def save_checkpoint(model, optimizer, scheduler, learning_rate, iteration,
output_directory, ema, wavenet_config):
checkpoint_path = "{}/wavenet_{}".format(output_directory, iteration)
print("Saving model and optimizer state at iteration {} to {}".format(
iteration, checkpoint_path))
model_for_saving = WaveNet(**wavenet_config).cuda()
model_for_saving.load_state_dict(model.state_dict())
torch.save(
{
'model': model_for_saving,
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'learning_rate': learning_rate
}, checkpoint_path)
ema_path = "{}/wavenet_ema_{}".format(output_directory, iteration)
print("Saving ema model at iteration {} to {}".format(iteration, ema_path))
state_dict = model_for_saving.state_dict()
for name, _ in model.named_parameters():
if name in ema.shadow:
state_dict[name] = ema.shadow[name]
model_for_saving.load_state_dict(state_dict)
torch.save(
{
'model': model_for_saving,
'iteration': iteration,
'optimizer': optimizer.state_dict(),
'scheduler': scheduler.state_dict(),
'learning_rate': learning_rate
}, ema_path)
def main():
parser = argparse.ArgumentParser()
# path setting
parser.add_argument("--waveforms",
required=True,
type=str,
help="directory or list of wav files")
parser.add_argument("--feats",
required=True,
type=str,
help="directory or list of aux feat files")
parser.add_argument("--stats",
required=True,
type=str,
help="hdf5 file including statistics")
parser.add_argument("--expdir",
required=True,
type=str,
help="directory to save the model")
# network structure setting
parser.add_argument("--n_quantize",
default=256,
type=int,
help="number of quantization")
parser.add_argument("--n_aux",
default=28,
type=int,
help="number of dimension of aux feats")
parser.add_argument("--n_resch",
default=512,
type=int,
help="number of channels of residual output")
parser.add_argument("--n_skipch",
default=256,
type=int,
help="number of channels of skip output")
parser.add_argument("--dilation_depth",
default=10,
type=int,
help="depth of dilation")
parser.add_argument("--dilation_repeat",
default=1,
type=int,
help="number of repeating of dilation")
parser.add_argument("--kernel_size",
default=2,
type=int,
help="kernel size of dilated causal convolution")
parser.add_argument("--upsampling_factor",
default=0,
type=int,
help="upsampling factor of aux features"
"(if set 0, do not apply)")
parser.add_argument("--use_speaker_code",
default=False,
type=strtobool,
help="flag to use speaker code")
# network training setting
parser.add_argument("--lr", default=1e-4, type=float, help="learning rate")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="weight decay coefficient")
parser.add_argument(
"--batch_size",
default=20000,
type=int,
help="batch size (if set 0, utterance batch will be used)")
parser.add_argument("--iters",
default=200000,
type=int,
help="number of iterations")
# other setting
parser.add_argument("--checkpoints",
default=10000,
type=int,
help="how frequent saving model")
parser.add_argument("--intervals",
default=100,
type=int,
help="log interval")
parser.add_argument("--seed", default=1, type=int, help="seed number")
parser.add_argument("--resume",
default=None,
type=str,
help="model path to restart training")
parser.add_argument("--verbose", default=1, type=int, help="log level")
args = parser.parse_args()
# make experimental directory
if not os.path.exists(args.expdir):
os.makedirs(args.expdir)
# set log level
if args.verbose == 1:
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S',
filename=args.expdir + "/train.log")
logging.getLogger().addHandler(logging.StreamHandler())
elif args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S',
filename=args.expdir + "/train.log")
logging.getLogger().addHandler(logging.StreamHandler())
else:
logging.basicConfig(
level=logging.WARN,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S',
filename=args.expdir + "/train.log")
logging.getLogger().addHandler(logging.StreamHandler())
logging.warn("logging is disabled.")
# fix seed
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# save args as conf
torch.save(args, args.expdir + "/model.conf")
# # define network
model = WaveNet(n_quantize=args.n_quantize,
n_aux=args.n_aux,
n_resch=args.n_resch,
n_skipch=args.n_skipch,
dilation_depth=args.dilation_depth,
dilation_repeat=args.dilation_repeat,
kernel_size=args.kernel_size,
upsampling_factor=args.upsampling_factor)
logging.info(model)
model.apply(initialize)
model.train()
# define loss and optimizer
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss()
# define transforms
scaler = StandardScaler()
scaler.mean_ = read_hdf5(args.stats, "/mean")
scaler.scale_ = read_hdf5(args.stats, "/scale")
wav_transform = transforms.Compose(
[lambda x: encode_mu_law(x, args.n_quantize)])
feat_transform = transforms.Compose([lambda x: scaler.transform(x)])
# define generator
if os.path.isdir(args.waveforms):
filenames = sorted(
find_files(args.waveforms, "*.wav", use_dir_name=False))
wav_list = [args.waveforms + "/" + filename for filename in filenames]
feat_list = [
args.feats + "/" + filename.replace(".wav", ".h5")
for filename in filenames
]
elif os.path.isfile(args.waveforms):
wav_list = read_txt(args.waveforms)
feat_list = read_txt(args.feats)
else:
logging.error("--waveforms should be directory or list.")
