Exemple #1
0
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')
Exemple #2
0
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()