def step(self, norm_type=2):
"""
Run an update eventually clipping the gradients
"""
if self.max_norm is not None:
clip_grad_norm(self.params, self.max_norm, norm_type=norm_type)
self.optim.step()
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1,5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print("clipping gradient: {} with coef {}".format(total_norm, args.clip_gradient / total_norm))
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print(('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5, lr=optimizer.param_groups[-1]['lr'])))
def step(self):
"""Update the model parameters based on current gradients.
Optionally, will employ gradient modification or update learning
rate.
"""
self._step += 1
# Decay method used in tensor2tensor.
if self.decay_method == "noam":
self._set_rate(
self.original_lr *
(self.model_size ** (-0.5) *
min(self._step ** (-0.5),
self._step * self.warmup_steps**(-1.5))))
if self.max_grad_norm:
clip_grad_norm(self.params, self.max_grad_norm)
self.optimizer.step()
def train(e, model, optimizer, train_iter, vocab_size, grad_clip, DE, EN):
model.train()
total_loss = 0
pad = EN.vocab.stoi['<pad>']
for b, batch in enumerate(train_iter):
src, len_src = batch.src
trg, len_trg = batch.trg
src, trg = src.cuda(), trg.cuda()
optimizer.zero_grad()
output = model(src, trg)
loss = F.cross_entropy(output[1:].view(-1, vocab_size),
trg[1:].contiguous().view(-1),
ignore_index=pad)
loss.backward()
clip_grad_norm(model.parameters(), grad_clip)
optimizer.step()
total_loss += loss.data[0]
if b % 100 == 0 and b != 0:
total_loss = total_loss / 100
print("[%d][loss:%5.2f][pp:%5.2f]" %
(b, total_loss, math.exp(total_loss)))
total_loss = 0
log_prob_actions_v = batch_scale_v * log_prob_v[range(BATCH_SIZE),
batch_actions_t]
loss_policy_v = -log_prob_actions_v.mean()
loss_policy_v.backward(retain_graph=True)
grads = np.concatenate([
p.grad.data.cpu().numpy().flatten() for p in net.parameters()
if p.grad is not None
])
prob_v = F.softmax(logits_v)
entropy_v = -(prob_v * log_prob_v).sum(dim=1).mean()
entropy_loss_v = -ENTROPY_BETA * entropy_v
loss_v = loss_policy_v + entropy_loss_v
loss_v.backward()
nn_utils.clip_grad_norm(net.parameters(), GRAD_L2_CLIP)
optimizer.step()
loss_v += loss_policy_v
# calc KL-div
new_logits_v = net(states_v)
new_prob_v = F.softmax(new_logits_v)
kl_div_v = -(
(new_prob_v / prob_v).log() * prob_v).sum(dim=1).mean()
writer.add_scalar("kl", kl_div_v.data.cpu().numpy()[0], step_idx)
writer.add_scalar("baseline", baseline, step_idx)
writer.add_scalar("entropy",
entropy_v.data.cpu().numpy()[0], step_idx)
writer.add_scalar("batch_scales", np.mean(batch_scales), step_idx)
writer.add_scalar("batch_scales_std", scale_std, step_idx)
def train(lr, net, epoch, train_loader, valid_loader, transform,
hyperparameters, batch_size):
# register hypercurve
agent = Agent(port=5001)
hyperparameters['criteria'] = 'train loss'
train_loss = agent.register(hyperparameters, 'loss')
hyperparameters['criteria'] = 'valid loss'
valid_loss = agent.register(hyperparameters, 'loss')
hyperparameters['criteria'] = 'valid bleu'
valid_bleu = agent.register(hyperparameters, 'bleu')
hyperparameters['criteria'] = 'train bleu'
train_bleu = agent.register(hyperparameters, 'bleu')
hyperparameters['criteria'] = 'scheduled sampling probability'
hyper_ssprob = agent.register(hyperparameters, 'probability')
if torch.cuda.is_available():
net.cuda()
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
net.train()
best_score = -1
global_steps = 0
best_valid_loss = 10000
for iepoch in range(epoch):
new_epoch = False
batchid = 0
for (_, data) in enumerate(train_loader, 0):
entext = data['entext']
enlen = data['enlen']
zhlabel = data['zhlabel']
zhgtruth = data['zhgtruth']
zhlen = data['zhlen']
ssprob = max(math.exp(-(global_steps - 100000) / 500000), 0.8)
print('scheduled sampling pro: ', ssprob)
logits, predic = net(entext, zhgtruth, enlen, ssprob, True)
loss = net.get_loss(logits, zhlabel)
optimizer.zero_grad()
loss.backward()
utils.clip_grad_norm(net.parameters(), 5)
optimizer.step()
batchid += 1
global_steps += 1
print(global_steps, iepoch, batchid, sum(loss.data.cpu().numpy()))
agent.append(train_loss, global_steps,
sum(loss.data.cpu().numpy()))
agent.append(hyper_ssprob, global_steps, ssprob)
if batchid % 50 == 0:
net.eval()
logits, predic = net(entext, zhgtruth, enlen, ssprob, True)
tmppre = [0 for i in range(len(entext))]
tmplabel = [0 for i in range(len(entext))]
for i in range(len(entext)):
tmppre[i] = transform.clip(predic[i], language='zh')
tmplabel[i] = zhlabel[i][:zhlen[i]]
tmpscore = bleuscore.score(tmppre, tmplabel)
for i in range(25):
ans_ = transform.i2t(tmplabel[i], language='zh')
pre_ = transform.i2t(tmppre[i], language='zh')
print(ans_)
print(pre_)
print('-------------------\n')
agent.append(train_bleu, global_steps, tmpscore)
del logits, predic
if batchid % 400 == 0:
print('\n------------------------\n')
net.eval()
all_pre = []
all_lable = []
all_len = []
all_loss = 0
bats = 0
for (_, data) in enumerate(valid_loader, 0):
entext = data['entext']
enlen = data['enlen']
zhlabel = data['zhlabel']
zhgtruth = data['zhgtruth']
zhlen = data['zhlen']
logits, predic = net(entext, zhgtruth, enlen, 0, False)
loss = net.get_loss(logits, zhlabel)
all_pre.extend(predic)
all_lable.extend(zhlabel)
all_len.extend(zhlen)
all_loss += sum(loss.data.cpu().numpy())
del loss, logits, predic
bats += 1
for i in range(len(all_pre)):
all_pre[i] = transform.clip(all_pre[i], language='zh')
all_lable[i] = all_lable[i][:all_len[i]]
score = bleuscore.score(all_pre, all_lable)
for i in range(0, 600, 6):
ans_ = transform.i2t(all_lable[i], language='zh')
pre_ = transform.i2t(all_pre[i], language='zh')
print(ans_)
print(pre_)
print('-------------------\n')
all_loss /= bats
print(global_steps, iepoch, batchid, all_loss, score,
'\n********************\n')
agent.append(valid_loss, global_steps, all_loss)
agent.append(valid_bleu, global_steps, score)
if best_valid_loss > all_loss or best_score < score:
best_valid_loss = all_loss
bestscore = score
torch.save(
net.state_dict(), model_dir +
"ssprob-{:3f}-loss-{:3f}-steps-{:d}-model.pkl".format(
ssprob, all_loss, global_steps))
del all_lable, all_len, all_loss, all_pre
net.train()
def train(hp_dict, args, data_dir, save_path):
use_chars = hp_dict['char_dim'] > 0
# load data
dp = preprocessing()
data = dp.preprocess(data_dir, no_training_set=False, use_chars=use_chars)
# build minibatch loader
train_batch_loader = mini_batch_loader(data.training,
BATCH_SIZE,
sample_rate=1.0,
len_bin=hp_dict['use_bin'])
valid_batch_loader = mini_batch_loader(data.validation,
BATCH_SIZE,
shuffle=False,
len_bin=hp_dict['use_bin'])
test_batch_loader = mini_batch_loader(data.test,
BATCH_SIZE,
shuffle=False,
len_bin=hp_dict['use_bin'])
logging.info("loading word2vec file ...")
embed_init, embed_dim = \
load_word2vec_embeddings(data.dictionary[0], hp_dict['embed_file'],EMBED_SIZE)
logging.info("embedding dim: {}".format(embed_dim))
logging.info("initialize model ...")
model = GA_reader(hp_dict['nhidden'], data.vocab_size, embed_dim,
embed_init, hp_dict['train_emb'], use_chars,
hp_dict['char_nhidden'], data.n_chars,
hp_dict['char_dim'], hp_dict['nlayers'],
hp_dict['gating_fn'], hp_dict['use_feat'],
hp_dict['dropout'])
if USE_CUDA:
model.cuda()
logging.info("Running on cuda: {}".format(USE_CUDA))
# training phase
opt = torch.optim.Adam(params=filter(lambda p: p.requires_grad,
model.parameters()),
lr=LEARNING_RATE)
shutil.copyfile('config.py', os.path.join(save_path, 'config.py'))
#
# load existing best model
if os.path.isfile(os.path.join(save_path, 'best_model.pkl')):
print('loading previously best model')
model.load_state_dict(
torch.load(os.path.join(save_path, 'best_model.pkl')))
# load existing train_model
elif os.path.isfile(os.path.join(save_path, 'init_model.pkl')):
print('loading init model')
model.load_state_dict(
torch.load(os.path.join(save_path, 'init_model.pkl')))
logging.info('-' * 50)
logging.info("Start training ...")
best_valid_acc = best_test_acc = 0
for epoch in range(NUM_EPOCHS):
new_max = False
if epoch >= 2:
for param_group in opt.param_groups:
param_group['lr'] /= 2
model.train()
acc = loss = n_examples = it = 0
start = time.time()
for dw, dw_m,qw,qw_m,dt,qt,tt,tm, \
answear, candidate, candi_m, cloze_pos, fnames in train_batch_loader:
n_examples += dw.shape[0]
feat = feat_fuc(dw, qw)
#-------train-------#
dw, dw_m,qw,qw_m,dt,qt,tt,tm, answear, candidate, candi_m, cloze_pos,feat=to_vars(\
[dw, dw_m,qw,qw_m,dt,qt,tt,tm, answear, candidate, candi_m, cloze_pos,feat], use_cuda=USE_CUDA)
loss_, acc_ = model(dw, dw_m, qw, qw_m, dt, qt, tt, tm, answear,
candidate, candi_m, cloze_pos,
feat) # tensor.float size 1
#print(acc_.cpu().data.numpy())
loss += loss_.cpu().data.numpy()[0] # numpy [1]
acc += acc_.cpu().data.numpy()[0]
it += 1
opt.zero_grad()
loss_.backward()
clip_grad_norm(parameters=filter(lambda p: p.requires_grad,
model.parameters()),
max_norm=GRAD_CLIP)
opt.step()
if it % print_every == 0 \
or it % len(train_batch_loader) == 0:
spend = (time.time() - start) / 60
statement = "Epoch: {}, it: {} (max: {}), "\
.format(epoch, it, len(train_batch_loader))
statement += "loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)"\
.format(loss / print_every, acc / n_examples, spend)
logging.info(statement)
del acc, loss, n_examples
acc = loss = n_examples = 0
start = time.time()
# save every print
torch.save(model.state_dict(),
os.path.join(save_path, 'init_model.pkl'))
# torch.save(model,os.path.join(save_path,'init_model.pkl'))
#-------valid-------#
if it % eval_every == 0:
start = time.time()
model.eval()
test_loss, test_acc = evaluate(model, valid_batch_loader,
USE_CUDA)
spend = (time.time() - start) / 60
statement = "Valid loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)"\
.format(test_loss, test_acc, spend)
logging.info(statement)
if best_valid_acc < test_acc:
best_valid_acc = test_acc
new_max = True
# store best valid model
torch.save(model.state_dict(),
os.path.join(save_path, 'best_model.pkl'))
#torch.save(model,os.path.join(save_path,'best_model.pkl'))
logging.info("Best valid acc: {:.3f}".format(best_valid_acc))
model.train()
start = time.time()
#-------test-------#
start = time.time()
model.eval()
test_loss, test_acc = evaluate(model, test_batch_loader, USE_CUDA)
spend = (time.time() - start) / 60
logging.info("Test loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)"\
.format(test_loss, test_acc, spend))
if best_test_acc < test_acc:
best_test_acc = test_acc
logging.info("Best test acc: {:.3f}".format(best_test_acc))
def main():
parser = argparse.ArgumentParser(
description='PyTorch PennTreeBank RNN/LSTM Language Model')
parser.add_argument('--data',
type=str,
default='../data/',
help='location of the data corpus')
parser.add_argument('--presaved',
action='store_true',
help='use presaved data')
parser.add_argument('--glovedata',
type=str,
default='../data/',
help='location of the pretrained glove embeddings')
parser.add_argument('--din', type=int, default=30, help='length of LSTM')
parser.add_argument('--demb',
type=int,
default=100,
help='size of word embeddings')
parser.add_argument('--dhid',
type=int,
default=100,
help='humber of hidden units per layer')
parser.add_argument('--dout',
type=int,
default=2,
help='number of output classes')
parser.add_argument('--nlayers',
type=int,
default=1,
help='number of layers')
parser.add_argument('--lr',
type=float,
default=0.001,
help='initial learning rate')
parser.add_argument('--clip',
type=float,
default=0.25,
help='gradient clipping')
parser.add_argument('--embinit',
type=str,
default='random',
help='embedding weight initialization type')
parser.add_argument('--decinit',
type=str,
default='random',
help='decoder weight initialization type')
parser.add_argument('--hidinit',
type=str,
default='random',
help='recurrent hidden weight initialization type')
parser.add_argument('--dropout',
type=float,
default=0.0,
help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--reweight',
action='store_true',
help='reweight loss function')
parser.add_argument('--epochs',
type=int,
default=50,
help='upper epoch limit')
parser.add_argument('--clean', action='store_true', help='clean text')
parser.add_argument('--rm_stops',
action='store_true',
help='remove stop words')
parser.add_argument('--batchsize',
type=int,
default=2000,
metavar='N',
help='batch size')
parser.add_argument('--seed', type=int, default=3, help='random seed')
parser.add_argument('--vocabsize',
type=int,
default=200000,
help='random seed')
parser.add_argument('--optimizer',
action='store_true',
help='use ADAM optimizer')
parser.add_argument('--cuda', action='store_true', help='use CUDA')
parser.add_argument('--loginterval',
type=int,
default=20,
metavar='N',
help='report interval')
parser.add_argument('--save',
type=str,
default='',
help='path to save the final model')
args = parser.parse_args()
pipe = None
corpus = TacoText(args.vocabsize, lower=True, vocab_pipe=pipe)
train_data = pd.read_csv('../data/train_data_shuffle.csv')
valid_data = pd.read_csv('../data/val_data_shuffle.csv')
train_data = train_data.fillna(' ')
valid_data = valid_data.fillna(' ')
if args.reweight:
print('Downsampling')
#downsample
pos_valid = valid_data[valid_data['is_duplicate'] == 1]
neg_valid = valid_data[valid_data['is_duplicate'] == 0]
p = 0.19
pl = len(pos_valid)
tl = len(pos_valid) + len(neg_valid)
val = int(pl - (pl - p * tl) / ((1 - p)))
pos_valid = pos_valid.iloc[:int(val)]
valid_data = pd.concat([pos_valid, neg_valid])
print('Splitting Train')
q1 = list(train_data['question1'].map(str))
q2 = list(train_data['question2'].map(str))
y = list(train_data['is_duplicate'])
print('Splitting Valid')
q1_val = list(valid_data['question1'].map(str))
q2_val = list(valid_data['question2'].map(str))
y_val = list(valid_data['is_duplicate'])
train_feat = pd.read_csv('../data/train_features_all_norm.csv')
val_feat = train_feat.iloc[valid_data['id']].values
train_feat = train_feat.iloc[train_data['id']].values
print('Splitting Data')
if args.clean:
print('Cleaning Data')
stops = None
if args.rm_stops:
stops = stops = set(stopwords.words("english"))
q1 = [split_text(x, stops) for x in q1]
q2 = [split_text(x, stops) for x in q2]
q1_val = [split_text(x, stops) for x in q1_val]
q2_val = [split_text(x, stops) for x in q2_val]
else:
q1 = [x.lower().split() for x in q1]
q2 = [x.lower().split() for x in q2]
q1_val = [x.lower().split() for x in q1_val]
q2_val = [x.lower().split() for x in q2_val]
print('Downsample Weight: ', np.mean(y_val))
corpus.gen_vocab(q1 + q2 + q2_val + q1_val)
n_feat = train_feat.shape[1]
d_in = args.din
feat_max = int(np.max([n_feat, d_in]))
X = torch.Tensor(len(train_data), 1, 3, feat_max)
X[:, 0, 0, :] = torch.from_numpy(corpus.pad_numericalize(q1,
feat_max)).long()
X[:, 0, 1, :] = torch.from_numpy(corpus.pad_numericalize(q2,
feat_max)).long()
X[:, 0, 2, :n_feat] = torch.from_numpy(np.array(train_feat))
y = torch.from_numpy(np.array(y)).long()
X_val = torch.Tensor(len(valid_data), 1, 3, feat_max)
X_val[:, 0,
0, :] = torch.from_numpy(corpus.pad_numericalize(q1_val,
feat_max)).long()
X_val[:, 0,
1, :] = torch.from_numpy(corpus.pad_numericalize(q2_val,
feat_max)).long()
X_val[:, 0, 2, :n_feat] = torch.from_numpy(np.array(val_feat))
y_val = torch.from_numpy(np.array(y_val)).long()
if args.cuda:
X, y = X.cuda(), y.cuda()
X_val, y_val = X_val.cuda(), y_val.cuda()
print('Generating Data Loaders')
#X.size len(train_data),1,2,fix_length
train_dataset = TensorDataset(X, y)
train_loader = DataLoader(train_dataset,
batch_size=args.batchsize,
shuffle=True)
valid_loader = DataLoader(TensorDataset(X_val, y_val),
batch_size=args.batchsize,
shuffle=False)
num_train = len(X)
del X, y, X_val, y_val, train_feat, val_feat, q1, q2, q1_val, q2_val
ntokens = len(corpus)
glove_embeddings = None
if args.embinit == 'glove':
assert args.demb in (50, 100, 200, 300)
glove_embeddings = get_glove_embeddings(args.glovedata,
corpus.dictionary.word2idx,
ntokens, args.demb)
model = LSTMModelMLPFeat(args.din, args.dhid, args.nlayers, args.dout,
args.demb, n_feat, args.vocabsize, args.dropout,
args.embinit, args.hidinit, args.decinit,
glove_embeddings, args.cuda)
if args.cuda:
model.cuda()
if args.reweight:
w_tensor = torch.Tensor([1.309028344, 0.472001959])
if args.cuda:
w_tensor = w_tensor.cuda()
criterion = nn.NLLLoss(weight=w_tensor)
else:
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
model_config = '\t'.join([
str(x) for x in (torch.__version__, args.clip, args.nlayers, args.din,
args.demb, args.dhid, args.embinit, args.decinit,
args.hidinit, args.dropout, args.optimizer,
args.reweight, args.lr, args.vocabsize,
args.batchsize, args.clean, args.rm_stops)
])
print(
'Pytorch | Clip | #Layers | InSize | EmbDim | HiddenDim | EncoderInit | DecoderInit | WeightInit | Dropout | Optimizer | Reweight | LR | VocabSize | batchsize | Clean | Stops'
)
print(model_config)
# best_val_acc = 0.78
best_ll = 0.3
for epoch in range(args.epochs):
model.train()
total_cost = 0
start_time = time.time()
cur_loss = 0
for ind, (qs, duplicate) in enumerate(train_loader):
model.zero_grad()
pred = model(qs[:, 0, 0, :d_in].long(), qs[:, 0, 1, :d_in].long(),
qs[:, 0, 2, :n_feat])
if args.cuda:
pred = pred.cuda()
duplicate = duplicate.cuda()
duplicate = Variable(duplicate)
loss = criterion(pred, duplicate)
loss.backward()
clip_grad_norm(model.parameters(), args.clip)
if optimizer:
optimizer.step()
else:
for p in model.parameters():
p.data.add_(-args.lr, p.grad.data)
total_cost += loss.data[0]
cur_loss += loss.data[0]
if ind % args.loginterval == 0 and ind > 0:
cur_loss = loss.data[0] / args.loginterval
elapsed = time.time() - start_time
print(
'| Epoch {:3d} | {:5d}/{:5d} Batches | ms/batch {:5.2f} | '
'Loss {:.6f}'.format(epoch, ind,
num_train // args.batchsize,
elapsed * 1000.0 / args.loginterval,
cur_loss))
start_time = time.time()
cur_loss = 0
model.eval()
train_acc, train_ll = evaluate(model, train_loader, args.cuda, d_in,
n_feat)
val_acc, val_ll = evaluate(model, valid_loader, args.cuda, d_in,
n_feat)
# if args.save and (val_acc > best_val_acc):
if args.save and (val_ll < best_ll):
with open(args.save + '_corpus.pkl', 'wb') as corp_f:
pkl.dump(corpus, corp_f, protocol=pkl.HIGHEST_PROTOCOL)
torch.save(model.cpu(), args.save)
torch.save(model.cpu().state_dict(), args.save + ".state_dict")
with open(args.save + ".state_dict.config", "w") as f:
f.write(model_config)
best_ll = val_ll
if args.cuda:
model.cuda()
print(
'Epoch: {} | Train Loss: {:.4f} | Train Accuracy: {:.4f} | Val Accuracy: {:.4f} | Train LL: {:.4f} | Val LL: {:.4f}'
.format(epoch, total_cost, train_acc, val_acc, train_ll, val_ll))
print('-' * 89)
del train_loader
print('Reloading Best Model')
model = torch.load(args.save)
model.cuda()
model.eval()
print('RELOADING VALID')
valid_data = pd.read_csv('../data/val_data_shuffle.csv')
valid_data = valid_data.fillna(' ')
q1_val = list(valid_data['question1'].map(str))
q2_val = list(valid_data['question2'].map(str))
y_val = list(valid_data['is_duplicate'])
train_feat = pd.read_csv('../data/train_features_all_norm.csv')
val_feat = train_feat.iloc[valid_data['id']].values
if args.clean:
print('Cleaning Data')
stops = None
if args.rm_stops:
stops = stops = set(stopwords.words("english"))
q1_val = [split_text(x, stops) for x in q1_val]
q2_val = [split_text(x, stops) for x in q2_val]
else:
q1_val = [x.lower().split() for x in q1_val]
q2_val = [x.lower().split() for x in q2_val]
X_val = torch.Tensor(len(valid_data), 1, 3, feat_max)
X_val[:, 0,
0, :] = torch.from_numpy(corpus.pad_numericalize(q1_val,
feat_max)).long()
X_val[:, 0,
1, :] = torch.from_numpy(corpus.pad_numericalize(q2_val,
feat_max)).long()
X_val[:, 0, 2, :n_feat] = torch.from_numpy(np.array(val_feat))
y_val = torch.from_numpy(np.array(y_val)).long()
if args.cuda:
X_val, y_val = X_val.cuda(), y_val.cuda()
valid_loader = DataLoader(TensorDataset(X_val, y_val),
batch_size=args.batchsize,
shuffle=False)
del X_val, y_val, train_feat, val_feat, q1_val, q2_val, valid_data
print('PREDICTING VALID')
pred_list = []
for ind, (qs, _) in enumerate(valid_loader):
out = model(qs[:, 0, 0, :d_in].long(), qs[:, 0, 1, :d_in].long(),
qs[:, 0, 2, :n_feat])
pred_list += list(out.exp()[:, 1].data.cpu().numpy())
with open('../predictions/' + args.save + '_val.pkl', 'wb') as f:
pkl.dump(pred_list, f, protocol=pkl.HIGHEST_PROTOCOL)
if args.reweight:
print('LOADING TEST DATA')
test_data = pd.read_csv('../data/test.csv')
test_data = test_data.fillna(' ')
q1 = list(test_data['question1'].map(str))
q2 = list(test_data['question2'].map(str))
q1 = [x.lower().split() for x in q1]
q2 = [x.lower().split() for x in q2]
print('LOADING TEST FEATURES')
test_feat = pd.read_csv('../data/test_features_all_norm.csv').values
n_feat = test_feat.shape[1]
d_in = args.din
feat_max = int(np.max([n_feat, d_in]))
X = torch.Tensor(len(test_data), 1, 3, feat_max)
X[:, 0,
0, :] = torch.from_numpy(corpus.pad_numericalize(q1,
feat_max)).long()
X[:, 0,
1, :] = torch.from_numpy(corpus.pad_numericalize(q2,
feat_max)).long()
X[:, 0, 2, :n_feat] = torch.from_numpy(np.array(test_feat))
y = torch.LongTensor(len(test_data)).zero_()
if args.cuda:
X = X.cuda()
y = y.cuda()
test_loader = DataLoader(TensorDataset(X, y),
batch_size=500,
shuffle=False)
print('PREDICTING')
pred_list = []
for ind, (qs, _) in enumerate(test_loader):
out = model(qs[:, 0, 0, :d_in].long(), qs[:, 0, 1, :d_in].long(),
qs[:, 0, 2, :n_feat])
pred_list += list(out.exp()[:, 1].data.cpu().numpy())
with open('../predictions/' + args.save + '.pkl', 'wb') as f:
pkl.dump(pred_list, f, protocol=pkl.HIGHEST_PROTOCOL)
def main():
cmd = argparse.ArgumentParser('Key-Value by H&Q')
cmd.add_argument('--data_path', help='', default='../data/')
cmd.add_argument('--hidden_size', help='', type=int, default=200)
cmd.add_argument('--embed_size', help='', type=int, default=200)
cmd.add_argument('--batch_size', help='', type=int, default=32)
cmd.add_argument('--lr', help='', type=float, default=0.001)
cmd.add_argument('--lr_decay', help='', type=float, default=1.0)
cmd.add_argument('--max_epoch', help='', type=int, default=200)
cmd.add_argument('--seed', help='', type=int, default=1234)
cmd.add_argument('--dropout', help='', type=float, default=0.8)
cmd.add_argument('--bleu_path', help='', default='../bleu/')
cmd.add_argument('--grad_clip', help='', type=float, default=10)
cmd.add_argument('--parallel_suffix', help='', type=str, default='123')
cmd.add_argument('--model_save_path',
help='',
type=str,
default='../model')
cmd.add_argument('--l2', help='', type=float, default=0.000005)
cmd.add_argument('--key_flag', help='', type=str, default='True')
args = cmd.parse_args(sys.argv[2:])
print(args)
# 存储参数配置
json.dump(
vars(args),
codecs.open(os.path.join(args.model_save_path, 'config.json'),
'w',
encoding='utf-8'))
random.seed(args.seed)
torch.manual_seed(args.seed)
