def __init__(self, pre_path=''):
self.all_data = ABSAData()
self.train_iter = self.all_data.train_iter
self.val_iter = self.all_data.val_iter
self.test_iter = self.all_data.test_iter
self.text_vocab = self.all_data.text_vocab
self.aspect_vocab = self.all_data.aspect_vocab
self.label_vocab = self.all_data.label_vocab
self.device_dict = {
-1: 'cpu',
0: 'cuda:0',
1: 'cuda:1',
2: 'cuda:2',
}
self.model = config.model().to(self.device_dict[config.device])
if config.pretrain:
self.load_model(config.pretrain_path)
self.criterion = config.criterion()
# TODO: Set momentum for optimizer, momentum=0.9
self.optimizer = config.optimizer(filter(lambda p: p.requires_grad,
self.model.parameters()),
lr=config.learning_rate,
lr_decay=config.lr_decay,
weight_decay=0.001)
if config.if_log:
self.writer = SummaryWriter(log_dir=config.log_dir)
# Create pretrained model folder
if not config.pretrain:
if pre_path != '':
self.pre_dir = pre_path
def __init__(self, model, train_dataset, checkpoint_path, best_model_path,
log_path):
os.environ['CUDA_VISIBLE_DEVICES'] = device_idx
self.bare_model = model.to(device)
self.model = nn.DataParallel(model) if num_gpu > 1 else model
self.train_dataloader = data.DataLoader(train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=num_workers)
self.optim = optimizer(self.model.parameters(), lr=learning_rate)
self.checkpoint_path = checkpoint_path
self.best_model_path = best_model_path
self.log_path = log_path
self.best_avg_loss = 987654321
def train(epoch, global_step):
optimizer = config.optimizer(steps=global_step, epoch=epoch)
# return {'accuracy': 10.0, 'loss': 10.0}
for batch in range(len(labels) // config.batch_size()):
L = batch * config.batch_size()
progress(epoch, (L * 10.0) / len(labels))
R = L + config.batch_size()
mini_batch_images = images[L:R]
mini_batch_labels = labels[L:R]
with tfe.GradientTape() as tape:
s = tf.concat([
tf.gather_nd(mini_batch_images, gather_map),
tf.zeros(list(np.shape(gather_map)[:2]) + [2])
], -1)
logits = model.flow(s)
input_labels_one_hot = tf.one_hot(mini_batch_labels, 10)
loss = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=input_labels_one_hot, logits=logits))
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(
zip(grads, model.trainable_variables),
global_step=tf.train.get_or_create_global_step())
global_step += 1
loss = 0.0
success = 0.0
for batch in range(len(labels) // config.batch_size()):
L = batch * config.batch_size()
R = L + config.batch_size()
mini_batch_images = images[L:R]
mini_batch_labels = labels[L:R]
s = tf.concat([
tf.gather_nd(mini_batch_images, gather_map),
tf.zeros(list(np.shape(gather_map)[:2]) + [2])
], -1)
logits = model.flow(s)
classes = tf.argmax(logits, 1)
loss += tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits_v2(
labels=input_labels_one_hot, logits=logits))
success += tf.reduce_sum(
tf.cast(tf.equal(classes, mini_batch_labels), tf.float32))
return {'accuracy': success * 100.0 / len(labels), 'loss': loss}
def align_classification_train_crf(self,
train_data,
valid_data,
test_data,
embed,
pretrain=True):
init_aspect = np.array(np.load("initAspect.npy"))
# init_aspect = init_aspect / np.linalg.norm(init_aspect, axis=-1, keepdims=True)
init_aspect = torch.from_numpy(init_aspect)
PreTrainABAE = clas_model.PreTrainABAE(init_aspect,
embed).to(config.device)
pre_trained_aspect = torch.load("AspectExtract/Aspect_Model.pkl")
aspect_dict = PreTrainABAE.state_dict()
pre_trained_dict = {
k: v
for k, v in pre_trained_aspect.items() if k in aspect_dict
}
aspect_dict.update(pre_trained_dict)
