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92 lines
3.4 KiB
92 lines
3.4 KiB
3 years ago
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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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#Ref: https://github.com/princeton-vl/RAFT/blob/master/core/update.py
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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 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 BasicMotionEncoder(nn.Module):
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def __init__(self, cor_planes):
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super(BasicMotionEncoder, self).__init__()
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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 BasicUpdateBlock(nn.Module):
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def __init__(self, hidden_dim, cor_planes, mask_size=8):
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super(BasicUpdateBlock, self).__init__()
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self.encoder = BasicMotionEncoder(cor_planes)
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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, mask_size**2 *9, 1, padding=0))
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def forward(self, net, inp, corr, flow, upsample=True):
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# print(inp.shape, corr.shape, flow.shape)
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motion_features = self.encoder(flow, corr)
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# print(motion_features.shape, inp.shape)
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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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# scale mask to balence gradients
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mask = .25 * self.mask(net)
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return net, mask, delta_flow
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