def setup_solvers(train_params):
solver_dict = dict()
solver_generator = S.Adam(alpha=train_params['lr_generator'],
beta1=0.5,
beta2=0.999)
with nn.parameter_scope("generator"):
solver_generator.set_parameters(nn.get_parameters())
solver_dict["generator"] = solver_generator
solver_discriminator = S.Adam(train_params['lr_discriminator'],
beta1=0.5,
beta2=0.999)
with nn.parameter_scope("discriminator"):
solver_discriminator.set_parameters(nn.get_parameters())
solver_dict["discriminator"] = solver_discriminator
solver_kp_detector = S.Adam(train_params['lr_kp_detector'],
beta1=0.5,
beta2=0.999)
with nn.parameter_scope("kp_detector"):
solver_kp_detector.set_parameters(nn.get_parameters())
solver_dict["kp_detector"] = solver_kp_detector
return solver_dict
def _build(self):
# inference
self.infer_obs_t = nn.Variable((1,) + self.obs_shape)
with nn.parameter_scope('trainable'):
self.infer_policy_t = policy_network(self.infer_obs_t,
self.action_size, 'actor')
# training
self.obss_t = nn.Variable((self.batch_size,) + self.obs_shape)
self.acts_t = nn.Variable((self.batch_size, self.action_size))
self.rews_tp1 = nn.Variable((self.batch_size, 1))
self.obss_tp1 = nn.Variable((self.batch_size,) + self.obs_shape)
self.ters_tp1 = nn.Variable((self.batch_size, 1))
# critic training
with nn.parameter_scope('trainable'):
q_t = q_network(self.obss_t, self.acts_t, 'critic')
with nn.parameter_scope('target'):
policy_tp1 = policy_network(self.obss_tp1, self.action_size,
'actor')
q_tp1 = q_network(self.obss_tp1, policy_tp1, 'critic')
y = self.rews_tp1 + self.gamma * q_tp1 * (1.0 - self.ters_tp1)
self.critic_loss = F.mean(F.squared_error(q_t, y))
# actor training
with nn.parameter_scope('trainable'):
policy_t = policy_network(self.obss_t, self.action_size, 'actor')
q_t_with_actor = q_network(self.obss_t, policy_t, 'critic')
self.actor_loss = -F.mean(q_t_with_actor)
# get neural network parameters
with nn.parameter_scope('trainable'):
with nn.parameter_scope('critic'):
critic_params = nn.get_parameters()
with nn.parameter_scope('actor'):
actor_params = nn.get_parameters()
# setup optimizers
self.critic_solver = S.Adam(self.critic_lr)
self.critic_solver.set_parameters(critic_params)
self.actor_solver = S.Adam(self.actor_lr)
self.actor_solver.set_parameters(actor_params)
with nn.parameter_scope('trainable'):
trainable_params = nn.get_parameters()
with nn.parameter_scope('target'):
target_params = nn.get_parameters()
# build target update
update_targets = []
sync_targets = []
for key, src in trainable_params.items():
dst = target_params[key]
updated_dst = (1.0 - self.tau) * dst + self.tau * src
update_targets.append(F.assign(dst, updated_dst))
sync_targets.append(F.assign(dst, src))
self.update_target_expr = F.sink(*update_targets)
self.sync_target_expr = F.sink(*sync_targets)
def main():
# argparse
args = get_args()
# Context Setting
# Get context.
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(
args.context, device_id=args.device_id)
nn.set_default_context(ctx)
model_path = args.model
if args.train:
# Data Loading
logger.info("Initialing DataSource.")
train_iterator = facade.facade_data_iterator(
args.traindir,
args.batchsize,
shuffle=True,
with_memory_cache=False)
val_iterator = facade.facade_data_iterator(
args.valdir,
args.batchsize,
random_crop=False,
shuffle=False,
with_memory_cache=False)
monitor = nm.Monitor(args.logdir)
solver_gen = S.Adam(alpha=args.lrate, beta1=args.beta1)
solver_dis = S.Adam(alpha=args.lrate, beta1=args.beta1)
generator = unet.generator
discriminator = unet.discriminator
model_path = train(generator, discriminator, args.patch_gan,
solver_gen, solver_dis,
args.weight_l1, train_iterator, val_iterator,
args.epoch, monitor, args.monitor_interval)
if args.generate:
if model_path is not None:
# Data Loading
logger.info("Generating from DataSource.")
test_iterator = facade.facade_data_iterator(
args.testdir,
args.batchsize,
shuffle=False,
with_memory_cache=False)
generator = unet.generator
generate(generator, model_path, test_iterator, args.logdir)
else:
logger.error("Trained model was NOT given.")
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--config', default=None, type=str)
parser.add_argument('--info', default=None, type=str)
args = parser.parse_args()
config = load_decoder_config(args.config)
if args.info:
config["experiment_name"] += args.info
pprint.pprint(config)
#########################
# Context Setting
# Get context.
from nnabla.ext_utils import get_extension_context
logger.info(f'Running in {config["context"]}.')
ctx = get_extension_context(config["context"],
device_id=config["device_id"])
nn.set_default_context(ctx)
#########################
# Data Loading
logger.info('Initialing Datasource')
train_iterator = data.celebv_data_iterator(
dataset_mode="decoder",
celeb_name=config["trg_celeb_name"],
data_dir=config["train_dir"],
ref_dir=config["ref_dir"],
mode=config["mode"],
batch_size=config["train"]["batch_size"],
shuffle=config["train"]["shuffle"],
with_memory_cache=config["train"]["with_memory_cache"],
with_file_cache=config["train"]["with_file_cache"],
)
monitor = nm.Monitor(
os.path.join(config["logdir"], "decoder", config["trg_celeb_name"],
config["experiment_name"]))
# Optimizer
solver_netG = S.Adam(alpha=config["train"]["lr"],
beta1=config["train"]["beta1"])
solver_netD = S.Adam(alpha=config["train"]["lr"],
beta1=config["train"]["beta1"])
# Network
netG = models.netG_decoder
netD = models.netD_decoder
train(config, netG, netD, solver_netG, solver_netD, train_iterator,
monitor)
def create_network(batch_size, num_dilations, learning_rate):
# model
x = nn.Variable(shape=(batch_size, data_config.duration, 1)) # (B, T, 1)
onehot = F.one_hot(x, shape=(data_config.q_bit_len, )) # (B, T, C)
wavenet_input = F.transpose(onehot, (0, 2, 1)) # (B, C, T)
# speaker embedding
s_emb = None
net = WaveNet(num_dilations)
wavenet_output = net(wavenet_input, s_emb)
pred = F.transpose(wavenet_output, (0, 2, 1))
# (B, T, 1)
t = nn.Variable(shape=(batch_size, data_config.duration, 1))
loss = F.mean(F.softmax_cross_entropy(pred, t))
