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b47a1ce 93d7b0a b47a1ce 93d7b0a b47a1ce | 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 | import torch
from dememwm_import_helper import install_dememwm_namespace
install_dememwm_namespace()
from algorithms.worldmem.dememwm.algorithm import MemoryDiTMixin
from algorithms.worldmem.dememwm.cache import StreamingCache
from algorithms.worldmem.dememwm.types import MemoryRecord, MemorySourceType
class Harness(MemoryDiTMixin):
def __init__(self):
self.n_tokens = 8
self.context_frames = 0
self.frame_stack = 1
self.dememwm_anchor_proj = torch.nn.Linear(12, 8)
self.dememwm_revisit_proj = torch.nn.Linear(12, 8)
self.project_call_lengths = []
self.project_call_values = []
def _project_latent_patch_tokens(self, latents, projection, patch_size):
self.project_call_lengths.append(int(latents.shape[0]))
self.project_call_values.append(latents[:, 0, 0, 0, 0].detach().cpu().tolist())
return MemoryDiTMixin._project_latent_patch_tokens(self, latents, projection, patch_size)
def test_training_window_bounds_samples_inside_long_clip():
harness = Harness()
torch.manual_seed(0)
starts = []
for _ in range(20):
start, end = harness._training_window_bounds(128, torch.device("cpu"))
starts.append(start)
assert end - start == 8
assert 0 <= start <= 120
assert any(start != 120 for start in starts)
def test_training_window_bounds_respects_context_frames():
harness = Harness()
harness.context_frames = 100
torch.manual_seed(0)
starts = []
for _ in range(20):
start, end = harness._training_window_bounds(128, torch.device("cpu"))
starts.append(start)
assert end - start == 8
assert 100 <= start <= 120
assert any(start != 120 for start in starts)
def test_revisit_local_context_exclusion_uses_n_tokens_times_frame_stack():
harness = Harness()
harness.n_tokens = 4
harness.frame_stack = 2
harness.context_frames = 100
assert harness._local_context_exclusion_frames() == 8
def test_diverse_anchor_selection_does_not_repeat_tied_pose_indices():
harness = Harness()
source_positions = torch.arange(5)
poses = torch.zeros((5, 5), dtype=torch.float32)
selected = harness._select_diverse_anchor_positions(source_positions, poses, 4)
assert selected.tolist() == [0, 1, 2, 3]
def test_diverse_anchor_selection_seeds_from_widest_pose_pair():
harness = Harness()
source_positions = torch.arange(4)
poses = torch.tensor([[0.0], [-10.0], [10.0], [0.1]], dtype=torch.float32)
selected = harness._select_diverse_anchor_positions(source_positions, poses, 2)
assert selected.tolist() == [1, 2]
def test_cached_revisit_prefilter_keeps_only_causal_records():
harness = Harness()
def record(frame: int) -> MemoryRecord:
return MemoryRecord(
tokens=torch.zeros((1, 8)),
mask=torch.ones(1, dtype=torch.bool),
source_start=frame,
source_end=frame + 1,
frame_indices=torch.tensor([frame]),
pose=None,
source_type=MemorySourceType.REVISIT,
is_generated=False,
chunk_id=f"revisit_{frame}",
)
selected = harness._causal_cached_revisit_records(
(record(0), record(2), record(5)),
target_frame=3,
)
assert [record.source_start for record in selected] == [0, 2]
def test_diverse_anchor_selection_uses_context_frames_not_literal_limit():
harness = Harness()
harness.context_frames = 2
latents = torch.randn(8, 1, 3, 2, 2)
frame_indices = torch.arange(8)[:, None]
poses = torch.zeros((8, 1, 5), dtype=torch.float32)
target_pose = torch.zeros((1, 1, 5), dtype=torch.float32)
anchor_banks, _, _, diag = harness._build_preselected_causal_memory_banks(
committed_latents=latents,
source_frame_indices=frame_indices,
source_is_generated=None,
pose=poses,
action=None,
target_frame_indices=torch.tensor([[6]]),
target_pose=target_pose,
target_action=None,
target_video_ids=None,
allow_generated_anchor=False,
anchor_indices=[0, 1, 2, 3],
anchor_pool_h=1,
anchor_pool_w=1,
anchor_diverse=True,
revisit_pool_h=1,
revisit_pool_w=1,
revisit_max_frames=0,
exclude_local_context_frames=4,
fov_overlap_threshold=0.0,
plucker_weight=0.1,
revisit_retrieval_kwargs=None,
token_patch_size=2,
)
assert [int(record.frame_indices.item()) for record in anchor_banks[0].records] == [0, 1]
assert diag["preselected_anchor_projected_frame_count"] == 2
def test_streaming_diverse_anchor_selection_uses_context_frames():
harness = Harness()
harness.context_frames = 2
latents = torch.randn(8, 1, 3, 2, 2)
frame_indices = torch.arange(8)[:, None]
