vgg16,
pretrained_holistic)
elif args.pretrained_model == 'vgg19':
pretrained_vgg19 = models.vgg19(pretrained=True)
# Freeze training for all layers
for child in pretrained_vgg19.children():
for param in child.parameters():
param.requires_grad = False
if args.pretrained_holistic == 0:
model = model_vgg19.DocClassificationHolistic(
args, pretrained_vgg19)
elif args.pretrained_holistic == 1:
pretrained_orig_vgg19 = model_vgg19.DocClassificationHolistic(
args, pretrained_vgg19)
pretrained_holistic = model_vgg19.DocClassificationHolistic(
args, pretrained_orig_vgg19.pretrained_model)
checkpoint = torch.load('./checkpoints/vgg19.pt')
pretrained_holistic.load_state_dict(checkpoint['model'])
model = model_vgg19.DocClassificationRest(args,
pretrained_orig_vgg19,
pretrained_holistic)
elif args.pretrained_model == 'resnet50':
pretrained_resnet50 = models.resnet50(pretrained=True)
# Freeze training for all layers
for child in pretrained_resnet50.children():
for param in child.parameters():
param.requires_grad = False
if args.pretrained_holistic == 0:
model = model_resnet50.DocClassificationHolistic(
args, pretrained_resnet50)
elif args.pretrained_holistic == 1:
pretrained_orig_resnet50 = model_resnet50.DocClassificationHolistic(
args, pretrained_resnet50)
pretrained_holistic = model_resnet50.DocClassificationHolistic(
args, pretrained_orig_resnet50.pretrained_model)
checkpoint = torch.load('./checkpoints/resnet50.pt')
pretrained_holistic.load_state_dict(checkpoint['model'])
model = model_resnet50.DocClassificationRest(
args, pretrained_orig_resnet50, pretrained_holistic)
elif args.pretrained_model == 'densenet121':
pretrained_densenet121 = models.densenet121(pretrained=True)
# Freeze training for all layers
for child in pretrained_densenet121.children():
for param in child.parameters():
param.requires_grad = False
if args.pretrained_holistic == 0:
model = model_densenet121.DocClassificationHolistic(
args, pretrained_densenet121)
elif args.pretrained_holistic == 1:
pretrained_orig_densenet121 = model_densenet121.DocClassificationHolistic(
args, pretrained_densenet121)
pretrained_holistic = model_densenet121.DocClassificationHolistic(
args, pretrained_orig_densenet121.pretrained_model)
checkpoint = torch.load('./checkpoints/densenet121.pt')
pretrained_holistic.load_state_dict(checkpoint['model'])
model = model_densenet121.DocClassificationRest(
args, pretrained_orig_densenet121, pretrained_holistic)
elif args.pretrained_model == 'inceptionv3':
pretrained_inceptionv3 = models.inception_v3(pretrained=True)
# Freeze training for all layers
for child in pretrained_inceptionv3.children():
for param in child.parameters():
param.requires_grad = False
if args.pretrained_holistic == 0:
model = model_inceptionv3.DocClassificationHolistic(
args, pretrained_inceptionv3)
elif args.pretrained_holistic == 1:
pretrained_orig_inceptionv3 = model_inceptionv3.DocClassificationHolistic(
args, pretrained_inceptionv3)
pretrained_holistic = model_inceptionv3.DocClassificationHolistic(
args, pretrained_orig_inceptionv3.pretrained_model)
checkpoint = torch.load('./checkpoints/inceptionv3.pt')
pretrained_holistic.load_state_dict(checkpoint['model'])
model = model_inceptionv3.DocClassificationRest(
args, pretrained_orig_inceptionv3, pretrained_holistic)
criterion = nn.CrossEntropyLoss(reduction='sum')
parameters = filter(lambda p: p.requires_grad, model.parameters())
if args.cuda:
model.cuda(), criterion.cuda()
if args.optim == 'adam':
optimizer = optim.Adam(parameters, lr=args.lr, weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(parameters, lr=args.lr, weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(parameters, lr=args.lr, weight_decay=args.wd)
elif args.optim == 'adadelta':
optimizer = optim.Adadelta(parameters,
lr=args.lr,
weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# create trainer object for training and testing
trainer = Trainer(args, model, criterion, optimizer)
train_idx = list(np.arange(len(train_dataset)))
dev_idx = list(np.arange(len(dev_dataset)))
test_idx = list(np.arange(len(test_dataset)))
best = float('inf')
columns = ['ExpName', 'ExpNo', 'Epoch', 'Loss', 'Accuracy']
results = []
early_stop_count = 0
for epoch in range(args.epochs):
train_loss = 0.0
dev_loss = 0.0
test_loss = 0.0
train_predictions = []
train_labels = []
