feat(deployment): centerpoint deployment integration

deployment/projects/centerpoint/README.md

@@ -35,7 +35,7 @@ Adjust at least:
 - `components` — per-subgraph ONNX/engine names, I/O dtypes, dynamic axes, and TensorRT profiles (must match your model grid and voxel limits).
 - `runtime_io` — sample `info_file` (relative to model config `data_root`) and `sample_idx`.
 
-Required component keys: `pts_voxel_encoder`, `pts_backbone_neck_head` (validated in `entrypoint.py`).
+Required component keys: `pts_voxel_encoder`, `pts_backbone_neck_head` (declared in project adapter metadata and validated by shared project validator).
 
 ## Layout
 

deployment/projects/centerpoint/__init__.py

@@ -0,0 +1,23 @@
+"""CenterPoint deployment bundle.
+
+This package owns all CenterPoint deployment-specific code (runner/evaluator/loader/pipelines/export).
+It registers a ProjectAdapter into the global `project_registry` so the unified CLI can invoke it.
+"""
+
+from __future__ import annotations
+
+from deployment.projects.centerpoint.cli import add_args
+from deployment.projects.centerpoint.entrypoint import run
+
+# Trigger pipeline factory registration for this project.
+from deployment.projects.centerpoint.pipelines.factory import CenterPointPipelineFactory  # noqa: F401
+from deployment.projects.registry import ProjectAdapter, project_registry
+
+project_registry.register(
+    ProjectAdapter(
+        name="centerpoint",
+        add_args=add_args,
+        run=run,
+        required_components=("pts_voxel_encoder", "pts_backbone_neck_head"),
+    )
+)

deployment/projects/centerpoint/cli.py

@@ -0,0 +1,14 @@
+"""CenterPoint CLI extensions."""
+
+from __future__ import annotations
+
+import argparse
+
+
+def add_args(parser: argparse.ArgumentParser) -> None:
+    """Register CenterPoint-specific CLI flags onto a project subparser."""
+    parser.add_argument(
+        "--rot-y-axis-reference",
+        action="store_true",
+        help="Convert rotation to y-axis clockwise reference (CenterPoint ONNX-compatible format)",
+    )

deployment/projects/centerpoint/config/deploy_config.py

@@ -2,17 +2,14 @@
 CenterPoint Deployment Configuration
 """
 
-# ============================================================================
-# Task type for pipeline building
-# Options: 'detection2d', 'detection3d', 'classification', 'segmentation'
-# ============================================================================
-task_type = "detection3d"
-
 # ============================================================================
 # Checkpoint Path - Single source of truth for PyTorch model
 # ============================================================================
 checkpoint_path = "work_dirs/centerpoint/best_checkpoint.pth"
 
+# Log file path (relative paths are under export.work_dir). Set to None to disable file logging.
+deploy_log_path = "deployment.log"
+
 # ============================================================================
 # Device settings (shared by export, evaluation, verification)
 # ============================================================================
@@ -21,33 +18,37 @@
     cuda="cuda:0",
 )
 
+# Single literal for deployment output root (used before `export` exists).
+_DEPLOY_WORK_DIR = "work_dirs/centerpoint_deployment"
+_WORK_DIR = _DEPLOY_WORK_DIR.rstrip("/")
+_ONNX_DIR = f"{_WORK_DIR}/onnx"
+_TENSORRT_DIR = f"{_WORK_DIR}/tensorrt"
+
 # ============================================================================
 # Export Configuration
+# mode: "onnx", "trt", "both", "none"
+# work_dir: path to the deployment output root
+# onnx_path: path to the ONNX output directory (if mode="trt" and ONNX already exists)
 # ============================================================================
 export = dict(
     mode="both",
-    work_dir="work_dirs/centerpoint_deployment",
-    onnx_path=None,
+    work_dir=_DEPLOY_WORK_DIR,
+    onnx_path=_ONNX_DIR,
 )
 
-# Derived artifact directories
-_WORK_DIR = str(export["work_dir"]).rstrip("/")
-_ONNX_DIR = f"{_WORK_DIR}/onnx"
-_TENSORRT_DIR = f"{_WORK_DIR}/tensorrt"
 
