def __init__(self):
    super(Mynet, self).__init__()
    enc1 = []
    enc1.append(torch.nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    self.enc1 = torch.nn.Sequential(*enc1)
    self.out = torch.nn.Conv2d(32, 1, kernel_size, padding=0, stride=1)
def __init__(self):
    super(Mynet, self).__init__()
    enc1 = []
    enc1.append(torch.nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    enc1.append(torch.nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1))
    enc1.append(torch.nn.BatchNorm2d(32))
    enc1.append(torch.nn.ReLU())
    self.enc1 = torch.nn.Sequential(*enc1)
    self.out = torch.nn.Conv2d(32, 1, kernel_size, padding=0, stride=1)