current_means = class_means[:, order[range(0, (b + 1) * P)]]
##############################################################
# Calculate validation error of model on the cumul of classes:
map_Y_valid_cumul = np.array(
[order_list.index(i) for i in Y_valid_cumul])
current_eval_set = merge_images_labels(X_valid_cumul,
map_Y_valid_cumul)
evalset.imgs = evalset.samples = current_eval_set
evalloader = torch.utils.data.DataLoader(
evalset,
batch_size=eval_batch_size,
shuffle=False,
num_workers=num_workers,
pin_memory=True)
cumul_acc = compute_accuracy(tg_model, tg_feature_model,
current_means, evalloader)
###############################
print('Saving protoset...')
map_Y_protoset_cumuls = np.array([
order_list.index(i) for i in np.concatenate(Y_protoset_cumuls)
])
current_eval_set = merge_images_labels(
np.concatenate(X_protoset_cumuls), map_Y_protoset_cumuls)
save_protosets(current_eval_set, ckp_prefix, b, output_dir)
##############################################################
if b == first_batch_number and cb_finetune: #for the convenience of evaluation
torch.save(
tg_model,
ckp_name.replace("/checkpoint/", "/checkpoint/AFTER_CBF_"))
torch.save(
])
else:
indices = np.array([
i in range(task_i * nb_cl, (task_i + 1) * nb_cl)
for i in map_input_labels
])
evalset.test_data = input_data[indices]
evalset.test_labels = map_input_labels[indices]
#print('Max and Min of valid labels: {}, {}'.format(min(evalset.test_labels), max(evalset.test_labels)))
evalloader = torch.utils.data.DataLoader(evalset,
batch_size=128,
shuffle=False,
num_workers=2)
acc = compute_accuracy(tg_model,
tg_feature_model,
current_means,
evalloader,
print_info=False)
all_accu_matrix[step_i, task_i - start_iter] = torch.tensor(acc)
# cumul
indices = np.array(
[i in range(0, (iteration + 1) * nb_cl) for i in map_input_labels])
evalset.test_data = input_data[indices]
evalset.test_labels = map_input_labels[indices]
#print('Max and Min of valid labels: {}, {}'.format(min(evalset.test_labels), max(evalset.test_labels)))
evalloader = torch.utils.data.DataLoader(evalset,
batch_size=128,
shuffle=False,
num_workers=2)
acc = compute_accuracy(tg_model,