# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
from __future__ import annotations
import contextlib
import glob
import importlib
import json
import operator
import os
import tempfile
import warnings
from collections import defaultdict
from collections.abc import Callable
from copy import copy, deepcopy
from functools import wraps
from pathlib import Path
from typing import TYPE_CHECKING, Any, Concatenate, ParamSpec, TypeVar
import numpy as np
import requests
import torch
import torchvision.transforms.functional as F # noqa: N812
import yaml
from PIL import Image
from rfdetr.assets.coco_classes import COCO_CLASS_NAMES, COCO_CLASSES
from rfdetr.assets.model_weights import download_pretrain_weights, get_model_cache_dir
from rfdetr.config import ModelConfig, TrainConfig
from rfdetr.datasets._keypoint_schema import (
active_keypoint_counts,
infer_coco_keypoint_schema,
infer_yolo_keypoint_schema,
)
from rfdetr.datasets.coco import is_valid_coco_dataset
from rfdetr.datasets.yolo import REQUIRED_YOLO_YAML_FILES, is_valid_yolo_dataset
from rfdetr.inference import ModelContext, _build_model_context
from rfdetr.utilities.distributed import is_main_process
from rfdetr.utilities.keypoints import _is_bg_first_schema, precision_cholesky_to_pixel_covariance
from rfdetr.utilities.logger import get_logger
if TYPE_CHECKING:
from supervision import Detections, KeyPoints
try:
torch.set_float32_matmul_precision("high")
except Exception:
pass
logger = get_logger()
_P = ParamSpec("_P")
_R = TypeVar("_R")
# ModelContext and _build_model_context are eagerly imported above (runtime use in get_model).
_VARIANT_EXPORTS = (
"RFDETRBase",
"RFDETRKeypointPreview",
"RFDETRLarge",
"RFDETRLargeDeprecated",
"RFDETRMedium",
"RFDETRNano",
"RFDETRSeg",
"RFDETRSeg2XLarge",
"RFDETRSegLarge",
"RFDETRSegMedium",
"RFDETRSegNano",
"RFDETRSegPreview",
"RFDETRSegSmall",
"RFDETRSegXLarge",
"RFDETRSmall",
)
__all__ = ["RFDETR", "ModelContext", *_VARIANT_EXPORTS]
_CHECKPOINT_MODEL_NAME_EXCLUDED_SYMBOLS = frozenset({"RFDETRLargeDeprecated", "RFDETRSeg"})
_CHECKPOINT_MODEL_NAME_CLASS_SYMBOLS: tuple[str, ...] = tuple(
class_symbol for class_symbol in _VARIANT_EXPORTS if class_symbol not in _CHECKPOINT_MODEL_NAME_EXCLUDED_SYMBOLS
)
_CHECKPOINT_PLUS_MODEL_NAME_CLASS_SYMBOLS: tuple[str, ...] = ("RFDETRXLarge", "RFDETR2XLarge")
_CHECKPOINT_MODEL_MAP_ENTRIES: tuple[tuple[str, str], ...] = (
("keypoint-preview", "RFDETRKeypointPreview"),
("seg-2xlarge", "RFDETRSeg2XLarge"),
("seg-xxlarge", "RFDETRSeg2XLarge"),
("seg-xlarge", "RFDETRSegXLarge"),
("seg-large", "RFDETRSegLarge"),
("seg-medium", "RFDETRSegMedium"),
("seg-small", "RFDETRSegSmall"),
("seg-nano", "RFDETRSegNano"),
("seg-preview", "RFDETRSegPreview"),
("large", "RFDETRLarge"),
("medium", "RFDETRMedium"),
("small", "RFDETRSmall"),
("nano", "RFDETRNano"),
("base", "RFDETRBase"),
)
_CHECKPOINT_PLUS_MODEL_MAP_ENTRIES: tuple[tuple[str, str], ...] = (
("2xlarge", "RFDETR2XLarge"),
("xxlarge", "RFDETR2XLarge"),
("xlarge", "RFDETRXLarge"),
)
def _validate_shape_dims(
shape: object,
block_size: int,
patch_size: int,
num_windows: int,
) -> tuple[int, int]:
"""Validate a user-supplied ``(height, width)`` shape tuple and return normalised plain-int dims.