# loss.visit(PrintFunc())
# Create Solver.
solver = S.Adam(learning_rate)
solver.set_parameters(nn.get_parameters())
return x, t, loss, solver
def main():
# 入力データのshape定義
x = nn.variable.Variable(
[BATCH_SIZE, IMAGE_DEPTH * IMAGE_WIDTH * IMAGE_HEIGHT])
# ラベルのshape定義
t = nn.variable.Variable([BATCH_SIZE, LABEL_NUM])
pred = convolution(x)
loss_ = loss(pred, t)
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
data = InputData()
for i in range(NUM_STEP):
# 100STEP毎にテスト実施
if i % 100 == 0:
l = 0
a = 0
for k, (t.d, x.d) in enumerate(data.test_data()):
loss_.forward()
l += loss_.d
a += accuracy(pred, t)
print("Step: %05d Test loss: %0.05f Test accuracy: %0.05f" %
(i, l / k, a / k))
t.d, x.d = data.next_batch()
loss_.forward()
solver.zero_grad()
loss_.backward()
solver.weight_decay(DECAY_RATE)
solver.update()
if i % 10 == 0:
print("Step: %05d Train loss: %0.05f Train accuracy: %0.05f" %
(i, loss_.d, accuracy(pred, t)))
def build_train_graph(self, batch):
self.solver = S.Adam(self.learning_rate)
obs, action, reward, terminal, newobs = batch
# Create input variables
s = nn.Variable(obs.shape)
a = nn.Variable(action.shape)
r = nn.Variable(reward.shape)
t = nn.Variable(terminal.shape)
snext = nn.Variable(newobs.shape)
with nn.parameter_scope(self.name_q):
q = self.q_builder(s, self.num_actions, test=False)
self.solver.set_parameters(nn.get_parameters())
with nn.parameter_scope(self.name_qnext):
qnext = self.q_builder(snext, self.num_actions, test=True)
qnext.need_grad = False
clipped_r = F.minimum_scalar(F.maximum_scalar(
r, -self.clip_reward), self.clip_reward)
q_a = F.sum(
q * F.one_hot(F.reshape(a, (-1, 1), inplace=False), (q.shape[1],)), axis=1)
target = clipped_r + self.gamma * (1 - t) * F.max(qnext, axis=1)
loss = F.mean(F.huber_loss(q_a, target))
Variables = namedtuple(
'Variables', ['s', 'a', 'r', 't', 'snext', 'q', 'loss'])
self.v = Variables(s, a, r, t, snext, q, loss)
self.sync_models()
self.built = True
def _setup_solvers(self):
# prepare training parameters
with nn.parameter_scope('%s/discriminator' % self.scope):
d_params = nn.get_parameters()
with nn.parameter_scope('%s/generator' % self.scope):
g_params = nn.get_parameters()
# create solver for discriminator
self.d_lr_scheduler = StepScheduler(self.d_lr, self.gamma,
[self.lr_milestone])
self.d_solver = S.Adam(self.d_lr, beta1=self.beta1, beta2=0.999)
self.d_solver.set_parameters(d_params)
# create solver for generator
self.g_lr_scheduler = StepScheduler(self.g_lr, self.gamma,
[self.lr_milestone])
self.g_solver = S.Adam(self.g_lr, beta1=self.beta1, beta2=0.999)
self.g_solver.set_parameters(g_params)
def train_model(model, data, labels):
model(data)
solver = S.Adam(alpha=0.001, beta1=0.9, beta2=0.999, eps=1e-08)
solver.set_parameters(nn.get_parameters())
for i in range(1500):
out = model(data)
loss = F.categorical_cross_entropy(out, labels)
loss.forward()
solver.zero_grad()
loss.backward()
solver.update()
def train(self):
# variables for training
tx_in = nn.Variable(
[self._batch_size, self._x_input_length, self._cols_size])
tx_out = nn.Variable(
[self._batch_size, self._x_output_length, self._cols_size])
tpred = self.network(tx_in, self._lstm_unit_name, self._lstm_units)
tpred.persistent = True
loss = F.mean(F.squared_error(tpred, tx_out))
solver = S.Adam(self._learning_rate)
solver.set_parameters(nn.get_parameters())
# variables for validation
vx_in = nn.Variable(
[self._batch_size, self._x_input_length, self._cols_size])
vx_out = nn.Variable(
[self._batch_size, self._x_output_length, self._cols_size])
vpred = self.network(vx_in, self._lstm_unit_name, self._lstm_units)
# data iterators
tdata = self._load_dataset(self._training_dataset_path,
self._batch_size,
shuffle=True)
vdata = self._load_dataset(self._validation_dataset_path,
self._batch_size,
shuffle=True)
# monitors
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(self._monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time",
monitor,
interval=100)
monitor_verr = MonitorSeries("Validation error", monitor, interval=10)
# Training loop
for i in range(self._max_iter):
if i % self._val_interval == 0:
ve = self._validate(vpred, vx_in, vx_out, vdata,
self._val_iter)
monitor_verr.add(i, ve / self._val_iter)
te = self._train(tpred, solver, loss, tx_in, tx_out, tdata.next(),
self._weight_decay)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, te)
monitor_time.add(i)
ve = self._validate(vpred, vx_in, vx_out, vdata, self._val_iter)
monitor_verr.add(i, ve / self._val_iter)
# Save a best model parameters
nn.save_parameters(self._model_params_path)
def __init__(self,
batch_size=32,
learning_rate=1e-4,
max_iter=5086,
total_epochs=20,
monitor_path=None,
val_weight=None,
model_load_path=None):
"""
Construct all the necessary attributes for the attribute classifier.
Args:
batch_size (int): number of samples contained in each generated batch
learning_rate (float) : learning rate
max_iter (int) : maximum iterations for an epoch
total_epochs (int) : total epochs to train the model
val_weight : sample weights
monitor_path (str) : model parameter to be saved
model_load_path (str) : load the model
"""
self.batch_size = batch_size
# Resnet 50
# training graph
model = ResNet50()
self.input_image = nn.Variable((self.batch_size, ) + model.input_shape)
self.label = nn.Variable([self.batch_size, 1])
# fine tuning
pool = model(self.input_image, training=True, use_up_to='pool')
self.clf = clf_resnet50(pool)
self.clf.persistent = True
# loss
self.loss = F.mean(F.sigmoid_cross_entropy(self.clf, self.label))
# hyper parameters
self.solver = S.Adam(learning_rate)
self.solver.set_parameters(nn.get_parameters())
# validation graph
self.x_v = nn.Variable((self.batch_size, ) + model.input_shape)
pool_v = model(self.x_v, training=False, use_up_to='pool')
self.v_clf = clf_resnet50(pool_v, train=False)
self.v_clf_out = F.sigmoid(self.v_clf)
self.print_freq = 100
self.validation_weight = val_weight
# val params
self.acc = 0.0
self.total_epochs = total_epochs
self.max_iter = max_iter
self.monitor_path = monitor_path
if model_load_path is not None:
_ = nn.load_parameters(model_load_path)
def data_distill(model, uniform_data_iterator, num_iter):
generated_img = []
for _ in range(uniform_data_iterator.size //
uniform_data_iterator.batch_size):
img, _ = uniform_data_iterator.next()
dst_img = nn.Variable(img.shape, need_grad=True)
dst_img.d = img
img_params = OrderedDict()
img_params['img'] = dst_img
init_lr = 0.5
solver = S.Adam(alpha=init_lr)
solver.set_parameters(img_params)
#scheduler = lr_scheduler.CosineScheduler(init_lr=0.5, max_iter=num_iter)
scheduler = ReduceLROnPlateauScheduler(init_lr=init_lr,
min_lr=1e-4,
verbose=False,
patience=100)
dummy_solver = S.Sgd(lr=0)
dummy_solver.set_parameters(nn.get_parameters())
for it in tqdm(range(num_iter)):
lr = scheduler.get_learning_rate()
solver.set_learning_rate(lr)
global outs
outs = []
global batch_stats
batch_stats = []
y = model(denormalize(dst_img),
force_global_pooling=True,
training=False) # denormalize to U(0, 255)
y.forward(function_post_hook=get_output)
assert len(outs) == len(batch_stats)
loss = zeroq_loss(batch_stats, outs, dst_img)
loss.forward()
solver.zero_grad()
dummy_solver.zero_grad()
loss.backward()
solver.weight_decay(1e-6)
solver.update()
scheduler.update_lr(loss.d)
generated_img.append(dst_img.d)
return generated_img
def __init__(self, generator, args):
self.generator = generator
self.solver = S.Adam()
self.base_lr = 0.1
self.img_size = 1024
self.n_latent = 10000
self.num_iters = 500
self.latent_dim = self.generator.mapping_network_dim
self.mse_c = 0.0
self.n_c = 1e5
self.lpips_distance = LPIPS(model='vgg')
self.project(args)
def train(max_iter=60000):
# Initialize data provider
di_l = I.data_iterator_mnist(batch_size, True)
di_t = I.data_iterator_mnist(batch_size, False)
# Network
shape_x = (1, 28, 28)
shape_z = (50, )
x = nn.Variable((batch_size, ) + shape_x)
loss_l = I.vae(x, shape_z, test=False)
loss_t = I.vae(x, shape_z, test=True)
# Create solver
solver = S.Adam(learning_rate)
solver.set_parameters(nn.get_parameters())
# Monitors for training and validation
path = cache_dir(os.path.join(I.name, "monitor"))
monitor = M.Monitor(path)
monitor_train_loss = M.MonitorSeries("train_loss", monitor, interval=600)
monitor_val_loss = M.MonitorSeries("val_loss", monitor, interval=600)
monitor_time = M.MonitorTimeElapsed("time", monitor, interval=600)
# Training Loop.
for i in range(max_iter):
# Initialize gradients
solver.zero_grad()
# Forward, backward and update
x.d, _ = di_l.next()
loss_l.forward(clear_no_need_grad=True)
loss_l.backward(clear_buffer=True)
solver.weight_decay(weight_decay)
solver.update()
# Forward for test
x.d, _ = di_t.next()
loss_t.forward(clear_no_need_grad=True)
# Monitor for logging
monitor_train_loss.add(i, loss_l.d.copy())
monitor_val_loss.add(i, loss_t.d.copy())
monitor_time.add(i)
return path
def main():
# Context
ctx = get_extension_context("cudnn", device_id="0")
nn.set_default_context(ctx)
nn.auto_forward(False)
# Inputs
b, c, h, w = 64, 1, 28, 28
x = nn.Variable([b, c, h, w])
t = nn.Variable([b, 1])
vx = nn.Variable([b, c, h, w])
vt = nn.Variable([b, 1])
# Model
model = Model()
pred = model(x)
loss = F.softmax_cross_entropy(pred, t)
vpred = model(vx, test=True)
verror = F.top_n_error(vpred, vt)
# Solver
solver = S.Adam()
solver.set_parameters(model.get_parameters(grad_only=True))
# Data Iterator
tdi = data_iterator_mnist(b, train=True)
vdi = data_iterator_mnist(b, train=False)
# Monitor
monitor = Monitor("tmp.monitor")
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_verr = MonitorSeries("Test error", monitor, interval=1)
# Training loop
for e in range(1):
for j in range(tdi.size // b):
i = e * tdi.size // b + j
x.d, t.d = tdi.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.update()
monitor_loss.add(i, loss.d)
error = 0.0
for _ in range(vdi.size // b):
vx.d, vt.d = vdi.next()
verror.forward(clear_buffer=True)
error += verror.d
error /= vdi.size // b
monitor_verr.add(i, error)
def create_model(net_name: str, batch_size=128, learning_rate=0.001):
nn.clear_parameters()
net = namedtuple("net", ("image", "label", "pred", "loss"))
model = namedtuple("model", ("name", "train", "val", "solver"))
mnist_cnn_prediction = get_net_prediction(net_name)
def create_net(test, persistent):
image = nn.Variable([batch_size, 1, 28, 28])
label = nn.Variable([batch_size, 1])
norm = 255 if net_name.startswith("binary") else 1
pred = mnist_cnn_prediction(image / norm, test=test)
if persistent:
pred.persistent = True
loss = F.mean(F.softmax_cross_entropy(pred, label))
return net(image, label, pred, loss)
net_train = create_net(test=False, persistent=True)
net_val = create_net(test=True, persistent=False)
solver = S.Adam(learning_rate)
solver.set_parameters(nn.get_parameters())
return model(net_name, net_train, net_val, solver)
def train(max_iter=5000, learning_rate=0.001, weight_decay=0):
train = create_net(False)
test = create_net(True)
# ソルバーの作成
solver = S.Adam(learning_rate)
solver.set_parameters(nn.get_parameters())
# モニタの作成
path = cache_dir(os.path.join(I.name, "monitor"))
monitor = M.Monitor(path)
monitor_loss_train = M.MonitorSeries("training_loss", monitor, interval=100)
monitor_time = M.MonitorTimeElapsed("time", monitor, interval=100)
monitor_loss_val = M.MonitorSeries("val_loss", monitor, interval=100)
# 訓練の実行
for i in range(max_iter):
if (i + 1) % 100 == 0:
val_error = 0.0
val_iter = 10
for j in range(val_iter):
test.image0.d, test.image1.d, test.label.d = test.data.next()
test.loss.forward(clear_buffer=True)
val_error += test.loss.d
monitor_loss_val.add(i, val_error / val_iter)
train.image0.d, train.image1.d, train.label.d = train.data.next()
solver.zero_grad()
train.loss.forward(clear_no_need_grad=True)
train.loss.backward(clear_buffer=True)
solver.weight_decay(weight_decay)
solver.update()
monitor_loss_train.add(i, train.loss.d.copy())
monitor_time.add(i)
nn.save_parameters(os.path.join(path, "params.h5"))
return path
def classification_svd():
args = get_args()
