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9877759 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 | 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):
"""
Entry point for training the model.
Args:
args (dict): Configuration arguments including model settings, dataset info, device, seed list, etc.
"""
seed_list = copy.deepcopy(args["seed"])
device = copy.deepcopy(args["device"])
for seed in seed_list:
args["seed"] = seed
args["device"] = device
_train(args)
def _train(args):
"""
Main training function to handle incremental learning across tasks.
Args:
args (dict): Configuration arguments including model settings, dataset info, etc.
"""
# Initialize class counter
init_cls = 0 if args ["init_cls"] == args["increment"] else args["init_cls"]
logs_name = "logs/{}/{}".format(args["model_name"],args["dataset"])
if not os.path.exists(logs_name):
os.makedirs(logs_name)
if not os.path.exists("predictions"):
os.makedirs("predictions")
logfilename = "logs/{}/{}/{}_{}_{}".format(
args["model_name"],
args["dataset"],
args["convnet_type"],
args["seed"],
args["summary_file"]
)
logging.basicConfig(
level=logging.INFO,
format="[%(asctime)s: %(filename)s: %(lineno)d: %(funcName)s]=> %(message)s",
handlers=[
logging.FileHandler(filename=logfilename + ".log", mode="w"),
logging.StreamHandler(sys.stdout),
],
)
_set_random()
_set_device(args)
# Initialize data manager for handling datasets
data_manager = DataManager(
args["dataset"],
args["shuffle"],
args["seed"],
args["init_cls"],
args["increment"],
)
model = factory.get_model(args["model_name"], args)
# Initialize accuracy tracking variables
cnn_curve, nme_curve = {"top1": [], "top3": []}, {"top1": [], "top3": []}
logging.info("nb_tasks: {}".format(data_manager.nb_tasks))
for task in range(data_manager.nb_tasks):
model.incremental_train(data_manager)
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["top3"].append(cnn_accy["top3"])
nme_curve["top1"].append(nme_accy["top1"])
nme_curve["top3"].append(nme_accy["top3"])
logging.info("CNN top1 curve: {}".format(cnn_curve["top1"]))
logging.info("CNN top3 curve: {}".format(cnn_curve["top3"]))
logging.info("NME top1 curve: {}".format(nme_curve["top1"]))
logging.info("NME top3 curve: {}\n".format(nme_curve["top3"]))
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_curve["top1"].append(cnn_accy["top1"])
cnn_curve["top3"].append(cnn_accy["top3"])
logging.info("CNN top1 curve: {}".format(cnn_curve["top1"]))
logging.info("CNN top3 curve: {}\n".format(cnn_curve["top3"]))
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"])))
def _set_device(args):
"""
Configure device settings for the model (CPU or GPU).
Args:
args (dict): Configuration arguments including device settings.
"""
device_type = args["device"]
gpus = []
for device in device_type:
if device_type == -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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