sys.exit(1)
assert len(wav_list) == len(feat_list)
logging.info("number of training data = %d." % len(wav_list))
generator = train_generator(wav_list,
feat_list,
receptive_field=model.receptive_field,
batch_size=args.batch_size,
wav_transform=wav_transform,
feat_transform=feat_transform,
shuffle=True,
upsampling_factor=args.upsampling_factor,
use_speaker_code=args.use_speaker_code)
while not generator.queue.full():
time.sleep(0.1)
# resume
if args.resume is not None:
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
iterations = checkpoint["iterations"]
logging.info("restored from %d-iter checkpoint." % iterations)
else:
iterations = 0
# send to gpu
if torch.cuda.is_available():
model.cuda()
criterion.cuda()
else:
logging.error("gpu is not available. please check the setting.")
sys.exit(1)
# train
loss = 0
total = 0
for i in six.moves.range(iterations, args.iters):
start = time.time()
(batch_x, batch_h), batch_t = generator.next()
batch_output = model(batch_x, batch_h)[0]
batch_loss = criterion(batch_output[model.receptive_field:],
batch_t[model.receptive_field:])
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
loss += batch_loss.data[0]
total += time.time() - start
logging.debug("batch loss = %.3f (%.3f sec / batch)" %
(batch_loss.data[0], time.time() - start))
# report progress
if (i + 1) % args.intervals == 0:
logging.info(
"(iter:%d) average loss = %.6f (%.3f sec / batch)" %
(i + 1, loss / args.intervals, total / args.intervals))
logging.info(
"estimated required time = "
"{0.days:02}:{0.hours:02}:{0.minutes:02}:{0.seconds:02}".
format(
relativedelta(seconds=int((args.iters - (i + 1)) *
(total / args.intervals)))))
loss = 0
total = 0
# save intermidiate model
if (i + 1) % args.checkpoints == 0:
save_checkpoint(args.expdir, model, optimizer, i + 1)
# save final model
model.cpu()
torch.save({"model": model.state_dict()},
args.expdir + "/checkpoint-final.pkl")
logging.info("final checkpoint created.")
def main():
args = parse_args()
cfg.resume = args.resume
cfg.exp_name = args.exp
cfg.work_root = '/zhzhao/code/wavenet_torch/torch_lyuan/exp_result/'
cfg.workdir = cfg.work_root + args.exp + '/debug'
cfg.sparse_mode = args.sparse_mode
cfg.batch_size = args.batch_size
cfg.lr = args.lr
cfg.load_from = args.load_from
cfg.save_excel = args.save_excel
if args.find_pattern == True:
cfg.find_pattern_num = 16
cfg.find_pattern_shape = [int(args.find_pattern_shape.split('_')[0]), int(args.find_pattern_shape.split('_')[1])]
cfg.find_zero_threshold = float(args.find_pattern_para.split('_')[0])
cfg.find_score_threshold = int(args.find_pattern_para.split('_')[1])
if int(cfg.find_pattern_shape[0] * cfg.find_pattern_shape[1]) <= cfg.find_score_threshold:
exit()
if args.skip_exist == True:
if os.path.exists(cfg.workdir):
exit()
print('initial training...')