# Get context.
from nnabla.ext_utils import get_extension_context
logger.info("Running in %s" % args.context)
ctx = get_extension_context(args.context,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
mnist_cnn_prediction = mnist_lenet_prediction_slim
# TRAIN
reference = "reference"
slim = "slim"
rrate = 0.5 # reduction rate
# Create input variables.
image = nn.Variable([args.batch_size, 1, 28, 28])
label = nn.Variable([args.batch_size, 1])
# Create `reference` and "slim" prediction graph.
model_load_path = args.model_load_path
pred = mnist_cnn_prediction(image, scope=slim, rrate=rrate, test=False)
pred.persistent = True
# Decompose and set parameters
decompose_network_and_set_params(model_load_path, reference, slim, rrate)
loss = F.mean(F.softmax_cross_entropy(pred, label))
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, 1, 28, 28])
vlabel = nn.Variable([args.batch_size, 1])
# Create reference prediction graph.
vpred = mnist_cnn_prediction(vimage, scope=slim, rrate=rrate, test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
with nn.parameter_scope(slim):
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=10)
# Initialize DataIterator for MNIST.
data = data_iterator_mnist(args.batch_size, True)
vdata = data_iterator_mnist(args.batch_size, False)
best_ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
if ve < best_ve:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
parameter_file = os.path.join(args.model_save_path,
'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
def main(args):
# Settings
device_id = args.device_id
batch_size = args.batch_size
batch_size_eval = args.batch_size_eval
n_l_train_data = 4000
n_train_data = 50000
n_cls = 10
learning_rate = 1. * 1e-3
n_epoch = 300
act = F.relu
iter_epoch = n_train_data / batch_size
n_iter = n_epoch * iter_epoch
extension_module = args.context
lambda_ = args.lambda_
# Model
## supervised cnn
batch_size, m, h, w = batch_size, 3, 32, 32
ctx = extension_context(extension_module, device_id=device_id)
x_l = nn.Variable((batch_size, m, h, w))
y_l = nn.Variable((batch_size, 1))
pred, log_var = cnn_model_003(ctx, "cnn", x_l)
one = F.constant(1., log_var.shape)
loss_ce = ce_loss(ctx, pred, y_l)
reg_sigma = sigma_regularization(ctx, log_var, one)
loss_supervised = loss_ce + er_loss(ctx, pred) + lambda_ * reg_sigma
## supervised resnet
pred_res = cifar10_resnet23_prediction(ctx, "resnet", x_l)
loss_res_ce = ce_loss(ctx, pred_res, y_l)
loss_res_supervised = loss_res_ce
## stochastic regularization
x_u0 = nn.Variable((batch_size, m, h, w))
x_u0.persistent = True
x_u1 = nn.Variable((batch_size, m, h, w))
pred_x_u0, log_var0 = cnn_model_003(ctx, "cnn", x_u0)
pred_x_u0.persistent = True
pred_x_u1, log_var1 = cnn_model_003(ctx, "cnn", x_u1)
loss_sr = sr_loss_with_uncertainty(ctx, pred_x_u0, pred_x_u1, log_var0,
log_var1)
reg_sigma0 = sigma_regularization(ctx, log_var0, one)
reg_sigma1 = sigma_regularization(ctx, log_var1, one)
reg_sigmas = sigmas_regularization(ctx, log_var0, log_var1)
loss_unsupervised = loss_sr + er_loss(ctx, pred_x_u0) + er_loss(ctx, pred_x_u1) \
+ lambda_ * (reg_sigma0 + reg_sigma1) + lambda_ * reg_sigmas
## knowledge transfer for resnet
pred_res_x_u0 = cifar10_resnet23_prediction(ctx, "resnet", x_u0)
loss_res_unsupervised = kl_divergence(ctx, pred_res_x_u0, pred_x_u0,
log_var0)
## evaluate
batch_size_eval, m, h, w = batch_size, 3, 32, 32
x_eval = nn.Variable((batch_size_eval, m, h, w))
x_eval.persistent = True
pred_eval, _ = cnn_model_003(ctx, "cnn", x_eval, test=True)
pred_res_eval = cifar10_resnet23_prediction(ctx,
"resnet",
x_eval,
test=True)
# Solver
with nn.context_scope(ctx):
# Solver
with nn.context_scope(ctx):
with nn.parameter_scope("cnn"):
solver = S.Adam(alpha=learning_rate)
solver.set_parameters(nn.get_parameters())
with nn.parameter_scope("resnet"):
solver_res = S.Adam(alpha=learning_rate)
solver_res.set_parameters(nn.get_parameters())
# Dataset
## separate dataset
home = os.environ.get("HOME")
fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
separator = Separator(n_l_train_data)
separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
# data reader
data_reader = Cifar10DataReader(l_train_path,
u_train_path,
test_path,
batch_size=batch_size,
n_cls=n_cls,
da=True,
shape=True)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
acc_prev = 0.
for i in range(n_iter):
# Get data and set it to the varaibles
x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
x_l.d, _, y_l.d = x_l0_data, x_l1_data, y_l_data
x_u0.d, x_u1.d = x_u0_data, x_u1_data
# Train cnn
loss_supervised.forward(clear_no_need_grad=True)
loss_unsupervised.forward(clear_no_need_grad=True)
solver.zero_grad()
loss_supervised.backward(clear_buffer=True)
loss_unsupervised.backward(clear_buffer=True)
solver.update()
# Train resnet
loss_res_supervised.forward(clear_no_need_grad=True)
loss_res_unsupervised.forward(clear_no_need_grad=True)
solver_res.zero_grad()
loss_res_supervised.backward(clear_buffer=True)
pred_x_u0.need_grad = False # no need grad for teacher
loss_res_unsupervised.backward(clear_buffer=True)
solver_res.update()
pred_x_u0.need_grad = True
# Evaluate
if (i + 1) % iter_epoch == 0:
# Get data and set it to the varaibles
x_data, y_data = data_reader.get_test_batch()
# Evaluation loop for cnn
ve = 0.
iter_val = 0
for k in range(0, len(x_data), batch_size_eval):
x_eval.d = get_test_data(x_data, k, batch_size_eval)
label = get_test_data(y_data, k, batch_size_eval)
pred_eval.forward(clear_buffer=True)
ve += categorical_error(pred_eval.d, label)
iter_val += 1
msg = "Epoch:{},ElapsedTime:{},Acc:{:02f}".format(
epoch,
time.time() - st, (1. - ve / iter_val) * 100)
print(msg)
# Evaluation loop for resnet
ve = 0.
iter_val = 0
for k in range(0, len(x_data), batch_size_eval):
x_eval.d = get_test_data(x_data, k, batch_size_eval)
label = get_test_data(y_data, k, batch_size_eval)
pred_res_eval.forward(clear_buffer=True)
ve += categorical_error(pred_res_eval.d, label)
iter_val += 1
msg = "Model:resnet,Epoch:{},ElapsedTime:{},Acc:{:02f}".format(
epoch,
time.time() - st, (1. - ve / iter_val) * 100)
print(msg)
st = time.time()
epoch += 1
def train(args):
if args.c_dim != len(args.selected_attrs):
print("c_dim must be the same as the num of selected attributes. Modified c_dim.")
args.c_dim = len(args.selected_attrs)
# Dump the config information.
config = dict()
print("Used config:")
for k in args.__dir__():
if not k.startswith("_"):
config[k] = getattr(args, k)
print("'{}' : {}".format(k, getattr(args, k)))
# Prepare Generator and Discriminator based on user config.
generator = functools.partial(
model.generator, conv_dim=args.g_conv_dim, c_dim=args.c_dim, num_downsample=args.num_downsample, num_upsample=args.num_upsample, repeat_num=args.g_repeat_num)
discriminator = functools.partial(model.discriminator, image_size=args.image_size,
conv_dim=args.d_conv_dim, c_dim=args.c_dim, repeat_num=args.d_repeat_num)
x_real = nn.Variable(
[args.batch_size, 3, args.image_size, args.image_size])
label_org = nn.Variable([args.batch_size, args.c_dim, 1, 1])
label_trg = nn.Variable([args.batch_size, args.c_dim, 1, 1])
with nn.parameter_scope("dis"):
dis_real_img, dis_real_cls = discriminator(x_real)
with nn.parameter_scope("gen"):
x_fake = generator(x_real, label_trg)
x_fake.persistent = True # to retain its value during computation.
