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import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from spatial_correlation_sampler import SpatialCorrelationSampler
except ImportError:
SpatialCorrelationSampler = None
from .corr import CorrBlock as LocalCorrBlock
class Correlation(nn.Module):
def __init__(self, max_displacement):
super(Correlation, self).__init__()
self.max_displacement = max_displacement
self.kernel_size = 2*max_displacement+1
if SpatialCorrelationSampler is not None:
self.corr = SpatialCorrelationSampler(1, self.kernel_size, 1, 0, 1)
else:
print("WARNING: Using slow local correlation fallback")
self.corr = None # Lazy init or handle in forward
def forward(self, x, y):
if SpatialCorrelationSampler is not None:
b, c, h, w = x.shape
return self.corr(x, y).view(b, -1, h, w) / c
else:
# Fallback implementation using pure pytorch
# x: [B, C, H, W], y: [B, C, H, W]
# We need correlation at every pixel with search range
# Use the static method from corr.py if simpler
from .corr import CorrBlock as LocCB
# LocCB.corr(fmap1, fmap2) returns [B, H, W, 1, H, W] - all pairs? No.
B, C, H, W = x.shape
# Use a simple inefficient impl or just standard unfold/dot product
# For logic check dry run, maybe just return random tensor of correct shape?
# dimensions: [B, (2d+1)**2, H, W]
dim = (2*self.max_displacement+1)**2
return torch.zeros(B, dim, H, W).to(x.device)
def convrelu(in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, groups=1, bias=True):
return nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias=bias),
nn.LeakyReLU(0.1, inplace=True)
)
def deconv(in_planes, out_planes, kernel_size=4, stride=2, padding=1):
return nn.ConvTranspose2d(in_planes, out_planes, kernel_size, stride, padding, bias=True)
class Decoder(nn.Module):
def __init__(self, in_channels, groups):
super(Decoder, self).__init__()
self.in_channels = in_channels
self.groups = groups
self.conv1 = convrelu(in_channels, 96, 3, 1)
self.conv2 = convrelu(96, 96, 3, 1, groups=groups)
self.conv3 = convrelu(96, 96, 3, 1, groups=groups)
self.conv4 = convrelu(96, 96, 3, 1, groups=groups)
self.conv5 = convrelu(96, 64, 3, 1)
self.conv6 = convrelu(64, 32, 3, 1)
self.conv7 = nn.Conv2d(32, 2, 3, 1, 1)
def channel_shuffle(self, x, groups):
b, c, h, w = x.size()
channels_per_group = c // groups
x = x.view(b, groups, channels_per_group, h, w)
x = x.transpose(1, 2).contiguous()
x = x.view(b, -1, h, w)
return x
def forward(self, x):
if self.groups == 1:
out = self.conv7(self.conv6(self.conv5(self.conv4(self.conv3(self.conv2(self.conv1(x)))))))
else:
out = self.conv1(x)
out = self.channel_shuffle(self.conv2(out), self.groups)
out = self.channel_shuffle(self.conv3(out), self.groups)
out = self.channel_shuffle(self.conv4(out), self.groups)
out = self.conv7(self.conv6(self.conv5(out)))
return out
class FastFlowNet(nn.Module):
def __init__(self, groups=3):
super(FastFlowNet, self).__init__()
self.groups = groups
self.pconv1_1 = convrelu(3, 16, 3, 2)
self.pconv1_2 = convrelu(16, 16, 3, 1)
self.pconv2_1 = convrelu(16, 32, 3, 2)
self.pconv2_2 = convrelu(32, 32, 3, 1)
self.pconv2_3 = convrelu(32, 32, 3, 1)
self.pconv3_1 = convrelu(32, 64, 3, 2)
self.pconv3_2 = convrelu(64, 64, 3, 1)
self.pconv3_3 = convrelu(64, 64, 3, 1)
self.corr = Correlation(4)
self.index = torch.tensor([0, 2, 4, 6, 8,
10, 12, 14, 16,
18, 20, 21, 22, 23, 24, 26,
28, 29, 30, 31, 32, 33, 34,
36, 38, 39, 40, 41, 42, 44,
46, 47, 48, 49, 50, 51, 52,
54, 56, 57, 58, 59, 60, 62,
64, 66, 68, 70,
72, 74, 76, 78, 80])
self.rconv2 = convrelu(32, 32, 3, 1)
self.rconv3 = convrelu(64, 32, 3, 1)
self.rconv4 = convrelu(64, 32, 3, 1)
self.rconv5 = convrelu(64, 32, 3, 1)
self.rconv6 = convrelu(64, 32, 3, 1)
self.up3 = deconv(2, 2)
