def create_main_loop(save_path):
model, bn_model, bn_updates = create_models()
ali, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=LEARNING_RATE, beta1=BETA1)
algorithm = ali_algorithm(discriminator_loss, ali.discriminator_parameters,
step_rule, generator_loss,
ali.generator_parameters, step_rule)
algorithm.add_updates(bn_updates)
streams = create_tiny_imagenet_data_streams(BATCH_SIZE,
MONITORING_BATCH_SIZE)
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
bn_monitored_variables = (
[v for v in bn_model.auxiliary_variables if 'norm' not in v.name] +
bn_model.outputs)
monitored_variables = (
[v for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=NUM_EPOCHS),
DataStreamMonitoring(
bn_monitored_variables, train_monitor_stream, prefix="train",
updates=bn_updates),
DataStreamMonitoring(
monitored_variables, valid_monitor_stream, prefix="valid"),
Checkpoint(save_path, after_epoch=True, after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
]
main_loop = MainLoop(model=bn_model, data_stream=main_loop_stream,
algorithm=algorithm, extensions=extensions)
return main_loop
def create_main_loop(save_path):
model, bn_model, bn_updates = create_models()
ali, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=LEARNING_RATE, beta1=BETA1)
algorithm = ali_algorithm(discriminator_loss, ali.discriminator_parameters,
step_rule, generator_loss,
ali.generator_parameters, step_rule)
algorithm.add_updates(bn_updates)
streams = create_celeba_data_streams(BATCH_SIZE, MONITORING_BATCH_SIZE)
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
bn_monitored_variables = (
[v for v in bn_model.auxiliary_variables if 'norm' not in v.name] +
bn_model.outputs)
monitored_variables = (
[v for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=NUM_EPOCHS),
DataStreamMonitoring(
bn_monitored_variables, train_monitor_stream, prefix="train",
updates=bn_updates),
DataStreamMonitoring(
monitored_variables, valid_monitor_stream, prefix="valid"),
Checkpoint(save_path, after_epoch=True, after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
]
main_loop = MainLoop(model=bn_model, data_stream=main_loop_stream,
algorithm=algorithm, extensions=extensions)
return main_loop
def create_main_loop():
model, bn_model, bn_updates = create_models()
ali, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=LEARNING_RATE, beta1=BETA1)
algorithm = ali_algorithm(discriminator_loss,
ali.discriminator_parameters, step_rule,
generator_loss, ali.generator_parameters,
step_rule)
algorithm.add_updates(bn_updates)
streams = create_gaussian_mixture_data_streams(
batch_size=BATCH_SIZE,
monitoring_batch_size=MONITORING_BATCH_SIZE,
means=MEANS,
variances=VARIANCES,
priors=PRIORS)
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
bn_monitored_variables = ([
v for v in bn_model.auxiliary_variables if 'norm' not in v.name
] + bn_model.outputs)
monitored_variables = (
[v
for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=NUM_EPOCHS),
DataStreamMonitoring(bn_monitored_variables,
train_monitor_stream,
prefix="train",
updates=bn_updates),
DataStreamMonitoring(monitored_variables,
valid_monitor_stream,
prefix="valid"),
Checkpoint(os.path.join(self._work_dir, "main_loop.tar"),
after_epoch=True,
after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
#ModelLogger(folder=self._work_dir, after_epoch=True),
GraphLogger(num_modes=1,
num_samples=2500,
dimension=2,
r=0,
std=1,
folder=self._work_dir,
after_epoch=True,
after_training=True),
MetricLogger(means=MEANS,
variances=VARIANCES,
folder=self._work_dir,
after_epoch=True)
]
main_loop = MainLoop(model=bn_model,
data_stream=main_loop_stream,
algorithm=algorithm,
extensions=extensions)
return main_loop
def create_main_loop(save_path):
model, bn_model, bn_updates = create_models()
ali, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=LEARNING_RATE, beta1=BETA1)
