x4d-devkit

Canonical contract library and x4d-devkit CLI for consuming X-4D data and public platform capabilities.

x4d-devkit owns X-4D data semantics, identity, coordinate/schema contracts, platform IO adapters, and official evaluation primitives. It is the stable interpretation layer between X-4D platform state and consumers such as training platforms, X-Points, external inference services, evaluation scripts, and automation.

It is not a downstream training, annotation-tool UI, experiment, deployment, or framework-specific cache-policy package. It may call public platform APIs or read standard X-4D files, but it must not import backend-private app.* modules or mutate DB/MinIO internals.

Design decisions for this boundary are tracked in GitHub issues, starting with:

• #42 - official external SDK and CLI boundary
• #43 - historical private-source runtime integration
• #44 - capability registry and CLI sync

Installation

pip install x4d-devkit==0.22.0rc1

Devkit is released independently as an immutable wheel. X-4D, XPoints, and other consumers must install one exact package version during image builds. Runtime source copies, submodule installs, editable installs, VCS main installs, and fallback dependency paths are not supported deployment modes. Use a published prerelease for coordinated cross-repository development.

Optional heavy dependencies:

# NuScenes format converter
pip install x4d-devkit[converters]

# External inference service SDK
pip install x4d-devkit[inference]

The platform API client and x4d-devkit CLI are base package features. See the release process for the immutable artifact and consumer cutover rules.

Scope

See docs/module-boundary.md for the module ownership table and command boundary.

Internal consumers should use the current-state identity contract for platform-to-platform data flow. See docs/current-state-identity.md for the generic identity model, and docs/training-current-state-data.md for work-session materialization and conditional annotation writeback. Label-schema bodies are resolved from X-4D and passed explicitly at every clip loading and validation boundary. Shared validation rules for platform submit, X-Points preflight, local clip validation, and training consumers are documented in docs/shared-contract-validation.md. Portable export producers and consumers share the exact 0.11 manifest parser documented in docs/export-bundle-contract.md. examples/current_state_identity_consumers.py provides an offline downstream fixture for read-only, read-write, and materialization consumers.

Included:

• Local X-4D dataset loading, validation, transforms, annotations, calibration, ego pose, converters, and evaluation helpers.
• Platform API client and public x4d-devkit CLI commands for external workflows such as clip listing/download, screening preview, model/checkpoint registration, status queries, and capability discovery.
• Strict validation of x4d-training-identity-v1 manifests.
• Strict producer-option and manifest parsing for export bundle 0.11.
• External inference service wrappers for third-party model-serving projects.

Excluded:

• Backend migrations, DB/MinIO repair scripts, cache rebuild internals, emergency operations, and one-off delivery importers.
• Any code that imports backend-private app.*.
• Downstream experiment taxonomy, CLASS_NAMES, model-class mappings, sampler policy, model acceptance thresholds, deployment policy, and framework-specific artifacts such as OpenPCDet infos/dbinfos or mmdetection3d pickle views.

Promotion rule: an internal operation becomes an x4d-devkit command only after it is productized as an external SDK/API workflow with explicit permissions, side effects, idempotency, and failure modes. Data deletion, clip production, and internal service dispatch stay out of devkit.

Training-specific convenience utilities must remain framework-neutral. Devkit may expose raw category stats, identity fingerprints, coordinate/schema validation, cache completeness checks, and official X-4D metrics; downstream training repos own taxonomy mapping, training-format generation, sampler configuration, and experiment lifecycle.

Quick Start

Connect to an X-4D platform

The CLI can authenticate with a bearer token passed explicitly, through environment variables, or from the user config written by x4d-devkit login.

x4d-devkit --api-url http://host:8000 --token <token> projects list
x4d-devkit --api-url http://host:8000 --token <token> clips list --project-id 1
x4d-devkit --api-url http://host:8000 --token <token> clips download --clip-ids clip_a,clip_b --output /data/x4d/clips

export X4D_API_URL=http://host:8000
export X4D_TOKEN=<token>
x4d-devkit projects list
x4d-devkit clips list --project-name nuscenes-mini --has-archive true --format json
x4d-devkit clips download --project-name nuscenes-mini --output /data/x4d/nuscenes-mini/clips --workers 8
x4d-devkit clips download --project-name nuscenes-mini --archive-profile keyframes_only --output /data/x4d/nuscenes-mini/keyframes --workers 8

x4d-devkit --api-url http://host:8000 login --username <user> --password <password>

login stores api_url and the returned access token as token in ~/.config/x4d/config.toml. Archive downloads go through the platform file proxy and extract standard X4D clip directories; training containers do not need database or MinIO access.

Clip archive downloads are profile-aware. --archive-profile keyframes_only is the default and requests the smaller archive with sweeps removed and metadata filtered accordingly. Use --archive-profile full when a consumer needs the complete clip payload including sweeps/. If the requested profile has not been built, is stale, or failed to build, the devkit reports that profile-specific reason instead of falling back to another tar.

