SAVE_DIR = RESULT_DIR + 'metric/save/%s/' % FILE_ID
LOG_DIR = RESULT_DIR + 'metric/log/'
CSV_DIR = RESULT_DIR + 'metric/csv/'
PKL_DIR = RESULT_DIR + 'metric/pkl/'
BOARD_DIR = RESULT_DIR + 'metric/board/%s/' % FILE_ID
create_muldir(SAVE_DIR, BOARD_DIR, LOG_DIR, CSV_DIR, PKL_DIR)
# load data
datasetmanager = DATASETMANAGER_DICT[args.dataset]
dm_train, dm_val, dm_test = datasetmanager(args.ltype,
nsclass=args.nsclass)
for v in [dm_train, dm_val, dm_test]:
v.print_shape()
model = DeepMetric(dm_train, dm_val, dm_test, LOG_DIR + FILE_ID + '.log',
args)
model.build()
model.set_up_train()
try:
model.restore(save_dir=SAVE_DIR)
except AttributeError:
model.initialize()
model.train(epoch=args.epoch, save_dir=SAVE_DIR, board_dir=BOARD_DIR)
model.restore(save_dir=SAVE_DIR)
model.prepare_test()
performance = model.test_metric(K_SET)
key_set = [
'te_tr_recall_at_k', 'te_tr_precision_at_k', 'te_te_recall_at_k',
'te_te_precision_at_k'
]
best_performance = performance
best_file_id = file_id
print("best performance : {} from {}".format(best_performance,
best_file_id))
copy_file(CSV_DIR + best_file_id + '.csv', copy_dst_csv)
copy_file(PKL_DIR + best_file_id + '.pkl', copy_dst_pkl)
copy_dir(RESULT_DIR + 'metric/save/' + best_file_id, SAVE_DIR)
# load data
datasetmanager = DATASETMANAGER_DICT[args.dataset]
dm_train, dm_val, dm_test = datasetmanager(args.ltype,
nsclass=args.nsclass)
for v in [dm_train, dm_val, dm_test]:
v.print_shape()
model = DeepMetric(dm_train, dm_val, dm_test, LOG_DIR + FILE_ID + '.log',
args)
model.build()
model.set_up_train()
model.restore(save_dir=SAVE_DIR)
model.prepare_test()
for activate_k in ACTIVATE_K_SET:
performance_th = model.test_th(activate_k, K_SET)
performance_vq = model.test_vq(activate_k, K_SET)
write_pkl({
'th': performance_th,
'vq': performance_vq
},
path=PKL_DIR + FILE_ID + '_{}.pkl'.format(activate_k))
LOG_DIR = RESULT_DIR + 'exp1/log/'
CSV_DIR = RESULT_DIR + 'exp1/csv/'
PKL_DIR = RESULT_DIR + 'exp1/pkl/'
BOARD_DIR = RESULT_DIR + 'exp1/board/%s/' % HASH_FILE_ID
create_muldir(SAVE_DIR, LOG_DIR, CSV_DIR, PKL_DIR, HASH_SAVE_DIR,
BOARD_DIR)
# load data
datasetmanager = DATASETMANAGER_DICT[args.dataset]
dm_train, dm_val, dm_test = datasetmanager(args.hltype,
nsclass=args.nsclass)
for v in [dm_train, dm_val, dm_test]:
v.print_shape()
model = DeepMetric(dm_train, dm_val, dm_test,
LOG_DIR + HASH_FILE_ID + '.log', args)
model.build()
model.restore(save_dir=SAVE_DIR)
model.prepare_test()
model.build_hash()
model.set_up_train_hash()
try:
model.restore_hash(save_dir=HASH_SAVE_DIR)
except AttributeError:
model.initialize()
model.train_hash(args.epoch,
save_dir=HASH_SAVE_DIR,
board_dir=BOARD_DIR)
model.restore_hash(save_dir=HASH_SAVE_DIR)
model.prepare_test_hash()
performance_hash = model.test_hash_metric(args.k, K_SET)