Datasets:
jblitzar
/
github-python-corpus

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val_cifar_c()
if args.mode in ['v2']:
val_cifar10_1()
if args.mode in ['sta']:
val_cifar_worst_of_k_affine(args.k)
elif args.dataset == 'tin':
if args.mode in ['clean', 'all']:
val_tin()
if args.mode in ['c', 'all']:
val_tin_c()
elif args.dataset == 'IN':
if args.mode in ['clean', 'all']:
val_IN()
if args.mode in ['c', 'all']:
val_IN_c()
# <FILESEP>
import os
import time
import torch
import numpy as np
import torch.nn as nn
import torch.nn.parallel
import torch.optim as optim
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
from tensorboardX import SummaryWriter
from utils import *
from options import get_args
from dataloader import nyudv2_dataloader
from models.loss import cal_spatial_loss, cal_temporal_loss
from models.backbone_dict import backbone_dict
from models import modules
from models import net
cudnn.benchmark = True
args = get_args('train')
os.environ['CUDA_VISIBLE_DEVICES'] = args.devices
# Create folder
makedir(args.checkpoint_dir)
makedir(args.logdir)
# creat summary logger
logger = SummaryWriter(args.logdir)
# dataset, dataloader
TrainImgLoader = nyudv2_dataloader.getTrainingData_NYUDV2(args.batch_size, args.trainlist_path, args.root_path)
# model, optimizer
device = 'cuda' if torch.cuda.is_available() and args.use_cuda else 'cpu'
backbone = backbone_dict[args.backbone]()
Encoder = modules.E_resnet(backbone)
if args.backbone in ['resnet50']:
model = net.model(Encoder, num_features=2048, block_channel=[256, 512, 1024, 2048], refinenet=args.refinenet)
elif args.backbone in ['resnet18', 'resnet34']:
model = net.model(Encoder, num_features=512, block_channel=[64, 128, 256, 512], refinenet=args.refinenet)
model = nn.DataParallel(model).cuda()
disc = net.C_C3D_1().cuda()
optimizer = build_optimizer(model = model,
learning_rate=args.lr,
optimizer_name=args.optimizer_name,
weight_decay = args.weight_decay,
epsilon=args.epsilon,
momentum=args.momentum
)
start_epoch = 0
if args.resume:
all_saved_ckpts = [ckpt for ckpt in os.listdir(args.checkpoint_dir) if ckpt.endswith(".pth.tar")]
print(all_saved_ckpts)
all_saved_ckpts = sorted(all_saved_ckpts, key=lambda x:int(x.split('_')[-1].split('.')[0]))
loadckpt = os.path.join(args.checkpoint_dir, all_saved_ckpts[-1])
start_epoch = int(all_saved_ckpts[-1].split('_')[-1].split('.')[0])
print("loading the lastest model in checkpoint_dir: {}".format(loadckpt))
state_dict = torch.load(loadckpt)
model.load_state_dict(state_dict)
elif args.loadckpt is not None:
print("loading model {}".format(args.loadckpt))
start_epoch = args.loadckpt.split('_')[-1].split('.')[0]
state_dict = torch.load(args.loadckpt)
model.load_state_dict(state_dict)
else:
print("start at epoch {}".format(start_epoch))
def train():
for epoch in range(start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch, args.lr)
batch_time = AverageMeter()
losses = AverageMeter()
model.train()
end = time.time()