Validate a training identity manifest

The manifest command has one contract and one operation. It validates an exact x4d-training-identity-v1 record; it does not create datasets, download clips, or translate earlier manifest shapes.

x4d-devkit manifest validate \
  --manifest training_identity.json \
  --format json

Unsupported versions, removed aliases, and malformed clip/schema identities fail validation. Producers own manifest construction and must emit the current contract directly.

Consume the project label schema

X-4D is the only source of truth for label-schema bodies. Consumers resolve the effective project record from the platform instead of maintaining their own category list or copying the record into each clip.

x4d-devkit label-schemas project-get --project-id 1 --format json
x4d-devkit label-schemas get \
  --schema-id object_3d_low_speed_roadside \
  --version 1.0.0 \
  --format json

from x4d_devkit import X4DClient
from x4d_devkit.label_schema import compare_project_label_schema_snapshot

client = X4DClient.from_config()
project = client.projects.resolve(project_name="nuscenes-mini")
project_schema = client.label_schemas.resolve_project(project_name="nuscenes-mini")
project_schema_view = client.label_schemas.get_project_view(project["id"])
label_schema = project_schema.label_schema

print(project["id"], project["name"])
print(project_schema.binding.to_dict())
print(sorted(label_schema.class_ids))
print([category["id"] for category in project_schema_view.to_dict()["categories"]])

label_schema.validate_category("tanker_truck")

stored_snapshot = project_schema.to_dict()
current = client.label_schemas.resolve_project(project_id=project["id"])
comparison = compare_project_label_schema_snapshot(stored_snapshot, current)
assert comparison.status == "same"

The native 0.11 clip stores only the immutable identity in meta.label_schema. It must not contain label_schema.json. The complete registry record is explicit runtime context for ClipLoader, validate_clip, conversion, and work-session parsing; its identity must exactly match meta.label_schema.

Training code may still define experiment-specific class mappings, but those mappings must be validated against the resolved project record before use.

Inspect or diff current state

Dataset identity clients, local manifests, portable cache identities, remote identity manifests, and update plans use one schema field only: label_schema, containing the complete x4d-label-schema-identity-v1 object. label_schema_fingerprint and clip_label_schema are not aliases and are rejected. A ready platform record without the complete identity is also rejected.

x4d-devkit dataset inspect writes x4d-remote-identity-v2, and x4d-devkit dataset diff writes x4d-update-plan-v2. These artifacts contain normalized clip records once; they do not embed a duplicate raw platform response. V1 artifacts are rejected rather than migrated.

Load a clip

from x4d_devkit import ClipLoader

loader = ClipLoader("/path/to/clip", label_schema=label_schema)
print(loader.meta)

for sample in loader.samples:
    for sd in loader.sample_data_for_sample(sample.token):
        print(sd.channel, sd.file_path)

Open a platform work session

Internal tools such as X-Points can open the platform's current clip state without waiting for a prebuilt archive:

from x4d_devkit import ClipLoader, X4DClient, materialize_work_session_clip
from x4d_devkit.client import WorkSessionConflictError
from x4d_devkit.core.loader import CLIP_WORLD_FRAME_ID

client = X4DClient.from_config()
manifest = client.clip_work_sessions.open(project_id=1812, clip_id="clip-id")
materialize_work_session_clip(
    manifest,
    output_dir="/data/x4d_cache/clips/clip-id",
    client=client,
    materialization_policy="keyframes_only",
)

loader = ClipLoader.from_work_session(manifest, api_url=client.api_url)
anns_world = loader.annotations_for_sample("sample-token", frame=CLIP_WORLD_FRAME_ID)
lidar_asset = loader.point_cloud_asset("sample-token", manifest.annotation_source_channel)

try:
    response = client.clip_work_sessions.submit_annotations(
        manifest=manifest,
        annotations=manifest.annotations,
        instances=manifest.instances,
    )
    print(response["annotation_revision"])
except WorkSessionConflictError as exc:
    print(exc.current_identities)
    raise

The work-session envelope may carry the complete registry record so ClipLoader.from_work_session() can validate without another request. That record is transient request context, not a second persisted schema source. Materialization writes the native tables with the identity in meta.label_schema and never writes label_schema.json. Loading or validating the materialized directory later therefore requires the caller to resolve and pass the complete registry record explicitly.

A ready work session is accepted only when its full registry record, clip_label_schema, and the resulting native meta.label_schema declare the same identity. A mismatch fails closed; there is no legacy work-session shape or schema-file fallback.

Submit is conditional on the manifest's source_revision, annotation_revision, and base_label_schema_fingerprint. If the platform state changed after the session was opened, the client raises WorkSessionConflictError with machine-readable conflicts, base_identities, and current_identities.

