File size: 11,803 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 |
import torch
import torch.nn.functional as F
from .geometry import coords_grid, generate_window_grid, normalize_coords
def global_correlation_softmax(feature0, feature1,
pred_bidir_flow=False,
):
# global correlation
b, c, h, w = feature0.shape
feature0 = feature0.view(b, c, -1).permute(0, 2, 1) # [B, H*W, C]
feature1 = feature1.view(b, c, -1) # [B, C, H*W]
correlation = torch.matmul(feature0, feature1).view(b, h, w, h, w) / (c ** 0.5) # [B, H, W, H, W]
# flow from softmax
init_grid = coords_grid(b, h, w).to(correlation.device) # [B, 2, H, W]
grid = init_grid.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2]
correlation = correlation.view(b, h * w, h * w) # [B, H*W, H*W]
if pred_bidir_flow:
correlation = torch.cat((correlation, correlation.permute(0, 2, 1)), dim=0) # [2*B, H*W, H*W]
init_grid = init_grid.repeat(2, 1, 1, 1) # [2*B, 2, H, W]
grid = grid.repeat(2, 1, 1) # [2*B, H*W, 2]
b = b * 2
prob = F.softmax(correlation, dim=-1) # [B, H*W, H*W]
correspondence = torch.matmul(prob, grid).view(b, h, w, 2).permute(0, 3, 1, 2) # [B, 2, H, W]
# when predicting bidirectional flow, flow is the concatenation of forward flow and backward flow
flow = correspondence - init_grid
return flow, prob
def local_correlation_softmax(feature0, feature1, local_radius,
padding_mode='zeros',
):
b, c, h, w = feature0.size()
coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W]
coords = coords_init.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2]
local_h = 2 * local_radius + 1
local_w = 2 * local_radius + 1
window_grid = generate_window_grid(-local_radius, local_radius,
-local_radius, local_radius,
local_h, local_w, device=feature0.device) # [2R+1, 2R+1, 2]
window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1)^2, 2]
sample_coords = coords.unsqueeze(-2) + window_grid # [B, H*W, (2R+1)^2, 2]
sample_coords_softmax = sample_coords
# exclude coords that are out of image space
valid_x = (sample_coords[:, :, :, 0] >= 0) & (sample_coords[:, :, :, 0] < w) # [B, H*W, (2R+1)^2]
valid_y = (sample_coords[:, :, :, 1] >= 0) & (sample_coords[:, :, :, 1] < h) # [B, H*W, (2R+1)^2]
valid = valid_x & valid_y # [B, H*W, (2R+1)^2], used to mask out invalid values when softmax
# normalize coordinates to [-1, 1]
sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1]
window_feature = F.grid_sample(feature1, sample_coords_norm,
padding_mode=padding_mode, align_corners=True
).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)^2]
feature0_view = feature0.permute(0, 2, 3, 1).view(b, h * w, 1, c) # [B, H*W, 1, C]
corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)^2]
# mask invalid locations
corr[~valid] = -1e9
prob = F.softmax(corr, -1) # [B, H*W, (2R+1)^2]
correspondence = torch.matmul(prob.unsqueeze(-2), sample_coords_softmax).squeeze(-2).view(
b, h, w, 2).permute(0, 3, 1, 2) # [B, 2, H, W]
flow = correspondence - coords_init
match_prob = prob
return flow, match_prob
def local_correlation_with_flow(feature0, feature1,
flow,
local_radius,
padding_mode='zeros',
dilation=1,
):
b, c, h, w = feature0.size()
coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W]
coords = coords_init.view(b, 2, -1).permute(0, 2, 1) # [B, H*W, 2]
local_h = 2 * local_radius + 1
local_w = 2 * local_radius + 1
window_grid = generate_window_grid(-local_radius, local_radius,
-local_radius, local_radius,
local_h, local_w, device=feature0.device) # [2R+1, 2R+1, 2]
window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1)^2, 2]
sample_coords = coords.unsqueeze(-2) + window_grid * dilation # [B, H*W, (2R+1)^2, 2]
# flow can be zero when using features after transformer
if not isinstance(flow, float):
sample_coords = sample_coords + flow.view(
b, 2, -1).permute(0, 2, 1).unsqueeze(-2) # [B, H*W, (2R+1)^2, 2]
else:
assert flow == 0.
