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ef296aa | 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 | import torch
from torch import nn
import torch.nn.functional as F
from tqdm import tqdm
from einops import rearrange, repeat
from .optical_flow import OpticalFlow
from .point_tracking import PointTracker
from dot.utils.torch import get_grid
class DenseOpticalTracker(nn.Module):
def __init__(self,
height=512,
width=512,
tracker_config="configs/cotracker2_patch_4_wind_8.json",
tracker_path="checkpoints/movi_f_cotracker2_patch_4_wind_8.pth",
estimator_config="configs/raft_patch_8.json",
estimator_path="checkpoints/cvo_raft_patch_8.pth",
refiner_config="configs/raft_patch_4_alpha.json",
refiner_path="checkpoints/movi_f_raft_patch_4_alpha.pth"):
super().__init__()
self.point_tracker = PointTracker(height, width, tracker_config, tracker_path, estimator_config, estimator_path)
self.optical_flow_refiner = OpticalFlow(height, width, refiner_config, refiner_path)
self.name = self.point_tracker.name + "_" + self.optical_flow_refiner.name
self.resolution = [height, width]
def forward(self, data, mode, **kwargs):
if mode == "flow_from_last_to_first_frame":
return self.get_flow_from_last_to_first_frame(data, **kwargs)
elif mode == "tracks_for_queries":
return self.get_tracks_for_queries(data, **kwargs)
elif mode == "tracks_from_first_to_every_other_frame":
return self.get_tracks_from_first_to_every_other_frame(data, **kwargs)
elif mode == "tracks_from_every_cell_in_every_frame":
return self.get_tracks_from_every_cell_in_every_frame(data, **kwargs)
else:
raise ValueError(f"Unknown mode {mode}")
def get_flow_from_last_to_first_frame(self, data, **kwargs):
B, T, C, h, w = data["video"].shape
init = self.point_tracker(data, mode="tracks_at_motion_boundaries", **kwargs)["tracks"]
init = torch.stack([init[..., 0] / (w - 1), init[..., 1] / (h - 1), init[..., 2]], dim=-1)
data = {
"src_frame": data["video"][:, -1],
"tgt_frame": data["video"][:, 0],
"src_points": init[:, -1],
"tgt_points": init[:, 0]
}
pred = self.optical_flow_refiner(data, mode="flow_with_tracks_init", **kwargs)
pred["src_points"] = data["src_points"]
pred["tgt_points"] = data["tgt_points"]
return pred
def get_tracks_for_queries(self, data, **kwargs):
time_steps = data["video"].size(1)
query_points = data["query_points"]
video = data["video"]
S = query_points.size(1)
B, T, C, h, w = video.shape
H, W = self.resolution
init = self.point_tracker(data, mode="tracks_at_motion_boundaries", **kwargs)["tracks"]
init = torch.stack([init[..., 0] / (w - 1), init[..., 1] / (h - 1), init[..., 2]], dim=-1)
if h != H or w != W:
video = video.reshape(B * T, C, h, w)
video = F.interpolate(video, size=(H, W), mode="bilinear")
video = video.reshape(B, T, C, H, W)
feats = self.optical_flow_refiner({"video": video}, mode="feats", **kwargs)["feats"]
grid = get_grid(H, W, device=video.device)
src_steps = [int(v) for v in torch.unique(query_points[..., 0])]
tracks = torch.zeros(B, T, S, 3, device=video.device)
for src_step in tqdm(src_steps, desc="Refine source step", leave=False):
src_points = init[:, src_step]
src_feats = feats[:, src_step]
tracks_from_src = []
for tgt_step in tqdm(range(time_steps), desc="Refine target step", leave=False):
if src_step == tgt_step:
flow = torch.zeros(B, H, W, 2, device=video.device)
alpha = torch.ones(B, H, W, device=video.device)
else:
tgt_points = init[:, tgt_step]
tgt_feats = feats[:, tgt_step]
data = {
"src_feats": src_feats,
"tgt_feats": tgt_feats,
"src_points": src_points,
"tgt_points": tgt_points
}
pred = self.optical_flow_refiner(data, mode="flow_with_tracks_init", **kwargs)
flow, alpha = pred["flow"], pred["alpha"]
flow[..., 0] = flow[..., 0] / (W - 1)
flow[..., 1] = flow[..., 1] / (H - 1)
tracks_from_src.append(torch.cat([flow + grid, alpha[..., None]], dim=-1))
tracks_from_src = torch.stack(tracks_from_src, dim=1)
for b in range(B):
cur = query_points[b, :, 0] == src_step
if torch.any(cur):
cur_points = query_points[b, cur]
cur_x = cur_points[..., 2] / (w - 1)
cur_y = cur_points[..., 1] / (h - 1)
cur_tracks = dense_to_sparse_tracks(cur_x, cur_y, tracks_from_src[b], h, w)
tracks[b, :, cur] = cur_tracks
return {"tracks": tracks}
def get_tracks_from_first_to_every_other_frame(self, data, return_flow=False, **kwargs):
video = data["video"]
B, T, C, h, w = video.shape
H, W = self.resolution
if h != H or w != W:
video = video.reshape(B * T, C, h, w)
video = F.interpolate(video, size=(H, W), mode="bilinear")
video = video.reshape(B, T, C, H, W)
init = self.point_tracker(data, mode="tracks_at_motion_boundaries", **kwargs)["tracks"]