# get an unlinked_variable of x_fake
x_fake_unlinked = x_fake.get_unlinked_variable()
with nn.parameter_scope("dis"):
dis_fake_img, dis_fake_cls = discriminator(x_fake_unlinked)
# ---------------- Define Loss for Discriminator -----------------
d_loss_real = (-1) * loss.gan_loss(dis_real_img)
d_loss_fake = loss.gan_loss(dis_fake_img)
d_loss_cls = loss.classification_loss(dis_real_cls, label_org)
d_loss_cls.persistent = True
# Gradient Penalty.
alpha = F.rand(shape=(args.batch_size, 1, 1, 1))
x_hat = F.mul2(alpha, x_real) + \
F.mul2(F.r_sub_scalar(alpha, 1), x_fake_unlinked)
with nn.parameter_scope("dis"):
dis_for_gp, _ = discriminator(x_hat)
grads = nn.grad([dis_for_gp], [x_hat])
l2norm = F.sum(grads[0] ** 2.0, axis=(1, 2, 3)) ** 0.5
d_loss_gp = F.mean((l2norm - 1.0) ** 2.0)
# total discriminator loss.
d_loss = d_loss_real + d_loss_fake + args.lambda_cls * \
d_loss_cls + args.lambda_gp * d_loss_gp
# ---------------- Define Loss for Generator -----------------
g_loss_fake = (-1) * loss.gan_loss(dis_fake_img)
g_loss_cls = loss.classification_loss(dis_fake_cls, label_trg)
g_loss_cls.persistent = True
# Reconstruct Images.
with nn.parameter_scope("gen"):
x_recon = generator(x_fake_unlinked, label_org)
x_recon.persistent = True
g_loss_rec = loss.recon_loss(x_real, x_recon)
g_loss_rec.persistent = True
# total generator loss.
g_loss = g_loss_fake + args.lambda_rec * \
g_loss_rec + args.lambda_cls * g_loss_cls
# -------------------- Solver Setup ---------------------
d_lr = args.d_lr # initial learning rate for Discriminator
g_lr = args.g_lr # initial learning rate for Generator
solver_dis = S.Adam(alpha=args.d_lr, beta1=args.beta1, beta2=args.beta2)
solver_gen = S.Adam(alpha=args.g_lr, beta1=args.beta1, beta2=args.beta2)
# register parameters to each solver.
with nn.parameter_scope("dis"):
solver_dis.set_parameters(nn.get_parameters())
with nn.parameter_scope("gen"):
solver_gen.set_parameters(nn.get_parameters())
# -------------------- Create Monitors --------------------
monitor = Monitor(args.monitor_path)
monitor_d_cls_loss = MonitorSeries(
'real_classification_loss', monitor, args.log_step)
monitor_g_cls_loss = MonitorSeries(
'fake_classification_loss', monitor, args.log_step)
monitor_loss_dis = MonitorSeries(
'discriminator_loss', monitor, args.log_step)
monitor_recon_loss = MonitorSeries(
'reconstruction_loss', monitor, args.log_step)
monitor_loss_gen = MonitorSeries('generator_loss', monitor, args.log_step)
monitor_time = MonitorTimeElapsed("Training_time", monitor, args.log_step)
# -------------------- Prepare / Split Dataset --------------------
using_attr = args.selected_attrs
dataset, attr2idx, idx2attr = get_data_dict(args.attr_path, using_attr)
random.seed(313) # use fixed seed.
random.shuffle(dataset) # shuffle dataset.
test_dataset = dataset[-2000:] # extract 2000 images for test
if args.num_data:
# Use training data partially.
training_dataset = dataset[:min(args.num_data, len(dataset) - 2000)]
else:
training_dataset = dataset[:-2000]
print("Use {} images for training.".format(len(training_dataset)))
# create data iterators.
load_func = functools.partial(stargan_load_func, dataset=training_dataset,
image_dir=args.celeba_image_dir, image_size=args.image_size, crop_size=args.celeba_crop_size)
data_iterator = data_iterator_simple(load_func, len(
training_dataset), args.batch_size, with_file_cache=False, with_memory_cache=False)
load_func_test = functools.partial(stargan_load_func, dataset=test_dataset,
image_dir=args.celeba_image_dir, image_size=args.image_size, crop_size=args.celeba_crop_size)
test_data_iterator = data_iterator_simple(load_func_test, len(
test_dataset), args.batch_size, with_file_cache=False, with_memory_cache=False)
# Keep fixed test images for intermediate translation visualization.
test_real_ndarray, test_label_ndarray = test_data_iterator.next()
test_label_ndarray = test_label_ndarray.reshape(
test_label_ndarray.shape + (1, 1))
# -------------------- Training Loop --------------------
one_epoch = data_iterator.size // args.batch_size
num_max_iter = args.max_epoch * one_epoch
for i in range(num_max_iter):
# Get real images and labels.
real_ndarray, label_ndarray = data_iterator.next()
label_ndarray = label_ndarray.reshape(label_ndarray.shape + (1, 1))
label_ndarray = label_ndarray.astype(float)
x_real.d, label_org.d = real_ndarray, label_ndarray
# Generate target domain labels randomly.
rand_idx = np.random.permutation(label_org.shape[0])
label_trg.d = label_ndarray[rand_idx]
# ---------------- Train Discriminator -----------------
# generate fake image.
x_fake.forward(clear_no_need_grad=True)
d_loss.forward(clear_no_need_grad=True)
solver_dis.zero_grad()
d_loss.backward(clear_buffer=True)
solver_dis.update()
monitor_loss_dis.add(i, d_loss.d.item())
monitor_d_cls_loss.add(i, d_loss_cls.d.item())
monitor_time.add(i)
# -------------- Train Generator --------------
if (i + 1) % args.n_critic == 0:
g_loss.forward(clear_no_need_grad=True)
solver_dis.zero_grad()
solver_gen.zero_grad()
x_fake_unlinked.grad.zero()
g_loss.backward(clear_buffer=True)
x_fake.backward(grad=None)
solver_gen.update()
monitor_loss_gen.add(i, g_loss.d.item())
monitor_g_cls_loss.add(i, g_loss_cls.d.item())
monitor_recon_loss.add(i, g_loss_rec.d.item())
monitor_time.add(i)
if (i + 1) % args.sample_step == 0:
# save image.
save_results(i, args, x_real, x_fake,
label_org, label_trg, x_recon)
if args.test_during_training:
# translate images from test dataset.
x_real.d, label_org.d = test_real_ndarray, test_label_ndarray
label_trg.d = test_label_ndarray[rand_idx]
x_fake.forward(clear_no_need_grad=True)
save_results(i, args, x_real, x_fake, label_org,
label_trg, None, is_training=False)
# Learning rates get decayed
if (i + 1) > int(0.5 * num_max_iter) and (i + 1) % args.lr_update_step == 0:
g_lr = max(0, g_lr - (args.lr_update_step *
args.g_lr / float(0.5 * num_max_iter)))
d_lr = max(0, d_lr - (args.lr_update_step *
args.d_lr / float(0.5 * num_max_iter)))
solver_gen.set_learning_rate(g_lr)
solver_dis.set_learning_rate(d_lr)
print('learning rates decayed, g_lr: {}, d_lr: {}.'.format(g_lr, d_lr))
