182 lines
6.5 KiB
Python
182 lines
6.5 KiB
Python
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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# VariationalHidDropout from https://github.com/locuslab/trellisnet/tree/master/TrellisNet
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class VariationalHidDropout(nn.Module):
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def __init__(self, dropout=0.0):
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"""
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Hidden-to-hidden (VD-based) dropout that applies the same mask at every time step and every layer of TrellisNet
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:param dropout: The dropout rate (0 means no dropout is applied)
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"""
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super(VariationalHidDropout, self).__init__()
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self.dropout = dropout
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self.mask = None
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def reset_mask(self, x):
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dropout = self.dropout
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# Dimension (N, C, L)
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n, c, h, w = x.shape
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m = x.data.new(n, c, 1, 1).bernoulli_(1 - dropout)
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with torch.no_grad():
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mask = m / (1 - dropout)
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self.mask = mask
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return mask
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def forward(self, x):
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if not self.training or self.dropout == 0:
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return x
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assert self.mask is not None, "You need to reset mask before using VariationalHidDropout"
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return self.mask * x
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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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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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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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# horizontal
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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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# vertical
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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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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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self.drop_inp = VariationalHidDropout(dropout=args.dropout)
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self.drop_net = VariationalHidDropout(dropout=args.dropout)
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def reset_mask(self, net, inp):
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self.drop_inp.reset_mask(inp)
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self.drop_net.reset_mask(net)
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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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if self.training:
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net = self.drop_net(net)
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inp = self.drop_inp(inp)
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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, 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.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.drop_inp = VariationalHidDropout(dropout=args.dropout)
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self.drop_net = VariationalHidDropout(dropout=args.dropout)
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def reset_mask(self, net, inp):
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self.drop_inp.reset_mask(inp)
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self.drop_net.reset_mask(net)
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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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if self.training:
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net = self.drop_net(net)
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inp = self.drop_inp(inp)
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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, delta_flow
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