print(f'work_dir:{cfg.workdir}, \n\
pretrained: {cfg.load_from}, \n\
batch_size: {cfg.batch_size}, \n\
lr : {cfg.lr}, \n\
epochs : {cfg.epochs}, \n\
sparse : {cfg.sparse_mode}')
writer = SummaryWriter(log_dir=cfg.workdir+'/runs')
# build train data
vctk_train = VCTK(cfg, 'train')
train_loader = DataLoader(vctk_train, batch_size=cfg.batch_size, num_workers=4, shuffle=True, pin_memory=True)
# train_loader = dataset.create("data/v28/train.record", cfg.batch_size, repeat=True)
vctk_val = VCTK(cfg, 'val')
if args.test_acc_cmodel == True:
val_loader = DataLoader(vctk_val, batch_size=1, num_workers=4, shuffle=False, pin_memory=True)
else:
val_loader = DataLoader(vctk_val, batch_size=cfg.batch_size, num_workers=4, shuffle=False, pin_memory=True)
# build model
model = WaveNet(num_classes=28, channels_in=40, dilations=[1,2,4,8,16])
model = nn.DataParallel(model)
model.cuda()
name_list = list()
para_list = list()
for name, para in model.named_parameters():
name_list.append(name)
para_list.append(para)
a = model.state_dict()
for i, name in enumerate(name_list):
if name.split(".")[-2] != "bn" \
and name.split(".")[-2] != "bn2" \
and name.split(".")[-2] != "bn3" \
and name.split(".")[-1] != "bias":
raw_w = para_list[i]
nn.init.xavier_normal_(raw_w, gain=1.0)
a[name] = raw_w
model.load_state_dict(a)
weights_dir = os.path.join(cfg.workdir, 'weights')
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
if not os.path.exists(cfg.vis_dir):
os.mkdir(cfg.vis_dir)
if args.vis_pattern == True or args.vis_mask == True:
cfg.vis_dir = os.path.join(cfg.vis_dir, cfg.exp_name)
if not os.path.exists(cfg.vis_dir):
os.mkdir(cfg.vis_dir)
model.train()
if cfg.resume and os.path.exists(cfg.workdir + '/weights/best.pth'):
model.load_state_dict(torch.load(cfg.workdir + '/weights/best.pth'), strict=True)
print("loading", cfg.workdir + '/weights/best.pth')
cfg.load_from = cfg.workdir + '/weights/best.pth'
if args.test_acc == True:
if os.path.exists(cfg.load_from):
model.load_state_dict(torch.load(cfg.load_from), strict=True)
print("loading", cfg.load_from)
else:
print("Error: model file not exists, ", cfg.load_from)
exit()
else:
if os.path.exists(cfg.load_from):
model.load_state_dict(torch.load(cfg.load_from), strict=True)
print("loading", cfg.load_from)
# Export the model
print("exporting onnx ...")
model.eval()
batch_size = 1
x = torch.randn(batch_size, 40, 720, requires_grad=True).cuda()
torch.onnx.export(model.module, # model being run
x, # model input (or a tuple for multiple inputs)
"wavenet.onnx", # where to save the model (can be a file or file-like object)
export_params=True, # store the trained parameter weights inside the model file
opset_version=10, # the ONNX version to export the model to
do_constant_folding=True, # whether to execute constant folding for optimization
input_names = ['input'], # the model's input names
output_names = ['output'], # the model's output names
dynamic_axes={'input' : {0 : 'batch_size'}, # variable lenght axes
'output' : {0 : 'batch_size'}})
if os.path.exists(args.load_from_h5):
# model.load_state_dict(torch.load(args.load_from_h5), strict=True)
print("loading", args.load_from_h5)
model.train()
model_dict = model.state_dict()
print(model_dict.keys())
#先将参数值numpy转换为tensor形式
pretrained_dict = dd.io.load(args.load_from_h5)
print(pretrained_dict.keys())
new_pre_dict = {}
for k,v in pretrained_dict.items():
new_pre_dict[k] = torch.Tensor(v)
#更新
model_dict.update(new_pre_dict)
#加载
model.load_state_dict(model_dict)
if args.find_pattern == True:
# cfg.find_pattern_num = 16
# cfg.find_pattern_shape = [int(args.find_pattern_shape.split('_')[0]), int(args.find_pattern_shape.split('_')[1])]
# cfg.find_zero_threshold = float(args.find_pattern_para.split('_')[0])
# cfg.find_score_threshold = int(args.find_pattern_para.split('_')[1])
# if cfg.find_pattern_shape[0] * cfg.find_pattern_shape[0] <= cfg.find_score_threshold:
# exit()
name_list = list()
para_list = list()
for name, para in model.named_parameters():
name_list.append(name)
para_list.append(para)
a = model.state_dict()
