def main():
args = parse_args()
with open(args.config_file) as fp:
config = yaml.safe_load(fp)
input_timesteps = config["input_timesteps"]
output_timesteps = config["output_timesteps"]
batch_size = config["batch_size"]
max_epochs = config["max_epochs"]
patience = config["patience"]
train_dataset = [(load_wave(_[0]).reshape(-1, 1),
load_wave(_[1]).reshape(-1, 1))
for _ in config["train_data"]]
train_dataflow = flow(train_dataset, input_timesteps, batch_size)
val_dataset = [(load_wave(_[0]).reshape(-1,
1), load_wave(_[1]).reshape(-1, 1))
for _ in config["val_data"]]
val_dataflow = flow(val_dataset, input_timesteps, batch_size)
model = build_model(input_timesteps)
model.compile(loss=LossFunc(output_timesteps), optimizer="adam")
train(model, train_dataflow, val_dataflow, max_epochs, patience)
def main():
args = parse_args()
with open(args.config_file) as fp:
config = yaml.safe_load(fp)
input_timesteps = config["input_timesteps"]
output_timesteps = config["output_timesteps"]
batch_size = config["batch_size"]
data = load_wave(args.input_file)
# padding and rounded up to the batch multiple
block_size = output_timesteps * batch_size
prepad = input_timesteps - output_timesteps
postpad = len(data) % block_size
padded = np.concatenate(
(np.zeros(prepad, np.float32), data, np.zeros(postpad, np.float32)))
x = sliding_window(padded, input_timesteps, output_timesteps)
x = x[:, :, np.newaxis]
model = load_model(args.model_file,
custom_objects={"LossFunc": LossFunc(output_timesteps)})
y = model.predict(x, batch_size=batch_size)
y = y[:, -output_timesteps:, :].reshape(-1)[:len(data)]
save_wave(y, args.output_file)
def main():
args = parse_args()
with open(args.config_file) as fp:
config = yaml.safe_load(fp)
input_timesteps = config["input_timesteps"]
output_timesteps = config["output_timesteps"]
batch_size = config["batch_size"]
data = load_wave(args.input_file)
print("data.shape is:", data.shape)
print("data.len is:", len(data))
"""
from nnabla.ext_utils import get_extension_context
cuda_device_id = 0
ctx = get_extension_context('cudnn', device_id=cuda_device_id)
print("Context: {}".format(ctx))
nn.set_default_context(ctx) # Set CUDA as a default context.
"""
# padding and rounded up to the batch multiple
block_size = output_timesteps * batch_size
prepad = input_timesteps - output_timesteps
postpad = len(data) % block_size
print("postpad", block_size - postpad)
padded = np.concatenate((np.zeros(prepad, np.float32), data,
np.zeros(block_size - postpad, np.float32)))
x = sliding_window(padded, input_timesteps, output_timesteps)
x = x[:, :, np.newaxis]
y = np.zeros_like(x)
batchlen = x.shape[0]
print("x.length is:", batchlen)
xx = nn.Variable((batch_size, input_timesteps, 1))
nn.load_parameters("best_result.h5")
print("xx.shape is:", xx.shape)
yy = build_model(xx)
print("yy.shape is:", yy.shape)
print("x.shape in the loop is:", x[32:32 + batch_size, :, :].shape)
start1 = time()
for step in range(0, batchlen, batch_size):
xx.d = x[step:step + batch_size, :, :]
yy.forward()
y[step:step + batch_size, :, :] = yy.d
proc_time = time() - start1
print(proc_time)
print(step)
y = y[:, -output_timesteps:, :].reshape(-1)[:len(data)]
save_wave(y, args.output_file)
print("finished\n")
proc_time = time() - start1
print(proc_time)
def main():
args = parse_args()
with open(args.config_file) as fp:
config = yaml.safe_load(fp)
input_timesteps = config["input_timesteps"]
output_timesteps = config["output_timesteps"]
batch_size = config["batch_size"]
max_epochs = config["max_epochs"]
patience = config["patience"]
train_dataset = [(load_wave(_[0]).reshape(-1, 1),
load_wave(_[1]).reshape(-1, 1))
for _ in config["train_data"]]
train_dataflow = flow(train_dataset, input_timesteps, batch_size)
val_dataset = [(load_wave(_[0]).reshape(-1,
1), load_wave(_[1]).reshape(-1, 1))
for _ in config["val_data"]]
val_dataflow = flow(val_dataset, input_timesteps, batch_size)
"""
from nnabla.ext_utils import get_extension_context
cuda_device_id = 0
ctx = get_extension_context('cudnn', device_id=cuda_device_id)
print("Context: {}".format(ctx))
nn.set_default_context(ctx) # Set CUDA as a default context.
