File size: 27,363 Bytes
1765773 3a1ad37 1765773 3a1ad37 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 | from __future__ import annotations
import time
from pathlib import Path
from typing import Any
import numpy as np
import portable_numpy_nmsfreecoder as portable_nms
from camera_grid_visualization import save_camera_grid_gif, save_camera_grid_summary, save_camera_grid_visualization
from utils import (
EXPECTED_TENSORS,
SHAPES,
as_encoder_img_feat,
elapsed_ms,
load_backbone_images,
load_json,
load_record,
normalize_rc,
rotate_prev_bev,
save_final_coordinates,
save_raw_outputs,
sha256_file,
stats,
)
DEFAULT_SHA256 = {
"backbone": "5eee1fe5cfdd6e5603e9bacf00c9d1bd73d81f4086151fbdb8f591cf425d51c0",
"encoder_temporal": "540798cabfe808601ef17600bb00136f38c6afad0671252830a178cec55030a4",
"encoder_scene_start": "7fb661a05e1a1d865e391d5ad6ddd43a83063a4de35c1176268e1f7c93de93ae",
"decoder": "a1f89cde2a000b11d8411d8ec39d32f448f8eb4572b41b72e7ecdd352f85ac76",
}
NUSCENES_CLASSES = (
"car",
"truck",
"construction_vehicle",
"bus",
"trailer",
"barrier",
"motorcycle",
"bicycle",
"pedestrian",
"traffic_cone",
)
class BevFormerModel:
def __init__(
self,
backbone_model: str,
encoder_temporal_model: str,
encoder_scene_start_model: str,
decoder_model: str,
model_type: str = "QNN240",
expected_sha256: dict[str, str] | None = None,
):
try:
import aidlite
except ModuleNotFoundError as exc:
raise RuntimeError(
"AidLite Python runtime is not available in this environment. "
"Use --dry_run to demonstrate the package structure in a normal container, "
"or run without --dry_run on the board / Container B where "
"`python3 -c \"import aidlite\"` succeeds."
) from exc
if model_type.upper() != "QNN240":
raise ValueError("This demo is pinned to QNN240 contexts")
if (
int(aidlite.FrameworkType.TYPE_QNN240),
int(aidlite.ImplementType.TYPE_LOCAL),
int(aidlite.AccelerateType.TYPE_DSP),
) != (109, 3, 3):
raise RuntimeError("AidLite enum contract mismatch")
self.aidlite = aidlite
self.expected_sha256 = expected_sha256 or DEFAULT_SHA256
self.interpreters: dict[str, Any] = {}
self.model_records: dict[str, Any] = {}
self.model_load_timing_ms: dict[str, float] = {}
model_load_start = time.perf_counter_ns()
for name, path in (
("backbone", backbone_model),
("encoder_temporal", encoder_temporal_model),
("encoder_scene_start", encoder_scene_start_model),
("decoder", decoder_model),
):
load_start = time.perf_counter_ns()
interpreter, record = self._create_loaded_interpreter(name, str(path))
self.model_load_timing_ms[name] = elapsed_ms(load_start)
self.interpreters[name] = interpreter
self.model_records[name] = record
self.model_load_timing_ms["total"] = elapsed_ms(model_load_start)
def __del__(self):
for interpreter in reversed(list(getattr(self, "interpreters", {}).values())):
for method_name in ("destroy", "destory"):
if hasattr(interpreter, method_name):
try:
getattr(interpreter, method_name)()
except Exception:
pass
break
def _create_model(self, model_path: str) -> Any:
try:
return self.aidlite.Model.create_instance(model_path=model_path)
except TypeError:
return self.aidlite.Model.create_instance(model_path)
def _build_interpreter(self, model: Any, config: Any) -> Any:
for method_name in ("build_interpreter_from_model_and_config", "build_interpretper_from_model_and_config"):
if hasattr(self.aidlite.InterpreterBuilder, method_name):
method = getattr(self.aidlite.InterpreterBuilder, method_name)
try:
return method(model=model, config=config)
except TypeError:
return method(model, config)
raise RuntimeError("No supported AidLite InterpreterBuilder method")
@staticmethod
def _flatten_tensor_info(groups: Any) -> list[dict[str, Any]]:
records: list[dict[str, Any]] = []
if groups is None:
return records
