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train.py
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import torch
import pickle
import os.path
import torch.nn.functional as F
import torchvision.models as models
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import numpy as np
import argparse
import subprocess
import collections
args = collections.namedtuple
import sys
srcFolder = os.path.join(os.path.abspath(os.path.dirname(__file__)), 'src')
sys.path.append(srcFolder)
from metrics import (nss, auc, cc)
from utils import *
from losses import *
from models import *
from training_scheme import *
parser = argparse.ArgumentParser(description='Saliency Training')
parser.add_argument('--lr', default=0.0005, type=float,
help='learning rate')
parser.add_argument('--lr_decay_epoch', default=2, type=float,
help='every n epochs to decay learning rate')
parser.add_argument('--lr_coef', default=.1, type=float,
help='lr coefficient to change learning rates')
parser.add_argument('--weight_decay', default=1e-4, type=float,
help='weight decay')
parser.add_argument('--epochs', default=10, type=int,
help='number of epochs')
parser.add_argument('--batch_size', default=10, type=int,
help='batch size for training')
parser.add_argument('--val_batch_size', default=1, type=int,
help='batch size for validation')
parser.add_argument('--model', default='resnet50', type=str,
help='backbone network: resnet50, din50')
parser.add_argument('--pretrainedModel', default='', type=str,
help='pretrained saliency model')
parser.add_argument('--train_img_dir', default='',
type=str,
help='training images path')
parser.add_argument('--train_gt_dir', default='',
type=str,
help='training human fixation maps path')
parser.add_argument('--val_img_dir', default='',
type=str,
help='validation images path')
parser.add_argument('--val_gt_dir', default='',
type=str,
help='validation human fixation maps path')
parser.add_argument('--image_size', nargs='+', type=int,
help='resized image resolution for training: (600, 800) | (480, 640) | (320, 640)')
parser.add_argument('--tr_fxt_size', nargs='+', type=int,
help='resized training fixation resolution: (600, 800) | (480, 640) | (320, 640)')
parser.add_argument('--val_fxt_size', nargs='+', type=int,
help='resized validation fixation resolution: (600, 800) | (480, 640) | (320, 640)')
parser.add_argument('--out_dir', default='logs/baseline/salicon_dinet',
type=str,
help='validation saliency maps path')
parser.add_argument('--fxt_loc_name', type=str, default='fixationPts', help='fixationPts|fixLocs')
parser.add_argument('--random_seed', default=0, type=int,
help='random seed')
args = parser.parse_args()
args.start_epoch = 0
args.pretrained = True
args.useMultiGPU = True
n_output = 256 # just for DINet
args.experiment_name = '{}'.format(args.model)
out_folder = args.out_dir
args.loggerdir = '{}/tsboard'.format(out_folder)
args.save_path = '{}/snapshots'.format(out_folder)
args.sal_path = '{}/salmap_val'.format(out_folder)
if args.image_size is None:
args.image_size = (480,640)
else:
args.image_size = (args.image_size[0], args.image_size[1])
modelzoo = {
'densenet169': models.densenet169,
'vgg16': models.vgg16,
'resnet101': models.resnet101,
'resnet50': models.resnet50,
'resnet34': models.resnet34,
'resnet18': models.resnet18,
}
print(vars(args))
criterion = TVdist
# create the model
if args.model == 'din50':
model = Saliency_DIN(args.model,modelzoo,args.pretrained,n_output=n_output)
elif args.model == 'resnet50':
model = Saliency_ResNet50(args.model,modelzoo,args.pretrained)
if args.pretrainedModel != '':
model.load_state_dict(torch.load(args.pretrainedModel))
if not args.useMultiGPU:
model = model.cuda()
param_groups = model.get_param_groups()
elif args.useMultiGPU:
model = nn.DataParallel(model).cuda()
# As the output of saliency prediction is a saliency map,
# whose size depends on the input size, we do a test here
# to quickly acquire the output size.
testImgs = load_allimages(args.val_img_dir)
oneimage = testImgs[0][0]
oneimage = datasets.folder.default_loader(oneimage)
oneimage = transforms.Resize(args.image_size)(oneimage)
oneimage = transforms.ToTensor()(oneimage)
oneimage = oneimage.view([1]+list(oneimage.size()))
oneimage = Variable(oneimage).cuda()
output = model(oneimage)
train_loader, val_loader = create_data_loaders(args,
outSize=tuple(output.size()[2:]),
imgSize=args.image_size,
trFxtSize=args.tr_fxt_size,
valFxtSize=args.val_fxt_size,
flip=False)
optimizer = torch.optim.Adam(model.parameters(), lr=args.lr,
weight_decay=args.weight_decay)
number_of_params = sum(p.numel() for p in model.parameters())
print('===Total parameters number: {}'.format(number_of_params))
snapshot_dir = args.save_path
ensure_dir(snapshot_dir)
pickle.dump(vars(args), open(snapshot_dir + 'args.pkl', 'wb'))
txtlogger = open('{}/log.txt'.format(out_folder), 'w')
print(vars(args),file=txtlogger, flush=True)
print(criterion,file=txtlogger, flush=True)
stat_file = os.path.join(out_folder, 'stat_training.csv')
with open(stat_file, 'w') as f:
f.write('nss, auc, cc, ep, tr_loss, val_loss, tr_batchtime, tr_datatime, val_batchtime, val_datatime\n')
for epoch in range(args.start_epoch, args.epochs):
adjust_learning_rate(args.lr, optimizer, epoch,
basenum=args.lr_decay_epoch, coef=args.lr_coef)
cur_lr = optimizer.param_groups[0]['lr']
train_loss, val_loss, train_batchtime, train_datatime, val_batchtime, val_datatime = train_val(model, criterion, optimizer, epoch,
train_loader, val_loader, args.sal_path, txtlogger)
save_filename = os.path.join(snapshot_dir, 'model_ep{epoch}.pth'.format(epoch=epoch+1))
save_gem_filename = os.path.join(snapshot_dir, 'model_gem_ep{epoch}.pth'.format(epoch=epoch+1))
save_checkpoint(model, save_filename)
sal_path = '{}/ep{}'.format(args.sal_path, epoch+1)
isnotify = 0 if epoch < args.epochs-1 else 1
appendix = ', {}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}, {:.4f}'.format(epoch+1, train_loss, val_loss, train_batchtime, train_datatime, val_batchtime, val_datatime)
evalCmd = 'python src/eval_command.py --output "{}" --fixation_folder "{}" --salmap_folder "{}" --fxt_loc_name "{}" --appendix "{}"'.format(stat_file, args.val_gt_dir, sal_path, args.fxt_loc_name, appendix)
subprocess.Popen(evalCmd, shell=True)
txtlogger.close()