PreTrainABAE.load_state_dict(aspect_dict)
# PreTrainABAE = PreTrainABAE.eval()
trained_aspect = pre_trained_aspect["aspect_lookup_mat"].data
run = clas_model.CrfWdeRnnEncoder(300, 300, 50, embed,
trained_aspect).to(config.device)
# params = []
# for param in run.parameters():
# if param.requires_grad:
# params.append(param)
# 加载预训练权重
if pretrain is True:
pre_trained_dict = torch.load(config.pretrained_model)
# pre_trained_dict = torch.load(config.pretrained_model, map_location=lambda storage, loc: storage)
model_dict = run.state_dict()
pre_trained_dict = {
k: v
for k, v in pre_trained_dict.items() if k in model_dict
}
model_dict.update(pre_trained_dict)
run.load_state_dict(model_dict)
criterion = config.criterion()
optimizer_rnn = config.optimizer(filter(lambda p: p.requires_grad,
run.parameters()),
lr=config.clas_lr)
optimizer_abae = config.optimizer(filter(lambda p: p.requires_grad,
PreTrainABAE.parameters()),
lr=config.clas_lr)
all_evaluate = []
best_test = 0
for epoch in range(config.epoch + 1):
run_hidden = run.initHidden(config.batch_size)
# context = torch.ones((config.batch_size, 50))
# loss_last = torch.tensor([0], dtype=torch.float)
optimizer_rnn.zero_grad()
optimizer_abae.zero_grad()
run.zero_grad()
for idx, sample_batch in enumerate(train_data):
run = run.train()
PreTrainABAE = PreTrainABAE.train()
input_data = sample_batch['input'].to(config.device)
label = sample_batch['label'].to(config.device)
aspect_info, trained_aspect, reg = PreTrainABAE(input_data)
input_data[:, 1] = aspect_info
out = run(input_data, run_hidden, trained_aspect,
"train").view(config.batch_size, 2).to(config.device)
# print("result :", out.size())
# print(label)
# loss = criterion(out, label) + reg.float()
loss = criterion(out, label)
loss.backward()
optimizer_rnn.step()
optimizer_abae.step()
# if epoch % 5 == 0:
# run.zero_grad()
# run = run.eval()
# valid_now = self.valid(run)
# print('epoch {} of {}: TEST : {}'.format(epoch, 100, valid_now))
print('epoch {} of {}: loss : {}'.format(epoch, config.epoch,
loss))
if epoch % 1 == 0:
with torch.no_grad():
total = 0
correct = 0
optimizer_rnn.zero_grad()
optimizer_abae.zero_grad()
run.zero_grad()
PreTrainABAE.zero_grad()
run_hidden = run.initHidden(1)
# context = torch.ones((1, 50))
for index, sample_batch in enumerate(valid_data):
run = run.eval()
PreTrainABAE = PreTrainABAE.eval()
input_data = sample_batch['input'].to(config.device)
label = sample_batch['label'].to(config.device)
aspect_info, trained_aspect, _ = PreTrainABAE(
input_data)
input_data[:, 1] = aspect_info
outputs = run(input_data, run_hidden, trained_aspect,
"test").view(1, 2).to(config.device)
_, predicted = torch.max(outputs.data, 1)
# print(outputs)
# print(predicted)
# print(label)
total += label.size(0)
# print(total)
correct += (predicted == label).sum().item()
# print(correct)
acc = correct / total
print("acc rate :", acc)
if acc > best_test:
best_test = acc
file_name = "ClassifyModelSave/Final_model.pkl"
file_name_aspect = "ClassifyModelSave/Final_model_aspect.pkl"
torch.save(run.state_dict(), file_name)
torch.save(PreTrainABAE.state_dict(), file_name_aspect)
all_evaluate.append(acc)
'''
Load the best models and Begin test
'''
PreTrainABAE_test = clas_model.PreTrainABAE(init_aspect,
embed).to(config.device)
pre_trained_aspect = torch.load(
"ClassifyModelSave/Final_model_aspect.pkl")
aspect_dict = PreTrainABAE_test.state_dict()
pre_trained_dict = {
k: v
for k, v in pre_trained_aspect.items() if k in aspect_dict
}
aspect_dict.update(pre_trained_dict)
PreTrainABAE_test.load_state_dict(aspect_dict)
