def get_parameters():
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 100, 100, 1
num_epochs = 5
data_loader = eu.examples_data_generator(N, D_in, D_out, binary_out=True)
data_loader_steps = 100
return N, D_in, H, D_out, num_epochs, data_loader, data_loader_steps
def test_metrics_validation(self):
device = tu.get_gpu_device_if_available()
model = eu.get_basic_model(10, 10, 10).to(device)
loss_func = nn.BCEWithLogitsLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
scheduler = None
metric = lambda x,y: x+y
callbacks = [
LossOptimizerHandler(),
StatsPrint()
]
data_loader = eu.examples_data_generator(10, 10, 10)
data_loader_steps = 100
# ASSERT BAD VALUE FOR metric
self.assertRaises(ValueError, Trainer, model=model,
device=device,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
metrics=metric,
train_data_loader=data_loader,
val_data_loader=data_loader,
train_steps=data_loader_steps,
val_steps=data_loader_steps,
callbacks=callbacks,
name='Trainer-Test')
# ASSERT GOOD VALUE FOR metrics
metrics = BinaryAccuracyWithLogits('acc')
trainer = Trainer(model=model,
device=device,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
metrics=metrics,
train_data_loader=data_loader,
val_data_loader=data_loader,
train_steps=data_loader_steps,
val_steps=data_loader_steps,
callbacks=callbacks,
name='Trainer-Test')
def test_loss_handler_validation(self):
device = tu.get_gpu_device_if_available()
model = eu.get_basic_model(10, 10, 10).to(device)
loss_func = nn.BCEWithLogitsLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
scheduler = KerasDecay(optimizer, 0.0001, last_step=-1)
metrics = BinaryAccuracyWithLogits(name='acc')
callbacks = [
StatsPrint()
]
data_loader = eu.examples_data_generator(10, 10, 10)
data_loader_steps = 100
num_epochs = 5
verbose = 0
trainer = Trainer(model=model,
device=device,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
metrics=metrics,
train_data_loader=data_loader,
val_data_loader=data_loader,
train_steps=data_loader_steps,
val_steps=data_loader_steps,
callbacks=callbacks,
name='Trainer-Test')
self.assertRaises(ValueError, trainer.train, num_epochs, verbose)
trainer.callbacks.append(LossOptimizerHandler())
trainer.train(num_epochs, verbose=0)
def test_save_and_load(self):
gu.seed_all(42)
save_to_dir = os.path.dirname(__file__) + '/trainer_checkpoint/'
trainer_file_name = 'trainer'
device = tu.get_gpu_device_if_available()
model = eu.get_basic_model(10, 10, 10).to(device)
loss_func = nn.CrossEntropyLoss().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-4)
scheduler = KerasDecay(optimizer, 0.0001, last_step=-1)
metrics = CategoricalAccuracyWithLogits(name='acc')
callbacks = [
LossOptimizerHandler(),
ModelCheckPoint(checkpoint_dir=save_to_dir,
checkpoint_file_name=trainer_file_name,
callback_monitor=CallbackMonitor(monitor_type=MonitorType.LOSS,
stats_type=StatsType.VAL,
monitor_mode=MonitorMode.MIN),
save_best_only=False,
save_full_trainer=True,
verbose=0),
SchedulerStep(apply_on_phase=Phase.BATCH_END, apply_on_states=State.TRAIN),
StatsPrint()
]
data_loader = eu.examples_data_generator(10, 10, 10, category_out=True)
data_loader_steps = 100
num_epochs = 5
trainer = Trainer(model=model,
device=device,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
metrics=metrics,
train_data_loader=data_loader,
val_data_loader=data_loader,
train_steps=data_loader_steps,
val_steps=data_loader_steps,
callbacks=callbacks,
name='Trainer-Test')
trainer.train(num_epochs, verbose=0)
loaded_trainer = Trainer.load_trainer(dir_path=save_to_dir,
file_name=trainer_file_name + f'_epoch_{num_epochs}',
model=model,
device=device,
loss_func=loss_func,
optimizer=optimizer,
scheduler=scheduler,
train_data_loader=data_loader,
val_data_loader=data_loader,
train_steps=data_loader_steps,
val_steps=data_loader_steps)
self.assertEqual(loaded_trainer.epoch, trainer.epoch)
self.assertListEqual(tu.get_lrs_from_optimizer(loaded_trainer.optimizer), tu.get_lrs_from_optimizer(trainer.optimizer))
self.assertEqual(loaded_trainer.callbacks[1].monitor._get_best(), trainer.callbacks[1].monitor._get_best())