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trainer.py
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trainer.py
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import sys
import logging
import copy
import torch
from utils import factory
from utils.data_manager import DataManager
from utils.toolkit import count_parameters
import os
def train(args):
# seed_list = copy.deepcopy(args["seed"])
# device = copy.deepcopy(args["device"])
# for seed in seed_list:
# args["seed"] = seed
# args["device"] = device
# _train(args)
seed_list = copy.deepcopy(args["seed"])
device = copy.deepcopy(args["device"])
args["device"] = device
args["seed"] = int(seed_list)
_train(args)
def _train(args):
logs_name = "logs/{}/".format(args["model_name"])
if not os.path.exists(logs_name):
os.makedirs(logs_name)
logfilename = "logs/{}/{}_{}_{}_{}_{}_{}_{}_{}_{}_mz{}_mp{}".format(
args["model_name"],
args["logtips"],
args["prefix"],
args["seed"],
args["model_name"],
args["convnet_type"],
args["dataset"],
args["init_cls"],
args["increment"],
args["oflocation"],
args["memory_size"],
args["memory_per_class"],
)
handlers = logging.getLogger().handlers
if len(handlers)==0:
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(filename)s] => %(message)s",
handlers=[
logging.FileHandler(filename=logfilename + ".log"),
logging.StreamHandler(sys.stdout),
],
)
else:
handlers[0] = logging.FileHandler(filename=logfilename + ".log") #change a new logfile
_set_random()
_set_device(args)
print_args(args)
data_manager = DataManager(
args["dataset"],
args["shuffle"],
args["seed"],
args["init_cls"],
args["increment"],
)
model = factory.get_model(args["model_name"], args)
cnn_curve, nme_curve = {"top1": [], "top5": []}, {"top1": [], "top5": []}
for task in range(data_manager.nb_tasks):
logging.info("All params: {}".format(count_parameters(model._network)))
logging.info(
"Trainable params: {}".format(count_parameters(model._network, True))
)
# model.incremental_train(data_manager)
if args["oflocation"]=='before':
model.outfit_before_train(data_manager)
logging.info("method: outfit before")
elif args["oflocation"]=='after':
model.outfit_after_train(data_manager)
logging.info("method: outfit after")
elif args["oflocation"]=='both':
model.outfit_both_train(data_manager)
logging.info("method: outfit both")
elif args["oflocation"]=='pull':
# model.pull_train(data_manager)
model.pull_train_da(data_manager)
logging.info("method: pull train")
else:
# model.incremental_train(data_manager)
model.incremental_train_ex(data_manager)
# logging.info("method: general incremental")
logging.info("method: extend data incremental")
cnn_accy, nme_accy = model.eval_task()
model.after_task()
if nme_accy is not None:
logging.info("CNN: {}".format(cnn_accy["grouped"]))
logging.info("NME: {}".format(nme_accy["grouped"]))
cnn_curve["top1"].append(cnn_accy["top1"])
cnn_curve["top5"].append(cnn_accy["top5"])
nme_curve["top1"].append(nme_accy["top1"])
nme_curve["top5"].append(nme_accy["top5"])
logging.info("CNN top1 curve: {}".format(cnn_curve["top1"]))
logging.info("CNN top5 curve: {}".format(cnn_curve["top5"]))
logging.info("NME top1 curve: {}".format(nme_curve["top1"]))
logging.info("NME top5 curve: {}\n".format(nme_curve["top5"]))
else:
logging.info("No NME accuracy.")
logging.info("CNN: {}".format(cnn_accy["grouped"]))
cnn_curve["top1"].append(cnn_accy["top1"])
cnn_curve["top5"].append(cnn_accy["top5"])
logging.info("CNN top1 curve: {}".format(cnn_curve["top1"]))
logging.info("CNN top5 curve: {}\n".format(cnn_curve["top5"]))
def _set_device(args):
device_type = args["device"]
gpus = []
for device in device_type:
if device_type == -1:
device = torch.device("cpu")
else:
if "cuda" in "{}".format(device):
pass
else:
device = torch.device("cuda:{}".format(device))
gpus.append(device)
args["device"] = gpus
def _set_random():
torch.manual_seed(1)
torch.cuda.manual_seed(1)
torch.cuda.manual_seed_all(1)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def print_args(args):
for key, value in args.items():
logging.info("{}: {}".format(key, value))