# 数据预处理: 把标点和词分开,没有标点的统一加上.
train_dialogs, valid_dialogs, test_dialogs = data_preprocess(
args.data_path)
# 提取keys, triples, entities
keys, triples, entities, value_to_abstract_keys = key_extraction(
train_dialogs, args.data_path)
# 生成词典,先将key变成下划线形式加入词典,再将对话加入词典
lang = Lang()
lang, underlined_keys = generate_dict(keys, train_dialogs, lang,
value_to_abstract_keys)
logging.info('dict generated! dict size:{0}'.format(lang.word_size))
# 存储词典
with codecs.open(os.path.join(args.model_save_path, 'dict'),
'w',
encoding='utf-8') as fs:
res_dict = []
for word, idx in lang.word2idx.items():
temp = word + '\t' + str(idx)
res_dict.append(temp)
res_dict = '\n'.join(res_dict)
fs.write(res_dict)
# 生成训练数据instances
train_instances = generate_instances(keys, train_dialogs, triples,
value_to_abstract_keys)
valid_instances = generate_instances(keys, valid_dialogs, triples,
value_to_abstract_keys)
test_instances = generate_instances(keys, test_dialogs, triples,
value_to_abstract_keys)
# valid_instances = test_instances = train_instances
#logging.info('instances sample: {0}'.format(train_instances))
# Word2idx
train_instances_idx = sentence_to_idx(lang,
train_instances) # [([],[]),()]
valid_instances_idx = sentence_to_idx(lang, valid_instances)
test_instances_idx = sentence_to_idx(lang, test_instances)
# valid_instances_idx = test_instances_idx = train_instances_idx
# keys2idx
keys_idx = key_to_idx(lang, underlined_keys)
train_instances_size = len(train_instances_idx)
valid_instances_size = len(valid_instances_idx)
test_instances_size = len(test_instances_idx)
logging.info('trainging size:{0} valid size:{1} test size:{2}'.format(train_instances_size, valid_instances_size, \
test_instances_size))
encoder = Encoder(args.embed_size, args.hidden_size, args.dropout, lang)
decoder = AttnDecoder(args.embed_size, args.hidden_size, args.dropout,
lang, args.key_flag)
encoderdecoder = EncoderDecoder(args.embed_size, args.hidden_size,
args.dropout, lang)
encoder = encoder.cuda() if use_cuda else encoder
decoder = decoder.cuda() if use_cuda else decoder
encoderdecoder = encoderdecoder.cuda() if use_cuda else encoderdecoder
encoder_optimizer = optim.Adam(encoder.parameters(),
lr=args.lr,
weight_decay=args.l2)
decoder_optimizer = optim.Adam(decoder.parameters(),
lr=args.lr,
weight_decay=args.l2)
encoderdecoder_optimizer = optim.Adam(decoder.parameters(),
lr=args.lr,
weight_decay=args.l2)
# train
best_valid_bleu_score, best_test_bleu_score = 0, 0
best_valid_f, best_test_f = 0, 0
order = list(range(len(train_instances_idx)))
for i in range(args.max_epoch):
logging.info(
'--------------------Round {0}---------------------'.format(i))
#random.shuffle(order)
start_id = 0
count = 0
total_loss = 0
for start_id in range(0, train_instances_size, args.batch_size):
end_id = start_id + args.batch_size if start_id + args.batch_size < train_instances_size else train_instances_size
batch_size = end_id - start_id
batch_to_be_generated = [
train_instances_idx[ids] for ids in order[start_id:end_id]
]
batch_gold = [
train_instances[ids] for ids in order[start_id:end_id]
] # 对于train来说没有用
batch_input, batch_output, _, sentence_lens = generate_batch(
batch_to_be_generated, batch_gold, batch_size,
lang.word2idx['pad'])
# train
encoder.train()
decoder.train()
encoderdecoder.train()
encoder.zero_grad()
decoder.zero_grad()
encoderdecoder.zero_grad()
loss = encoderdecoder.forward(batch_input, batch_output, sentence_lens, keys_idx, \
encoder, decoder, lang.word2idx['pad'], args.embed_size)
loss.backward()
clip_grad_norm(encoder.parameters(), args.grad_clip)
clip_grad_norm(decoder.parameters(), args.grad_clip)
clip_grad_norm(encoderdecoder.parameters(), args.grad_clip)
encoder_optimizer.step()
decoder_optimizer.step()
encoderdecoder_optimizer.step()
total_loss += loss.data
count += 1
# if (count % 100 == 0):
# logging.info('average loss: {0}'.format(total_loss*1.0/count))
valid_bleu_score, valid_f = evaluate(keys_idx, encoder, decoder, encoderdecoder, valid_instances_idx, valid_instances, lang, \
args.batch_size, args.embed_size, args.hidden_size, args.bleu_path, args.parallel_suffix)
# if (valid_bleu_score > best_valid_bleu_score):
# test_bleu_score, test_f = evaluate(keys_idx, encoder, decoder, encoderdecoder, test_instances_idx, test_instances, lang, \
# args.batch_size, args.embed_size, args.hidden_size, args.bleu_path, args.parallel_suffix)
# best_test_bleu_score = max(best_test_bleu_score, test_bleu_score)
#
# logging.info('New Record! test bleu score now: {0} best test bleu score ever: {1}'.format(\
# test_bleu_score, best_test_bleu_score))
if (valid_f > best_valid_f):
torch.save(
encoder.state_dict(),
os.path.join(args.model_save_path,
'encoder' + args.parallel_suffix))
torch.save(
decoder.state_dict(),
os.path.join(args.model_save_path,
'decoder' + args.parallel_suffix))
torch.save(
encoderdecoder.state_dict(),
os.path.join(args.model_save_path,
'encoderdecoder' + args.parallel_suffix))
test_bleu_score, test_f = evaluate(keys_idx, encoder, decoder, encoderdecoder, test_instances_idx, test_instances, lang, \
args.batch_size, args.embed_size, args.hidden_size, args.bleu_path, args.parallel_suffix)
best_test_f = max(best_test_f, test_f)
best_test_bleu_score = max(best_test_bleu_score, test_bleu_score)
logging.info('New Record! test F now: {0} best test F ever: {1} test bleu score now: {2} best test bleu score ever: {3}'.format(\
test_f, best_test_f, test_bleu_score, best_test_bleu_score))
best_valid_f = max(best_valid_f, valid_f)
best_valid_bleu_score = max(best_valid_bleu_score, valid_bleu_score)
logging.info('valid F: {0} best valid F ever: {1}'.format(
valid_f, best_valid_f))
logging.info(
'valid bleu score: {0} best valid bleu score ever: {1}'.format(
valid_bleu_score, best_valid_bleu_score))
logging.info('Trianing complete! best valid bleu score: {0} best test bleu score: {1} best valid F: {2} best test F: {3}'\
.format(best_valid_bleu_score, best_test_bleu_score, best_valid_f, best_test_f))
logging.info('suffix is {0}'.format(args.parallel_suffix))
print(args)
def train(self):
info = {}
if self.T % self.args.nec_update != 0:
return info
# print("Training")
for _ in range(self.args.iters):
# TODO: Use a named tuple for experience replay
batch = self.replay.Sample(self.args.batch_size)
columns = list(zip(*batch))
states = Variable(torch.from_numpy(np.array(columns[0])).float().transpose_(1, 3))
# print(states)
actions = columns[1]
# print(actions)
targets = Variable(torch.FloatTensor(columns[2]))
# print(targets)
keys = self.embedding(states).cpu()
# print(keys)
# print("Keys", keys.requires_grad)
# for action in actions:
# print(action)
# for action, key in zip(actions, keys):
# print(action, key)
# kk = key.unsqueeze(0)
# print("kk", kk.requires_grad)
# k = self.dnds[action].lookup(key.unsqueeze(0))
# print("key", key.requires_grad, key.volatile)
model_predictions = torch.cat([self.dnds[action].lookup(key.unsqueeze(0)) for action, key in zip(actions, keys)])
# print(model_predictions)
# print(targets)
td_error = model_predictions - targets
# print(td_error)
info["TD_Error"] = td_error.mean().data[0]
l2_loss = (td_error).pow(2).mean()
info["Loss"] = l2_loss.data[0]
# Update
self.optimizer.zero_grad()
l2_loss.backward()
# Taken from pytorch clip_grad_norm
# Remove once the pip version it up to date with source
gradient_norm = clip_grad_norm(self.embedding.parameters(), self.args.clip_value)
if gradient_norm is not None:
info["Norm"] = gradient_norm
self.optimizer.step()
if "States" in info:
states_trained = info["States"]
info["States"] = states_trained + columns[0]
else:
info["States"] = columns[0]
return info
def forward(data_loader,
model,
criterion,
epoch=0,
training=True,
optimizer=None):
if args.gpus and len(args.gpus) > 1:
model = torch.nn.DataParallel(model, args.gpus)
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
if training:
optimizer.zero_grad()
for i, (inputs, target) in enumerate(data_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.gpus is not None:
target = target.cuda(async=True)
input_var = Variable(inputs.type(args.type), volatile=not training)
target_var = Variable(target)
# compute output
if not training:
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target_var.data, topk=(1, 5))
losses.update(loss.data[0], input_var.size(0))
top1.update(prec1[0], input_var.size(0))
top5.update(prec5[0], input_var.size(0))
else:
is_updating = ((i + 1) % args.batch_multiplier
== 0) or (i + 1 == len(data_loader))
mini_inputs = input_var.chunk(args.batch_size //
args.mini_batch_size)
mini_targets = target_var.chunk(args.batch_size //
args.mini_batch_size)
# get the coefficent to scale noise
eq_batch_num = (len(data_loader) + args.batch_multiplier -
1) // args.batch_multiplier
if args.smoothing_type == 'constant':
noise_coef = 1.
elif args.smoothing_type == 'anneal':
noise_coef = 1.0 / (
(1 + epoch * eq_batch_num + i // args.batch_multiplier)**
args.anneal_index)
noise_coef = noise_coef**0.5
elif args.smoothing_type == 'tanh':
noise_coef = np.tanh(
args.tanh_scale *
((float)(epoch * eq_batch_num + i // args.batch_multiplier)
/ (float)(args.epochs * eq_batch_num) - .5))
noise_coef = (noise_coef + 1.) / 2.0
else:
raise ValueError('Unknown smoothing-type')
if i % args.print_freq == 0:
logging.info(
'{phase} - Epoch: [{0}][{1}/{2}]\t'
'Noise Coefficient: {noise_coef:.4f}\t'.format(
epoch,
i,
len(data_loader),
phase='TRAINING' if training else 'EVALUATING',
noise_coef=noise_coef))
for k, mini_input_var in enumerate(mini_inputs):
noises = {}
# randomly change current model @ each mini-mini-batch
if args.sharpness_smoothing:
for key, p in model.named_parameters():
if hasattr(model, 'quiet_parameters') and (
key in model.quiet_parameters):
continue
if args.adapt_type == 'weight':
noise = (
torch.cuda.FloatTensor(p.size()).uniform_() *
2. -
1.) * args.sharpness_smoothing * torch.abs(
p.data) * noise_coef
elif args.adapt_type == 'filter':
noise = (
torch.cuda.FloatTensor(p.size()).uniform_() *
2. - 1.)
noise_shape = noise.shape
noise_norms = noise.view(
[noise_shape[0], -1]).norm(p=2, dim=1) + 1.0e-6
p_norms = p.view([noise_shape[0], -1]).norm(p=2,
dim=1)
for shape_idx in range(1, len(noise_shape)):
noise_norms = noise_norms.unsqueeze(-1)
p_norms = p_norms.unsqueeze(-1)
noise = noise / noise_norms * p_norms.data
#for idx in range(0, noise.shape[0]):
# if 1 == len(noise.shape):
# if np.abs(np.linalg.norm(noise[idx]))>1.0e-6:
# noise[idx] = noise[idx] / np.linalg.norm(noise[idx]) * np.linalg.norm(p.data[idx])
# else:
# if np.abs(noise[idx].norm())>1.0e-6:
# noise[idx] = noise[idx] / noise[idx].norm() * p.data[idx].norm()
noise = noise * args.sharpness_smoothing * noise_coef
elif args.adapt_type == 'none':
noise = (
torch.cuda.FloatTensor(p.size()).uniform_() *
2. -
1.) * args.sharpness_smoothing * noise_coef
else:
raise ValueError('Unkown --adapt-type')
noises[key] = noise
p.data.add_(noise)
mini_target_var = mini_targets[k]
output = model(mini_input_var)
loss = criterion(output, mini_target_var)
prec1, prec5 = accuracy(output.data,
mini_target_var.data,
topk=(1, 5))
losses.update(loss.data[0], mini_input_var.size(0))
top1.update(prec1[0], mini_input_var.size(0))
top5.update(prec5[0], mini_input_var.size(0))
# compute gradient and do SGD step
loss.backward()
# denoise @ each mini-mini-batch.
if args.sharpness_smoothing:
for key, p in model.named_parameters():
if key in noises:
p.data.sub_(noises[key])
if is_updating:
n_batches = args.batch_multiplier
if (i + 1) == len(data_loader):
n_batches = (i % args.batch_multiplier) + 1
for p in model.parameters():
p.grad.data.div_(len(mini_inputs) * n_batches)
clip_grad_norm(model.parameters(), 5.)
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(data_loader),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5))
return {'loss': losses.avg, 'prec1': top1.avg, 'prec5': top5.avg}
def fit_model(model, loss_op, optim_op, train_gen, val_gen, epochs,
checkpoint_path, patience):
""" Analog to Keras fit_generator function.
# Arguments:
model: Model to be finetuned.
loss_op: loss operation (BCEWithLogitsLoss or CrossEntropy for e.g.)
optim_op: optimization operation (Adam e.g.)
train_gen: Training data iterator (DataLoader)
val_gen: Validation data iterator (DataLoader)
epochs: Number of epochs.
checkpoint_path: Filepath where weights will be checkpointed to
during training. This file will be rewritten by the function.
patience: Patience for callback methods.
verbose: Verbosity flag.
# Returns:
Accuracy of the trained model, ONLY if 'evaluate' is set.
"""
# Save original checkpoint
torch.save(model.state_dict(), checkpoint_path)
model.eval()
best_loss = np.mean([loss_op(model(Variable(xv)).squeeze(), Variable(yv.float()).squeeze()).data.cpu().numpy()[0] for xv, yv in val_gen])
print("original val loss", best_loss)
epoch_without_impr = 0
for epoch in range(epochs):
for i, data in enumerate(train_gen):
X_train, y_train = data
X_train = Variable(X_train, requires_grad=False)
y_train = Variable(y_train, requires_grad=False)
model.train()
optim_op.zero_grad()
output = model(X_train)
loss = loss_op(output, y_train.float())
loss.backward()
clip_grad_norm(model.parameters(), 1)
optim_op.step()
acc = evaluate_using_acc(model, [(X_train.data, y_train.data)])
print("== Epoch", epoch, "step", i, "train loss", loss.data.cpu().numpy()[0], "train acc", acc)
model.eval()
acc = evaluate_using_acc(model, val_gen)
print("val acc", acc)
val_loss = np.mean([loss_op(model(Variable(xv)).squeeze(), Variable(yv.float()).squeeze()).data.cpu().numpy()[0] for xv, yv in val_gen])
print("val loss", val_loss)
if best_loss is not None and val_loss >= best_loss:
epoch_without_impr += 1
print('No improvement over previous best loss: ', best_loss)
# Save checkpoint
if best_loss is None or val_loss < best_loss:
best_loss = val_loss
torch.save(model.state_dict(), checkpoint_path)
print('Saving model at', checkpoint_path)
# Early stopping
if epoch_without_impr >= patience:
break
def train(train_iter, dev_iter, test_iter, model, args):
if args.cuda:
model.cuda()
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-8)
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
# optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,momentum=)
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr)
if args.Adam is True:
print("Adam Training......")
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
elif args.SGD is True:
print("SGD Training.......")