Args:
shape: The raw value supplied by the caller (e.g. from ``export(shape=...)`` or
``predict(shape=...)``). Must be a two-element sequence of positive integers (or integer-compatible types
accepted by :func:`operator.index`).
block_size: Required divisor for both dimensions. Equals ``patch_size * num_windows``.
patch_size: Backbone patch size — used only in error messages.
num_windows: Number of attention windows — used only in error messages.
Returns:
A ``(height, width)`` tuple of plain Python :class:`int` values.
Raises:
ValueError: If ``shape`` cannot be unpacked as a two-element sequence, if either
dimension is a bool, float, or other non-integer type, if either dimension is not positive, or if either
dimension is not divisible by ``block_size``.
"""
try:
height, width = shape # type: ignore[misc]
except (TypeError, ValueError):
raise ValueError(f"shape must be a sequence of two positive integers (height, width), got {shape!r}.") from None
for dim_name, dim in (("height", height), ("width", width)):
if isinstance(dim, bool):
raise ValueError(f"shape {dim_name} must be an integer, got {type(dim).__name__} (shape={shape!r}).")
try:
operator.index(dim)
except TypeError:
raise ValueError(
f"shape {dim_name} must be an integer, got {type(dim).__name__} (shape={shape!r}).",
) from None
if dim <= 0:
raise ValueError(f"shape must contain positive integers for height and width, got {shape!r}.")
# Normalise to plain Python ints; also accepts numpy.int64, torch scalars, etc.
height, width = operator.index(height), operator.index(width)
if height % block_size != 0 or width % block_size != 0:
raise ValueError(
f"shape must have both dimensions divisible by {block_size} "
f"(patch_size={patch_size} * num_windows={num_windows}), got {shape!r}.",
)
return height, width
def _resolve_patch_size(patch_size: int | None, model_config: object, caller: str) -> int:
"""Resolve and validate the ``patch_size`` argument for :meth:`RFDETR.export` and :meth:`RFDETR.predict`.
Args:
patch_size: Value supplied by the caller, or ``None`` to read from ``model_config``.
model_config: The model's configuration object. Must expose ``patch_size`` as a
positive integer attribute when ``patch_size`` is ``None`` or when a mismatch check is needed.
caller: Name of the calling method (``"export"`` or ``"predict"``) — used in
error messages to help the caller locate the problem.
Returns:
A validated, positive :class:`int` patch size.
Raises:
ValueError: If the resolved or provided ``patch_size`` is not a positive integer,
or if a caller-provided value disagrees with ``model_config.patch_size``.
"""
if patch_size is None:
patch_size = getattr(model_config, "patch_size", 14)
else:
if isinstance(patch_size, bool) or not isinstance(patch_size, int) or patch_size <= 0:
raise ValueError(f"patch_size must be a positive integer, got {patch_size!r}")
model_patch_size = getattr(model_config, "patch_size", None)
if model_patch_size is not None and patch_size != model_patch_size:
raise ValueError(
f"{caller}(patch_size={patch_size}) does not match the instantiated model's "
f"patch_size={model_patch_size}. Patch size is an architectural parameter; "
f"omit patch_size to use the model's configured value.",
)
if isinstance(patch_size, bool) or not isinstance(patch_size, int) or patch_size <= 0:
raise ValueError(f"patch_size must be a positive integer, got {patch_size!r}")
return patch_size
def _move_model_context_to_device(model_ctx: Any) -> None:
"""Move model weights to the target device recorded in *model_ctx*.
``_build_model_context`` intentionally keeps the ``nn.Module`` on CPU so that ``RFDETR.__init__`` does not
initialise CUDA (which would prevent DDP strategies from forking in notebook environments). This helper performs
the deferred ``.to(device)`` on first use.
It is safe to call on duck-typed stand-ins (e.g. ``SimpleNamespace``); the function silently returns when the
expected attributes are missing.
