print(str1)
#####
# t6 = timer()
if ii % num_summary_steps == 0:
summary = sess.run(merged_summaries, feed_dict=train_mix_dict_eval)
summary_writer_adv.add_summary(summary, global_step.eval(sess))
if ii % num_checkpoint_steps == 0 and ii > 0 or is_finetune and ii == 0:
print('-' * 10, 'FGSM', '-' * 10)
_ = eval_in_train_vanilla(config,
model_var,
raw_dataset,
sess,
global_step,
test_summary_writer_FGSM,
False,
fp,
dataset_type,
attack_test=FGSM)
# print('-' * 10, '7PGD', '-' * 10)
# _ = eval_in_train_vanilla(config, model_var, raw_dataset, sess, global_step, test_summary_writer_list[0],
# False, fp, dataset_type, attack_test=attack_mild)
print('-' * 10, '20PGD', '-' * 10)
adv_acc = eval_in_train_vanilla(config,
model_var,
raw_dataset,
sess,
global_step,
test_summary_writer_20PGD,
False,
#####
# t6 = timer()
if ii % num_summary_steps == 0:
summary = sess.run(merged_summaries, feed_dict=train_mix_dict_eval)
summary_writer_adv.add_summary(summary, global_step.eval(sess))
if ii % num_checkpoint_steps == 0 and ii > 0 or is_finetune and ii == 0:
print('-' * 10, 'FGSM', '-' * 10)
_ = eval_in_train_vanilla(config,
model_var,
raw_dataset,
sess,
global_step,
test_summary_writer_FGSM,
False,
fp,
dataset_type,
attack_test=FGSM)
# print("past eval!")
# import pdb; pdb.set_trace();
# print('-' * 10, '7PGD', '-' * 10)
# _ = eval_in_train_vanilla(config, model_var, raw_dataset, sess, global_step, test_summary_writer_list[0],
# False, fp, dataset_type, attack_test=attack_mild)
print('-' * 10, '20PGD', '-' * 10)
adv_acc = eval_in_train_vanilla(config,
model_var,
raw_dataset,
sess,