| | import torch |
| | import torch.nn as nn |
| | import torch.nn.functional as F |
| | from nnutils import make_conv_2d, make_upscale_2d, make_downscale_2d, ResBlock2d, Identity |
| |
|
| | class FlowHead(nn.Module): |
| | def __init__(self, input_dim=128, hidden_dim=256): |
| | super(FlowHead, self).__init__() |
| | self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) |
| | self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1) |
| | self.relu = nn.ReLU(inplace=True) |
| |
|
| | def forward(self, x): |
| | x = self.relu(self.conv1(x)) |
| | return self.conv2(x), x |
| |
|
| | class ConvGRU(nn.Module): |
| | def __init__(self, hidden_dim=128, input_dim=192+128): |
| | super(ConvGRU, self).__init__() |
| | self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) |
| | self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) |
| | self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) |
| |
|
| | def forward(self, h, x): |
| | hx = torch.cat([h, x], dim=1) |
| |
|
| | z = torch.sigmoid(self.convz(hx)) |
| | r = torch.sigmoid(self.convr(hx)) |
| | q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) |
| |
|
| | h = (1-z) * h + z * q |
| | return h |
| |
|
| | class SepConvGRU(nn.Module): |
| | def __init__(self, hidden_dim=128, input_dim=192+128): |
| | super(SepConvGRU, self).__init__() |
| | self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) |
| | self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) |
| | self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) |
| |
|
| | self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) |
| | self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) |
| | self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) |
| |
|
| |
|
| | def forward(self, h, x): |
| | |
| | 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 |
| |
|
| | |
| | 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 SmallMotionEncoder(nn.Module): |
| | def __init__(self, args): |
| | super(SmallMotionEncoder, self).__init__() |
| | cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2 |
| | self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0) |
| | self.convf1 = nn.Conv2d(2, 64, 7, padding=3) |
| | self.convf2 = nn.Conv2d(64, 32, 3, padding=1) |
| | self.conv = nn.Conv2d(128, 80, 3, padding=1) |
| |
|
| | def forward(self, flow, corr): |
| | cor = F.relu(self.convc1(corr)) |
| | 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 BasicMotionEncoder(nn.Module): |
| | def __init__(self, args): |
| | super(BasicMotionEncoder, self).__init__() |
| | cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2 |
| | self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0) |
| | self.convc2 = nn.Conv2d(256, 192, 3, padding=1) |
| | self.convf1 = nn.Conv2d(2, 128, 7, padding=3) |
| | self.convf2 = nn.Conv2d(128, 64, 3, padding=1) |
| | self.conv = nn.Conv2d(64+192, 128-2, 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 SmallUpdateBlock(nn.Module): |
| | def __init__(self, args, hidden_dim=96): |
| | super(SmallUpdateBlock, self).__init__() |
| | self.encoder = SmallMotionEncoder(args) |
| | self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64) |
| | self.flow_head = FlowHead(hidden_dim, hidden_dim=128) |
| |
|
| | 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, feature = self.flow_head(net) |
| |
|
| | return net, None, delta_flow, feature |
| |
|
| | class BasicUpdateBlock(nn.Module): |
| | def __init__(self, args, hidden_dim=128, input_dim=128): |
| | super(BasicUpdateBlock, self).__init__() |
| | self.args = args |
| | self.encoder = BasicMotionEncoder(args) |
| | self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim) |
| | self.flow_head = FlowHead(hidden_dim, hidden_dim=256) |
| |
|
| | self.mask = nn.Sequential( |
| | nn.Conv2d(128, 256, 3, padding=1), |
| | nn.ReLU(inplace=True), |
| | nn.Conv2d(256, 64*9, 1, padding=0)) |
| |
|
| | def forward(self, net, inp, corr, flow, upsample=True): |
| | motion_features = self.encoder(flow, corr) |
| | inp = torch.cat([inp, motion_features], dim=1) |
| |
|
| | net = self.gru(net, inp) |
| | delta_flow, feature = self.flow_head(net) |
| |
|
| | |
| | mask = .25 * self.mask(net) |
| | return net, mask, delta_flow, feature |
| |
|
| | class BasicWeightsNet(nn.Module): |
| | def __init__(self, opt): |
| | super(BasicUpdateBlock, self).__init__() |
| | if opt.small: |
| | in_dim = 128 |
| | else: |
| | in_dim = 256 |
| | fn_0 = 16 |
| | self.input_fn = fn_0 + 2 |
| | fn_1 = 16 |
| | self.conv1 = torch.nn.Conv2d(in_channels=in_dim, out_channels=fn_0, kernel_size=3, stride=1, padding=1) |
| | if opt.use_batch_norm: |
| | custom_batch_norm = torch.nn.BatchNorm2d |
| | else: |
| | custom_batch_norm = Identity |
| | self.model = nn.Sequential( |
| | make_conv_2d(self.input_fn, fn_1, n_blocks=1, normalization=custom_batch_norm), |
| | ResBlock2d(fn_1, normalization=custom_batch_norm), |
| | ResBlock2d(fn_1, normalization=custom_batch_norm), |
| | ResBlock2d(fn_1, normalization=custom_batch_norm), |
| | nn.Conv2d(fn_1, 1, kernel_size=3, padding=1), |
| | torch.nn.Sigmoid() |
| | ) |
| |
|
| | def forward(self, flow, feature): |
| | features = self.conv1(features) |
| | x = torch.cat([features, flow], 1) |
| | return self.model(x) |
| |
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