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import torch
import torch.nn as nn
import torch.nn.functional as F
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256,
out_dim=2,
):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, out_dim, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
out = self.conv2(self.relu(self.conv1(x)))
return out
class SepConvGRU(nn.Module):
def __init__(self, hidden_dim=128, input_dim=192 + 128,
kernel_size=5,
):
padding = (kernel_size - 1) // 2
super(SepConvGRU, self).__init__()
self.convz1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding))
self.convr1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding))
self.convq1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding))
self.convz2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0))
self.convr2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0))
self.convq2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0))
def forward(self, h, x):
# horizontal
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz1(hx))
r = torch.sigmoid(self.convr1(hx))
q = torch.tanh(self.convq1(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
# vertical
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz2(hx))
r = torch.sigmoid(self.convr2(hx))
q = torch.tanh(self.convq2(torch.cat([r * h, x], dim=1)))
h = (1 - z) * h + z * q
return h
class BasicMotionEncoder(nn.Module):
def __init__(self, corr_channels=324,
flow_channels=2,
):
super(BasicMotionEncoder, self).__init__()
self.convc1 = nn.Conv2d(corr_channels, 256, 1, padding=0)
self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
self.convf1 = nn.Conv2d(flow_channels, 128, 7, padding=3)
self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
self.conv = nn.Conv2d(64 + 192, 128 - flow_channels, 3, padding=1)
def forward(self, flow, corr):
cor = F.relu(self.convc1(corr))
cor = F.relu(self.convc2(cor))
flo = F.relu(self.convf1(flow))
flo = F.relu(self.convf2(flo))
cor_flo = torch.cat([cor, flo], dim=1)
out = F.relu(self.conv(cor_flo))
return torch.cat([out, flow], dim=1)
class BasicUpdateBlock(nn.Module):
def __init__(self, corr_channels=324,
hidden_dim=128,
context_dim=128,
downsample_factor=8,
flow_dim=2,
bilinear_up=False,
):
super(BasicUpdateBlock, self).__init__()
self.encoder = BasicMotionEncoder(corr_channels=corr_channels,
flow_channels=flow_dim,
)
self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=context_dim + hidden_dim)
self.flow_head = FlowHead(hidden_dim, hidden_dim=256,
out_dim=flow_dim,
)
if bilinear_up:
self.mask = None
else:
self.mask = nn.Sequential(
nn.Conv2d(hidden_dim, 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, downsample_factor ** 2 * 9, 1, padding=0))
def forward(self, net, inp, corr, flow):
motion_features = self.encoder(flow, corr)
inp = torch.cat([inp, motion_features], dim=1)
net = self.gru(net, inp)
delta_flow = self.flow_head(net)
if self.mask is not None:
mask = self.mask(net)
else:
mask = None
return net, mask, delta_flow
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