"""
target = getattr(model_ctx, "device", None)
inner = getattr(model_ctx, "model", None)
if target is None or inner is None or not hasattr(inner, "parameters"):
return
if isinstance(target, str):
target = torch.device(target)
first_param = next(inner.parameters(), None)
if first_param is not None and first_param.device != target:
model_ctx.model = inner.to(target)
def _ensure_model_on_device(method: Callable[Concatenate[Any, _P], _R]) -> Callable[Concatenate[Any, _P], _R]:
"""Decorate RF-DETR instance methods that require lazy model device placement.
The wrapped method receives the same arguments and return value as the original method. Before calling it, the
decorator moves ``self.model.model`` to ``self.model.device`` if the model context is available and the weights are
still on a different device. This keeps public inference methods clean while preserving deferred CUDA initialization
during ``RFDETR.__init__``.
"""
@wraps(method)
def wrapper(self: Any, *args: _P.args, **kwargs: _P.kwargs) -> _R:
_move_model_context_to_device(getattr(self, "model", None))
return method(self, *args, **kwargs)
return wrapper
class RFDETR:
"""The base RF-DETR class implements the core methods for training RF-DETR models, running inference on the models,
optimising models, and uploading trained models for deployment."""
means = [0.485, 0.456, 0.406]
stds = [0.229, 0.224, 0.225]
size = None
_model_config_class: type[ModelConfig] = ModelConfig
_train_config_class: type[TrainConfig] = TrainConfig
def __init__(self, **kwargs):
self.model_config = self.get_model_config(**kwargs)
self.maybe_download_pretrain_weights()
self.model = self.get_model(self.model_config)
self.callbacks = defaultdict(list)
# repeat means and stds for non-rgb images
if self.model_config.num_channels != 3:
from itertools import cycle
self.means = [val for _, val in zip(range(self.model_config.num_channels), cycle(self.means))]
self.stds = [val for _, val in zip(range(self.model_config.num_channels), cycle(self.stds))]
self.model.inference_model = None
self._is_optimized_for_inference = False
self._has_warned_about_not_being_optimized_for_inference = False
self._optimized_has_been_compiled = False
self._optimized_batch_size = None
self._optimized_resolution = None
self._optimized_dtype = None
self._optimized_inplace = False
def maybe_download_pretrain_weights(self):
"""Download pre-trained weights if they are not already downloaded.
Bare filenames (no directory component, e.g. ``rf-detr-base.pth``) are resolved to the model cache directory —
set the ``RF_HOME`` environment variable to override the location (default: ``~/.roboflow/models``). Resolution
happens in ``ModelConfig.expand_path`` for explicitly-provided values, and here as a fallback for field defaults
(which Pydantic does not validate by default).
Paths that already contain a directory component are used as-is; the parent directory is created if it does not
yet exist.
"""
pretrain_weights = self.model_config.pretrain_weights
if pretrain_weights is None:
return
if not os.path.dirname(pretrain_weights):
# Field default was not processed by expand_path — resolve to cache dir.
cache_dir = get_model_cache_dir()
os.makedirs(cache_dir, exist_ok=True)
pretrain_weights = os.path.join(cache_dir, pretrain_weights)
else:
os.makedirs(os.path.dirname(pretrain_weights), exist_ok=True)
self.model_config.pretrain_weights = pretrain_weights
download_pretrain_weights(self.model_config.pretrain_weights)
def get_model_config(self, **kwargs) -> ModelConfig:
"""Retrieve the configuration parameters used by the model."""
return self._model_config_class(**kwargs)
@classmethod
def from_checkpoint(cls, path: str | os.PathLike[str], **kwargs: Any) -> RFDETR:
"""Load an RF-DETR model from a training checkpoint, automatically inferring the model class.
The correct subclass is resolved in order of preference:
1. ``model_name`` key in the checkpoint (written by the PTL training
stack since v1.7.0).
2. ``pretrain_weights`` field in the checkpoint's ``args`` entry
(legacy fallback for older checkpoints).
