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import torch |
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import torch.nn as nn |
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import torch.nn.init as init |
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import torch.nn.functional as F |
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from opt_einsum import contract |
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class FlowHead(nn.Module): |
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def __init__(self, input_dim=128, hidden_dim=256, output_dim=2): |
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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, output_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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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, input_dim, kernel_size=3): |
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super(ConvGRU, self).__init__() |
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self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2) |
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self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2) |
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self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2) |
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self._initialize_weights() |
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def forward(self, h, cz, cr, cq, *x_list): |
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x = torch.cat(x_list, dim=1) |
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hx = torch.cat([h, x], dim=1) |
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z = torch.sigmoid(self.convz(hx) + cz) |
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r = torch.sigmoid(self.convr(hx) + cr) |
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q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)) + cq) |
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h = (1-z) * h + z * q |
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return h |
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def _initialize_weights(self): |
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for m in self.modules(): |
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if isinstance(m, nn.Conv2d): |
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init.kaiming_normal_(m.weight) |
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if m.bias is not None: |
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m.bias.data.zero_() |
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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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x = torch.cat(x, dim=1) |
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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, args): |
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super(BasicMotionEncoder, self).__init__() |
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self.args = args |
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cor_planes = args.corr_levels * (2*args.corr_radius + 1) |
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self.convc1 = nn.Conv2d(cor_planes, 64, 1, padding=0) |
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self.convc2 = nn.Conv2d(64, 64, 3, padding=1) |
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self.convf1 = nn.Conv2d(2, 64, 7, padding=3) |
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self.convf2 = nn.Conv2d(64, 64, 3, padding=1) |
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self.conv = nn.Conv2d(64+64, 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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def pool2x(x): |
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return F.avg_pool2d(x, 3, stride=2, padding=1) |
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def pool4x(x): |
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return F.avg_pool2d(x, 5, stride=4, padding=1) |
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def interp(x, dest): |
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interp_args = {'mode': 'bilinear', 'align_corners': True} |
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return F.interpolate(x, dest.shape[2:], **interp_args) |
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class BasicMultiUpdateBlock(nn.Module): |
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def __init__(self, args, hidden_dims=[]): |
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super().__init__() |
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self.args = args |
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self.encoder = BasicMotionEncoder(args) |
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encoder_output_dim = 128 |
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self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1)) |
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self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2]) |
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self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1]) |
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self.flow_head = FlowHead(hidden_dims[2], hidden_dim=256, output_dim=2) |
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factor = 2**self.args.n_downsample |
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self.mask = nn.Sequential( |
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nn.Conv2d(hidden_dims[2], 256, 3, padding=1), |
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nn.ReLU(inplace=True), |
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nn.Conv2d(256, (factor**2)*9, 1, padding=0)) |
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def forward(self, net, inp, corr=None, flow=None, iter08=True, iter16=True, iter32=True, update=True): |
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if iter32: |
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net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1])) |
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if iter16: |
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if self.args.n_gru_layers > 2: |
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1])) |
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else: |
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0])) |
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if iter08: |
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motion_features = self.encoder(flow, corr) |
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if self.args.n_gru_layers > 1: |
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net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0])) |
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else: |
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net[0] = self.gru08(net[0], *(inp[0]), motion_features) |
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if not update: |
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return net |
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delta_flow = self.flow_head(net[0]) |
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mask = .25 * self.mask(net[0]) |
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return net, mask, delta_flow |
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class ManifoldBasicMultiUpdateBlock(nn.Module): |
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def __init__(self, args, hidden_dims=[]): |
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super().__init__() |
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self.args = args |
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self.encoder = BasicMotionEncoder(args) |
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encoder_output_dim = 128 |
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output_dim = 2 |
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self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1)) |
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self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2]) |
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self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1]) |
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self.flow_head = FlowHead(hidden_dims[2], hidden_dim=256, output_dim=output_dim) |
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factor = 2**self.args.n_downsample |
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self.mask = nn.Sequential( |
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nn.Conv2d(hidden_dims[2], 256, 3, padding=1), |
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nn.ReLU(inplace=True), |
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nn.Conv2d(256, (factor**2)*9, 1, padding=0)) |
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self.mask2 = nn.Sequential( |
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nn.Conv2d(hidden_dims[2], 256, 3, padding=1), |
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nn.ReLU(inplace=True), |
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nn.Conv2d(256, (factor**2)*9, 1, padding=0)) |
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def forward(self, net, inp, corr=None, flow=None, iter08=True, iter16=True, iter32=True, update=True): |
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if iter32: |
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net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1])) |
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if iter16: |
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if self.args.n_gru_layers > 2: |
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1])) |
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else: |
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net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0])) |
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if iter08: |
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motion_features = self.encoder(flow, corr) |
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if self.args.n_gru_layers > 1: |
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net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0])) |
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else: |
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net[0] = self.gru08(net[0], *(inp[0]), motion_features) |
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if not update: |
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return net |
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delta_flow = self.flow_head(net[0]) |
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mask = .25 * self.mask(net[0]) |
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mask_disp = None |
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mask_disp = .25 * self.mask2(net[0]) |
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return net, mask, delta_flow, mask_disp |
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