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, padding=1, bias=False),
        nn.LeakyReLU(0.1,inplace=True)
    )

"""
# used for FlowNetC
def correlate(input1, input2):
    out_corr = spatial_correlation_sample(input1,
                                          input2,
                                          kernel_size=1,
                                          patch_size=21,
                                          stride=1,
                                          padding=0,
                                          dilation_patch=2)
    # collate dimensions 1 and 2 in order to be treated as a
    # regular 4D tensor
    b, ph, pw, h, w = out_corr.size()
    out_corr = out_corr.view(b, ph * pw, h, w)/input1.size(1)
    return F.leaky_relu_(out_corr, 0.1)
"""

def crop_like(input, target):
    if input.size()[2:] == target.size()[2:]:
        return input
    else:
        return input[:, :, :target.size(2), :target.size(3)]

  model.py

import torch
from torch.nn.init import kaiming_normal_, constant_
import torch.nn as nn
from util import *

class FlowNetS(nn.Module):
    expansion = 1

    def __init__(self, batchNorm=True):
        super(FlowNetS, self).__init__()

        self.batchNorm = batchNorm
        self.conv1 = conv(self.batchNorm, 6, 64, kernel_size=7, stride=2)
        self.conv2 = conv(self.batchNorm, 64, 128, kernel_size=5, stride=2)
        self.conv3 = conv(self.batchNorm, 128, 256, kernel_size=5, stride=2)
        self.conv3_1 = conv(self.batchNorm, 256, 256)
        self.conv4 = conv(self.batchNorm, 256, 512, stride=2)
        self.conv4_1 = conv(self.batchNorm, 512, 512)
        self.conv5 = conv(self.batchNorm, 512, 512, stride=2)
        self.conv5_1 = conv(self.batchNorm, 512, 512)
        self.conv6 = conv(self.batchNorm, 512, 1024, stride=2)
        self.conv6_1 = conv(self.batchNorm, 1024, 1024)

        self.deconv5 = deconv(1024, 512)
        self.deconv4 = deconv(1026, 256)
        self.deconv3 = deconv(770, 128)
        self.deconv2 = deconv(386, 64)

        self.predict_flow6 = predict_flow(1024)
        self.predict_flow5 = predict_flow(1026)
        self.predict_flow4 = predict_flow(770)
        self.predict_flow3 = predict_flow(386)
        self.predict_flow2 = predict_flow(194)

        self.upsampled_flow6_to_5 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
        self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
        self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)
        self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=False)

        for m in self.modules():
            if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
                kaiming_normal_(m.weight, 0.1)
                if m.bias is not None:
                    constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                constant_(m.weight, 1)
                constant_(m.bias, 0)

    def forward(self, x):
        out_conv2 = self.conv2(self.conv1(x))
        out_conv3 = self.conv3_1(self.conv3(out_conv2))
        out_conv4 = self.conv4_1(self.conv4(out_conv3))
        out_conv5 = self.conv5_1(self.conv5(out_conv4))
        out_conv6 = self.conv6_1(self.conv6(out_conv5))

        flow6 = self.predict_flow6(out_conv6)
        flow6_up = crop_like(self.upsampled_flow6_to_5(flow6), out_conv5)
        out_deconv5 = crop_like(self.deconv5(out_conv6), out_conv5)

        concat5 = torch.cat((out_conv5, out_deconv5, flow6_up), 1)
        flow5 = self.predict_flow5(concat5)
        flow5_up = crop_like(self.upsampled_flow5_to_4(flow5), out_conv4)
        out_deconv4 = crop_like(self.deconv4(concat5), out_conv4)

        concat4 = torch.cat((out_conv4, out_deconv4, flow5_up), 1)
        flow4 = self.predict_flow4(concat4)
        flow4_up = crop_like(self.upsampled_flow4_to_3(flow4), out_conv3)
        out_deconv3 = crop_like(self.deconv3(concat4), out_conv3)

        concat3 = torch.cat((out_conv3, out_deconv3, flow4_up), 1)
        flow3 = self.predict_flow3(concat3)
        flow3_up = crop_like(self.upsampled_flow3_to_2(flow3), out_conv2)
        out_deconv2 = crop_like(self.deconv2(concat3), out_conv2)

        concat2 = torch.cat((out_conv2, out_deconv2, flow3_up), 1)
        flow2 = self.predict_flow2(concat2)

        self.training = False
        if self.training:
            return flow2, flow3, flow4, flow5, flow6
        else:
            return flow2

    def weight_parameters(self):
        return [param for name, param in self.named_parameters() if 'weight' in name]

    def bias_parameters(self):
        return [param for name, param in self.named_parameters() if 'bias' in name]


if __name__ == '__main__':
    import time
    def time_synchronized():
        torch.cuda.synchronize() if torch.cuda.is_available() else None
        return time.time()

    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

    model = FlowNetS(batchNorm=False).to(device)
    input_frames = torch.randn(1, 6, 384, 512).to(device)  #

    time1 = time_synchronized()
    flow = model(input_frames)

    print("time: ", time_synchronized() - time1)
    print(flow.shape)