cfg = configurations[args.config]
cuda = torch.cuda.is_available
"""""" """""~~~ dataset loader ~~~""" """"""
train_dataRoot = args.train_dataroot
test_dataRoot = args.test_dataroot
if not os.path.exists(args.snapshot_root):
os.mkdir(args.snapshot_root)
if not os.path.exists(args.salmap_root):
os.mkdir(args.salmap_root)
if args.phase == 'train':
SnapRoot = args.snapshot_root # checkpoint
train_loader = torch.utils.data.DataLoader(MyData(train_dataRoot,
transform=True),
batch_size=2,
shuffle=True,
num_workers=4,
pin_memory=True)
if args.phase == 'test':
MapRoot = args.salmap_root
test_loader = torch.utils.data.DataLoader(MyTestData(test_dataRoot,
transform=True),
batch_size=1,
shuffle=True,
num_workers=4,
pin_memory=True)
print('data already')
"""""" """"" ~~~nets~~~ """ """"""
""" Title: Depth-induced Multi-scale Recurrent Attention Network for Saliency Detection Author: Wei Ji, Jingjing Li E-mail: [email protected] """ import torch from torch.autograd import Variable from torch.utils.data import DataLoader import torchvision import torch.nn.functional as F import torch.optim as optim from dataset_loader import MyData, MyTestData from model import RGBNet,DepthNet from fusion import ConvLSTM from functions import imsave import argparse from trainer import Trainer import os configurations = { # same configuration as original work # https://github.com/shelhamer/fcn.berkeleyvision.org 1: dict( max_iteration=1000000, lr=1.0e-10, momentum=0.99, weight_decay=0.0005, spshot=20000, nclass=2, sshow=10,
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torchvision
import torch.nn.functional as F
import torch.optim as optim
from dataset_loader import MyData, MyTestData, DTestData
from model import FocalNet, FocalNet_sub
from conv_lstm import ConvLSTM
from functions import imsave
import argparse
from Trainer_Teacher import Trainer
import os
if __name__ == '__main__':
configurations = {
1: dict(
max_iteration=500000,
lr=1.0e-10,
momentum=0.99,
weight_decay=0.0005,
spshot=10000,
nclass=2,
sshow=10,
focal_num=12,
)
}
parser=argparse.ArgumentParser()
parser.add_argument('--phase', type=str, default='test', help='train or test')
parser.add_argument('--param', type=str, default=True, help='path to pre-trained parameters')
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torchvision
import torch.nn.functional as F
import torch.optim as optim
from dataset_loader import MyData, MyTestData
from model import FocalNet, FocalNet_sub
from conv_lstm import ConvLSTM
from functions import imsave
import argparse
from Trainer_Student import Trainer
from resnet_18 import Resnet_18
import os
import imageio
if __name__ == '__main__':
configurations = {
1: dict(
max_iteration=300000,
lr=1.0e-10,
momentum=0.99,
weight_decay=0.0005,
spshot=10000,
nclass=2,
sshow=10,
focal_num=12,
)
}
parser=argparse.ArgumentParser()