def train(model, tokenizer, train_data, valid_data, args, eos=False):
model.train()
train_dataset = TextDataset(train_data)
train_dataloader = DataLoader(train_dataset, sampler=RandomSampler(train_dataset),
batch_size=args.train_batch_size, num_workers=args.num_workers,
collate_fn=lambda x: collate_fn(x, tokenizer, args.max_seq_length, eos=eos, tokenizer_type=args.tokenizer))
valid_dataset = TextDataset(valid_data)
valid_dataloader = DataLoader(valid_dataset, sampler=SequentialSampler(valid_dataset),
batch_size=args.eval_batch_size, num_workers=args.num_workers,
collate_fn=lambda x: collate_fn(x, tokenizer, args.max_seq_length, eos=eos, tokenizer_type=args.tokenizer))
valid_noisy = [x['noisy'] for x in valid_data]
valid_clean = [x['clean'] for x in valid_data]
epochs = (args.max_steps - 1) // len(train_dataloader) + 1
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,
betas=eval(args.adam_betas), eps=args.eps,
weight_decay=args.weight_decay)
lr_lambda = lambda x: x / args.num_warmup_steps if x <= args.num_warmup_steps else (x / args.num_warmup_steps) ** -0.5
scheduler = LambdaLR(optimizer, lr_lambda)
step = 0
best_val_gleu = -float("inf")
meter = Meter()
for epoch in range(1, epochs + 1):
print("===EPOCH: ", epoch)
for batch in train_dataloader:
step += 1
batch = tuple(t.to(args.device) for t in batch)
loss, items = calc_loss(model, batch)
meter.add(*items)
loss.backward()
if args.max_grad_norm > 0:
nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)
optimizer.step()
model.zero_grad()
scheduler.step()
if step % args.log_interval == 0:
lr = scheduler.get_lr()[0]
loss_sent, loss_token = meter.average()
logger.info(f' [{step:5d}] lr {lr:.6f} | {meter.print_str(True)}')
nsml.report(step=step, scope=locals(), summary=True,
train__lr=lr, train__loss_sent=loss_sent, train__token_ppl=math.exp(loss_token))
meter.init()
if step % args.eval_interval == 0:
start_eval = time.time()
(val_loss, val_loss_token), valid_str = evaluate(model, valid_dataloader, args)
prediction = correct(model, tokenizer, valid_noisy, args, eos=eos, length_limit=0.1)
val_em = em(prediction, valid_clean)
cnt = 0
for noisy, pred, clean in zip(valid_noisy, prediction, valid_clean):
print(f'[{noisy}], [{pred}], [{clean}]')
# 10개만 출력하기
cnt += 1
if cnt == 20:
break
val_gleu = gleu(prediction, valid_clean)
logger.info('-' * 89)
logger.info(f' [{step:6d}] valid | {valid_str} | em {val_em:5.2f} | gleu {val_gleu:5.2f}')
logger.info('-' * 89)
nsml.report(step=step, scope=locals(), summary=True,
valid__loss_sent=val_loss, valid__token_ppl=math.exp(val_loss_token),
valid__em=val_em, valid__gleu=val_gleu)
if val_gleu > best_val_gleu:
best_val_gleu = val_gleu
nsml.save("best")
meter.start += time.time() - start_eval
if step >= args.max_steps:
break
#nsml.save(epoch)
if step >= args.max_steps:
break
def main():
global opts
opts = parser.parse_args()
opts.cuda = 0
# Set GPU
seed = opts.seed
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
os.environ['CUDA_VISIBLE_DEVICES'] = opts.gpu_ids
use_gpu = torch.cuda.is_available()
if use_gpu:
opts.cuda = 1
print("Number of device: {}".format(torch.cuda.device_count()))
print("Currently using GPU {}".format(opts.gpu_ids))
cudnn.benchmark = True
torch.cuda.manual_seed_all(seed)
else:
print("Currently using CPU (GPU is highly recommended)")
if opts.model == 'resnet18':
model = resnet18(pretrained=False)
if opts.batchsize == -1:
opts.batchsize = 1024 if opts.half else 512
elif opts.model == 'resnet50':
model = resnet50(pretrained=False)
if opts.batchsize == -1:
opts.batchsize = 512 if opts.half else 256
elif opts.model == 'resnet101':
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet101', pretrained=False)
if opts.batchsize == -1:
opts.batchsize = 360 if opts.half else 180
elif opts.model == 'resnet152':
model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet152', pretrained=False)
if opts.batchsize == -1:
opts.batchsize = 256 if opts.half else 128
elif opts.model == 'ran56':
model = ResidualAttentionModel_56()
if opts.batchsize == -1:
opts.batchsize = 140
elif opts.model == 'ran92':
model = ResidualAttentionModel_92()
if opts.batchsize == -1:
opts.batchsize = 80
ch = model.fc.in_features
model.fc = nn.Sequential(nn.Linear(ch, ch), nn.ReLU(), nn.Linear(ch, ch))
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# model = torch.nn.DataParallel(model)
model.eval()
if opts.half:
model.half()
for layer in model.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.float()
parameters = filter(lambda p: p.requires_grad, model.parameters())
n_parameters = sum([p.data.nelement() for p in model.parameters()])
print(' + Number of params: {}'.format(n_parameters))
### GPU Setup ###
if use_gpu:
model.cuda()
### DO NOT MODIFY THIS BLOCK ###
if IS_ON_NSML:
bind_nsml(model)
if opts.pause:
nsml.paused(scope=locals())
################################
bind_nsml(model)
if opts.mode == 'train':
model.train()
# Set dataloader
train_ids, val_ids, unl_ids = split_ids(os.path.join(DATASET_PATH, 'train/train_label'), 0.2)
print('found {} train, {} validation and {} unlabeled images'.format(len(train_ids), len(val_ids), len(unl_ids)))
label_loader = torch.utils.data.DataLoader(
SimpleImageLoader(DATASET_PATH, 'train', train_ids,
transform=transforms.Compose([
transforms.Resize(opts.imResize),
transforms.RandomResizedCrop(opts.imsize),
# transforms.Resize((opts.imsize, opts.imsize)),
transforms.RandomHorizontalFlip(),
# transforms.ColorJitter(0.5, 0.5, 0.5, 0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])),
batch_size=opts.batchsize * 2, shuffle=True, num_workers=4, pin_memory=True, drop_last=True)
print('train_loader done')
unlabel_loader = torch.utils.data.DataLoader(
SimpleImageLoader(DATASET_PATH, 'unlabel', unl_ids,
transform=transforms.Compose([
transforms.Resize(opts.imResize),
transforms.RandomResizedCrop(opts.imsize),
transforms.ColorJitter(0.5, 0.5, 0.5, 0.5),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])),
batch_size=opts.batchsize, shuffle=True, num_workers=0, pin_memory=True, drop_last=True)
print('unlabel_loader done')
validation_loader = torch.utils.data.DataLoader(
SimpleImageLoader(DATASET_PATH, 'val', val_ids,
transform=transforms.Compose([
transforms.Resize(opts.imResize),
transforms.CenterCrop(opts.imsize),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])),
batch_size=opts.batchsize, shuffle=False, num_workers=0, pin_memory=True, drop_last=False)
print('validation_loader done')
model = torch.nn.DataParallel(model)
model.to(device)
bind_nsml(model)
#Set optimizer
if opts.optimizer == 'SGD':
if opts.adaptive_lr:
base_optimizer = optim.SGD(model.parameters(), lr=0.3*opts.batchsize/256)
else:
if opts.lr == -1:
base_optimizer = optim.SGD(model.parameters(), lr=0.001)
else:
base_optimizer = optim.SGD(model.parameters(), lr=opts.lr)
elif opts.optimizer == 'Adam':
if opts.adaptive_lr:
base_optimizer = optim.Adam(model.parameters(), lr=0.3*opts.batchsize/256)
else:
if opts.lr == -1:
base_optimizer = optim.Adam(model.parameters(), lr=0.001)
else:
base_optimizer = optim.Adam(model.parameters(), lr=opts.lr)
if opts.LARS:
optimizer = torchlars.LARS(optimizer=base_optimizer, eps=1e-8, trust_coef=0.001)
else:
optimizer = base_optimizer
# INSTANTIATE LOSS CLASS
unlabel_criterion = nt_cross_entropy
# INSTANTIATE STEP LEARNING SCHEDULER CLASS
if opts.scheduler == 'linear':
scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[50, 150], gamma=0.1)
elif opts.scheduler == 'exp':
scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=1e-6)
model.train()
print('==================================')
print(opts)
print('==================================')
print('starting pretask')
total_iter = 0
for epoch in range(1, 201):
for it, data in enumerate(unlabel_loader):
total_iter += 1
d = data.size()
if opts.half:
x = data.view(d[0]*2, d[2], d[3], d[4]).half().to(device)
else:
x = data.view(d[0]*2, d[2], d[3], d[4]).to(device)
optimizer.zero_grad()
p = model(x)
if opts.half:
loss = unlabel_criterion(p.float())
else:
loss = unlabel_criterion(p)
loss.backward()
if opts.half:
model.float()
optimizer.step()
if opts.half:
model.half()
for layer in model.modules():
if isinstance(layer, nn.BatchNorm2d):
layer.float()
print("epoch: ", epoch, "loss: ", loss.item())
nsml.report(summary=True, loss=loss.item(), step=total_iter)
scheduler.step()
print("epoch: ", epoch, "loss: ", loss.item())
if (epoch) % 2 == 0:
if IS_ON_NSML:
nsml.save(opts.name + '_pre{}'.format(epoch))
else:
torch.save(model.state_dict(), os.path.join('runs', opts.name + '_pre{}'.format(epoch)))
labels = np.asarray(label_list)
labels = np.concatenate((labels, labels), axis=0)
y_train = keras.utils.to_categorical(labels, num_classes=num_classes)
x_train = x_train.astype('float32')
x_train /= 255
print(len(labels), 'train samples')
""" Callback """
monitor = 'get_categorical_accuracy_keras'
reduce_lr = ReduceLROnPlateau(monitor=monitor, patience=3)
""" Training loop """
for epoch in range(nb_epoch):
res = model.fit(x_train,
y_train,
batch_size=batch_size,
initial_epoch=epoch,
epochs=epoch + 1,
callbacks=[reduce_lr],
verbose=1,
shuffle=True)
print(res.history)
train_loss, train_acc = res.history['loss'][0], res.history[
'get_categorical_accuracy_keras'][0]
nsml.report(summary=True,
epoch=epoch,
epoch_total=nb_epoch,
loss=train_loss,
acc=train_acc)
nsml.save(epoch)
def main():
global char2index
global index2char
global SOS_token
global EOS_token
global PAD_token
parser = argparse.ArgumentParser(description='Speech hackathon Baseline')
parser.add_argument('--hidden_size',
type=int,
default=512,
help='hidden size of model (default: 256)')
parser.add_argument('--layer_size',
type=int,
default=3,
help='number of layers of model (default: 3)')
parser.add_argument(
'--bidirectional',
action='store_true',
help='use bidirectional RNN for encoder (default: False)')
parser.add_argument(
'--use_attention',
action='store_true',
help='use attention between encoder-decoder (default: False)')
parser.add_argument('--batch_size',
type=int,
default=32,
help='batch size in training (default: 32)')
parser.add_argument(
'--workers',
type=int,
default=4,
help='number of workers in dataset loader (default: 4)')
parser.add_argument('--max_epochs',
type=int,
default=10,
help='number of max epochs in training (default: 10)')
parser.add_argument('--lr',
type=float,
default=1e-04,
help='learning rate (default: 0.0001)')
parser.add_argument('--teacher_forcing',
type=float,
default=0.5,
help='teacher forcing ratio in decoder (default: 0.5)')
parser.add_argument('--max_len',
type=int,
default=80,
help='maximum characters of sentence (default: 80)')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
help='random seed (default: 1)')
parser.add_argument('--save_name',
type=str,
default='model',
help='the name of model in nsml or local')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument("--pause", type=int, default=0)
parser.add_argument("--visdom", type=bool, default=False)
parser.add_argument("--use_stft",
type=bool,
default=False,
help="use stft or log mel + specaugmentation")
parser.add_argument("--mels", type=int, default=128)
parser.add_argument("--use_rnn", type=bool, default=False)
# Low Frame Rate (stacking and skipping frames)
parser.add_argument('--LFR_m',
default=4,
type=int,
help='Low Frame Rate: number of frames to stack')
parser.add_argument('--LFR_n',
default=3,
type=int,
help='Low Frame Rate: number of frames to skip')
# EncoderTrans
parser.add_argument('--n_layers_enc',
default=2,
type=int,
help='Number of encoder stacks')
parser.add_argument('--n_head',
default=4,
type=int,
help='Number of Multi Head Attention (MHA)')
parser.add_argument('--d_k', default=64, type=int, help='Dimension of key')
parser.add_argument('--d_v',
default=64,
type=int,
help='Dimension of value')
parser.add_argument('--d_model',
default=512,
type=int,
help='Dimension of model')
parser.add_argument('--d_inner',
default=512,
type=int,
help='Dimension of inner')
parser.add_argument('--dropout',
default=0.1,
type=float,
help='Dropout rate')
parser.add_argument('--pe_maxlen',
default=5000,
type=int,
help='Positional Encoding max len')
# Decoder Trans
parser.add_argument('--d_word_vec',
default=512,
type=int,
help='Dim of decoder embedding')
parser.add_argument('--n_layers_dec',
default=2,
type=int,
help='Number of decoder stacks')
parser.add_argument('--tgt_emb_prj_weight_sharing',
default=1,
type=int,
help='share decoder embedding with decoder projection')
# TransLoss
parser.add_argument('--label_smoothing',
default=0.1,
type=float,
help='label smoothing')
# Optimizer
parser.add_argument('--k',
default=1.0,
type=float,
help='tunable scalar multiply to learning rate')
parser.add_argument('--warmup_steps',
default=4000,
type=int,
help='warmup steps')
args = parser.parse_args()
char2index, index2char = label_loader.load_label('./data/hackathon.labels')
SOS_token = char2index['<s>']
EOS_token = char2index['</s>']
PAD_token = char2index['_']
# Setting seed
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
# Setting device
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if args.cuda else 'cpu')
# Feature extractor
if args.use_stft:
feature_size = N_FFT / 2 + 1
else:
feature_size = args.mels
# Actual model
if args.use_rnn: # RNN structure
# Define model
enc = EncoderRNN(feature_size,
args.hidden_size,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
n_layers=args.layer_size,
bidirectional=args.bidirectional,
rnn_cell='gru',
variable_lengths=False)
dec = DecoderRNN(len(char2index),
args.max_len,
args.hidden_size * (2 if args.bidirectional else 1),
SOS_token,
EOS_token,
n_layers=args.layer_size,
rnn_cell='gru',
bidirectional=args.bidirectional,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
use_attention=args.use_attention)
model = Seq2seq(enc, dec)
model.flatten_parameters()
# Parameters initialization
for param in model.parameters():
param.data.uniform_(-0.08, 0.08)
model = nn.DataParallel(model).to(device)
optimizer = optim.Adam(model.module.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss(reduction='sum',
ignore_index=PAD_token).to(device)
bind_model(args, model, optimizer)
if args.pause == 1:
nsml.paused(scope=locals())
if args.mode != "train":
return
data_list = os.path.join(DATASET_PATH, 'train_data', 'data_list.csv')
wav_paths = list()
script_paths = list()
with open(data_list, 'r') as f:
for line in f:
# line: "aaa.wav,aaa.label"
wav_path, script_path = line.strip().split(',')
wav_paths.append(
os.path.join(DATASET_PATH, 'train_data', wav_path))
script_paths.append(
