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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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class FlowHead(nn.Module): |
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def __init__(self, input_dim=128, hidden_dim=256, |
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out_dim=2, |
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): |
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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, out_dim, 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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out = self.conv2(self.relu(self.conv1(x))) |
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return out |
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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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kernel_size=5, |
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): |
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padding = (kernel_size - 1) // 2 |
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super(SepConvGRU, self).__init__() |
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self.convz1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) |
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self.convr1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) |
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self.convq1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) |
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self.convz2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) |
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self.convr2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) |
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self.convq2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) |
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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.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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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, corr_channels=324, |
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flow_channels=2, |
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): |
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super(BasicMotionEncoder, self).__init__() |
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self.convc1 = nn.Conv2d(corr_channels, 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(flow_channels, 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 - flow_channels, 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, corr_channels=324, |
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hidden_dim=128, |
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context_dim=128, |
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downsample_factor=8, |
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flow_dim=2, |
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bilinear_up=False, |
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): |
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super(BasicUpdateBlock, self).__init__() |
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self.encoder = BasicMotionEncoder(corr_channels=corr_channels, |
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flow_channels=flow_dim, |
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) |
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self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=context_dim + hidden_dim) |
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self.flow_head = FlowHead(hidden_dim, hidden_dim=256, |
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out_dim=flow_dim, |
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) |
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if bilinear_up: |
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self.mask = None |
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else: |
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self.mask = nn.Sequential( |
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nn.Conv2d(hidden_dim, 256, 3, padding=1), |
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nn.ReLU(inplace=True), |
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nn.Conv2d(256, downsample_factor ** 2 * 9, 1, padding=0)) |
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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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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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if self.mask is not None: |
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mask = self.mask(net) |
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else: |
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mask = None |
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return net, mask, delta_flow |
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