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train_projector.py
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train_projector.py
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import argparse
import os
import socket
import time
import tensorboard_logger as tb_logger
import torch
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
from torchinfo import summary
from criterion.criterion import CrossEntropy_SNNL
from dataset import kaggle
from helper.loops import train_SNNL as train
from helper.loops import validate_SNNL as validate
from helper.utils import adjust_learning_rate
from models import model_dict
def parse_option():
hostname = socket.gethostname()
parser = argparse.ArgumentParser("argument for training")
parser.add_argument("--print_freq", type=int, default=100, help="print frequency")
parser.add_argument("--tb_freq", type=int, default=500, help="tb frequency")
parser.add_argument("--save_freq", type=int, default=400, help="save frequency")
parser.add_argument("--batch_size", type=int, default=45, help="batch_size")
parser.add_argument(
"--num_workers", type=int, default=4, help="num of workers to use"
)
parser.add_argument(
"--epochs", type=int, default=30, help="number of training epochs"
)
# optimization
parser.add_argument(
"--learning_rate", type=float, default=1e-3, help="learning rate"
)
parser.add_argument(
"--lr_decay_epochs",
type=str,
default="10,20",
help="where to decay lr, can be a list",
)
parser.add_argument(
"--lr_decay_rate", type=float, default=0.1, help="decay rate for learning rate"
)
parser.add_argument("--weight_decay", type=float, default=5e-4, help="weight decay")
# parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
# dataset
parser.add_argument(
"--model",
type=str,
default="resnet50",
choices=[
"resnet18",
"resnet34",
"resnet50",
"wrn_16_1",
"wrn_16_2",
"wrn_40_1",
"wrn_40_2",
"vgg8",
"vgg11",
"vgg13",
"vgg16",
"vgg19",
"MobileNetV2",
"ShuffleV1",
"ShuffleV2",
"resnext50_32x4d",
"resnext101_32x8d",
"wide_resnet50_2",
"wide_resnet101_2",
],
)
parser.add_argument(
"--d_rep", type=int, default=128, help="dimension of representation layer"
)
parser.add_argument(
"--dataset", type=str, default="kaggle", choices=["kaggle"], help="dataset"
)
parser.add_argument(
"-T", "--temperature", type=float, default=50, help="temperature"
)
parser.add_argument(
"-a", "--alpha", type=float, default=-5.0, help="alpha multiplier"
)
parser.add_argument("-c", "--check-model", default=False, action="store_true")
parser.add_argument("-t", "--trial", type=int, default=0, help="the experiment id")
parser.add_argument("--parallel-training", type=bool, default=False)
parser.add_argument("--info", type=str, default="", help="more infomation")
opt = parser.parse_args()
# set different learning rate from these 4 models
if opt.model in ["MobileNetV2", "ShuffleV1", "ShuffleV2"]:
opt.learning_rate = 0.01
opt.model_path = "./save/models"
opt.tb_path = "./save/tensorboard"
iterations = opt.lr_decay_epochs.split(",")
opt.lr_decay_epochs = [int(it) for it in iterations]
opt.model_name = f"Teacher_{opt.model}_epochs{opt.epochs}_alpha{opt.alpha}_T{opt.temperature}_{opt.info}"
opt.tb_folder = os.path.join(opt.tb_path, opt.model_name)
if not os.path.isdir(opt.tb_folder):
os.makedirs(opt.tb_folder)
opt.save_folder = os.path.join(opt.model_path, opt.model_name)
if not os.path.isdir(opt.save_folder):
os.makedirs(opt.save_folder)
return opt
def main(): # sourcery skip: use-fstring-for-formatting
best_acc = 0
opt = parse_option()
print("a={},T={}".format(opt.alpha, opt.temperature))
# dataset check
if opt.dataset != "kaggle":
raise NotImplementedError(opt.dataset)
train_loader, val_loader = kaggle.get_kaggle_dataloaders(
batch_size=opt.batch_size, num_workers=opt.num_workers
)
n_classes = 4
# model
base = model_dict[opt.model](input_channel=1, num_classes=opt.d_rep)
model = model_dict["rep_net"](base, opt.d_rep, n_classes)
# *check model
if opt.check_model:
summary(model, input_size=(32, 1, 224, 224), device=torch.device("cuda:0"))
exit(0)
# exit()
# optimizer
optimizer = optim.Adam(
model.parameters(), lr=opt.learning_rate, weight_decay=opt.weight_decay
)
criterion = CrossEntropy_SNNL(T=opt.temperature, alpha=opt.alpha)
use_cuda = torch.cuda.is_available()
device = torch.device("cuda:0" if use_cuda else "cpu")
assert torch.cuda.is_available(), "Not with GPU"
base = base.to(device)
model = model.to(device)
criterion = criterion.to(device)
torch.backends.cudnn.benchmark = True
if opt.parallel_training:
model = nn.DataParallel(model)
logger = tb_logger.Logger(logdir=opt.tb_folder, flush_secs=2)
# routine
print("==> Start training...")
start_time = time.time()
for epoch in range(1, opt.epochs + 1):
adjust_learning_rate(epoch, opt, optimizer)
now_time = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime())
print(now_time)
time1 = time.time()
train_acc, train_loss = train(
epoch, train_loader, model, criterion, optimizer, device, opt
)
time2 = time.time()
print("epoch {}, total time {:.2f}".format(epoch, (time2 - time1) / 60))
logger.log_value("train_acc", train_acc, epoch)
logger.log_value("train_loss", train_loss, epoch)
# test_acc, test_acc_top5, test_loss = validate(val_loader, model, criterion, opt)
test_acc, test_loss = validate(val_loader, model, criterion, opt)
logger.log_value("test_acc", test_acc, epoch)
# logger.log_value('test_acc_top5', test_acc_top5, epoch)
logger.log_value("test_losss", test_loss, epoch)
# save the best model
if test_acc > best_acc:
best_acc = test_acc
state = {
"epoch": epoch,
"model": model.state_dict(),
"best_acc": best_acc,
"optimizer": optimizer.state_dict(),
}
save_file = os.path.join(opt.save_folder, "{}_best.pth".format(opt.model))
print("saving the best model!")
torch.save(state, save_file)
# regular saving
if epoch % opt.save_freq == 0:
print("==> Saving...")
state = {
"epoch": epoch,
"model": model.state_dict(),
"accuracy": test_acc,
"optimizer": optimizer.state_dict(),
}
save_file = os.path.join(
opt.save_folder, "ckpt_epoch_{epoch}.pth".format(epoch=epoch)
)
torch.save(state, save_file)
# This best accuracy is only for printing purpose.
# The results reported in the paper/README is from the last epoch.
print("best accuracy:", best_acc)
end_time = time.time()
print(time.strftime("%Hh:%Mm:%Ss", time.gmtime(end_time - start_time)))
# save model
state = {
"opt": opt,
"model": model.state_dict(),
"optimizer": optimizer.state_dict(),
}
save_file = os.path.join(opt.save_folder, "{}_last.pth".format(opt.model))
torch.save(state, save_file)
if __name__ == "__main__":
main()