"""
timestamp = datetime.datetime.now()
# For tensorboard
tb_log_dir = "tensorboard/{:%Y%m%d_%H%M%S}".format(timestamp)
if not os.path.exists(tb_log_dir):
os.makedirs(tb_log_dir)
tb_writer = SummaryWriter(tb_log_dir)
x = nn.Variable((batch_size, input_timesteps, 1), need_grad=True)
y = nn.Variable((batch_size, input_timesteps, 1), need_grad=True)
vx = nn.Variable((batch_size, input_timesteps, 1), need_grad=True)
vy = nn.Variable((batch_size, input_timesteps, 1), need_grad=True)
t = build_model(x)
vt = build_model(vx)
loss = F.mean(
F.squared_error(t[:, -output_timesteps:, :], y[:,
-output_timesteps:, :]))
solver = S.AdaBound()
solver.set_parameters(nn.get_parameters(grad_only=False))
tloop = tqdm.trange(1, max_epochs + 1)
es = EarlyStopping(patience=patience)
steps = 100
validation_time = 10
cp_dir = "checkpoint/{:%Y%m%d_%H%M%S}".format(timestamp)
if not os.path.exists(cp_dir):
os.makedirs(cp_dir)
idx = 0
for i in tloop:
# TRAINING
tloop.set_description("Training Epoch")
st = tqdm.trange(0, steps)
losses = AverageMeter()
for j in st:
x.d, y.d = train_dataflow.__next__()
st.set_description("Training Steps")
loss.forward()
solver.zero_grad()
loss.backward()
solver.update()
losses.update(loss.d.copy().mean())
st.set_postfix(train_loss=losses.avg)
# write train graph
tb_writer.add_scalar('train/loss', losses.avg, global_step=idx)
idx += 1
# VALIDATION
vlosses = AverageMeter()
for j in range(validation_time):
vx.d, vy.d = val_dataflow.__next__()
vloss = F.mean(
F.squared_error(vt[:, -output_timesteps:, :],
vy[:, -output_timesteps:, :]))
vloss.forward(clear_buffer=True)
vlosses.update(vloss.d.copy().mean())
tloop.set_postfix(train_loss=losses.avg, val_loss=vlosses.avg)
tb_writer.add_scalar('test/loss', vlosses.avg, global_step=i)
stop = es.step(vlosses.avg)
is_best = vlosses.avg == es.best
# save current model
nn.save_parameters(os.path.join(cp_dir, 'checkpoint_{}.h5'.format(i)))
if is_best:
nn.save_parameters(os.path.join(cp_dir, 'best_result.h5'))
"""
contents = {
'networks': [
{'name': 'MyChain',
'batch_size': batch_size,
'outputs': {'t': t },
'names': {'x': x}}],
'executors': [
{'name': 'runtime',
'network': 'MyChain',
'data': ['x'],
'output': ['t']}]}
nnabla.utils.save.save(os.path.join(cp_dir ,'MyChain.nnp'), contents)
"""
if stop:
print("Apply Early Stopping")
break