| 31 | return h |
| 32 | |
| 33 | class SepConvGRU(nn.Module): |
| 34 | def __init__(self, hidden_dim=128, input_dim=192+128): |
| 35 | super(SepConvGRU, self).__init__() |
| 36 | self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) |
| 37 | self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) |
| 38 | self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) |
| 39 | |
| 40 | self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) |
| 41 | self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) |
| 42 | self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) |
| 43 | |
| 44 | |
| 45 | def forward(self, h, x): |
| 46 | # horizontal |
| 47 | hx = torch.cat([h, x], dim=1) |
| 48 | z = torch.sigmoid(self.convz1(hx)) |
| 49 | r = torch.sigmoid(self.convr1(hx)) |
| 50 | q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) |
| 51 | h = (1-z) * h + z * q |
| 52 | |
| 53 | # vertical |
| 54 | hx = torch.cat([h, x], dim=1) |
| 55 | z = torch.sigmoid(self.convz2(hx)) |
| 56 | r = torch.sigmoid(self.convr2(hx)) |
| 57 | q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) |
| 58 | h = (1-z) * h + z * q |
| 59 | |
| 60 | return h |
| 61 | |
| 62 | class SmallMotionEncoder(nn.Module): |
| 63 | def __init__(self, args): |