3. The **filename** of *path* itself, used as a last resort when
``pretrain_weights`` is absent or an unset-like sentinel value
(empty string, ``"none"``, or ``"null"``). Starter weights
published by Roboflow store ``pretrain_weights="none"`` in their
``args``; passing the canonical filename (e.g.
``rf-detr-small.pth``) lets ``from_checkpoint`` infer the class
automatically.
Both legacy ``argparse.Namespace`` checkpoints (produced by ``engine.py``) and dict-style checkpoints (produced
by the PTL training stack) are supported.
Args:
path: Path to a checkpoint file (e.g. ``checkpoint_best_total.pth``).
**kwargs: Additional keyword arguments forwarded to the model
constructor (e.g. ``accept_platform_model_license=True`` for XLarge / 2XLarge models).
``num_classes`` is resolved in this priority order:
1. Explicit caller kwarg — always wins.
2. Weight inference from ``class_embed.weight`` shape in the checkpoint
(``shape[0] - 1``, since the head includes a background class). This
overrides a stale ``model_config`` value written before fine-tuning
changed the class count.
3. ``saved_model_config["num_classes"]`` from the checkpoint's
``model_config`` entry — may be stale for older checkpoints.
4. Legacy ``args["num_classes"]`` dict entry.
5. Constructor default.
In cases 2–5 the field is not recorded as a user-set override, so
:meth:`train` can still adapt the detection head to the training
dataset's class count. Pass an explicit ``num_classes=N`` to pin
the head and prevent adaptation.
Returns:
An instance of the appropriate :class:`RFDETR` subclass loaded from the checkpoint.
Warning:
This method calls ``torch.load`` with ``weights_only=False``, which
unpickles arbitrary Python objects. Only load checkpoints from trusted sources.
Raises:
FileNotFoundError: If *path* does not exist.
OSError: If *path* exists but cannot be read.
KeyError: If the checkpoint does not contain an ``"args"`` key.
ValueError: If the model class cannot be inferred from ``model_name``,
``pretrain_weights``, or the checkpoint filename.
Examples:
>>> model = RFDETR.from_checkpoint("checkpoint_best_total.pth") # doctest: +SKIP
>>> model = RFDETRSmall.from_checkpoint("checkpoint_best_total.pth") # doctest: +SKIP
"""
# Local import breaks the variants → detr import cycle.
import rfdetr.variants as rfdetr_variants
_plus_available = False
_plus_symbols: dict[str, type[RFDETR]] = {}
_plus_entries: list[tuple[str, type[RFDETR]]] = []
from rfdetr.platform import _IS_RFDETR_PLUS_AVAILABLE
if _IS_RFDETR_PLUS_AVAILABLE:
try:
import rfdetr.platform.models as platform_models
for class_symbol in _CHECKPOINT_PLUS_MODEL_NAME_CLASS_SYMBOLS:
plus_obj = getattr(platform_models, class_symbol)
_plus_symbols[class_symbol] = plus_obj
_plus_entries = [
(name, _plus_symbols[class_symbol]) for name, class_symbol in _CHECKPOINT_PLUS_MODEL_MAP_ENTRIES
]
_plus_available = True
except ModuleNotFoundError as ex:
if ex.name not in {"rfdetr_plus", "rfdetr_plus.models"}:
raise
# weights_only=False is required because legacy checkpoints embed
# argparse.Namespace objects that cannot be deserialised with
# weights_only=True.
ckpt: dict[str, Any] = torch.load(path, map_location="cpu", weights_only=False)
args = ckpt["args"]
_variant_name_to_class: dict[str, type[RFDETR]] = {
getattr(variant_obj, "__name__", symbol): variant_obj
for symbol in dir(rfdetr_variants)
if symbol.startswith("RFDETR")
for variant_obj in [getattr(rfdetr_variants, symbol)]
}
_variant_symbols: dict[str, type[RFDETR]] = {
class_symbol: _variant_name_to_class[class_symbol] for class_symbol in _CHECKPOINT_MODEL_NAME_CLASS_SYMBOLS
