File size: 7,448 Bytes
998bb30 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 |
import json
import argparse
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
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
train(args)
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)
def _train(args):
init_cls = 0 if args["init_cls"] == args["increment"] else args["init_cls"]
logs_name = "logs/{}/{}/init_cls_{}/per_classes_{}/{}".format(args["model_name"], args["dataset"], init_cls,
args['increment'], args["convnet_type"])
if not os.path.exists(logs_name):
os.makedirs(logs_name)
logfilename = "logs/{}/{}/init_cls_{}/per_classes_{}/{}/{}_{}_{}".format(
args["model_name"],
args["dataset"],
init_cls,
args["increment"],
args["convnet_type"],
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()
_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": []}
cnn_matrix, nme_matrix = [], []
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)
cnn_accy, nme_accy = model.eval_task()
model.save_checkpoint(logs_name, args["convnet_type"])
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"])))
if len(cnn_matrix) > 0:
np_acctable = np.zeros([task + 1, task + 1])
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[:, task])[:task])
print('Accuracy Matrix (CNN):')
print(np_acctable)
formatted_str = "\n".join(
["\t[" + ", ".join("{:.1f}".format(float(val)) for val in row) + "]" for row in np_acctable])
logging.info("Accuracy Matrix (CNN):\n{}".format(formatted_str))
# logging.info('Accuracy Matrix (CNN): {}'.format(np_acctable.tolist()))
print('Forgetting (CNN):', forgetting)
logging.info('Forgetting (CNN): {}'.format(forgetting))
if len(nme_matrix) > 0:
np_acctable = np.zeros([task + 1, task + 1])
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[:, task])[:task])
print('Accuracy Matrix (NME):')
print(np_acctable)
formatted_str = "\n".join(
["\t[" + ", ".join("{:.1f}".format(float(val)) for val in row) + "]" for row in np_acctable])
logging.info("Accuracy Matrix (NME):\n{}".format(formatted_str))
print('Forgetting (NME):', forgetting)
logging.info('Forgetting (NME): {}'.format(forgetting))
def _set_device(args):
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))
def load_json(settings_path):
with open(settings_path) as data_file:
param = json.load(data_file)
return param
def setup_parser():
parser = argparse.ArgumentParser(description='Reproduce of multiple continual learning algorithms.')
parser.add_argument('--config', type=str, default='./exps/gem.json',
help='Json file of settings.')
return parser
if __name__ == '__main__':
main()
|