File size: 7,778 Bytes
f06f310 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
import torch
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
from opt_einsum import contract
class FlowHead(nn.Module):
def __init__(self, input_dim=128, hidden_dim=256, output_dim=2):
super(FlowHead, self).__init__()
self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1)
self.conv2 = nn.Conv2d(hidden_dim, output_dim, 3, padding=1)
self.relu = nn.ReLU(inplace=True)
def forward(self, x):
return self.conv2(self.relu(self.conv1(x)))
class ConvGRU(nn.Module):
def __init__(self, hidden_dim, input_dim, kernel_size=3):
super(ConvGRU, self).__init__()
self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2)
self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2)
self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, kernel_size, padding=kernel_size//2)
self._initialize_weights()
def forward(self, h, cz, cr, cq, *x_list):
x = torch.cat(x_list, dim=1)
hx = torch.cat([h, x], dim=1)
z = torch.sigmoid(self.convz(hx) + cz)
r = torch.sigmoid(self.convr(hx) + cr)
q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1)) + cq)
h = (1-z) * h + z * q
return h
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.zero_()
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):
# horizontal
x = torch.cat(x, dim=1)
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, args):
super(BasicMotionEncoder, self).__init__()
self.args = args
cor_planes = args.corr_levels * (2*args.corr_radius + 1)
self.convc1 = nn.Conv2d(cor_planes, 64, 1, padding=0)
self.convc2 = nn.Conv2d(64, 64, 3, padding=1)
self.convf1 = nn.Conv2d(2, 64, 7, padding=3)
self.convf2 = nn.Conv2d(64, 64, 3, padding=1)
self.conv = nn.Conv2d(64+64, 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)
def pool2x(x):
return F.avg_pool2d(x, 3, stride=2, padding=1)
def pool4x(x):
return F.avg_pool2d(x, 5, stride=4, padding=1)
def interp(x, dest):
interp_args = {'mode': 'bilinear', 'align_corners': True}
return F.interpolate(x, dest.shape[2:], **interp_args)
class BasicMultiUpdateBlock(nn.Module):
def __init__(self, args, hidden_dims=[]):
super().__init__()
self.args = args
self.encoder = BasicMotionEncoder(args)
encoder_output_dim = 128
self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1))
self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2])
self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1])
self.flow_head = FlowHead(hidden_dims[2], hidden_dim=256, output_dim=2)
factor = 2**self.args.n_downsample
self.mask = nn.Sequential(
nn.Conv2d(hidden_dims[2], 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, (factor**2)*9, 1, padding=0))
def forward(self, net, inp, corr=None, flow=None, iter08=True, iter16=True, iter32=True, update=True):
if iter32:
net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1]))
if iter16:
if self.args.n_gru_layers > 2:
net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1]))
else:
net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]))
if iter08:
motion_features = self.encoder(flow, corr)
if self.args.n_gru_layers > 1:
net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0]))
else:
net[0] = self.gru08(net[0], *(inp[0]), motion_features)
if not update:
return net
delta_flow = self.flow_head(net[0])
# scale mask to balence gradients
mask = .25 * self.mask(net[0])
return net, mask, delta_flow
class ManifoldBasicMultiUpdateBlock(nn.Module):
def __init__(self, args, hidden_dims=[]):
super().__init__()
self.args = args
self.encoder = BasicMotionEncoder(args)
encoder_output_dim = 128
output_dim = 2
self.gru08 = ConvGRU(hidden_dims[2], encoder_output_dim + hidden_dims[1] * (args.n_gru_layers > 1))
self.gru16 = ConvGRU(hidden_dims[1], hidden_dims[0] * (args.n_gru_layers == 3) + hidden_dims[2])
self.gru32 = ConvGRU(hidden_dims[0], hidden_dims[1])
self.flow_head = FlowHead(hidden_dims[2], hidden_dim=256, output_dim=output_dim)
factor = 2**self.args.n_downsample
self.mask = nn.Sequential(
nn.Conv2d(hidden_dims[2], 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, (factor**2)*9, 1, padding=0))
self.mask2 = nn.Sequential(
nn.Conv2d(hidden_dims[2], 256, 3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(256, (factor**2)*9, 1, padding=0))
def forward(self, net, inp, corr=None, flow=None, iter08=True, iter16=True, iter32=True, update=True):
if iter32:
net[2] = self.gru32(net[2], *(inp[2]), pool2x(net[1]))
if iter16:
if self.args.n_gru_layers > 2:
net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]), interp(net[2], net[1]))
else:
net[1] = self.gru16(net[1], *(inp[1]), pool2x(net[0]))
if iter08:
motion_features = self.encoder(flow, corr)
if self.args.n_gru_layers > 1:
net[0] = self.gru08(net[0], *(inp[0]), motion_features, interp(net[1], net[0]))
else:
net[0] = self.gru08(net[0], *(inp[0]), motion_features)
if not update:
return net
delta_flow = self.flow_head(net[0])
# scale mask to balence gradients
mask = .25 * self.mask(net[0])
# return net, mask, delta_flow
mask_disp = None
mask_disp = .25 * self.mask2(net[0])
return net, mask, delta_flow, mask_disp
|