}
# Build in three explicit segments: seg-* entries, then plus-model entries
# (xlarge/2xlarge), then base entries — order determines lookup priority.
_seg_map: list[tuple[str, type[RFDETR]]] = [
(name, _variant_symbols[class_symbol])
for name, class_symbol in _CHECKPOINT_MODEL_MAP_ENTRIES
if name.startswith("seg-")
]
_keypoint_map: list[tuple[str, type[RFDETR]]] = [
(name, _variant_symbols[class_symbol])
for name, class_symbol in _CHECKPOINT_MODEL_MAP_ENTRIES
if "keypoint" in name
]
_base_map: list[tuple[str, type[RFDETR]]] = [
(name, _variant_symbols[class_symbol])
for name, class_symbol in _CHECKPOINT_MODEL_MAP_ENTRIES
if not name.startswith("seg-") and "keypoint" not in name
]
_model_map: list[tuple[str, type[RFDETR]]] = _seg_map + _keypoint_map + _plus_entries + _base_map
# New checkpoints store model_name directly — use it when available.
_name_map: dict[str, type[RFDETR]] = dict(_variant_symbols)
# Plus-model classes are resolved only when rfdetr_plus is installed.
if _plus_available:
_name_map.update(_plus_symbols)
saved_model_name = ckpt.get("model_name")
model_cls: type[RFDETR] | None = None
if isinstance(saved_model_name, str):
normalized_name = saved_model_name.strip()
if normalized_name:
model_cls = _name_map.get(normalized_name)
else:
normalized_name = ""
# Fall back to pretrain_weights (legacy) or, when unset-like, the checkpoint filename.
if isinstance(args, dict):
weights_name = str(args.get("pretrain_weights", "")).strip().lower()
else:
weights_name = str(getattr(args, "pretrain_weights", "")).strip().lower()
# The sentinel set {"", "none", "null"} covers unset-like checkpoint values:
# "" — pretrain_weights key absent entirely
# "none" — checkpoint value was None or the literal string "none";
# after str(...).strip().lower() both normalize to the same sentinel.
# This is NOT an intentional "no pretraining" flag (see
# test_pretrain_weights_none_warns, which operates at the config
# level, not the checkpoint level)
# "null" — checkpoint stored the literal string "null" (for example from a
# YAML-originated value), which is also treated as unset-like here
_filename_fallback = False
if weights_name in {"", "none", "null"}:
weights_name = os.path.basename(os.fspath(path)).lower()
_filename_fallback = True
if model_cls is None:
# Guard: plus-only checkpoints should raise an actionable install error
# when rfdetr_plus is missing, regardless of whether class inference
# relies on model_name (new format) or pretrain_weights (legacy format).
plus_by_model_name = normalized_name in _CHECKPOINT_PLUS_MODEL_NAME_CLASS_SYMBOLS
plus_by_weights_name = (
"xlarge" in weights_name and "seg-" not in weights_name and "keypoint-preview" not in weights_name
)
if not _plus_available and (plus_by_model_name or plus_by_weights_name):
from rfdetr.platform import _INSTALL_MSG
raise ImportError(
f"Checkpoint model_name={saved_model_name!r}, pretrain_weights={weights_name!r} requires the "
f"rfdetr_plus package. " + _INSTALL_MSG.format(name="platform model downloads")
)
for name, klass in _model_map:
if name in weights_name:
model_cls = klass
break
if _filename_fallback and model_cls is not None:
logger.info(
"pretrain_weights unset in checkpoint %r; inferred model class %s from filename %r",
path,
getattr(model_cls, "__name__", repr(model_cls)),
weights_name,
)
if model_cls is None:
raise ValueError(
f"Could not infer model class from checkpoint at {path!r} "
f"(model_name={saved_model_name!r}, pretrain_weights={weights_name!r}). "
f"Please instantiate the model class directly."