os.path.join(DATASET_PATH, 'train_data', script_path))
best_loss = 1e10
best_cer = 1e10
begin_epoch = 0
# load all target scripts for reducing disk i/o
target_path = os.path.join(DATASET_PATH, 'train_label')
load_targets(target_path)
train_batch_num, train_dataset_list, valid_dataset = split_dataset(
args, wav_paths, script_paths, valid_ratio=0.05)
logger.info('start')
if args.visdom:
train_visual = Visual(train_batch_num)
eval_visual = Visual(1)
train_begin = time.time()
for epoch in range(begin_epoch, args.max_epochs):
train_queue = queue.Queue(args.workers * 2)
train_loader = MultiLoader(train_dataset_list, train_queue,
args.batch_size, args.workers)
train_loader.start()
if args.visdom:
train_loss, train_cer = trainRNN(model, train_batch_num,
train_queue, criterion,
optimizer, device,
train_begin, args.workers, 10,
args.teacher_forcing,
train_visual)
else:
train_loss, train_cer = trainRNN(model, train_batch_num,
train_queue, criterion,
optimizer, device,
train_begin, args.workers, 10,
args.teacher_forcing)
logger.info('Epoch %d (Training) Loss %0.4f CER %0.4f' %
(epoch, train_loss, train_cer))
train_loader.join()
valid_queue = queue.Queue(args.workers * 2)
valid_loader = BaseDataLoader(valid_dataset, valid_queue,
args.batch_size, 0)
valid_loader.start()
if args.visdom:
eval_loss, eval_cer = evaluateRNN(model, valid_loader,
valid_queue, criterion,
device, eval_visual)
else:
eval_loss, eval_cer = evaluateRNN(model, valid_loader,
valid_queue, criterion,
device)
logger.info('Epoch %d (Evaluate) Loss %0.4f CER %0.4f' %
(epoch, eval_loss, eval_cer))
valid_loader.join()
nsml.report(False,
step=epoch,
train_epoch__loss=train_loss,
train_epoch__cer=train_cer,
eval__loss=eval_loss,
eval__cer=eval_cer)
best_loss_model = (eval_loss < best_loss)
best_cer_model = (eval_cer < best_cer)
nsml.save(args.save_name)
if best_loss_model:
nsml.save('best_loss')
best_loss = eval_loss
if best_cer_model:
nsml.save('best_cer')
best_cer = eval_cer
else: # Transformer structure
# Define model
enc = EncoderTrans(feature_size,
args.n_layers_enc,
args.n_head,
args.d_k,
args.d_v,
args.d_model,
args.d_inner,
dropout=args.dropout,
pe_maxlen=args.pe_maxlen)
dec = DecoderTrans(
SOS_token,
EOS_token,
len(char2index),
args.d_word_vec,
args.n_layers_dec,
args.n_head,
args.d_k,
args.d_v,
args.d_model,
args.d_inner,
dropout=args.dropout,
tgt_emb_prj_weight_sharing=args.tgt_emb_prj_weight_sharing,
pe_maxlen=args.pe_maxlen)
model = Transformer(enc, dec)
# Parameter initialization
for param in model.parameters():
param.data.uniform_(-0.08, 0.08)
model = nn.DataParallel(model).to(device)
optimizer = TransformerOptimizer(
torch.optim.Adam(model.parameters(), betas=(0.9, 0.98), eps=1e-09),
args.k, args.d_model, args.warmup_steps)
criterion = nn.CrossEntropyLoss(reduction='sum',
ignore_index=PAD_token).to(device)
bind_model(args, model, optimizer)
if args.pause == 1:
nsml.paused(scope=locals())
if args.mode != "train":
return
data_list = os.path.join(DATASET_PATH, 'train_data', 'data_list.csv')
wav_paths = list()
script_paths = list()
with open(data_list, 'r') as f:
for line in f:
# line: "aaa.wav,aaa.label"
wav_path, script_path = line.strip().split(',')
wav_paths.append(
os.path.join(DATASET_PATH, 'train_data', wav_path))
script_paths.append(
os.path.join(DATASET_PATH, 'train_data', script_path))
best_loss = 1e10
best_cer = 1e10
begin_epoch = 0
# load all target scripts for reducing disk i/o
target_path = os.path.join(DATASET_PATH, 'train_label')
load_targets(target_path)
train_batch_num, train_dataset_list, valid_dataset = split_dataset(
args, wav_paths, script_paths, valid_ratio=0.05)
logger.info('start')
train_begin = time.time()
for epoch in range(begin_epoch, args.max_epochs):
train_queue = queue.Queue(args.workers * 2)
train_loader = MultiLoader(train_dataset_list, train_queue,
args.batch_size, args.workers)
train_loader.start()
if args.visdom:
train_loss, train_cer = trainTrans(model, train_batch_num,
train_queue, criterion,
optimizer, device,
train_begin, args.workers,
10, args.teacher_forcing,
train_visual)
else:
train_loss, train_cer = trainTrans(
model,
train_batch_num,
train_queue,
criterion,
optimizer,
device,
train_begin,
args.workers,
10,
args.teacher_forcing,
label_smoothing=args.label_smoothing)
logger.info('Epoch %d (Training) Loss %0.4f CER %0.4f' %
(epoch, train_loss, train_cer))
train_loader.join()
valid_queue = queue.Queue(args.workers * 2)
valid_loader = BaseDataLoader(valid_dataset, valid_queue,
args.batch_size, 0)
valid_loader.start()
if args.visdom:
eval_loss, eval_cer = evaluateTrans(model, valid_loader,
valid_queue, criterion,
device, eval_visual)
else:
eval_loss, eval_cer = evaluateTrans(
model,
valid_loader,
valid_queue,
criterion,
device,
label_smoothing=args.label_smoothing)
logger.info('Epoch %d (Evaluate) Loss %0.4f CER %0.4f' %
(epoch, eval_loss, eval_cer))
valid_loader.join()
nsml.report(False,
step=epoch,
train_epoch__loss=train_loss,
train_epoch__cer=train_cer,
eval__loss=eval_loss,
eval__cer=eval_cer)
best_loss_model = (eval_loss < best_loss)
best_cer_model = (eval_cer < best_cer)
nsml.save(args.save_name)
if best_loss_model:
nsml.save('best_loss')
best_loss = eval_loss
if best_cer_model:
nsml.save('best_cer')
best_cer = eval_cer
def train_fine(opts, train_loader, model, criterion, optimizer, ema_optimizer,
epoch, use_gpu, scheduler, is_mixsim):
global global_step
scaler = torch.cuda.amp.GradScaler()
losses = AverageMeter()
losses_x = AverageMeter()
losses_curr = AverageMeter()
losses_x_curr = AverageMeter()
weight_scale = AverageMeter()
acc_top1 = AverageMeter()
acc_top5 = AverageMeter()
model.train()
# nCnt =0
out = False
local_step = 0
while not out:
labeled_train_iter = iter(train_loader)
for batch_idx in range(len(train_loader)):
try:
data = labeled_train_iter.next()
inputs_x, targets_x = data
except:
labeled_train_iter = iter(train_loader)
data = labeled_train_iter.next()
inputs_x, targets_x = data
batch_size = inputs_x.size(0)
# Transform label to one-hot
classno = NUM_CLASSES
targets_org = targets_x
targets_x = torch.zeros(batch_size,
classno).scatter_(1, targets_x.view(-1, 1),
1)
if use_gpu:
# Send input value to device 0, where first parameters of MixSim_Model is.
if is_mixsim:
dev0 = 'cuda:{}'.format(opts.gpu_ids.split(',')[0])
else:
dev0 = 'cuda'
inputs_x, targets_x = inputs_x.to(dev0), targets_x.to(dev0)
optimizer.zero_grad()
with torch.cuda.amp.autocast():
_, logits = model(inputs_x)
loss = criterion(logits, targets_x, opts.temperature)
losses.update(loss.item(), inputs_x.size(0))
losses_curr.update(loss.item(), inputs_x.size(0))
# compute gradient and do SGD step
scaler.scale(loss).backward()
scaler.step(optimizer)
ema_optimizer.step()
scaler.update()
scheduler.step()
with torch.no_grad():
# compute guessed labels of unlabel samples
_, pred_x1 = model(inputs_x)
if IS_ON_NSML and global_step % opts.log_interval == 0:
nsml.report(step=global_step, loss=losses_curr.avg)
losses_curr.reset()
acc_top1b = top_n_accuracy_score(targets_org.data.cpu().numpy(),
pred_x1.data.cpu().numpy(),
n=1) * 100
acc_top5b = top_n_accuracy_score(targets_org.data.cpu().numpy(),
pred_x1.data.cpu().numpy(),
n=5) * 100
acc_top1.update(torch.as_tensor(acc_top1b), inputs_x.size(0))
acc_top5.update(torch.as_tensor(acc_top5b), inputs_x.size(0))
local_step += 1
global_step += 1
if local_step >= opts.steps_per_epoch:
out = True
break
return losses.avg, acc_top1.avg, acc_top5.avg
for epoch in range(config.epochs):
for i, (data,
labels) in enumerate(_batch_loader(dataset, config.batch)):
_, c = sess.run([train, cost], feed_dict={x: data, y: labels})
w_v, b_v, h, c, p, a = sess.run(
[w, b, hypothesis, cost, prediction, accuracy],
feed_dict={
x: data,
y: labels
})
print('epoch:', epoch, "accuracy:", a)
nsml.report(summary=True,
scope=locals(),
epoch=epoch,
accuracy=accuracy,
step=epoch)
# save session
if IS_ON_NSML: # on nsml
nsml.save(epoch)
else: # on local
if epoch % 1000 == 0:
print(b_v)
save_path = saver.save(
sess, os.path.join('save2',
str(epoch) + '.ckpt'))
print("Model saved in file: %s" % save_path)
# local test debug mode
def on_epoch_end(self, epoch, logs={}):
nsml.report(summary=True, epoch=epoch, loss=logs.get('loss'), val_loss=logs.get('val_loss'))
nsml.save(self.prefix +'_'+ str(self.seed)+'_' +str(epoch))
def main():
global char2index
global index2char
global SOS_token
global EOS_token
global PAD_token
parser = argparse.ArgumentParser(description='Speech hackathon Baseline')
parser.add_argument('--hidden_size',
type=int,
default=512,
help='hidden size of model (default: 256)')
parser.add_argument('--layer_size',
type=int,
default=3,
help='number of layers of model (default: 3)')
parser.add_argument('--dropout',
type=float,
default=0.2,
help='dropout rate in training (default: 0.2)')
parser.add_argument(
'--bidirectional',
action='store_true',
help='use bidirectional RNN for encoder (default: False)')
parser.add_argument(
'--use_attention',
action='store_true',
help='use attention between encoder-decoder (default: False)')
parser.add_argument('--batch_size',
type=int,
default=32,
help='batch size in training (default: 32)')
parser.add_argument(
'--workers',
type=int,
default=4,
help='number of workers in dataset loader (default: 4)')
parser.add_argument('--max_epochs',
type=int,
default=50,
help='number of max epochs in training (default: 10)')
parser.add_argument('--lr',
type=float,
default=1e-04,
help='learning rate (default: 0.0001)')
parser.add_argument('--teacher_forcing',
type=float,
default=0.5,
help='teacher forcing ratio in decoder (default: 0.5)')
parser.add_argument('--max_len',
type=int,
default=80,
help='maximum characters of sentence (default: 80)')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
help='random seed (default: 1)')
parser.add_argument('--save_name',
type=str,
default='model',
help='the name of model in nsml or local')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument("--pause", type=int, default=0)
args = parser.parse_args()
char2index, index2char = label_loader.load_label('./hackathon.labels')
SOS_token = char2index['<s>']
EOS_token = char2index['</s>']
PAD_token = char2index['_']
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if args.cuda else 'cpu')
# N_FFT: defined in loader.py
#feature_size = N_FFT / 2 + 1
#feature_size = 40
feature_size = 45
#Mel function이면 다르게 해야할듯?
enc = EncoderRNN(feature_size,
args.hidden_size,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
n_layers=args.layer_size,
bidirectional=args.bidirectional,
rnn_cell='gru',
variable_lengths=False)
dec = DecoderRNN(len(char2index),
args.max_len,
args.hidden_size * (2 if args.bidirectional else 1),
SOS_token,
EOS_token,
n_layers=args.layer_size,
rnn_cell='gru',
bidirectional=args.bidirectional,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
use_attention=args.use_attention)
model = Seq2seq(enc, dec)
model.flatten_parameters()
for param in model.parameters():
param.data.uniform_(-0.08, 0.08)
model = nn.DataParallel(model).to(device)
optimizer = optim.Adam(model.module.parameters(),
lr=args.lr,
weight_decay=1e-06)
criterion = nn.CrossEntropyLoss(reduction='sum',
ignore_index=PAD_token).to(device)
bind_model(model, optimizer)
if args.pause == 1:
nsml.paused(scope=locals())
if args.mode != "train":
return
'''
nsml.load(checkpoint='best', session='team228/sr-hack-2019-dataset/414')
nsml.save('best')
'''
data_list = os.path.join(DATASET_PATH, 'train_data', 'data_list.csv')
wav_paths = list()
script_paths = list()
with open(data_list, 'r') as f:
for line in f:
# line: "aaa.wav,aaa.label"
wav_path, script_path = line.strip().split(',')
wav_paths.append(os.path.join(DATASET_PATH, 'train_data',
wav_path))
script_paths.append(
os.path.join(DATASET_PATH, 'train_data', script_path))
best_loss = 1e10
begin_epoch = 0
# load all target scripts for reducing disk i/o
target_path = os.path.join(DATASET_PATH, 'train_label')
load_targets(target_path)
train_batch_num, train_dataset_list, valid_dataset = split_dataset(
args, wav_paths, script_paths, valid_ratio=0.05)
logger.info('start')
train_begin = time.time()
for epoch in range(begin_epoch, args.max_epochs):
train_queue = queue.Queue(args.workers * 2)
train_loader = MultiLoader(train_dataset_list, train_queue,
args.batch_size, args.workers)
train_loader.start()
train_loss, train_cer = train(model, train_batch_num, train_queue,
criterion, optimizer, device,
train_begin, args.workers, 10,
args.teacher_forcing)
logger.info('Epoch %d (Training) Loss %0.4f CER %0.4f' %
(epoch, train_loss, train_cer))
train_loader.join()
valid_queue = queue.Queue(args.workers * 2)
valid_loader = BaseDataLoader(valid_dataset, valid_queue,
args.batch_size, 0)
valid_loader.start()
eval_loss, eval_cer = evaluate(model, valid_loader, valid_queue,
criterion, device)
logger.info('Epoch %d (Evaluate) Loss %0.4f CER %0.4f' %
(epoch, eval_loss, eval_cer))
valid_loader.join()
nsml.report(False,
step=epoch,
train_epoch__loss=train_loss,
train_epoch__cer=train_cer,
eval__loss=eval_loss,
eval__cer=eval_cer)
best_model = (eval_loss < best_loss)
nsml.save(args.save_name)
if best_model:
nsml.save('best')
best_loss = eval_loss
t0 = time.time()
for epoch in range(nb_epoch):
t1 = time.time()
res = model.fit_generator(generator=train_generator,
steps_per_epoch=STEP_SIZE_TRAIN,
initial_epoch=epoch,
validation_data=val_generator,
epochs=epoch + 1,
callbacks=[reduce_lr],
verbose=1,
shuffle=True,
validation_steps=val_generator.n //
batch_size)
t2 = time.time()
print(res.history)
print('Training time for one epoch : %.1f' % ((t2 - t1)))
train_loss, train_acc = res.history['loss'][0], res.history['acc'][
0]
val_loss, val_acc = res.history['val_loss'][0], res.history[
'val_acc'][0]
nsml.report(summary=True,
step=epoch,
epoch=epoch,
epoch_total=nb_epoch,
loss=train_loss,
acc=train_acc,
val_loss=val_loss,
val_acc=val_acc)
nsml.save(epoch)
print('Total training time : %.1f' % (time.time() - t0))
loss = criterion(predictions, label_vars)
optimizer.zero_grad()
loss.backward()
if grad_clip:
grad_norm = nn.utils.clip_grad_norm(model.parameters(), 1)
grad_norm = grad_norm
else:
grad_norm = 0
optimizer.step()
avg_loss += loss.data[0]
accumulate(model_run, model)
print('batch', i, 'loss', loss.data[0], 'norm', grad_norm)
nsml.report(summary=True, scope=locals(), epoch=epoch,
epoch_total=config.epochs, train__loss=float(loss.data[0]),
step=epoch * total_batch + i)
#print('batch:', i, 'loss:', loss.data[0])
print('epoch:', epoch, ' train_loss:', float(avg_loss/total_batch))
if epoch in [4, 6]:
optimizer.param_groups[0]['lr'] /= 10
print('reduce learning rate')