dev_predictions = []
dev_labels = []
test_predictions = []
test_labels = []
random.shuffle(train_idx)
random.shuffle(dev_idx)
random.shuffle(test_idx)
batch_train_data = [
train_idx[i:i + args.batchsize]
for i in range(0, len(train_idx), args.batchsize)
]
batch_dev_data = [
dev_idx[i:i + args.batchsize]
for i in range(0, len(dev_idx), args.batchsize)
]
batch_test_data = [
test_idx[i:i + args.batchsize]
for i in range(0, len(test_idx), args.batchsize)
]
for batch in tqdm(batch_train_data, desc='Training batches..'):
train_batch_holistic, \
train_batch_header, \
train_batch_footer, \
train_batch_left_body, \
train_batch_right_body, \
train_batch_labels = train_dataset[batch]
if args.pretrained_holistic == 0:
_ = trainer.train_holistic(train_batch_holistic,
train_batch_labels)
elif args.pretrained_holistic == 1:
_ = trainer.train_rest(train_batch_holistic, \
train_batch_header, \
train_batch_footer, \
train_batch_left_body, \
train_batch_right_body, \
train_batch_labels)
for batch in tqdm(batch_train_data, desc='Training batches..'):
train_batch_holistic, \
train_batch_header, \
train_batch_footer, \
train_batch_left_body, \
train_batch_right_body, \
train_batch_labels = train_dataset[batch]
if args.pretrained_holistic == 0:
train_batch_loss, train_batch_predictions, train_batch_labels = trainer.test_holistic(
train_batch_holistic, train_batch_labels)
elif args.pretrained_holistic == 1:
train_batch_loss, train_batch_predictions, train_batch_labels = trainer.test_rest(train_batch_holistic, \
train_batch_header, \
train_batch_footer, \
train_batch_left_body, \
train_batch_right_body, \
train_batch_labels)
train_predictions.append(train_batch_predictions)
train_labels.append(train_batch_labels)
train_loss = train_loss + train_batch_loss
train_accuracy = metrics.accuracy(np.concatenate(train_predictions),
np.concatenate(train_labels))
for batch in tqdm(batch_dev_data, desc='Dev batches..'):
dev_batch_holistic, \
dev_batch_header, \
dev_batch_footer, \
dev_batch_left_body, \
dev_batch_right_body, \
dev_batch_labels = dev_dataset[batch]
if args.pretrained_holistic == 0:
dev_batch_loss, dev_batch_predictions, dev_batch_labels = trainer.test_holistic(
dev_batch_holistic, dev_batch_labels)
elif args.pretrained_holistic == 1:
dev_batch_loss, dev_batch_predictions, dev_batch_labels = trainer.test_rest(dev_batch_holistic, \
dev_batch_header, \
dev_batch_footer, \
dev_batch_left_body, \
dev_batch_right_body, \
dev_batch_labels)
dev_predictions.append(dev_batch_predictions)
dev_labels.append(dev_batch_labels)
dev_loss = dev_loss + dev_batch_loss
dev_accuracy = metrics.accuracy(np.concatenate(dev_predictions),
np.concatenate(dev_labels))
for batch in tqdm(batch_test_data, desc='Test batches..'):
test_batch_holistic, \
test_batch_header, \
test_batch_footer, \
test_batch_left_body, \
test_batch_right_body, \
test_batch_labels = test_dataset[batch]
if args.pretrained_holistic == 0:
test_batch_loss, test_batch_predictions, test_batch_labels = trainer.test_holistic(
test_batch_holistic, test_batch_labels)
elif args.pretrained_holistic == 1:
test_batch_loss, test_batch_predictions, test_batch_labels = trainer.test_rest(test_batch_holistic, \
test_batch_header, \
test_batch_footer, \
test_batch_left_body, \
test_batch_right_body, \
test_batch_labels)
test_predictions.append(test_batch_predictions)
test_labels.append(test_batch_labels)
test_loss = test_loss + test_batch_loss
test_accuracy = metrics.accuracy(np.concatenate(test_predictions),
np.concatenate(test_labels))
print('==> Training Epoch: %d, \
\nLoss: %f, \
\nAccuracy: %f' %(epoch + 1, \
train_loss/(len(batch_train_data) * args.batchsize), \
train_accuracy))
print('==> Dev Epoch: %d, \
\nLoss: %f, \
\nAccuracy: %f' %(epoch + 1, \
dev_loss/(len(batch_dev_data) * args.batchsize), \
dev_accuracy))
print('==> Test Epoch: %d, \
\nLoss: %f, \
\nAccuracy: %f' %(epoch + 1, \
test_loss/(len(batch_test_data) * args.batchsize), \
test_accuracy))
#quit()
results.append((args.expname, \
args.expno, \
epoch+1, \
test_loss/(len(batch_test_data) * args.batchsize), \
test_accuracy))
if best > test_loss:
best = test_loss
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'loss': test_loss,
'accuracy': test_accuracy,
'args': args,
'epoch': epoch
}
print('==> New optimum found, checkpointing everything now...')