)
if isinstance(args, dict):
num_classes: int | None = args.get("num_classes")
else:
num_classes = getattr(args, "num_classes", None)
constructor_kwargs: dict[str, Any] = {}
checkpoint_config_keys: set[str] = set() # keys injected from checkpoint, not from caller
# Resolve model config field set once — used for both saved_model_config parsing and
# weight-based schema inference guards (BaseConfig has extra="forbid"; unknown fields raise).
_model_config_class = getattr(model_cls, "_model_config_class", None)
_mc_fields: dict[str, Any] = {}
_mc_model_fields = getattr(_model_config_class, "model_fields", None)
if isinstance(_mc_model_fields, dict):
_mc_fields = _mc_model_fields
else:
_mc_legacy = getattr(_model_config_class, "__fields__", None)
if isinstance(_mc_legacy, dict):
_mc_fields = _mc_legacy
saved_model_config = ckpt.get("model_config")
if isinstance(saved_model_config, dict):
for key, value in saved_model_config.items():
if key == "pretrain_weights":
continue
if not _mc_fields or key in _mc_fields:
constructor_kwargs[key] = value
checkpoint_config_keys.add(key)
if num_classes is not None and "num_classes" not in kwargs:
constructor_kwargs["num_classes"] = num_classes
checkpoint_config_keys.add("num_classes")
# Infer schema-critical fields from checkpoint weights — these are authoritative when
# ``model_config`` is absent or stale (saved before ``model_config`` persistence was added,
# or saved with default values before fine-tuning changed the trained schema).
# User-supplied ``kwargs`` take precedence and are applied in the ``update`` call below.
_ckpt_weights: dict[str, Any] = ckpt.get("model") or {}
if not _ckpt_weights and "state_dict" in ckpt:
_pfx = "model."
_ckpt_weights = {}
for k, v in ckpt["state_dict"].items():
if k.startswith(_pfx):
key = k[len(_pfx) :]
# Strip optional torch.compile() wrapper prefix
if key.startswith("_orig_mod."):
key = key[len("_orig_mod.") :]
_ckpt_weights[key] = v
if _ckpt_weights:
# num_keypoints_per_class — inferred from _kp_active_mask (shape [num_classes, max_kp]).
# Reflects what the model actually learned; saved model_config may carry the COCO default
# [0, 17] even after fine-tuning on a different keypoint schema.
if "num_keypoints_per_class" not in kwargs and (not _mc_fields or "num_keypoints_per_class" in _mc_fields):
_kp_mask = _ckpt_weights.get("_kp_active_mask")
if isinstance(_kp_mask, torch.Tensor) and _kp_mask.ndim == 2:
_inferred_kp = [int(n) for n in _kp_mask.sum(dim=1).tolist()]
_current_kp = constructor_kwargs.get("num_keypoints_per_class")
if _inferred_kp != _current_kp:
logger.debug(
"from_checkpoint: overriding num_keypoints_per_class %s → %s "
"(inferred from _kp_active_mask; saved model_config may be stale).",
_current_kp,
_inferred_kp,
)
constructor_kwargs["num_keypoints_per_class"] = _inferred_kp
checkpoint_config_keys.add("num_keypoints_per_class")
# num_classes — inferred from class_embed.weight shape.
# The head shape is ground truth for what num_classes the checkpoint uses.
if "num_classes" not in kwargs:
_ce_weight = _ckpt_weights.get("class_embed.weight")
if isinstance(_ce_weight, torch.Tensor) and _ce_weight.ndim == 2:
_inferred_nc = _ce_weight.shape[0] - 1 # shape[0] = num_classes + 1 (background)
_current_nc = constructor_kwargs.get("num_classes")
if _inferred_nc != _current_nc:
logger.debug(
"from_checkpoint: overriding num_classes %s → %s "
"(inferred from class_embed.weight; saved model_config may be stale).",
_current_nc,
_inferred_nc,
)
constructor_kwargs["num_classes"] = _inferred_nc
checkpoint_config_keys.add("num_classes")
constructor_kwargs.update(kwargs)