for graph_index, group in enumerate(groups):
try:
tensors = list(group)
except TypeError:
tensors = [group]
for tensor_index, info in enumerate(tensors):
records.append({
"graph_index": graph_index,
"tensor_index": tensor_index,
"name": str(getattr(info, "name", "")),
"element_count": int(getattr(info, "element_count", -1)),
"shape": [int(v) for v in getattr(info, "shape", [])],
"element_type": str(getattr(info, "element_type", "")),
})
return records
def _create_loaded_interpreter(self, name: str, model_path: str) -> tuple[Any, dict[str, Any]]:
path = Path(model_path).expanduser().resolve()
if not path.is_file():
raise FileNotFoundError(path)
actual_sha = sha256_file(path)
expected_sha = self.expected_sha256[name]
if actual_sha != expected_sha:
raise RuntimeError(f"{name} context SHA mismatch: expected={expected_sha} actual={actual_sha}")
print(f"{name.upper()}_CONTEXT_SHA_GATE=PASS")
model = self._create_model(str(path))
config = self.aidlite.Config.create_instance()
if model is None or config is None:
raise RuntimeError(f"{name}: Model/Config creation failed")
config.framework_type = self.aidlite.FrameworkType.TYPE_QNN240
config.implement_type = self.aidlite.ImplementType.TYPE_LOCAL
config.accelerate_type = self.aidlite.AccelerateType.TYPE_DSP
config.qnn_shared_buffer = 0
interpreter = self._build_interpreter(model, config)
if interpreter is None:
raise RuntimeError(f"{name}: interpreter creation failed")
init_rc = normalize_rc(interpreter.init())
load_rc = normalize_rc(interpreter.load_model())
if init_rc != 0 or load_rc != 0:
raise RuntimeError(f"{name}: init/load failed init={init_rc} load={load_rc}")
inputs = self._flatten_tensor_info(interpreter.get_input_tensor_info())
outputs = self._flatten_tensor_info(interpreter.get_output_tensor_info())
actual_inputs = {item["name"]: item["element_count"] for item in inputs}
actual_outputs = {item["name"]: item["element_count"] for item in outputs}
expected = EXPECTED_TENSORS[name]
if actual_inputs != expected["inputs"] or actual_outputs != expected["outputs"]:
raise RuntimeError(f"{name}: tensor contract mismatch inputs={actual_inputs} outputs={actual_outputs}")
print(f"{name.upper()}_LOAD_GATE=PASS")
return interpreter, {
"name": name,
"path": str(path),
"sha256": actual_sha,
"inputs": inputs,
"outputs": outputs,
}
def _set_input(self, interpreter: Any, name: str, value: np.ndarray) -> float:
tensor = np.ascontiguousarray(value, dtype=np.float32)
start = time.perf_counter_ns()
rc = normalize_rc(interpreter.set_input_tensor(in_tensor_tag=name, input_data=tensor))
duration = elapsed_ms(start)
if rc != 0:
raise RuntimeError(f"set_input_tensor failed name={name} rc={rc}")
return duration
def _invoke(self, interpreter: Any, name: str) -> float:
start = time.perf_counter_ns()
rc = normalize_rc(interpreter.invoke())
duration = elapsed_ms(start)
if rc != 0:
raise RuntimeError(f"{name}: invoke failed rc={rc}")
return duration
def _get_output(self, interpreter: Any, name: str, shape: tuple[int, ...]) -> tuple[np.ndarray, float]:
start = time.perf_counter_ns()
value = interpreter.get_output_tensor(out_tensor_tag=name)
duration = elapsed_ms(start)
if value is None:
raise RuntimeError(f"get_output_tensor returned None name={name}")
array = np.asarray(value, dtype=np.float32).reshape(shape)
if not np.isfinite(array).all():
raise RuntimeError(f"{name}: non-finite output")
return np.ascontiguousarray(array, dtype=np.float32), duration
def run_frame(
self,
frame_index: int,
frame_manifest: dict[str, Any],
repo_root: str | Path,
previous_frame_bev_embed: np.ndarray | None,
check_image_sha: bool = False,
preprocess_workers: int = 6,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, dict[str, Any]]:
"""Run one BEVFormer frame on the board.