torch.save(checkpoint,
'%s.pt' % os.path.join(args.save, args.expname))
#np.savetxt("test_pred.csv", test_pred.numpy(), delimiter=",")
else:
early_stop_count = early_stop_count + 1
if early_stop_count == 20:
quit()
def main():
global args
args = parse_args()
args.input_dim, args.mem_dim = 300, 150
args.hidden_dim, args.num_classes = 20, 2
args.cuda = args.cuda and torch.cuda.is_available()
if args.sparse and args.wd != 0:
print('Sparsity and weight decay are incompatible, pick one!')
exit()
print(args)
torch.manual_seed(args.seed)
random.seed(args.seed)
numpy.random.seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
torch.backends.cudnn.benchmark = True
if not os.path.exists(args.save):
os.makedirs(args.save)
train_dir = os.path.join(args.data, 'train/')
dev_dir = os.path.join(args.data, 'dev/')
test_dir = os.path.join(args.data, 'test/')
# write unique words from all token files
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
if not os.path.isfile(sick_vocab_file):
token_files_a = [
os.path.join(split, 'toks.a')
for split in [train_dir, dev_dir, test_dir]
]
token_files_b = [
os.path.join(split, 'toks.b')
for split in [train_dir, dev_dir, test_dir]
]
token_files = token_files_a + token_files_b
sick_vocab_file = os.path.join(args.data, 'sick.vocab')
build_vocab(token_files, sick_vocab_file)
# get vocab object from vocab file previously written
vocab = Vocab(filename=sick_vocab_file,
data=[
Constants.PAD_WORD, Constants.UNK_WORD,
Constants.BOS_WORD, Constants.EOS_WORD
])
print('==> SICK vocabulary size : %d ' % vocab.size())
# load SICK dataset splits
train_file = os.path.join(args.data, 'sick_train.pth')
if os.path.isfile(train_file):
train_dataset = torch.load(train_file)
else:
train_dataset = SICKDataset(train_dir, vocab, args.num_classes)
torch.save(train_dataset, train_file)
print('==> Size of train data : %d ' % len(train_dataset))
dev_file = os.path.join(args.data, 'sick_dev.pth')
if os.path.isfile(dev_file):
dev_dataset = torch.load(dev_file)
else:
dev_dataset = SICKDataset(dev_dir, vocab, args.num_classes)
torch.save(dev_dataset, dev_file)
print('==> Size of dev data : %d ' % len(dev_dataset))
test_file = os.path.join(args.data, 'sick_test.pth')
if os.path.isfile(test_file):
test_dataset = torch.load(test_file)
else:
test_dataset = SICKDataset(test_dir, vocab, args.num_classes)
torch.save(test_dataset, test_file)
print('==> Size of test data : %d ' % len(test_dataset))
# initialize model, criterion/loss_function, optimizer
model = SimilarityTreeLSTM(args.cuda, vocab.size(), args.input_dim,
args.mem_dim, args.hidden_dim, args.num_classes,
args.sparse)
criterion = nn.KLDivLoss()
if args.cuda:
model.cuda(), criterion.cuda()
if args.optim == 'adam':
optimizer = optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'adagrad':
optimizer = optim.Adagrad(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
elif args.optim == 'sgd':
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.wd)
metrics = Metrics(args.num_classes)
# for words common to dataset vocab and GLOVE, use GLOVE vectors
# for other words in dataset vocab, use random normal vectors
emb_file = os.path.join(args.data, 'sick_embed.pth')
if os.path.isfile(emb_file):
emb = torch.load(emb_file)
else:
# load glove embeddings and vocab
glove_vocab, glove_emb = load_word_vectors(
os.path.join(args.glove, 'glove.840B.300d'))
print('==> GLOVE vocabulary size: %d ' % glove_vocab.size())
emb = torch.Tensor(vocab.size(),
glove_emb.size(1)).normal_(-0.05, 0.05)
# zero out the embeddings for padding and other special words if they are absent in vocab
for idx, item in enumerate([
