def __init__(self, metrics_prefix='', **kwargs):
"""
:param metrics_prefix: prefix will be added to each metric - f.e. you can add val_ to validation engine
:param kwargs: key-word arguments of PlotLosses
"""
self.liveplot = PlotLosses(**kwargs)
self.metrics_prefix = metrics_prefix
class PlotLossesCallback:
def __init__(self,
train_engine: Optional[ignite.engine.Engine] = None,
**kwargs):
"""
:param train_egine - engine with global setep info:
:param kwargs key-word arguments of PlotLosses:
"""
self.liveplot = PlotLosses(**kwargs)
self.train_engine = train_engine
def attach(self, engine: ignite.engine.Engine):
"""
Attach callback to ignite engine, attached method will be called on the end of each epoch
and optionally on the end of every iteration
"""
engine.add_event_handler(ignite.engine.Events.EPOCH_COMPLETED,
self.store)
engine.add_event_handler(ignite.engine.Events.ITERATION_COMPLETED,
self.store)
def store(self, engine: ignite.engine.Engine):
"""Evaluation engine store state with computed metrics, that will be send to main logger"""
metrics = {}
if not hasattr(engine.state, 'metrics') or len(
engine.state.metrics) == 0:
return
kwargs = dict(current_step=global_step_from_engine(self.train_engine)(
self.train_engine,
self.train_engine.last_event_name)) if self.train_engine else {}
for key, val in engine.state.metrics.items():
metric_name = key
metrics[metric_name] = val
self.liveplot.update(metrics, **kwargs)
self.liveplot.send()
class _PlotLossesCallback:
"""Base keras callback class for keras and tensorflow.keras"""
def __init__(self, **kwargs):
self.liveplot = PlotLosses(**kwargs)
def on_epoch_end(self, epoch, logs):
"""Send metrics to livelossplot"""
self.liveplot.update(logs.copy(), epoch)
self.liveplot.send()
def __init__(self,
train_engine: Optional[ignite.engine.Engine] = None,
**kwargs):
"""
:param train_egine - engine with global setep info:
:param kwargs key-word arguments of PlotLosses:
"""
self.liveplot = PlotLosses(**kwargs)
self.train_engine = train_engine
class PlotLossesCallback:
def __init__(self,
train_engine: Optional[ignite.engine.Engine] = None,
**kwargs):
"""
Args:
train_engine: engine with global step information, send metohod callback will be attached to it
if None send method will be called on the end of each store call it may cause warnings and errors in
the case of multiple engines attached
Keyword Args:
**kwargs: keyword args that will be passed to livelossplot PlotLosses class
"""
self.liveplot = PlotLosses(**kwargs)
self.train_engine = train_engine
if self.train_engine:
self.train_engine.add_event_handler(
ignite.engine.Events.EPOCH_STARTED, self.send)
self.train_engine.add_event_handler(ignite.engine.Events.COMPLETED,
self.send)
def attach(self, engine: ignite.engine.Engine):
"""Attach callback to ignite engine, attached method will be called on the end of each epoch
Args:
engine: engine that computes metrics on the end of each epoch and / or on the end of each iteration
Notes:
metrics computation plugins have to be attached before this one
"""
engine.add_event_handler(ignite.engine.Events.EPOCH_COMPLETED,
self.store)
engine.add_event_handler(ignite.engine.Events.ITERATION_COMPLETED,
self.store)
def store(self, engine: ignite.engine.Engine):
"""Store computed metrics, that will be send to main logger
Args:
engine: engine with state.metrics
"""
metrics = {}
if not hasattr(engine.state, 'metrics') or len(
engine.state.metrics) == 0:
return
kwargs = dict(current_step=global_step_from_engine(self.train_engine)(
self.train_engine,
self.train_engine.last_event_name)) if self.train_engine else {}
for key, val in engine.state.metrics.items():
metrics[key] = val
self.liveplot.update(metrics, **kwargs)