poses = torch.zeros((8, 1, 5), dtype=torch.float32)
anchor_banks, _ = harness._build_streaming_cache_records(
source_latents=latents,
source_frame_indices=frame_indices,
source_is_generated=None,
pose=poses,
action=None,
allow_generated_anchor=False,
anchor_indices=[0, 1, 2, 3],
anchor_pool_h=1,
anchor_pool_w=1,
anchor_diverse=True,
token_patch_size=2,
)
assert [int(record.frame_indices.item()) for record in anchor_banks[0].records] == [0, 1]
assert harness.project_call_lengths == [2]
def test_preselected_memory_banks_project_only_selected_frames():
harness = Harness()
latents = torch.randn(20, 1, 3, 2, 2)
frame_indices = torch.arange(20)[:, None]
target_frame_indices = torch.tensor([[10], [11]])
poses = torch.zeros((20, 1, 5), dtype=torch.float32)
target_pose = torch.zeros((2, 1, 5), dtype=torch.float32)
anchor_banks, revisit_banks, tokens_per_frame, diag = harness._build_preselected_causal_memory_banks(
committed_latents=latents,
source_frame_indices=frame_indices,
source_is_generated=None,
pose=poses,
action=None,
target_frame_indices=target_frame_indices,
target_pose=target_pose,
target_action=None,
target_video_ids=None,
allow_generated_anchor=False,
anchor_indices=[0, 1, 2, 3],
anchor_pool_h=1,
anchor_pool_w=1,
anchor_diverse=False,
revisit_pool_h=1,
revisit_pool_w=1,
revisit_max_frames=2,
exclude_local_context_frames=4,
fov_overlap_threshold=0.0,
plucker_weight=0.1,
revisit_retrieval_kwargs=None,
token_patch_size=2,
)
assert tokens_per_frame == 1
assert len(anchor_banks[0].records) == 4
assert len(revisit_banks[0].records) == 3
assert diag["preselected_anchor_projected_frame_count"] == 4
assert diag["preselected_revisit_projected_frame_count"] == 3
assert diag["preselected_revisit_projected_frame_record_count"] == 3
assert harness.project_call_lengths == [4, 1, 1, 1]
assert 20 not in harness.project_call_lengths
def test_preselected_revisit_projects_best_fov_frame_not_latest():
harness = Harness()
latents = torch.arange(8, dtype=torch.float32).view(8, 1, 1, 1, 1).expand(8, 1, 3, 2, 2).clone()
frame_indices = torch.arange(8)[:, None]
pose_rows = torch.tensor(
[
[0.0, 0.0, 0.0, 0.0, 180.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 180.0],
[0.0, 0.0, 0.0, 0.0, 180.0],
[0.0, 0.0, 0.0, 0.0, 180.0],
[0.0, 0.0, 0.0, 0.0, 180.0],
[0.0, 0.0, 0.0, 0.0, 180.0],
[0.0, 0.0, 0.0, 0.0, 180.0],
],
dtype=torch.float32,
)
poses = pose_rows[:, None, :]
_, revisit_banks, _, _ = harness._build_preselected_causal_memory_banks(
committed_latents=latents,
source_frame_indices=frame_indices,
source_is_generated=None,
pose=poses,
action=None,
target_frame_indices=torch.tensor([[8]]),
target_pose=torch.tensor([[[0.0, 0.0, 0.0, 0.0, 0.0]]]),
target_action=None,
target_video_ids=None,
allow_generated_anchor=False,
anchor_indices=[],
anchor_pool_h=1,
anchor_pool_w=1,
anchor_diverse=False,
revisit_pool_h=1,
revisit_pool_w=1,
revisit_max_frames=1,
exclude_local_context_frames=4,
fov_overlap_threshold=0.30,
plucker_weight=0.1,
revisit_retrieval_kwargs={"high_quality_fov_threshold": 0.70},
token_patch_size=2,
)
assert len(revisit_banks[0].records) == 1
assert revisit_banks[0].records[0].metadata["dememwm_selected_frame_index"] == 1
assert harness.project_call_values == [[1.0]]
def test_streaming_revisit_projection_uses_selected_frame_metadata():
harness = Harness()
cache = StreamingCache(enabled=True, keep_raw_latents="all", keep_compressed_records=True)
latents = torch.arange(4, dtype=torch.float32).view(4, 1, 1, 1, 1).expand(4, 1, 3, 2, 2).clone()
cache.add_raw_latents(latents, torch.arange(4)[:, None])
record = MemoryRecord(
tokens=torch.zeros((1, 8)),
mask=torch.ones(1, dtype=torch.bool),
source_start=0,
source_end=4,
frame_indices=torch.tensor([0, 1, 2, 3]),
pose=None,
source_type=MemorySourceType.PREFIX_GT,
is_generated=False,
chunk_id="frame",
metadata={
"dememwm_revisit_metadata_only": True,
"dememwm_selected_frame_index": 1,
},
)
projected = harness._project_streaming_revisit_records(
cache=cache,
batch_idx=0,
records=[record],
device=torch.device("cpu"),
dtype=torch.float32,
token_patch_size=2,
revisit_pool_h=1,
revisit_pool_w=1,
projection_cache={},
)
assert len(projected) == 1
assert projected[0].metadata["dememwm_selected_frame_index"] == 1
assert harness.project_call_values == [[1.0]]
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