# pretrain_weights is placed after **kwargs so it always wins even if
# a caller accidentally passes pretrain_weights inside kwargs.
constructor_kwargs["pretrain_weights"] = str(path)
# Fields injected from the checkpoint but not supplied by the caller must not be
# treated as explicit user overrides in Pydantic's model_fields_set. Downstream
# alignment guards (e.g. _align_num_classes_from_dataset,
# _align_keypoint_schema_from_dataset, load_pretrain_weights) all read
# model_fields_set to decide whether to adapt model internals to the training
# dataset — leaving checkpoint-derived fields marked as user-set breaks them.
checkpoint_derived_keys = checkpoint_config_keys - set(kwargs)
model = model_cls(**constructor_kwargs)
if checkpoint_derived_keys:
loaded_config = getattr(model, "model_config", None)
# model_fields_set is the public API and returns the live backing set
# in Pydantic v2; fall back to the private attribute only if that changes.
fields_set = getattr(loaded_config, "model_fields_set", None)
if fields_set is None:
fields_set = getattr(loaded_config, "__pydantic_fields_set__", None)
if fields_set is not None:
fields_set.difference_update(checkpoint_derived_keys)
# Verify num_classes specifically — if Pydantic ever returns a snapshot instead
# of the live backing set, this assertion will catch the silent regression before
# it causes a training-time head-adaptation failure.
if "num_classes" in checkpoint_derived_keys:
assert "num_classes" not in getattr(loaded_config, "model_fields_set", set()), (
"num_classes still in model_fields_set after checkpoint load; "
"Pydantic may return a snapshot rather than the live backing set — "
"switch to model_construct(_fields_set=...) for Pydantic v3 compatibility."
)
return model
@staticmethod
def _resolve_trainer_device_kwargs(device: Any) -> tuple[str | None, list[int] | None]:
"""Map a torch-style device specifier to PTL ``accelerator``/``devices`` kwargs.
Args:
device: A device specifier accepted by ``torch.device``.
Returns:
``(accelerator, devices)`` where ``devices`` is ``None`` unless an explicit device index is provided (for
example ``cuda:1``).
Raises:
ValueError: If ``device`` is not a valid torch device specifier.
"""
if device is None:
return None, None
try:
resolved_device = torch.device(device)
except (TypeError, ValueError, RuntimeError) as exc:
raise ValueError(
f"Invalid device specifier for train(): {device!r}. "
"Expected values like 'cpu', 'cuda', 'cuda:0', or torch.device(...).",
) from exc
if resolved_device.type == "cpu":
return "cpu", None
if resolved_device.type == "cuda":
return "gpu", [resolved_device.index] if resolved_device.index is not None else None
if resolved_device.type == "mps":
return "mps", [resolved_device.index] if resolved_device.index is not None else None
warnings.warn(
f"Device type {resolved_device.type!r} is not explicitly mapped to a PyTorch Lightning "
"accelerator; falling back to PTL auto-detection. Training may use an unexpected device.",
UserWarning,
stacklevel=2,
)
return None, None
def train(self, **kwargs):
"""Train an RF-DETR model via the PyTorch Lightning stack.
All keyword arguments are forwarded to :meth:`get_train_config` to build a :class:`~rfdetr.config.TrainConfig`.
Several kwargs are absorbed and handled specially so that existing call-sites do not break:
* ``resolution`` — updates the model's input resolution by mutating
:attr:`model_config.resolution` in place before the train config is built. This change persists on
:attr:`model_config` after :meth:`train` returns. The value must be a positive integer divisible by
``patch_size * num_windows`` for the model variant; a :class:`ValueError` is raised otherwise.
:attr:`model_config.positional_encoding_size` is also updated when the config derives it formulaically (``PE
== resolution // patch_size``); configs with a pretrained-specific PE value (e.g. ``RFDETRBase`` uses DINOv2's
PE=37 at 560 px) are left unchanged to preserve checkpoint compatibility.
* ``device`` — normalized via :class:`torch.device` and mapped to PyTorch
Lightning trainer arguments. ``"cpu"`` becomes ``accelerator="cpu"``; ``"cuda"`` and ``"cuda:N"`` become
``accelerator="gpu"`` and optionally ``devices=[N]``; ``"mps"`` becomes ``accelerator="mps"``. Other valid
torch device types fall back to PTL auto-detection and emit a :class:`UserWarning`.