# nsml ps, 혹은 웹 상의 텐서보드에 나타나는 값을 리포트하는 함수입니다.
#
#nsml.report(summary=True, scope=locals(), epoch=epoch, epoch_total=config.epochs,
# train__loss=float(avg_loss/total_batch), step=epoch)
# DONOTCHANGE (You can decide how often you want to save the model)
nsml.save(epoch)
def train(*, env_id, num_env, hps, num_timesteps, seed):
venv = VecFrameStack(
make_atari_env(env_id,
num_env,
seed,
wrapper_kwargs=dict(),
start_index=num_env * MPI.COMM_WORLD.Get_rank(),
max_episode_steps=hps.pop('max_episode_steps')),
hps.pop('frame_stack'))
venv.score_multiple = 1
venv.record_obs = False
ob_space = venv.observation_space
ac_space = venv.action_space
gamma = hps.pop('gamma')
policy = {'rnn': CnnGruPolicy, 'cnn': CnnPolicy}[hps.pop('policy')]
agent = PpoAgent(
scope='ppo',
ob_space=ob_space,
ac_space=ac_space,
stochpol_fn=functools.partial(
policy,
scope='pol',
ob_space=ob_space,
ac_space=ac_space,
update_ob_stats_independently_per_gpu=hps.pop(
'update_ob_stats_independently_per_gpu'),
proportion_of_exp_used_for_predictor_update=hps.pop(
'proportion_of_exp_used_for_predictor_update'),
exploration_type=hps.pop("exploration_type"),
beta=hps.pop("beta"),
),
gamma=gamma,
gamma_ext=hps.pop('gamma_ext'),
lam=hps.pop('lam'),
nepochs=hps.pop('nepochs'),
nminibatches=hps.pop('nminibatches'),
lr=hps.pop('lr'),
cliprange=0.1,
nsteps=128,
ent_coef=0.001,
max_grad_norm=hps.pop('max_grad_norm'),
use_news=hps.pop("use_news"),
comm=MPI.COMM_WORLD if MPI.COMM_WORLD.Get_size() > 1 else None,
update_ob_stats_every_step=hps.pop('update_ob_stats_every_step'),
int_coeff=hps.pop('int_coeff'),
ext_coeff=hps.pop('ext_coeff'),
noise_type=hps.pop('noise_type'),
noise_p=hps.pop('noise_p'),
use_sched=hps.pop('use_sched'),
num_env=num_env,
exp_name=hps.pop('exp_name'),
)
agent.start_interaction([venv])
if hps.pop('update_ob_stats_from_random_agent'):
agent.collect_random_statistics(num_timesteps=128 * 50)
assert len(hps) == 0, "Unused hyperparameters: %s" % list(hps.keys())
counter = 0
while True:
info = agent.step()
n_updates = 0
if info['update']:
logger.logkvs(info['update'])
logger.dumpkvs()
if NSML:
n_updates = int(info['update']['n_updates'])
nsml_dict = {
k: np.float64(v)
for k, v in info['update'].items()
if isinstance(v, Number)
}
nsml.report(step=n_updates, **nsml_dict)
counter += 1
#if n_updates >= 40*1000: # 40K updates
# break
if agent.I.stats['tcount'] > num_timesteps:
break
agent.stop_interaction()
def main():
global char2index
global index2char
global SOS_token
global EOS_token
global PAD_token
parser = argparse.ArgumentParser(description='Speech hackathon Baseline')
parser.add_argument('--hidden_size', type=int, default=512, help='hidden size of model (default: 512)')
parser.add_argument('--layer_size', type=int, default=3, help='number of layers of model (default: 3)')
parser.add_argument('--dropout', type=float, default=0.2, help='dropout rate in training (default: 0.2)')
parser.add_argument('--bidirectional', action='store_true', help='use bidirectional RNN for encoder (default: False)')
parser.add_argument('--use_attention', action='store_true', help='use attention between encoder-decoder (default: False)')
parser.add_argument('--batch_size', type=int, default=32, help='batch size in training (default: 32)')
parser.add_argument('--workers', type=int, default=4, help='number of workers in dataset loader (default: 4)')
parser.add_argument('--max_epochs', type=int, default=10, help='number of max epochs in training (default: 10)')
parser.add_argument('--lr', type=float, default=1e-04, help='learning rate (default: 0.0001)')
parser.add_argument('--teacher_forcing', type=float, default=0.5, help='teacher forcing ratio in decoder (default: 0.5)')
parser.add_argument('--max_len', type=int, default=80, help='maximum characters of sentence (default: 80)')
parser.add_argument('--no_cuda', action='store_true', help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, help='random seed (default: 1)')
parser.add_argument('--save_name', type=str, default='model', help='the name of model in nsml or local')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument('--pause', type=int, default=0)
parser.add_argument('--log_dir', help='directory for logging, valid in local only')
parser.add_argument('--patience', type=int, help='patience before early stopping (default to None)')
parser.add_argument('--weight_decay', type=float, default=0, help='weight for L2 regularization')
parser.add_argument('--save_from', type=int, default=0, help='starting epoch to save models')
parser.add_argument('--load_ckpt', nargs=2, help='session and checkpoint to load')
parser.add_argument('--transformer_encoder', action='store_true')
parser.add_argument('--share_params', action='store_true')
args = parser.parse_args()
for name, value in args.__dict__.items():
print('{}:\t{}'.format(name, value))
print()
if nsml.IS_ON_NSML:
args.log_dir = None
if args.log_dir is not None:
if not osp.exists(args.log_dir):
os.makedirs(args.log_dir)
with open(osp.join(args.log_dir, 'args.txt'), 'w') as f:
for name, value in args.__dict__.items():
f.write('{}\t{}\n'.format(name, value))
char2index, index2char = label_loader.load_label('./hackathon.labels')
SOS_token = char2index['<s>']
EOS_token = char2index['</s>']
PAD_token = char2index['_']
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if args.cuda else 'cpu')
# N_FFT: defined in loader.py
feature_size = N_FFT / 2 + 1
if args.transformer_encoder:
enc = Encoder(len_max_seq=1248, d_word_vec=257, n_layers=6, n_head=8, d_k=64, d_v=64,
d_model=257, d_inner=2048, dropout=0.1, share_params=args.share_params)
else:
enc = EncoderRNN(
feature_size, args.hidden_size, input_dropout_p=args.dropout, dropout_p=args.dropout,
n_layers=args.layer_size, bidirectional=args.bidirectional, rnn_cell='gru',
variable_lengths=False)
dec = DecoderRNN(
len(char2index), args.max_len, args.hidden_size * (2 if args.bidirectional else 1),
SOS_token, EOS_token, n_layers=args.layer_size, rnn_cell='gru',
bidirectional=args.bidirectional, input_dropout_p=args.dropout, dropout_p=args.dropout,
use_attention=args.use_attention)
if args.transformer_encoder:
model = Seq2SeqTransformerEncoder(enc, dec)
else:
model = Seq2seq(enc, dec)
model.flatten_parameters()
for param in model.parameters():
param.data.uniform_(-0.08, 0.08)
model = nn.DataParallel(model).to(device)
optimizer = optim.Adam(model.module.parameters(), lr=args.lr, weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss(reduction='sum', ignore_index=PAD_token).to(device)
bind_model(model, optimizer)
if args.load_ckpt is not None:
nsml.load(session=args.load_ckpt[0], checkpoint=args.load_ckpt[1])
if args.pause == 1:
nsml.paused(scope=locals())
if args.mode != "train":
return
data_list = osp.join(DATASET_PATH, 'train_data', 'data_list.csv')
wav_paths = list()
script_paths = list()
with open(data_list, 'r') as f:
for line in f:
# line: "aaa.wav,aaa.label"
wav_path, script_path = line.strip().split(',')
wav_paths.append(osp.join(DATASET_PATH, 'train_data', wav_path))
script_paths.append(osp.join(DATASET_PATH, 'train_data', script_path))
cnt_converged = 0
best_loss = 1e10
begin_epoch = 0
# load all target scripts for reducing disk i/o
target_path = osp.join(DATASET_PATH, 'train_label')
load_targets(target_path)
train_batch_num, train_dataset_list, valid_dataset = split_dataset(
args, wav_paths, script_paths, valid_ratio=0.05)
logger.info('start')
train_begin = time.time()
if args.log_dir is not None:
train_writer = SummaryWriter(logdir=osp.join(args.log_dir, 'train'))
valid_writer = SummaryWriter(logdir=osp.join(args.log_dir, 'valid'))
else:
train_writer, valid_writer = None, None
for epoch in range(begin_epoch, args.max_epochs):
if args.load_ckpt is not None:
valid_queue = queue.Queue(args.workers * 2)
valid_loader = BaseDataLoader(valid_dataset, valid_queue, args.batch_size, 0)
valid_loader.start()
eval_loss, eval_cer = evaluate(model, valid_loader, valid_queue, criterion, device)
logger.info('Eval right after model loading (just for checking)')
logger.info('Epoch %d (Evaluate) Loss %0.4f CER %0.4f' % (epoch, eval_loss, eval_cer))
train_queue = queue.Queue(args.workers * 2)
train_loader = MultiLoader(train_dataset_list, train_queue, args.batch_size, args.workers)
train_loader.start()
train_loss, train_cer = train(model, train_batch_num, train_queue, criterion, optimizer,
device, train_begin, args.workers, 100, args.teacher_forcing)
logger.info('Epoch %d (Training) Loss %0.4f CER %0.4f' % (epoch, train_loss, train_cer))
if args.log_dir is not None:
train_writer.add_scalar('epoch/loss', train_loss, epoch)
train_writer.add_scalar('epoch/CER', train_cer, epoch)
train_loader.join()
valid_queue = queue.Queue(args.workers * 2)
valid_loader = BaseDataLoader(valid_dataset, valid_queue, args.batch_size, 0)
valid_loader.start()
eval_loss, eval_cer = evaluate(model, valid_loader, valid_queue, criterion, device)
logger.info('Epoch %d (Evaluate) Loss %0.4f CER %0.4f' % (epoch, eval_loss, eval_cer))
if args.log_dir is not None:
valid_writer.add_scalar('epoch/loss', eval_loss, epoch)
valid_writer.add_scalar('epoch/CER', eval_cer, epoch)
with open(osp.join(args.log_dir, 'loss.txt'), 'a') as f:
f.write('epoch: {}, train: {:.6f}, valid: {:.6f}\n'.format(epoch, train_loss, eval_loss))
with open(osp.join(args.log_dir, 'CER.txt'), 'a') as f:
f.write('epoch: {}, train: {:.6f}, valid: {:.6f}\n'.format(epoch, train_cer, eval_cer))
valid_loader.join()
nsml.report(False, step=epoch, train_epoch__loss=train_loss, train_epoch__cer=train_cer,
eval__loss=eval_loss, eval__cer=eval_cer)
if epoch > args.save_from:
nsml.save(args.save_name + '_e{}'.format(epoch))
best_model = (eval_loss < best_loss)
if best_model:
nsml.save('best')
best_loss = eval_loss
if eval_loss > best_loss:
cnt_converged += 1
if args.patience is not None and cnt_converged > args.patience:
break
else:
cnt_converged = 0
def main():
global char2index
global index2char
global SOS_token
global EOS_token
global PAD_token
parser = argparse.ArgumentParser(description='Speech hackathon Baseline')
parser.add_argument('--hidden_size',
type=int,
default=512,
help='hidden size of model (default: 256)')
parser.add_argument('--layer_size',
type=int,
default=3,
help='number of layers of model (default: 3)')
parser.add_argument('--dropout',
type=float,
default=0.2,
help='dropout rate in training (default: 0.2)')
parser.add_argument(
'--bidirectional',
action='store_true',
help='use bidirectional RNN for encoder (default: False)')
parser.add_argument(
'--use_attention',
action='store_true',
help='use attention between encoder-decoder (default: False)')
parser.add_argument('--batch_size',
type=int,
default=32,
help='batch size in training (default: 32)')
parser.add_argument(
'--workers',
type=int,
default=4,
help='number of workers in dataset loader (default: 4)')
parser.add_argument('--max_epochs',
type=int,
default=10,
help='number of max epochs in training (default: 10)')
parser.add_argument('--lr',
type=float,
default=1e-04,
help='learning rate (default: 0.0001)')
parser.add_argument('--teacher_forcing',
type=float,
default=0.5,
help='teacher forcing ratio in decoder (default: 0.5)')
parser.add_argument('--max_len',
type=int,
default=80,
help='maximum characters of sentence (default: 80)')
parser.add_argument('--no_cuda',
action='store_true',
default=False,
help='disables CUDA training')
parser.add_argument('--seed',
type=int,
default=1,
help='random seed (default: 1)')
parser.add_argument('--save_name',
type=str,
default='model',
help='the name of model in nsml or local')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument("--pause", type=int, default=0)
parser.add_argument('--rnn_cell', type=str, default='gru')
parser.add_argument("--iteration", type=int, default=0)
parser.add_argument('--feature', type=str, default='spec')
parser.add_argument('--save_dir', type=str, default='')
args = parser.parse_args()
char2index, index2char = label_loader.load_label('./hackathon.labels')
SOS_token = char2index['<s>']
EOS_token = char2index['</s>']
PAD_token = char2index['_']
random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if args.cuda else 'cpu')
logger.info('Using %s as feature' % args.feature)
if args.save_dir:
logger.info('Save directory: %s' % args.save_dir)
os.makedirs(args.save_dir, exist_ok=True)
# N_FFT: defined in loader.py
if args.feature == 'mfcc':
feature_size = N_MFCC * 3 # concat of mfcc, mfcc' mfcc''
elif args.feature == 'melspec':
feature_size = N_MELS
elif args.feature == 'spec':
feature_size = N_FFT / 2 + 1
else:
raise ValueError('Unsupported feature %s' % args.feature)
enc = EncoderRNN(feature_size,
args.hidden_size,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
n_layers=args.layer_size,
bidirectional=args.bidirectional,
rnn_cell=args.rnn_cell,
variable_lengths=False)
dec = DecoderRNN(len(char2index),
args.max_len,
args.hidden_size * (2 if args.bidirectional else 1),
SOS_token,
EOS_token,
n_layers=args.layer_size,
rnn_cell=args.rnn_cell,
bidirectional=args.bidirectional,
input_dropout_p=args.dropout,
dropout_p=args.dropout,
use_attention=args.use_attention)
model = Seq2seq(enc, dec)
model.flatten_parameters()
for param in model.parameters():
param.data.uniform_(-0.08, 0.08)
model = nn.DataParallel(model).to(device)
optimizer = optim.Adam(model.module.parameters(), lr=args.lr)
criterion = nn.CrossEntropyLoss(reduction='sum',
ignore_index=PAD_token).to(device)
bind_model(model, optimizer, args.feature)
if args.pause != 1:
nsml.load(checkpoint='10', session='team236/sr-hack-2019-dataset/122')
nsml.save('init')
logger.info('Saved!')
if args.pause == 1:
nsml.paused(scope=locals())
if args.mode != "train":
return
data_list = os.path.join(DATASET_PATH, 'train_data', 'data_list.csv')
wav_paths = list()
script_paths = list()
# load all target scripts for reducing disk i/o
target_path = os.path.join(DATASET_PATH, 'train_label')
target_dict = load_targets(target_path)
with open(data_list, 'r') as f:
for line in f:
# line: "aaa.wav,aaa.label"
wav_path, script_path = line.strip().split(',')
wav_paths.append(os.path.join(DATASET_PATH, 'train_data',
wav_path))
script_paths.append(
os.path.join(DATASET_PATH, 'train_data', script_path))
best_loss = 1e10
begin_epoch = 0
train_dataset, valid_dataset = split_dataset(args,
wav_paths,
script_paths,
target_dict,
args.feature,
valid_ratio=0.05)
train_begin = time.time()
for epoch in range(begin_epoch, args.max_epochs):
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
collate_fn=collate_fn)
train_loss, train_cer = train(model, train_loader, criterion,
optimizer, device, train_begin, 10,
args.teacher_forcing)
logger.info('Epoch %d (Training) Loss %0.4f CER %0.4f' %
(epoch, train_loss, train_cer))
valid_loader = torch.utils.data.DataLoader(valid_dataset,
batch_size=4,
shuffle=False,
num_workers=args.workers,
collate_fn=collate_fn)
eval_loss, eval_cer = evaluate(model, valid_loader, criterion, device)
logger.info('Epoch %d (Evaluate) Loss %0.4f CER %0.4f' %
(epoch, eval_loss, eval_cer))
nsml.report(False,
step=epoch,
train_epoch__loss=train_loss,
train_epoch__cer=train_cer,
eval__loss=eval_loss,
eval__cer=eval_cer)
best_model = (eval_loss < best_loss)
nsml.save(args.save_name)
nsml.save(str(epoch))
if args.save_dir:
save_model(
model, optimizer,
os.path.join(args.save_dir,
'./epoch-%d-cer-%d.pt' % (epoch, eval_cer)))
if best_model:
nsml.save('best')
best_loss = eval_loss
def train(model,
data_loader,
criterion,
optimizer,
device,
train_begin,
print_batch=5,
teacher_forcing_ratio=1):
total_loss = 0.