# Save parameters and training config.
param_name = 'trained_params_{}.h5'.format(
datetime.datetime.today().strftime("%m%d%H%M"))
param_path = os.path.join(args.model_save_path, param_name)
nn.save_parameters(param_path)
config["pretrained_params"] = param_name
with open(os.path.join(args.model_save_path, "training_conf_{}.json".format(datetime.datetime.today().strftime("%m%d%H%M"))), "w") as f:
json.dump(config, f)
# -------------------- Translation on test dataset --------------------
for i in range(args.num_test):
real_ndarray, label_ndarray = test_data_iterator.next()
label_ndarray = label_ndarray.reshape(label_ndarray.shape + (1, 1))
label_ndarray = label_ndarray.astype(float)
x_real.d, label_org.d = real_ndarray, label_ndarray
rand_idx = np.random.permutation(label_org.shape[0])
label_trg.d = label_ndarray[rand_idx]
x_fake.forward(clear_no_need_grad=True)
save_results(i, args, x_real, x_fake, label_org,
label_trg, None, is_training=False)
def train(data_iterator, monitor, config, comm, args):
monitor_train_loss, monitor_train_recon = None, None
monitor_val_loss, monitor_val_recon = None, None
if comm.rank == 0:
monitor_train_loss = MonitorSeries(
config['monitor']['train_loss'], monitor, interval=config['train']['logger_step_interval'])
monitor_train_recon = MonitorImageTile(config['monitor']['train_recon'], monitor, interval=config['train']['logger_step_interval'],
num_images=config['train']['batch_size'])
monitor_val_loss = MonitorSeries(
config['monitor']['val_loss'], monitor, interval=config['train']['logger_step_interval'])
monitor_val_recon = MonitorImageTile(config['monitor']['val_recon'], monitor, interval=config['train']['logger_step_interval'],
num_images=config['train']['batch_size'])
model = VQVAE(config)
if not args.sample_from_pixelcnn:
if config['train']['solver'] == 'adam':
solver = S.Adam()
else:
solver = S.momentum()
solver.set_learning_rate(config['train']['learning_rate'])
train_loader = data_iterator(config, comm, train=True)
if config['dataset']['name'] != 'imagenet':
val_loader = data_iterator(config, comm, train=False)
else:
val_loader = None
else:
solver, train_loader, val_loader = None, None, None
if not args.pixelcnn_prior:
trainer = VQVAEtrainer(model, solver, train_loader, val_loader, monitor_train_loss,
monitor_train_recon, monitor_val_loss, monitor_val_recon, config, comm)
num_epochs = config['train']['num_epochs']
else:
pixelcnn_model = GatedPixelCNN(config['prior'])
trainer = TrainerPrior(model, pixelcnn_model, solver, train_loader, val_loader, monitor_train_loss,
monitor_train_recon, monitor_val_loss, monitor_val_recon, config, comm, eval=args.sample_from_pixelcnn)
num_epochs = config['prior']['train']['num_epochs']
if os.path.exists(config['model']['checkpoint']) and (args.load_checkpoint or args.sample_from_pixelcnn):
checkpoint_path = config['model']['checkpoint'] if not args.pixelcnn_prior else config['prior']['checkpoint']
trainer.load_checkpoint(checkpoint_path, msg='Parameters loaded from {}'.format(
checkpoint_path), pixelcnn=args.pixelcnn_prior, load_solver=not args.sample_from_pixelcnn)
if args.sample_from_pixelcnn:
trainer.random_generate(
args.sample_from_pixelcnn, args.sample_save_path)
return
for epoch in range(num_epochs):
trainer.train(epoch)
if epoch % config['val']['interval'] == 0 and val_loader != None:
trainer.validate(epoch)
if comm.rank == 0:
if epoch % config['train']['save_param_step_interval'] == 0 or epoch == config['train']['num_epochs']-1:
trainer.save_checkpoint(
config['model']['saved_models_dir'], epoch, pixelcnn=args.pixelcnn_prior)
def main(args):
# Settings
device_id = args.device_id
batch_size = 100
batch_size_eval = 100
n_l_train_data = 4000
n_train_data = 50000
n_cls = 10
learning_rate = 1. * 1e-3
n_epoch = 300
act = F.relu
iter_epoch = n_train_data / batch_size
n_iter = n_epoch * iter_epoch
extension_module = args.context
# Model
## supervised
batch_size, m, h, w = batch_size, 3, 32, 32
ctx = extension_context(extension_module, device_id=device_id)
x_l = nn.Variable((batch_size, m, h, w))
y_l = nn.Variable((batch_size, 1))
pred = cnn_model_003(ctx, x_l)
loss_ce = ce_loss(ctx, pred, y_l)
loss_er = er_loss(ctx, pred)
loss_supervised = loss_ce + loss_er
## stochastic regularization
x_u0 = nn.Variable((batch_size, m, h, w))
x_u1 = nn.Variable((batch_size, m, h, w))
pred_x_u0 = cnn_model_003(ctx, x_u0)
pred_x_u1 = cnn_model_003(ctx, x_u1)
loss_sr = sr_loss(ctx, pred_x_u0, pred_x_u1)
loss_er0 = er_loss(ctx, pred_x_u0)
loss_er1 = er_loss(ctx, pred_x_u1)
loss_unsupervised = loss_sr + loss_er0 + loss_er1
## evaluate
batch_size_eval, m, h, w = batch_size, 3, 32, 32
x_eval = nn.Variable((batch_size_eval, m, h, w))
pred_eval = cnn_model_003(ctx, x_eval, test=True)
# Solver
with nn.context_scope(ctx):
solver_superrvised = S.Adam(alpha=learning_rate)
solver_superrvised.set_parameters(nn.get_parameters())
solver_unsuperrvised = S.Adam(alpha=learning_rate)
solver_unsuperrvised.set_parameters(nn.get_parameters())
# Gradient Scale Container
gsc = GradScaleContainer(len(nn.get_parameters()))
# Dataset
## separate dataset
home = os.environ.get("HOME")
fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
separator = Separator(n_l_train_data)
separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
# data reader
data_reader = Cifar10DataReader(
l_train_path,
u_train_path,
test_path,
batch_size=batch_size,
n_cls=n_cls,
da=True, #TODO: use F.image_augmentation
shape=True)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
acc_prev = 0.
for i in range(n_iter):
# Get data and set it to the varaibles
x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
x_l.d, _, y_l.d = x_l0_data, x_l1_data, y_l_data
x_u0.d, x_u1.d = x_u0_data, x_u1_data
# Train
loss_supervised.forward(clear_no_need_grad=True)
loss_unsupervised.forward(clear_no_need_grad=True)
## compute scales and update with grads of supervised loss
solver_superrvised.zero_grad()
loss_supervised.backward(clear_buffer=True)
gsc.set_scales_supervised_loss(nn.get_parameters())
solver_superrvised.update()
## compute scales and update with grads of unsupervised loss
solver_unsuperrvised.zero_grad()
loss_unsupervised.backward(clear_buffer=True)
gsc.set_scales_unsupervised_loss(nn.get_parameters())
gsc.scale_grad(ctx, nn.get_parameters())
solver_unsuperrvised.update()
# Evaluate
if (i + 1) % iter_epoch == 0:
# Get data and set it to the varaibles
x_data, y_data = data_reader.get_test_batch()
# Evaluation loop
ve = 0.
iter_val = 0
for k in range(0, len(x_data), batch_size_eval):
x_eval.d = x_data[k:k + batch_size_eval, :]
label = y_data[k:k + batch_size_eval, :]
pred_eval.forward(clear_buffer=True)
ve += categorical_error(pred_eval.d, label)
iter_val += 1
msg = "Epoch:{},ElapsedTime:{},Acc:{:02f}".format(
epoch,
time.time() - st, (1. - ve / iter_val) * 100)
print(msg)
st = time.time()
epoch += 1
def train(args):
# Context
ctx = get_extension_context(args.context,
device_id=args.device_id,
type_config=args.type_config)
nn.set_default_context(ctx)
# Args
latent = args.latent
maps = args.maps
batch_size = args.batch_size
image_size = args.image_size
lambda_ = args.lambda_
# Model
# generator loss
z = nn.Variable([batch_size, latent])
x_fake = generator(z, maps=maps, up=args.up).apply(persistent=True)
p_fake = discriminator(x_fake, maps=maps)
loss_gen = gan_loss(p_fake).apply(persistent=True)
# discriminator loss
p_fake = discriminator(x_fake, maps=maps)
x_real = nn.Variable([batch_size, 3, image_size, image_size])
p_real = discriminator(x_real, maps=maps)
loss_dis = gan_loss(p_fake, p_real).apply(persistent=True)
# gradient penalty
eps = F.rand(shape=[batch_size, 1, 1, 1])
x_rmix = eps * x_real + (1.0 - eps) * x_fake
p_rmix = discriminator(x_rmix, maps=maps)
x_rmix.need_grad = True # Enabling gradient computation for double backward
grads = nn.grad([p_rmix], [x_rmix])
l2norms = [F.sum(g**2.0, [1, 2, 3])**0.5 for g in grads]
gp = sum([F.mean((l - 1.0)**2.0) for l in l2norms])
loss_dis += lambda_ * gp
# generator with fixed value for test
z_test = nn.Variable.from_numpy_array(np.random.randn(batch_size, latent))
x_test = generator(z_test, maps=maps, test=True,
up=args.up).apply(persistent=True)
# Solver
solver_gen = S.Adam(args.lrg, args.beta1, args.beta2)
solver_dis = S.Adam(args.lrd, args.beta1, args.beta2)
with nn.parameter_scope("generator"):
params_gen = nn.get_parameters()
solver_gen.set_parameters(params_gen)
with nn.parameter_scope("discriminator"):
params_dis = nn.get_parameters()
solver_dis.set_parameters(params_dis)
# Monitor
monitor = Monitor(args.monitor_path)
monitor_loss_gen = MonitorSeries("Generator Loss", monitor, interval=10)
monitor_loss_cri = MonitorSeries("Negative Critic Loss",
monitor,
interval=10)
monitor_time = MonitorTimeElapsed("Training Time", monitor, interval=10)
monitor_image_tile_train = MonitorImageTile("Image Tile Train",
monitor,
num_images=batch_size,
interval=1,
normalize_method=denormalize)
monitor_image_tile_test = MonitorImageTile("Image Tile Test",
monitor,
num_images=batch_size,
interval=1,
normalize_method=denormalize)
# Data Iterator
di = data_iterator_cifar10(batch_size, True)
# Train loop
for i in range(args.max_iter):
# Train discriminator
x_fake.need_grad = False # no need backward to generator
for _ in range(args.n_critic):
solver_dis.zero_grad()
x_real.d = di.next()[0] / 127.5 - 1.0
z.d = np.random.randn(batch_size, latent)
loss_dis.forward(clear_no_need_grad=True)
loss_dis.backward(clear_buffer=True)
solver_dis.update()
# Train generator
x_fake.need_grad = True # need backward to generator
solver_gen.zero_grad()
z.d = np.random.randn(batch_size, latent)
loss_gen.forward(clear_no_need_grad=True)
loss_gen.backward(clear_buffer=True)
solver_gen.update()
# Monitor
monitor_loss_gen.add(i, loss_gen.d)
monitor_loss_cri.add(i, -loss_dis.d)
monitor_time.add(i)
# Save
if i % args.save_interval == 0:
monitor_image_tile_train.add(i, x_fake)
monitor_image_tile_test.add(i, x_test)
nn.save_parameters(
os.path.join(args.monitor_path, "params_{}.h5".format(i)))
# Last
x_test.forward(clear_buffer=True)
nn.save_parameters(
os.path.join(args.monitor_path, "params_{}.h5".format(i)))
monitor_image_tile_train.add(i, x_fake)
monitor_image_tile_test.add(i, x_test)
def train():
args = get_args()