trained_aspect = pre_trained_aspect["aspect_lookup_mat"].data
model_test = clas_model.CrfWdeRnnEncoder(
300, 300, 50, embed, trained_aspect).to(config.device)
pre_trained_dict = torch.load("ClassifyModelSave/Final_model.pkl")
model_dict = model_test.state_dict()
pre_trained_dict = {
k: v
for k, v in pre_trained_dict.items() if k in model_dict
}
model_dict.update(pre_trained_dict)
model_test.load_state_dict(model_dict)
with torch.no_grad():
total = 0
correct = 0
model_test.zero_grad()
PreTrainABAE_test.zero_grad()
run_hidden = model_test.initHidden(1)
# context = torch.ones((1, 50))
for index, sample_batch in enumerate(test_data):
model_test = model_test.eval()
input_data = sample_batch['input'].to(config.device)
label = sample_batch['label'].to(config.device)
aspect_info, trained_aspect, _ = PreTrainABAE_test(input_data)
input_data[:, 1] = aspect_info
outputs = model_test(input_data, run_hidden, trained_aspect,
"test").view(1, 2).to(config.device)
_, predicted = torch.max(outputs.data, 1)
# print(outputs)
# print(predicted)
# print(label)
total += label.size(0)
# print(total)
correct += (predicted == label).sum().item()
# print(correct)
acc = correct / total
print("Test acc rate (final result) :", acc)
return all_evaluate
def classification_train_fix(self,
train_data,
test_data,
embed,
pretrain=True):
run = clas_model.AttentionEncoder(300, 300, 50,
embed).to(config.device)
params = []
for param in run.parameters():
if param.requires_grad:
params.append(param)
# 加载预训练权重
if pretrain is True:
pre_trained_dict = torch.load(
config.pretrained_model,
map_location=lambda storage, loc: storage)
model_dict = run.state_dict()
pre_trained_dict = {
k: v
for k, v in pre_trained_dict.items() if k in model_dict
}
model_dict.update(pre_trained_dict)
run.load_state_dict(model_dict)
optimizer = config.optimizer(params, lr=config.clas_lr)
criterion = config.criterion
all_evaluate = []
for epoch in range(config.epoch):
context = torch.ones((config.batch_size, 50))
# loss_last = torch.tensor([0], dtype=torch.float)
optimizer.zero_grad()
# run.zero_grad()
for idx, sample_batch in enumerate(train_data):
run = run.train()
input_data = sample_batch['input'].to(config.device)
label = sample_batch['label'].to(config.device)
out = run(input_data,
context.cuda()).view(config.batch_size,
2).to(config.device)
# print("result :", out.size())
# print(label)
loss = criterion(out, label)
loss.backward()
optimizer.step()
# if epoch % 5 == 0:
# run.zero_grad()
# run = run.eval()
# valid_now = self.valid(run)
# print('epoch {} of {}: TEST : {}'.format(epoch, 100, valid_now))
print('epoch {} of {}: loss : {}'.format(epoch, config.epoch,
loss))
if epoch % 5 == 0:
with torch.no_grad():
total = 0
correct = 0
optimizer.zero_grad()
run.zero_grad()
context = torch.ones((1, 50))
for index, sample_batch in enumerate(test_data):
run = run.eval()
input_data = sample_batch['input'].to(config.device)
label = sample_batch['label'].to(config.device)
outputs = run(input_data,
context.cuda()).view(1,
2).to(config.device)
_, predicted = torch.max(outputs.data, 1)
# print(outputs)
# print(predicted)
# print(label)
total += label.size(0)
# print(total)
correct += (predicted == label).sum().item()
# print(correct)
print("acc rate :", correct / total)
all_evaluate.append(correct / total)
return all_evaluate
def weakly_train(self, train_data, test_pos, test_neg, embed, asp_list):