total_num = 0
total_dist = 0
total_length = 0
total_sent_num = 0
batch = 0
model.train()
total_batch_size = len(data_loader)
logger.info('train() start')
begin = epoch_begin = time.time()
for batch_index, batch in enumerate(data_loader):
feats, scripts, feat_lengths, script_lengths = batch
optimizer.zero_grad()
feats = feats.to(device)
scripts = scripts.to(device)
src_len = scripts.size(1)
target = scripts[:, 1:]
model.module.flatten_parameters()
logit = model(feats,
feat_lengths,
scripts,
teacher_forcing_ratio=teacher_forcing_ratio)
logit = torch.stack(logit, dim=1).to(device)
y_hat = logit.max(-1)[1]
loss = criterion(logit.contiguous().view(-1, logit.size(-1)),
target.contiguous().view(-1))
total_loss += loss.item()
total_num += sum(feat_lengths)
display = random.randrange(0, 100) == 0
dist, length = get_distance(target, y_hat, display=display)
total_dist += dist
total_length += length
total_sent_num += target.size(0)
loss.backward()
optimizer.step()
if batch_index % print_batch == 0:
current = time.time()
elapsed = current - begin
epoch_elapsed = (current - epoch_begin) / 60.0
train_elapsed = (current - train_begin) / 3600.0
logger.info(
'batch: {:4d}/{:4d}, loss: {:.4f}, cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h'
.format(batch_index, total_batch_size, total_loss / total_num,
total_dist / total_length, elapsed, epoch_elapsed,
train_elapsed))
begin = time.time()
nsml.report(False,
step=train.cumulative_batch_count,
train_step__loss=total_loss / total_num,
train_step__cer=total_dist / total_length)
train.cumulative_batch_count += 1
logger.info('train() completed')
return total_loss / total_num, total_dist / total_length
def train(self):
with tf.control_dependencies(self.update_ops):
step = tf.Variable(0, trainable=False)
lr_ = config.lr
lr = tf.placeholder(tf.float32)
optimizer = tf.train.AdamOptimizer(lr, beta1=config.Beta1, beta2=config.Beta2)
if (config.loss_seq in ('two', 'first', 'second')) & (config.hybrid == True):
self.trainer_pre = optimizer.minimize(
self.loss_pre, var_list=tf.trainable_variables(), global_step=step)
self.trainer = optimizer.minimize(
self.loss, var_list=tf.trainable_variables(), global_step=step)
NUM_THREADS = 3
configuration = tf.ConfigProto(inter_op_parallelism_threads=NUM_THREADS, \
intra_op_parallelism_threads=NUM_THREADS, \
allow_soft_placement=True, \
device_count={'CPU': 3}, \
)
with tf.Session(config=configuration) as sess:
path = os.path.join(os.getcwd(), 'Results', 'Spectrograms')
os.makedirs(path, exist_ok=True)
path = os.path.join(os.getcwd(), 'Results', 'Test')
os.makedirs(path, exist_ok=True)
path = os.path.join(os.getcwd(), 'Results', 'Simulation')
os.makedirs(path, exist_ok=True)
path = os.path.join(os.getcwd(), 'Results', 'CheckPoint')
os.makedirs(path, exist_ok=True)
path = os.path.join(os.getcwd(), 'Results', 'Logs_Training')
os.makedirs(path, exist_ok=True)
tf.global_variables_initializer().run()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord=coord)
saver = tf.train.Saver(max_to_keep=10)
writer = tf.summary.FileWriter('./Results/Logs_Training', sess.graph)
"""
Restore
Restore checkpoint only when the user specify the 'config.restore' to be True
"""
if config.restore:
ckpt = tf.train.get_checkpoint_state(checkpoint_dir="./Results/CheckPoint")
try:
if ckpt and ckpt.model_checkpoint_path:
print("check point path : ", ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
print('Restored!')
except AttributeError:
print("No checkpoint")
else:
print("No model restoration")
training_loss = 0
training_alpha_sum = 0
training_alpha_list = []
f = open(os.path.join(os.getcwd(), 'Results', 'Logs_Training', 'training_loss.txt'), 'a+')
f.write("Variable Size : {}\n".format(self.variable_size))
f.close()
for iter in range(config.iter + 1):
if coord.should_stop():
print("break data pipeline")
break
if (config.loss_seq in ('two', 'first', 'second')) & (config.hybrid == True):
if iter <= config.loss_thereshold:
_, loss_, alpha_ = sess.run([self.trainer_pre, self.loss_pre, self.graph.alpha], feed_dict={lr: lr_})
else:
_, loss_, alpha_ = sess.run([self.trainer, self.loss, self.graph.alpha], feed_dict={lr: lr_})
else:
_, loss_, alpha_ = sess.run([self.trainer, self.loss, self.graph.alpha], feed_dict={lr: lr_})
training_loss += loss_
training_alpha_sum += np.mean(alpha_)
training_alpha_list.append(np.squeeze(alpha_))
if iter % 10 == 0:
print("iter : {} (loss : {:.3f}, alpha : {:.3f}/ {:.3f})".format(iter, loss_, np.mean(alpha_), np.std(alpha_)))
summary_ = sess.run(self.summary)
writer.add_summary(summary_, iter)
# print spectrograms
if iter % 5000 == 0:
spec_mix_, spec_tgt_, spec_est_ = sess.run(
[self.graph.input_spec, self.graph.source_spec, self.graph.masked_spec])
mag_mix_ = np.sqrt(np.square(spec_mix_[:, :, :, 0]) + np.square(spec_mix_[:, :, :, 1]))
mag_tgt_ = np.sqrt(np.square(spec_tgt_[:, :, :, 0]) + np.square(spec_tgt_[:, :, :, 1]))
mag_est_ = np.sqrt(np.square(spec_est_[:, :, :, 0]) + np.square(spec_est_[:, :, :, 1]))
mag_image1 = np.log10(np.concatenate((mag_mix_[0], mag_mix_[1], mag_mix_[2]), axis=1) + 1e-02)
mag_image2 = np.log10(np.concatenate((mag_tgt_[0], mag_tgt_[1], mag_tgt_[2]), axis=1) + 1e-02)
mag_image3 = np.log10(np.concatenate((mag_est_[0], mag_est_[1], mag_est_[2]), axis=1) + 1e-02)
mag_image4 = np.log10(
np.concatenate((mag_mix_[-3], mag_mix_[-2], mag_mix_[-1]), axis=1) + 1e-02)
mag_image5 = np.log10(
np.concatenate((mag_tgt_[-3], mag_tgt_[-2], mag_tgt_[-1]), axis=1) + 1e-02)
mag_image6 = np.log10(
np.concatenate((mag_est_[-3], mag_est_[-2], mag_est_[-1]), axis=1) + 1e-02)
fig = plt.figure(figsize=(15, 15))
fig.suptitle('Spectrograms', fontsize=20, family='serif')
font = {'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 15,
}
ax1 = fig.add_subplot(1, 3, 1)
ax1.set_title('Mixture', fontdict=font)
ax1.imshow(mag_image1, interpolation='nearest', aspect='auto', cmap='jet')
ax1.xaxis.set_tick_params(labelsize=15)
ax1.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax2 = fig.add_subplot(1, 3, 2)
ax2.set_title('True Vocal', fontdict=font)
ax2.imshow(mag_image2, interpolation='nearest', aspect='auto', cmap='jet')
ax2.xaxis.set_tick_params(labelsize=15)
ax2.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax3 = fig.add_subplot(1, 3, 3)
ax3.set_title('Estimated Vocal', fontdict=font)
ax3.imshow(mag_image3, interpolation='nearest', aspect='auto', cmap='jet')
ax3.xaxis.set_tick_params(labelsize=15)
ax3.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
plt.tight_layout(pad=0.4, w_pad=1, h_pad=1.0)
plt.subplots_adjust(top=0.85)
fig.savefig("./Results/Spectrograms/Spectrogram{}.png".format(iter))
plt.close(fig)
fig = plt.figure(figsize=(15, 15))
fig.suptitle('NoiseSpectrograms', fontsize=20, family='serif')
font = {'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 15,
}
ax1 = fig.add_subplot(1, 3, 1)
ax1.set_title('Noise', fontdict=font)
ax1.imshow(mag_image4, interpolation='nearest', aspect='auto', cmap='jet')
ax1.xaxis.set_tick_params(labelsize=15)
ax1.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax2 = fig.add_subplot(1, 3, 2)
ax2.set_title('True Zeros', fontdict=font)
ax2.imshow(mag_image5, interpolation='nearest', aspect='auto', cmap='jet')
ax2.xaxis.set_tick_params(labelsize=15)
ax2.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax3 = fig.add_subplot(1, 3, 3)
ax3.set_title('Estimated Zeros', fontdict=font)
ax3.imshow(mag_image6, interpolation='nearest', aspect='auto', cmap='jet')
ax3.xaxis.set_tick_params(labelsize=15)
ax3.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
plt.tight_layout(pad=0.4, w_pad=1, h_pad=1.0)
plt.subplots_adjust(top=0.85)
fig.savefig(
"./Results/Spectrograms/Noise_input_Spectrogram{}.png".format(iter))
plt.close(fig)
if (iter) % 10000 == 0:
training_alpha_std = np.std(np.array(training_alpha_list))
f = open(os.path.join(os.getcwd(), 'Results', 'Logs_Training', 'training_loss.txt'), 'a+')
f.write("average {} loss : {:.4f} (iter {} ~ {})\n".format(config.loss, training_loss / 10000,
iter - 9999, iter + 1))
f.close()
f = open(os.path.join(os.getcwd(), 'Results', 'Logs_Training', 'training_alpha.txt'), 'a+')
f.write("average alpha : {:.4f} / std : {:.4f} (iter {} ~ {})\n".format(training_alpha_sum / 10000,
training_alpha_std ,iter - 9999, iter + 1))
f.close()
nsml.report(summary=True,
iter=iter,
train__loss=float(training_loss / 10000),
alpha=float(training_alpha_sum / 10000),
alpha_std=float(training_alpha_std),
lr=float(lr_)*10000
)
training_loss = 0
training_alpha_sum = 0
training_alpha_list = []
if (iter+1) % config.lr_decay_iter == 0:
if config.lr_decay:
lr_ = lr_/config.lr_decay_rate
print("learning rate is decayed : {}".format(lr_))
# test songs
if (iter + 1) % config.model_save_iter == 0:
moving_rate = config.moving_rate
hop = int(config.audio_size / moving_rate) # This has to be an interger without any rounding
mask = (signal.get_window('hanning', config.audio_size)) / (moving_rate / 2)
# mask = (1 / moving_rate)
for folder, root in [('Test', config.test_sample_root), ('Simulation', config.simul_sample_root)]:
test_sample_path = glob(os.path.join(root, "noisy/*.wav"))
print("{} audios for test".format(len(test_sample_path)))
SNR_list = []
infer_time = []
wb = openpyxl.Workbook()
ws = wb.active
for k, song_path in enumerate(sorted(test_sample_path)):
print("\n %d th song" % k)
# Load song
name = os.path.basename(song_path)
print("Loading {}...".format(name))
noisy, _ = librosa.core.load(song_path, sr=16000)
clean, _ = librosa.core.load(os.path.join(root, "spk", name[:8]+".wav"),
sr=16000)
noise = noisy - clean
print("song shape : ", noisy.shape)
song_length = noisy.shape[0]
num_frame = int(song_length / config.audio_size) + 1
# pad zeros to song to make the length of it be the multiple of config.audio_size
noisy_pad = np.lib.pad(noisy, (
config.audio_size, num_frame * config.audio_size - song_length + config.audio_size),
'constant')
clean_pad = np.lib.pad(clean, (
config.audio_size, num_frame * config.audio_size - song_length + config.audio_size),
'constant')
padded_song_shape = noisy_pad.shape[0]
# Slice mixture and source
noisy_slice_list = []
clean_slice_list = []
hop = int(
config.audio_size / config.moving_rate) # This has to be an interger without any rounding
num_to_move = int((padded_song_shape - config.audio_size) / hop) + 1
for i in range(num_to_move):
start_point = int(i * hop)
end_point = int(start_point + config.audio_size)
noisy_slice = noisy_pad[start_point:end_point] # ex: (1,16384)
noisy_slice_list.append(noisy_slice)
clean_slice = clean_pad[start_point:end_point]
clean_slice_list.append(clean_slice)
num_slice = len(noisy_slice_list)
noisy_stacked = np.array(noisy_slice_list)
clean_stacked = np.array(clean_slice_list)
# Separation
segments = []
spec_mix_list = []
spec_tgt_list = []
spec_est_list = []
spec_est1_list = []
spec_est2_list = []
alpha_list = []
for n, i in enumerate(range(num_slice)):
start_time = time.time()
print("processing {}: {}/{}".format(name, n, num_slice))
estimated_sample, spec_mix_, spec_tgt_, spec_est_, spec_est1_, spec_est2_, alpha_ \
= sess.run([self.graph_test.estimated, self.graph_test.input_spec,
self.graph_test.source_spec, self.graph_test.masked_spec,
self.graph_test.masked_spec1, self.graph_test.masked_spec2,
self.graph_test.alpha],
feed_dict={self.mix_test: noisy_stacked[i:i + 1],
self.source_test: clean_stacked[i:i + 1]})
infer_time.append(time.time() - start_time)
masked_sample = np.expand_dims(mask, axis=0) * estimated_sample
segments.append(masked_sample)
alpha_list.append(np.mean(alpha_))
if (n+1) % 4 == 0:
spec_mix_list.append(spec_mix_)
spec_tgt_list.append(spec_tgt_)
spec_est_list.append(spec_est_)
spec_est1_list.append(spec_est1_)
spec_est2_list.append(spec_est2_)
spec_mix_ = np.concatenate(spec_mix_list, axis=0)
spec_tgt_ = np.concatenate(spec_tgt_list, axis=0)
spec_est_ = np.concatenate(spec_est_list, axis=0)
spec_est1_ = np.concatenate(spec_est1_list, axis=0)
spec_est2_ = np.concatenate(spec_est2_list, axis=0)
# spectrograms
mag_mix_ = np.sqrt(np.square(spec_mix_[:, :, :, 0]) + np.square(spec_mix_[:, :, :, 1]))
mag_tgt_ = np.sqrt(np.square(spec_tgt_[:, :, :, 0]) + np.square(spec_tgt_[:, :, :, 1]))
mag_est_ = np.sqrt(np.square(spec_est_[:, :, :, 0]) + np.square(spec_est_[:, :, :, 1]))
mag_est1_ = np.sqrt(np.square(spec_est1_[:, :, :, 0]) + np.square(spec_est1_[:, :, :, 1]))
mag_est2_ = np.sqrt(np.square(spec_est2_[:, :, :, 0]) + np.square(spec_est2_[:, :, :, 1]))
mag_image1 = np.log10(np.concatenate((mag_mix_[0], mag_mix_[1], mag_mix_[2]), axis=1) + 1e-02)
mag_image2 = np.log10(np.concatenate((mag_tgt_[0], mag_tgt_[1], mag_tgt_[2]), axis=1) + 1e-02)
mag_image3 = np.log10(np.concatenate((mag_est_[0], mag_est_[1], mag_est_[2]), axis=1) + 1e-02)
mag_image4 = np.log10(np.concatenate((mag_est1_[0], mag_est1_[1], mag_est1_[2]), axis=1) + 1e-02)
mag_image5 = np.log10(np.concatenate((mag_est2_[0], mag_est1_[1], mag_est2_[2]), axis=1) + 1e-02)
fig = plt.figure(figsize=(25, 15))
fig.suptitle('Spectrograms', fontsize=20, family='serif')
font = {'family': 'serif',
'color': 'darkred',
'weight': 'normal',
'size': 15,
}
ax1 = fig.add_subplot(1, 5, 1)
ax1.set_title('Mixture', fontdict=font)
ax1.imshow(mag_image1, interpolation='nearest', aspect='auto', cmap='jet')
ax1.xaxis.set_tick_params(labelsize=15)
ax1.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax2 = fig.add_subplot(1, 5, 2)
ax2.set_title('True Vocal', fontdict=font)
ax2.imshow(mag_image2, interpolation='nearest', aspect='auto', cmap='jet')
ax2.xaxis.set_tick_params(labelsize=15)
ax2.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax3 = fig.add_subplot(1, 5, 3)
ax3.set_title('Estimated Vocal {:.3f}'.format(np.mean(alpha_)), fontdict=font)
ax3.imshow(mag_image3, interpolation='nearest', aspect='auto', cmap='jet')
ax3.xaxis.set_tick_params(labelsize=15)
ax3.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax3 = fig.add_subplot(1, 5, 4)
ax3.set_title('Estimated-1 Vocal', fontdict=font)
ax3.imshow(mag_image4, interpolation='nearest', aspect='auto', cmap='jet')
ax3.xaxis.set_tick_params(labelsize=15)
ax3.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
ax3 = fig.add_subplot(1, 5, 5)
ax3.set_title('Estimated-2 Vocal', fontdict=font)
ax3.imshow(mag_image5, interpolation='nearest', aspect='auto', cmap='jet')
ax3.xaxis.set_tick_params(labelsize=15)
ax3.yaxis.set_tick_params(labelsize=15)
plt.gca().invert_yaxis()
plt.tight_layout(pad=0.4, w_pad=1, h_pad=1.0)
plt.subplots_adjust(top=0.85)
fig.savefig("./Results/{}/{}_{}.png".format(folder, iter, os.path.splitext(name)[0]))
plt.close(fig)
# Post-processing(triangle mask)
# num_to_pad = int((config.audio_size/2)*(num_slice-1))
# temp = np.zeros(shape=(config.audio_size + num_to_pad))
temp = np.zeros(shape=(padded_song_shape))
for i in range(len(segments)):
start_point = int(i * (config.audio_size / config.moving_rate))
end_point = int(start_point + config.audio_size)
temp[start_point:end_point] = temp[start_point:end_point] + segments[i]
# Match the original song length
estimation = np.squeeze(temp[config.audio_size:config.audio_size + song_length])
# print("estimation shape: {}".format(estimation.shape))
# save separated source as audio
OUTPUT_FILENAME = "./Results/{}/{}_{}_{}_{}.wav".format(folder, iter, os.path.splitext(name)[0],
config.network, config.network2)
librosa.output.write_wav(OUTPUT_FILENAME, estimation, 16000)
s_target = clean * np.sum(estimation * clean) / np.sum(clean ** 2)
error = estimation - s_target
snr = 10 * np.log10(sum(s_target ** 2) / sum(error ** 2))
SNR_list.append(snr)
noise_type = os.path.splitext(name)[0].split("_")[2]
noise_snr = os.path.splitext(name)[0].split("_")[3]
ws.cell(row= 3, column= k % 3 + 2).value = noise_snr
ws.cell(row= k//3 + 4, column= k % 3 + 2).value = snr
ws.cell(row= k//3 + 4, column= 1).value = noise_type
ws.cell(row= 3, column= k % 3 + 6).value = noise_snr
ws.cell(row= k//3 + 4, column= k % 3 + 6).value = np.mean(np.array(alpha_list))
snr_mean = np.mean(np.array(SNR_list))
snr_std = np.std(np.array(SNR_list))
print("snr mean : {}, std : {}".format(snr_mean, snr_std))
ws.cell(row=1, column=2).value = "mean"
ws.cell(row=1, column=3).value = "std"
ws.cell(row=1, column=4).value = "infer_time per frame"
ws.cell(row=2, column=2).value = snr_mean
ws.cell(row=2, column=3).value = snr_std
ws.cell(row=2, column=4).value = np.mean(np.array(infer_time))
ws.cell(row=2, column=6).value = "alpha"
wb.save('./Results/{}/snr_{}_{}_{}.xlsx'.format(folder, iter, config.network, config.network2))
wb.close()
print("Save model for epoch {}".format(iter))
saver.save(sess, "./Results/CheckPoint/model.ckpt", global_step=(iter+1))
print("-------------------Model saved-------------------")
coord.request_stop()
coord.join(threads)
def train(model,
total_batch_size,
queue,
criterion,
optimizer,
device,
train_begin,
train_loader_count,
print_batch=5,
teacher_forcing_ratio=1):
total_loss = 0.