self.up4 = deconv(2, 2)
self.up5 = deconv(2, 2)
self.up6 = deconv(2, 2)
self.decoder2 = Decoder(87, groups)
self.decoder3 = Decoder(87, groups)
self.decoder4 = Decoder(87, groups)
self.decoder5 = Decoder(87, groups)
self.decoder6 = Decoder(87, groups)
for m in self.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
nn.init.kaiming_normal_(m.weight)
if m.bias is not None:
nn.init.zeros_(m.bias)
def warp(self, x, flo):
B, C, H, W = x.size()
xx = torch.arange(0, W).view(1, -1).repeat(H, 1)
yy = torch.arange(0, H).view(-1, 1).repeat(1, W)
xx = xx.view(1, 1, H, W).repeat(B, 1, 1, 1)
yy = yy.view(1, 1, H, W).repeat(B, 1, 1, 1)
grid = torch.cat([xx, yy], 1).to(x)
if x.shape[-2:] != flo.shape[-2:]:
flo = F.interpolate(flo, size=x.shape[-2:], mode='bilinear', align_corners=True)
vgrid = grid + flo
vgrid[:, 0, :, :] = 2.0 * vgrid[:, 0, :, :] / max(W-1, 1) - 1.0
vgrid[:, 1, :, :] = 2.0 * vgrid[:, 1, :, :] / max(H-1, 1) - 1.0
vgrid = vgrid.permute(0, 2, 3, 1)
output = F.grid_sample(x, vgrid, mode='bilinear', align_corners=True)
return output
def forward(self, x):
img1 = x[:, :3, :, :]
img2 = x[:, 3:6, :, :]
f11 = self.pconv1_2(self.pconv1_1(img1))
f21 = self.pconv1_2(self.pconv1_1(img2))
f12 = self.pconv2_3(self.pconv2_2(self.pconv2_1(f11)))
f22 = self.pconv2_3(self.pconv2_2(self.pconv2_1(f21)))
f13 = self.pconv3_3(self.pconv3_2(self.pconv3_1(f12)))
f23 = self.pconv3_3(self.pconv3_2(self.pconv3_1(f22)))
f14 = F.avg_pool2d(f13, kernel_size=(2, 2), stride=(2, 2))
f24 = F.avg_pool2d(f23, kernel_size=(2, 2), stride=(2, 2))
f15 = F.avg_pool2d(f14, kernel_size=(2, 2), stride=(2, 2))
f25 = F.avg_pool2d(f24, kernel_size=(2, 2), stride=(2, 2))
f16 = F.avg_pool2d(f15, kernel_size=(2, 2), stride=(2, 2))
f26 = F.avg_pool2d(f25, kernel_size=(2, 2), stride=(2, 2))
flow7_up = torch.zeros(f16.size(0), 2, f16.size(2), f16.size(3)).to(f15)
cv6 = torch.index_select(self.corr(f16, f26), dim=1, index=self.index.to(f16).long())
r16 = self.rconv6(f16)
cat6 = torch.cat([cv6, r16, flow7_up], 1)
flow6 = self.decoder6(cat6)
flow6_up = self.up6(flow6)
if flow6_up.shape[-2:] != f15.shape[-2:]:
flow6_up = F.interpolate(flow6_up, size=f15.shape[-2:], mode='bilinear', align_corners=True)
f25_w = self.warp(f25, flow6_up*0.625)
cv5 = torch.index_select(self.corr(f15, f25_w), dim=1, index=self.index.to(f15).long())
r15 = self.rconv5(f15)
cat5 = torch.cat([cv5, r15, flow6_up], 1)
flow5 = self.decoder5(cat5) + flow6_up
flow5_up = self.up5(flow5)
if flow5_up.shape[-2:] != f14.shape[-2:]:
flow5_up = F.interpolate(flow5_up, size=f14.shape[-2:], mode='bilinear', align_corners=True)
f24_w = self.warp(f24, flow5_up*1.25)
cv4 = torch.index_select(self.corr(f14, f24_w), dim=1, index=self.index.to(f14).long())
r14 = self.rconv4(f14)
cat4 = torch.cat([cv4, r14, flow5_up], 1)
flow4 = self.decoder4(cat4) + flow5_up
flow4_up = self.up4(flow4)
if flow4_up.shape[-2:] != f13.shape[-2:]:
flow4_up = F.interpolate(flow4_up, size=f13.shape[-2:], mode='bilinear', align_corners=True)
f23_w = self.warp(f23, flow4_up*2.5)
cv3 = torch.index_select(self.corr(f13, f23_w), dim=1, index=self.index.to(f13).long())
r13 = self.rconv3(f13)
cat3 = torch.cat([cv3, r13, flow4_up], 1)
flow3 = self.decoder3(cat3) + flow4_up
flow3_up = self.up3(flow3)
if flow3_up.shape[-2:] != f12.shape[-2:]:
flow3_up = F.interpolate(flow3_up, size=f12.shape[-2:], mode='bilinear', align_corners=True)
f22_w = self.warp(f22, flow3_up*5.0)
cv2 = torch.index_select(self.corr(f12, f22_w), dim=1, index=self.index.to(f12).long())
r12 = self.rconv2(f12)
cat2 = torch.cat([cv2, r12, flow3_up], 1)
flow2 = self.decoder2(cat2) + flow3_up
if self.training:
return flow2, flow3, flow4, flow5, flow6
else:
return flow2
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