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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
import numpy as np
from load_model import load_model, get_methods
def train_more(args):
seed_list = copy.deepcopy(args["seed"])
device = copy.deepcopy(args["device"])
for seed in seed_list:
args["seed"] = seed
args["device"] = device
_train_more(args)
def _train_more(args):
init_cls = 0 if args ["init_cls"] == args["increment"] else args["init_cls"]
logs_name = "logs/{}/{}/{}/{}".format(args["model_name"],args["dataset"], init_cls, args['increment'])
if not os.path.exists(logs_name):
os.makedirs(logs_name)
save_name = "models/{}/{}/{}/{}".format(args["model_name"],args["dataset"], init_cls, args['increment'])
if not os.path.exists(save_name):
os.makedirs(save_name)
logfilename = "logs/{}/{}/{}/{}/{}_{}_{}".format(
args["model_name"],
args["dataset"],
init_cls,
args["increment"],
args["prefix"],
args["seed"],
args["convnet_type"],
)
if not os.path.exists(logs_name):
os.makedirs(logs_name)
args['logfilename'] = logs_name
args['csv_name'] = "{}_{}_{}".format(
args["prefix"],
args["seed"],
args["convnet_type"],
)
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s [%(filename)s] => %(message)s",
handlers=[
logging.FileHandler(filename=logfilename + ".log"),
logging.StreamHandler(sys.stdout),
],
)
_set_random()
print_args(args)
model = load_model(args)
data_manager = DataManager(
args["dataset"],
args["shuffle"],
args["seed"],
args["init_cls"],
args["increment"],
resume = True,
path = args["data"],
class_list = model.class_list
)
cnn_curve, nme_curve = {"top1": [], "top5": []}, {"top1": [], "top5": []}
cnn_matrix, nme_matrix = [], []
for task in range(data_manager.nb_tasks):
print(args["device"])
logging.info("All params: {}".format(count_parameters(model._network)))
logging.info(
"Trainable params: {}".format(count_parameters(model._network, True))
)
model.incremental_train(data_manager)
cnn_accy, nme_accy = model.eval_task(save_conf=True)
model.after_task()
if nme_accy is not None:
logging.info("CNN: {}".format(cnn_accy["grouped"]))
logging.info("NME: {}".format(nme_accy["grouped"]))
cnn_keys = [key for key in cnn_accy["grouped"].keys() if '-' in key]
cnn_keys_sorted = sorted(cnn_keys)
cnn_values = [cnn_accy["grouped"][key] for key in cnn_keys_sorted]
cnn_matrix.append(cnn_values)
nme_keys = [key for key in nme_accy["grouped"].keys() if '-' in key]
nme_keys_sorted = sorted(nme_keys)
nme_values = [nme_accy["grouped"][key] for key in nme_keys_sorted]
nme_matrix.append(nme_values)
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"]))
print('Average Accuracy (CNN):', sum(cnn_curve["top1"])/len(cnn_curve["top1"]))
print('Average Accuracy (NME):', sum(nme_curve["top1"])/len(nme_curve["top1"]))
logging.info("Average Accuracy (CNN): {}".format(sum(cnn_curve["top1"])/len(cnn_curve["top1"])))
logging.info("Average Accuracy (NME): {}".format(sum(nme_curve["top1"])/len(nme_curve["top1"])))
else:
logging.info("No NME accuracy.")
logging.info("CNN: {}".format(cnn_accy["grouped"]))
cnn_keys = [key for key in cnn_accy["grouped"].keys() if '-' in key]
cnn_keys_sorted = sorted(cnn_keys)
cnn_values = [cnn_accy["grouped"][key] for key in cnn_keys_sorted]
cnn_matrix.append(cnn_values)
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"]))
print('Average Accuracy (CNN):', sum(cnn_curve["top1"])/len(cnn_curve["top1"]))
logging.info("Average Accuracy (CNN): {}".format(sum(cnn_curve["top1"])/len(cnn_curve["top1"])))
model.save_checkpoint(save_name)
if len(cnn_matrix)>0:
np_acctable = np.zeros([ task + 1, int((args["init_cls"] // 10) + task * (args["increment"] // 10))])
for idxx, line in enumerate(cnn_matrix):
idxy = len(line)
np_acctable[idxx, :idxy] = np.array(line)
np_acctable = np_acctable.T
forgetting = np.mean((np.max(np_acctable, axis=1) - np_acctable[:, -1])[:-1])
logging.info('Forgetting (CNN): {}'.format(forgetting))
logging.info('Accuracy Matrix (CNN): {}'.format(np_acctable))
print('Accuracy Matrix (CNN):')
print(np_acctable)
print('Forgetting (CNN):', forgetting)
if len(nme_matrix)>0:
np_acctable = np.zeros([ task + 1, int((args["init_cls"] // 10) + task * (args["increment"] // 10))])
for idxx, line in enumerate(nme_matrix):
idxy = len(line)
np_acctable[idxx, :idxy] = np.array(line)
np_acctable = np_acctable.T
forgetting = np.mean((np.max(np_acctable, axis=1) - np_acctable[:, -1])[:-1])
logging.info('Forgetting (NME): {}'.format(forgetting))
logging.info('Accuracy Matrix (NME): {}'.format(np_acctable))
print('Accuracy Matrix (NME):')
print(np_acctable)
print('Forgetting (NME):', forgetting)
def _set_device(args):
device_type = args["device"]
gpus = []
for device in device_type:
if device == -1:
device = torch.device("cpu")
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))
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