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay,
momentum=args.momentum_value)
elif args.Adadelta is True:
print("Adadelta Training.......")
optimizer = torch.optim.Adadelta(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
# lambda1 = lambda epoch: epoch // 30
# lambda2 = lambda epoch: 0.99 ** epoch
# print("lambda1 {} lambda2 {} ".format(lambda1, lambda2))
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda2])
# scheduler = lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.9)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
steps = 0
epoch_step = 0
model_count = 0
model.train()
for epoch in range(1, args.epochs+1):
print("\n## 第{} 轮迭代，共计迭代 {} 次 ！##\n".format(epoch, args.epochs))
# scheduler.step()
# print("now lr is {} \n".format(scheduler.get_lr()))
print("now lr is {} \n".format(optimizer.param_groups[0].get("lr")))
for batch in train_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
logit = model(feature)
loss = F.cross_entropy(logit, target)
loss.backward()
if args.init_clip_max_norm is not None:
utils.clip_grad_norm(model.parameters(), max_norm=args.init_clip_max_norm)
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
train_size = len(train_iter.dataset)
corrects = (torch.max(logit, 1)[1].view(target.size()).data == target.data).sum()
accuracy = float(corrects)/batch.batch_size * 100.0
sys.stdout.write(
'\rBatch[{}/{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.format(steps,
train_size, loss.data[0], accuracy,
corrects, batch.batch_size))
# eval and test after every epoch
eval(dev_iter, model, args, scheduler)
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
save_prefix = os.path.join(args.save_dir, 'snapshot')
save_path = '{}_steps{}.pt'.format(save_prefix, steps)
torch.save(model, save_path)
print("\n", save_path, end=" ")
test_model = torch.load(save_path)
model_count += 1
test_eval(test_iter, test_model, save_path, args, model_count)
return model_count
def train():
global_steps = 0
best_em = -1
best_f1 = -1
for iepoch in range(epochs):
batch = 0
for tdata in train_loader:
passage_tokens = tdata['passage_tokens']
passage_len = tdata['passage_len']
char_start_end = tdata['char_start_end']
question_tokens = tdata['question_tokens']
question_len = tdata['question_len']
ground_truth = tdata['ground_truth']
answer_tokens = tdata['answer_tokens']
answer_len = tdata['answer_len']
boundary = tdata['boundary']
passage_str = tdata['passage_str']
question_str = tdata['question_str']
answer_str = tdata['answer_str']
key = tdata['key']
classification_labels, answer_index = prepare_classify(question_tokens, answer_tokens)
fw_res = net(passage=passage_tokens,
question=question_tokens,
answer=answer_tokens,
answer_index=answer_index,
decoder_inputs=ground_truth,
is_classification = True,
is_generation = True,
is_teacher_forcing = True)
match_logits = fw_res['match_logits']
match_predictions = fw_res['match_predictions']
generation_logits = fw_res['generation_logits']
generation_predictions = fw_res['generation_predictions']
classification_logits = fw_res['classification_logits']
classification_predictions = fw_res['classification_predictions']
print (classification_predictions)
print (classification_labels.numpy())
print (sum(classification_labels.numpy() == classification_predictions) / len(classification_predictions))
loss_return = net.get_loss(match_logits=match_logits,
match_labels=boundary,
generation_logits=generation_logits,
generation_labels=question_tokens,
classification_logits=classification_logits,
classification_labels= classification_labels,
is_match = False,
is_generation = False,
is_classification = True,
lambda_m = lambda_m,
lambda_g = lambda_g,
lambda_c = lambda_c)
match_loss = loss_return['match_loss']
loss = loss_return['loss']
generation_loss = loss_return['generation_loss']
classification_loss = loss_return['classification_loss']
optimizer.zero_grad()
loss.backward()
utils.clip_grad_norm(net.parameters(), 5)
optimizer.step()
print (global_steps, iepoch, batch, 'match loss: ', match_loss, 'generation loss: ', generation_loss,'classification loss: ', classification_loss )
agent.append(train_match_loss, global_steps, match_loss)
agent.append(train_generation_loss, global_steps, generation_loss)
agent.append(train_classification_loss, global_steps, classification_loss)
agent.append(train_loss, global_steps, sum(loss.cpu().data.numpy()))
batch += 1
global_steps += 1
del fw_res, match_logits, match_predictions, loss, match_loss, generation_loss, loss_return
'''
if global_steps % 10 == 0:
match_loss, loss = check(net, tdata)
net.train()
if global_steps % 20 == 0:
dev_loss, em, f1 = valid()
agent.append(valid_match_loss, global_steps, dev_loss)
agent.append(valid_match_em, global_steps, em)
agent.append(valid_match_f1, global_steps, f1)
print (global_steps, iepoch, batch, dev_loss, em, f1)
'''
'''
if em > best_em and f1 > best_f1:
save_model(net, dev_loss, em, f1, global_steps)
'''
'''
def train(self):
if self.T - self.target_sync_T > self.args.target:
self.sync_target_network()
self.target_sync_T = self.T
info = {}
for _ in range(self.args.iters):
self.dqn.eval()
# TODO: Use a named tuple for experience replay
n_step_sample = 1
if np.random.random() < self.args.n_step_mixing:
n_step_sample = self.args.n_step
batch, indices, is_weights = self.replay.Sample_N(self.args.batch_size, n_step_sample, self.args.gamma)
columns = list(zip(*batch))
states = Variable(torch.from_numpy(np.array(columns[0])).float().transpose_(1, 3))
actions = Variable(torch.LongTensor(columns[1]))
terminal_states = Variable(torch.FloatTensor(columns[5]))
rewards = Variable(torch.FloatTensor(columns[2]))
# Have to clip rewards for DQN
rewards = torch.clamp(rewards, -1, 1)
steps = Variable(torch.FloatTensor(columns[4]))
new_states = Variable(torch.from_numpy(np.array(columns[3])).float().transpose_(1, 3))
target_dqn_qvals = self.target_dqn(new_states).cpu()
# Make a new variable with those values so that these are treated as constants
target_dqn_qvals_data = Variable(target_dqn_qvals.data)
q_value_targets = (Variable(torch.ones(terminal_states.size()[0])) - terminal_states)
inter = Variable(torch.ones(terminal_states.size()[0]) * self.args.gamma)
# print(steps)
q_value_targets = q_value_targets * torch.pow(inter, steps)
if self.args.double:
# Double Q Learning
new_states_qvals = self.dqn(new_states).cpu()
new_states_qvals_data = Variable(new_states_qvals.data)
q_value_targets = q_value_targets * target_dqn_qvals_data.gather(1, new_states_qvals_data.max(1)[1])
else:
q_value_targets = q_value_targets * target_dqn_qvals_data.max(1)[0]
q_value_targets = q_value_targets + rewards
self.dqn.train()
if self.args.gpu:
actions = actions.cuda()
q_value_targets = q_value_targets.cuda()
model_predictions = self.dqn(states).gather(1, actions.view(-1, 1))
# info = {}
td_error = model_predictions - q_value_targets
info["TD_Error"] = td_error.mean().data[0]
# Update the priorities
if not self.args.density_priority:
self.replay.Update_Indices(indices, td_error.cpu().data.numpy(), no_pseudo_in_priority=self.args.count_td_priority)
# If using prioritised we need to weight the td_error
if self.args.prioritized and self.args.prioritized_is:
# print(td_error)
weights_tensor = torch.from_numpy(is_weights).float()
weights_tensor = Variable(weights_tensor)
if self.args.gpu:
weights_tensor = weights_tensor.cuda()
# print(weights_tensor)
td_error = td_error * weights_tensor
l2_loss = (td_error).pow(2).mean()
info["Loss"] = l2_loss.data[0]
# Update
self.optimizer.zero_grad()
l2_loss.backward()
# Taken from pytorch clip_grad_norm
# Remove once the pip version it up to date with source
gradient_norm = clip_grad_norm(self.dqn.parameters(), self.args.clip_value)
if gradient_norm is not None:
info["Norm"] = gradient_norm
self.optimizer.step()
if "States" in info:
states_trained = info["States"]
info["States"] = states_trained + columns[0]
else:
info["States"] = columns[0]
# Pad out the states to be of size batch_size
if len(info["States"]) < self.args.batch_size:
old_states = info["States"]
new_states = old_states[0] * (self.args.batch_size - len(old_states))
info["States"] = new_states
return info
def train(train_loader, model, act_criterion, comp_criterion, regression_criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
act_losses = AverageMeter()
comp_losses = AverageMeter()
reg_losses = AverageMeter()
act_accuracies = AverageMeter()
fg_accuracies = AverageMeter()
bg_accuracies = AverageMeter()
# switch to train mode
model.train()
end = time.time()
optimizer.zero_grad()
ohem_num = train_loader.dataset.fg_per_video
comp_group_size = train_loader.dataset.fg_per_video + train_loader.dataset.incomplete_per_video
for i, (out_frames, out_prop_len, out_prop_scaling, out_prop_type, out_prop_labels,
out_prop_reg_targets, out_stage_split) \
in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
input_var = torch.autograd.Variable(out_frames)
scaling_var = torch.autograd.Variable(out_prop_scaling)
target_var = torch.autograd.Variable(out_prop_labels)
reg_target_var = torch.autograd.Variable(out_prop_reg_targets)
prop_type_var = torch.autograd.Variable(out_prop_type)
# compute output
activity_out, activity_target, \
completeness_out, completeness_target, \
regression_out, regression_labels, regression_target = model(input_var, scaling_var, target_var,
reg_target_var, prop_type_var)
act_loss = act_criterion(activity_out, activity_target)
comp_loss = comp_criterion(completeness_out, completeness_target, ohem_num, comp_group_size)
reg_loss = regression_criterion(regression_out, regression_labels, regression_target)
loss = act_loss + comp_loss * args.comp_loss_weight + reg_loss * args.reg_loss_weight
reg_losses.update(reg_loss.data[0], out_frames.size(0))
# measure mAP and record loss
losses.update(loss.data[0], out_frames.size(0))
act_losses.update(act_loss.data[0], out_frames.size(0))
comp_losses.update(comp_loss.data[0], out_frames.size(0))
act_acc = accuracy(activity_out, activity_target)
act_accuracies.update(act_acc[0].data[0], activity_out.size(0))
fg_acc = accuracy(activity_out.view(-1, 2, activity_out.size(1))[:, 0, :].contiguous(),
activity_target.view(-1, 2)[:, 0].contiguous())
bg_acc = accuracy(activity_out.view(-1, 2, activity_out.size(1))[:, 1, :].contiguous(),
activity_target.view(-1, 2)[:, 1].contiguous())
fg_accuracies.update(fg_acc[0].data[0], activity_out.size(0) // 2)
bg_accuracies.update(bg_acc[0].data[0], activity_out.size(0) // 2)
# compute gradient and do SGD step
loss.backward()
if i % args.iter_size == 0:
# scale down gradients when iter size is functioning
if args.iter_size != 1:
for g in optimizer.param_groups:
for p in g['params']:
p.grad /= args.iter_size
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print("clipping gradient: {} with coef {}".format(total_norm, args.clip_gradient / total_norm))
else:
total_norm = 0
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Act. Loss {act_losses.val:.3f} ({act_losses.avg: .3f}) \t'
'Comp. Loss {comp_losses.val:.3f} ({comp_losses.avg: .3f}) '
.format(
epoch, i, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, act_losses=act_losses,
comp_losses=comp_losses, lr=optimizer.param_groups[0]['lr'], ) +
'\tReg. Loss {reg_loss.val:.3f} ({reg_loss.avg:.3f})'.format(
reg_loss=reg_losses)
+ '\n Act. FG {fg_acc.val:.02f} ({fg_acc.avg:.02f}) Act. BG {bg_acc.avg:.02f} ({bg_acc.avg:.02f})'
.format(act_acc=act_accuracies,
fg_acc=fg_accuracies, bg_acc=bg_accuracies)
)
def train(lr, net, epoch, train_loader, valid_loader, transform, hyperparameters, batch_size):
# register hypercurve
agent = Agent(port=5005)
hyperparameters['criteria'] = 'train loss'
train_loss = agent.register(hyperparameters, 'loss')
hyperparameters['criteria'] = 'valid loss'
valid_loss = agent.register(hyperparameters, 'loss')
hyperparameters['criteria'] = 'valid bleu'
valid_bleu = agent.register(hyperparameters, 'bleu')
hyperparameters['criteria'] = 'train bleu'
train_bleu = agent.register(hyperparameters, 'bleu')
hyperparameters['criteria'] = 'teacher_forcing_ratio'
hyper_tfr = agent.register(hyperparameters, 'ratio')
hyperparameters['criteria'] = 'teacher_forcing_loss'
valid_tf_loss = agent.register(hyperparameters, 'loss')
if torch.cuda.is_available():
net.cuda()
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr = lr)
net.train()
best_score = -1
global_steps = 578800
best_valid_loss = 10000
for iepoch in range(epoch):
batchid = 0
for (_, tdata) in enumerate(train_loader, 0):
entext = tdata['entext']
enlen = tdata['enlen']
zhlabel = tdata['zhlabel']
zhgtruth = tdata['zhgtruth']
zhlen = tdata['zhlen']
enstr = tdata['enstr']
zhstr = tdata['zhstr']
teacher_forcing_ratio = 1
print ('teacher_forcing_ratio: ', teacher_forcing_ratio)
decoder_outputs, ret_dict = net(entext, zhgtruth,True, teacher_forcing_ratio)
loss = net.get_loss(decoder_outputs, zhlabel)
optimizer.zero_grad()
loss.backward()
utils.clip_grad_norm(net.parameters(), 5)
optimizer.step()
batchid += 1
global_steps += 1
print (global_steps, iepoch, batchid, max(enlen), sum(loss.data.cpu().numpy()))
agent.append(train_loss, global_steps, sum(loss.data.cpu().numpy()))
agent.append(hyper_tfr, global_steps, teacher_forcing_ratio)
if global_steps % 50 == 0:
net.eval()
decoder_outputs, ret_dict = net(entext, zhgtruth, True, teacher_forcing_ratio)
length = ret_dict['length']
prediction = [0 for i in range(len(length))]
tmppre = [_.squeeze().cpu().data.tolist() for _ in ret_dict['sequence']]
tmppre = np.array(tmppre).transpose(1, 0)
for i in range(len(tmppre)):
prediction[i] = tmppre[i][:length[i]]
prediction[i] = transform.i2t(prediction[i], language = 'zh')
prediction[i] = re.sub(r'nuk#', '', prediction[i])
prediction[i] = re.sub(r'eos#', '', prediction[i])
tmpscore = bleuscore.score(prediction, zhstr)
for i in range(5):
print (prediction[i])
print (zhstr[i])
print ('-------------------\n')
del decoder_outputs, ret_dict
agent.append(train_bleu, global_steps, tmpscore)
net.train()
if global_steps % 200 == 0:
print ('\n------------------------\n')
net.eval()
all_pre = []
all_label = []
all_loss = 0
all_en = []
bats = 0
teacher_forcing_loss = 0
for (_, vdata) in enumerate(valid_loader, 0):
entext = vdata['entext']
enlen = vdata['enlen']
zhlabel = vdata['zhlabel']
zhgtruth = vdata['zhgtruth']
zhlen = vdata['zhlen']
enstr = vdata['enstr']
zhstr = vdata['zhstr']
decoder_outputs, ret_dict = net(entext, None, True, 0)
length = ret_dict['length']
prediction = [0 for i in range(len(length))]
tmppre = [_.squeeze().cpu().data.tolist() for _ in ret_dict['sequence']]
tmppre = np.array(tmppre).transpose(1, 0)
for i in range(len(tmppre)):
prediction[i] = tmppre[i][:length[i]]
prediction[i] = transform.i2t(prediction[i], language = 'zh')
prediction[i] = re.sub(r'nuk#', '', prediction[i])
prediction[i] = re.sub(r'eos#', '', prediction[i])
loss = net.get_loss(decoder_outputs, zhlabel)
all_pre.extend(prediction)
all_label.extend(zhstr)
all_en.extend(enstr)
all_loss += sum(loss.data.cpu().numpy())
del loss, decoder_outputs, ret_dict
# teacher forcing loss, to judge if overfit
decoder_outputs, _ = net(entext, zhgtruth, True, 1)
loss = net.get_loss(decoder_outputs, zhlabel)
teacher_forcing_loss += sum(loss.data.cpu().numpy())
bats += 1
score = bleuscore.score(all_pre, all_label)
for i in range(0, 400):
print (all_en[i])
print (all_pre[i])
print (all_label[i])
print ('-------------------\n')
all_loss /= bats
teacher_forcing_loss /= bats
print (global_steps, iepoch, batchid, all_loss, teacher_forcing_loss, score, '\n********************\n')
agent.append(valid_loss, global_steps, all_loss)
agent.append(valid_bleu, global_steps, score)
agent.append(valid_tf_loss, global_steps, teacher_forcing_loss)
if best_valid_loss > all_loss:
best_valid_loss = all_loss
#bestscore = score
_ = model_dir + "ratio-{:3f}-loss-{:3f}-score-{:3f}-steps-{:d}-model.pkl".format(teacher_forcing_ratio, all_loss, score, global_steps)
torch.save(net.state_dict(), _)
elif global_steps % 400 == 0:
_ = model_dir + "ratio-{:3f}-loss-{:3f}-score-{:3f}-steps-{:d}-model.pkl".format(teacher_forcing_ratio, all_loss, score, global_steps)
torch.save(net.state_dict(), _)
del all_label, all_loss, all_pre
net.train()
def train(args, word_idx, train_data, valid_data, dev_data, writer=None):
# build minibatch loader
train_batch_loader = minibatch_loader(train_data,
args.batch_size,
sample=1.0,
punc=args.punc)
valid_batch_loader = minibatch_loader(valid_data,
args.batch_size,
shuffle=False,
punc=args.punc)
dev_batch_loader = minibatch_loader(dev_data,
args.batch_size,
shuffle=False,
punc=args.punc)
# training phase
if args.restore_model != None:
logging.info("restore from previous training...")
_, embed_dim = load_word2vec_embeddings(word_idx, args.embed_file,
args.embed_dim, False)
model = AttSum(args.n_layers, args.vocab_size, args.drop_out,
args.gru_size, None, embed_dim, args.train_emb)
checkpoint = torch.load(args.restore_model + '.chkpt')
opt = torch.optim.Adam(params=filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.init_learning_rate)
model.load_state_dict(checkpoint)
'''
model.load_state_dict(checkpoint['state_dict'])
opt.load_state_dict(checkpoint['optimizer'])
'''
else:
embed_init, embed_dim = load_word2vec_embeddings(
word_idx, args.embed_file, args.embed_dim, True)
logging.info("embedding dim: {}".format(embed_dim))
logging.info("initialize model ...")
model = AttSum(args.n_layers, args.vocab_size, args.drop_out,
args.gru_size, embed_init, embed_dim, args.train_emb)
opt = torch.optim.Adam(params=filter(lambda p: p.requires_grad,
model.parameters()),
lr=args.init_learning_rate)
if USE_CUDA:
model.cuda()
logging.info("Running on cuda: {}".format(USE_CUDA))
logging.info('-' * 50)
logging.info("Start training ...")
best_valid_acc = best_dev_acc = 0
for epoch in range(args.epoch):
'''
if epoch >= 2:
for param_group in opt.param_groups:
param_group['lr'] /= 2
'''
model.train()
train_acc = acc = train_loss = loss = n_examples = train_examples = it = 0
start = time.time()
for docs, anss, docs_mask, \
cand_position in train_batch_loader:
train_examples += docs.shape[0]
n_examples += docs.shape[0]
docs, anss, docs_mask, \
cand_position = to_vars([docs, anss, docs_mask, \
cand_position], use_cuda=USE_CUDA)
opt.zero_grad()
loss_, acc_ = model(docs, anss, docs_mask, cand_position)
train_loss += loss_.cpu().data.numpy()[0]
loss += loss_.cpu().data.numpy()[0]
train_acc += acc_.cpu().data.numpy()[0]
acc += acc_.cpu().data.numpy()[0]
it += 1
loss_.backward()
clip_grad_norm(parameters=filter(lambda p: p.requires_grad,
model.parameters()),
max_norm=args.grad_clip)
opt.step()
if (it % args.print_every == 0):
# on training
spend = (time.time() - start) / 60
statement = "it: {} (max: {}), "\
.format(it, len(train_batch_loader))
statement += "train loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)"\
.format(loss / float(args.print_every), acc / n_examples, spend)
logging.info(statement)
# on valid
model.eval()
start = time.time()
valid_loss, valid_acc = evaluate(model, valid_batch_loader,
USE_CUDA, False)
spend = (time.time() - start) / 60
logging.info(
"Valid loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)".format(
valid_loss, valid_acc, spend))
if best_valid_acc < valid_acc:
best_valid_acc = valid_acc
logging.info(
"Best valid acc: {:.3f}".format(best_valid_acc))
# on lambada dev
start = time.time()
dev_loss, dev_acc = evaluate(model, dev_batch_loader, USE_CUDA,
True)
spend = (time.time() - start) / 60
logging.info(
"dev loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)".format(
dev_loss, dev_acc, spend))
if best_dev_acc < dev_acc:
best_dev_acc = dev_acc
logging.info("Best dev acc: {:.3f}".format(best_dev_acc))
if args.save_mode == 'best':
model_name = args.save_model + '.chkpt'
torch.save(model.state_dict(), model_name)
logging.info(
' - [Info] The checkpoint file has been updated [best].'