# normalize coordinates to [-1, 1]
sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1]
window_feature = F.grid_sample(feature1, sample_coords_norm,
padding_mode=padding_mode, align_corners=True
).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)^2]
feature0_view = feature0.permute(0, 2, 3, 1).view(b, h * w, 1, c) # [B, H*W, 1, C]
corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)^2]
corr = corr.view(b, h, w, -1).permute(0, 3, 1, 2).contiguous() # [B, (2R+1)^2, H, W]
return corr
def global_correlation_softmax_stereo(feature0, feature1,
):
# global correlation on horizontal direction
b, c, h, w = feature0.shape
x_grid = torch.linspace(0, w - 1, w, device=feature0.device) # [W]
feature0 = feature0.permute(0, 2, 3, 1) # [B, H, W, C]
feature1 = feature1.permute(0, 2, 1, 3) # [B, H, C, W]
correlation = torch.matmul(feature0, feature1) / (c ** 0.5) # [B, H, W, W]
# mask subsequent positions to make disparity positive
mask = torch.triu(torch.ones((w, w)), diagonal=1).type_as(feature0) # [W, W]
valid_mask = (mask == 0).unsqueeze(0).unsqueeze(0).repeat(b, h, 1, 1) # [B, H, W, W]
correlation[~valid_mask] = -1e9
prob = F.softmax(correlation, dim=-1) # [B, H, W, W]
correspondence = (x_grid.view(1, 1, 1, w) * prob).sum(-1) # [B, H, W]
# NOTE: unlike flow, disparity is typically positive
disparity = x_grid.view(1, 1, w).repeat(b, h, 1) - correspondence # [B, H, W]
return disparity.unsqueeze(1), prob # feature resolution
def local_correlation_softmax_stereo(feature0, feature1, local_radius,
):
b, c, h, w = feature0.size()
coords_init = coords_grid(b, h, w).to(feature0.device) # [B, 2, H, W]
coords = coords_init.view(b, 2, -1).permute(0, 2, 1).contiguous() # [B, H*W, 2]
local_h = 1
local_w = 2 * local_radius + 1
window_grid = generate_window_grid(0, 0,
-local_radius, local_radius,
local_h, local_w, device=feature0.device) # [1, 2R+1, 2]
window_grid = window_grid.reshape(-1, 2).repeat(b, 1, 1, 1) # [B, 1, (2R+1), 2]
sample_coords = coords.unsqueeze(-2) + window_grid # [B, H*W, (2R+1), 2]
sample_coords_softmax = sample_coords
# exclude coords that are out of image space
valid_x = (sample_coords[:, :, :, 0] >= 0) & (sample_coords[:, :, :, 0] < w) # [B, H*W, (2R+1)^2]
valid_y = (sample_coords[:, :, :, 1] >= 0) & (sample_coords[:, :, :, 1] < h) # [B, H*W, (2R+1)^2]
valid = valid_x & valid_y # [B, H*W, (2R+1)^2], used to mask out invalid values when softmax
# normalize coordinates to [-1, 1]
sample_coords_norm = normalize_coords(sample_coords, h, w) # [-1, 1]
window_feature = F.grid_sample(feature1, sample_coords_norm,
padding_mode='zeros', align_corners=True
).permute(0, 2, 1, 3) # [B, H*W, C, (2R+1)]
feature0_view = feature0.permute(0, 2, 3, 1).contiguous().view(b, h * w, 1, c) # [B, H*W, 1, C]
corr = torch.matmul(feature0_view, window_feature).view(b, h * w, -1) / (c ** 0.5) # [B, H*W, (2R+1)]
# mask invalid locations
corr[~valid] = -1e9
