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'''
This code is partially borrowed from RAFT (https://github.com/princeton-vl/RAFT).
'''
import torch
import torch.nn as nn
import torch.nn.functional as F
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 = corr_levels * (2 * radius + 1) **2
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.corr_pyramid = []
self.corr_pyramid_T = []
corr = BidirCorrBlock.corr(fmap1, fmap2)
batch, h1, w1, dim, h2, w2 = corr.shape
corr_T = corr.clone().permute(0, 4, 5, 3, 1, 2)
corr = corr.reshape(batch*h1*w1, dim, h2, w2)
corr_T = corr_T.reshape(batch*h2*w2, dim, h1, w1)
self.corr_pyramid.append(corr)
self.corr_pyramid_T.append(corr_T)
for _ in range(self.num_levels-1):
corr = F.avg_pool2d(corr, 2, stride=2)
corr_T = F.avg_pool2d(corr_T, 2, stride=2)
self.corr_pyramid.append(corr)
self.corr_pyramid_T.append(corr_T)
def __call__(self, coords0, coords1):
r = self.radius
coords0 = coords0.permute(0, 2, 3, 1)
coords1 = coords1.permute(0, 2, 3, 1)
assert coords0.shape == coords1.shape, f"coords0 shape: [{coords0.shape}] is not equal to [{coords1.shape}]"
batch, h1, w1, _ = coords0.shape
out_pyramid = []
out_pyramid_T = []
for i in range(self.num_levels):
corr = self.corr_pyramid[i]
corr_T = self.corr_pyramid_T[i]
dx = torch.linspace(-r, r, 2*r+1, device=coords0.device)
dy = torch.linspace(-r, r, 2*r+1, device=coords0.device)
delta = torch.stack(torch.meshgrid(dy, dx, indexing='ij'), axis=-1)
delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2)
centroid_lvl_0 = coords0.reshape(batch*h1*w1, 1, 1, 2) / 2**i
centroid_lvl_1 = coords1.reshape(batch*h1*w1, 1, 1, 2) / 2**i
coords_lvl_0 = centroid_lvl_0 + delta_lvl
coords_lvl_1 = centroid_lvl_1 + delta_lvl
corr = bilinear_sampler(corr, coords_lvl_0)
corr_T = bilinear_sampler(corr_T, coords_lvl_1)
corr = corr.view(batch, h1, w1, -1)
corr_T = corr_T.view(batch, h1, w1, -1)
out_pyramid.append(corr)
out_pyramid_T.append(corr_T)
out = torch.cat(out_pyramid, dim=-1)
out_T = torch.cat(out_pyramid_T, dim=-1)
return out.permute(0, 3, 1, 2).contiguous().float(), out_T.permute(0, 3, 1, 2).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())
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