def test_log(mock_now):
"""Testa se uma nova mensagem foi adicionada à lista de mensagens do objeto da classe Logger"""
test_now = 123
test_message = 'A test message'
mock_now.now.return_value = test_now
log = Logger()
log.log(test_message)
assert log.messages == [(test_now, test_message)]
ind_permute = np.arange(0, 28)
np.random.seed(0)
np.random.shuffle(ind_permute)
np.save("permuteB.npy", ind_permute)
# Load the previous FIM
fishers_cpu = torch.load("fisherA.pth")
fishers = []
for fisher in fishers_cpu:
fishers.append(fisher.to(model.device))
# Create callbacks
checkpoint = CheckPoint(model, "modelB.ckpt")
earlystop = EarlyStopping(**earlystop_params)
list_metrics = ["loss_trainB", "loss_testB", "acc_testA", "acc_testB"]
logger = Logger(list_metrics=list_metrics, logger_file="log-metrics.npy")
# Train and evaluate
flg_stop = False
for epoch in range(1, params["n_epochs"] + 1):
print("\n[EPOCH %d]" % (epoch))
loss_trainB = train_ewc(model,
trainB_loader,
optimizer,
base_loss_fn,
params["lamda"],
fishers,
prev_opt_thetas,
epoch,
description="Train on task B")
print()
def test_init():
"""Testa se a classe Logger é instanciado corretamente."""
log = Logger()
assert log.messages == []