total_num = 0
total_dist = 0
total_length = 0
total_sent_num = 0
batch = 0
model.train()
logger.info('train() start')
begin = epoch_begin = time.time()
while True:
if queue.empty():
logger.debug('queue is empty')
feats, scripts, feat_lengths, script_lengths = queue.get()
if feats.shape[0] == 0:
# empty feats means closing one loader
train_loader_count -= 1
logger.debug('left train_loader: %d' % (train_loader_count))
if train_loader_count == 0:
break
else:
continue
optimizer.zero_grad()
feats = feats.to(device)
scripts = scripts.to(device)
src_len = scripts.size(1)
target = scripts[:, 1:]
model.module.flatten_parameters()
logit = model(feats,
feat_lengths,
scripts,
teacher_forcing_ratio=teacher_forcing_ratio)
logit = torch.stack(logit, dim=1).to(device)
y_hat = logit.max(-1)[1]
loss = criterion(logit.contiguous().view(-1, logit.size(-1)),
target.contiguous().view(-1))
total_loss += loss.item()
total_num += sum(feat_lengths)
display = random.randrange(0, 100) == 0
dist, length = get_distance(target, y_hat, display=display)
total_dist += dist
total_length += length
total_sent_num += target.size(0)
loss.backward()
optimizer.step()
if batch % print_batch == 0:
current = time.time()
elapsed = current - begin
epoch_elapsed = (current - epoch_begin) / 60.0
train_elapsed = (current - train_begin) / 3600.0
logger.info(
'batch: {:4d}/{:4d}, loss: {:.4f}, cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h'
.format(
batch,
#len(dataloader),
total_batch_size,
total_loss / total_num,
total_dist / total_length,
elapsed,
epoch_elapsed,
train_elapsed))
begin = time.time()
nsml.report(False,
step=train.cumulative_batch_count,
train_step__loss=total_loss / total_num,
train_step__cer=total_dist / total_length)
batch += 1
train.cumulative_batch_count += 1
logger.info('train() completed')
return total_loss / total_num, total_dist / total_length
def on_epoch_end(self, epoch, logs={}):
nsml.report(summary=True, scope=locals(), epoch=epoch, epoch_total=config.epochs, step=epoch)
nsml.save(epoch)
', Accuracy:', round(accuracy, 2), ', time: %.2f' % (time.time() - t0))
t0 = time.time()
if (i+1) >= total_eval_batch:
break
if total_eval_batch > 0:
eval_accuracy = float(avg_accuracy / total_eval_batch)
eval_loss = float(avg_loss/total_eval_batch)
print('\t eval_loss: %.3f' % eval_loss, 'accuracy: %.2f' % eval_accuracy,
', time: %.1fs' % (time.time() - t1))
else:
eval_accuracy = 0
eval_loss = 0
# nsml ps, 혹은 웹 상의 텐서보드에 나타나는 값을 리포트하는 함수입니다.
nsml.report(summary=True, scope=locals(), epoch=epoch, epoch_total=config.epochs,
train__loss=train_loss, train_accuracy=train_accuracy,
eval__loss=eval_loss, eval_accuracy=eval_accuracy,
step=epoch)
# DONOTCHANGE (You can decide how often you want to save the model)
nsml.save(epoch)
# 로컬 테스트 모드일때 사용합니다
# 결과가 아래와 같이 나온다면, nsml submit을 통해서 제출할 수 있습니다.
# [(0.0, 9.045), (0.0, 5.91), ... ]
elif config.mode == 'test_local':
with open(os.path.join(DATASET_PATH, 'train/train_data'), 'rt', encoding='utf-8') as f:
reviews = f.readlines()[:config.max_dataset]
res = nsml.infer(reviews)
print(res)
def train(model, train_loader, args):
# Set mode to 'train' (for batch norm.)
model.train()
# Enable CUDA if specified
use_cuda = args.cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
model = model.to(device)
# Initialize visdom object
if not args.local:
viz = Visdom(visdom=visdom)
# Specify loss function & optimizer
if args.loss == 'L1':
criterion = nn.L1Loss()
elif args.loss == 'MSE':
criterion = nn.MSELoss()
else:
raise NotImplementedError("Only L1Loss(cmd arg:'L1') and MSELoss(cmd arg:'MSE') are supported.")
parameters = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.Adam(params = parameters, lr = args.learning_rate)
# Compute the number of iterations in an epoch
num_iters = len(train_loader)
if args.max_dataset_size > 0:
num_iters = args.max_dataset_size // args.batch_size
min_loss = 10000000
# For NSML saving function
bind_model(model=model, optimizer=optimizer)
# Add perceptual loss term (use upto 'relu2-2' layer of VGG-16)
if args.gamma > 0:
vgg16_model = models.vgg16_bn(pretrained=True).to(device)
layers_list = list(vgg16_model.features.children())[:14]
'''
for idx in [2,5,9,12]:
layers_list[idx] = nn.ReLU(inplace=False)
for layer in layers_list:
print(layer)
'''
perceptual_model = torch.nn.Sequential(*layers_list).to(device)
for param in perceptual_model.parameters():
param.requires_grad = False
perceptual_criterion = nn.MSELoss()
# Conduct training
for epoch in range(args.epochs):
total_loss = 0
if args.verbose:
print('=============== Epoch {} ==============='.format(epoch+1))
for it, (covers, secrets) in enumerate(train_loader):
print('\tIter: {} / {}'.format(it+1, num_iters))
# Enable CUDA if specified
covers, secrets = covers.to(device), secrets.to(device)
# Forward run
prepped, container, revealed = model.forward(covers, secrets, device=device,
noise_level=args.noise_level)
# Compute loss
loss = criterion(container, covers)
loss += args.beta * criterion(revealed, secrets)
if args.gamma > 0:
# Normalize each image in a minibatch of {covers, container}
size = container.size()
means = torch.tensor([0.485, 0.456, 0.406])
stds = torch.tensor([0.229, 0.224, 0.225])
means, stds = [item.view(3,1,1).expand(container.size()).to(device) for item in [means, stds]]
normalized = [torch.div(torch.add(minibatch,-1,means),stds).to(device) \
for minibatch in [container, covers]]
loss += args.gamma * criterion(*normalized)
# Do back-propagation
model.zero_grad()
loss.backward()
optimizer.step()
# Collect statistics
total_loss += loss.item()
if args.verbose:
print("\t\tLoss sum at iter {}: {}".format(it+1, loss.item()))
if args.max_dataset_size > 0 and (it+1) * args.batch_size >= args.max_dataset_size:
break
if args.verbose:
print('Loss at epoch {}: {}'.format(epoch+1, total_loss / num_iters))
# Report statistics to NSML (if not draft)
if not args.local:
nsml.report(summary = True,
step = (epoch+1) * len(train_loader),
epoch_total = args.epochs,
train__loss = total_loss / num_iters)
# Visualize input & output images
for idx in range(args.viz_count):
images = [images.detach()[idx] \
for images in [covers, secrets, container, revealed]] # select example images
visualize(viz, images, epoch,
c_mode=args.c_mode, s_mode = args.s_mode,
c_draw_rgb=args.c_draw_rgb, s_draw_rgb = args.s_draw_rgb,
use_cuda = use_cuda)
# Save session if minimum loss is renewed
if not args.draft and not args.local and total_loss < min_loss:
nsml.save(epoch)
min_loss = total_loss
if args.draft:
break
def train_distill(opts, train_loader, unlabel_loader, model, criterion,
optimizer, ema_optimizer, epoch, use_gpu, scheduler,
is_mixsim):
global global_step
scaler = torch.cuda.amp.GradScaler()
losses = AverageMeter()
losses_x = AverageMeter()
losses_un = AverageMeter()
losses_curr = AverageMeter()
losses_x_curr = AverageMeter()
losses_un_curr = AverageMeter()
weight_scale = AverageMeter()
acc_top1 = AverageMeter()
acc_top5 = AverageMeter()
model.train()
# nCnt =0
out = False
local_step = 0
while not out:
labeled_train_iter = iter(train_loader)
unlabeled_train_iter = iter(unlabel_loader)
for batch_idx in range(len(train_loader)):
try:
data = labeled_train_iter.next()
inputs_x, targets_x = data
except:
labeled_train_iter = iter(train_loader)
data = labeled_train_iter.next()
inputs_x, targets_x = data
try:
data = unlabeled_train_iter.next()
inputs_u1, inputs_u2 = data
except:
unlabeled_train_iter = iter(unlabel_loader)
data = unlabeled_train_iter.next()
inputs_u1, inputs_u2 = data
batch_size = inputs_x.size(0)
# Transform label to one-hot
classno = NUM_CLASSES
targets_org = targets_x
targets_x = torch.zeros(batch_size,
classno).scatter_(1, targets_x.view(-1, 1),
1)
if use_gpu:
# Send input value to device 0, where first parameters of MixSim_Model is.
if is_mixsim:
dev0 = 'cuda:{}'.format(opts.gpu_ids.split(',')[0])
else:
dev0 = 'cuda'
inputs_x, targets_x = inputs_x.to(dev0), targets_x.to(dev0)
inputs_u1, inputs_u2 = inputs_u1.to(dev0), inputs_u2.to(dev0)
with torch.no_grad():
# compute guessed labels of unlabel samples
_, pred_u1 = model(inputs_u1)
_, pred_u2 = model(inputs_u2)
pred_u_all = (torch.softmax(pred_u1, dim=1) +
torch.softmax(pred_u2, dim=1)) / 2
pt = pred_u_all**(1 / opts.T)
targets_u = pt / pt.sum(dim=1, keepdim=True)
targets_u = targets_u.detach()
# mixup
all_inputs = torch.cat([inputs_x, inputs_u1, inputs_u2], dim=0)
all_targets = torch.cat([targets_x, targets_u, targets_u], dim=0)
lamda = np.random.beta(opts.alpha, opts.alpha)
lamda = max(lamda, 1 - lamda)
newidx = torch.randperm(all_inputs.size(0))
input_a, input_b = all_inputs, all_inputs[newidx]
target_a, target_b = all_targets, all_targets[newidx]
mixed_input = lamda * input_a + (1 - lamda) * input_b
mixed_target = lamda * target_a + (1 - lamda) * target_b
# interleave labeled and unlabed samples between batches to get correct batchnorm calculation
mixed_input = list(torch.split(mixed_input, batch_size))
mixed_input = interleave(mixed_input, batch_size)
optimizer.zero_grad()
with torch.cuda.amp.autocast():
_, logits_temp = model(mixed_input[0])
logits = [logits_temp]
for newinput in mixed_input[1:]:
_, logits_temp = model(newinput)
logits.append(logits_temp)
# put interleaved samples back
logits = interleave(logits, batch_size)
logits_x = logits[0]
logits_u = torch.cat(logits[1:], dim=0)
loss_x, loss_un, weigts_mixing = criterion(
logits_x, mixed_target[:batch_size], logits_u,
mixed_target[batch_size:],
epoch + batch_idx / len(train_loader), opts.epochs)
loss = loss_x + weigts_mixing * loss_un
losses.update(loss.item(), inputs_x.size(0))
losses_x.update(loss_x.item(), inputs_x.size(0))
losses_un.update(loss_un.item(), inputs_x.size(0))
weight_scale.update(weigts_mixing, inputs_x.size(0))
losses_curr.update(loss.item(), inputs_x.size(0))
losses_x_curr.update(loss_x.item(), inputs_x.size(0))
losses_un_curr.update(loss_un.item(), inputs_x.size(0))
# compute gradient and do SGD step using amp
scaler.scale(loss).backward()
scaler.step(optimizer)
ema_optimizer.step()
scaler.update()
scheduler.step()
with torch.no_grad():
# compute guessed labels of unlabel samples
_, pred_x1 = model(inputs_x)
if IS_ON_NSML and global_step % opts.log_interval == 0:
nsml.report(step=global_step,
loss=losses_curr.avg,
loss_x=losses_x_curr.avg,
loss_un=losses_un_curr.avg)
losses_curr.reset()
losses_x_curr.reset()
losses_un_curr.reset()
acc_top1b = top_n_accuracy_score(targets_org.data.cpu().numpy(),
pred_x1.data.cpu().numpy(),
n=1) * 100
acc_top5b = top_n_accuracy_score(targets_org.data.cpu().numpy(),
pred_x1.data.cpu().numpy(),
n=5) * 100
acc_top1.update(torch.as_tensor(acc_top1b), inputs_x.size(0))
acc_top5.update(torch.as_tensor(acc_top5b), inputs_x.size(0))
local_step += 1
global_step += 1
if local_step >= opts.steps_per_epoch:
out = True
break
return losses.avg, losses_x.avg, losses_un.avg, acc_top1.avg, acc_top5.avg
train_loss = float(avg_loss / total_batch)
val_loss = sess.run(textRNN.loss_val,
feed_dict={
textRNN.input_y: y_test,
textRNN.input_x: x_test,
textRNN.dropout_keep_prob: 1
})
if val_loss < min_val_loss:
min_val_loss = val_loss
print('epoch:', epoch, ' train_loss:', train_loss, ' val_loss:',
val_loss)
nsml.report(summary=True,
scope=locals(),
epoch=epoch,
epoch_total=config.epochs,
train__loss=float(avg_loss / total_batch),
step=epoch)
# DONOTCHANGE (You can decide how often you want to save the model)
nsml.save(epoch)
# 로컬 테스트 모드일때 사용합니다
# 결과가 아래와 같이 나온다면, nsml submit을 통해서 제출할 수 있습니다.
# [(0.3, 0), (0.7, 1), ... ]
elif config.mode == 'test_local':
with open(os.path.join(DATASET_PATH, 'train/train_data'),
'rt',
encoding='utf-8') as f:
reviews = f.readlines()
res = nsml.infer(reviews)
data, y = data_loader(img_path, label_path)
X = preprocessing(data)
# Data loader
batch_loader = DataLoader(
dataset=MammoDataset(X, y), ## pytorch data loader 사용
batch_size=batch_size,
shuffle=True)
# Train the model
total_step = len(batch_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(batch_loader):
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
nsml.report(summary=True,
step=epoch,
epoch_total=num_epochs,
loss=loss.item()) #, acc=train_acc)
nsml.save(epoch)
if step % 150 == 0 or (config.debug and step % 1 == 0):
infer_result = local_infer(queries, references, queries_img, reference_img, batch_size)
mAP, mean_recall_at_K, min_first_1_at_K = evaluate_rank(infer_result)
if best_min_first_K >= min_first_1_at_K:
best_min_first_K = min_first_1_at_K
best_min_first_K_step = step
print("----> First_K @ 1 recall : %d / %d" % (min_first_1_at_K, len(mean_recall_at_K)))
do_save = True
if best_mAP <= mAP :
best_mAP = mAP
print("----> Best mAP : best-mAP {:g}".format(best_mAP))
do_save = True
if epoch - prev_epoch == 1:
print("----> Epoch changed saving")
do_save = True
if do_save:
# save model
nsml.report(summary=True, epoch=str(step), epoch_total=nb_epoch)
nsml.save(step)
print("Model saved : %d step" % step)
print("=============================================================================================================")
except tf.errors.OutOfRangeError:
print("finish train!")