# Get context.
from nnabla.contrib.context import extension_context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = extension_context(extension_module, device_id=args.device_id)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
if args.net == "cifar10_resnet23_prediction":
model_prediction = cifar10_resnet23_prediction
# TRAIN
maps = 64
data_iterator = data_iterator_cifar10
c = 3
h = w = 32
n_train = 50000
n_valid = 10000
# Create input variables.
image = nn.Variable([args.batch_size, c, h, w])
label = nn.Variable([args.batch_size, 1])
# Create model_prediction graph.
pred = model_prediction(image, maps=maps, test=False)
pred.persistent = True
# Create loss function.
loss = F.mean(F.softmax_cross_entropy(pred, label))
# SSL Regularization
loss += ssl_regularization(nn.get_parameters(), args.filter_decay,
args.channel_decay)
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, c, h, w])
vlabel = nn.Variable([args.batch_size, 1])
# Create predition graph.
vpred = model_prediction(vimage, maps=maps, test=True)
# Create Solver.
solver = S.Adam(args.learning_rate)
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=1)
# Initialize DataIterator
data = data_iterator(args.batch_size, True)
vdata = data_iterator(args.batch_size, False)
best_ve = 1.0
ve = 1.0
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
if ve < best_ve:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
best_ve = ve
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(int(n_valid / args.batch_size)):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
ve /= int(n_valid / args.batch_size)
monitor_verr.add(i, ve)
parameter_file = os.path.join(args.model_save_path,
'params_{:06}.h5'.format(args.max_iter))
nn.save_parameters(parameter_file)
def train():
"""
Main script.
Steps:
* Parse command line arguments.
* Specify a context for computation.
* Initialize DataIterator for MNIST.
* Construct a computation graph for training and validation.
* Initialize a solver and set parameter variables to it.
* Create monitor instances for saving and displaying training stats.
* Training loop
* Computate error rate for validation data (periodically)
* Get a next minibatch.
* Execute forwardprop on the training graph.
* Compute training error
* Set parameter gradients zero
* Execute backprop.
* Solver updates parameters by using gradients computed by backprop.
"""
args = get_args()
from numpy.random import seed
seed(0)
# Get context.
from nnabla.contrib.context import extension_context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = extension_context(extension_module, device_id=args.device_id)
nn.set_default_context(ctx)
# Create CNN network for both training and testing.
if args.net == 'lenet':
mnist_cnn_prediction = mnist_lenet_prediction
elif args.net == 'resnet':
mnist_cnn_prediction = mnist_resnet_prediction
else:
raise ValueError("Unknown network type {}".format(args.net))
# TRAIN
# Create input variables.
image = nn.Variable([args.batch_size, 1, 28, 28])
label = nn.Variable([args.batch_size, 1])
# Create prediction graph.
pred = mnist_cnn_prediction(image, test=False, aug=args.augment_train)
pred.persistent = True
# Create loss function.
loss = F.mean(F.softmax_cross_entropy(pred, label))
# TEST
# Create input variables.
vimage = nn.Variable([args.batch_size, 1, 28, 28])
vlabel = nn.Variable([args.batch_size, 1])
# Create predition graph.
vpred = mnist_cnn_prediction(vimage, test=True, aug=args.augment_test)
# Create Solver.
solver = S.Adam(args.learning_rate)
solver.set_parameters(nn.get_parameters())
# Create monitor.
from nnabla.monitor import Monitor, MonitorSeries, MonitorTimeElapsed
monitor = Monitor(args.monitor_path)
monitor_loss = MonitorSeries("Training loss", monitor, interval=10)
monitor_err = MonitorSeries("Training error", monitor, interval=10)
monitor_time = MonitorTimeElapsed("Training time", monitor, interval=100)
monitor_verr = MonitorSeries("Test error", monitor, interval=10)
# Initialize DataIterator for MNIST.
from numpy.random import RandomState
data = data_iterator_mnist(args.batch_size, True, rng=RandomState(1223))
vdata = data_iterator_mnist(args.batch_size, False)
# Training loop.
for i in range(args.max_iter):
if i % args.val_interval == 0:
# Validation
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
if i % args.model_save_interval == 0:
nn.save_parameters(
os.path.join(args.model_save_path, 'params_%06d.h5' % i))
# Training forward
image.d, label.d = data.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
loss.backward(clear_buffer=True)
solver.weight_decay(args.weight_decay)
solver.update()
e = categorical_error(pred.d, label.d)
monitor_loss.add(i, loss.d.copy())
monitor_err.add(i, e)
monitor_time.add(i)
ve = 0.0
for j in range(args.val_iter):
vimage.d, vlabel.d = vdata.next()
vpred.forward(clear_buffer=True)
ve += categorical_error(vpred.d, vlabel.d)
monitor_verr.add(i, ve / args.val_iter)
parameter_file = os.path.join(
args.model_save_path,
'{}_params_{:06}.h5'.format(args.net, args.max_iter))
nn.save_parameters(parameter_file)
def train():
# Check NNabla version
if utils.get_nnabla_version_integer() < 11900:
raise ValueError(
'Please update the nnabla version to v1.19.0 or latest version since memory efficiency of core engine is improved in v1.19.0'
)
parser, args = get_train_args()
# Get context.
ctx = get_extension_context(args.context, device_id=args.device_id)
comm = CommunicatorWrapper(ctx)
nn.set_default_context(comm.ctx)
ext = import_extension_module(args.context)
# Monitors
# setting up monitors for logging
monitor_path = args.output
monitor = Monitor(monitor_path)
monitor_best_epoch = MonitorSeries('Best epoch', monitor, interval=1)
monitor_traing_loss = MonitorSeries('Training loss', monitor, interval=1)
monitor_validation_loss = MonitorSeries('Validation loss',
monitor,
interval=1)
monitor_lr = MonitorSeries('learning rate', monitor, interval=1)
monitor_time = MonitorTimeElapsed("training time per iteration",
monitor,
interval=1)
if comm.rank == 0:
print("Mixing coef. is {}, i.e., MDL = {}*TD-Loss + FD-Loss".format(
args.mcoef, args.mcoef))
if not os.path.isdir(args.output):
os.makedirs(args.output)
# Initialize DataIterator for MUSDB.
train_source, valid_source, args = load_datasources(parser, args)
train_iter = data_iterator(train_source,
args.batch_size,
RandomState(args.seed),
with_memory_cache=False,
with_file_cache=False)
valid_iter = data_iterator(valid_source,
1,
RandomState(args.seed),
with_memory_cache=False,
with_file_cache=False)
if comm.n_procs > 1:
train_iter = train_iter.slice(rng=None,
num_of_slices=comm.n_procs,
slice_pos=comm.rank)
valid_iter = valid_iter.slice(rng=None,
num_of_slices=comm.n_procs,
slice_pos=comm.rank)
# Calculate maxiter per GPU device.
max_iter = int((train_source._size // args.batch_size) // comm.n_procs)
weight_decay = args.weight_decay * comm.n_procs
print("max_iter", max_iter)
# Calculate the statistics (mean and variance) of the dataset
scaler_mean, scaler_std = utils.get_statistics(args, train_source)
max_bin = utils.bandwidth_to_max_bin(train_source.sample_rate, args.nfft,
args.bandwidth)
unmix = OpenUnmix_CrossNet(input_mean=scaler_mean,
input_scale=scaler_std,
nb_channels=args.nb_channels,
hidden_size=args.hidden_size,
n_fft=args.nfft,
n_hop=args.nhop,
max_bin=max_bin)
# Create input variables.
mixture_audio = nn.Variable([args.batch_size] +
list(train_source._get_data(0)[0].shape))
target_audio = nn.Variable([args.batch_size] +
list(train_source._get_data(0)[1].shape))
vmixture_audio = nn.Variable(
[1] + [2, valid_source.sample_rate * args.valid_dur])
vtarget_audio = nn.Variable([1] +
[8, valid_source.sample_rate * args.valid_dur])
# create training graph
mix_spec, M_hat, pred = unmix(mixture_audio)
Y = Spectrogram(*STFT(target_audio, n_fft=unmix.n_fft, n_hop=unmix.n_hop),
mono=(unmix.nb_channels == 1))
loss_f = mse_loss(mix_spec, M_hat, Y)
loss_t = sdr_loss(mixture_audio, pred, target_audio)
