| 112 | return net, None, delta_flow |
| 113 | |
| 114 | class BasicUpdateBlock(nn.Module): |
| 115 | def __init__(self, args, hidden_dim=128, input_dim=128): |
| 116 | super(BasicUpdateBlock, self).__init__() |
| 117 | self.args = args |
| 118 | self.encoder = BasicMotionEncoder(args) |
| 119 | self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim) |
| 120 | self.flow_head = FlowHead(hidden_dim, hidden_dim=256) |
| 121 | |
| 122 | self.mask = nn.Sequential( |
| 123 | nn.Conv2d(128, 256, 3, padding=1), |
| 124 | nn.ReLU(inplace=True), |
| 125 | nn.Conv2d(256, 64*9, 1, padding=0)) |
| 126 | |
| 127 | def forward(self, net, inp, corr, flow, upsample=True): |
| 128 | motion_features = self.encoder(flow, corr) |
| 129 | inp = torch.cat([inp, motion_features], dim=1) |
| 130 | |
| 131 | net = self.gru(net, inp) |
| 132 | delta_flow = self.flow_head(net) |
| 133 | |
| 134 | # scale mask to balence gradients |
| 135 | mask = .25 * self.mask(net) |
| 136 | return net, mask, delta_flow |
| 137 | |
| 138 | |
| 139 | |