break
def trainTrans(model,
total_batch_size,
queue,
criterion,
optimizer,
device,
train_begin,
train_loader_count,
print_batch=5,
teacher_forcing_ratio=1,
visual=None,
label_smoothing=0.1):
total_loss = 0.
total_num = 0
total_dist = 0
total_length = 0
total_sent_num = 0
batch = 0
model.train()
logger.info('train() start')
begin = epoch_begin = time.time()
while True:
if queue.empty():
logger.debug('queue is empty')
feats, scripts, feat_lengths, script_lengths = queue.get()
if feats.shape[0] == 0:
# empty feats means closing one loader
train_loader_count -= 1
logger.debug('left train_loader: %d' % (train_loader_count))
if train_loader_count == 0:
break
else:
continue
optimizer.zero_grad()
feats = feats.to(device)
scripts = scripts.to(device)
src_len = scripts.size(1)
target = scripts[:, 1:]
pred, gold = model(feats, feat_lengths, scripts)
loss, n_correct = cal_performance(pred,
gold,
smoothing=label_smoothing)
y_hat = pred.max(-1)[1]
total_loss += loss.item()
total_num += sum(feat_lengths)
display = random.randrange(0, 100) == 0
dist, length = get_distance(target, y_hat, display=display)
total_dist += dist
total_length += length
total_sent_num += target.size(0)
loss.backward()
optimizer.step()
if visual:
vis_log = {
'Train Loss': total_loss / total_num,
'Train CER': total_dist / total_length
}
visual.log(vis_log)
if batch % print_batch == 0:
current = time.time()
elapsed = current - begin
epoch_elapsed = (current - epoch_begin) / 60.0
train_elapsed = (current - train_begin) / 3600.0
logger.info(
'batch: {:4d}/{:4d}, loss: {:.4f}, cer: {:.2f}, elapsed: {:.2f}s {:.2f}m {:.2f}h'
.format(
batch,
#len(dataloader),
total_batch_size,
total_loss / total_num,
total_dist / total_length,
elapsed,
epoch_elapsed,
train_elapsed))
begin = time.time()
nsml.report(False,
step=trainTrans.cumulative_batch_count,
train_step__loss=total_loss / total_num,
train_step__cer=total_dist / total_length)
batch += 1
trainTrans.cumulative_batch_count += 1
logger.info('train() completed')
return total_loss / total_num, total_dist / total_length
def run(self):
mb_obs, mb_td_targets, mb_base_actions, \
mb_xy0, mb_xy1, \
mb_values, mb_dones \
= [],[],[],[],[],[], []
# ,[],[],[],[],[],[],[],[],[],[],[]
mb_states = self.states
for n in range(self.nsteps):
# pi, pi2, x1, y1, x2, y2, v0
pi1, pi_xy0, pi_xy1, values, states = self.model.step(
self.obs, self.states, self.dones)
pi1_noise = np.random.random_sample((self.nenv, 2)) * 0.3
# avail = self.env.available_actions()
# print("pi1 : ", pi1)
# print("pi1 * self.base_act_mask : ", pi1 * self.base_act_mask)
# print("pi1 * self.base_act_mask + pi1_noise : ", pi1 * self.base_act_mask + pi1_noise)
base_actions = np.argmax(pi1 * self.base_act_mask + pi1_noise,
axis=1)
xy0 = np.argmax(pi_xy0, axis=1)
x0 = (xy0 % 32).astype(int)
y0 = (xy0 / 32).astype(int)
xy1 = np.argmax(pi_xy1, axis=1)
x1 = (xy1 % 32).astype(int)
y1 = (xy1 / 32).astype(int)
# pi (2?, 524) * (2?, 524) masking
# print("base_actions : ", base_actions)
# print("base_action_spec : ", base_action_spec)
# sub1_act_mask, sub2_act_mask, sub3_act_mask = self.get_sub_act_mask(base_action_spec)
# print("base_actions : ", base_actions, "base_action_spec", base_action_spec,
# "sub1_act_mask :", sub1_act_mask, "sub2_act_mask :", sub2_act_mask, "sub3_act_mask :", sub3_act_mask)
# sub3_actions = np.argmax(pi_sub3, axis=1) # pi (2?, 2) [1 0]
# sub4_actions = np.argmax(pi_sub4, axis=1) # pi (2?, 5) [4 4]
# sub5_actions = np.argmax(pi_sub5, axis=1) # pi (2?, 10) [1 4]
# sub6_actions = np.argmax(pi_sub6, axis=1) # pi (2?, 4) [3 1]
# sub7_actions = np.argmax(pi_sub7, axis=1) # pi (2?, 2)
# sub8_actions = np.argmax(pi_sub8, axis=1) # pi (2?, 4)
# sub9_actions = np.argmax(pi_sub9, axis=1) # pi (2?, 500)
# sub10_actions = np.argmax(pi_sub10, axis=1) # pi (2?, 4)
# sub11_actions = np.argmax(pi_sub11, axis=1) # pi (2?, 10)
# sub12_actions = np.argmax(pi_sub12, axis=1) # pi (2?, 500)
# Scripted Agent Hacking
for env_num in range(self.nenv):
if (env_num >= self.nscripts): # only for scripted agents
continue
ob = self.obs[env_num, :, :, :]
# extra = ob[:,:,-1]
# selected = ob[:, :, -2]
player_relative = ob[:, :, -1]
#if(common.check_group_list())
self.group_list[env_num] = common.update_group_list2(
self.control_groups[env_num])
# if(len(self.action_queue[env_num]) == 0 and len(self.group_list[env_num]) == 0):
#
# # Scripted Agent is only for even number agents
# self.action_queue[env_num] = common.group_init_queue(player_relative)
if (len(self.action_queue[env_num]) == 0):
self.action_queue[env_num], self.group_id[env_num], self.dest_per_marine[env_num], self.xy_per_marine[env_num] = \
common.solve_tsp(player_relative,
self.selected[env_num][0],
self.group_list[env_num],
self.group_id[env_num],
self.dest_per_marine[env_num],
self.xy_per_marine[env_num])
base_actions[env_num] = 0
x0[env_num] = 0
y0[env_num] = 0
x1[env_num] = 0
y1[env_num] = 0
if (len(self.action_queue[env_num]) > 0):
action = self.action_queue[env_num].pop(0)
# print("action :", action)
base_actions[env_num] = action.get("base_action", 0)
x0[env_num] = action.get("x0", 0)
y0[env_num] = action.get("y0", 0)
xy0[env_num] = y0[env_num] * 32 + x0[env_num]
x1[env_num] = action.get("x1", 0)
y1[env_num] = action.get("y1", 0)
xy1[env_num] = y1[env_num] * 32 + x1[env_num]
base_actions = self.valid_base_action(base_actions)
# print("valid_base_actions : ", base_actions)
new_base_actions = self.trans_base_actions(base_actions)
# print("new_base_actions : ", new_base_actions)
base_action_spec = self.env.action_spec(new_base_actions)
actions = self.construct_action(
base_actions,
base_action_spec,
# sub3_actions, sub4_actions, sub5_actions,
# sub6_actions,
# sub7_actions, sub8_actions,
# sub9_actions, sub10_actions,
# sub11_actions, sub12_actions,
x0,
y0,
x1,
y1
# , x2, y2
)
mb_obs.append(np.copy(self.obs))
mb_base_actions.append(base_actions)
# mb_sub3_actions.append(sub3_actions)
# mb_sub4_actions.append(sub4_actions)
# mb_sub5_actions.append(sub5_actions)
# mb_sub6_actions.append(sub6_actions)
# mb_sub7_actions.append(sub7_actions)
# mb_sub8_actions.append(sub8_actions)
# mb_sub9_actions.append(sub9_actions)
# mb_sub10_actions.append(sub10_actions)
# mb_sub11_actions.append(sub11_actions)
# mb_sub12_actions.append(sub12_actions)
mb_xy0.append(xy0)
# mb_y0.append(y0)
mb_xy1.append(xy1)
# mb_y1.append(y1)
# mb_x2.append(x2)
# mb_y2.append(y2)
mb_values.append(values)
mb_dones.append(self.dones)
#print("final acitons : ", actions)
obs, rewards, dones, available_actions, army_counts, control_groups, selected, xy_per_marine = self.env.step(
actions=actions)
self.army_counts = army_counts
self.control_groups = control_groups
self.selected = selected
for env_num, data in enumerate(xy_per_marine):
# print("env_num", env_num, "xy_per_marine:", data)
self.xy_per_marine[env_num] = data
self.update_available(available_actions)
self.states = states
self.dones = dones
for n, done in enumerate(dones):
self.total_reward[n] += float(rewards[n])
if done:
self.obs[n] = self.obs[n] * 0
self.episodes += 1
num_episodes = self.episodes
self.episode_rewards.append(self.total_reward[n])
mean_100ep_reward = round(
np.mean(self.episode_rewards[-101:-1]), 1)
if (n < self.nscripts): # scripted agents
self.episode_rewards_script.append(
self.total_reward[n])
mean_100ep_reward_script = round(
np.mean(self.episode_rewards_script[-101:-1]), 1)
# logger.record_tabular("reward script",
# self.total_reward[n])
# logger.record_tabular("mean reward script",
# mean_100ep_reward_script)
nsml.report(
reward_script=self.total_reward[n],
mean_reward_script=mean_100ep_reward_script,
reward=self.total_reward[n],
mean_100ep_reward=mean_100ep_reward,
episodes=self.episodes,
step=self.episodes,
scope=locals())
else:
self.episode_rewards_a2c.append(self.total_reward[n])
mean_100ep_reward_a2c = round(
np.mean(self.episode_rewards_a2c[-101:-1]), 1)
# logger.record_tabular("reward a2c",
# self.total_reward[n])
# logger.record_tabular("mean reward a2c",
# mean_100ep_reward_a2c)
nsml.report(reward_a2c=self.total_reward[n],
mean_reward_a2c=mean_100ep_reward_a2c,
reward=self.total_reward[n],
mean_100ep_reward=mean_100ep_reward,
episodes=self.episodes,
step=self.episodes,
scope=locals())
#print("env %s done! reward : %s mean_100ep_reward : %s " %
# (n, self.total_reward[n], mean_100ep_reward))
# logger.record_tabular("reward", self.total_reward[n])
# logger.record_tabular("mean 100 episode reward",
# mean_100ep_reward)
# logger.record_tabular("episodes", self.episodes)
# logger.dump_tabular()
self.total_reward[n] = 0
self.group_list[n] = []
model = self.model
if self.callback is not None:
self.callback(locals(), globals())
#print("rewards : ", rewards)
#print("self.total_reward :", self.total_reward)
self.update_obs(obs)
mb_td_targets.append(rewards)
mb_dones.append(self.dones)
#batch of steps to batch of rollouts
mb_obs = np.asarray(mb_obs, dtype=np.uint8).swapaxes(1, 0).reshape(
self.batch_ob_shape)
mb_td_targets = np.asarray(mb_td_targets,
dtype=np.float32).swapaxes(1, 0)
mb_base_actions = np.asarray(mb_base_actions,
dtype=np.int32).swapaxes(1, 0)
# mb_sub3_actions = np.asarray(mb_sub3_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub4_actions = np.asarray(mb_sub4_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub5_actions = np.asarray(mb_sub5_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub6_actions = np.asarray(mb_sub6_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub7_actions = np.asarray(mb_sub7_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub8_actions = np.asarray(mb_sub8_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub9_actions = np.asarray(mb_sub9_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub10_actions = np.asarray(mb_sub10_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub11_actions = np.asarray(mb_sub11_actions, dtype=np.int32).swapaxes(1, 0)
# mb_sub12_actions = np.asarray(mb_sub12_actions, dtype=np.int32).swapaxes(1, 0)
mb_xy0 = np.asarray(mb_xy0, dtype=np.int32).swapaxes(1, 0)
# mb_y0 = np.asarray(mb_y0, dtype=np.int32).swapaxes(1, 0)
mb_xy1 = np.asarray(mb_xy1, dtype=np.int32).swapaxes(1, 0)
# mb_y1 = np.asarray(mb_y1, dtype=np.int32).swapaxes(1, 0)
# mb_x2 = np.asarray(mb_x2, dtype=np.int32).swapaxes(1, 0)
# mb_y2 = np.asarray(mb_y2, dtype=np.int32).swapaxes(1, 0)
mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
mb_masks = mb_dones[:, :-1]
mb_dones = mb_dones[:, 1:]
last_values = self.model.value(self.obs, self.states,
self.dones).tolist()
#discount/bootstrap off value fn
for n, (rewards, dones,
value) in enumerate(zip(mb_td_targets, mb_dones, last_values)):
rewards = rewards.tolist()
dones = dones.tolist()
if dones[-1] == 0:
rewards = discount_with_dones(rewards + [value], dones + [0],
self.gamma)[:-1]
else:
rewards = discount_with_dones(rewards, dones, self.gamma)
mb_td_targets[n] = rewards
mb_td_targets = mb_td_targets.flatten()
mb_base_actions = mb_base_actions.flatten()
# mb_sub3_actions = mb_sub3_actions.flatten()
# mb_sub4_actions = mb_sub4_actions.flatten()
# mb_sub5_actions = mb_sub5_actions.flatten()
# mb_sub6_actions = mb_sub6_actions.flatten()
# mb_sub7_actions = mb_sub7_actions.flatten()
# mb_sub8_actions = mb_sub8_actions.flatten()
# mb_sub9_actions = mb_sub9_actions.flatten()
# mb_sub10_actions = mb_sub10_actions.flatten()
# mb_sub11_actions = mb_sub11_actions.flatten()
# mb_sub12_actions = mb_sub12_actions.flatten()
mb_xy0 = mb_xy0.flatten()
# mb_y0 = mb_y0.flatten()
mb_xy1 = mb_xy1.flatten()
# mb_y1 = mb_y1.flatten()
# mb_x2 = mb_x2.flatten()
# mb_y2 = mb_y2.flatten()
mb_values = mb_values.flatten()
mb_masks = mb_masks.flatten()
return mb_obs, mb_states, mb_td_targets, mb_masks, \
mb_base_actions, mb_xy0, mb_xy1, mb_values
for iter_, (image, image_id, label) in enumerate(train_loader):
image = image.cuda()
label = label.cuda()
pred = model(image)
loss = criterion(pred, label)
total_loss += loss.item() * image.size(0)
num_images += image.size(0)
optimizer.zero_grad()
loss.backward()
optimizer.step()
loss_average = total_loss / float(num_images)
scheduler.step(metrics=loss_average, epoch=epoch)
if IS_ON_NSML:
nsml.save(str(epoch + 1))
gt_label = os.path.join(DATASET_PATH, 'train/train_data/val_label')
acc = local_eval(model, val_loader, gt_label)
print(f'[{epoch + 1}/{config.num_epochs}] '
f'Validation performance: {acc:.3f}')
nsml.report(step=epoch, val_acc=acc)
nsml.report(step=epoch,
train_loss=loss_average,
learning_rate=optimizer.param_groups[0]['lr'])
if IS_ON_NSML and acc > best_accuracy:
nsml.save(str(epoch + 1))
best_accuracy = acc
def learn(policy,
env,
seed,
total_timesteps=int(40e6),
gamma=0.99,
log_interval=1,
nprocs=24,
nscripts=12,
nsteps=20,
nstack=4,
ent_coef=0.01,
vf_coef=0.5,
vf_fisher_coef=1.0,
lr=0.25,
max_grad_norm=0.01,
kfac_clip=0.001,
save_interval=None,
lrschedule='linear',
callback=None):
tf.reset_default_graph()
set_global_seeds(seed)
nenvs = nprocs
ob_space = (32, 32, 3) # env.observation_space
ac_space = (32, 32)
make_model = lambda: Model(policy,
ob_space,
ac_space,
nenvs,
total_timesteps,
nprocs=nprocs,
nscripts=nscripts,
nsteps=nsteps,
nstack=nstack,
ent_coef=ent_coef,
vf_coef=vf_coef,
vf_fisher_coef=vf_fisher_coef,
lr=lr,
max_grad_norm=max_grad_norm,
kfac_clip=kfac_clip,
lrschedule=lrschedule)
if save_interval and logger.get_dir():
import cloudpickle
with open(osp.join(logger.get_dir(), 'make_model.pkl'), 'wb') as fh:
fh.write(cloudpickle.dumps(make_model))
model = make_model()
print("make_model complete!")
