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import sys
import logging
import copy
import torch
from PIL import Image
import torchvision.transforms as transforms
from utils import factory
from utils.data_manager import DataManager
from torch.utils.data import DataLoader
from utils.toolkit import count_parameters
import os
import numpy as np
import json
import argparse
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
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 get_methods(object, spacing=20):
methodList = []
for method_name in dir(object):
try:
if callable(getattr(object, method_name)):
methodList.append(str(method_name))
except Exception:
methodList.append(str(method_name))
processFunc = (lambda s: ' '.join(s.split())) or (lambda s: s)
for method in methodList:
try:
print(str(method.ljust(spacing)) + ' ' +
processFunc(str(getattr(object, method).__doc__)[0:90]))
except Exception:
print(method.ljust(spacing) + ' ' + ' getattr() failed')
def load_model(args):
_set_device(args)
model = factory.get_model(args["model_name"], args)
model.load_checkpoint(args["checkpoint"])
return model
def evaluate(args):
logs_name = "logs/{}/{}_{}/{}/{}".format(args["model_name"],args["dataset"], args['data'], args['init_cls'], args['increment'])
if not os.path.exists(logs_name):
os.makedirs(logs_name)
logfilename = "logs/{}/{}_{}/{}/{}/{}_{}_{}".format(
args["model_name"],
args["dataset"],
args['data'],
args['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"],
False,
args["seed"],
args["init_cls"],
args["increment"],
path = args["data"]
)
loader = DataLoader(data_manager.get_dataset(model.class_list, source = "test", mode = "test"), batch_size=args['batch_size'], shuffle=True, num_workers=8)
cnn_acc, nme_acc = model.eval_task(loader, group = 1, mode = "test")
print(cnn_acc, nme_acc)
def main():
args = setup_parser().parse_args()
param = load_json(args.config)
args = vars(args) # Converting argparse Namespace to a dict.
args.update(param) # Add parameters from json
evaluate(args)
def load_json(settings_path):
with open(settings_path) as data_file:
param = json.load(data_file)
return param
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 setup_parser():
parser = argparse.ArgumentParser(description='Reproduce of multiple continual learning algorthms.')
parser.add_argument('--config', type=str, default='./exps/finetune.json',
help='Json file of settings.')
parser.add_argument('-d','--data', type=str, help='Path of the data folder')
parser.add_argument('-c','--checkpoint', type=str, help='Path of checkpoint file if resume training')
return parser
def print_args(args):
for key, value in args.items():
logging.info("{}: {}".format(key, value))
if __name__ == '__main__':
main()
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