for i, name in enumerate(name_list):
if name.split(".")[-2] != "bn" \
and name.split(".")[-2] != "bn2" \
and name.split(".")[-2] != "bn3" \
and name.split(".")[-1] != "bias":
raw_w = para_list[i]
if raw_w.size(0) == 128 and raw_w.size(1) == 128:
patterns, pattern_match_num, pattern_coo_nnz, pattern_nnz, pattern_inner_nnz \
= find_pattern_by_similarity(raw_w
, cfg.find_pattern_num
, cfg.find_pattern_shape
, cfg.find_zero_threshold
, cfg.find_score_threshold)
pattern_num_memory_dict, pattern_num_cal_num_dict, pattern_num_coo_nnz_dict \
= pattern_curve_analyse(raw_w.shape
, cfg.find_pattern_shape
, patterns
, pattern_match_num
, pattern_coo_nnz
, pattern_nnz
, pattern_inner_nnz)
write_pattern_curve_analyse(os.path.join(cfg.work_root, args.save_pattern_count_excel)
, cfg.exp_name + " " + args.find_pattern_shape + " " + args.find_pattern_para
, patterns, pattern_match_num, pattern_coo_nnz, pattern_nnz
, pattern_inner_nnz
, pattern_num_memory_dict, pattern_num_cal_num_dict, pattern_num_coo_nnz_dict)
# write_pattern_count(os.path.join(cfg.work_root, args.save_pattern_count_excel)
# , cfg.exp_name + " " + args.find_pattern_shape +" " + args.find_pattern_para
# , all_nnzs.values(), all_patterns.values())
exit()
if cfg.sparse_mode == 'sparse_pruning':
cfg.sparsity = args.sparsity
print(f'sparse_pruning {cfg.sparsity}')
elif cfg.sparse_mode == 'pattern_pruning':
print(args.pattern_para)
pattern_num = int(args.pattern_para.split('_')[0])
pattern_shape = [int(args.pattern_para.split('_')[1]), int(args.pattern_para.split('_')[2])]
pattern_nnz = int(args.pattern_para.split('_')[3])
print(f'pattern_pruning {pattern_num} [{pattern_shape[0]}, {pattern_shape[1]}] {pattern_nnz}')
cfg.patterns = generate_pattern(pattern_num, pattern_shape, pattern_nnz)
cfg.pattern_mask = generate_pattern_mask(model, cfg.patterns)
elif cfg.sparse_mode == 'coo_pruning':
cfg.coo_shape = [int(args.coo_para.split('_')[0]), int(args.coo_para.split('_')[1])]
cfg.coo_nnz = int(args.coo_para.split('_')[2])
# cfg.patterns = generate_pattern(pattern_num, pattern_shape, pattern_nnz)
print(f'coo_pruning [{cfg.coo_shape[0]}, {cfg.coo_shape[1]}] {cfg.coo_nnz}')
elif cfg.sparse_mode == 'ptcoo_pruning':
cfg.pattern_num = int(args.pattern_para.split('_')[0])
cfg.pattern_shape = [int(args.ptcoo_para.split('_')[1]), int(args.ptcoo_para.split('_')[2])]
cfg.pt_nnz = int(args.ptcoo_para.split('_')[3])
cfg.coo_nnz = int(args.ptcoo_para.split('_')[4])
cfg.patterns = generate_pattern(cfg.pattern_num, cfg.pattern_shape, cfg.pt_nnz)
cfg.pattern_mask = generate_pattern_mask(model, cfg.patterns)
print(f'ptcoo_pruning {cfg.pattern_num} [{cfg.pattern_shape[0]}, {cfg.pattern_shape[1]}] {cfg.pt_nnz} {cfg.coo_nnz}')
elif cfg.sparse_mode == 'find_retrain':
cfg.pattern_num = int(args.find_retrain_para.split('_')[0])
cfg.pattern_shape = [int(args.find_retrain_para.split('_')[1]), int(args.find_retrain_para.split('_')[2])]
cfg.pattern_nnz = int(args.find_retrain_para.split('_')[3])
cfg.coo_num = float(args.find_retrain_para.split('_')[4])
cfg.layer_or_model_wise = str(args.find_retrain_para.split('_')[5])
# cfg.fd_rtn_pattern_candidates = generate_complete_pattern_set(
# cfg.pattern_shape, cfg.pattern_nnz)
print(f'find_retrain {cfg.pattern_num} [{cfg.pattern_shape[0]}, {cfg.pattern_shape[1]}] {cfg.pattern_nnz} {cfg.coo_num} {cfg.layer_or_model_wise}')
elif cfg.sparse_mode == 'hcgs_pruning':
print(args.pattern_para)
cfg.block_shape = [int(args.hcgs_para.split('_')[0]), int(args.hcgs_para.split('_')[1])]
cfg.reserve_num1 = int(args.hcgs_para.split('_')[2])
cfg.reserve_num2 = int(args.hcgs_para.split('_')[3])
print(f'hcgs_pruning {cfg.reserve_num1}/8 {cfg.reserve_num2}/16')
cfg.hcgs_mask = generate_hcgs_mask(model, cfg.block_shape, cfg.reserve_num1, cfg.reserve_num2)
if args.vis_mask == True:
name_list = list()
para_list = list()
for name, para in model.named_parameters():
name_list.append(name)
para_list.append(para)
for i, name in enumerate(name_list):
if name.split(".")[-2] != "bn" \
and name.split(".")[-2] != "bn2" \
and name.split(".")[-2] != "bn3" \
and name.split(".")[-1] != "bias":