if not self.train_engine:
self.send()
def send(self, _: Optional[ignite.engine.Engine] = None):
self.liveplot.send()
class _PlotLossesCallback:
"""Base keras callback class for keras and tensorflow.keras"""
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments that will be passed to PlotLosses constructor
"""
self.liveplot = PlotLosses(**kwargs)
def on_epoch_end(self, epoch: int, logs: Dict[str, float]):
"""Send metrics to livelossplot
Args:
epoch: epoch number
logs: metrics with values
"""
self.liveplot.update(logs.copy(), epoch)
self.liveplot.send()
def __init__(self,
train_engine: Optional[ignite.engine.Engine] = None,
**kwargs):
"""
Args:
train_engine: engine with global step information, send metohod callback will be attached to it
if None send method will be called on the end of each store call it may cause warnings and errors in
the case of multiple engines attached
Keyword Args:
**kwargs: keyword args that will be passed to livelossplot PlotLosses class
"""
self.liveplot = PlotLosses(**kwargs)
self.train_engine = train_engine
if self.train_engine:
self.train_engine.add_event_handler(
ignite.engine.Events.EPOCH_STARTED, self.send)
self.train_engine.add_event_handler(ignite.engine.Events.COMPLETED,
self.send)
class PlotLossesCallback:
def __init__(self, metrics_prefix='', **kwargs):
"""
:param metrics_prefix: prefix will be added to each metric - f.e. you can add val_ to validation engine
:param kwargs: key-word arguments of PlotLosses
"""
self.liveplot = PlotLosses(**kwargs)
self.metrics_prefix = metrics_prefix
def attach(self, engine):
"""Attach callback to ignite engine, attached method will be called on the end of each epoch"""
engine.add_event_handler(ignite.engine.Events.EPOCH_COMPLETED, self.on_epoch_end)
def on_epoch_end(self, engine):
"""Evaluation engine store state with computed metrics, that will be send to main logger"""
metrics = {}
for key, val in engine.state.metrics.items():
metric_name = '{}{}'.format(self.metrics_prefix, key)
metrics[metric_name] = val
self.liveplot.update(metrics)
self.liveplot.send()
def __init__(self, parameters: ModelParameters):
self.parameters = parameters
self.dataloader = get_dataloader(parameters)
groups = {'VAE': ['recons_loss'], 'GAN': ['g_loss', 'd_loss']}
self.liveloss = PlotLosses(outputs=[BokehPlot(max_cols=2)],
mode='script',
groups=groups)
self.vae_gan = VAE_GAN(parameters.image_size,
parameters.image_channels,
parameters.latent_dim,
self.dataloader,
layers_per_size=parameters.layers_per_size,
channel_scaling=parameters.channel_scaling)
self.vae_gan.eval()
if parameters.cuda:
self.vae_gan.cuda()
if parameters.print_summary:
print('Model Summary:')
summary(self.vae_gan, depth=10)
def train(self, dataset, val_dataset=None, epochs=int(3e4), n_itr=100):
try:
z = tf.constant(
np.load(f'{self.save_path}/{self.model_name}_z.npy'))
except FileNotFoundError:
z = tf.constant(random.normal((self.batch_size, 1, 1, self.z_dim)))
os.makedirs(self.save_path, exist_ok=True)
np.save(f'{self.save_path}/{self.model_name}_z', z.numpy())
liveplot = PlotLosses()
try:
losses_list = pickle.load(
open(f'{self.save_path}/{self.model_name}_losses_list.pkl',
'rb'))
except:
losses_list = []
for i, losses in enumerate(losses_list):
liveplot.update(losses, i)
start_epoch = len(losses_list)
g_train_loss = metrics.Mean()
d_train_loss = metrics.Mean()
d_val_loss = metrics.Mean()
for epoch in range(start_epoch, epochs):
train_bar = pbar(n_itr, epoch, epochs)
for itr_c, batch in zip(range(n_itr), dataset):
if train_bar.n >= n_itr:
break
for _ in range(self.n_critic):
d_loss = self.train_d(batch['images'])
d_train_loss(d_loss)
g_loss = self.train_g()
g_train_loss(g_loss)
self.train_g()
train_bar.postfix['g_loss'] = f'{g_train_loss.result():6.3f}'