The QNN context tensor names still follow the exported model contract
such as ``can_bus``, ``lidar2img`` and ``prev_bev``. The Python
variable names below describe their deployment meaning more explicitly:
- scene-start frame: use the scene-start encoder; no previous BEV state.
- temporal frame: rotate the previous frame BEV state, then feed it as
the current frame ``prev_bev`` tensor.
"""
frame_start = time.perf_counter_ns()
is_scene_start_frame = bool(frame_manifest.get("is_scene_start", False))
asset_records = frame_manifest["assets"]
timing: dict[str, float] = {}
preprocess_start = time.perf_counter_ns()
padded_camera_tensor, image_preprocess = load_backbone_images(
asset_records,
repo_root,
check_image_sha=check_image_sha,
preprocess_workers=preprocess_workers,
)
timing["image_preprocess_ms"] = elapsed_ms(preprocess_start)
ego_motion_can_bus = load_record(asset_records["can_bus"], repo_root).astype(np.float32)
camera_lidar2img_matrices = load_record(asset_records["lidar2img"], repo_root).astype(np.float32)
timing["backbone_set_input_ms"] = self._set_input(
self.interpreters["backbone"],
"images",
padded_camera_tensor,
)
timing["backbone_invoke_ms"] = self._invoke(self.interpreters["backbone"], "backbone")
backbone_image_features, timing["backbone_get_output_ms"] = self._get_output(
self.interpreters["backbone"], "img_feat", SHAPES["img_feat"]
)
encoder_image_features = as_encoder_img_feat(backbone_image_features)
if is_scene_start_frame:
selected_encoder_name = "encoder_scene_start"
selected_encoder_route = "scene_start_reset_prev_bev"
selected_encoder = self.interpreters[selected_encoder_name]
timing["encoder_set_input_ms"] = 0.0
scene_start_inputs = (
("can_bus", ego_motion_can_bus),
("img_feat", encoder_image_features),
("lidar2img", camera_lidar2img_matrices),
)
for tensor_name, tensor_value in scene_start_inputs:
timing["encoder_set_input_ms"] += self._set_input(selected_encoder, tensor_name, tensor_value)
else:
if previous_frame_bev_embed is None:
raise RuntimeError(f"frame{frame_index:03d}: previous BEV state is missing for temporal frame")
ego_motion_shift = load_record(asset_records["shift"], repo_root).astype(np.float32)
ego_rotation_can_bus = load_record(asset_records["rotation_can_bus"], repo_root).astype(np.float32)
rotate_start = time.perf_counter_ns()
rotated_previous_bev_embed = rotate_prev_bev(previous_frame_bev_embed, ego_rotation_can_bus)
timing["prev_bev_rotate_ms"] = elapsed_ms(rotate_start)
selected_encoder_name = "encoder_temporal"
selected_encoder_route = "temporal_reuse_previous_bev"
selected_encoder = self.interpreters[selected_encoder_name]
timing["encoder_set_input_ms"] = 0.0
temporal_inputs = (
("can_bus", ego_motion_can_bus),
("img_feat", encoder_image_features),
("lidar2img", camera_lidar2img_matrices),
("shift", ego_motion_shift),
("prev_bev", rotated_previous_bev_embed),
)
for tensor_name, tensor_value in temporal_inputs:
timing["encoder_set_input_ms"] += self._set_input(selected_encoder, tensor_name, tensor_value)
timing["encoder_invoke_ms"] = self._invoke(selected_encoder, selected_encoder_name)
current_frame_bev_embed, timing["encoder_get_output_ms"] = self._get_output(
selected_encoder,
"bev_embed",
SHAPES["bev"],
)
decoder = self.interpreters["decoder"]
timing["decoder_set_input_ms"] = self._set_input(decoder, "bev_embed", current_frame_bev_embed)
timing["decoder_invoke_ms"] = self._invoke(decoder, "decoder")
decoder_cls_scores, timing["decoder_get_cls_ms"] = self._get_output(decoder, "cls_scores", SHAPES["decoder"])