raw_w = para_list[i]
zero = torch.zeros_like(raw_w)
one = torch.ones_like(raw_w)
mask = torch.where(raw_w == 0, zero, one)
vis.save_visualized_mask(mask, name)
exit()
if args.vis_pattern == True:
pattern_count_dict = find_pattern_model(model, [8,8])
patterns = list(pattern_count_dict.keys())
counts = list(pattern_count_dict.values())
print(len(patterns))
print(counts)
vis.save_visualized_pattern(patterns)
exit()
# build loss
loss_fn = nn.CTCLoss(blank=27)
# loss_fn = nn.CTCLoss()
#
scheduler = optim.Adam(model.parameters(), lr=cfg.lr, eps=1e-4)
# scheduler = optim.lr_scheduler.MultiStepLR(train_step, milestones=[50, 150, 250], gamma=0.5)
if args.test_acc == True:
f1, val_loss, tps, preds, poses = test_acc(val_loader, model, loss_fn)
# f1, val_loss, tps, preds, poses = test_acc(val_loader, model, loss_fn)
write_test_acc(os.path.join(cfg.work_root, args.test_acc_excel),
cfg.exp_name, f1, val_loss, tps, preds, poses)
exit()
if args.test_acc_cmodel == True:
f1, val_loss, tps, preds, poses = test_acc_cmodel(val_loader, model, loss_fn)
# f1, val_loss, tps, preds, poses = test_acc(val_loader, model, loss_fn)
write_test_acc(os.path.join(cfg.work_root, args.test_acc_excel),
cfg.exp_name, f1, val_loss, tps, preds, poses)
exit()
# train
train(train_loader, scheduler, model, loss_fn, val_loader, writer)
x = batch[:-1]
logits = net(x)
sz = logits.size(0)
loss = loss + nn.functional.cross_entropy(logits, batch[-sz:])
loss = loss / batch_size
loss.backward()
optimizer.step()
loss_save.append(loss.data[0])
# monitor progress
if epoch % 100 == 0:
batch = next(g)
print('epoch %i, loss %.4f' % (epoch, loss.data[0]))
logits = net(batch[:-1])
_, i = logits.max(dim=1)
plt.figure(figsize=[16, 4])
plt.plot(i.data.tolist())
plt.plot(batch.data.tolist()[sum(net.dilations) + 1:])
plt.legend(['generated', 'data'])
plt.title('epoch %i' % epoch)
plt.tight_layout()
plt.savefig('train/epoch_%i.png' % epoch)
y_gen = net.generate(batch, 4000)
plt.figure(figsize=[16, 4])
plt.plot(y_gen, '--', c='b')
plt.plot(batch.data.tolist(), ms=2, c='k')
plt.legend(['generated', 'data'])
plt.savefig('train/generation.png')
torch.save(net.state_dict(), 'train/wavenet.pt')
def main():
args = parse_args()
cfg.resume = args.resume
cfg.exp_name = args.exp
cfg.work_root = '/zhzhao/code/wavenet_torch/torch_lyuan/exp_result/'
cfg.workdir = cfg.work_root + args.exp + '/debug'
cfg.sparse_mode = args.sparse_mode
cfg.batch_size = args.batch_size
cfg.lr = args.lr
cfg.load_from = args.load_from
cfg.save_excel = args.save_excel
if args.skip_exist == True:
if os.path.exists(cfg.workdir):
exit()
print('initial training...')
print(f'work_dir:{cfg.workdir}, \n\
pretrained: {cfg.load_from}, \n\
batch_size: {cfg.batch_size}, \n\
lr : {cfg.lr}, \n\
epochs : {cfg.epochs}, \n\
sparse : {cfg.sparse_mode}')
writer = SummaryWriter(log_dir=cfg.workdir+'/runs')
# build train data
vctk_train = VCTK(cfg, 'train')
train_loader = DataLoader(vctk_train, batch_size=cfg.batch_size, num_workers=8, shuffle=True, pin_memory=True)
# train_loader = dataset.create("data/v28/train.record", cfg.batch_size, repeat=True)
vctk_val = VCTK(cfg, 'val')
val_loader = DataLoader(vctk_val, batch_size=cfg.batch_size, num_workers=8, shuffle=False, pin_memory=True)
# build model
model = WaveNet(num_classes=28, channels_in=20, dilations=[1,2,4,8,16])
model = nn.DataParallel(model)
model.cuda()
name_list = list()
para_list = list()
for name, para in model.named_parameters():
name_list.append(name)
para_list.append(para)
a = model.state_dict()
for i, name in enumerate(name_list):
if name.split(".")[-2] != "bn" and name.split(".")[-1] != "bias":
raw_w = para_list[i]
nn.init.xavier_normal_(raw_w, gain=1.0)
a[name] = raw_w
model.load_state_dict(a)
weights_dir = os.path.join(cfg.workdir, 'weights')
if not os.path.exists(weights_dir):
os.mkdir(weights_dir)
if not os.path.exists(cfg.vis_dir):
os.mkdir(cfg.vis_dir)
if args.vis_pattern == True or args.vis_mask == True:
cfg.vis_dir = os.path.join(cfg.vis_dir, cfg.exp_name)
if not os.path.exists(cfg.vis_dir):
os.mkdir(cfg.vis_dir)
model.train()
