143 lines
5.6 KiB
Python
143 lines
5.6 KiB
Python
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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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 DispHead(nn.Module):
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def __init__(self, input_dim=128, hidden_dim=256, output_dim=1):
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super(DispHead, 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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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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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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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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# 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, 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) * (8+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.convd1 = nn.Conv2d(1, 64, 7, padding=3)
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self.convd2 = nn.Conv2d(64, 64, 3, padding=1)
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self.conv = nn.Conv2d(64+64, 128-1, 3, padding=1)
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def forward(self, disp, 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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disp_ = F.relu(self.convd1(disp))
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disp_ = F.relu(self.convd2(disp_))
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cor_disp = torch.cat([cor, disp_], dim=1)
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out = F.relu(self.conv(cor_disp))
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return torch.cat([out, disp], 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.gru04 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1))
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self.gru08 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2])
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self.gru16 = ConvGRU(hidden_dims[0], hidden_dims[1])
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self.disp_head = DispHead(hidden_dims[2], hidden_dim=256, output_dim=1)
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factor = 2**self.args.n_downsample
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self.mask_feat_4 = nn.Sequential(
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nn.Conv2d(hidden_dims[2], 32, 3, padding=1),
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nn.ReLU(inplace=True))
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def forward(self, net, inp, corr=None, disp=None, iter04=True, iter08=True, iter16=True, update=True):
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if iter16:
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net[2] = self.gru16(net[2], *(inp[2]), pool2x(net[1]))
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if iter08:
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if self.args.n_gru_layers > 2:
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net[1] = self.gru08(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1]))
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else:
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net[1] = self.gru08(net[1], *(inp[1]), pool2x(net[0]))
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if iter04:
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motion_features = self.encoder(disp, corr)
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if self.args.n_gru_layers > 1:
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net[0] = self.gru04(net[0], *(inp[0]), motion_features, interp(net[1], net[0]))
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else:
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net[0] = self.gru04(net[0], *(inp[0]), motion_features)
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if not update:
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return net
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delta_disp = self.disp_head(net[0])
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mask_feat_4 = self.mask_feat_4(net[0])
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return net, mask_feat_4, delta_disp
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