)
if writer != None:
it_w = it / args.print_every
writer.add_scalar('data/train_loss',
loss / float(args.print_every), it_w)
writer.add_scalar('data/train_acc', acc / n_examples, it_w)
writer.add_scalar('data/valid_loss', valid_loss, it_w)
writer.add_scalar('data/valid_acc', valid_acc, it_w)
writer.add_scalar('data/valid_loss', dev_loss, it_w)
writer.add_scalar('data/valid_acc', dev_acc, it_w)
model.train()
start = time.time()
acc = loss = n_examples = 0
logging.info(
"End: train loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)".format(
train_loss / len(train_batch_loader),
train_acc / train_examples, spend))
# on valid
start = time.time()
model.eval()
valid_loss, valid_acc = evaluate(model, valid_batch_loader, USE_CUDA,
False)
spend = (time.time() - start) / 60
logging.info(
"End: Valid loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)".format(
valid_loss, valid_acc, spend))
if best_valid_acc < valid_acc:
best_valid_acc = valid_acc
logging.info("Best valid acc: {:.3f}".format(best_valid_acc))
# on lambada dev
start = time.time()
dev_loss, dev_acc = evaluate(model, dev_batch_loader, USE_CUDA, True)
spend = (time.time() - start) / 60
logging.info(
"End: dev loss: {:.3f}, acc: {:.3f}, time: {:.1f}(m)".format(
dev_loss, dev_acc, spend))
if best_dev_acc < dev_acc:
best_dev_acc = dev_acc
logging.info("Best dev acc: {:.3f}".format(best_dev_acc))
#save checkpoint
checkpoint = {
'state_dict': model.state_dict(),
'optimizer': opt.state_dict()
}
if args.save_model:
if args.save_mode == 'series':
model_name = args.save_model + '.chkpt'
torch.save(model.state_dict(), model_name)
#torch.save(checkpoint, model_name)
logging.info(
' - [Info] The checkpoint file has been updated [series].')
elif args.save_mode == 'all':
model_name = args.save_model + '_accu_{accu:3.3f}.chkpt'.format(
accu=100 * valid_accu)
torch.save(checkpoint, model_name)
logging.info(
' - [Info] The checkpoint file has been updated [all].')
'''
def forward(data_loader, model, criterion, epoch=0, training=True, optimizer=None, U=None, V=None):
if args.gpus and len(args.gpus) > 1:
model = torch.nn.DataParallel(model, args.gpus)
batch_time = AverageMeter()
pruning_time = AverageMeter()
select_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
masks = [torch.zeros(w.size()).cuda() for w in list(model.parameters())]
for i, (inputs, target) in enumerate(data_loader):
# measure data loading time
data_time.update(time.time() - end)
if args.gpus is not None:
target = target.cuda(async=True)
input_var = Variable(inputs.type(args.type), volatile=not training)
target_var = Variable(target)
# compute output
if not training:
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target_var.data, topk=(1, 5))
losses.update(loss.data[0], input_var.size(0))
top1.update(prec1[0], input_var.size(0))
top5.update(prec5[0], input_var.size(0))
else:
mini_inputs = input_var.chunk(args.batch_size // args.mini_batch_size)
mini_targets = target_var.chunk(args.batch_size // args.mini_batch_size)
#TODO for debug shoul be delete
if(0 == i):
print('number of ghost batch is ', len(mini_inputs))
optimizer.zero_grad()
# fjr simulate distributed senario
acc_grad = []
if args.use_residue_acc:
if torch.cuda.is_available():
acc_grad = [torch.zeros(w.size()).cuda() for w in list(model.parameters())]
else:
print("gpu is not avaiable for acc_grad allocation")
for k, mini_input_var in enumerate(mini_inputs):
mini_target_var = mini_targets[k]
output = model(mini_input_var)
loss = criterion(output, mini_target_var)
prec1, prec5 = accuracy(output.data, mini_target_var.data, topk=(1, 5))
losses.update(loss.data[0], mini_input_var.size(0))
top1.update(prec1[0], mini_input_var.size(0))
top5.update(prec5[0], mini_input_var.size(0))
# compute gradient and do SGD step
# fjr
if args.use_residue_acc:
# clear grad before accumulating
optimizer.zero_grad()
loss.backward()
if args.use_residue_acc:
if args.use_pruning:
clip_grad_norm(model.parameters(), 5. * (len(mini_inputs) ** -0.5))
idx = 0
for u, v, p in zip(U[k], V[k], model.parameters()):
prune_begin = time.time()
if args.use_pruning:
# TODO how to set rho (momentum)
g = p.grad.data / len(mini_inputs)
g += p.data * args.weight_decay / len(mini_inputs)
if args.use_nesterov:
u = args.momentum * (u + g)
v = v + u + g
else:
u = args.momentum * u + g
v = v + u
select_begin = time.time()
if args.use_sync and i % args.sync_interval == 0:
masks[idx] = 1;
else:
if args.use_warmup:
# print("iter", i, "node ", k, " pruning layer ", idx)
if (epoch == 0):
masks[idx] = select_top_k(v, 1 - 0.75, masks[idx])
elif (epoch == 1):
masks[idx] = select_top_k(v, 1 - 0.9375, masks[idx])
elif (epoch == 2):
masks[idx] = select_top_k(v, 1 - 0.984375, masks[idx])
elif (epoch == 3):
masks[idx] = select_top_k(v, 1 - 0.996, masks[idx])
else:
masks[idx] = select_top_k(v, 1 - 0.999, masks[idx])
else:
masks[idx] = select_top_k(v, 1 - 0.999, masks[idx])
select_time.update(time.time() - select_begin)
p.grad.data = v * masks[idx]
v = v * (1 - masks[idx])
u = u * (1 - masks[idx])
acc_grad[idx] += p.grad.data
U[k][idx] = u #new_residue
V[k][idx] = v
else:
acc_grad[idx] += p.grad.data / len(mini_inputs)
pruning_time.update(time.time() - prune_begin)
idx = idx + 1
if args.use_residue_acc:
# Master
idx = 0
for g, p in zip(acc_grad, model.parameters()):
# print("accumulated sparsity is", check_sparsity(g))
if args.use_pruning:
# TODO 1. use pytorch sgd optimizer to calculate mom and weight_decay, set mom and wd
# used with pruning
p.grad.data = g
else:
# TODO 2. implement weight_decay and momentum by myself, set mom=0 and wd = 0
# used with baseline
g += p.data * args.weight_decay
V[k][idx] = args.momentum * V[k][idx] + g
p.grad.data = V[k][idx]
# clip_grad_norm(model.parameters(), 5.)
idx = idx+1
else:
for p in model.parameters():
p.grad.data.div_(len(mini_inputs))
#print("original grad norm before clip", p.grad.data.norm())
clip_grad_norm(model.parameters(), 5.)
#print("original grad norm after clip", p.grad.data.norm())
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
logging.info('{phase} - Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Prune {pruning_time.val:.9f} ({pruning_time.avg:.3f})\t'
'Select {select_time.val:.9f} ({select_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, i, len(data_loader),
phase='TRAINING' if training else 'EVALUATING',
batch_time=batch_time,
data_time=data_time,
pruning_time = pruning_time,
select_time = select_time,
loss=losses, top1=top1, top5=top5))
return {'loss': losses.avg,
'prec1': top1.avg,
'prec5': top5.avg,
'U' : U,
'V' : V}
def train(
train_loader,
model,
act_criterion,
comp_criterion,
regression_criterion,
optimizer,
epoch,
):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
act_losses = AverageMeter()
comp_losses = AverageMeter()
reg_losses = AverageMeter()
act_accuracies = AverageMeter()
fg_accuracies = AverageMeter()
bg_accuracies = AverageMeter()
# switch to train mode
model.train()
end = time.time()
optimizer.zero_grad()
ohem_num = train_loader.dataset.fg_per_video
comp_group_size = (
train_loader.dataset.fg_per_video + train_loader.dataset.incomplete_per_video
)
for (
i,
(
out_frames,
out_prop_len,
out_prop_scaling,
out_prop_type,
out_prop_labels,
out_prop_reg_targets,
out_stage_split,
),
) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
input_var = torch.autograd.Variable(out_frames)
scaling_var = torch.autograd.Variable(out_prop_scaling)
target_var = torch.autograd.Variable(out_prop_labels)
reg_target_var = torch.autograd.Variable(out_prop_reg_targets)
prop_type_var = torch.autograd.Variable(out_prop_type)
# compute output
activity_out, activity_target, completeness_out, completeness_target, regression_out, regression_labels, regression_target = model(
input_var, scaling_var, target_var, reg_target_var, prop_type_var
)
act_loss = act_criterion(activity_out, activity_target)
comp_loss = comp_criterion(
completeness_out, completeness_target, ohem_num, comp_group_size
)
reg_loss = regression_criterion(
regression_out, regression_labels, regression_target
)
loss = (
act_loss
+ comp_loss * args.comp_loss_weight
+ reg_loss * args.reg_loss_weight
)
reg_losses.update(reg_loss.data[0], out_frames.size(0))
# measure mAP and record loss
losses.update(loss.data[0], out_frames.size(0))
act_losses.update(act_loss.data[0], out_frames.size(0))
comp_losses.update(comp_loss.data[0], out_frames.size(0))
act_acc = accuracy(activity_out, activity_target)
act_accuracies.update(act_acc[0].data[0], activity_out.size(0))
fg_acc = accuracy(
activity_out.view(-1, 2, activity_out.size(1))[:, 0, :].contiguous(),
activity_target.view(-1, 2)[:, 0].contiguous(),
)
bg_acc = accuracy(
activity_out.view(-1, 2, activity_out.size(1))[:, 1, :].contiguous(),
activity_target.view(-1, 2)[:, 1].contiguous(),
)
fg_accuracies.update(fg_acc[0].data[0], activity_out.size(0) // 2)
bg_accuracies.update(bg_acc[0].data[0], activity_out.size(0) // 2)
# compute gradient and do SGD step
loss.backward()
if i % args.iter_size == 0:
# scale down gradients when iter size is functioning
if args.iter_size != 1:
for g in optimizer.param_groups:
for p in g["params"]:
p.grad /= args.iter_size
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print(
(
"clipping gradient: {} with coef {}".format(
total_norm, args.clip_gradient / total_norm
)
)
)
else:
total_norm = 0
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print(
(
"Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t"
"Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t"
"Data {data_time.val:.3f} ({data_time.avg:.3f})\t"
"Loss {loss.val:.4f} ({loss.avg:.4f})\t"
"Act. Loss {act_losses.val:.3f} ({act_losses.avg: .3f}) \t"
"Comp. Loss {comp_losses.val:.3f} ({comp_losses.avg: .3f}) ".format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
act_losses=act_losses,
comp_losses=comp_losses,
lr=optimizer.param_groups[0]["lr"],
)
+ "\tReg. Loss {reg_loss.val:.3f} ({reg_loss.avg:.3f})".format(
reg_loss=reg_losses
)
+ "\n Act. FG {fg_acc.val:.02f} ({fg_acc.avg:.02f}) Act. BG {bg_acc.avg:.02f} ({bg_acc.avg:.02f})".format(
act_acc=act_accuracies,
fg_acc=fg_accuracies,
bg_acc=bg_accuracies,
)
)
)
def train(args: Dict[str, str]):
# LJ: source corpus and target corpus
train_data_src = read_corpus(args['--train-src'], source='src')
train_data_tgt = read_corpus(args['--train-tgt'], source='tgt')
# LJ: the validation set (source and target)
dev_data_src = read_corpus(args['--dev-src'], source='src')
dev_data_tgt = read_corpus(args['--dev-tgt'], source='tgt')
# LJ: the training and validation sentences pairs
train_data = list(zip(train_data_src, train_data_tgt))
dev_data = list(zip(dev_data_src, dev_data_tgt))
# LJ: the configurations
train_batch_size = int(args['--batch-size'])
clip_grad = float(args['--clip-grad'])
valid_niter = int(args['--valid-niter'])
log_every = int(args['--log-every'])
model_save_path = args['--save-to']
# LJ: read the vocabulary
vocab = pickle.load(open(args['--vocab'], 'rb'))
# LJ: set up the loss function (ignore to <pad>)
nll_loss = nn.NLLLoss(ignore_index=0)
# LJ: build the model
model = NMT(embed_size=int(args['--embed-size']),
hidden_size=int(args['--hidden-size']),
dropout_rate=float(args['--dropout']),
local_att=bool(args['--local']),
conv=bool(args['--conv']),
vocab=vocab,
loss=nll_loss)
bound = float(args['--uniform-init'])
for p in model.parameters():
torch.nn.init.uniform_(p.data, a=-bound, b=bound)
src_embed_fn = args['--src_ebed_fn']
tgt_embed_fn = args['--tgt_ebed_fn']
#print(src_embed_fn)
#print(tgt_embed_fn)
if not src_embed_fn == "None":
src_vectors = np.load(src_embed_fn)['embedding']
model.src_embed.weight.data = torch.from_numpy(
src_vectors).float().cuda()
if not tgt_embed_fn == "None":
tgt_vectors = np.load(tgt_embed_fn)['embedding']
model.tgt_embed.weight.data = torch.from_numpy(
tgt_vectors).float().cuda()
# LJ: the learning rate
lr = float(args['--lr'])
# LJ: setting some initial losses, etc.
num_trial = 0
train_iter = patience = cum_loss = report_loss = cumulative_tgt_words = report_tgt_words = 0
cumulative_examples = report_examples = epoch = valid_num = 0
hist_valid_scores = []
train_time = begin_time = time.time()
print('begin Maximum Likelihood training')
# LJ: setup the optimizer
# optimizer = optim.Adam(list(model.encoder.parameters())+list(model.decoder.parameters()), lr=lr)
optimizer = optim.Adam(filter(lambda x: x.requires_grad,
model.parameters()),
lr=lr)
while True:
# start the epoch
epoch += 1
# LJ: ok, we yield the sentences in a shuffle manner.
for src_sents, tgt_sents in batch_iter(train_data,
batch_size=train_batch_size,
shuffle=True):
model.set_model_to_train()
train_iter += 1
# LJ: current batch size
batch_size = len(src_sents)
# (batch_size)
# LJ: train on the mini-batch and get the loss, backpropagation
# loss = -model(src_sents, tgt_sents)
optimizer.zero_grad()
loss, num_words = model(src_sents, tgt_sents)
loss.backward()
clip_grad_norm(
list(model.encoder.parameters()) +
list(model.decoder.parameters()), clip_grad)
optimizer.step()
# add the loss to cumlinative loss
report_loss += loss.detach().cpu().numpy() * num_words
cum_loss += loss.detach().cpu().numpy() * num_words
# LJ: how many targets words are there in all target sentences in current batch
tgt_words_num_to_predict = sum(
len(s[1:]) for s in tgt_sents) # omitting leading `<s>`
# LJ: all cumulative words
report_tgt_words += tgt_words_num_to_predict
cumulative_tgt_words += tgt_words_num_to_predict
# LJ: all number of instances handled
report_examples += batch_size
cumulative_examples += batch_size
# LJ: print out the training loss
if train_iter % log_every == 0:
print('epoch %d, iter %d, avg. loss %.2f, avg. ppl %.2f ' \
'cum. examples %d, speed %.2f words/sec, time elapsed %.2f sec' % (epoch, train_iter,
report_loss / report_examples,
math.exp(
report_loss / report_tgt_words),
cumulative_examples,
report_tgt_words / (
time.time() - train_time),
time.time() - begin_time),
file=sys.stderr)
train_time = time.time()
report_loss = report_tgt_words = report_examples = 0.
# the following code performs validation on dev set, and controls the learning schedule
# if the dev score is better than the last check point, then the current model is saved.
# otherwise, we allow for that performance degeneration for up to `--patience` times;
# if the dev score does not increase after `--patience` iterations, we reload the previously
# saved best model (and the state of the optimizer), halve the learning rate and continue
# training. This repeats for up to `--max-num-trial` times.
if train_iter % valid_niter == 0:
model.set_model_to_eval()
print(
'epoch %d, iter %d, cum. loss %.2f, cum. ppl %.2f cum. examples %d'
% (epoch, train_iter, cum_loss / cumulative_examples,
np.exp(cum_loss / cumulative_tgt_words),
cumulative_examples),
file=sys.stderr)
cum_loss = cumulative_examples = cumulative_tgt_words = 0.
valid_num += 1
print('begin validation ...', file=sys.stderr)
# compute dev. ppl and bleu
# LJ: the validation is implemented in a seperate function
cum_loss, dev_ppl = model.evaluate_ppl(
dev_data,
batch_size=128) # dev batch size can be a bit larger
# valid_metric = -dev_ppl
valid_metric = -cum_loss
print('validation: iter %d, dev. ppl %f, val cum loss: %f' %
(train_iter, dev_ppl, cum_loss),
file=sys.stderr)
# LJ: a new better model is found.
is_better = len(hist_valid_scores
) == 0 or valid_metric > max(hist_valid_scores)
hist_valid_scores.append(valid_metric)
if is_better:
patience = 0
print('save currently the best model to [%s]' %
model_save_path,
file=sys.stderr)
model.save(model_save_path)
# You may also save the optimizer's state, adjust the training weight, since we found there are too
# much iterations without improvements.
elif patience < int(args['--patience']):
patience += 1
print('hit patience %d' % patience, file=sys.stderr)
if patience == int(args['--patience']):
num_trial += 1
print('hit #%d trial' % num_trial, file=sys.stderr)
if num_trial == int(args['--max-num-trial']):
print('early stop!', file=sys.stderr)
exit(0)
# decay learning rate, and restore from previously best checkpoint
lr = lr * float(args['--lr-decay'])
optimizer = optim.Adam(filter(
lambda x: x.requires_grad, model.parameters()),
lr=lr)
print(
'load previously best model and decay learning rate to %f'
% lr,
file=sys.stderr)
# load model
# model.load(model_save_path)
model = utils.load_model_by_state_dict(
model, model_save_path)
print('restore parameters of the optimizers',
file=sys.stderr)
# You may also need to load the state of the optimizer saved before
# reset patience
patience = 0
if epoch == int(args['--max-epoch']):
print('reached maximum number of epochs!', file=sys.stderr)
exit(0)
def train(train_loader, model, criterion, optimizer, epoch, log):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data[0], input.size(0))
top1.update(prec1[0], input.size(0))
top5.update(prec5[0], input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
# if total_norm > args.clip_gradient:
# print("clipping gradient: {} with coef {}".format(total_norm, args.clip_gradient / total_norm))
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
output = ('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5,
lr=optimizer.param_groups[-1]['lr']))
print(output)
log.write(output + '\n')
log.flush()
objective_loss = F.cross_entropy(input=logits_squeezed, target=Variable(torch_labels))
if objective_loss.data[0] > 5 and epoch > 10:
#interested in phrase that have large loss (i.e. incorrectly classified)
print(' '.join(tree.get_words()))
loss_history.append(objective_loss.data[0])
if step % 20 == 0 and step > 0:
print("step %3d, last loss %0.3f, mean loss (%d steps) %0.3f" % (step, objective_loss.data[0], average_over, np.mean(loss_history[-average_over:])))
optimizer.zero_grad()
if np.isnan(objective_loss.data[0]):
print("object_loss was not a number")
sys.exit(1)
else:
objective_loss.backward()
clip_grad_norm(model.parameters(), 5, norm_type=2.)