runner = Runner(env,
model,
nsteps=nsteps,
nscripts=nscripts,
nstack=nstack,
gamma=gamma,
callback=callback)
nbatch = nenvs * nsteps
tstart = time.time()
#enqueue_threads = model.q_runner.create_threads(model.sess, coord=tf.train.Coordinator(), start=True)
for update in range(1, total_timesteps // nbatch + 1):
# print('iteration: %d' % update)
obs, states, td_targets, masks, actions, xy0, xy1, values = runner.run(
)
# print('values: %s' % values)
policy_loss, value_loss, policy_entropy, \
policy_loss_xy0, policy_entropy_xy0, \
policy_loss_xy1, policy_entropy_xy1, \
= model.train(obs, states, td_targets,
masks, actions,
xy0, xy1, values)
model.old_obs = obs
nseconds = time.time() - tstart
fps = int((update * nbatch) / nseconds)
if update % log_interval == 0 or update == 1:
ev = explained_variance(values, td_targets)
nsml.report(nupdates=update,
total_timesteps=update * nbatch,
fps=fps,
policy_entropy=float(policy_entropy),
policy_loss=float(policy_loss),
policy_loss_xy0=float(policy_loss_xy0),
policy_entropy_xy0=float(policy_entropy_xy0),
policy_loss_xy1=float(policy_loss_xy1),
policy_entropy_xy1=float(policy_entropy_xy1),
value_loss=float(value_loss),
explained_variance=float(ev),
batch_size=nbatch,
step=update * nbatch,
scope=locals())
# logger.record_tabular("nupdates", update)
# logger.record_tabular("total_timesteps", update * nbatch)
# logger.record_tabular("fps", fps)
# logger.record_tabular("policy_entropy", float(policy_entropy))
# logger.record_tabular("policy_loss", float(policy_loss))
# logger.record_tabular("policy_loss_xy0", float(policy_loss_xy0))
# logger.record_tabular("policy_entropy_xy0",
# float(policy_entropy_xy0))
# logger.record_tabular("policy_loss_xy1", float(policy_loss_xy1))
# logger.record_tabular("policy_entropy_xy1",
# float(policy_entropy_xy1))
# # logger.record_tabular("policy_loss_y0", float(policy_loss_y0))
# # logger.record_tabular("policy_entropy_y0", float(policy_entropy_y0))
# logger.record_tabular("value_loss", float(value_loss))
# logger.record_tabular("explained_variance", float(ev))
# logger.dump_tabular()
if save_interval and (update % save_interval == 0
or update == 1) and logger.get_dir():
savepath = osp.join(logger.get_dir(), 'checkpoint%.5i' % update)
print('Saving to', savepath)
model.save(savepath)
env.close()
def main():
global char2index
global index2char
global SOS_token
global EOS_token
global PAD_token
parser = argparse.ArgumentParser(description='Speech hackathon lilililill model')
parser.add_argument('--max_epochs', type=int, default=1000, help='number of max epochs in training (default: 1000)')
parser.add_argument('--no_cuda', action='store_true', default=False, help='disables CUDA training')
parser.add_argument('--save_name', type=str, default='model', help='the name of model in nsml or local')
# parser.add_argument('--dropout', type=float, default=0.2, help='dropout rate in training (default: 0.2)')
parser.add_argument('--lr_1', type=float, default=1e-04, help='learning rate 1 (default: 0.0001)')
parser.add_argument('--lr_2', type=float, default=1e-04, help='learning rate 2 (default: 0.0001)')
parser.add_argument('--num_mels', type=int, default=160, help='number of the mel bands (default: 160)')
parser.add_argument('--num_layers', type=int, default=1, help='number of layers (default: 1)')
parser.add_argument('--batch_size', type=int, default=128, help='batch size in training (default: 128)')
parser.add_argument("--num_thread", type=int, default=4, help='number of the loading thread (default: 4)')
parser.add_argument('--num_hidden_enc', type=int, default=1024, help='hidden size of model (default: 1024)')
parser.add_argument('--num_hidden_dec', type=int, default=512, help='hidden size of model decoder (default: 512)')
parser.add_argument('--num_hidden_seq', type=int, default=1024, help='hidden size of model seq2seq (default: 1024)')
parser.add_argument('--nsc_in_ms', type=int, default=40,
help='Number of sample size per time segment in ms (default: 40)')
parser.add_argument('--ref_repeat', type=int, default=1,
help='Number of repetition of reference seq2seq (default: 1)')
parser.add_argument('--loss_lim', type=float, default=0.05, help='Minimum loss threshold (default: 0.05)')
parser.add_argument('--mode', type=str, default='train')
parser.add_argument("--pause", type=int, default=0)
parser.add_argument('--memo', type=str, default='', help='Comment you wish to leave')
parser.add_argument('--train_ratio', type=float, default=0.5, help='Training ratio (default: 0.5)')
parser.add_argument('--load', type=str, default=None)
parser.add_argument('--debug', type=str, default='False', help='debug mode')
args = parser.parse_args()
batch_size = args.batch_size
num_thread = args.num_thread
num_mels = args.num_mels
char2index, index2char = load_label('./hackathon.labels')
SOS_token = char2index['<s>'] # '<sos>' or '<s>'
EOS_token = char2index['</s>'] # '<eos>' or '</s>'
PAD_token = char2index['_'] # '-' or '_'
unicode_jamo_list = My_Unicode_Jamo_v2()
tokenizer = Tokenizer(unicode_jamo_list)
jamo_tokens = tokenizer.word2num(unicode_jamo_list)
args.cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device('cuda' if args.cuda else 'cpu')
net = Mel2SeqNet_General(num_mels, args.num_hidden_enc, args.num_hidden_dec, len(unicode_jamo_list), args.num_layers, device)
net_optimizer = optim.Adam(net.parameters(), lr=args.lr_1)
ctc_loss = nn.CTCLoss().to(device)
net_B = Seq2SeqNet_v2(args.num_hidden_seq, jamo_tokens, char2index, device)
net_B_optimizer = optim.Adam(net_B.parameters(), lr=args.lr_2)
net_B_criterion = nn.NLLLoss(reduction='none').to(device)
bind_model(net, net_B, net_optimizer, net_B_optimizer, index2char, tokenizer)
if args.pause == 1:
nsml.paused(scope=locals())
if args.mode != "train":
return
if args.load != None:
nsml.load(checkpoint='model', session='team47/sr-hack-2019-50000/' + args.load)
nsml.save('saved')
for g in net_optimizer.param_groups:
g['lr'] = args.lr_1
logger.info('Learning rate of the net: {}'.format(g['lr']))
for g in net_B_optimizer.param_groups:
g['lr'] = args.lr_2
logger.info('Learning rate of the net B: {}'.format(g['lr']))
print(net)
print(net_B)
wav_paths, script_paths, korean_script_paths = get_paths(DATASET_PATH)
logger.info('wav_paths len: {}'.format(len(wav_paths)))
logger.info('script_paths len: {}'.format(len(script_paths)))
logger.info('korean_script_paths len: {}'.format(len(korean_script_paths)))
korean_script_list, jamo_script_list = get_korean_and_jamo_list_v2(korean_script_paths)
logger.info('Korean script 0: {}'.format(korean_script_list[0]))
logger.info('Korean script 0 length: {}'.format(len(korean_script_list[0])))
logger.info('Jamo script 0: {}'.format(jamo_script_list[0]))
logger.info('Jamo script 0 length: {}'.format(len(jamo_script_list[0])))
script_path_list = get_script_list(script_paths, SOS_token, EOS_token)
ground_truth_list = [(tokenizer.word2num(['<s>'] + list(jamo_script_list[i]) + ['</s>'])) for i in
range(len(jamo_script_list))]
logger.info('Train Ratio: {}'.format(args.train_ratio))
split_index = int(args.train_ratio * len(wav_paths))
# split_index = int(0.9 * len(wav_paths))
wav_path_list_train = wav_paths[:split_index]
ground_truth_list_train = ground_truth_list[:split_index]
korean_script_list_train = korean_script_list[:split_index]
script_path_list_train = script_path_list[:split_index]
wav_path_list_eval = wav_paths[split_index:]
ground_truth_list_eval = ground_truth_list[split_index:]
korean_script_list_eval = korean_script_list[split_index:]
script_path_list_eval = script_path_list[split_index:]
logger.info('Total:Train:Eval = {}:{}:{}'.format(len(wav_paths), len(wav_path_list_train), len(wav_path_list_eval)))
preloader_train = Threading_Batched_Preloader_v2(wav_path_list_train, ground_truth_list_train,
script_path_list_train, korean_script_list_train, batch_size,
num_mels, args.nsc_in_ms, is_train=True)
preloader_eval = Threading_Batched_Preloader_v2(wav_path_list_eval, ground_truth_list_eval, script_path_list_eval,
korean_script_list_eval, batch_size, num_mels, args.nsc_in_ms,
is_train=False)
best_loss = 1e10
best_eval_cer = 1e10
# load all target scripts for reducing disk i/o
target_path = os.path.join(DATASET_PATH, 'train_label')
load_targets(target_path)
logger.info('start')
train_begin = time.time()
for epoch in range(args.max_epochs):
logger.info((datetime.now().strftime('%m-%d %H:%M:%S')))
net.train()
net_B.train()
preloader_train.initialize_batch(num_thread)
loss_list_train = list()
seq2seq_loss_list_train = list()
seq2seq_loss_list_train_ref = list()
logger.info("Initialized Training Preloader")
count = 0
total_dist = 0
total_length = 1
total_dist_ref = 0
total_length_ref = 1
while not preloader_train.end_flag:
batch = preloader_train.get_batch()
if batch is not None:
tensor_input, ground_truth, loss_mask, length_list, batched_num_script, batched_num_script_loss_mask = batch
pred_tensor, loss = train(net, net_optimizer, ctc_loss, tensor_input.to(device),
ground_truth.to(device), length_list.to(device), device)
loss_list_train.append(loss)
jamo_result = Decode_Prediction_No_Filtering(pred_tensor, tokenizer)
true_string_list = Decode_Num_Script(batched_num_script.detach().cpu().numpy(), index2char)
for i in range(args.ref_repeat):
lev_input_ref = ground_truth
lev_pred_ref, attentions_ref, seq2seq_loss_ref = net_B.net_train(lev_input_ref.to(device),
batched_num_script.to(device),
batched_num_script_loss_mask.to(
device),
net_B_optimizer,
net_B_criterion)
pred_string_list_ref = Decode_Lev_Prediction(lev_pred_ref, index2char)
seq2seq_loss_list_train_ref.append(seq2seq_loss_ref)
dist_ref, length_ref = char_distance_list(true_string_list, pred_string_list_ref)
pred_string_list = [None]
dist = 0
length = 0
if (loss < args.loss_lim):
lev_input = Decode_CTC_Prediction_And_Batch(pred_tensor)
lev_pred, attentions, seq2seq_loss = net_B.net_train(lev_input.to(device),
batched_num_script.to(device),
batched_num_script_loss_mask.to(device),
net_B_optimizer, net_B_criterion)
pred_string_list = Decode_Lev_Prediction(lev_pred, index2char)
seq2seq_loss_list_train.append(seq2seq_loss)
dist, length = char_distance_list(true_string_list, pred_string_list)
total_dist_ref += dist_ref
total_length_ref += length_ref
total_dist += dist
total_length += length
count += 1
if count % 25 == 0:
logger.info(
"Train: Count {} | {} => {}".format(count, true_string_list[0], pred_string_list_ref[0]))
logger.info("Train: Count {} | {} => {} => {}".format(count, true_string_list[0], jamo_result[0],
pred_string_list[0]))
else:
logger.info("Training Batch is None")
train_loss = np.mean(np.asarray(loss_list_train))
train_cer = np.mean(np.asarray(total_dist / total_length))
train_cer_ref = np.mean(np.asarray(total_dist_ref / total_length_ref))
logger.info("Mean Train Loss: {}".format(train_loss))
logger.info("Total Train CER: {}".format(train_cer))
logger.info("Total Train Reference CER: {}".format(train_cer_ref))
preloader_eval.initialize_batch(num_thread)
loss_list_eval = list()
seq2seq_loss_list_eval = list()
seq2seq_loss_list_eval_ref = list()
logger.info("Initialized Evaluation Preloader")
count = 0
total_dist = 0
total_length = 1
total_dist_ref = 0
total_length_ref = 1
net.eval()
net_B.eval()
while not preloader_eval.end_flag:
batch = preloader_eval.get_batch()
if batch is not None:
tensor_input, ground_truth, loss_mask, length_list, batched_num_script, batched_num_script_loss_mask = batch
pred_tensor, loss = evaluate(net, ctc_loss, tensor_input.to(device), ground_truth.to(device),
length_list.to(device), device)
loss_list_eval.append(loss)
jamo_result = Decode_Prediction_No_Filtering(pred_tensor, tokenizer)
true_string_list = Decode_Num_Script(batched_num_script.detach().cpu().numpy(), index2char)
lev_input_ref = ground_truth
lev_pred_ref, attentions_ref, seq2seq_loss_ref = net_B.net_eval(lev_input_ref.to(device),
batched_num_script.to(device),
batched_num_script_loss_mask.to(device),
net_B_criterion)
pred_string_list_ref = Decode_Lev_Prediction(lev_pred_ref, index2char)
seq2seq_loss_list_train_ref.append(seq2seq_loss_ref)
dist_ref, length_ref = char_distance_list(true_string_list, pred_string_list_ref)
pred_string_list = [None]
dist = 0
length = 0
if (loss < args.loss_lim):
lev_input = Decode_CTC_Prediction_And_Batch(pred_tensor)
lev_pred, attentions, seq2seq_loss = net_B.net_eval(lev_input.to(device),
batched_num_script.to(device),
batched_num_script_loss_mask.to(device),
net_B_criterion)
pred_string_list = Decode_Lev_Prediction(lev_pred, index2char)
seq2seq_loss_list_train.append(seq2seq_loss)
dist, length = char_distance_list(true_string_list, pred_string_list)
total_dist_ref += dist_ref
total_length_ref += length_ref
total_dist += dist
total_length += length
count += 1
if count % 10 == 0:
logger.info("Eval: Count {} | {} => {}".format(count, true_string_list[0], pred_string_list_ref[0]))
logger.info("Eval: Count {} | {} => {} => {}".format(count, true_string_list[0], jamo_result[0],
pred_string_list[0]))
else:
logger.info("Training Batch is None")
eval_cer = total_dist / total_length
eval_cer_ref = total_dist_ref / total_length_ref
eval_loss = np.mean(np.asarray(loss_list_eval))
logger.info("Mean Evaluation Loss: {}".format(eval_loss))
logger.info("Total Evaluation CER: {}".format(eval_cer))
logger.info("Total Evaluation Reference CER: {}".format(eval_cer_ref))
nsml.report(False, step=epoch, train_epoch__loss=train_loss, train_epoch__cer=train_cer,
train_epoch__cer_ref=train_cer_ref,
eval__loss=eval_loss, eval__cer=eval_cer, eval__cer_ref=eval_cer_ref)
nsml.save(args.save_name)
best_model = (eval_cer < best_eval_cer)
if best_model:
nsml.save('best')
best_eval_cer = eval_cer
def run(self):
mb_obs, mb_td_targets, mb_base_actions, \
mb_xy0, mb_xy1, \
mb_values, mb_dones \
= [], [], [], [], [], [], []
mb_states = self.states
for n in range(self.nsteps):
# pi, pi2, x1, y1, x2, y2, v0
pi1, pi_xy0, pi_xy1, values, states = self.model.step(
self.obs, self.states, self.dones)
pi1_noise = np.random.random_sample((self.nenv, 3)) * 0.3
base_actions = np.argmax(
pi1 * self.base_act_mask + pi1_noise, axis=1)
xy0 = np.argmax(pi_xy0, axis=1)
x0 = (xy0 % 32).astype(int)
y0 = (xy0 / 32).astype(int)
xy1 = np.argmax(pi_xy1, axis=1)
x1 = (xy1 % 32).astype(int)
y1 = (xy1 / 32).astype(int)
# Scripted Agent Hacking
for env_num in range(self.nenv):
if env_num >= self.nscripts: # only for scripted agents
continue
ob = self.obs[env_num, :, :, :]
player_relative = ob[:, :, -1]
