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e340a84 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 | import torch
class StreamSession:
def __init__(
self,
model,
mode: str,
window_size: int = 5,
keep_first_frame_anchor: bool = True,
):
self.model = model
self.core_model = getattr(model, "longstream", model)
self.mode = mode
self.window_size = window_size
self.keep_first_frame_anchor = keep_first_frame_anchor
if self.mode not in ["causal", "window"]:
raise ValueError(f"Unsupported attention mode: {self.mode}")
self.aggregator_kv_cache_depth = self.core_model.aggregator.depth
self.use_camera_head = self.core_model.camera_head is not None
if self.use_camera_head:
self.camera_head_kv_cache_depth = self.core_model.camera_head.trunk_depth
self.camera_head_iterations = 4
else:
self.camera_head_kv_cache_depth = 0
self.camera_head_iterations = 0
self.use_rel_pose_head = (
hasattr(self.core_model, "rel_pose_head")
and self.core_model.rel_pose_head is not None
)
if self.use_rel_pose_head:
self.rel_pose_head_trunk_depth = self.core_model.rel_pose_head.trunk_depth
self.rel_pose_head_iterations = 4
self.clear()
def _clear_predictions(self):
self.sequence_predictions = {}
self.scalar_predictions = {}
def _update_predictions(self, predictions):
sequence_keys = [
"pose_enc",
"rel_pose_enc",
"world_points",
"world_points_conf",
"depth",
"depth_conf",
]
scalar_keys = ["predicted_scale_factor", "global_scale"]
for k in sequence_keys:
if k in predictions:
self.sequence_predictions.setdefault(k, []).append(
predictions[k].detach().cpu()
)
for k in scalar_keys:
if k in predictions:
value = predictions[k]
self.scalar_predictions[k] = (
value.detach().cpu() if isinstance(value, torch.Tensor) else value
)
def _clear_cache(self):
self.aggregator_kv_cache_list = [
[None, None] for _ in range(self.aggregator_kv_cache_depth)
]
if self.use_camera_head:
self.camera_head_kv_cache_list = [
[[None, None] for _ in range(self.camera_head_kv_cache_depth)]
for _ in range(self.camera_head_iterations)
]
else:
self.camera_head_kv_cache_list = None
if self.use_rel_pose_head:
self.rel_pose_kv_cache_list = [
[[None, None] for _ in range(self.rel_pose_head_trunk_depth)]
for _ in range(self.rel_pose_head_iterations)
]
else:
self.rel_pose_kv_cache_list = None
def _update_cache(
self, aggregator_kv_cache_list, camera_head_kv_cache_list, frame_hw
):
if self.mode == "causal":
self.aggregator_kv_cache_list = aggregator_kv_cache_list
if self.use_camera_head:
self.camera_head_kv_cache_list = camera_head_kv_cache_list
return
if self.mode == "window":
h, w = frame_hw
P = (
h
* w
// self.core_model.aggregator.patch_size
// self.core_model.aggregator.patch_size
+ self.core_model.aggregator.patch_start_idx
)
for k in range(2):
for i in range(self.aggregator_kv_cache_depth):
cache_size = aggregator_kv_cache_list[i][k].size(2)
if self.keep_first_frame_anchor:
if cache_size <= P:
self.aggregator_kv_cache_list[i][
k
] = aggregator_kv_cache_list[i][k].contiguous()
elif cache_size <= self.window_size * P:
self.aggregator_kv_cache_list[i][
k
] = aggregator_kv_cache_list[i][k].contiguous()
else:
anchor = aggregator_kv_cache_list[i][k][:, :, :P]
recent_start = cache_size - (self.window_size - 1) * P
recent = aggregator_kv_cache_list[i][k][:, :, recent_start:]
self.aggregator_kv_cache_list[i][k] = torch.cat(
[anchor, recent], dim=2
).contiguous()
else:
start_idx = max(0, cache_size - self.window_size * P)
self.aggregator_kv_cache_list[i][k] = aggregator_kv_cache_list[
i
][k][:, :, start_idx:].contiguous()
if camera_head_kv_cache_list is not None:
for k in range(2):
for i in range(self.camera_head_iterations):
for j in range(self.camera_head_kv_cache_depth):
cache_size = camera_head_kv_cache_list[i][j][k].size(2)
if self.keep_first_frame_anchor:
if cache_size <= 1:
self.camera_head_kv_cache_list[i][j][
k
] = camera_head_kv_cache_list[i][j][k].contiguous()
elif cache_size <= self.window_size:
self.camera_head_kv_cache_list[i][j][
k
] = camera_head_kv_cache_list[i][j][k].contiguous()
else:
anchor = camera_head_kv_cache_list[i][j][k][
:, :, :1
]
recent_start = cache_size - (self.window_size - 1)
recent = camera_head_kv_cache_list[i][j][k][