#temp_grad += model.fcl._parameters['weight'].grad.data
# # Update weights using gradient descent; w1.data and w2.data are Tensors,
# # w1.grad and w2.grad are Variables and w1.grad.data and w2.grad.data are
# # Tensors.
# loss.backward()
# w1.data -= learning_rate * w1.grad.data
# w2.data -= learning_rate * w2.grad.data
optimizer.step()
print("total root predicted correctly = ", total_root_prediction/ float(train_size))
print("total node (including root) predicted correctly = ", total_summed_accuracy / float(train_size))
total_dev_loss = 0.
dev_correct_at_root = 0.
dev_correct_all = 0.
for step, dev_example in enumerate(dev_data):
def train():
logging.info('Loading vocab,train and val dataset.Wait a second,please')
embed = torch.Tensor(np.load(args.embedding)['embedding'])
with open(args.word2id) as f:
word2id = json.load(f)
vocab = utils.Vocab(embed, word2id)
with open(args.train_dir) as f:
examples = [json.loads(line) for line in f]
train_dataset = utils.Dataset(examples)
with open(args.val_dir) as f:
examples = [json.loads(line) for line in f]
val_dataset = utils.Dataset(examples)
# update args
args.embed_num = embed.size(0)
args.embed_dim = embed.size(1)
args.kernel_sizes = [int(ks) for ks in args.kernel_sizes.split(',')]
# build model
net = getattr(models, args.model)(args, embed)
if use_gpu:
net.cuda()
# load dataset
train_iter = DataLoader(dataset=train_dataset,
batch_size=args.batch_size,
shuffle=True)
val_iter = DataLoader(dataset=val_dataset,
batch_size=args.batch_size,
shuffle=False)
# loss function
criterion = nn.BCELoss()
# model info
print(net)
params = sum(p.numel() for p in list(net.parameters())) / 1e6
print('#Params: %.1fM' % (params))
min_loss = float('inf')
optimizer = torch.optim.Adam(net.parameters(), lr=args.lr)
net.train()
t1 = time()
for epoch in range(1, args.epochs + 1):
for i, batch in enumerate(train_iter):
features, targets, _, doc_lens = vocab.make_features(batch)
features, targets = Variable(features), Variable(targets.float())
if use_gpu:
features = features.cuda()
targets = targets.cuda()
probs = net(features, doc_lens)
loss = criterion(probs, targets)
optimizer.zero_grad()
loss.backward()
clip_grad_norm(net.parameters(), args.max_norm)
optimizer.step()
if args.debug:
print('Batch ID:%d Loss:%f' % (i, loss.data[0]))
continue
if i % args.report_every == 0:
cur_loss = eval(net, vocab, val_iter, criterion)
if cur_loss < min_loss:
min_loss = cur_loss
best_path = net.save()
logging.info('Epoch: %2d Min_Val_Loss: %f Cur_Val_Loss: %f' %
(epoch, min_loss, cur_loss))
t2 = time()
logging.info('Total Cost:%f h' % ((t2 - t1) / 3600))
def train(self):
if self.T - self.target_sync_T > self.args.target:
self.sync_target_network()
self.target_sync_T = self.T
info = {}
for _ in range(self.args.iters):
self.dqn.eval()
batch, indices, is_weights = self.replay.Sample_N(self.args.batch_size, self.args.n_step, self.args.gamma)
columns = list(zip(*batch))
states = Variable(torch.from_numpy(np.array(columns[0])).float().transpose_(1, 3))
actions = Variable(torch.LongTensor(columns[1]))
terminal_states = Variable(torch.FloatTensor(columns[5]))
rewards = Variable(torch.FloatTensor(columns[2]))
# Have to clip rewards for DQN
rewards = torch.clamp(rewards, -1, 1)
steps = Variable(torch.FloatTensor(columns[4]))
new_states = Variable(torch.from_numpy(np.array(columns[3])).float().transpose_(1, 3))
target_dqn_qvals = self.target_dqn(new_states).cpu()
# Make a new variable with those values so that these are treated as constants
target_dqn_qvals_data = Variable(target_dqn_qvals.data)
q_value_gammas = (Variable(torch.ones(terminal_states.size()[0])) - terminal_states)
inter = Variable(torch.ones(terminal_states.size()[0]) * self.args.gamma)
# print(steps)
q_value_gammas = q_value_gammas * torch.pow(inter, steps)
values = torch.linspace(self.args.v_min, self.args.v_max, steps=self.args.atoms)
values = Variable(values)
values = values.view(1, 1, self.args.atoms)
values = values.expand(self.args.batch_size, self.args.actions, self.args.atoms)
# print(values)
q_value_gammas = q_value_gammas.view(self.args.batch_size, 1, 1)
q_value_gammas = q_value_gammas.expand(self.args.batch_size, self.args.actions, self.args.atoms)
# print(q_value_gammas)
gamma_values = q_value_gammas * values
# print(gamma_values)
rewards = rewards.view(self.args.batch_size, 1, 1)
rewards = rewards.expand(self.args.batch_size, self.args.actions, self.args.atoms)
# print(rewards)
operator_q_values = rewards + gamma_values
# print(operator_q_values)
clipped_operator_q_values = torch.clamp(operator_q_values, self.args.v_min, self.args.v_max)
delta_z = (self.args.v_max - self.args.v_min) / (self.args.atoms - 1)
# Using the notation from the categorical paper
b_j = (clipped_operator_q_values - self.args.v_min) / delta_z
# print(b_j)
lower_bounds = torch.floor(b_j)
upper_bounds = torch.ceil(b_j)
# Work out the max action
atom_values = Variable(torch.linspace(self.args.v_min, self.args.v_max, steps=self.args.atoms))
atom_values = atom_values.view(1, 1, self.args.atoms)
atom_values = atom_values.expand(self.args.batch_size, self.args.actions, self.args.atoms)
# Sum over the atoms dimension
target_expected_qvalues = torch.sum(target_dqn_qvals_data * atom_values, dim=2)
# Get the maximum actions index across the batch size
max_actions = target_expected_qvalues.max(dim=1)[1].view(-1)
# Project back onto the original support for the max actions
q_value_distribution_targets = torch.zeros(self.args.batch_size, self.args.atoms)
# Distributions for the max actions
# print(target_dqn_qvals_data, max_actions)
q_value_max_actions_distribs = target_dqn_qvals_data.index_select(dim=1, index=max_actions)[:,0,:]
# print(q_value_max_actions_distribs)
# Lower_bounds_actions
lower_bounds_actions = lower_bounds.index_select(dim=1, index=max_actions)[:,0,:]
upper_bounds_actions = upper_bounds.index_select(dim=1, index=max_actions)[:,0,:]
b_j_actions = b_j.index_select(dim=1, index=max_actions)[:,0,:]
lower_bound_values_to_add = q_value_max_actions_distribs * (upper_bounds_actions - b_j_actions)
upper_bound_values_to_add = q_value_max_actions_distribs * (b_j_actions - lower_bounds_actions)
# print(lower_bounds_actions)
# print(lower_bound_values_to_add)
# Naive looping
for b in range(self.args.batch_size):
for l, pj in zip(lower_bounds_actions.data.type(torch.LongTensor)[b], lower_bound_values_to_add[b].data):
q_value_distribution_targets[b][l] += pj
for u, pj in zip(upper_bounds_actions.data.type(torch.LongTensor)[b], upper_bound_values_to_add[b].data):
q_value_distribution_targets[b][u] += pj
self.dqn.train()
if self.args.gpu:
actions = actions.cuda()
# q_value_targets = q_value_targets.cuda()
q_value_distribution_targets = q_value_distribution_targets.cuda()
model_predictions = self.dqn(states).index_select(1, actions.view(-1))[:,0,:]
q_value_distribution_targets = Variable(q_value_distribution_targets)
# print(q_value_distribution_targets)
# print(model_predictions)
# Cross entropy loss
ce_loss = -torch.sum(q_value_distribution_targets * torch.log(model_predictions), dim=1)
ce_batch_loss = ce_loss.mean()
info = {}
self.log("DQN/X_Entropy_Loss", ce_batch_loss.data[0], step=self.T)
# Update
self.optimizer.zero_grad()
ce_batch_loss.backward()
# Taken from pytorch clip_grad_norm
# Remove once the pip version it up to date with source
gradient_norm = clip_grad_norm(self.dqn.parameters(), self.args.clip_value)
if gradient_norm is not None:
info["Norm"] = gradient_norm
self.optimizer.step()
if "States" in info:
states_trained = info["States"]
info["States"] = states_trained + columns[0]
else:
info["States"] = columns[0]
# Pad out the states to be of size batch_size
if len(info["States"]) < self.args.batch_size:
old_states = info["States"]
new_states = old_states[0] * (self.args.batch_size - len(old_states))
info["States"] = new_states
return info
def train(model_str, embeddings, train_iter, val_iter=None, context_size=None, early_stopping=False, save=False, save_path=None,
model_params={}, opt_params={}, train_params={}, cuda=CUDA_DEFAULT, reshuffle_train=False, TEXT=None):
# Initialize model and other variables
train_iter_, val_iter_, model, criterion, optimizer, scheduler = _train_initialize_variables(model_str, embeddings, model_params, train_iter, val_iter, opt_params, cuda)
# First validation round before any training
if val_iter_ is not None:
model.eval()
print("Model initialized")
val_loss = predict(model, val_iter_, context_size=context_size,
save_loss=False, expt_name="dummy_expt", cuda=cuda)
model.train()
if scheduler is not None:
scheduler.step(val_loss)
print("All set. Actual Training begins")
for epoch in range(train_params.get('n_ep', 30)):
# Monitoring loss
total_loss = 0
count = 0
# if using NNLM, reshuffle sentences
if model_str == 'NNLM' and reshuffle_train:
train_iter_, _, _ = rebuild_iterators(TEXT, batch_size=int(model_params['batch_size']))
# Initialize hidden layer and memory(for LSTM). Converting to variable later.
if model_str in recur_models:
model.hidden = model.init_hidden()
# Actual training loop.
for x_train, y_train in data_generator(train_iter_, model_str, context_size=context_size, cuda=cuda):
optimizer.zero_grad()
if model_str in recur_models:
output, model_hidden = model(x_train)
if model.model_str == 'LSTM':
model.hidden = model.hidden[0].detach(), model.hidden[1].detach() # to break the computational graph epxlictly (backprop through `bptt_steps` steps only)
else:
model.hidden = model.hidden.detach() # to break the computational graph epxlictly (backprop through `bptt_steps` steps only)
else:
output = model(x_train)
# Dimension matching to cut it right for loss function.
if model_str in recur_models:
batch_size, sent_length = y_train.size(0), y_train.size(1)
loss = criterion(output.view(batch_size, -1, sent_length), y_train)
else:
loss = criterion(output, y_train)
# backprop
loss.backward()
# Clip gradients to prevent exploding gradients in RNN/LSTM/GRU
if model_str in recur_models:
clip_grad_norm(model.parameters(), model_params.get("clip_grad_norm", 5))
optimizer.step()
# monitoring
count += x_train.size(0) if model.model_str == 'NNLM2' else x_train.size(0) * x_train.size(1) # in that case there are batch_size x bbp_length classifications per batch
total_loss += t.sum(loss.data) # .data to break so that you dont keep references
# monitoring
avg_loss = total_loss / count
print("Average loss after %d epochs is %.4f" % (epoch, avg_loss))
if val_iter_ is not None:
model.eval()
former_val_loss = val_loss * 1.
val_loss = predict(model, val_iter_, context_size=context_size,
save_loss=False, expt_name="dummy_expt", cuda=cuda)
if scheduler is not None:
scheduler.step(val_loss)
if val_loss > former_val_loss:
if early_stopping:
break
else:
if save:
assert save_path is not None
# weights
save_model(model, save_path + '.pytorch')
# params
with open(save_path + '.params.json', 'w') as fp:
json.dump(model.params, fp)
# loss
with open(save_path + '.losses.txt', 'w') as fp:
fp.write('val: ' + str(val_loss))
fp.write('train: ' + str(avg_loss))
model.train()
return model
def step(self):
"""Compute gradients norm."""
if self.max_grad_norm:
clip_grad_norm(self.params, self.max_grad_norm)
self.optimizer.step()
def main():
args_parser = argparse.ArgumentParser(
description='Tuning with stack pointer parser')
args_parser.add_argument('--mode',
choices=['RNN', 'LSTM', 'GRU', 'FastLSTM'],
help='architecture of rnn',
required=True)
args_parser.add_argument('--num_epochs',
type=int,
default=200,
help='Number of training epochs')
args_parser.add_argument('--batch_size',
type=int,
default=64,
help='Number of sentences in each batch')
args_parser.add_argument('--hidden_size',
type=int,
default=256,
help='Number of hidden units in RNN')
args_parser.add_argument('--arc_space',
type=int,
default=128,
help='Dimension of tag space')
args_parser.add_argument('--type_space',
type=int,
default=128,
help='Dimension of tag space')
args_parser.add_argument('--num_layers',
type=int,
default=1,
help='Number of layers of RNN')
args_parser.add_argument('--num_filters',
type=int,
default=50,
help='Number of filters in CNN')
args_parser.add_argument('--pos_dim',
type=int,
default=50,
help='Dimension of POS embeddings')
args_parser.add_argument('--char_dim',
type=int,
default=50,
help='Dimension of Character embeddings')
args_parser.add_argument('--opt',
choices=['adam', 'sgd', 'adadelta'],
help='optimization algorithm')
args_parser.add_argument('--learning_rate',
type=float,
default=0.001,
help='Learning rate')
args_parser.add_argument('--decay_rate',
type=float,
default=0.5,
help='Decay rate of learning rate')
args_parser.add_argument('--clip',
type=float,
default=5.0,
help='gradient clipping')
args_parser.add_argument('--gamma',
type=float,
default=0.0,
help='weight for regularization')
args_parser.add_argument('--coverage',
type=float,
default=0.0,
help='weight for coverage loss')
args_parser.add_argument('--p_rnn',
nargs=2,
type=float,
required=True,
help='dropout rate for RNN')
args_parser.add_argument('--p_in',
type=float,
default=0.33,
help='dropout rate for input embeddings')
args_parser.add_argument('--p_out',
type=float,
default=0.33,
help='dropout rate for output layer')
args_parser.add_argument(
'--prior_order',
choices=['inside_out', 'left2right', 'deep_first', 'shallow_first'],
help='prior order of children.',
required=True)
args_parser.add_argument('--schedule',
type=int,
help='schedule for learning rate decay')
args_parser.add_argument(
'--unk_replace',
type=float,
default=0.,
help='The rate to replace a singleton word with UNK')
args_parser.add_argument('--punctuation',
nargs='+',
type=str,
help='List of punctuations')
args_parser.add_argument('--beam',
type=int,
default=1,
help='Beam size for decoding')
args_parser.add_argument('--word_embedding',
choices=['glove', 'senna', 'sskip', 'polyglot'],
help='Embedding for words',
required=True)
args_parser.add_argument('--word_path',
help='path for word embedding dict')
args_parser.add_argument('--char_embedding',
choices=['random', 'polyglot'],
help='Embedding for characters',
required=True)
args_parser.add_argument('--char_path',
help='path for character embedding dict')
args_parser.add_argument(
'--train') # "data/POS-penn/wsj/split1/wsj1.train.original"
args_parser.add_argument(
'--dev') # "data/POS-penn/wsj/split1/wsj1.dev.original"
args_parser.add_argument(
'--test') # "data/POS-penn/wsj/split1/wsj1.test.original"
args_parser.add_argument('--model_path',
help='path for saving model file.',
required=True)
args_parser.add_argument('--model_name',
help='name for saving model file.',
required=True)
args = args_parser.parse_args()
logger = get_logger("PtrParser")
mode = args.mode
train_path = args.train
dev_path = args.dev
test_path = args.test
model_path = args.model_path
model_name = args.model_name
num_epochs = args.num_epochs
batch_size = args.batch_size
hidden_size = args.hidden_size
arc_space = args.arc_space
type_space = args.type_space
num_layers = args.num_layers
num_filters = args.num_filters
learning_rate = args.learning_rate
opt = args.opt
momentum = 0.9
betas = (0.9, 0.9)
rho = 0.9
eps = 1e-6
decay_rate = args.decay_rate
clip = args.clip
gamma = args.gamma
cov = args.coverage
schedule = args.schedule
p_rnn = tuple(args.p_rnn)
p_in = args.p_in
p_out = args.p_out
unk_replace = args.unk_replace
prior_order = args.prior_order
beam = args.beam
punctuation = args.punctuation
word_embedding = args.word_embedding
word_path = args.word_path
char_embedding = args.char_embedding
char_path = args.char_path
pos_dim = args.pos_dim
word_dict, word_dim = utils.load_embedding_dict(word_embedding, word_path)
char_dict = None
char_dim = args.char_dim
if char_embedding != 'random':
char_dict, char_dim = utils.load_embedding_dict(
char_embedding, char_path)
logger.info("Creating Alphabets")
alphabet_path = os.path.join(model_path, 'alphabets/')
model_name = os.path.join(model_path, model_name)
word_alphabet, char_alphabet, pos_alphabet, type_alphabet = conllx_stacked_data.create_alphabets(
alphabet_path,
train_path,
data_paths=[dev_path, test_path],
max_vocabulary_size=50000,
embedd_dict=word_dict)
num_words = word_alphabet.size()
num_chars = char_alphabet.size()
num_pos = pos_alphabet.size()
num_types = type_alphabet.size()
logger.info("Word Alphabet Size: %d" % num_words)
logger.info("Character Alphabet Size: %d" % num_chars)
logger.info("POS Alphabet Size: %d" % num_pos)
logger.info("Type Alphabet Size: %d" % num_types)
logger.info("Reading Data")
use_gpu = torch.cuda.is_available()
data_train = conllx_stacked_data.read_stacked_data_to_variable(
train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
prior_order=prior_order)
num_data = sum(data_train[1])
data_dev = conllx_stacked_data.read_stacked_data_to_variable(
dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
data_test = conllx_stacked_data.read_stacked_data_to_variable(
test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
type_alphabet,
use_gpu=use_gpu,
volatile=True,
prior_order=prior_order)
punct_set = None
if punctuation is not None:
punct_set = set(punctuation)
logger.info("punctuations(%d): %s" %
(len(punct_set), ' '.join(punct_set)))
def construct_word_embedding_table():
scale = np.sqrt(3.0 / word_dim)
table = np.empty([word_alphabet.size(), word_dim], dtype=np.float32)
table[conllx_stacked_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, word_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if word in word_dict:
embedding = word_dict[word]
elif word.lower() in word_dict:
embedding = word_dict[word.lower()]
else:
embedding = np.random.uniform(-scale, scale,
[1, word_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('word OOV: %d' % oov)
return torch.from_numpy(table)
def construct_char_embedding_table():
if char_dict is None:
return None
scale = np.sqrt(3.0 / char_dim)
table = np.empty([num_chars, char_dim], dtype=np.float32)
table[conllx_stacked_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, char_dim]).astype(np.float32)
oov = 0
for char, index, in char_alphabet.items():
if char in char_dict:
embedding = char_dict[char]
else:
embedding = np.random.uniform(-scale, scale,
[1, char_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('character OOV: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
char_table = construct_char_embedding_table()
window = 3
network = StackPtrNet(word_dim,
num_words,
char_dim,
num_chars,
pos_dim,
num_pos,
num_filters,
window,
mode,
hidden_size,
num_layers,
num_types,
arc_space,
type_space,
embedd_word=word_table,
embedd_char=char_table,
p_in=p_in,
p_out=p_out,
p_rnn=p_rnn,
biaffine=True,
prior_order=prior_order)
if use_gpu:
network.cuda()
pred_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
gold_writer = CoNLLXWriter(word_alphabet, char_alphabet, pos_alphabet,
type_alphabet)
def generate_optimizer(opt, lr, params):
if opt == 'adam':
return Adam(params,
lr=lr,
betas=betas,
weight_decay=gamma,
eps=eps)
elif opt == 'sgd':
return SGD(params,
lr=lr,
momentum=momentum,
weight_decay=gamma,
nesterov=True)
elif opt == 'adadelta':
return Adadelta(params,
lr=lr,
rho=rho,
weight_decay=gamma,
eps=eps)
else:
raise ValueError('Unknown optimization algorithm: %s' % opt)
lr = learning_rate
optim = generate_optimizer(opt, lr, network.parameters())
opt_info = 'opt: %s, ' % opt
if opt == 'adam':
opt_info += 'betas=%s, eps=%.1e' % (betas, eps)
elif opt == 'sgd':
opt_info += 'momentum=%.2f' % momentum
elif opt == 'adadelta':
opt_info += 'rho=%.2f, eps=%.1e' % (rho, eps)
logger.info("Embedding dim: word=%d, char=%d, pos=%d" %
(word_dim, char_dim, pos_dim))
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, arc_space=%d, type_space=%d"
% (mode, num_layers, hidden_size, num_filters, arc_space, type_space))
logger.info(
"train: cov: %.1f, (#data: %d, batch: %d, clip: %.2f, dropout(in, out, rnn): (%.2f, %.2f, %s), unk_repl: %.2f)"
% (cov, num_data, batch_size, clip, p_in, p_out, p_rnn, unk_replace))
logger.info('prior order: %s, beam: %d' % (prior_order, beam))
logger.info(opt_info)
num_batches = num_data / batch_size + 1
dev_ucorrect = 0.0
dev_lcorrect = 0.0
dev_ucomlpete_match = 0.0
dev_lcomplete_match = 0.0
dev_ucorrect_nopunc = 0.0
dev_lcorrect_nopunc = 0.0
dev_ucomlpete_match_nopunc = 0.0
dev_lcomplete_match_nopunc = 0.0
dev_root_correct = 0.0
best_epoch = 0
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_root_correct = 0.0
test_total = 0
test_total_nopunc = 0
test_total_inst = 0
test_total_root = 0
patient = 0
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s, optim: %s, learning rate=%.6f, decay rate=%.2f (schedule=%d, patient=%d)): '
% (epoch, mode, opt, lr, decay_rate, schedule, patient))
train_err_arc_leaf = 0.
train_err_arc_non_leaf = 0.
train_err_type_leaf = 0.
train_err_type_non_leaf = 0.
train_err_cov = 0.
train_total_leaf = 0.
train_total_non_leaf = 0.
