| import torch
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| import torch.nn as nn
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| import torch.nn.functional as F
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|
|
|
|
| class FlowHead(nn.Module):
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| def __init__(self, input_dim=128, hidden_dim=256):
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| super(FlowHead, self).__init__()
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| self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
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| self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1)
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| self.relu = nn.ReLU(inplace=True)
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|
|
| def forward(self, x):
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| return self.conv2(self.relu(self.conv1(x)))
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|
|
| class ConvGRU(nn.Module):
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| def __init__(self, hidden_dim=128, input_dim=192+128):
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| super(ConvGRU, self).__init__()
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| self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
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| self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
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| self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1)
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|
|
| def forward(self, h, x):
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| hx = torch.cat([h, x], dim=1)
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|
|
| z = torch.sigmoid(self.convz(hx))
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| r = torch.sigmoid(self.convr(hx))
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| q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)))
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|
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| h = (1-z) * h + z * q
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| return h
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|
|
| class SepConvGRU(nn.Module):
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| def __init__(self, hidden_dim=128, input_dim=192+128):
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| super(SepConvGRU, self).__init__()
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| self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
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| self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
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| self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2))
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|
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| self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
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| self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
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| self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0))
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|
|
|
|
| def forward(self, h, x):
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|
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| hx = torch.cat([h, x], dim=1)
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| z = torch.sigmoid(self.convz1(hx))
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| r = torch.sigmoid(self.convr1(hx))
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| q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1)))
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| h = (1-z) * h + z * q
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|
|
|
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| hx = torch.cat([h, x], dim=1)
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| z = torch.sigmoid(self.convz2(hx))
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| r = torch.sigmoid(self.convr2(hx))
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| q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1)))
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| h = (1-z) * h + z * q
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|
|
| return h
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|
|
| class SmallMotionEncoder(nn.Module):
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| def __init__(self, args):
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| super(SmallMotionEncoder, self).__init__()
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| cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
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| self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0)
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| self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
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| self.convf2 = nn.Conv2d(64, 32, 3, padding=1)
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| self.conv = nn.Conv2d(128, 80, 3, padding=1)
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|
|
| def forward(self, flow, corr):
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| cor = F.relu(self.convc1(corr))
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| flo = F.relu(self.convf1(flow))
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| flo = F.relu(self.convf2(flo))
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| cor_flo = torch.cat([cor, flo], dim=1)
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| out = F.relu(self.conv(cor_flo))
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| return torch.cat([out, flow], dim=1)
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|
|
| class BasicMotionEncoder(nn.Module):
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| def __init__(self, args):
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| super(BasicMotionEncoder, self).__init__()
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| cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2
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| self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0)
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| self.convc2 = nn.Conv2d(256, 192, 3, padding=1)
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| self.convf1 = nn.Conv2d(2, 128, 7, padding=3)
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| self.convf2 = nn.Conv2d(128, 64, 3, padding=1)
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| self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1)
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|
|
| def forward(self, flow, corr):
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| cor = F.relu(self.convc1(corr))
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| cor = F.relu(self.convc2(cor))
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| flo = F.relu(self.convf1(flow))
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| flo = F.relu(self.convf2(flo))
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|
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| cor_flo = torch.cat([cor, flo], dim=1)
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| out = F.relu(self.conv(cor_flo))
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| return torch.cat([out, flow], dim=1)
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|
|
| class SmallUpdateBlock(nn.Module):
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| def __init__(self, args, hidden_dim=96):
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| super(SmallUpdateBlock, self).__init__()
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| self.encoder = SmallMotionEncoder(args)
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| self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64)
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| self.flow_head = FlowHead(hidden_dim, hidden_dim=128)
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|
|
| def forward(self, net, inp, corr, flow):
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| motion_features = self.encoder(flow, corr)
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| inp = torch.cat([inp, motion_features], dim=1)
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| net = self.gru(net, inp)
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| delta_flow = self.flow_head(net)
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|
|
| return net, None, delta_flow
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|
|
| class BasicUpdateBlock(nn.Module):
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| def __init__(self, args, hidden_dim=128, input_dim=128):
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| super(BasicUpdateBlock, self).__init__()
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| self.args = args
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| self.encoder = BasicMotionEncoder(args)
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| self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim)
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| self.flow_head = FlowHead(hidden_dim, hidden_dim=256)
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|
|
| self.mask = nn.Sequential(
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| nn.Conv2d(128, 256, 3, padding=1),
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| nn.ReLU(inplace=True),
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| nn.Conv2d(256, 64*9, 1, padding=0))
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|
|
| def forward(self, net, inp, corr, flow, upsample=True):
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| motion_features = self.encoder(flow, corr)
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| inp = torch.cat([inp, motion_features], dim=1)
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|
|
| net = self.gru(net, inp)
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| delta_flow = self.flow_head(net)
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|
|
|
|
| mask = .25 * self.mask(net)
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| return net, mask, delta_flow
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