loss = args.mcoef * loss_t + loss_f
loss.persistent = True
# Create Solver and set parameters.
solver = S.Adam(args.lr)
solver.set_parameters(nn.get_parameters())
# create validation graph
vmix_spec, vM_hat, vpred = unmix(vmixture_audio, test=True)
vY = Spectrogram(*STFT(vtarget_audio, n_fft=unmix.n_fft,
n_hop=unmix.n_hop),
mono=(unmix.nb_channels == 1))
vloss_f = mse_loss(vmix_spec, vM_hat, vY)
vloss_t = sdr_loss(vmixture_audio, vpred, vtarget_audio)
vloss = args.mcoef * vloss_t + vloss_f
vloss.persistent = True
# Initialize Early Stopping
es = utils.EarlyStopping(patience=args.patience)
# Initialize LR Scheduler (ReduceLROnPlateau)
lr_scheduler = ReduceLROnPlateau(lr=args.lr,
factor=args.lr_decay_gamma,
patience=args.lr_decay_patience)
best_epoch = 0
# Training loop.
for epoch in trange(args.epochs):
# TRAINING
losses = utils.AverageMeter()
for batch in range(max_iter):
mixture_audio.d, target_audio.d = train_iter.next()
solver.zero_grad()
loss.forward(clear_no_need_grad=True)
if comm.n_procs > 1:
all_reduce_callback = comm.get_all_reduce_callback()
loss.backward(clear_buffer=True,
communicator_callbacks=all_reduce_callback)
else:
loss.backward(clear_buffer=True)
solver.weight_decay(weight_decay)
solver.update()
losses.update(loss.d.copy(), args.batch_size)
training_loss = losses.avg
# clear cache memory
ext.clear_memory_cache()
# VALIDATION
vlosses = utils.AverageMeter()
for batch in range(int(valid_source._size // comm.n_procs)):
x, y = valid_iter.next()
dur = int(valid_source.sample_rate * args.valid_dur)
sp, cnt = 0, 0
loss_tmp = nn.NdArray()
loss_tmp.zero()
while 1:
vmixture_audio.d = x[Ellipsis, sp:sp + dur]
vtarget_audio.d = y[Ellipsis, sp:sp + dur]
vloss.forward(clear_no_need_grad=True)
cnt += 1
sp += dur
loss_tmp += vloss.data
if x[Ellipsis,
sp:sp + dur].shape[-1] < dur or x.shape[-1] == cnt * dur:
break
loss_tmp = loss_tmp / cnt
if comm.n_procs > 1:
comm.all_reduce(loss_tmp, division=True, inplace=True)
vlosses.update(loss_tmp.data.copy(), 1)
validation_loss = vlosses.avg
# clear cache memory
ext.clear_memory_cache()
lr = lr_scheduler.update_lr(validation_loss, epoch=epoch)
solver.set_learning_rate(lr)
stop = es.step(validation_loss)
if comm.rank == 0:
monitor_best_epoch.add(epoch, best_epoch)
monitor_traing_loss.add(epoch, training_loss)
monitor_validation_loss.add(epoch, validation_loss)
monitor_lr.add(epoch, lr)
monitor_time.add(epoch)
if validation_loss == es.best:
# save best model
nn.save_parameters(os.path.join(args.output, 'best_xumx.h5'))
best_epoch = epoch
if stop:
print("Apply Early Stopping")
break
def main(args):
# Settings
device_id = args.device_id
batch_size = args.batch_size
batch_size_eval = args.batch_size_eval
n_l_train_data = 4000
n_train_data = 50000
n_cls = 10
learning_rate = 1. * 1e-3
n_epoch = 300
act = F.relu
iter_epoch = n_train_data / batch_size
n_iter = int(n_epoch * iter_epoch)
extension_module = args.context
lambda_ = args.lambda_
# Model
## supervised
batch_size, m, h, w = batch_size, 3, 32, 32
ctx = extension_context(extension_module, device_id=device_id)
x_l = nn.Variable((batch_size, m, h, w))
y_l = nn.Variable((batch_size, 1))
pred, log_var = cnn_model_003(ctx, x_l)
one = F.constant(1., log_var.shape)
loss_ce = ce_loss(ctx, pred, y_l)
reg_sigma = sigma_regularization(ctx, log_var, one)
loss_supervised = loss_ce + er_loss(ctx, pred) + lambda_ * reg_sigma
## stochastic regularization
x_u0 = nn.Variable((batch_size, m, h, w))
x_u1 = nn.Variable((batch_size, m, h, w))
pred_x_u0, log_var0 = cnn_model_003(ctx, x_u0)
pred_x_u1, log_var1 = cnn_model_003(ctx, x_u1)
loss_sr = sr_loss_with_uncertainty(ctx, pred_x_u0, pred_x_u1, log_var0,
log_var1)
reg_sigma0 = sigma_regularization(ctx, log_var0, one)
reg_sigma1 = sigma_regularization(ctx, log_var1, one)
reg_sigmas = sigmas_regularization(ctx, log_var0, log_var1)
loss_unsupervised = loss_sr + er_loss(ctx, pred_x_u0) + er_loss(ctx, pred_x_u1) \
+ lambda_ * (reg_sigma0 + reg_sigma1) + lambda_ * reg_sigmas
## evaluate
batch_size_eval, m, h, w = batch_size, 3, 32, 32
x_eval = nn.Variable((batch_size_eval, m, h, w))
pred_eval, _ = cnn_model_003(ctx, x_eval, test=True)
# Solver
with nn.context_scope(ctx):
solver = S.Adam(alpha=learning_rate)
solver.set_parameters(nn.get_parameters())
# Dataset
## separate dataset
home = os.environ.get("HOME")
fpath = os.path.join(home, "datasets/cifar10/cifar-10.npz")
separator = Separator(n_l_train_data)
#separator.separate_then_save(fpath)
l_train_path = os.path.join(home, "datasets/cifar10/l_cifar-10.npz")
u_train_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
test_path = os.path.join(home, "datasets/cifar10/cifar-10.npz")
# data reader
data_reader = Cifar10DataReader(l_train_path,
u_train_path,
test_path,
batch_size=batch_size,
n_cls=n_cls,
da=True,
shape=True)
# Training loop
print("# Training loop")
epoch = 1
st = time.time()
acc_prev = 0.
ve_best = 1.
save_path_prev = ""
for i in range(n_iter):
# Get data and set it to the varaibles
x_l0_data, x_l1_data, y_l_data = data_reader.get_l_train_batch()
x_u0_data, x_u1_data, y_u_data = data_reader.get_u_train_batch()
x_l.d, _, y_l.d = x_l0_data, x_l1_data, y_l_data
x_u0.d, x_u1.d = x_u0_data, x_u1_data
# Train
loss_supervised.forward(clear_no_need_grad=True)
loss_unsupervised.forward(clear_no_need_grad=True)
solver.zero_grad()
loss_supervised.backward(clear_buffer=True)
loss_unsupervised.backward(clear_buffer=True)
solver.update()
# Evaluate
if int((i + 1) % iter_epoch) == 0:
# Get data and set it to the varaibles
#x_data, y_data = data_reader.get_test_batch()
u_train_data = data_reader.u_train_data
x_data, y_data = u_train_data["train_x"].reshape(
-1, 3, 32, 32) / 255.0, u_train_data["train_y"],
# Evaluation loop
ve = 0.
iter_val = 0
for k in range(0, len(x_data), batch_size_eval):
x_eval.d = get_test_data(x_data, k, batch_size_eval)
label = get_test_data(y_data, k, batch_size_eval)
pred_eval.forward(clear_buffer=True)
ve += categorical_error(pred_eval.d, label)
iter_val += 1
ve /= iter_val
msg = "Epoch:{},ElapsedTime:{},Acc:{:02f}".format(
epoch,
time.time() - st, (1. - ve) * 100)
print(msg)
if ve < ve_best:
ve_best = ve
st = time.time()
epoch += 1
def train(args):
# Settings
b, c, h, w = 1, 3, 256, 256
beta1 = 0.5
beta2 = 0.999
pool_size = 50
lambda_recon = args.lambda_recon
lambda_idt = args.lambda_idt
base_lr = args.learning_rate
init_method = args.init_method
# Context
extension_module = args.context
if args.context is None:
extension_module = 'cpu'
logger.info("Running in %s" % extension_module)
ctx = get_extension_context(extension_module,
device_id=args.device_id, type_config=args.type_config)
nn.set_default_context(ctx)
# Inputs
x_raw = nn.Variable([b, c, h, w], need_grad=False)
y_raw = nn.Variable([b, c, h, w], need_grad=False)
x_real = image_augmentation(x_raw)
y_real = image_augmentation(y_raw)
x_history = nn.Variable([b, c, h, w])
y_history = nn.Variable([b, c, h, w])
x_real_test = nn.Variable([b, c, h, w], need_grad=False)
y_real_test = nn.Variable([b, c, h, w], need_grad=False)
# Models for training
# Generate
y_fake = models.g(x_real, unpool=args.unpool, init_method=init_method)
x_fake = models.f(y_real, unpool=args.unpool, init_method=init_method)
y_fake.persistent, x_fake.persistent = True, True
# Reconstruct
x_recon = models.f(y_fake, unpool=args.unpool, init_method=init_method)
y_recon = models.g(x_fake, unpool=args.unpool, init_method=init_method)
# Discriminate
d_y_fake = models.d_y(y_fake, init_method=init_method)
d_x_fake = models.d_x(x_fake, init_method=init_method)
d_y_real = models.d_y(y_real, init_method=init_method)
d_x_real = models.d_x(x_real, init_method=init_method)
d_y_history = models.d_y(y_history, init_method=init_method)
d_x_history = models.d_x(x_history, init_method=init_method)
# Models for test
y_fake_test = models.g(
x_real_test, unpool=args.unpool, init_method=init_method)
x_fake_test = models.f(
y_real_test, unpool=args.unpool, init_method=init_method)
y_fake_test.persistent, x_fake_test.persistent = True, True
# Reconstruct
x_recon_test = models.f(
y_fake_test, unpool=args.unpool, init_method=init_method)
y_recon_test = models.g(
x_fake_test, unpool=args.unpool, init_method=init_method)
# Losses
# Reconstruction Loss
loss_recon = models.recon_loss(x_recon, x_real) \
+ models.recon_loss(y_recon, y_real)
# Generator loss
loss_gen = models.lsgan_loss(d_y_fake) \
+ models.lsgan_loss(d_x_fake) \
+ lambda_recon * loss_recon
# Identity loss
if lambda_idt != 0:
logger.info("Identity loss was added.")
