import torch import torch.nn as nn import torch.nn.functional as F # VariationalHidDropout from https://github.com/locuslab/trellisnet/tree/master/TrellisNet class VariationalHidDropout(nn.Module): def __init__(self, dropout=0.0): """ Hidden-to-hidden (VD-based) dropout that applies the same mask at every time step and every layer of TrellisNet :param dropout: The dropout rate (0 means no dropout is applied) """ super(VariationalHidDropout, self).__init__() self.dropout = dropout self.mask = None def reset_mask(self, x): dropout = self.dropout # Dimension (N, C, L) n, c, h, w = x.shape m = x.data.new(n, c, 1, 1).bernoulli_(1 - dropout) with torch.no_grad(): mask = m / (1 - dropout) self.mask = mask return mask def forward(self, x): if not self.training or self.dropout == 0: return x assert self.mask is not None, "You need to reset mask before using VariationalHidDropout" return self.mask * x class FlowHead(nn.Module): def __init__(self, input_dim=128, hidden_dim=256): super(FlowHead, self).__init__() self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1) self.relu = nn.ReLU(inplace=True) def forward(self, x): return self.conv2(self.relu(self.conv1(x))) class ConvGRU(nn.Module): def __init__(self, hidden_dim=128, input_dim=192+128): super(ConvGRU, self).__init__() self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) def forward(self, h, x): hx = torch.cat([h, x], dim=1) z = torch.sigmoid(self.convz(hx)) r = torch.sigmoid(self.convr(hx)) q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) h = (1-z) * h + z * q return h class SepConvGRU(nn.Module): def __init__(self, hidden_dim=128, input_dim=192+128): super(SepConvGRU, self).__init__() self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) 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 SmallMotionEncoder(nn.Module): def __init__(self, args): super(SmallMotionEncoder, self).__init__() cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2 self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0) self.convf1 = nn.Conv2d(2, 64, 7, padding=3) self.convf2 = nn.Conv2d(64, 32, 3, padding=1) self.conv = nn.Conv2d(128, 80, 3, padding=1) def forward(self, flow, corr): cor = F.relu(self.convc1(corr)) 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 BasicMotionEncoder(nn.Module): def __init__(self, args): super(BasicMotionEncoder, self).__init__() cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2 self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0) self.convc2 = nn.Conv2d(256, 192, 3, padding=1) self.convf1 = nn.Conv2d(2, 128, 7, padding=3) self.convf2 = nn.Conv2d(128, 64, 3, padding=1) self.conv = nn.Conv2d(64+192, 128-2, 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 SmallUpdateBlock(nn.Module): def __init__(self, args, hidden_dim=96): super(SmallUpdateBlock, self).__init__() self.encoder = SmallMotionEncoder(args) self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64) self.flow_head = FlowHead(hidden_dim, hidden_dim=128) 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) return net, delta_flow class BasicUpdateBlock(nn.Module): def __init__(self, args, hidden_dim=128, input_dim=128): super(BasicUpdateBlock, self).__init__() self.encoder = BasicMotionEncoder(args) self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim) self.flow_head = FlowHead(hidden_dim, hidden_dim=256) self.drop_inp = VariationalHidDropout(dropout=args.dropout) self.drop_net = VariationalHidDropout(dropout=args.dropout) def reset_mask(self, net, inp): self.drop_inp.reset_mask(inp) self.drop_net.reset_mask(net) def forward(self, net, inp, corr, flow): motion_features = self.encoder(flow, corr) inp = torch.cat([inp, motion_features], dim=1) if self.training: net = self.drop_net(net) inp = self.drop_inp(inp) net = self.gru(net, inp) delta_flow = self.flow_head(net) return net, delta_flow