decoder_bbox_preds, timing["decoder_get_bbox_ms"] = self._get_output(decoder, "bbox_preds", SHAPES["decoder"])
timing["qnn_invoke_ms"] = (
timing["backbone_invoke_ms"]
+ timing["encoder_invoke_ms"]
+ timing["decoder_invoke_ms"]
)
timing["frame_total_ms"] = elapsed_ms(frame_start)
frame_result = {
"frame_index": int(frame_index),
"sample_token": frame_manifest.get("sample_token"),
"frame_type": "scene_start" if is_scene_start_frame else "temporal",
"is_scene_start": is_scene_start_frame,
"encoder": selected_encoder_name,
"encoder_route": selected_encoder_route,
"image_preprocess": image_preprocess,
"timing_ms": timing,
"status": "PASS",
}
return current_frame_bev_embed, decoder_cls_scores, decoder_bbox_preds, frame_result
def run_manifest(
self,
manifest_path: str | Path,
repo_root: str | Path,
output_dir: str | Path,
nms_contract_path: str | Path,
frame_start: int = 0,
frame_count: int | None = None,
save_all_raw: bool = False,
visualize: bool = True,
vis_score_thr: float = 0.0,
vis_max_boxes: int = 80,
check_image_sha: bool = False,
preprocess_workers: int = 6,
) -> dict[str, Any]:
run_manifest_start = time.perf_counter_ns()
manifest_load_start = time.perf_counter_ns()
manifest = load_json(manifest_path)
nms_contract = load_json(nms_contract_path)
manifest_load_ms = elapsed_ms(manifest_load_start)
total_frames = int(manifest.get("total_frames", len(manifest["frames"])))
end = total_frames if frame_count is None else min(total_frames, frame_start + frame_count)
output_path = Path(output_dir)
output_path.mkdir(parents=True, exist_ok=True)
previous_frame_bev_embed: np.ndarray | None = None
frames: dict[str, Any] = {}
scene_start_count = 0
temporal_count = 0
frame_ms: list[float] = []
qnn_invoke_ms: list[float] = []
backbone_invoke_ms: list[float] = []
encoder_invoke_ms: list[float] = []
scene_start_encoder_invoke_ms: list[float] = []
temporal_encoder_invoke_ms: list[float] = []
decoder_invoke_ms: list[float] = []
image_preprocess_ms: list[float] = []
raw_save_ms: list[float] = []
nms_decode_ms: list[float] = []
final_coordinates_save_ms: list[float] = []
postprocess_ms: list[float] = []
visualization_ms: list[float] = []
final_outputs: dict[str, Any] | None = None
final_coordinates: dict[str, Any] = {}
visualizations: dict[str, Any] = {}
for frame_index in range(frame_start, end):
sample = f"sample_{frame_index:03d}"
current_frame_bev_embed, decoder_cls_scores, decoder_bbox_preds, frame_record = self.run_frame(
frame_index,
manifest["frames"][sample],
repo_root,
previous_frame_bev_embed,
check_image_sha=check_image_sha,
preprocess_workers=preprocess_workers,
)
previous_frame_bev_embed = np.ascontiguousarray(current_frame_bev_embed, dtype=np.float32)
frames[sample] = frame_record
frame_ms.append(frame_record["timing_ms"]["frame_total_ms"])
qnn_invoke_ms.append(frame_record["timing_ms"]["qnn_invoke_ms"])
backbone_invoke_ms.append(frame_record["timing_ms"]["backbone_invoke_ms"])
encoder_time = frame_record["timing_ms"]["encoder_invoke_ms"]
encoder_invoke_ms.append(encoder_time)
if frame_record["encoder"] == "encoder_scene_start":
scene_start_encoder_invoke_ms.append(encoder_time)
elif frame_record["encoder"] == "encoder_temporal":
temporal_encoder_invoke_ms.append(encoder_time)
decoder_invoke_ms.append(frame_record["timing_ms"]["decoder_invoke_ms"])
image_preprocess_ms.append(frame_record["timing_ms"].get("image_preprocess_ms", 0.0))
if frame_record["is_scene_start"]:
scene_start_count += 1
else:
temporal_count += 1
raw_save_value = 0.0
if save_all_raw or frame_index == end - 1:
