File size: 7,050 Bytes
8d015d4 |
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 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from .BilateralCorrelation_NN import bilateralcorrelation_nn as bicorr_nn
def resize(x, scale_factor):
return F.interpolate(x, scale_factor=scale_factor, mode="bilinear", align_corners=False)
def bilinear_sampler(img, coords, mask=False):
""" Wrapper for grid_sample, uses pixel coordinates """
H, W = img.shape[-2:]
xgrid, ygrid = coords.split([1,1], dim=-1)
xgrid = 2*xgrid/(W-1) - 1
ygrid = 2*ygrid/(H-1) - 1
grid = torch.cat([xgrid, ygrid], dim=-1)
img = F.grid_sample(img, grid, align_corners=True)
if mask:
mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1)
return img, mask.float()
return img
def coords_grid(batch, ht, wd, device):
coords = torch.meshgrid(torch.arange(ht, device=device),
torch.arange(wd, device=device),
indexing='ij')
coords = torch.stack(coords[::-1], dim=0).float()
return coords[None].repeat(batch, 1, 1, 1)
class SmallUpdateBlock(nn.Module):
def __init__(self, cdim, hidden_dim, flow_dim, corr_dim, fc_dim,
corr_levels=4, radius=3, scale_factor=None):
super(SmallUpdateBlock, self).__init__()
cor_planes = corr_levels * (2 * radius + 1) **2
self.scale_factor = scale_factor
self.convc1 = nn.Conv2d(2 * cor_planes, corr_dim, 1, padding=0)
self.convf1 = nn.Conv2d(4, flow_dim*2, 7, padding=3)
self.convf2 = nn.Conv2d(flow_dim*2, flow_dim, 3, padding=1)
self.conv = nn.Conv2d(corr_dim+flow_dim, fc_dim, 3, padding=1)
self.gru = nn.Sequential(
nn.Conv2d(fc_dim+4+cdim, hidden_dim, 3, padding=1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
)
self.feat_head = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(hidden_dim, cdim, 3, padding=1),
)
self.flow_head = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(hidden_dim, 4, 3, padding=1),
)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def forward(self, net, flow, corr):
net = resize(net, 1 / self.scale_factor
) if self.scale_factor is not None else net
cor = self.lrelu(self.convc1(corr))
flo = self.lrelu(self.convf1(flow))
flo = self.lrelu(self.convf2(flo))
cor_flo = torch.cat([cor, flo], dim=1)
inp = self.lrelu(self.conv(cor_flo))
inp = torch.cat([inp, flow, net], dim=1)
out = self.gru(inp)
delta_net = self.feat_head(out)
delta_flow = self.flow_head(out)
if self.scale_factor is not None:
delta_net = resize(delta_net, scale_factor=self.scale_factor)
delta_flow = self.scale_factor * resize(delta_flow, scale_factor=self.scale_factor)
return delta_net, delta_flow
class BasicUpdateBlock(nn.Module):
def __init__(self, cdim, hidden_dim, flow_dim, corr_dim, corr_dim2,
fc_dim, corr_levels=4, radius=3, scale_factor=None, out_num=1):
super(BasicUpdateBlock, self).__init__()
cor_planes = (2 * radius + 1) ** 2 * corr_levels
self.scale_factor = scale_factor
self.convc1 = nn.Conv2d(2 * cor_planes, corr_dim, 1, padding=0)
self.convc2 = nn.Conv2d(corr_dim, corr_dim2, 3, padding=1)
self.convf1 = nn.Conv2d(4, flow_dim*2, 7, padding=3)
self.convf2 = nn.Conv2d(flow_dim*2, flow_dim, 3, padding=1)
self.conv = nn.Conv2d(flow_dim+corr_dim2, fc_dim, 3, padding=1)
self.gru = nn.Sequential(
nn.Conv2d(fc_dim+4+cdim, hidden_dim, 3, padding=1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
)
self.feat_head = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(hidden_dim, cdim, 3, padding=1),
)
self.flow_head = nn.Sequential(
nn.Conv2d(hidden_dim, hidden_dim, 3, padding=1),
nn.LeakyReLU(negative_slope=0.1, inplace=True),
nn.Conv2d(hidden_dim, 4*out_num, 3, padding=1),
)
self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)
def forward(self, net, flow, corr):
net = resize(net, 1 / self.scale_factor
) if self.scale_factor is not None else net
cor = self.lrelu(self.convc1(corr))
cor = self.lrelu(self.convc2(cor))
flo = self.lrelu(self.convf1(flow))
flo = self.lrelu(self.convf2(flo))
cor_flo = torch.cat([cor, flo], dim=1)
inp = self.lrelu(self.conv(cor_flo))
inp = torch.cat([inp, flow, net], dim=1)
out = self.gru(inp)
delta_net = self.feat_head(out)
delta_flow = self.flow_head(out)
if self.scale_factor is not None:
delta_net = resize(delta_net, scale_factor=self.scale_factor)
delta_flow = self.scale_factor * resize(delta_flow, scale_factor=self.scale_factor)
return delta_net, delta_flow
class BidirCorrBlock:
def __init__(self, fmap1, fmap2, num_levels=4, radius=4):
self.num_levels = num_levels
self.radius = radius
self.fmap1_pyramid = [fmap1]
self.fmap2_pyramid = [fmap2]
for _ in range(self.num_levels - 1):
fmap1 = F.avg_pool2d(fmap1, 2, stride=2)
fmap2 = F.avg_pool2d(fmap2, 2, stride=2)
self.fmap1_pyramid.append(fmap1)
self.fmap2_pyramid.append(fmap2)
def __call__(self, flowt0, flowt1, time_step):
r = self.radius
out_pyramid = []
out_pyramid_T = []
flowt0 = flowt0.contiguous()
flowt1 = flowt1.contiguous()
for i in range(self.num_levels):
fmap1 = self.fmap1_pyramid[i]
fmap2 = self.fmap2_pyramid[i]
corr0 = bicorr_nn.apply(fmap2, fmap1, flowt0, time_step, self.radius)
corr1 = bicorr_nn.apply(fmap1, fmap2, flowt1, time_step, self.radius)
out_pyramid.append(corr0)
out_pyramid_T.append(corr1)
out = torch.cat(out_pyramid, dim=1)
out_T = torch.cat(out_pyramid_T, dim=1)
return out.contiguous().float(), out_T.contiguous().float()
@staticmethod
def corr(fmap1, fmap2):
batch, dim, ht, wd = fmap1.shape
fmap1 = fmap1.view(batch, dim, ht*wd)
fmap2 = fmap2.view(batch, dim, ht*wd)
corr = torch.matmul(fmap1.transpose(1,2), fmap2)
corr = corr.view(batch, ht, wd, 1, ht, wd)
return corr / torch.sqrt(torch.tensor(dim).float()) |