3D Resnet

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
import math
from functools import partial

__all__ = ['ResNet', 'resnet10', 'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152', 'resnet200']


def conv3x3x3(in_planes, out_planes, stride=1):
    # 3x3x3 convolution with padding
    return nn.Conv3d(in_planes, out_planes, kernel_size=3,
                     stride=stride, padding=1, bias=False)


def downsample_basic_block(x, planes, stride):
    out = F.avg_pool3d(x, kernel_size=1, stride=stride)
    zero_pads = torch.Tensor(out.size(0), planes - out.size(1),
                             out.size(2), out.size(3),
                             out.size(4)).zero_()
    if isinstance(out.data, torch.cuda.FloatTensor):
        zero_pads = zero_pads.cuda()

    out = Variable(torch.cat([out.data, zero_pads], dim=1))

    return out


class BasicBlock(nn.Module):
    expansion = 1