if cfg.resume and os.path.exists(cfg.workdir + '/weights/best.pth'):
model.load_state_dict(torch.load(cfg.workdir + '/weights/best.pth'), strict=False)
print("loading", cfg.workdir + '/weights/best.pth')
if os.path.exists(cfg.load_from):
model.load_state_dict(torch.load(cfg.load_from), strict=False)
print("loading", cfg.load_from)
if os.path.exists(args.load_from_h5):
# model.load_state_dict(torch.load(args.load_from_h5), strict=True)
print("loading", args.load_from_h5)
model.train()
model_dict = model.state_dict()
print(model_dict.keys())
#先将参数值numpy转换为tensor形式
pretrained_dict = dd.io.load(args.load_from_h5)
print(pretrained_dict.keys())
new_pre_dict = {}
for k,v in pretrained_dict.items():
new_pre_dict[k] = torch.Tensor(v)
#更新
model_dict.update(new_pre_dict)
#加载
model.load_state_dict(model_dict)
if args.find_pattern == True:
cfg.find_pattern_num = 16
cfg.find_pattern_shape = [int(args.find_pattern_shape.split('_')[0]), int(args.find_pattern_shape.split('_')[1])]
cfg.find_zero_threshold = float(args.find_pattern_para.split('_')[0])
cfg.find_score_threshold = int(args.find_pattern_para.split('_')[1])
name_list = list()
para_list = list()
for name, para in model.named_parameters():
name_list.append(name)
para_list.append(para)
a = model.state_dict()
for i, name in enumerate(name_list):
if name.split(".")[-2] != "bn" and name.split(".")[-1] != "bias":
raw_w = para_list[i]
if raw_w.size(0) == 128 and raw_w.size(1) == 128:
patterns, pattern_match_num, pattern_coo_nnz, pattern_nnz, pattern_inner_nnz \
= find_pattern_by_similarity(raw_w
, cfg.find_pattern_num
, cfg.find_pattern_shape
, cfg.find_zero_threshold
, cfg.find_score_threshold)
pattern_num_memory_dict, pattern_num_coo_nnz_dict \
= pattern_curve_analyse(raw_w.shape
, cfg.find_pattern_shape
, patterns
, pattern_match_num
, pattern_coo_nnz
, pattern_nnz
, pattern_inner_nnz)
write_pattern_curve_analyse(os.path.join(cfg.work_root, args.save_pattern_count_excel)
, cfg.exp_name + " " + args.find_pattern_shape + " " + args.find_pattern_para
, patterns, pattern_match_num, pattern_coo_nnz, pattern_nnz
, pattern_num_memory_dict, pattern_num_coo_nnz_dict)
# write_pattern_count(os.path.join(cfg.work_root, args.save_pattern_count_excel)
# , cfg.exp_name + " " + args.find_pattern_shape +" " + args.find_pattern_para
# , all_nnzs.values(), all_patterns.values())
exit()
if cfg.sparse_mode == 'sparse_pruning':
cfg.sparsity = args.sparsity
print(f'sparse_pruning {cfg.sparsity}')
elif cfg.sparse_mode == 'pattern_pruning':
print(args.pattern_para)
pattern_num = int(args.pattern_para.split('_')[0])
pattern_shape = [int(args.pattern_para.split('_')[1]), int(args.pattern_para.split('_')[2])]
pattern_nnz = int(args.pattern_para.split('_')[3])
print(f'pattern_pruning {pattern_num} [{pattern_shape[0]}, {pattern_shape[1]}] {pattern_nnz}')
cfg.patterns = generate_pattern(pattern_num, pattern_shape, pattern_nnz)
cfg.pattern_mask = generate_pattern_mask(model, cfg.patterns)
elif cfg.sparse_mode == 'coo_pruning':
cfg.coo_shape = [int(args.coo_para.split('_')[0]), int(args.coo_para.split('_')[1])]
cfg.coo_nnz = int(args.coo_para.split('_')[2])
# cfg.patterns = generate_pattern(pattern_num, pattern_shape, pattern_nnz)
print(f'coo_pruning [{cfg.coo_shape[0]}, {cfg.coo_shape[1]}] {cfg.coo_nnz}')
elif cfg.sparse_mode == 'ptcoo_pruning':
cfg.pattern_num = int(args.pattern_para.split('_')[0])
cfg.pattern_shape = [int(args.ptcoo_para.split('_')[1]), int(args.ptcoo_para.split('_')[2])]
cfg.pt_nnz = int(args.ptcoo_para.split('_')[3])
cfg.coo_nnz = int(args.ptcoo_para.split('_')[4])
cfg.patterns = generate_pattern(cfg.pattern_num, cfg.pattern_shape, cfg.pt_nnz)
cfg.pattern_mask = generate_pattern_mask(model, cfg.patterns)
print(f'ptcoo_pruning {cfg.pattern_num} [{cfg.pattern_shape[0]}, {cfg.pattern_shape[1]}] {cfg.pt_nnz} {cfg.coo_nnz}')
if args.vis_mask == True:
name_list = list()
para_list = list()
for name, para in model.named_parameters():
name_list.append(name)
para_list.append(para)
for i, name in enumerate(name_list):
if name.split(".")[-2] != "bn" and name.split(".")[-1] != "bias":