raw_save_start = time.perf_counter_ns()
final_outputs = save_raw_outputs(output_path, frame_index, decoder_cls_scores, decoder_bbox_preds)
raw_save_value = elapsed_ms(raw_save_start)
raw_save_ms.append(raw_save_value)
nms_start = time.perf_counter_ns()
boxes, scores, labels = portable_nms.decode_numpy_nmsfreecoder(
decoder_cls_scores,
decoder_bbox_preds,
nms_contract,
)
nms_value = elapsed_ms(nms_start)
nms_decode_ms.append(nms_value)
final_save_start = time.perf_counter_ns()
final_coordinates[f"frame{frame_index:03d}"] = save_final_coordinates(
output_path,
frame_index,
boxes,
scores,
labels,
)
final_save_value = elapsed_ms(final_save_start)
final_coordinates_save_ms.append(final_save_value)
postprocess_ms.append(raw_save_value + nms_value + final_save_value)
visualization_value = 0.0
if visualize:
visualization_start = time.perf_counter_ns()
visualizations[f"frame{frame_index:03d}"] = save_camera_grid_visualization(
output_path,
frame_index,
manifest["frames"][sample],
repo_root,
boxes,
scores,
labels,
score_thr=vis_score_thr,
max_boxes=vis_max_boxes,
result_dir=output_path,
)
visualization_value = elapsed_ms(visualization_start)
visualization_ms.append(visualization_value)
frame_record["timing_ms"]["raw_save_ms"] = raw_save_value
frame_record["timing_ms"]["nms_decode_ms"] = nms_value
frame_record["timing_ms"]["final_coordinates_save_ms"] = final_save_value
frame_record["timing_ms"]["postprocess_ms"] = raw_save_value + nms_value + final_save_value
frame_record["timing_ms"]["visualization_ms"] = visualization_value
top_detections = []
for det_index in range(min(5, len(scores))):
label_id = int(labels[det_index])
class_name = NUSCENES_CLASSES[label_id] if label_id < len(NUSCENES_CLASSES) else str(label_id)
top_detections.append({
"box": [float(value) for value in boxes[det_index].tolist()],
"score": float(scores[det_index]),
"label": label_id,
"class_name": class_name,
})
frame_record["detections"] = {
"count": int(len(scores)),
"top": top_detections,
}
route_text = (
"first frame: reset prev_bev state"
if frame_record["encoder_route"] == "scene_start_reset_prev_bev"
else "later frame: previous bev_embed -> prev_bev"
)
print("----------------------------------------")
print(f"Frame {frame_index:03d} | PASS")
print(f" frame type : {frame_record['frame_type']}")
print(f" encoder context : {frame_record['encoder']}")
print(f" temporal policy : {route_text}")
print(f" input source : 6 raw camera JPG images")
print(" timing (ms)")
print(f" preprocess (6 JPG -> tensor) : {frame_record['timing_ms']['image_preprocess_ms']:.3f}")
print(f" QNN execute (3 contexts) : {frame_record['timing_ms']['qnn_invoke_ms']:.3f}")
print(f" backbone context execute : {frame_record['timing_ms']['backbone_invoke_ms']:.3f}")
print(f" encoder context execute : {frame_record['timing_ms']['encoder_invoke_ms']:.3f}")
print(f" decoder context execute : {frame_record['timing_ms']['decoder_invoke_ms']:.3f}")
print(f" postprocess (NMS + save boxes) : {frame_record['timing_ms']['postprocess_ms']:.3f}")
print(f" visualization (camera-grid PNG) : {frame_record['timing_ms']['visualization_ms']:.3f}")
print(f" model path total (no drawing) : {frame_record['timing_ms']['frame_total_ms']:.3f}")
print(f" final detections : {len(scores)} BEV boxes")
if visualize:
print(f" camera grid image : {visualizations[f'frame{frame_index:03d}']['path']}")
print(" top detections")
print(" rank class score box[x, y, z, w, l, h, yaw]")
for rank, det in enumerate(top_detections, start=1):
box = det["box"]
box_text = ", ".join(f"{value:.3f}" for value in box[:7])
