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FlowNetCImg.py
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import torch
import torch.nn as nn
from torch.nn import init
import math
import numpy as np
from correlation_package.modules.correlation import Correlation
from submodules import *
'Parameter count , 39,175,298 '
class FlowNetCImg(nn.Module):
#def __init__(self,args, batchNorm=True, div_flow = 20):
def __init__(self, batchNorm=True, div_flow = 20):
super(FlowNetCImg,self).__init__()
self.batchNorm = batchNorm
self.div_flow = div_flow
self.conv1 = conv(self.batchNorm, 3, 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.conv_redir = conv(self.batchNorm, 256, 32, kernel_size=1, stride=1)
#if args.fp16:
# self.corr = nn.Sequential(
# tofp32(),
# Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1),
# tofp16())
#else:
self.corr = Correlation(pad_size=20, kernel_size=1, max_displacement=20, stride1=1, stride2=2, corr_multiply=1)
self.corr_activation = nn.LeakyReLU(0.1,inplace=True)
self.conv3_1 = conv(self.batchNorm, 473, 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=True)
self.upsampled_flow5_to_4 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
self.upsampled_flow4_to_3 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
self.upsampled_flow3_to_2 = nn.ConvTranspose2d(2, 2, 4, 2, 1, bias=True)
for m in self.modules():
if isinstance(m, nn.Conv2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
if isinstance(m, nn.ConvTranspose2d):
if m.bias is not None:
init.uniform(m.bias)
init.xavier_uniform(m.weight)
# init_deconv_bilinear(m.weight)
self.upsample1 = nn.Upsample(scale_factor=4, mode='bilinear')
def forward(self, x):
x1 = x[:,0:3,:,:]
x2 = x[:,3::,:,:]
out_conv1a = self.conv1(x1)
out_conv2a = self.conv2(out_conv1a)
out_conv3a = self.conv3(out_conv2a)
# FlownetC bottom input stream
out_conv1b = self.conv1(x2)
out_conv2b = self.conv2(out_conv1b)
out_conv3b = self.conv3(out_conv2b)
# Merge streams
out_corr = self.corr(out_conv3a, out_conv3b) # False
out_corr = self.corr_activation(out_corr)
# Redirect top input stream and concatenate
out_conv_redir = self.conv_redir(out_conv3a)
in_conv3_1 = torch.cat((out_conv_redir, out_corr), 1)
# Merged conv layers
out_conv3_1 = self.conv3_1(in_conv3_1)
out_conv4 = self.conv4_1(self.conv4(out_conv3_1))
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 = self.upsampled_flow6_to_5(flow6)
out_deconv5 = self.deconv5(out_conv6)
concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
flow5 = self.predict_flow5(concat5)
flow5_up = self.upsampled_flow5_to_4(flow5)
out_deconv4 = self.deconv4(concat5)
concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
flow4 = self.predict_flow4(concat4)
flow4_up = self.upsampled_flow4_to_3(flow4)
out_deconv3 = self.deconv3(concat4)
concat3 = torch.cat((out_conv3_1,out_deconv3,flow4_up),1)
flow3 = self.predict_flow3(concat3)
flow3_up = self.upsampled_flow3_to_2(flow3)
out_deconv2 = self.deconv2(concat3)
concat2 = torch.cat((out_conv2a,out_deconv2,flow3_up),1)
flow2 = self.predict_flow2(concat2)
#if self.training:
# return flow2,flow3,flow4,flow5,flow6
#else:
# return flow2,
#return out_conv6 # (1 , 1024, 6, 8)
return flow2