raw_w = para_list[i]
zero = torch.zeros_like(raw_w)
one = torch.ones_like(raw_w)
mask = torch.where(raw_w == 0, zero, one)
vis.save_visualized_mask(mask, name)
exit()
if args.vis_pattern == True:
pattern_count_dict = find_pattern_model(model, [8,8])
patterns = list(pattern_count_dict.keys())
counts = list(pattern_count_dict.values())
print(len(patterns))
print(counts)
vis.save_visualized_pattern(patterns)
exit()
# build loss
loss_fn = nn.CTCLoss(blank=27)
# loss_fn = nn.CTCLoss()
#
scheduler = optim.Adam(model.parameters(), lr=cfg.lr, eps=1e-4)
# scheduler = optim.lr_scheduler.MultiStepLR(train_step, milestones=[50, 150, 250], gamma=0.5)
if args.test_acc == True:
f1, val_loss, tps, preds, poses = test_acc(val_loader, model, loss_fn)
write_test_acc(os.path.join(cfg.work_root, args.test_acc_excel),
cfg.exp_name, f1, val_loss, tps, preds, poses)
exit()
# train
train(train_loader, scheduler, model, loss_fn, val_loader, writer)
def main():
parser = argparse.ArgumentParser()
# path setting
parser.add_argument("--waveforms",
required=True,
type=str,
help="directory or list of wav files")
parser.add_argument("--feats",
required=True,
type=str,
help="directory or list of aux feat files")
parser.add_argument("--stats",
required=True,
type=str,
help="hdf5 file including statistics")
parser.add_argument("--expdir",
required=True,
type=str,
help="directory to save the model")
parser.add_argument("--feature_type",
default="world",
choices=["world", "melspc"],
type=str,
help="feature type")
# network structure setting
parser.add_argument("--n_quantize",
default=256,
type=int,
help="number of quantization")
parser.add_argument("--n_aux",
default=28,
type=int,
help="number of dimension of aux feats")
parser.add_argument("--n_resch",
default=512,
type=int,
help="number of channels of residual output")
parser.add_argument("--n_skipch",
default=256,
type=int,
help="number of channels of skip output")
parser.add_argument("--dilation_depth",
default=10,
type=int,
help="depth of dilation")
parser.add_argument("--dilation_repeat",
default=1,
type=int,
help="number of repeating of dilation")
parser.add_argument("--kernel_size",
default=2,
type=int,
help="kernel size of dilated causal convolution")
parser.add_argument("--upsampling_factor",
default=80,
type=int,
help="upsampling factor of aux features")
parser.add_argument("--use_upsampling_layer",
default=True,
type=strtobool,
help="flag to use upsampling layer")
parser.add_argument("--use_speaker_code",
default=False,
type=strtobool,
help="flag to use speaker code")
# network training setting
parser.add_argument("--lr", default=1e-4, type=float, help="learning rate")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="weight decay coefficient")
parser.add_argument(
"--batch_length",
default=20000,
type=int,
help="batch length (if set 0, utterance batch will be used)")
parser.add_argument(
"--batch_size",
default=1,
type=int,
help="batch size (if use utterance batch, batch_size will be 1.")
parser.add_argument("--iters",
default=200000,
type=int,
help="number of iterations")
# other setting
parser.add_argument("--checkpoints",
default=10000,
type=int,
help="how frequent saving model")
parser.add_argument("--intervals",
default=100,
type=int,
help="log interval")
parser.add_argument("--seed", default=1, type=int, help="seed number")
parser.add_argument("--resume",
default=None,
nargs="?",
type=str,
help="model path to restart training")
parser.add_argument("--n_gpus", default=1, type=int, help="number of gpus")
parser.add_argument("--verbose", default=1, type=int, help="log level")
args = parser.parse_args()
# set log level
if args.verbose == 1:
logging.basicConfig(
level=logging.INFO,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
elif args.verbose > 1:
logging.basicConfig(
level=logging.DEBUG,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
else:
logging.basicConfig(
level=logging.WARNING,
format=
'%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s',
datefmt='%m/%d/%Y %I:%M:%S')
logging.warning("logging is disabled.")