start_time = time.time()
num_back = 0
network.train()
for batch in range(1, num_batches + 1):
input_encoder, input_decoder = conllx_stacked_data.get_batch_stacked_variable(
data_train, batch_size, unk_replace=unk_replace)
word, char, pos, heads, types, masks_e, lengths_e = input_encoder
stacked_heads, children, stacked_types, masks_d, lengths_d = input_decoder
optim.zero_grad()
loss_arc_leaf, loss_arc_non_leaf, \
loss_type_leaf, loss_type_non_leaf, \
loss_cov, num_leaf, num_non_leaf = network.loss(word, char, pos, stacked_heads, children, stacked_types,
mask_e=masks_e, length_e=lengths_e, mask_d=masks_d, length_d=lengths_d)
loss_arc = loss_arc_leaf + loss_arc_non_leaf
loss_type = loss_type_leaf + loss_type_non_leaf
loss = loss_arc + loss_type + cov * loss_cov
loss.backward()
clip_grad_norm(network.parameters(), clip)
optim.step()
num_leaf = num_leaf.data[0]
num_non_leaf = num_non_leaf.data[0]
train_err_arc_leaf += loss_arc_leaf.data[0] * num_leaf
train_err_arc_non_leaf += loss_arc_non_leaf.data[0] * num_non_leaf
train_err_type_leaf += loss_type_leaf.data[0] * num_leaf
train_err_type_non_leaf += loss_type_non_leaf.data[0] * num_non_leaf
train_err_cov += loss_cov.data[0] * (num_leaf + num_non_leaf)
train_total_leaf += num_leaf
train_total_non_leaf += num_non_leaf
time_ave = (time.time() - start_time) / batch
time_left = (num_batches - batch) * time_ave
# update log
if batch % 10 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf +
train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
log_info = 'train: %d/%d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time left (estimated): %.2fs' % (
batch, num_batches, err, err_arc, err_arc_leaf,
err_arc_non_leaf, err_type, err_type_leaf,
err_type_non_leaf, err_cov, time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
err_arc_leaf = train_err_arc_leaf / train_total_leaf
err_arc_non_leaf = train_err_arc_non_leaf / train_total_non_leaf
err_arc = err_arc_leaf + err_arc_non_leaf
err_type_leaf = train_err_type_leaf / train_total_leaf
err_type_non_leaf = train_err_type_non_leaf / train_total_non_leaf
err_type = err_type_leaf + err_type_non_leaf
err_cov = train_err_cov / (train_total_leaf + train_total_non_leaf)
err = err_arc + err_type + cov * err_cov
print(
'train: %d loss (leaf, non_leaf): %.4f, arc: %.4f (%.4f, %.4f), type: %.4f (%.4f, %.4f), coverage: %.4f, time: %.2fs'
% (num_batches, err, err_arc, err_arc_leaf, err_arc_non_leaf,
err_type, err_type_leaf, err_type_non_leaf, err_cov,
time.time() - start_time))
# evaluate performance on dev data
network.eval()
pred_filename = 'tmp/%spred_dev%d' % (str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = 'tmp/%sgold_dev%d' % (str(uid), epoch)
gold_writer.start(gold_filename)
dev_ucorr = 0.0
dev_lcorr = 0.0
dev_total = 0
dev_ucomlpete = 0.0
dev_lcomplete = 0.0
dev_ucorr_nopunc = 0.0
dev_lcorr_nopunc = 0.0
dev_total_nopunc = 0
dev_ucomlpete_nopunc = 0.0
dev_lcomplete_nopunc = 0.0
dev_root_corr = 0.0
dev_total_root = 0.0
dev_total_inst = 0.0
for batch in conllx_stacked_data.iterate_batch_stacked_variable(
data_dev, batch_size):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(word,
pos,
heads,
types,
lengths,
symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser.eval(
word,
pos,
heads_pred,
types_pred,
heads,
types,
word_alphabet,
pos_alphabet,
lengths,
punct_set=punct_set,
symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
dev_ucorr += ucorr
dev_lcorr += lcorr
dev_total += total
dev_ucomlpete += ucm
dev_lcomplete += lcm
dev_ucorr_nopunc += ucorr_nopunc
dev_lcorr_nopunc += lcorr_nopunc
dev_total_nopunc += total_nopunc
dev_ucomlpete_nopunc += ucm_nopunc
dev_lcomplete_nopunc += lcm_nopunc
dev_root_corr += corr_root
dev_total_root += total_root
dev_total_inst += num_inst
pred_writer.close()
gold_writer.close()
print(
'W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%'
% (dev_ucorr, dev_lcorr, dev_total, dev_ucorr * 100 / dev_total,
dev_lcorr * 100 / dev_total, dev_ucomlpete * 100 /
dev_total_inst, dev_lcomplete * 100 / dev_total_inst))
print(
'Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%%'
% (dev_ucorr_nopunc, dev_lcorr_nopunc, dev_total_nopunc,
dev_ucorr_nopunc * 100 / dev_total_nopunc, dev_lcorr_nopunc *
100 / dev_total_nopunc, dev_ucomlpete_nopunc * 100 /
dev_total_inst, dev_lcomplete_nopunc * 100 / dev_total_inst))
print('Root: corr: %d, total: %d, acc: %.2f%%' %
(dev_root_corr, dev_total_root,
dev_root_corr * 100 / dev_total_root))
if dev_ucorrect_nopunc <= dev_ucorr_nopunc:
dev_ucorrect_nopunc = dev_ucorr_nopunc
dev_lcorrect_nopunc = dev_lcorr_nopunc
dev_ucomlpete_match_nopunc = dev_ucomlpete_nopunc
dev_lcomplete_match_nopunc = dev_lcomplete_nopunc
dev_ucorrect = dev_ucorr
dev_lcorrect = dev_lcorr
dev_ucomlpete_match = dev_ucomlpete
dev_lcomplete_match = dev_lcomplete
dev_root_correct = dev_root_corr
best_epoch = epoch
patient = 0
torch.save(network, model_name)
pred_filename = 'tmp/%spred_test%d' % (str(uid), epoch)
pred_writer.start(pred_filename)
gold_filename = 'tmp/%sgold_test%d' % (str(uid), epoch)
gold_writer.start(gold_filename)
test_ucorrect = 0.0
test_lcorrect = 0.0
test_ucomlpete_match = 0.0
test_lcomplete_match = 0.0
test_total = 0
test_ucorrect_nopunc = 0.0
test_lcorrect_nopunc = 0.0
test_ucomlpete_match_nopunc = 0.0
test_lcomplete_match_nopunc = 0.0
test_total_nopunc = 0
test_total_inst = 0
test_root_correct = 0.0
test_total_root = 0
for batch in conllx_stacked_data.iterate_batch_stacked_variable(
data_test, batch_size):
input_encoder, _ = batch
word, char, pos, heads, types, masks, lengths = input_encoder
heads_pred, types_pred, _, _ = network.decode(
word,
char,
pos,
mask=masks,
length=lengths,
beam=beam,
leading_symbolic=conllx_stacked_data.NUM_SYMBOLIC_TAGS)
word = word.data.cpu().numpy()
pos = pos.data.cpu().numpy()
lengths = lengths.cpu().numpy()
heads = heads.data.cpu().numpy()
types = types.data.cpu().numpy()
pred_writer.write(word,
pos,
heads_pred,
types_pred,
lengths,
symbolic_root=True)
gold_writer.write(word,
pos,
heads,
types,
lengths,
symbolic_root=True)
stats, stats_nopunc, stats_root, num_inst = parser.eval(
word,
pos,
heads_pred,
types_pred,
heads,
types,
word_alphabet,
pos_alphabet,
lengths,
punct_set=punct_set,
symbolic_root=True)
ucorr, lcorr, total, ucm, lcm = stats
ucorr_nopunc, lcorr_nopunc, total_nopunc, ucm_nopunc, lcm_nopunc = stats_nopunc
corr_root, total_root = stats_root
test_ucorrect += ucorr
test_lcorrect += lcorr
test_total += total
test_ucomlpete_match += ucm
test_lcomplete_match += lcm
test_ucorrect_nopunc += ucorr_nopunc
test_lcorrect_nopunc += lcorr_nopunc
test_total_nopunc += total_nopunc
test_ucomlpete_match_nopunc += ucm_nopunc
test_lcomplete_match_nopunc += lcm_nopunc
test_root_correct += corr_root
test_total_root += total_root
test_total_inst += num_inst
pred_writer.close()
gold_writer.close()
else:
if patient < schedule:
patient += 1
else:
network = torch.load(model_name)
lr = lr * decay_rate
optim = generate_optimizer(opt, lr, network.parameters())
patient = 0
print(
'----------------------------------------------------------------------------------------------------------------------------'
)
print(
'best dev W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
% (dev_ucorrect, dev_lcorrect, dev_total,
dev_ucorrect * 100 / dev_total, dev_lcorrect * 100 / dev_total,
dev_ucomlpete_match * 100 / dev_total_inst,
dev_lcomplete_match * 100 / dev_total_inst, best_epoch))
print(
'best dev Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
% (dev_ucorrect_nopunc, dev_lcorrect_nopunc, dev_total_nopunc,
dev_ucorrect_nopunc * 100 / dev_total_nopunc,
dev_lcorrect_nopunc * 100 / dev_total_nopunc,
dev_ucomlpete_match_nopunc * 100 / dev_total_inst,
dev_lcomplete_match_nopunc * 100 / dev_total_inst, best_epoch))
print('best dev Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' %
(dev_root_correct, dev_total_root,
dev_root_correct * 100 / dev_total_root, best_epoch))
print(
'----------------------------------------------------------------------------------------------------------------------------'
)
print(
'best test W. Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
% (test_ucorrect, test_lcorrect, test_total, test_ucorrect * 100 /
test_total, test_lcorrect * 100 / test_total,
test_ucomlpete_match * 100 / test_total_inst,
test_lcomplete_match * 100 / test_total_inst, best_epoch))
print(
'best test Wo Punct: ucorr: %d, lcorr: %d, total: %d, uas: %.2f%%, las: %.2f%%, ucm: %.2f%%, lcm: %.2f%% (epoch: %d)'
%
(test_ucorrect_nopunc, test_lcorrect_nopunc, test_total_nopunc,
test_ucorrect_nopunc * 100 / test_total_nopunc,
test_lcorrect_nopunc * 100 / test_total_nopunc,
test_ucomlpete_match_nopunc * 100 / test_total_inst,
test_lcomplete_match_nopunc * 100 / test_total_inst, best_epoch))
print('best test Root: corr: %d, total: %d, acc: %.2f%% (epoch: %d)' %
(test_root_correct, test_total_root,
test_root_correct * 100 / test_total_root, best_epoch))
print(
'============================================================================================================================'
)
def train(self):
if self.T - self.target_sync_T > self.args.target:
self.sync_target_network()
self.target_sync_T = self.T
info = {}
for _ in range(self.args.iters):
self.dqn.eval()
# TODO: Use a named tuple for experience replay
n_step_sample = self.args.n_step
batch, indices, is_weights = self.replay.Sample_N(self.args.batch_size, n_step_sample, self.args.gamma)
columns = list(zip(*batch))
states = Variable(torch.from_numpy(np.array(columns[0])).float().transpose_(1, 3))
actions = Variable(torch.LongTensor(columns[1]))
terminal_states = Variable(torch.FloatTensor(columns[5]))
rewards = Variable(torch.FloatTensor(columns[2]))
# Have to clip rewards for DQN
rewards = torch.clamp(rewards, -1, 1)
steps = Variable(torch.FloatTensor(columns[4]))
new_states = Variable(torch.from_numpy(np.array(columns[3])).float().transpose_(1, 3))
target_dqn_qvals = self.target_dqn(new_states).cpu()
# Make a new variable with those values so that these are treated as constants
target_dqn_qvals_data = Variable(target_dqn_qvals.data)
q_value_targets = (Variable(torch.ones(terminal_states.size()[0])) - terminal_states)
inter = Variable(torch.ones(terminal_states.size()[0]) * self.args.gamma)
# print(steps)
q_value_targets = q_value_targets * torch.pow(inter, steps)
if self.args.double:
# Double Q Learning
new_states_qvals = self.dqn(new_states).cpu()
new_states_qvals_data = Variable(new_states_qvals.data)
q_value_targets = q_value_targets * target_dqn_qvals_data.gather(1, new_states_qvals_data.max(1)[1])
else:
q_value_targets = q_value_targets * target_dqn_qvals_data.max(1)[0]
q_value_targets = q_value_targets + rewards
self.dqn.train()
one_hot_actions = torch.zeros(self.args.batch_size, self.args.actions)
for i in range(self.args.batch_size):
one_hot_actions[i][actions[i].data] = 1
if self.args.gpu:
actions = actions.cuda()
one_hot_actions = one_hot_actions.cuda()
q_value_targets = q_value_targets.cuda()
new_states = new_states.cuda()
model_predictions_q_vals, model_predictions_state = self.dqn(states, Variable(one_hot_actions))
model_predictions = model_predictions_q_vals.gather(1, actions.view(-1, 1))
# info = {}
td_error = model_predictions - q_value_targets
info["TD_Error"] = td_error.mean().data[0]
# Update the priorities
if not self.args.density_priority:
self.replay.Update_Indices(indices, td_error.cpu().data.numpy(), no_pseudo_in_priority=self.args.count_td_priority)
# If using prioritised we need to weight the td_error
if self.args.prioritized and self.args.prioritized_is:
# print(td_error)
weights_tensor = torch.from_numpy(is_weights).float()
weights_tensor = Variable(weights_tensor)
if self.args.gpu:
weights_tensor = weights_tensor.cuda()
# print(weights_tensor)
td_error = td_error * weights_tensor
# Model 1 step state transition error
# Save them every x steps
if self.T % self.args.model_save_image == 0:
os.makedirs("{}/transition_model/{}".format(self.args.log_path, self.T))
for ii, image, action, next_state, current_state in zip(range(self.args.batch_size), model_predictions_state.cpu().data, actions.data, new_states.cpu().data, states.cpu().data):
image = image.numpy()[0]
image = np.clip(image, 0, 1)
# print(next_state)
next_state = next_state.numpy()[0]
current_state = current_state.numpy()[0]
black_bars = np.zeros_like(next_state[:1, :])
# print(black_bars.shape)
joined_image = np.concatenate((current_state, black_bars, image, black_bars, next_state), axis=0)
joined_image = np.transpose(joined_image)
self.log_image("{}/transition_model/{}/{}_____Action_{}".format(self.args.log_path, self.T, ii + 1, action), joined_image * 255)
# self.log_image("{}/transition_model/{}/{}_____Action_{}".format(self.args.log_path, self.T, ii + 1, action), image * 255)
# self.log_image("{}/transition_model/{}/{}_____Correct".format(self.args.log_path, self.T, ii + 1), next_state * 255)
# print(model_predictions_state)
# Cross Entropy Loss
# TODO
# Regresssion loss
state_error = model_predictions_state - new_states
# state_error_val = state_error.mean().data[0]
info["State_Error"] = state_error.mean().data[0]
self.log("DQN/State_Loss", state_error.mean().data[0], step=self.T)
self.log("DQN/State_Loss_Squared", state_error.pow(2).mean().data[0], step=self.T)
self.log("DQN/State_Loss_Max", state_error.abs().max().data[0], step=self.T)
# self.log("DQN/Action_Matrix_Norm", self.dqn.action_matrix.weight.norm().cpu().data[0], step=self.T)
combined_loss = (1 - self.args.model_loss) * td_error.pow(2).mean() + (self.args.model_loss) * state_error.pow(2).mean()
l2_loss = combined_loss
# l2_loss = (combined_loss).pow(2).mean()
info["Loss"] = l2_loss.data[0]
# Update
self.optimizer.zero_grad()
l2_loss.backward()
# Taken from pytorch clip_grad_norm
# Remove once the pip version it up to date with source
gradient_norm = clip_grad_norm(self.dqn.parameters(), self.args.clip_value)
if gradient_norm is not None:
info["Norm"] = gradient_norm
self.optimizer.step()
if "States" in info:
states_trained = info["States"]
info["States"] = states_trained + columns[0]
else:
info["States"] = columns[0]
# Pad out the states to be of size batch_size
if len(info["States"]) < self.args.batch_size:
old_states = info["States"]
new_states = old_states[0] * (self.args.batch_size - len(old_states))
info["States"] = new_states
return info
def step(self, closure=None):
"""Gradient clipping aware step()."""
if self.gclip is not None and self.gclip > 0:
# print("aaaa")
clip_grad_norm(self.params, self.gclip)
self.optim.step(closure)
def train(train_loader, model, criterion, optimizer, epoch, batch_logger):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
acc = AverageMeter()
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
end = time.time()
for i, (input, target) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(async=True)
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
loss = criterion(output, target_var)
# measure accuracy and record loss
prec = calculate_accuracy(output.data, target)
losses.update(loss.item(), input.size(0))
acc.update(prec, input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print("clipping gradient: {} with coef {}".format(
total_norm, args.clip_gradient / total_norm))
optimizer.step()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
batch_logger.log({
'epoch': epoch,
'batch': i + 1,
'loss': losses.val,
'acc': acc.val,
'lr': optimizer.param_groups[-1]['lr']
})
if i % args.print_freq == 0:
print(('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec {acc.val:.3f} ({acc.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
acc=acc,
lr=optimizer.param_groups[-1]['lr'])))
def model_train(self, epoch_offset=0):
create_dir(MODEL_SAVE_PATH)
loss_for_regression = MSELoss()
img_coors_json = read_json_file(BBOX_XYWH_JSON_PATH)
optimizer = RMSprop(self.parameters(),
lr=LEARNING_RATE,
momentum=MOMENTUM)
# optimizer = Adam(self.parameters(), lr=LEARNING_RATE)
# optimizer = SGD(self.parameters(), lr=LEARNING_RATE, momentum=MOMENTUM)
scheduler = StepLR(optimizer,
step_size=SCHEDULER_STEP,
gamma=SCHEDULER_GAMMA)
for epoch in range(EPOCHS):
epoch_loss = 0.0
scheduler.step(epoch)
LOGGER.debug('Epoch: %s, Current Learning Rate: %s',
str(epoch + epoch_offset), str(scheduler.get_lr()))
for image, coors in img_coors_json.items():
path_of_image = NORMALISED_IMAGES_PATH + image
path_of_image = path_of_image.replace('%', '_')
img = cv2.imread(path_of_image)
img = torch.tensor(img).float().permute(2, 0, 1).unsqueeze(0)
img = img.to(self.device)
predicted_width, predicted_height, predicted_midpoint = self.forward(
img)
#all are scaled
mp_x = coors[0][0]
mp_y = coors[0][1]
mp = torch.cat((torch.tensor([[mp_x]]).to(
self.device), torch.tensor([[mp_y]]).to(self.device)),
dim=1).float()
w = coors[0][2]
h = coors[0][3]
loss1 = loss_for_regression(
predicted_height,
torch.tensor([[h]]).float().to(self.device))
loss2 = loss_for_regression(
predicted_width,
torch.tensor([[w]]).float().to(self.device))
loss3 = loss_for_regression(predicted_midpoint,
mp.to(self.device))
loss = loss1 + loss2 + loss3 / 2
optimizer.zero_grad()
loss.backward()
clip_grad_norm(self.parameters(), 0.5)
optimizer.step()
epoch_loss = epoch_loss + loss.item()
if epoch % 5 == 0:
print('epoch: ' + str(epoch) + ' ' + 'loss: ' +
str(epoch_loss))
if epoch % EPOCH_SAVE_INTERVAL == 0:
print('saving')
torch.save(
self.state_dict(), MODEL_SAVE_PATH + 'model_epc_' +
str(epoch + epoch_offset) + '.pt')
torch.save(
self.state_dict(),
MODEL_SAVE_PATH + 'model_epc_' + str(epoch + epoch_offset) + '.pt')
logits_v = net(states_v)
log_prob_v = F.log_softmax(logits_v)
log_prob_actions_v = batch_scale_v * log_prob_v[range(BATCH_SIZE), batch_actions_t]
loss_policy_v = -log_prob_actions_v.mean()
loss_policy_v.backward(retain_graph=True)
grads = np.concatenate([p.grad.data.cpu().numpy().flatten()
for p in net.parameters()
if p.grad is not None])
prob_v = F.softmax(logits_v)
entropy_v = -(prob_v * log_prob_v).sum(dim=1).mean()
entropy_loss_v = -ENTROPY_BETA * entropy_v
loss_v = loss_policy_v + entropy_loss_v
loss_v.backward()
nn_utils.clip_grad_norm(net.parameters(), GRAD_L2_CLIP)
optimizer.step()
loss_v += loss_policy_v
# calc KL-div
new_logits_v = net(states_v)
new_prob_v = F.softmax(new_logits_v)
kl_div_v = -((new_prob_v / prob_v).log() * prob_v).sum(dim=1).mean()
writer.add_scalar("kl", kl_div_v.data.cpu().numpy()[0], step_idx)
writer.add_scalar("baseline", baseline, step_idx)
writer.add_scalar("entropy", entropy_v.data.cpu().numpy()[0], step_idx)
writer.add_scalar("batch_scales", np.mean(batch_scales), step_idx)
writer.add_scalar("batch_scales_std", scale_std, step_idx)
writer.add_scalar("loss_entropy", entropy_loss_v.data.cpu().numpy()[0], step_idx)
writer.add_scalar("loss_policy", loss_policy_v.data.cpu().numpy()[0], step_idx)
def train(train_data_loader, net, criterion, optimizer, scheduler, epoch):
net.train()
# loss counters
batch_time = AverageMeter()
losses = AverageMeter()
loc_losses = AverageMeter()
cls_losses = AverageMeter()
#### shuffle ####
xxxx = copy.deepcopy(train_data_loader.dataset.ids)
np.random.shuffle(arr)
iii = 0
for arr_i in arr:
key = keys[arr_i]
rang = my_dict[key]
xxxx[iii:(iii + rang[1] - rang[0])] = xxx[rang[0]:rang[1]]
iii += rang[1] - rang[0]
train_data_loader.dataset.ids = copy.deepcopy(xxxx)
# create batch iterator
batch_iterator = None
iter_count = 0
t0 = time.perf_counter()
for iteration in range(len(train_data_loader)):
if not batch_iterator:
batch_iterator = iter(train_data_loader)
# load train data
images, targets, img_indexs = next(batch_iterator)
if args.cuda:
images = Variable(images.cuda())
targets = [
Variable(anno.cuda(), volatile=True) for anno in targets
]
else:
images = Variable(images)
targets = [Variable(anno, volatile=True) for anno in targets]
# forward
out = net(images, img_indexs)
# backprop
optimizer.zero_grad()
loss_l, loss_c = criterion(out, targets)
loss = loss_l + loss_c
loss.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(net.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print("clipping gradient: {} with coef {}".format(
total_norm, args.clip_gradient / total_norm))
optimizer.step()
if scheduler is not None:
scheduler.step()
loc_loss = loss_l.data[0]
conf_loss = loss_c.data[0]
# print('Loss data type ',type(loc_loss))
loc_losses.update(loc_loss)
cls_losses.update(conf_loss)
losses.update((loc_loss + conf_loss) / 2.0)
if iteration % args.print_step == 0 and iteration > 0:
torch.cuda.synchronize()
t1 = time.perf_counter()
batch_time.update(t1 - t0)
print_line = 'Epoch {:02d}/{:02d} Iteration {:06d}/{:06d} loc-loss {:.3f}({:.3f}) cls-loss {:.3f}({:.3f}) ' \
'average-loss {:.3f}({:.3f}) Timer {:0.3f}({:0.3f}) lr {:0.5f}'.format(
epoch, args.epochs, iteration, len(train_data_loader), loc_losses.val, loc_losses.avg, cls_losses.val,
cls_losses.avg, losses.val, losses.avg, batch_time.val, batch_time.avg, args.lr)
torch.cuda.synchronize()
t0 = time.perf_counter()
log_file.write(print_line + '\n')
print(print_line)
iter_count += 1
if iter_count % args.loss_reset_step == 0 and iter_count > 0:
loc_losses.reset()
cls_losses.reset()
losses.reset()
batch_time.reset()
print('Reset accumulators of ', args.snapshot_pref, ' at',
iter_count * args.print_step)
iter_count = 0
def train(model_str,
train_iter,
val_iter=None,
source_embedding=None,
target_embedding=None,
early_stopping=False,
save=False,
save_path=None,
model_params={},
opt_params={},
train_params={},
cuda=CUDA_DEFAULT):
# Initialize model and other variables
model, criterion, optimizer, scheduler = _train_initialize_variables(model_str, model_params, opt_params, cuda, source_embedding, target_embedding)
val_loss = 1e6
best_val_loss = 1e6
if scheduler is not None:
assert val_iter is not None
scheduler.step(val_loss)
print("All set. Actual Training begins")
for epoch in range(train_params.get('n_ep', 30)):
# Monitoring loss
total_loss = 0
count = 0
# Actual training loop.
for batch in train_iter:
# Get data
source = batch.src.transpose(0, 1) # batch first
target = batch.trg.transpose(0, 1)
if cuda:
source = source.cuda()
target = target.cuda()
# Initialize hidden layer and memory
if model.model_str == 'LSTM': # for LSTMA it is done in the forward because the init of the dec needs the last h of the enc
model.hidden_enc = model.init_hidden('enc', source.size(0))
model.hidden_dec = model.init_hidden('dec', source.size(0))
# zero gradients
optimizer.zero_grad()
model.zero_grad()
# predict
output = model(source, target)
# Dimension matching to cut it right for loss function.
batch_size, sent_length = target.size(0), target.size(1)-1
loss = criterion(output.view(batch_size, -1, sent_length), target[:, 1:]) # remove the first element of target (it is the SOS token)
# Compute gradients, clip, and backprop
loss.backward()
clip_grad_norm(model.parameters(), model_params.get("clip_gradients", 5.))