:, :, recent_start:
]
self.camera_head_kv_cache_list[i][j][k] = torch.cat(
[anchor, recent], dim=2
).contiguous()
else:
start_idx = max(0, cache_size - self.window_size)
self.camera_head_kv_cache_list[i][j][
k
] = camera_head_kv_cache_list[i][j][k][
:, :, start_idx:
].contiguous()
return
raise ValueError(f"Unsupported attention mode: {self.mode}")
def _get_cache(self):
return self.aggregator_kv_cache_list, self.camera_head_kv_cache_list
def get_all_predictions(self):
predictions = {}
for key, chunks in self.sequence_predictions.items():
if not chunks:
continue
predictions[key] = (
chunks[0] if len(chunks) == 1 else torch.cat(chunks, dim=1)
)
predictions.update(self.scalar_predictions)
return predictions
def get_last_prediction(self):
last_predictions = {}
keys_to_extract = [
"pose_enc",
"rel_pose_enc",
"world_points",
"world_points_conf",
"depth",
"depth_conf",
"predicted_scale_factor",
]
for k in keys_to_extract:
if k in self.sequence_predictions and self.sequence_predictions[k]:
last_predictions[k] = self.sequence_predictions[k][-1][:, -1:]
elif k in self.scalar_predictions:
last_predictions[k] = self.scalar_predictions[k]
return last_predictions
def clear(self):
self._clear_predictions()
self._clear_cache()
if self.use_rel_pose_head:
if hasattr(self.core_model.rel_pose_head, "_keyframe_tokens_cache"):
self.core_model.rel_pose_head._keyframe_tokens_cache = {}
if hasattr(self.core_model.rel_pose_head, "_current_frame_id"):
self.core_model.rel_pose_head._current_frame_id = 0
if hasattr(self.core_model.rel_pose_head, "_frame_info"):
self.core_model.rel_pose_head._frame_info = []
def clear_cache_only(self):
self._clear_cache()
if self.use_rel_pose_head:
if hasattr(self.core_model.rel_pose_head, "_keyframe_tokens_cache"):
self.core_model.rel_pose_head._keyframe_tokens_cache = {}
if hasattr(self.core_model.rel_pose_head, "_current_frame_id"):
self.core_model.rel_pose_head._current_frame_id = 0
if hasattr(self.core_model.rel_pose_head, "_frame_info"):
self.core_model.rel_pose_head._frame_info = []
def forward_stream(
self, images, is_keyframe=None, keyframe_indices=None, record: bool = True
):
aggregator_kv_cache_list, camera_head_kv_cache_list = self._get_cache()
rel_pose_inputs = None
if (
self.use_rel_pose_head
and is_keyframe is not None
and keyframe_indices is not None
):
rel_pose_inputs = {
"is_keyframe": is_keyframe,
"keyframe_indices": keyframe_indices,
"kv_cache_list": self.rel_pose_kv_cache_list,
}
outputs = self.model(
images=images,
mode=self.mode,
aggregator_kv_cache_list=aggregator_kv_cache_list,
camera_head_kv_cache_list=camera_head_kv_cache_list,
rel_pose_inputs=rel_pose_inputs,
is_keyframe=is_keyframe,
)
if record:
self._update_predictions(outputs)
camera_head_kv_cache_list = outputs.get("camera_head_kv_cache_list", None)
depth_hw = (
outputs["depth"].shape[2:4] if "depth" in outputs else images.shape[-2:]
)
self._update_cache(
outputs["aggregator_kv_cache_list"], camera_head_kv_cache_list, depth_hw
)
if self.use_rel_pose_head and "rel_pose_kv_cache_list" in outputs:
rel_pose_kv_cache = outputs["rel_pose_kv_cache_list"]
if self.mode == "causal":
self.rel_pose_kv_cache_list = rel_pose_kv_cache
elif self.mode == "window":
for k in range(2):
for i in range(self.rel_pose_head_iterations):
for j in range(self.rel_pose_head_trunk_depth):
if rel_pose_kv_cache[i][j][k] is None:
continue
cache_len = rel_pose_kv_cache[i][j][k].size(2)
if self.keep_first_frame_anchor:
if cache_len <= 1:
self.rel_pose_kv_cache_list[i][j][
k
] = rel_pose_kv_cache[i][j][k].contiguous()
elif cache_len <= self.window_size:
self.rel_pose_kv_cache_list[i][j][
k
] = rel_pose_kv_cache[i][j][k].contiguous()
else:
anchor = rel_pose_kv_cache[i][j][k][:, :, :1]
recent_start = cache_len - (self.window_size - 1)
recent = rel_pose_kv_cache[i][j][k][
:, :, recent_start:
]
self.rel_pose_kv_cache_list[i][j][k] = torch.cat(
[anchor, recent], dim=2
).contiguous()
else:
start_idx = max(0, cache_len - self.window_size)
self.rel_pose_kv_cache_list[i][j][
k
] = rel_pose_kv_cache[i][j][k][
:, :, start_idx:
].contiguous()
return outputs
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