print(f" {rank:<5} {det['class_name']:<12} {det['score']:.6f} [{box_text}]")
camera_grid_gif = None
camera_grid_summary = None
gif_ms = 0.0
if visualize and visualizations:
gif_start = time.perf_counter_ns()
ordered_records = [visualizations[key] for key in sorted(visualizations)]
camera_grid_gif = save_camera_grid_gif(ordered_records, output_path)
camera_grid_summary = save_camera_grid_summary(output_path, ordered_records, camera_grid_gif)
gif_ms = elapsed_ms(gif_start)
if camera_grid_gif:
print(f"Camera grid GIF saved: {camera_grid_gif['path']}")
total_with_visualization_ms = elapsed_ms(run_manifest_start)
total_without_visualization_ms = (
manifest_load_ms
+ sum(frame_ms)
+ sum(postprocess_ms)
)
total_visualization_ms = sum(visualization_ms) + gif_ms
return {
"status": "PASS",
"manifest": str(Path(manifest_path).resolve()),
"nms_contract": str(Path(nms_contract_path).resolve()),
"repo_root": str(Path(repo_root).resolve()),
"frame_range": [int(frame_start), int(end - 1)] if end > frame_start else [],
"completed_frames": int(end - frame_start),
"scene_start_encoder_count": scene_start_count,
"temporal_encoder_count": temporal_count,
"models": self.model_records,
"model_load_timing_ms": self.model_load_timing_ms,
"end_to_end_timing_ms": {
"manifest_and_contract_load_ms": manifest_load_ms,
"complete_inference_no_visualization_ms": total_without_visualization_ms,
"visualization_total_ms": total_visualization_ms,
"complete_inference_with_visualization_ms": total_with_visualization_ms,
"camera_grid_gif_ms": gif_ms,
},
"timing_ms": stats(frame_ms),
"qnn_invoke_ms": stats(qnn_invoke_ms),
"component_invoke_ms": {
"backbone": stats(backbone_invoke_ms),
"encoder": stats(encoder_invoke_ms),
"encoder_scene_start": stats(scene_start_encoder_invoke_ms),
"encoder_temporal": stats(temporal_encoder_invoke_ms),
"decoder": stats(decoder_invoke_ms),
},
"per_bin_qnn_invoke_ms": {
"backbone_context.bin": stats(backbone_invoke_ms),
"scene_start_encoder_context.bin": stats(scene_start_encoder_invoke_ms),
"temporal_encoder_context.bin": stats(temporal_encoder_invoke_ms),
"decoder_context.bin": stats(decoder_invoke_ms),
},
"image_preprocess_ms": stats(image_preprocess_ms),
"postprocess_ms": stats(postprocess_ms),
"nms_decode_ms": stats(nms_decode_ms),
"raw_save_ms": stats(raw_save_ms),
"final_coordinates_save_ms": stats(final_coordinates_save_ms),
"visualization_ms": stats(visualization_ms),
"timing_contract": {
"frame_total_ms": "Per-frame inference path: image preprocessing plus model input/output and QNN invoke time; excludes NMS, result saving, and camera-grid visualization rendering.",
"qnn_invoke_ms": "Backbone + selected encoder + decoder invoke time only. Excludes preprocessing, tensor set/get, NMS, result saving, and visualization.",
"per_bin_qnn_invoke_ms": "Pure AidLite/QNN interpreter.invoke() time grouped by delivered .bin context file.",
"image_preprocess_ms": "Six-camera JPG decode, RGB conversion, normalization, resize, CHW conversion, and zero padding.",
"complete_inference_no_visualization_ms": "Manifest load plus all frame inference, NMS, and result saving; excludes PNG/GIF rendering.",
"complete_inference_with_visualization_ms": "Full run_manifest wall time including PNG/GIF rendering; excludes Python process startup and model loading, which are reported separately.",
},
"frames": frames,
"final_outputs": final_outputs,
"final_coordinates": final_coordinates,
"visualizations": visualizations,
"camera_grid_gif": camera_grid_gif,
"camera_grid_summary": camera_grid_summary,
}
|