optimizer.step()
# monitoring
count += t.sum((target.data[:, 1:] != PAD_TOKEN).long()) # in that case there are batch_size x bbp_length classifications per batch, minus the pad tokens
total_loss += t.sum(loss.data) # .data so that you dont keep references
# monitoring
avg_loss = total_loss / count
print("Average loss after %d epochs is %.4f" % (epoch, avg_loss))
if val_iter is not None:
model.eval()
former_val_loss = val_loss * 1.
val_loss = predict(model, val_iter, cuda=cuda)
if scheduler is not None:
scheduler.step(val_loss)
if val_loss > former_val_loss:
if early_stopping:
break
else:
if save and best_val_loss > val_loss: # save only the best
best_val_loss = val_loss * 1.
assert save_path is not None
# weights
save_model(model, save_path + '.pytorch')
# params
with open(save_path + '.params.json', 'w') as fp:
json.dump(model.params, fp)
# loss
with open(save_path + '.losses.txt', 'w') as fp:
fp.write('val: ' + str(val_loss))
fp.write('train: ' + str(avg_loss))
model.train()
return model
def step(self):
# Compute gradients norm.
if self.max_grad_norm:
clip_grad_norm(self.params, self.max_grad_norm)
self.optimizer.step()
def train(train_loader, model, act_criterion, comp_criterion,
regression_criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
act_losses = AverageMeter()
comp_losses = AverageMeter()
reg_losses = AverageMeter()
act_accuracies = AverageMeter()
fg_accuracies = AverageMeter()
bg_accuracies = AverageMeter()
# switch to train mode
model.train()
end = time.time()
optimizer.zero_grad()
ohem_num = train_loader.dataset.fg_per_video
comp_group_size = train_loader.dataset.fg_per_video + train_loader.dataset.incomplete_per_video
for i, (prop_fts, prop_type, prop_labels,
prop_reg_targets) in enumerate(train_loader):
# measure data loading time
data_time.update(time.time() - end)
batch_size = prop_fts[0].size(0)
activity_out, activity_target, activity_prop_type, \
completeness_out, completeness_target, \
regression_out, regression_labels, regression_target = model((prop_fts[0], prop_fts[1]), prop_labels,
prop_reg_targets, prop_type)
act_loss = act_criterion(activity_out, activity_target)
comp_loss = comp_criterion(completeness_out, completeness_target,
ohem_num, comp_group_size)
reg_loss = regression_criterion(regression_out, regression_labels,
regression_target)
loss = act_loss + comp_loss * args.comp_loss_weight + reg_loss * args.reg_loss_weight
losses.update(loss.item(), batch_size)
act_losses.update(act_loss.item(), batch_size)
comp_losses.update(comp_loss.item(), batch_size)
reg_losses.update(reg_loss.item(), batch_size)
act_acc = accuracy(activity_out, activity_target)
act_accuracies.update(act_acc[0].item(), activity_out.size(0))
fg_indexer = (activity_prop_type == 0).nonzero().squeeze()
bg_indexer = (activity_prop_type == 2).nonzero().squeeze()
fg_acc = accuracy(activity_out[fg_indexer, :],
activity_target[fg_indexer])
fg_accuracies.update(fg_acc[0].item(), len(fg_indexer))
if len(bg_indexer) > 0:
bg_acc = accuracy(activity_out[bg_indexer, :],
activity_target[bg_indexer])
bg_accuracies.update(bg_acc[0].item(), len(bg_indexer))
loss.backward()
if i % args.iter_size == 0:
# scale down gradients when iter size is functioning
if args.iter_size != 1:
for g in optimizer.param_groups:
for p in g['params']:
p.grad /= args.iter_size
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(),
args.clip_gradient)
if total_norm > args.clip_gradient:
logger.info("clipping gradient: {} with coef {}".format(
total_norm, args.clip_gradient / total_norm))
else:
total_norm = 0
optimizer.step()
optimizer.zero_grad()
batch_time.update(time.time() - end)
end = time.time()
writer.add_scalar('data/loss', losses.val,
epoch * len(train_loader) + i + 1)
writer.add_scalar('data/Reg_loss', reg_losses.val,
epoch * len(train_loader) + i + 1)
writer.add_scalar('data/Act_loss', act_losses.val,
epoch * len(train_loader) + i + 1)
writer.add_scalar('data/comp_loss', comp_losses.val,
epoch * len(train_loader) + i + 1)
if i % args.print_freq == 0:
logger.info(
'Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Act. Loss {act_losses.val:.3f} ({act_losses.avg: .3f}) \t'
'Comp. Loss {comp_losses.val:.3f} ({comp_losses.avg: .3f}) '.
format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
act_losses=act_losses,
comp_losses=comp_losses,
lr=optimizer.param_groups[0]['lr'],
) +
'\tReg. Loss {reg_loss.val:.3f} ({reg_loss.avg:.3f})'.format(
reg_loss=reg_losses) +
'\n Act. FG {fg_acc.val:.02f} ({fg_acc.avg:.02f}) Act. BG {bg_acc.avg:.02f} ({bg_acc.avg:.02f})'
.format(act_acc=act_accuracies,
fg_acc=fg_accuracies,
bg_acc=bg_accuracies))
def train(train_loader, model, criterion, optimizer, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
cls_losses = AverageMeter()
res_losses = AverageMeter()
losses = AverageMeter()
model.train()
end = time.time()
optimizer.zero_grad()
for i, (feats, labels, start_offsets,
end_offsets) in enumerate(train_loader):
data_time.update(time.time() - end)
input_feats = torch.autograd.Variable(feats).cuda()
input_labels = torch.autograd.Variable(labels).cuda()
start_offsets = torch.autograd.Variable(start_offsets).cuda().float()
end_offsets = torch.autograd.Variable(end_offsets).cuda().float()
pred_labels = model(input_feats)
cls_loss = criterion[0](pred_labels[:, :2], input_labels)
res_loss = criterion[1](pred_labels[:, 2:], input_labels.float(),
start_offsets, end_offsets)
cls_losses.update(cls_loss.cpu().item(), feats.size(0))
res_losses.update(res_loss.cpu().item(), torch.sum(labels))
loss = cls_loss + args.lambda_reg * res_loss
losses.update(loss.cpu().item(), feats.size(0))
# compute gradient and do SGD step
loss.backward()
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
if total_norm > args.clip_gradient:
print('Clipping gradient: {} with coef {}'.format(
total_norm, args.clip_gradient / total_norm))
optimizer.step()
optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print(
'Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\n'
'Classification Loss {cls_loss.val:.4f} ({cls_loss.avg:.4f})\t'
'Regression Loss {res_loss.val:.4f} ({res_loss.avg:.4f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\n'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
cls_loss=cls_losses,
res_loss=res_losses,
lr=optimizer.param_groups[0]['lr']))
# Load batch
progress, notFinished, batch_input, batch_metadata, batch_labels = get_batch_data(args.batch_size,
pick_data)
if not notFinished:
break
# zero the parameter gradients
optimizer.zero_grad()
# Run neural net + Calculate Loss
outputs = net(Variable(batch_input), Variable(batch_metadata)).cuda()
loss = criterion(outputs, Variable(batch_labels))
# Backprop
loss.backward()
nnutils.clip_grad_norm(net.parameters(), 1.0)
optimizer.step()
# Update progress bar
pb.animate(progress)
batch_counter += 1
sum_counter += 1
sum += loss.data[0]
if sum_counter == 80:
log_file.write(
'\n' + str(batch_counter) + ',' + str(sum / sum_counter))
log_file.flush()
sum = 0
sum_counter = 0
def train(train_iter, dev_iter, test_iter, model, args):
if args.cuda:
model.cuda()
torch.cuda.manual_seed(hyperparams.seed_num)
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=1e-8)
# optimizer = torch.optim.Adam(model.parameters(), lr=args.lr, weight_decay=args.init_weight_decay)
# optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,momentum=)
# optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=args.lr)
if args.Adam is True:
print("Adam Training......")
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.init_weight_decay)
elif args.SGD is True:
print("SGD Training.......")
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
weight_decay=args.init_weight_decay,
momentum=args.momentum_value)
elif args.Adadelta is True:
print("Adadelta Training.......")
optimizer = torch.optim.Adadelta(model.parameters(),
lr=args.lr,
weight_decay=args.init_weight_decay)
# lambda1 = lambda epoch: epoch // 30
# lambda2 = lambda epoch: 0.99 ** epoch
# print("lambda1 {} lambda2 {} ".format(lambda1, lambda2))
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=[lambda2])
# scheduler = lr_scheduler.StepLR(optimizer, step_size=3, gamma=0.9)
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
steps = 0
epoch_step = 0
model_count = 0
model.train()
for epoch in range(1, args.epochs + 1):
print("\n## 第{} 轮迭代，共计迭代 {} 次 ！##\n".format(epoch, args.epochs))
# scheduler.step()
# print("now lr is {} \n".format(scheduler.get_lr()))
print("now lr is {} \n".format(optimizer.param_groups[0].get("lr")))
for batch in train_iter:
feature, target = batch.text, batch.label
feature.data.t_(), target.data.sub_(1) # batch first, index align
if args.cuda:
feature, target = feature.cuda(), target.cuda()
optimizer.zero_grad()
model.zero_grad()
logit = model(feature)
loss = F.cross_entropy(logit, target)
loss.backward()
if args.init_clip_max_norm is not None:
utils.clip_grad_norm(model.parameters(),
max_norm=args.init_clip_max_norm)
optimizer.step()
steps += 1
if steps % args.log_interval == 0:
train_size = len(train_iter.dataset)
corrects = (torch.max(logit, 1)[1].view(
target.size()).data == target.data).sum()
accuracy = float(corrects) / batch.batch_size * 100.0
sys.stdout.write(
'\rBatch[{}/{}] - loss: {:.6f} acc: {:.4f}%({}/{})'.
format(steps, train_size, loss.data[0], accuracy, corrects,
batch.batch_size))
if steps % args.test_interval == 0:
eval(dev_iter, model, args)
if steps % args.save_interval == 0:
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
save_prefix = os.path.join(args.save_dir, 'snapshot')
save_path = '{}_steps{}.pt'.format(save_prefix, steps)
torch.save(model, save_path)
print("\n", save_path, end=" ")
test_model = torch.load(save_path)
model_count += 1
test_eval(test_iter, test_model, save_path, args, model_count)
return model_count
def main():
parser = argparse.ArgumentParser(
description='Tuning with Multitask bi-directional RNN-CNN-CRF')
parser.add_argument('--config',
help='Config file (Python file format)',
default="config_multitask.py")
parser.add_argument('--grid', help='Grid Search Options', default="{}")
args = parser.parse_args()
logger = get_logger("Multi-Task")
use_gpu = torch.cuda.is_available()
# Config Tensorboard Writer
log_writer = SummaryWriter()
# Load from config file
spec = importlib.util.spec_from_file_location("config", args.config)
config_module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(config_module)
config = config_module.entries
# Load options from grid search
options = eval(args.grid)
for k, v in options.items():
if isinstance(v, six.string_types):
cmd = "%s = \"%s\"" % (k, v)
else:
cmd = "%s = %s" % (k, v)
log_writer.add_scalar(k, v, 1)
exec(cmd)
# Load embedding dict
embedding = config.embedding.embedding_type
embedding_path = config.embedding.embedding_dict
embedd_dict, embedd_dim = utils.load_embedding_dict(
embedding, embedding_path)
# Collect data path
data_dir = config.data.data_dir
data_names = config.data.data_names
train_paths = [
os.path.join(data_dir, data_name, "train.tsv")
for data_name in data_names
]
dev_paths = [
os.path.join(data_dir, data_name, "devel.tsv")
for data_name in data_names
]
test_paths = [
os.path.join(data_dir, data_name, "test.tsv")
for data_name in data_names
]
# Create alphabets
logger.info("Creating Alphabets")
if not os.path.exists('tmp'):
os.mkdir('tmp')
word_alphabet, char_alphabet, pos_alphabet, chunk_alphabet, ner_alphabet, ner_alphabet_task, label_reflect = \
bionlp_data.create_alphabets(os.path.join(Path(data_dir).abspath(), "alphabets", "_".join(data_names)), train_paths,
data_paths=dev_paths + test_paths, use_cache=True,
embedd_dict=embedd_dict, max_vocabulary_size=50000)
logger.info("Word Alphabet Size: %d" % word_alphabet.size())
logger.info("Character Alphabet Size: %d" % char_alphabet.size())
logger.info("POS Alphabet Size: %d" % pos_alphabet.size())
logger.info("Chunk Alphabet Size: %d" % chunk_alphabet.size())
logger.info("NER Alphabet Size: %d" % ner_alphabet.size())
logger.info(
"NER Alphabet Size per Task: %s",
str([task_alphabet.size() for task_alphabet in ner_alphabet_task]))
#task_reflects = torch.LongTensor(reverse_reflect(label_reflect, ner_alphabet.size()))
#if use_gpu:
# task_reflects = task_reflects.cuda()
if embedding == 'elmo':
logger.info("Loading ELMo Embedder")
ee = ElmoEmbedder(options_file=config.embedding.elmo_option,
weight_file=config.embedding.elmo_weight,
cuda_device=config.embedding.elmo_cuda)
else:
ee = None
logger.info("Reading Data")
# Prepare dataset
data_trains = [
bionlp_data.read_data_to_variable(train_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet_task[task_id],
use_gpu=use_gpu,
elmo_ee=ee)
for task_id, train_path in enumerate(train_paths)
]
num_data = [sum(data_train[1]) for data_train in data_trains]
num_labels = ner_alphabet.size()
num_labels_task = [task_item.size() for task_item in ner_alphabet_task]
data_devs = [
bionlp_data.read_data_to_variable(dev_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet_task[task_id],
use_gpu=use_gpu,
volatile=True,
elmo_ee=ee)
for task_id, dev_path in enumerate(dev_paths)
]
data_tests = [
bionlp_data.read_data_to_variable(test_path,
word_alphabet,
char_alphabet,
pos_alphabet,
chunk_alphabet,
ner_alphabet_task[task_id],
use_gpu=use_gpu,
volatile=True,
elmo_ee=ee)
for task_id, test_path in enumerate(test_paths)
]
writer = BioNLPWriter(word_alphabet, char_alphabet, pos_alphabet,
chunk_alphabet, ner_alphabet)
def construct_word_embedding_table():
scale = np.sqrt(3.0 / embedd_dim)
table = np.empty([word_alphabet.size(), embedd_dim], dtype=np.float32)
table[bionlp_data.UNK_ID, :] = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov = 0
for word, index in word_alphabet.items():
if not embedd_dict == None and word in embedd_dict:
embedding = embedd_dict[word]
elif not embedd_dict == None and word.lower() in embedd_dict:
embedding = embedd_dict[word.lower()]
else:
embedding = np.random.uniform(
-scale, scale, [1, embedd_dim]).astype(np.float32)
oov += 1
table[index, :] = embedding
print('oov: %d' % oov)
return torch.from_numpy(table)
word_table = construct_word_embedding_table()
logger.info("constructing network...")
# Construct network
window = 3
num_layers = 1
mode = config.rnn.mode
hidden_size = config.rnn.hidden_size
char_dim = config.rnn.char_dim
num_filters = config.rnn.num_filters
tag_space = config.rnn.tag_space
bigram = config.rnn.bigram
attention_mode = config.rnn.attention
if config.rnn.dropout == 'std':
network = MultiTaskBiRecurrentCRF(
len(data_trains),
embedd_dim,
word_alphabet.size(),
char_dim,
char_alphabet.size(),
num_filters,
window,
mode,
hidden_size,
num_layers,
num_labels,
num_labels_task=num_labels_task,
tag_space=tag_space,
embedd_word=word_table,
p_in=config.rnn.p,
p_rnn=config.rnn.p,
bigram=bigram,
elmo=(embedding == 'elmo'),
attention_mode=attention_mode,
adv_loss_coef=config.multitask.adv_loss_coef,
diff_loss_coef=config.multitask.diff_loss_coef,
char_level_rnn=config.rnn.char_level_rnn)
else:
raise NotImplementedError
if use_gpu:
network.cuda()
# Prepare training
unk_replace = config.embedding.unk_replace
num_epochs = config.training.num_epochs
batch_size = config.training.batch_size
lr = config.training.learning_rate
momentum = config.training.momentum
alpha = config.training.alpha
lr_decay = config.training.lr_decay
schedule = config.training.schedule
gamma = config.training.gamma
# optim = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=gamma, nesterov=True)
optim = RMSprop(network.parameters(),
lr=lr,
alpha=alpha,
momentum=momentum,
weight_decay=gamma)
logger.info(
"Network: %s, num_layer=%d, hidden=%d, filter=%d, tag_space=%d, crf=%s"
% (mode, num_layers, hidden_size, num_filters, tag_space,
'bigram' if bigram else 'unigram'))
logger.info(
"training: l2: %f, (#training data: %s, batch: %d, dropout: %.2f, unk replace: %.2f)"
% (gamma, num_data, batch_size, config.rnn.p, unk_replace))
num_batches = [x // batch_size + 1 for x in num_data]
dev_f1 = [0.0 for x in num_data]
dev_acc = [0.0 for x in num_data]
dev_precision = [0.0 for x in num_data]
dev_recall = [0.0 for x in num_data]
test_f1 = [0.0 for x in num_data]
test_acc = [0.0 for x in num_data]
test_precision = [0.0 for x in num_data]
test_recall = [0.0 for x in num_data]
best_epoch = [0 for x in num_data]
# Training procedure
for epoch in range(1, num_epochs + 1):
print(
'Epoch %d (%s(%s), learning rate=%.4f, decay rate=%.4f (schedule=%d)): '
% (epoch, mode, config.rnn.dropout, lr, lr_decay, schedule))
train_err = 0.
train_total = 0.
