def run_all(root=DATA_FOLDER,
data_file=DATA_FILE,
store_path=Path(DATA_FOLDER).joinpath(STORE_FOLDER)):
ensure_folder(store_path)
setup_logger(store_path, LOGGER_FILE_NAME, console_output=True)
setup_seed(SEED)
# load data
file_path = Path(root).joinpath(data_file)
data = load_data(file_path)
logging.info(">>>>> Summary of original dataset.")
print_info(data)
logger_endl()
# wash data, explicit->implicit
data = filter_data(data)
logging.info(">>>>> Summary of filtered dataset.")
print_info(data)
logger_endl()
# index -> make session -> gen neg sample
data, user_to_idx, item_to_idx = make_index(data)
logger_endl()
user_to_seqs, user_to_pos_items = gen_session_sample(data)
logger_endl()
user_to_neg_items = gen_neg_sample(user_to_seqs, user_to_pos_items,
set(item_to_idx.values()))
logger_endl()
# save all
save_data(user_to_seqs, user_to_pos_items, user_to_idx, item_to_idx,
store_path)
save_neg_data(user_to_neg_items, store_path)
def __init__(
self,
model,
output_labels,
save_checkpoint_path=generate_checkpoint_path(),
epochs=DEFAULT_EPOCHS,
mean=DEFAULT_MEAN,
std=DEFAULT_STD,
lr=DEFAULT_LR - 3,
image_resize=DEFAULT_IMAGE_RESIZE,
batch_size=DEFAULT_BATCH_SIZE,
images_dir=None,
only_labels=None,
use_cpu=False,
seed=DEFAULT_SEED,
no_warmup=False,
no_scheduler=None,
device=None,
**kwargs # to pass values from argparse
):
self.seed = setup_seed(seed)
self.model = model
self.device = device
self.n_epochs = epochs
self.mean = mean
self.std = std
self.lr = lr
self.image_resize = image_resize
self.batch_size = batch_size
self.only_labels = only_labels
self.images_dir = images_dir
self.optimizer = None
self.scheduler = None
self.save_path = save_checkpoint_path
self.index_to_label = None
self.output_labels = [x for x in output_labels]
self.no_warmup = no_warmup
self.labels_indices = {}
self.use_cpu = use_cpu
self.current_epoch = -1
self.no_scheduler = no_scheduler
parser = student_search_parser(teacher_type=teacher_type)
args = parser.parse_args(cmd.split(" "))
else: # 2. Running
preset_parser = student_search_preset_parser()
preset_args, unk = preset_parser.parse_known_args()
teacher_type, dataset = preset_args.T, preset_args.D
parser = student_search_parser(teacher_type=teacher_type)
args = parser.parse_args(unk)
assert args.search_teacher_folder is not None
store_folder = setup_folder(store_root=args.aux_store_root,
project_name=args.name)
folder = Path(store_folder)
setup_logger(folder_path=folder, console_output=args.aux_console_output)
setup_seed(args.seed)
if args.gpu_teacher == args.gpu_student:
gpu_str = args.gpu_teacher
device_student = torch.device("cuda:0")
device_teacher = torch.device("cuda:0")
else: # args.gpu_teacher != args.gpu_student:
if args.gpu_teacher > args.gpu_student:
gpu_str = f"{args.gpu_student},{args.gpu_teacher}"
device_student = torch.device("cuda:0")
device_teacher = torch.device("cuda:1")
else: # args.gpu_teacher < args.gpu_student:
gpu_str = f"{args.gpu_teacher},{args.gpu_student}"
device_student = torch.device("cuda:1")
device_teacher = torch.device("cuda:0")
setup_gpu(gpu_str)