# show argmument
for key, value in vars(args).items():
logging.info("%s = %s" % (key, str(value)))
# make experimental directory
if not os.path.exists(args.expdir):
os.makedirs(args.expdir)
# fix seed
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
# fix slow computation of dilated conv
# https://github.com/pytorch/pytorch/issues/15054#issuecomment-450191923
torch.backends.cudnn.benchmark = True
# save args as conf
torch.save(args, args.expdir + "/model.conf")
# define network
if args.use_upsampling_layer:
upsampling_factor = args.upsampling_factor
else:
upsampling_factor = 0
model = WaveNet(n_quantize=args.n_quantize,
n_aux=args.n_aux,
n_resch=args.n_resch,
n_skipch=args.n_skipch,
dilation_depth=args.dilation_depth,
dilation_repeat=args.dilation_repeat,
kernel_size=args.kernel_size,
upsampling_factor=upsampling_factor)
logging.info(model)
model.apply(initialize)
model.train()
if args.n_gpus > 1:
device_ids = range(args.n_gpus)
model = torch.nn.DataParallel(model, device_ids)
model.receptive_field = model.module.receptive_field
if args.n_gpus > args.batch_size:
logging.warning("batch size is less than number of gpus.")
# define optimizer and loss
optimizer = torch.optim.Adam(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss()
# define transforms
scaler = StandardScaler()
scaler.mean_ = read_hdf5(args.stats, "/" + args.feature_type + "/mean")
scaler.scale_ = read_hdf5(args.stats, "/" + args.feature_type + "/scale")
wav_transform = transforms.Compose(
[lambda x: encode_mu_law(x, args.n_quantize)])
feat_transform = transforms.Compose([lambda x: scaler.transform(x)])
# define generator
if os.path.isdir(args.waveforms):
filenames = sorted(
find_files(args.waveforms, "*.wav", use_dir_name=False))
wav_list = [args.waveforms + "/" + filename for filename in filenames]
feat_list = [
args.feats + "/" + filename.replace(".wav", ".h5")
for filename in filenames
]
elif os.path.isfile(args.waveforms):
wav_list = read_txt(args.waveforms)
feat_list = read_txt(args.feats)
else:
logging.error("--waveforms should be directory or list.")
sys.exit(1)
assert len(wav_list) == len(feat_list)
logging.info("number of training data = %d." % len(wav_list))
generator = train_generator(wav_list,
feat_list,
receptive_field=model.receptive_field,
batch_length=args.batch_length,
batch_size=args.batch_size,
feature_type=args.feature_type,
wav_transform=wav_transform,
feat_transform=feat_transform,
shuffle=True,
upsampling_factor=args.upsampling_factor,
use_upsampling_layer=args.use_upsampling_layer,
use_speaker_code=args.use_speaker_code)
# charge minibatch in queue
while not generator.queue.full():
time.sleep(0.1)
# resume model and optimizer
if args.resume is not None and len(args.resume) != 0:
checkpoint = torch.load(args.resume,
map_location=lambda storage, loc: storage)
iterations = checkpoint["iterations"]
if args.n_gpus > 1:
model.module.load_state_dict(checkpoint["model"])
else:
model.load_state_dict(checkpoint["model"])
optimizer.load_state_dict(checkpoint["optimizer"])
logging.info("restored from %d-iter checkpoint." % iterations)
else:
iterations = 0
# check gpu and then send to gpu
if torch.cuda.is_available():
model.cuda()
criterion.cuda()
for state in optimizer.state.values():
for key, value in state.items():
if torch.is_tensor(value):
state[key] = value.cuda()
else:
logging.error("gpu is not available. please check the setting.")
sys.exit(1)
# train
loss = 0
total = 0
for i in six.moves.range(iterations, args.iters):
start = time.time()
(batch_x, batch_h), batch_t = generator.next()
batch_output = model(batch_x, batch_h)
batch_loss = criterion(
batch_output[:, model.receptive_field:].contiguous().view(
-1, args.n_quantize),
batch_t[:, model.receptive_field:].contiguous().view(-1))
optimizer.zero_grad()
batch_loss.backward()
optimizer.step()
loss += batch_loss.item()
total += time.time() - start
logging.debug("batch loss = %.3f (%.3f sec / batch)" %
(batch_loss.item(), time.time() - start))
# report progress
if (i + 1) % args.intervals == 0:
logging.info(
"(iter:%d) average loss = %.6f (%.3f sec / batch)" %
(i + 1, loss / args.intervals, total / args.intervals))
logging.info(
"estimated required time = "
"{0.days:02}:{0.hours:02}:{0.minutes:02}:{0.seconds:02}".
format(
relativedelta(seconds=int((args.iters - (i + 1)) *
(total / args.intervals)))))
loss = 0
total = 0
# save intermidiate model
if (i + 1) % args.checkpoints == 0:
if args.n_gpus > 1:
save_checkpoint(args.expdir, model.module, optimizer, i + 1)
else:
save_checkpoint(args.expdir, model, optimizer, i + 1)
# save final model
if args.n_gpus > 1:
torch.save({"model": model.module.state_dict()},
args.expdir + "/checkpoint-final.pkl")
else:
torch.save({"model": model.state_dict()},
args.expdir + "/checkpoint-final.pkl")
logging.info("final checkpoint created.")