train_bar.postfix['d_loss'] = f'{d_train_loss.result():6.3f}'
train_bar.update(n=itr_c)
train_bar.close()
if val_dataset:
val_bar = vbar(n_itr // 5, epoch, epochs)
for itr_c, batch in zip(range(n_itr // 5), val_dataset):
if val_bar.n >= n_itr // 5:
break
d_val_l = self.val_d(batch['images'])
d_val_loss(d_val_l)
val_bar.postfix[
'd_val_loss'] = f'{d_val_loss.result():6.3f}'
val_bar.update(n=itr_c)
val_bar.close()
losses = {
'g_loss': g_train_loss.result(),
'd_loss': d_train_loss.result(),
'd_val_loss': d_val_loss.result()
}
losses_list += [losses]
pickle.dump(
losses_list,
open(f'{self.save_path}/{self.model_name}_losses_list.pkl',
'wb'))
liveplot.update(losses, epoch)
liveplot.send()
g_train_loss.reset_states()
d_train_loss.reset_states()
d_val_loss.reset_states()
del train_bar
del val_bar
self.G.save_weights(
filepath=f'{self.save_path}/{self.model_name}_generator')
self.D.save_weights(
filepath=f'{self.save_path}/{self.model_name}_discriminator')
if epoch >= int(2e4):
if epoch % 1000 == 0:
self.G.save_weights(
filepath=
f'{self.save_path}/{self.model_name}_generator{epoch}')
self.D.save_weights(
filepath=
f'{self.save_path}/{self.model_name}_discriminator{epoch}'
)
if epoch % 5 == 0:
samples = self.generate_samples(z)
image_grid = img_merge(samples, n_rows=6).squeeze()
img_path = f'./images/{self.model_name}'
os.makedirs(img_path, exist_ok=True)
save_image_grid(image_grid,
epoch + 1,
self.model_name,
output_dir=img_path)
class Model:
def __init__(self, parameters: ModelParameters):
self.parameters = parameters
self.dataloader = get_dataloader(parameters)
groups = {'VAE': ['recons_loss'], 'GAN': ['g_loss', 'd_loss']}
self.liveloss = PlotLosses(outputs=[BokehPlot(max_cols=2)],
mode='script',
groups=groups)
self.vae_gan = VAE_GAN(parameters.image_size,
parameters.image_channels,
parameters.latent_dim,
self.dataloader,
layers_per_size=parameters.layers_per_size,
channel_scaling=parameters.channel_scaling)
self.vae_gan.eval()
if parameters.cuda:
self.vae_gan.cuda()
if parameters.print_summary:
print('Model Summary:')
summary(self.vae_gan, depth=10)
def _epoch_callback(self,
epoch,
recons_loss=0,
kld_loss=0,
g_loss=0,
d_loss=0):
self.recons_loss = recons_loss
self.kld_loss = kld_loss
self.g_loss = g_loss
self.d_loss = d_loss
if self.parameters.print_info:
print('Finished epoch {} with a loss of {:.4f}.'.format(
epoch, recons_loss))
if self.parameters.save_snapshots:
save_vae_snapshot(self.vae_gan.vae, self.dataloader, epoch,
self.parameters.cuda)
save_gan_snapshot(self.vae_gan.gan, epoch)
if self.parameters.save_model:
save_model(self.vae_gan, epoch)
if self.parameters.plot_loss:
logs = {
'recons_loss': recons_loss + kld_loss,
'g_loss': g_loss,
'd_loss': d_loss
}
self.liveloss.update(logs, current_step=epoch)
self.liveloss.send()
if self.parameters.epoch_callback:
self.parameters.epoch_callback(epoch, recons_loss, kld_loss,
g_loss, d_loss)
def train(self):
self.vae_gan.train()
if self.parameters.vae_pretraining_epochs > 0:
if self.parameters.print_info:
print('Pre-training VAE.')
self.vae_gan.train_vae(
epochs=self.parameters.vae_pretraining_epochs,
epoch_callback=self._epoch_callback,
print_info=self.parameters.print_info)
if self.parameters.print_info:
print('Converting to dual-training.')
self.vae_gan.train_dual(
epochs=self.parameters.epochs,
epoch_offset=self.parameters.vae_pretraining_epochs,
epoch_callback=self._epoch_callback,
print_info=self.parameters.print_info)
self.vae_gan.eval()
def __init__(self, **kwargs):
self.liveplot = PlotLosses(**kwargs)
def __init__(self, **kwargs):
"""
Args:
**kwargs: keyword arguments that will be passed to PlotLosses constructor
"""
self.liveplot = PlotLosses(**kwargs)