# run = models.AttentionEncoder(300, 300, 50, embed).to(config.device)
# init_aspect = np.array(np.load("initAspect.npy"))
# # init_aspect = init_aspect / np.linalg.norm(init_aspect, axis=-1, keepdims=True)
# init_aspect = torch.from_numpy(init_aspect)
# pre_train_abae = weak_model.PreTrainABAE(init_aspect, embed).to(config.device)
#
# pre_trained_aspect = torch.load("AspectExtract/Aspect_Model.pkl")
# aspect_dict = pre_train_abae.state_dict()
# pre_trained_dict = {k: v for k, v in pre_trained_aspect.items() if k in aspect_dict}
# aspect_dict.update(pre_trained_dict)
# pre_train_abae.load_state_dict(aspect_dict)
# pre_train_abae = pre_train_abae.eval()
#
# trained_aspect = pre_trained_aspect["aspect_lookup_mat"].data
# run = weak_model.WdeRnnEncoderFix(300, 300, 50, embed, trained_aspect).to(config.device)
run = weak_model.WdeRnnEncoderFix(300, 300, 50,
embed).to(config.device)
# context = torch.ones((config.batch_size, 50))
# optimizer = optim.Adagrad(params, lr=0.003)
# params = []
# for param in run.parameters():
# if param.requires_grad:
# params.append(param)
# optimizer = optim.SGD(filter(lambda p: p.requires_grad, run.parameters()), lr=0.0001)
optimizer = config.optimizer(filter(lambda p: p.requires_grad,
run.parameters()),
lr=config.weak_lr)
loss_func = config.criterion(margin=config.margin, p=config.margin_p)
for epoch in range(config.epoch):
run_hidden = run.initHidden(config.batch_size)
loss_last = torch.tensor([0], dtype=torch.float)
optimizer.zero_grad()
# run.zero_grad()
for idx, sample_batch in enumerate(train_data):
# now = time.time()
run = run.train()
input1 = sample_batch['input1'].to(config.device)
input2 = sample_batch['input2'].to(config.device)
input3 = sample_batch['input3'].to(config.device)
aspect1 = sample_batch['aspect1'].to(config.device)
aspect2 = sample_batch['aspect2'].to(config.device)
aspect3 = sample_batch['aspect3'].to(config.device)
# get aspect info
# aspect_info = pre_train_abae(input1)
# input1[:, 1] = aspect_info
# aspect_info = pre_train_abae(input2)
# input2[:, 1] = aspect_info
# aspect_info = pre_train_abae(input3)
# input3[:, 1] = aspect_info
# feed input data
out1 = run(input1, run_hidden,
aspect1).view(config.batch_size, 300)
out2 = run(input2, run_hidden,
aspect2).view(config.batch_size, 300)
out3 = run(input3, run_hidden,
aspect3).view(config.batch_size, 300)
# count loss
loss_last = loss_func(out1, out2, out3)
loss_last.backward()
optimizer.step()
if epoch % config.valid_step == 0:
run.zero_grad()
run = run.eval()
valid_now = self.valid(asp_list, run, test_pos, test_neg,
embed)
a = round((loss_last).item(), 5)
b = round(valid_now, 5)
if config.save_model and valid_now > config.valid_thres:
file_name = config.save_model_path + "model_loss_" + str(
a) + "valid_" + str(b) + ".pkl"
torch.save(run.state_dict(), file_name)
print('epoch {} of {}: TEST : {}'.format(
epoch, config.epoch, valid_now))
print('epoch {} of {}: loss : {}'.format(epoch, config.epoch,
loss_last.item()))
param.requires_grad = False
for param in model.classifier.parameters():
param.requires_grad = True
model = model.to(device)
###### TRAIN NEURAL NET ######
dataloaders = {
'train': train_loader,
'val': val_loader,
'test': test_loader
}
criterion = cfg.criterion
optimizer = cfg.optimizer(model.parameters(), weight_decay=0.1)
###### SAVE / LOAD MODEL #####
filename = "prediction_network_epochs" + str(num_epochs) + "_bs" + str(
batch_size) + "_num_classes" + str(num_classes)
load_model = os.path.exists(filename)
#load_model = False
if load_model:
model.load_state_dict(torch.load(filename))
else:
model, val_acc_history = train_model(model,
dataloaders,
criterion,
optimizer,
num_epochs=num_epochs,
filename=filename)