# Gradient decent on training data
start_time = time.time()
num_back = 0
network.train()
batch_count = 0
for batch in range(1, 2 * num_batches[0] + 1):
r = random.random()
task_id = 0 if r <= 0.5 else random.randint(1, len(num_data) - 1)
#if batch > num_batches[task_id]:
# batch = batch % num_batches[task_id] + 1
batch_count += 1
word, char, _, _, labels, masks, lengths, elmo_embedding = bionlp_data.get_batch_variable(
data_trains[task_id], batch_size, unk_replace=unk_replace)
optim.zero_grad()
loss, task_loss, adv_loss, diff_loss = network.loss(
task_id,
word,
char,
labels,
mask=masks,
elmo_word=elmo_embedding)
#log_writer.add_scalars(
# 'train_loss_task' + str(task_id),
# {'all_loss': loss, 'task_loss': task_loss, 'adv_loss': adv_loss, 'diff_loss': diff_loss},
# (epoch - 1) * (num_batches[task_id] + 1) + batch
#)
#log_writer.add_scalars(
# 'train_loss_overview',
# {'all_loss': loss, 'task_loss': task_loss, 'adv_loss': adv_loss, 'diff_loss': diff_loss},
# (epoch - 1) * (sum(num_batches) + 1) + batch_count
#)
loss.backward()
clip_grad_norm(network.parameters(), 5.0)
optim.step()
num_inst = word.size(0)
train_err += loss.data[0] * num_inst
train_total += num_inst
time_ave = (time.time() - start_time) / batch
time_left = (2 * num_batches[0] - batch) * time_ave
# update log
if batch % 100 == 0:
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
log_info = 'train: %d/%d loss: %.4f, time left (estimated): %.2fs' % (
batch, 2 * num_batches[0], train_err / train_total,
time_left)
sys.stdout.write(log_info)
sys.stdout.flush()
num_back = len(log_info)
sys.stdout.write("\b" * num_back)
sys.stdout.write(" " * num_back)
sys.stdout.write("\b" * num_back)
print('train: %d loss: %.4f, time: %.2fs' %
(2 * num_batches[0], train_err / train_total,
time.time() - start_time))
# Evaluate performance on dev data
network.eval()
for task_id in range(len(num_batches)):
tmp_filename = 'tmp/%s_dev%d%d' % (str(uid), epoch, task_id)
writer.start(tmp_filename)
for batch in bionlp_data.iterate_batch_variable(
data_devs[task_id], batch_size):
word, char, pos, chunk, labels, masks, lengths, elmo_embedding = batch
preds, _ = network.decode(
task_id,
word,
char,
target=labels,
mask=masks,
leading_symbolic=bionlp_data.NUM_SYMBOLIC_TAGS,
elmo_word=elmo_embedding)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
acc, precision, recall, f1 = evaluate(tmp_filename)
log_writer.add_scalars(
'dev_task' + str(task_id), {
'accuracy': acc,
'precision': precision,
'recall': recall,
'f1': f1
}, epoch)
print(
'dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%%'
% (acc, precision, recall, f1))
if dev_f1[task_id] < f1:
dev_f1[task_id] = f1
dev_acc[task_id] = acc
dev_precision[task_id] = precision
dev_recall[task_id] = recall
best_epoch[task_id] = epoch
# Evaluate on test data when better performance detected
tmp_filename = 'tmp/%s_test%d%d' % (str(uid), epoch, task_id)
writer.start(tmp_filename)
for batch in bionlp_data.iterate_batch_variable(
data_tests[task_id], batch_size):
word, char, pos, chunk, labels, masks, lengths, elmo_embedding = batch
preds, _ = network.decode(
task_id,
word,
char,
target=labels,
mask=masks,
leading_symbolic=bionlp_data.NUM_SYMBOLIC_TAGS,
elmo_word=elmo_embedding)
writer.write(word.data.cpu().numpy(),
pos.data.cpu().numpy(),
chunk.data.cpu().numpy(),
preds.cpu().numpy(),
labels.data.cpu().numpy(),
lengths.cpu().numpy())
writer.close()
test_acc[task_id], test_precision[task_id], test_recall[
task_id], test_f1[task_id] = evaluate(tmp_filename)
log_writer.add_scalars(
'test_task' + str(task_id), {
'accuracy': test_acc[task_id],
'precision': test_precision[task_id],
'recall': test_recall[task_id],
'f1': test_f1[task_id]
}, epoch)
print(
"================================================================================"
)
print("dataset: %s" % data_names[task_id])
print(
"best dev acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
% (dev_acc[task_id], dev_precision[task_id],
dev_recall[task_id], dev_f1[task_id], best_epoch[task_id]))
print(
"best test acc: %.2f%%, precision: %.2f%%, recall: %.2f%%, F1: %.2f%% (epoch: %d)"
%
(test_acc[task_id], test_precision[task_id],
test_recall[task_id], test_f1[task_id], best_epoch[task_id]))
print(
"================================================================================\n"
)
if epoch % schedule == 0:
# lr = learning_rate / (1.0 + epoch * lr_decay)
# optim = SGD(network.parameters(), lr=lr, momentum=momentum, weight_decay=gamma, nesterov=True)
lr = lr * lr_decay
optim.param_groups[0]['lr'] = lr
# writer.export_scalars_to_json("./all_scalars.json")
writer.close()
def fit_model(model, loss_op, optim_op, train_gen, val_gen, epochs,
checkpoint_path, patience):
""" Analog to Keras fit_generator function.
# Arguments:
model: Model to be finetuned.
loss_op: loss operation (BCEWithLogitsLoss or CrossEntropy for e.g.)
optim_op: optimization operation (Adam e.g.)
train_gen: Training data iterator (DataLoader)
val_gen: Validation data iterator (DataLoader)
epochs: Number of epochs.
checkpoint_path: Filepath where weights will be checkpointed to
during training. This file will be rewritten by the function.
patience: Patience for callback methods.
verbose: Verbosity flag.
# Returns:
Accuracy of the trained model, ONLY if 'evaluate' is set.
"""
# Save original checkpoint
torch.save(model.state_dict(), checkpoint_path)
model.eval()
best_loss = np.mean([calc_loss(loss_op, model(Variable(xv)), Variable(yv)).data.cpu().numpy()[0] for xv, yv in val_gen])
print("original val loss", best_loss)
epoch_without_impr = 0
for epoch in range(epochs):
for i, data in enumerate(train_gen):
X_train, y_train = data
X_train = Variable(X_train, requires_grad=False)
y_train = Variable(y_train, requires_grad=False)
model.train()
optim_op.zero_grad()
output = model(X_train)
loss = calc_loss(loss_op, output, y_train)
loss.backward()
clip_grad_norm(model.parameters(), 1)
optim_op.step()
acc = evaluate_using_acc(model, [(X_train.data, y_train.data)])
print("== Epoch", epoch, "step", i, "train loss", loss.data.cpu().numpy()[0], "train acc", acc)
model.eval()
acc = evaluate_using_acc(model, val_gen)
print("val acc", acc)
val_loss = np.mean([calc_loss(loss_op, model(Variable(xv)), Variable(yv)).data.cpu().numpy()[0] for xv, yv in val_gen])
print("val loss", val_loss)
if best_loss is not None and val_loss >= best_loss:
epoch_without_impr += 1
print('No improvement over previous best loss: ', best_loss)
# Save checkpoint
if best_loss is None or val_loss < best_loss:
best_loss = val_loss
torch.save(model.state_dict(), checkpoint_path)
print('Saving model at', checkpoint_path)
# Early stopping
if epoch_without_impr >= patience:
break
def main():
parser = argparse.ArgumentParser(
description='PyTorch PennTreeBank RNN/LSTM Language Model')
parser.add_argument('--data',
type=str,
default='../data/',
help='location of the data corpus')
parser.add_argument('--presaved',
action='store_true',
help='use presaved data')
parser.add_argument('--glovedata',
type=str,
default='../data/glove.6B',
help='location of the pretrained glove embeddings')
parser.add_argument('--din', type=int, default=30, help='length of LSTM')
parser.add_argument('--demb',
type=int,
default=100,
help='size of word embeddings')
parser.add_argument('--dhid',
type=int,
default=100,
help='humber of hidden units per layer')
parser.add_argument('--dout',
type=int,
default=2,
help='number of output classes')
parser.add_argument('--nlayers',
type=int,
default=1,
help='number of layers')
parser.add_argument('--lr',
type=float,
default=0.001,
help='initial learning rate')
parser.add_argument('--clip',
type=float,
default=0.25,
help='gradient clipping')
parser.add_argument('--embinit',
type=str,
default='random',
help='embedding weight initialization type')
parser.add_argument('--decinit',
type=str,
default='random',
help='decoder weight initialization type')
parser.add_argument('--hidinit',
type=str,
default='random',
help='recurrent hidden weight initialization type')
parser.add_argument('--dropout',
type=float,
default=0.0,
help='dropout applied to layers (0 = no dropout)')
parser.add_argument('--reweight',
action='store_true',
help='reweight loss function')
parser.add_argument('--epochs',
type=int,
default=40,
help='upper epoch limit')
parser.add_argument('--batchsize',
type=int,
default=20,
metavar='N',
help='batch size')
parser.add_argument('--seed', type=int, default=3, help='random seed')
parser.add_argument('--vocabsize',
type=int,
default=200000,
help='random seed')
parser.add_argument('--optimizer',
action='store_true',
help='use ADAM optimizer')
parser.add_argument('--pipeline',
action='store_true',
help='use pipeline file')
parser.add_argument('--psw', type=int, default=1, help='remove stop words')
parser.add_argument('--ppunc',
action='store_true',
help='remove punctuation')
parser.add_argument('--pntok',
action='store_true',
help='use number tokens')
parser.add_argument('--pkq',
action='store_true',
help='keep question words')
parser.add_argument('--stem', action='store_true', help='use stemmer')
parser.add_argument('--lemma', action='store_true', help='use lemmatizer')
parser.add_argument('--freezeemb',
action='store_false',
help='freezes embeddings')
parser.add_argument('--cuda', action='store_true', help='use CUDA')
parser.add_argument('--loginterval',
type=int,
default=100,
metavar='N',
help='report interval')
parser.add_argument('--save',
type=str,
default='',
help='path to save the final model')
args = parser.parse_args()
pipe = None
if args.pipeline:
stemmer, lemmatizer = None, None
if args.stem:
stemmer = SnowballStemmer('english')
elif args.lemma:
lemmatizer = WordNetLemmatizer()
if not args.presaved:
pipe = functools.partial(pipeline,
rm_stop_words=args.psw,
rm_punc=args.ppunc,
number_token=args.pntok,
keep_questions=args.pkq,
stemmer=stemmer,
lemmatizer=lemmatizer)
corpus = TacoText(args.vocabsize, lower=True, vocab_pipe=pipe)
X, y, X_val, y_val = load_data(args.data,
corpus,
args.din,
train_split=0.9)
else:
print('Loading Presaved Data')
X = torch.load(args.data + 'train_x.t')
y = torch.load(args.data + 'train_y.t')
X_val = torch.load(args.data + 'val_x.t')
y_val = torch.load(args.data + 'val_y.t')
with open(args.data + 'corpus.pkl', 'rb') as f:
corpus = pkl.load(f)
if args.cuda:
X, y = X.cuda(), y.cuda()
X_val, y_val = X_val.cuda(), y_val.cuda()
print('Generating Data Loaders')
#X.size len(train_data),1,2,fix_length
train_dataset = TensorDataset(X, y)
train_loader = DataLoader(train_dataset,
batch_size=args.batchsize,
shuffle=True)
valid_loader = DataLoader(TensorDataset(X_val, y_val),
batch_size=args.batchsize,
shuffle=False)
ntokens = len(corpus)
glove_embeddings = None
if args.embinit == 'glove':
assert args.demb in (50, 100, 200, 300)
glove_embeddings = get_glove_embeddings(args.glovedata,
corpus.dictionary.word2idx,
ntokens, args.demb)
model = LSTMModelMLP(args.din, args.dhid, args.nlayers, args.dout,
args.demb, args.vocabsize, args.dropout, args.embinit,
args.hidinit, args.decinit, glove_embeddings,
args.cuda)
if args.cuda:
model.cuda()
if args.reweight:
w_tensor = torch.Tensor([1.309028344, 0.472001959])
if args.cuda:
w_tensor = w_tensor.cuda()
criterion = nn.NLLLoss(weight=w_tensor)
else:
criterion = nn.NLLLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)
model_config = '\t'.join([
str(x)
for x in (torch.__version__, args.clip, args.nlayers, args.din,
args.demb, args.dhid, args.embinit, args.decinit,
args.hidinit, args.dropout, args.optimizer, args.reweight,
args.lr, args.vocabsize, args.pipeline, args.psw, args.ppunc,
args.pntok, args.pkq, args.stem, args.lemma)
])
print(
'Pytorch | Clip | #Layers | InSize | EmbDim | HiddenDim | EncoderInit | DecoderInit | WeightInit | Dropout | Optimizer | Reweight | LR | VocabSize | pipeline | stop | punc | ntoken | keep_ques | stem | lemma'
)
print(model_config)
# best_val_acc = 0.78
best_ll = 0.48
for epoch in range(args.epochs):
model.train()
total_cost = 0
start_time = time.time()
cur_loss = 0
for ind, (qs, duplicate) in enumerate(train_loader):
model.zero_grad()
pred = model(qs[:, 0, 0, :], qs[:, 0, 1, :])
if args.cuda:
pred = pred.cuda()
duplicate = duplicate.cuda()
duplicate = Variable(duplicate)
loss = criterion(pred, duplicate)
loss.backward()
clip_grad_norm(model.parameters(), args.clip)
if optimizer:
optimizer.step()
else:
for p in model.parameters():
p.data.add_(-args.lr, p.grad.data)
total_cost += loss.data[0]
cur_loss += loss.data[0]
if ind % args.loginterval == 0 and ind > 0:
cur_loss = loss.data[0] / args.loginterval
elapsed = time.time() - start_time
print(
'| Epoch {:3d} | {:5d}/{:5d} Batches | ms/batch {:5.2f} | '
'Loss {:.6f}'.format(epoch, ind,
len(X) // args.batchsize,
elapsed * 1000.0 / args.loginterval,
cur_loss))
start_time = time.time()
cur_loss = 0
model.eval()
train_acc, train_ll = evaluate(model, train_loader, args.cuda)
val_acc, val_ll = evaluate(model, valid_loader, args.cuda)
# if args.save and (val_acc > best_val_acc):
if args.save and (val_ll < best_ll):
with open(args.save + '_corpus.pkl', 'wb') as corp_f:
pkl.dump(corpus, corp_f, protocol=pkl.HIGHEST_PROTOCOL)
torch.save(model.cpu(), args.save)
torch.save(model.cpu().state_dict(), args.save + ".state_dict")
with open(args.save + ".state_dict.config", "w") as f:
f.write(model_config)
best_ll = val_ll
if args.cuda:
model.cuda()
print(
'Epoch: {} | Train Loss: {:.4f} | Train Accuracy: {:.4f} | Val Accuracy: {:.4f} | Train LL: {:.4f} | Val LL: {:.4f}'
.format(epoch, total_cost, train_acc, val_acc, train_ll, val_ll))
print('-' * 89)
# add summary to logger
logger.scalar_summary('square loss', square_loss.data[0], step)
# Next train Generator on Criterion from Discriminator
real_labels = Variable(label.fill_(1))
g_loss = 0 if not use_adv_model else label_loss(model_adv.forward(outputs), real_labels)
loss = square_loss + g_loss
losses.append(loss.data[0])
if use_adv_model:
logger.scalar_summary('Generator Loss', g_loss.data[0], step)
# Clip gradient norms
optimizer.zero_grad()
loss.backward()
logger.scalar_summary('Composite Loss', loss.data[0], step)
clip_grad_norm(model.parameters(), 1.0)
optimizer.step()
step += args.batch
print('Composite loss: ', np.array(losses).mean())
if epoch % args.snapshot == 0:
# snapshot model and optimizer
snapshot = {
'epoch': epoch + 1,
'model': model.state_dict(),
'adv_model': None if not use_adv_model else model_adv.state_dict(),
'optimizer': optimizer.state_dict(),
'optimizer_adv': None if not use_adv_model else optimizer_adv.state_dict()
}
torch.save(snapshot, os.path.join(exp_path, 'best.pth'))
def train(train_loader, model, criterion, optimizer, optimizer_cent, epoch):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
if args.no_partialbn:
model.module.partialBN(False)
else:
model.module.partialBN(True)
# switch to train mode
model.train()
end = time.time()
#centers = model.centers
#print(center)
for i, (input, target) in enumerate(train_loader):
# print('##### i:', i)
# measure data loading time
data_time.update(time.time() - end)
target = target.cuda(async=True)
# print('input size ====>', input.size())
# print('input size', input.size())
# input = input.view(-1,3,224,224)
# print('input size ====>', input.size())
input_var = torch.autograd.Variable(input)
target_var = torch.autograd.Variable(target)
# compute output
output = model(input_var)
#print(output.shape)
#feature = output(0)
#center_loss = compute_center_loss(feature, model.centers, target_var)
#print("tar shape {}".format(target_var.shape))
#print("ourput shape {}".format(output.shape))
#print("feature shape {}".format(feature.shape))
loss = criterion(output, target_var)
#print(loss)
#print(output.shape)
#sys.exit()
#loss_cent = criterion_cent(feature, output[target_var)
#print(loss_cent)
#loss_cent = 0.03 * center_loss
#loss = loss_cent + loss
# measure accuracy and record loss
prec1, prec5 = accuracy(output.data, target, topk=(1, 5))
losses.update(loss.data.item(), input.size(0))
top1.update(prec1.item(), input.size(0))
top5.update(prec5.item(), input.size(0))
# compute gradient and do SGD step
optimizer.zero_grad()
#optimizer_cent.zero_grad()
loss.backward(retain_graph=True)
if args.clip_gradient is not None:
total_norm = clip_grad_norm(model.parameters(), args.clip_gradient)
#print("total_norm: {}".format(total_norm))
if total_norm > args.clip_gradient:
print("clipping gradient: {} with coef {}".format(
total_norm, args.clip_gradient / total_norm))
optimizer.step()
#for param in criterion_cent.parameters():
# param.grad.data *= (1./0.1)
#center_deltas =get_center_delta(feature, centers, target, alpha=0.5)
#model.centers = centers - center_deltas
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % args.print_freq == 0:
print(('Epoch: [{0}][{1}/{2}], lr: {lr:.5f}\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Prec@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Prec@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch,
i,
len(train_loader),
batch_time=batch_time,
data_time=data_time,
loss=losses,
top1=top1,
top5=top5,
lr=optimizer.param_groups[-1]['lr'])))
torch.zeros(num_layers, batch_size, hidden_size).to(device))
for i in range(0, ids.size(1) - seq_length, seq_length):
# Get mini-batch inputs and targets
inputs = ids[:, i:i+seq_length].to(device)
targets = ids[:, (i+1):(i+1)+seq_length].to(device)
# Forward pass
states = detach(states)
outputs, states = model(inputs, states)
loss = criterion(outputs, targets.reshape(-1))
# Backward and optimize
model.zero_grad()
loss.backward()
clip_grad_norm(model.parameters(), 0.5)
optimizer.step()
step = (i+1) // seq_length
if step % 100 == 0:
print ('Epoch [{}/{}], Step[{}/{}], Loss: {:.4f}, Perplexity: {:5.2f}'
.format(epoch+1, num_epochs, step, num_batches, loss.item(), np.exp(loss.item())))
# Test the model
with torch.no_grad():
with open('sample.txt', 'w') as f:
# Set intial hidden ane cell states
state = (torch.zeros(num_layers, 1, hidden_size).to(device),
torch.zeros(num_layers, 1, hidden_size).to(device))
# Select one word id randomly
def main(args):
CUDA = False
folder_name = 'RL_' + args.name + '_' + args.task + '_' + args.architecture
folder_path = os.path.join('./', folder_name)
create_folder(folder_name)
datasets = [IntegersLargerThanAverage(10000, i, 10) for i in range(4, 5)]
critic = MovingAverageBaseline(0.9)
if args.architecture == 'set':
policy = BernoulliPolicy(IntegerSubsetNet())
elif args.architecture == 'null':
policy = BernoulliPolicy(IntegerSubsetNet(null_model=True))
else:
raise ValueError('Unknown architecture. Must be set or null!')
optimizer = torch.optim.Adam(policy.parameters(), lr=1e-3, eps=1e-2)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 5000, gamma=0.99)
if torch.cuda.is_available() and args.gpu != '':
policy.cuda()
CUDA = True
print('Using GPU')
environment = RLWrapper(datasets, 64, use_cuda=CUDA)
data = environment.reset()
rewards_list = []
for n in range(args.n_episodes): # run for epochs
actions, log_prob_actions = policy(data)
#policy_p = F.sigmoid(policy.fn_approximator(data))
log_prob_actions = log_prob_actions.sum(1)
baseline = critic(data).view(-1, 1)
if n % 100 == 0:
y_target = torch.FloatTensor(
environment.current_dataset.supervised_objective(
data.data.int()))
data, reward, _, info = environment.step(actions)
advantage = reward - baseline
critic.update_baseline(None, reward)
loss = gradients.calculate_policy_gradient_terms(
log_prob_actions, advantage)
loss = loss.mean(
) # mean is fine since there is only really "one action"?
optimizer.zero_grad()
loss.backward()
clip_grad_norm(policy.fn_approximator.parameters(), 40)
optimizer.step()
scheduler.step()
rewards_list.append(reward.mean())
if n % 100 == 0:
set_acc, elem_acc = set_accuracy(y_target, actions.data)
print('{}: loss {:3g}, episode_reward {:3g}, set acc: {},'
' elem_acc: {}, set_size {}, entropy {}'.format(
n,
loss.cpu().data[0], reward.mean(), set_acc, elem_acc,
environment.current_dataset.set_size,
(-log_prob_actions *
log_prob_actions.exp()).sum().data[0]))
print('reward distribution: {}'.format(
Counter(reward.numpy().ravel().tolist())))
# now put this into "supervised" mode
datasets = [
(i,
torch.utils.data.DataLoader(IntegerSubsetsSupervised(256,
i,
10,
target='mean',
seed=5),
batch_size=256)) for i in range(4, 10)
]
set_sizes = []
mse = []
set_accs = []
elem_accs = []
torch.save(policy, os.path.join(folder_path, 'model-gpu.pyt'))
criterion = torch.nn.BCELoss()
for set_size, dataset in datasets:
for i, (x, y) in enumerate(dataset):
# prepare the data
if CUDA:
x = x.cuda()
y = y.cuda()
x, y = Variable(x, volatile=True), Variable(y,
volatile=True).float()
# run it through the network
y_hat, _ = policy(x)
y_hat = y_hat.view_as(y)
# calculate the loss
loss = criterion(y_hat, y)
if CUDA:
loss = loss.cpu()
set_sizes.append(set_size)
mse.append(loss.data[0])
set_acc, elem_acc = set_accuracy(y.squeeze(), y_hat.squeeze())
set_accs.append(set_acc.data[0])
elem_accs.append(elem_acc.data[0])
print(set_sizes)
print(mse)
print(set_accs)
print(torch.FloatTensor(set_accs).mean())
policy.cpu()
torch.save(
{
'set_sizes': set_sizes,
'rewards_list': rewards_list,
'mse': mse,
'set_acc': set_accs,
'elem_accs': elem_accs,
'mean_acc': torch.FloatTensor(set_accs).mean()
}, os.path.join(folder_path, 'results.json'))
torch.save(policy, os.path.join(folder_path, 'model.pyt'))