Coordinate frame transforms

Frames are real frame_id strings: any node in the calibration tree (e.g. "LIDAR_TOP", "base_link", "cam_front") plus the constant "clip_world" (the SLAM-anchored clip-local world). The legacy aliases "sensor", "ego", "world" are not accepted.

Training converters should read the clip's self-described frame contract rather than assume fixed channel names. The native annotation frame is derived from meta.sensors[meta.annotation_source_channel].frame_id; clip_world is local to one clip and is not a global frame across clips.

The installed package exposes the training coordinate contract for logs and debugging:

x4d-devkit dataset coordinate-contract
x4d-devkit dataset coordinate-contract --format json

from x4d_devkit import COORDINATE_CONTRACT_VERSION, get_training_coordinate_contract

from x4d_devkit import ClipLoader
from x4d_devkit.core.loader import CLIP_WORLD_FRAME_ID

loader = ClipLoader("/path/to/clip", label_schema=label_schema)
sd = loader.sample_data_for_channel("LIDAR_TOP")[0]

# Load point cloud in different frames
pts_sensor = loader.load_point_cloud(sd)                                 # raw sensor (default)
pts_ego    = loader.load_point_cloud(sd, frame=loader.ego_pose_frame_id) # sensor → ego
pts_world  = loader.load_point_cloud(sd, frame=CLIP_WORLD_FRAME_ID)      # sensor → clip_world

# Get annotations transformed to clip-local world
anns_world = loader.annotations_for_sample(sample.token, frame=CLIP_WORLD_FRAME_ID)

# Or to a specific sensor's frame
anns_lidar = loader.annotations_for_sample(sample.token, frame="LIDAR_TOP")

# Get the transform matrix directly (sd is required when clip_world is involved)
T = loader.get_transform(loader.sensor_frame_id(sd), CLIP_WORLD_FRAME_ID, sd=sd)
pts_world = T.apply(pts_sensor[:, :3])  # or use T.as_matrix for 4x4

Validate a clip

x4d-devkit validate /path/to/clip --label-schema project_schema_record.json

from x4d_devkit import validate_clip

report = validate_clip("/path/to/clip", label_schema=label_schema)
print(report)

For a remote clip, call validate_clip_payload(..., asset_sizes=...) with a mapping of clip-relative object paths to byte sizes. This applies the same asset existence and point-cloud layout checks without downloading the assets.

Serve an external inference backend

Third-party model projects should use x4d-devkit[inference] instead of hand-writing the X-4D inference HTTP protocol.

from x4d_devkit.inference import DetectionModel, InferenceService, detection_3d_item


class MyDetector(DetectionModel):
    model_id = "centerpoint-v1"
    display_name = "CenterPoint v1"
    raw_classes = ["car", "truck", "pedestrian"]

    def predict_clip(self, clip, config):
        return [
            detection_3d_item(
                sample_token=clip.samples[0].token,
                raw_class="car",
                score=0.91,
                translation=(1.0, 2.0, 0.5),
                size=(4.5, 1.8, 1.6),
                yaw=0.2,
            )
        ]


InferenceService(
    service_name="my-detector",
    models=[MyDetector()],
    clip_resolver=resolve_clip,
).run(port=9000)

See docs/external-inference.md and examples/external_detection_service.py for the standard integration flow.

Detection evaluation

from x4d_devkit.eval import DetectionEval, DetectionConfig

config = DetectionConfig(
    class_names=["car", "pedestrian", "bicycle"],
    dist_thresholds=[0.5, 1.0, 2.0, 4.0],
)
evaluator = DetectionEval(config, gt_clips=[...], pred_clips=[...])
result = evaluator.evaluate()
print(f"mAP: {result.mAP:.3f}, NDS: {result.NDS:.3f}")

For detectors that intentionally do not predict velocity, construct 7D boxes and evaluate with with_velocity=False so velocity error is excluded from the primary detection score:

from x4d_devkit.eval import Box, DetectionConfig, evaluate

pred = {
    "sample_1": [
        Box.from_xyzlwhyaw([0, 0, 0, 4, 2, 1.5, 0.0], category="car", score=0.9)
    ]
}
config = DetectionConfig(
    class_names=["car"],
    dist_thresholds=[0.5, 1.0, 2.0, 4.0],
    dist_th_tp=2.0,
    min_recall=0.1,
    min_precision=0.1,
    max_boxes_per_sample=500,
    class_range={"car": 50.0},
    with_velocity=False,
)
result = evaluate(gt, pred, config)
assert result.with_velocity is False

Convert from NuScenes

from x4d_devkit.converters import NuScenesConverter

converter = NuScenesConverter("/path/to/nuscenes")
converter.convert_scene("scene-0001", output_dir="/path/to/output")

Modules

License

Apache License 2.0