ponta256

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from layers import *
import os

def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,

ponta256

from  __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import os
import pprint

import torch
import torch.nn.parallel
import torch.backends.cudnn as cudnn