prob = F.softmax(corr, -1) # [B, H*W, (2R+1)]
correspondence = torch.matmul(prob.unsqueeze(-2),
sample_coords_softmax).squeeze(-2).view(
b, h, w, 2).permute(0, 3, 1, 2).contiguous() # [B, 2, H, W]
flow = correspondence - coords_init # flow at feature resolution
match_prob = prob
flow_x = -flow[:, :1] # [B, 1, H, W]
return flow_x, match_prob
def correlation_softmax_depth(feature0, feature1,
intrinsics,
pose,
depth_candidates,
depth_from_argmax=False,
pred_bidir_depth=False,
):
b, c, h, w = feature0.size()
assert depth_candidates.dim() == 4 # [B, D, H, W]
scale_factor = c ** 0.5
if pred_bidir_depth:
feature0, feature1 = torch.cat((feature0, feature1), dim=0), torch.cat((feature1, feature0), dim=0)
intrinsics = intrinsics.repeat(2, 1, 1)
pose = torch.cat((pose, torch.inverse(pose)), dim=0)
depth_candidates = depth_candidates.repeat(2, 1, 1, 1)
# depth candidates are actually inverse depth
warped_feature1 = warp_with_pose_depth_candidates(feature1, intrinsics, pose,
1. / depth_candidates,
) # [B, C, D, H, W]
correlation = (feature0.unsqueeze(2) * warped_feature1).sum(1) / scale_factor # [B, D, H, W]
match_prob = F.softmax(correlation, dim=1) # [B, D, H, W]
# for cross-task transfer (flow -> depth), extract depth with argmax at test time
if depth_from_argmax:
index = torch.argmax(match_prob, dim=1, keepdim=True)
depth = torch.gather(depth_candidates, dim=1, index=index)
else:
depth = (match_prob * depth_candidates).sum(dim=1, keepdim=True) # [B, 1, H, W]
return depth, match_prob
def warp_with_pose_depth_candidates(feature1, intrinsics, pose, depth,
clamp_min_depth=1e-3,
):
"""
feature1: [B, C, H, W]
intrinsics: [B, 3, 3]
pose: [B, 4, 4]
depth: [B, D, H, W]
"""
assert intrinsics.size(1) == intrinsics.size(2) == 3
assert pose.size(1) == pose.size(2) == 4
assert depth.dim() == 4
b, d, h, w = depth.size()
c = feature1.size(1)
with torch.no_grad():
# pixel coordinates
grid = coords_grid(b, h, w, homogeneous=True, device=depth.device) # [B, 3, H, W]
# back project to 3D and transform viewpoint
points = torch.inverse(intrinsics).bmm(grid.view(b, 3, -1)) # [B, 3, H*W]
points = torch.bmm(pose[:, :3, :3], points).unsqueeze(2).repeat(
1, 1, d, 1) * depth.view(b, 1, d, h * w) # [B, 3, D, H*W]
points = points + pose[:, :3, -1:].unsqueeze(-1) # [B, 3, D, H*W]
# reproject to 2D image plane
points = torch.bmm(intrinsics, points.view(b, 3, -1)).view(b, 3, d, h * w) # [B, 3, D, H*W]
pixel_coords = points[:, :2] / points[:, -1:].clamp(min=clamp_min_depth) # [B, 2, D, H*W]
# normalize to [-1, 1]
x_grid = 2 * pixel_coords[:, 0] / (w - 1) - 1
y_grid = 2 * pixel_coords[:, 1] / (h - 1) - 1
grid = torch.stack([x_grid, y_grid], dim=-1) # [B, D, H*W, 2]
# sample features
warped_feature = F.grid_sample(feature1, grid.view(b, d * h, w, 2), mode='bilinear',
padding_mode='zeros',
align_corners=True).view(b, c, d, h, w) # [B, C, D, H, W]
return warped_feature
|