# Identity
y_idt = models.g(y_real, unpool=args.unpool, init_method=init_method)
x_idt = models.f(x_real, unpool=args.unpool, init_method=init_method)
loss_idt = models.recon_loss(x_idt, x_real) \
+ models.recon_loss(y_idt, y_real)
loss_gen += lambda_recon * lambda_idt * loss_idt
# Discriminator losses
loss_dis_y = models.lsgan_loss(d_y_history, d_y_real)
loss_dis_x = models.lsgan_loss(d_x_history, d_x_real)
# Solvers
solver_gen = S.Adam(base_lr, beta1, beta2)
solver_dis_x = S.Adam(base_lr, beta1, beta2)
solver_dis_y = S.Adam(base_lr, beta1, beta2)
with nn.parameter_scope('generator'):
solver_gen.set_parameters(nn.get_parameters())
with nn.parameter_scope('discriminator'):
with nn.parameter_scope("x"):
solver_dis_x.set_parameters(nn.get_parameters())
with nn.parameter_scope("y"):
solver_dis_y.set_parameters(nn.get_parameters())
# Datasets
rng = np.random.RandomState(313)
ds_train_B = cycle_gan_data_source(
args.dataset, train=True, domain="B", shuffle=True, rng=rng)
ds_train_A = cycle_gan_data_source(
args.dataset, train=True, domain="A", shuffle=True, rng=rng)
ds_test_B = cycle_gan_data_source(
args.dataset, train=False, domain="B", shuffle=False, rng=rng)
ds_test_A = cycle_gan_data_source(
args.dataset, train=False, domain="A", shuffle=False, rng=rng)
di_train_B = cycle_gan_data_iterator(ds_train_B, args.batch_size)
di_train_A = cycle_gan_data_iterator(ds_train_A, args.batch_size)
di_test_B = cycle_gan_data_iterator(ds_test_B, args.batch_size)
di_test_A = cycle_gan_data_iterator(ds_test_A, args.batch_size)
# Monitors
monitor = Monitor(args.monitor_path)
def make_monitor(name):
return MonitorSeries(name, monitor, interval=1)
monitor_loss_gen = make_monitor('generator_loss')
monitor_loss_dis_x = make_monitor('discriminator_B_domain_loss')
monitor_loss_dis_y = make_monitor('discriminator_A_domain_loss')
def make_monitor_image(name):
return MonitorImage(name, monitor, interval=1,
normalize_method=lambda x: (x + 1.0) * 127.5)
monitor_train_gx = make_monitor_image('fake_images_train_A')
monitor_train_fy = make_monitor_image('fake_images_train_B')
monitor_train_x_recon = make_monitor_image('fake_images_B_recon_train')
monitor_train_y_recon = make_monitor_image('fake_images_A_recon_train')
monitor_test_gx = make_monitor_image('fake_images_test_A')
monitor_test_fy = make_monitor_image('fake_images_test_B')
monitor_test_x_recon = make_monitor_image('fake_images_recon_test_B')
monitor_test_y_recon = make_monitor_image('fake_images_recon_test_A')
monitor_train_list = [
(monitor_train_gx, y_fake),
(monitor_train_fy, x_fake),
(monitor_train_x_recon, x_recon),
(monitor_train_y_recon, y_recon),
(monitor_loss_gen, loss_gen),
(monitor_loss_dis_x, loss_dis_x),
(monitor_loss_dis_y, loss_dis_y),
]
monitor_test_list = [
(monitor_test_gx, y_fake_test),
(monitor_test_fy, x_fake_test),
(monitor_test_x_recon, x_recon_test),
(monitor_test_y_recon, y_recon_test)]
# ImagePool
pool_x = ImagePool(pool_size)
pool_y = ImagePool(pool_size)
# Training loop
epoch = 0
n_images = np.max([ds_train_B.size, ds_train_A.size]
) # num. images for each domain
max_iter = args.max_epoch * n_images // args.batch_size
for i in range(max_iter):
# Validation
if int((i+1) % (n_images // args.batch_size)) == 0:
logger.info("Mode:Test,Epoch:{}".format(epoch))
# Monitor for train
for monitor, v in monitor_train_list:
monitor.add(i, v.d)
# Use training graph since there are no test mode
x_data, _ = di_test_B.next()
y_data, _ = di_test_A.next()
x_real_test.d = x_data
y_real_test.d = y_data
x_recon_test.forward()
y_recon_test.forward()
# Monitor for test
for monitor, v in monitor_test_list:
monitor.add(i, v.d)
# Save model
nn.save_parameters(os.path.join(
args.model_save_path, 'params_%06d.h5' % i))
# Learning rate decay
for solver in [solver_gen, solver_dis_x, solver_dis_y]:
linear_decay(solver, base_lr, epoch, args.max_epoch)
epoch += 1
# Get data
x_data, _ = di_train_B.next()
y_data, _ = di_train_A.next()
x_raw.d = x_data
y_raw.d = y_data
# Train Generators
loss_gen.forward(clear_no_need_grad=False)
solver_gen.zero_grad()
loss_gen.backward(clear_buffer=True)
solver_gen.update()
# Insert and Get to/from pool
x_history.d = pool_x.insert_then_get(x_fake.d)
y_history.d = pool_y.insert_then_get(y_fake.d)
# Train Discriminator Y
loss_dis_y.forward(clear_no_need_grad=False)
solver_dis_y.zero_grad()
loss_dis_y.backward(clear_buffer=True)
solver_dis_y.update()
# Train Discriminator X
loss_dis_x.forward(clear_no_need_grad=False)
solver_dis_x.zero_grad()
loss_dis_x.backward(clear_buffer=True)
solver_dis_x.update()
output = F.relu(PF.affine(m, output_mlp_size))
if train:
output = F.dropout(output, p=dropout_ratio)
with nn.parameter_scope('output'):
y = F.sigmoid(PF.affine(output, 1))
accuracy = F.mean(F.equal(F.round(y), t))
loss = F.mean(F.binary_cross_entropy(
y, t)) + attention_penalty_coef * frobenius(
F.batch_matmul(a, a, transpose_a=True) - batch_eye(batch_size, r))
return x, t, accuracy, loss
# Create solver.
x, t, accuracy, loss = build_self_attention_model(train=True)
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
x, t, accuracy, loss = build_self_attention_model(train=True)
trainer = Trainer(inputs=[x, t],
loss=loss,
metrics={
'cross entropy': loss,
'accuracy': accuracy
},
solver=solver)
for epoch in range(max_epoch):
x, t, accuracy, loss = build_self_attention_model(train=True)
trainer.update_variables(inputs=[x, t],
loss=loss,
metrics={
loss_gen = F.mean(
F.sigmoid_cross_entropy(pred_fake, F.constant(1, pred_fake.shape)))
fake_dis = fake.get_unlinked_variable(need_grad=True)
fake_dis.need_grad = True # TODO: Workaround until v1.0.2
pred_fake_dis = I.discriminator(fake_dis)
loss_dis = F.mean(
F.sigmoid_cross_entropy(pred_fake_dis, F.constant(0, pred_fake_dis.shape)))
# Realパスの設定
x = nn.Variable([batch_size, 1, 28, 28])
pred_real = I.discriminator(x)
loss_dis += F.mean(
F.sigmoid_cross_entropy(pred_real, F.constant(1, pred_real.shape)))
# ソルバーの作成
solver_gen = S.Adam(learning_rate, beta1=0.5)
solver_dis = S.Adam(learning_rate, beta1=0.5)
with nn.parameter_scope("gen"):
solver_gen.set_parameters(nn.get_parameters())
with nn.parameter_scope("dis"):
solver_dis.set_parameters(nn.get_parameters())
# パラメータスコープの使い方を見ておく。
print(len(nn.get_parameters()))
with nn.parameter_scope("gen"):
print(len(nn.get_parameters()))
# パラメータスコープ内では、`get_parameters()`で取得できるパラメータがフィルタリングされ
# る。
# モニターの設定
path = cache_dir(os.path.join(I.name, "monitor"))
