FlowNets

 

 util.py

import torch.nn as nn
import torch.nn.functional as F

def conv(batchNorm, in_planes, out_planes, kernel_size=3, stride=1):
    if batchNorm:
        return nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=False),
            nn.BatchNorm2d(out_planes),
            nn.LeakyReLU(0.1,inplace=True)
        )
    else:
        return nn.Sequential(
            nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride, padding=(kernel_size-1)//2, bias=True),
            nn.LeakyReLU(0.1,inplace=True)
        )


def predict_flow(in_planes):
    return nn.Conv2d(in_planes,2,kernel_size=3,stride=1,padding=1,bias=False)


def deconv(in_planes, out_planes):
    return nn.Sequential(
        nn.ConvTranspose2d(in_planes, out_planes, kernel_size=4, stride=2, padd