 # ============================================================================
 # Unified Component Configuration (Single Source of Truth)
 #
+# Component key is the unique identifier (used for config lookup, filenames, logs).
 # Each component defines:
-#   - name: Component identifier used in export
 #   - onnx_file: Output ONNX filename
 #   - engine_file: Output TensorRT engine filename
 #   - io: Input/output specification for ONNX export
 #   - tensorrt_profile: TensorRT optimization profile (min/opt/max shapes)
 # ============================================================================
 components = dict(
-    voxel_encoder=dict(
-        name="pts_voxel_encoder",
+    pts_voxel_encoder=dict(
         onnx_file="pts_voxel_encoder.onnx",
         engine_file="pts_voxel_encoder.engine",
         io=dict(
@@ -64,14 +65,14 @@
         ),
         tensorrt_profile=dict(
             input_features=dict(
+                # Make sure to match the shape of the input to the model
                 min_shape=[1000, 32, 11],
                 opt_shape=[20000, 32, 11],
-                max_shape=[64000, 32, 11],
+                max_shape=[96000, 32, 11],
             ),
         ),
     ),
-    backbone_head=dict(
-        name="pts_backbone_neck_head",
+    pts_backbone_neck_head=dict(
         onnx_file="pts_backbone_neck_head.onnx",
         engine_file="pts_backbone_neck_head.engine",
         io=dict(
@@ -98,6 +99,8 @@
         ),
         tensorrt_profile=dict(
             spatial_features=dict(
+                # Make sure to match the shape of the input to the model
+                # check grid size in the model config
                 min_shape=[1, 32, 1020, 1020],
                 opt_shape=[1, 32, 1020, 1020],
                 max_shape=[1, 32, 1020, 1020],
@@ -111,8 +114,8 @@
 # ============================================================================
 runtime_io = dict(
     # This should be a path relative to `data_root` in the model config.
-    info_file="info/t4dataset_j6gen2_infos_val.pkl",
-    sample_idx=1,
+    info_file="info/t4dataset_j6gen2_base_infos_test.pkl",
+    sample_idx=5,
 )
 
 # ============================================================================
@@ -128,6 +131,7 @@
 
 # ============================================================================
 # TensorRT Build Settings (shared across all components)
+# Supports `auto`, `fp16`, `fp32_tf32`, and `strongly_typed`
 # ============================================================================
 tensorrt_config = dict(
     precision_policy="auto",
@@ -161,10 +165,19 @@
 
 # ============================================================================
 # Verification Configuration
+#
+# Tolerance is backend- and machine-dependent:
+# - The same scenario can show very different max/mean diffs on different machines: GPU
+#   architecture, driver, ORT/CUDA/TRT versions, and ORT's CUDA graph partitioning (CPU
+#   fallback nodes for small ops) all change numerics. ONNX on CPU, ONNX on CUDA, and
+#   TensorRT on CUDA are not directly comparable to each other as "one true" references.
+# - Additionally, the verification configuration should use a precision-aware tolerance,
+#   especially when FP16 is enabled.
 # ============================================================================
 verification = dict(
     enabled=False,
-    tolerance=1e-1,
+    # TODO(vividf): double check the tolerance value
+    tolerance=1,
     num_verify_samples=1,
     devices=devices,
     scenarios=dict(

deployment/projects/centerpoint/entrypoint.py

@@ -0,0 +1,70 @@
+"""CenterPoint deployment entrypoint invoked by the unified CLI."""
+
+from __future__ import annotations
+
+import argparse
+import logging
+
+from mmengine.config import Config
+
+from deployment.cli.args import add_deployment_file_logging, setup_logging
+from deployment.configs.base import BaseDeploymentConfig
+from deployment.core.contexts import CenterPointExportContext
+from deployment.projects.centerpoint.eval.evaluator import CenterPointEvaluator
+from deployment.projects.centerpoint.eval.metrics_utils import extract_t4metric_v2_config
+from deployment.projects.centerpoint.io.data_loader import CenterPointDataLoader
+from deployment.projects.centerpoint.runner import CenterPointDeploymentRunner
+from deployment.projects.registry import project_registry
+
+
+def run(args: argparse.Namespace) -> int:
+    """Run the CenterPoint deployment workflow for the unified CLI.
+
+    Args:
+        args: Parsed command-line arguments containing deploy_cfg and model_cfg paths.
+
+    Returns:
+        Exit code (0 for success).
+    """
+    logger = setup_logging(args.log_level)
+
+    deploy_cfg = Config.fromfile(args.deploy_cfg)
+    model_cfg = Config.fromfile(args.model_cfg)
+    config = BaseDeploymentConfig(deploy_cfg)
+
+    log_file = config.resolved_deploy_log_file
+    if log_file:
+        add_deployment_file_logging(log_file)
+        logger.info("Deployment log file: %s", log_file)
+
+    project_registry.validate_required_components("centerpoint", config.components_cfg)
+
+    logger.info("=" * 80)
+    logger.info("CenterPoint Deployment Pipeline")
+    logger.info("=" * 80)
+
+    data_loader = CenterPointDataLoader(
+        info_file=config.runtime_config.info_file,
+        model_cfg=model_cfg,
+    )
+    logger.info(f"Loaded {data_loader.num_samples} samples")
+
+    metrics_config = extract_t4metric_v2_config(model_cfg, logger=logger)
+
+    evaluator = CenterPointEvaluator(
+        model_cfg=model_cfg,
+        metrics_config=metrics_config,
+        components_cfg=config.components_cfg,
+    )
+
+    runner = CenterPointDeploymentRunner(
+        data_loader=data_loader,
+        evaluator=evaluator,
+        config=config,
+        model_cfg=model_cfg,
+        logger=logger,
+    )
+
+    context = CenterPointExportContext(rot_y_axis_reference=bool(getattr(args, "rot_y_axis_reference", False)))
+    runner.run(context=context)
+    return 0