algorithm = ali_algorithm(discriminator_loss, ali.discriminator_parameters,
step_rule, generator_loss,
ali.generator_parameters, step_rule)
algorithm.add_updates(bn_updates)
streams = create_cifar10_data_streams(BATCH_SIZE, MONITORING_BATCH_SIZE)
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
for d in main_loop_stream.get_epoch_iterator(as_dict=True):
print(d.keys)
print(d['features'].shape, d['features'].dtype)
break
main_loop_stream = ShapesDataset(num_examples=600, img_size=32, min_diameter=3, seed=1234).create_stream(batch_size=BATCH_SIZE, is_train=True)
for d in main_loop_stream.get_epoch_iterator(as_dict=True):
print(d.keys)
print(d['features'].shape, d['features'].dtype)
break
train_monitor_stream = ShapesDataset(num_examples=100, img_size=32, min_diameter=3, seed=1234).create_stream(batch_size=BATCH_SIZE, is_train=False)
valid_monitor_stream = ShapesDataset(num_examples=100, img_size=32, min_diameter=3, seed=5678).create_stream(batch_size=BATCH_SIZE, is_train=False)
bn_monitored_variables = (
[v for v in bn_model.auxiliary_variables if 'norm' not in v.name] +
bn_model.outputs)
monitored_variables = (
[v for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=NUM_EPOCHS),
DataStreamMonitoring(
bn_monitored_variables, train_monitor_stream, prefix="train",
updates=bn_updates),
DataStreamMonitoring(
monitored_variables, valid_monitor_stream, prefix="valid"),
Checkpoint(save_path, after_epoch=True, after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
]
main_loop = MainLoop(model=bn_model, data_stream=main_loop_stream,
algorithm=algorithm, extensions=extensions)
return main_loop
def create_main_loop(self):
model, bn_model, bn_updates = self.create_models()
gan, = bn_model.top_bricks
discriminator_loss, generator_loss = bn_model.outputs
step_rule = Adam(learning_rate=self._config["learning_rate"],
beta1=self._config["beta1"])
algorithm = ali_algorithm(discriminator_loss,
gan.discriminator_parameters, step_rule,
generator_loss, gan.generator_parameters,
step_rule)
algorithm.add_updates(bn_updates)
streams = create_packing_gaussian_mixture_data_streams(
num_packings=self._config["num_packing"],
batch_size=self._config["batch_size"],
monitoring_batch_size=self._config["monitoring_batch_size"],
means=self._config["x_mode_means"],
variances=self._config["x_mode_variances"],
priors=self._config["x_mode_priors"],
num_examples=self._config["num_sample"])
main_loop_stream, train_monitor_stream, valid_monitor_stream = streams
bn_monitored_variables = (
[v for v in bn_model.auxiliary_variables if 'norm' not in v.name] +
bn_model.outputs)
monitored_variables = (
[v for v in model.auxiliary_variables if 'norm' not in v.name] +
model.outputs)
extensions = [
Timing(),
FinishAfter(after_n_epochs=self._config["num_epoch"]),
DataStreamMonitoring(bn_monitored_variables,
train_monitor_stream,
prefix="train",
updates=bn_updates),
DataStreamMonitoring(monitored_variables,
valid_monitor_stream,
prefix="valid"),
Checkpoint(os.path.join(self._work_dir,
self._config["main_loop_file"]),
after_epoch=True,
after_training=True,
use_cpickle=True),
ProgressBar(),
Printing(),
]
if self._config["log_models"]:
extensions.append(
ModelLogger(folder=self._work_dir, after_epoch=True))
if self._config["log_figures"]:
extensions.append(
GraphLogger(num_modes=self._config["num_zmode"],
num_samples=self._config["num_log_figure_sample"],
dimension=self._config["num_zdim"],
r=self._config["z_mode_r"],
std=self._config["z_mode_std"],
folder=self._work_dir,
after_epoch=True,
after_training=True))
if self._config["log_metrics"]:
extensions.append(
MetricLogger(means=self._config["x_mode_means"],
variances=self._config["x_mode_variances"],
folder=self._work_dir,
after_epoch=True))
main_loop = MainLoop(model=bn_model,
data_stream=main_loop_stream,
algorithm=algorithm,
extensions=extensions)
return main_loop
