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| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| class FlowHead(nn.Module): | |
| def __init__(self, input_dim=128, hidden_dim=256, | |
| out_dim=2, | |
| ): | |
| super(FlowHead, self).__init__() | |
| self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) | |
| self.conv2 = nn.Conv2d(hidden_dim, out_dim, 3, padding=1) | |
| self.relu = nn.ReLU(inplace=True) | |
| def forward(self, x): | |
| out = self.conv2(self.relu(self.conv1(x))) | |
| return out | |
| class SepConvGRU(nn.Module): | |
| def __init__(self, hidden_dim=128, input_dim=192 + 128, | |
| kernel_size=5, | |
| ): | |
| padding = (kernel_size - 1) // 2 | |
| super(SepConvGRU, self).__init__() | |
| self.convz1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) | |
| self.convr1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) | |
| self.convq1 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (1, kernel_size), padding=(0, padding)) | |
| self.convz2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) | |
| self.convr2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 0)) | |
| self.convq2 = nn.Conv2d(hidden_dim + input_dim, hidden_dim, (kernel_size, 1), padding=(padding, 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 BasicMotionEncoder(nn.Module): | |
| def __init__(self, corr_channels=324, | |
| flow_channels=2, | |
| ): | |
| super(BasicMotionEncoder, self).__init__() | |
| self.convc1 = nn.Conv2d(corr_channels, 256, 1, padding=0) | |
| self.convc2 = nn.Conv2d(256, 192, 3, padding=1) | |
| self.convf1 = nn.Conv2d(flow_channels, 128, 7, padding=3) | |
| self.convf2 = nn.Conv2d(128, 64, 3, padding=1) | |
| self.conv = nn.Conv2d(64 + 192, 128 - flow_channels, 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 BasicUpdateBlock(nn.Module): | |
| def __init__(self, corr_channels=324, | |
| hidden_dim=128, | |
| context_dim=128, | |
| downsample_factor=8, | |
| flow_dim=2, | |
| bilinear_up=False, | |
| ): | |
| super(BasicUpdateBlock, self).__init__() | |
| self.encoder = BasicMotionEncoder(corr_channels=corr_channels, | |
| flow_channels=flow_dim, | |
| ) | |
| self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=context_dim + hidden_dim) | |
| self.flow_head = FlowHead(hidden_dim, hidden_dim=256, | |
| out_dim=flow_dim, | |
| ) | |
| if bilinear_up: | |
| self.mask = None | |
| else: | |
| self.mask = nn.Sequential( | |
| nn.Conv2d(hidden_dim, 256, 3, padding=1), | |
| nn.ReLU(inplace=True), | |
| nn.Conv2d(256, downsample_factor ** 2 * 9, 1, padding=0)) | |
| 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) | |
| if self.mask is not None: | |
| mask = self.mask(net) | |
| else: | |
| mask = None | |
| return net, mask, delta_flow | |