self.group_list[env_num] = common.update_group_list2(
self.control_groups[env_num])
if len(self.action_queue[env_num]) == 0:
self.action_queue[env_num], self.group_id[env_num], self.dest_per_marine[env_num], self.xy_per_marine[env_num] = \
common.solve_tsp(player_relative,
self.selected[env_num][0],
self.group_list[env_num],
self.group_id[env_num],
self.dest_per_marine[env_num],
self.xy_per_marine[env_num])
base_actions[env_num] = 0
x0[env_num] = 0
y0[env_num] = 0
x1[env_num] = 0
y1[env_num] = 0
if len(self.action_queue[env_num]) > 0:
action = self.action_queue[env_num].pop(0)
base_actions[env_num] = action.get("base_action", 0)
x0[env_num] = action.get("x0", 0)
y0[env_num] = action.get("y0", 0)
xy0[env_num] = y0[env_num] * 32 + x0[env_num]
x1[env_num] = action.get("x1", 0)
y1[env_num] = action.get("y1", 0)
xy1[env_num] = y1[env_num] * 32 + x1[env_num]
base_actions = self.valid_base_action(base_actions)
new_base_actions = self.trans_base_actions(base_actions)
base_action_spec = self.env.action_spec(new_base_actions)
# print("base_actions:", base_actions)
actions = self.construct_action(
base_actions,
base_action_spec,
x0,
y0,
x1,
y1
)
mb_obs.append(np.copy(self.obs))
mb_base_actions.append(base_actions)
mb_xy0.append(xy0)
mb_xy1.append(xy1)
mb_values.append(values)
mb_dones.append(self.dones)
#print("final acitons : ", actions)
obs, rewards, dones,\
available_actions, army_counts,\
control_groups, selected, xy_per_marine\
= self.env.step(
actions=actions)
self.army_counts = army_counts
self.control_groups = control_groups
self.selected = selected
for env_num, data in enumerate(xy_per_marine):
self.xy_per_marine[env_num] = data
self.update_available(available_actions)
self.states = states
self.dones = dones
mean_100ep_reward_a2c = 0
for n, done in enumerate(dones):
self.total_reward[n] += float(rewards[n])
if done:
self.obs[n] = self.obs[n] * 0
self.episodes += 1
num_episodes = self.episodes
self.episode_rewards.append(self.total_reward[n])
model = self.model
mean_100ep_reward = round(
np.mean(self.episode_rewards[-101:]), 1)
if (n < self.nscripts): # scripted agents
self.episode_rewards_script.append(
self.total_reward[n])
mean_100ep_reward_script = round(
np.mean(self.episode_rewards_script[-101:]), 1)
nsml.report(
reward_script=self.total_reward[n],
mean_reward_script=mean_100ep_reward_script,
reward=self.total_reward[n],
mean_100ep_reward=mean_100ep_reward,
episodes=self.episodes,
step=self.episodes,
scope=locals()
)
else:
self.episode_rewards_a2c.append(self.total_reward[n])
mean_100ep_reward_a2c = round(
np.mean(self.episode_rewards_a2c[-101:]), 1)
nsml.report(
reward_a2c=self.total_reward[n],
mean_reward_a2c=mean_100ep_reward_a2c,
reward=self.total_reward[n],
mean_100ep_reward=mean_100ep_reward,
episodes=self.episodes,
step=self.episodes,
scope=locals()
)
print("mean_100ep_reward_a2c", mean_100ep_reward_a2c)
if self.callback is not None:
self.callback(locals(), globals())
self.total_reward[n] = 0
self.group_list[n] = []
self.update_obs(obs)
mb_td_targets.append(rewards)
mb_dones.append(self.dones)
#batch of steps to batch of rollouts
mb_obs = np.asarray(
mb_obs, dtype=np.uint8).swapaxes(1, 0).reshape(
self.batch_ob_shape)
mb_td_targets = np.asarray(mb_td_targets, dtype=np.float32).swapaxes(1, 0)
mb_base_actions = np.asarray(
mb_base_actions, dtype=np.int32).swapaxes(1, 0)
mb_xy0 = np.asarray(mb_xy0, dtype=np.int32).swapaxes(1, 0)
mb_xy1 = np.asarray(mb_xy1, dtype=np.int32).swapaxes(1, 0)
mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
mb_masks = mb_dones[:, :-1]
mb_dones = mb_dones[:, 1:]
last_values = self.model.value(self.obs, self.states,
self.dones).tolist()
#discount/bootstrap off value fn
for n, (rewards, dones, value) in enumerate(
zip(mb_td_targets, mb_dones, last_values)):
rewards = rewards.tolist()
dones = dones.tolist()
if dones[-1] == 0:
rewards = discount_with_dones(rewards + [value], dones + [0],
self.gamma)[:-1]
else:
rewards = discount_with_dones(rewards, dones, self.gamma)
mb_td_targets[n] = rewards
mb_td_targets = mb_td_targets.flatten()
mb_base_actions = mb_base_actions.flatten()
mb_xy0 = mb_xy0.flatten()
mb_xy1 = mb_xy1.flatten()
mb_values = mb_values.flatten()
mb_masks = mb_masks.flatten()
return mb_obs, mb_states, mb_td_targets, mb_masks, \
mb_base_actions, mb_xy0, mb_xy1, mb_values
def main():
seed_everything()
# yml = 'configs/base.yml'
# config = utils.config.load(yml)
# pprint.pprint(config, indent=2)
model = get_model(config).cuda()
bind_model(model)
args = get_args()
if args.pause: ## test mode일 때
print('Inferring Start...')
nsml.paused(scope=locals())
if args.mode == 'train': ### training mode일 때
print('Training Start...')
# no bias decay
if config.OPTIMIZER.NO_BIAS_DECAY:
group_decay, group_no_decay = group_weight(model)
params = [{'params': group_decay},
{'params': group_no_decay, 'weight_decay': 0.0}]
else:
params = model.parameters()
optimizer = get_optimizer(config, params)
optimizer.param_groups[0]['initial_lr'] = config.OPTIMIZER.LR
if config.OPTIMIZER.NO_BIAS_DECAY:
optimizer.param_groups[1]['initial_lr'] = config.OPTIMIZER.LR
###############################################################################################
if IS_LOCAL:
prepare_train_directories(config)
utils.config.save_config(yml, config.LOCAL_TRAIN_DIR)
checkpoint = utils.checkpoint.get_initial_checkpoint(config)
if checkpoint is not None:
last_epoch, score, loss = utils.checkpoint.load_checkpoint(config, model, checkpoint)
else:
print('[*] no checkpoint found')
last_epoch, score, loss = -1, -1, float('inf')
print('last epoch:{} score:{:.4f} loss:{:.4f}'.format(last_epoch, score, loss))
else:
last_epoch = -1
###############################################################################################
scheduler = get_scheduler(config, optimizer, last_epoch=last_epoch)
###############################################################################################
if IS_LOCAL:
if config.SCHEDULER.NAME == 'multi_step':
if config.SCHEDULER.WARMUP:
scheduler_dict = scheduler.state_dict()['after_scheduler'].state_dict()
else:
scheduler_dict = scheduler.state_dict()
milestones = scheduler_dict['milestones']
step_count = len([i for i in milestones if i < last_epoch])
optimizer.param_groups[0]['lr'] *= scheduler_dict['gamma'] ** step_count
if config.OPTIMIZER.NO_BIAS_DECAY:
optimizer.param_groups[1]['initial_lr'] *= scheduler_dict['gamma'] ** step_count
if last_epoch != -1:
scheduler.step()
###############################################################################################
# for dirname, _, filenames in os.walk(DATASET_PATH):
# for filename in filenames:
# print(os.path.join(dirname, filename))
# if preprocessing possible
preprocess_type = config.DATA.PREPROCESS
cv2_size = (config.DATA.IMG_W, config.DATA.IMG_H)
if not IS_LOCAL:
preprocess(os.path.join(DATASET_PATH, 'train', 'train_data', 'NOR'), os.path.join(preprocess_type, 'NOR'), preprocess_type, cv2_size)
preprocess(os.path.join(DATASET_PATH, 'train', 'train_data', 'AMD'), os.path.join(preprocess_type, 'AMD'), preprocess_type, cv2_size)
preprocess(os.path.join(DATASET_PATH, 'train', 'train_data', 'RVO'), os.path.join(preprocess_type, 'RVO'), preprocess_type, cv2_size)
preprocess(os.path.join(DATASET_PATH, 'train', 'train_data', 'DMR'), os.path.join(preprocess_type, 'DMR'), preprocess_type, cv2_size)
data_dir = preprocess_type
# data_dir = os.path.join(DATASET_PATH, 'train/train_data')
else: # IS_LOCAL
data_dir = os.path.join(DATASET_PATH, preprocess_type)
# eda
# train_std(data_dir, preprocess_type, cv2_size)
fold_df = split_cv(data_dir, n_splits=config.NUM_FOLDS)
val_fold_idx = config.IDX_FOLD
###############################################################################################
train_loader = get_dataloader(config, data_dir, fold_df, val_fold_idx, 'train', transform=Albu())
val_loader = get_dataloader(config, data_dir, fold_df, val_fold_idx, 'val')
postfix = dict()
num_epochs = config.TRAIN.NUM_EPOCHS
val_acc_list = []
for epoch in range(last_epoch+1, num_epochs):
if epoch >= config.LOSS.FINETUNE_EPOCH:
criterion = get_loss(config.LOSS.FINETUNE_LOSS)
else:
criterion = get_loss(config.LOSS.NAME)
train_values = train_single_epoch(config, model, train_loader, criterion, optimizer, scheduler, epoch)
val_values = evaluate_single_epoch(config, model, val_loader, criterion, epoch)
val_acc_list.append((epoch, val_values[2]))
if config.SCHEDULER.NAME != 'one_cyle_lr':
scheduler.step()
if IS_LOCAL:
utils.checkpoint.save_checkpoint(config, model, epoch, val_values[1], val_values[0])
else:
postfix['train_loss'] = train_values[0]
postfix['train_res'] = train_values[1]
postfix['train_acc'] = train_values[2]
postfix['train_sens'] = train_values[3]
postfix['train_spec'] = train_values[4]
postfix['val_loss'] = val_values[0]
postfix['val_res'] = val_values[1]
postfix['val_acc'] = val_values[2]
postfix['val_sens'] = val_values[3]
postfix['val_spec'] = val_values[4]
nsml.report(**postfix, summary=True, step=epoch)
val_res = '%.10f' % val_values[1]
val_res = val_res.replace(".", "")
val_res = val_res[:4] + '.' + val_res[4:8] + '.' + val_res[8:]
save_name = 'epoch_%02d_score%s_loss%.4f.pth' % (epoch, val_res, val_values[0])
# nsml.save(save_name)
nsml.save(epoch)
for e, val_acc in val_acc_list:
print('%02d %s' % (e, val_acc))
def run(self):
mb_obs, mb_td_targets, mb_base_actions, \
mb_xy0, mb_xy1, \
mb_values, mb_dones \
= [], [], [], [], [], [], []
mb_states = self.states
for n in range(self.nsteps):
# pi, pi2, x1, y1, x2, y2, v0
pi1, pi_xy0, pi_xy1, values, states = self.model.step(
self.obs, self.states, self.dones)
pi1_noise = np.random.random_sample((self.nenv, 3)) * 0.3
base_actions = np.argmax(
pi1 * self.base_act_mask + pi1_noise, axis=1)
xy0 = np.argmax(pi_xy0, axis=1)
x0 = (xy0 % 32).astype(int)
y0 = (xy0 / 32).astype(int)
xy1 = np.argmax(pi_xy1, axis=1)
x1 = (xy1 % 32).astype(int)
y1 = (xy1 / 32).astype(int)
# Scripted Agent Hacking
for env_num in range(self.nenv):
if env_num >= self.nscripts: # only for scripted agents
continue
ob = self.obs[env_num, :, :, :]
player_relative = ob[:, :, -1]
self.group_list[env_num] = common.update_group_list2(
self.control_groups[env_num])
if len(self.action_queue[env_num]) == 0:
self.action_queue[env_num], self.group_id[env_num], self.dest_per_marine[env_num], self.xy_per_marine[env_num] = \
common.solve_tsp(player_relative,
self.selected[env_num][0],
self.group_list[env_num],
self.group_id[env_num],
self.dest_per_marine[env_num],
self.xy_per_marine[env_num])
base_actions[env_num] = 0
x0[env_num] = 0
y0[env_num] = 0
x1[env_num] = 0
y1[env_num] = 0
if len(self.action_queue[env_num]) > 0:
action = self.action_queue[env_num].pop(0)
base_actions[env_num] = action.get("base_action", 0)
x0[env_num] = action.get("x0", 0)
y0[env_num] = action.get("y0", 0)
xy0[env_num] = y0[env_num] * 32 + x0[env_num]
x1[env_num] = action.get("x1", 0)
y1[env_num] = action.get("y1", 0)
xy1[env_num] = y1[env_num] * 32 + x1[env_num]
base_actions = self.valid_base_action(base_actions)
new_base_actions = self.trans_base_actions(base_actions)
base_action_spec = self.env.action_spec(new_base_actions)
# print("base_actions:", base_actions)
actions = self.construct_action(
base_actions,
base_action_spec,
x0,
y0,
x1,
y1
)
mb_obs.append(np.copy(self.obs))
mb_base_actions.append(base_actions)
mb_xy0.append(xy0)
mb_xy1.append(xy1)
mb_values.append(values)
mb_dones.append(self.dones)
#print("final acitons : ", actions)
obs, rewards, dones,\
available_actions, army_counts,\
control_groups, selected, xy_per_marine\
= self.env.step(
actions=actions)
self.army_counts = army_counts
self.control_groups = control_groups
self.selected = selected
for env_num, data in enumerate(xy_per_marine):
self.xy_per_marine[env_num] = data
self.update_available(available_actions)
self.states = states
self.dones = dones
mean_100ep_reward_a2c = 0
for n, done in enumerate(dones):
self.total_reward[n] += float(rewards[n])
if done:
self.obs[n] = self.obs[n] * 0
self.episodes += 1
num_episodes = self.episodes
self.episode_rewards.append(self.total_reward[n])
model = self.model
mean_100ep_reward = round(
np.mean(self.episode_rewards[-101:]), 1)
if (n < self.nscripts): # scripted agents
self.episode_rewards_script.append(
self.total_reward[n])
mean_100ep_reward_script = round(
np.mean(self.episode_rewards_script[-101:]), 1)
nsml.report(
reward_script=self.total_reward[n],
mean_reward_script=mean_100ep_reward_script,
reward=self.total_reward[n],
mean_100ep_reward=mean_100ep_reward,
episodes=self.episodes,
step=self.episodes,
scope=locals()
)
else:
self.episode_rewards_a2c.append(self.total_reward[n])
mean_100ep_reward_a2c = round(
np.mean(self.episode_rewards_a2c[-101:]), 1)
nsml.report(
reward_a2c=self.total_reward[n],
mean_reward_a2c=mean_100ep_reward_a2c,
reward=self.total_reward[n],
mean_100ep_reward=mean_100ep_reward,
episodes=self.episodes,
step=self.episodes,
scope=locals()
)
print("mean_100ep_reward_a2c", mean_100ep_reward_a2c)
if self.callback is not None:
self.callback(locals(), globals())
self.total_reward[n] = 0
self.group_list[n] = []
self.update_obs(obs)
mb_td_targets.append(rewards)
mb_dones.append(self.dones)
#batch of steps to batch of rollouts
mb_obs = np.asarray(
mb_obs, dtype=np.uint8).swapaxes(1, 0).reshape(
self.batch_ob_shape)
mb_td_targets = np.asarray(mb_td_targets, dtype=np.float32).swapaxes(1, 0)
mb_base_actions = np.asarray(
mb_base_actions, dtype=np.int32).swapaxes(1, 0)
mb_xy0 = np.asarray(mb_xy0, dtype=np.int32).swapaxes(1, 0)
mb_xy1 = np.asarray(mb_xy1, dtype=np.int32).swapaxes(1, 0)
mb_values = np.asarray(mb_values, dtype=np.float32).swapaxes(1, 0)
mb_dones = np.asarray(mb_dones, dtype=np.bool).swapaxes(1, 0)
mb_masks = mb_dones[:, :-1]
mb_dones = mb_dones[:, 1:]
last_values = self.model.value(self.obs, self.states,
self.dones).tolist()
#discount/bootstrap off value fn
for n, (rewards, dones, value) in enumerate(
zip(mb_td_targets, mb_dones, last_values)):
rewards = rewards.tolist()
dones = dones.tolist()
if dones[-1] == 0:
rewards = discount_with_dones(rewards + [value], dones + [0],
self.gamma)[:-1]
else:
rewards = discount_with_dones(rewards, dones, self.gamma)
mb_td_targets[n] = rewards
mb_td_targets = mb_td_targets.flatten()
mb_base_actions = mb_base_actions.flatten()
mb_xy0 = mb_xy0.flatten()
mb_xy1 = mb_xy1.flatten()
mb_values = mb_values.flatten()
mb_masks = mb_masks.flatten()
return mb_obs, mb_states, mb_td_targets, mb_masks, \
mb_base_actions, mb_xy0, mb_xy1, mb_values