init = torch.stack([init[..., 0] / (w - 1), init[..., 1] / (h - 1), init[..., 2]], dim=-1)
grid = get_grid(H, W, device=video.device)
grid[..., 0] *= (W - 1)
grid[..., 1] *= (H - 1)
src_step = 0
src_points = init[:, src_step]
src_frame = video[:, src_step]
tracks = []
for tgt_step in tqdm(range(T), desc="Refine target step", leave=False):
if src_step == tgt_step:
flow = torch.zeros(B, H, W, 2, device=video.device)
alpha = torch.ones(B, H, W, device=video.device)
else:
tgt_points = init[:, tgt_step]
tgt_frame = video[:, tgt_step]
data = {
"src_frame": src_frame,
"tgt_frame": tgt_frame,
"src_points": src_points,
"tgt_points": tgt_points
}
pred = self.optical_flow_refiner(data, mode="flow_with_tracks_init", **kwargs)
flow, alpha = pred["flow"], pred["alpha"]
if return_flow:
tracks.append(torch.cat([flow, alpha[..., None]], dim=-1))
else:
tracks.append(torch.cat([flow + grid, alpha[..., None]], dim=-1)) # flow means: 1->i pixel moving values, grid is the fisrt frame pixel ori cood, alpha is confidence
tracks = torch.stack(tracks, dim=1)
return {"tracks": tracks}
def get_tracks_from_every_cell_in_every_frame(self, data, cell_size=1, cell_time_steps=20, **kwargs):
video = data["video"]
B, T, C, h, w = video.shape
H, W = self.resolution
ch, cw, ct = h // cell_size, w // cell_size, min(T, cell_time_steps)
if h != H or w != W:
video = video.reshape(B * T, C, h, w)
video = F.interpolate(video, size=(H, W), mode="bilinear")
video = video.reshape(B, T, C, H, W)
init = self.point_tracker(data, mode="tracks_at_motion_boundaries", **kwargs)["tracks"]
init = torch.stack([init[..., 0] / (w - 1), init[..., 1] / (h - 1), init[..., 2]], dim=-1)
feats = self.optical_flow_refiner({"video": video}, mode="feats", **kwargs)["feats"]
grid = get_grid(H, W, device=video.device)
visited_cells = torch.zeros(B, T, ch, cw, device=video.device)
src_steps = torch.linspace(0, T - 1, T // ct).long()
tracks = [[] for _ in range(B)]
for k, src_step in enumerate(tqdm(src_steps, desc="Refine source step", leave=False)):
if visited_cells[:, src_step].all():
continue
src_points = init[:, src_step]
src_feats = feats[:, src_step]
tracks_from_src = []
for tgt_step in tqdm(range(T), desc="Refine target step", leave=False):
if src_step == tgt_step:
flow = torch.zeros(B, H, W, 2, device=video.device)
alpha = torch.ones(B, H, W, device=video.device)
else:
tgt_points = init[:, tgt_step]
tgt_feats = feats[:, tgt_step]
data = {
"src_feats": src_feats,
"tgt_feats": tgt_feats,
"src_points": src_points,
"tgt_points": tgt_points
}
pred = self.optical_flow_refiner(data, mode="flow_with_tracks_init", **kwargs)
flow, alpha = pred["flow"], pred["alpha"]
flow[..., 0] = flow[..., 0] / (W - 1)
flow[..., 1] = flow[..., 1] / (H - 1)
tracks_from_src.append(torch.cat([flow + grid, alpha[..., None]], dim=-1))
tracks_from_src = torch.stack(tracks_from_src, dim=1)
for b in range(B):
src_cell = visited_cells[b, src_step]
if src_cell.all():
continue
cur_y, cur_x = (1 - src_cell).nonzero(as_tuple=True)
cur_x = (cur_x + 0.5) / cw
cur_y = (cur_y + 0.5) / ch
cur_tracks = dense_to_sparse_tracks(cur_x, cur_y, tracks_from_src[b], h, w)
visited_cells[b] = update_visited(visited_cells[b], cur_tracks, h, w, ch, cw)
tracks[b].append(cur_tracks)
tracks = [torch.cat(t, dim=1) for t in tracks]
return {"tracks": tracks}
def dense_to_sparse_tracks(x, y, tracks, height, width):
h, w = height, width
T = tracks.size(0)
grid = torch.stack([x, y], dim=-1) * 2 - 1
grid = repeat(grid, "s c -> t s r c", t=T, r=1)
tracks = rearrange(tracks, "t h w c -> t c h w")
tracks = F.grid_sample(tracks, grid, align_corners=True, mode="bilinear")
tracks = rearrange(tracks[..., 0], "t c s -> t s c")
tracks[..., 0] = tracks[..., 0] * (w - 1)
tracks[..., 1] = tracks[..., 1] * (h - 1)
tracks[..., 2] = (tracks[..., 2] > 0).float()
return tracks
def update_visited(visited_cells, tracks, height, width, cell_height, cell_width):
T = tracks.size(0)
h, w = height, width
ch, cw = cell_height, cell_width
for tgt_step in range(T):
tgt_points = tracks[tgt_step]
tgt_vis = tgt_points[:, 2]
visited = tgt_points[tgt_vis.bool()]
if len(visited) > 0:
visited_x, visited_y = visited[:, 0], visited[:, 1]
visited_x = (visited_x / (w - 1) * cw).floor().long()
visited_y = (visited_y / (h - 1) * ch).floor().long()
valid = (visited_x >= 0) & (visited_x < cw) & (visited_y >= 0) & (visited_y < ch)
visited_x = visited_x[valid]
visited_y = visited_y[valid]
tgt_cell = visited_cells[tgt_step].view(-1)
tgt_cell[visited_y * cw + visited_x] = 1.
tgt_cell = tgt_cell.view_as(visited_cells[tgt_step])
visited_cells[tgt_step] = tgt_cell
return visited_cells |