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()