Constants.PAD_WORD, Constants.UNK_WORD, Constants.BOS_WORD,
Constants.EOS_WORD
]):
emb[idx].zero_()
for word in vocab.labelToIdx.keys():
word_new = word.decode("utf8")
idx_set = [
glove_vocab.getIndex(token)
for token in word_tokenize(word_new)
]
idx_set = [id for id in idx_set if id is not None]
if len(idx_set) != 0:
idx_set = torch.LongTensor(idx_set)
sum_emb = F.torch.sum(glove_emb.index_select(0, idx_set), 0)
else:
sum_emb = glove_emb[1] * 0
# for token in word_tokenize(word_new):
# idx = glove_vocab.getIndex(token)
# if idx is not None:
# if sum_emb is None:
# sum_emb = glove_emb[idx]
# else:
# sum_emb += glove_emb[idx]
emb[vocab.getIndex(word)] = sum_emb
torch.save(emb, emb_file)
# plug these into embedding matrix inside model
if args.cuda:
emb = emb.cuda()
model.childsumtreelstm.emb.state_dict()['weight'].copy_(emb)
# create trainer object for training and testing
trainer = Trainer(args, model, criterion, optimizer)
best = -float('inf')
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
print(train_pred)
dev_loss, dev_pred = trainer.test(dev_dataset)
print(dev_pred)
test_loss, test_pred = trainer.test(test_dataset)
train_pearson = metrics.pearson(train_pred, train_dataset.labels)
train_mse = metrics.accuracy(train_pred, train_dataset.labels)
print('==> Train Loss: {}\tPearson: {}\tL1: {}'.format(
train_loss, train_pearson, train_mse))
dev_pearson = metrics.pearson(dev_pred, dev_dataset.labels)
dev_mse = metrics.accuracy(dev_pred, dev_dataset.labels)
print('==> Dev Loss: {}\tPearson: {}\tL1: {}'.format(
dev_loss, dev_pearson, dev_mse))
test_pearson = metrics.pearson(test_pred, test_dataset.labels)
test_mse = metrics.accuracy(test_pred, test_dataset.labels)
print('==> Test Loss: {}\tPearson: {}\tL1: {}'.format(
test_loss, test_pearson, test_mse))
if best < test_pearson:
best = test_pearson
checkpoint = {
'model': trainer.model.state_dict(),
'optim': trainer.optimizer,
'pearson': test_pearson,
'mse': test_mse,
'args': args,
'epoch': epoch
}
print('==> New optimum found, checkpointing everything now...')
torch.save(
checkpoint,
'%s.pt' % os.path.join(args.save, args.expname + '.pth'))
optimizer = optim.SGD(filter(lambda p: p.requires_grad,
model.parameters()), lr=args.lr, weight_decay=args.wd)
model.to(device), criterion.to(device)
trainer = Trainer(args, model, criterion, optimizer, device)
best = - float("inf")
metrics = Metrics(8)
for epoch in range(args.epochs):
train_loss = trainer.train(train_dataset)
train_loss, train_pred = trainer.test(train_dataset)
test_loss, test_preds = trainer.test(test_dataset)
train_pred = train_pred[:, 1]
test_preds = test_preds[:, 1]
train_labels = utils.get_labels(train_dataset).squeeze(1)
train_targets = utils.map_labels_to_targets(train_labels, args.num_classes)
train_mse = metrics.mse(train_pred, train_labels)
acc = metrics.accuracy(train_pred, train_labels) # label！！！
fpr, tpr, threshold = roc_curve(train_labels, train_pred)
train_auc = auc(fpr, tpr)
logger.info("==> Epoch {}, Train \t Loss: {}\t Auc: {}\tMSE{} \t Accuracy{}".format(
epoch, train_loss, train_auc, train_mse, acc
))
test_lables = utils.get_labels(test_dataset).squeeze(1)
test_targets = utils.map_labels_to_targets(test_lables, args.num_classes)
test_mse = metrics.mse(test_preds, test_lables)
test_acc = metrics.accuracy(test_preds, test_lables)
fpr, tpr, t = roc_curve(test_lables, test_preds)
test_auc = auc(fpr, tpr)
logger.info("==> Epoch {}, Test \t Loss: {}\tAuc: {}\tMSE{} \t Accuracy{} \t".format(
epoch, test_loss, test_auc, test_mse, test_acc
))