* ``callbacks`` — if the dict contains any non-empty lists a
:class:`DeprecationWarning` is emitted; the dict is then discarded. Use PTL
:class:`~pytorch_lightning.Callback` objects passed via :func:`~rfdetr.training.build_trainer` instead.
* ``start_epoch`` — emits :class:`DeprecationWarning` and is dropped.
* ``do_benchmark`` — emits :class:`DeprecationWarning` and is dropped.
* ``notes`` — optional user-defined metadata (string, dict, list, or
any JSON-serialisable value) stored under the ``"notes"`` key in every ``.pth`` checkpoint produced during
training. The value is also available inside ``args["notes"]`` for full provenance. Pass the same value to
:meth:`export` to embed it in the ONNX file as well.
After training completes the underlying ``nn.Module`` is synced back onto ``self.model.model`` so that
:meth:`predict` and :meth:`export` continue to work without reloading the checkpoint.
Raises:
ImportError: If training dependencies are not installed. Install with
``pip install "rfdetr[train,loggers]"``.
ValueError: If ``resolution`` is not a positive integer or is not
divisible by ``patch_size * num_windows`` for the model variant.
"""
# Both imports are grouped in a single try block because they both live in
# the `rfdetr[train]` extras group — a missing `pytorch_lightning` (or any
# other training-extras package) causes either import to fail, and the
# remediation is identical: `pip install "rfdetr[train,loggers]"`.
try:
from rfdetr.training import RFDETRDataModule, RFDETRModelModule, build_trainer
from rfdetr.training.auto_batch import resolve_auto_batch_config
except ModuleNotFoundError as exc:
# Preserve internal import errors so packaging/regression issues in
# rfdetr.* are not misreported as missing optional extras.
if exc.name and exc.name.startswith("rfdetr."):
raise
raise ImportError(
"RF-DETR training dependencies are missing. "
'Install them with `pip install "rfdetr[train,loggers]"` and try again.',
) from exc
# Absorb legacy `callbacks` dict — warn if non-empty, then discard.
callbacks_dict = kwargs.pop("callbacks", None)
if callbacks_dict and any(callbacks_dict.values()):
warnings.warn(
"Custom callbacks dict is not forwarded to PTL. "
"Deprecated since v1.7.0, will be removed in v1.9.0. "
"Use PTL Callback objects instead.",
DeprecationWarning,
stacklevel=2,
)
# Parse `device` kwarg and map it to PTL accelerator/devices.
# Supports torch-style strings and torch.device (e.g. "cuda:1").
_device = kwargs.pop("device", None)
_accelerator, _devices = RFDETR._resolve_trainer_device_kwargs(_device)
# Absorb legacy `start_epoch` — PTL resumes automatically via ckpt_path.
if "start_epoch" in kwargs:
warnings.warn(
"`start_epoch` is deprecated since v1.7.0 and will be removed in v1.9.0; "
"PTL resumes automatically via `resume`.",
DeprecationWarning,
stacklevel=2,
)
kwargs.pop("start_epoch")
# Pop `do_benchmark`; benchmarking via `.train()` is deprecated.
run_benchmark = bool(kwargs.pop("do_benchmark", False))
if run_benchmark:
warnings.warn(
"`do_benchmark` in `.train()` is deprecated since v1.7.0 and will be removed in v1.9.0; "
"use `rfdetr benchmark`.",
DeprecationWarning,
stacklevel=2,
)
# Apply resolution override to model_config before building the train config.
# resolution is a ModelConfig field, not a TrainConfig field, so we pop it
# here to avoid it being silently ignored by TrainConfig.
_resolution = kwargs.pop("resolution", None)
if _resolution is not None:
if isinstance(_resolution, bool):
raise ValueError("resolution must be a positive integer")
try:
_resolution = operator.index(_resolution)
except TypeError as error:
raise ValueError("resolution must be a positive integer") from error
if _resolution <= 0:
raise ValueError("resolution must be a positive integer")
block_size = self.model_config.patch_size * self.model_config.num_windows
if _resolution % block_size != 0:
raise ValueError(
f"resolution={_resolution} is not divisible by "
f"patch_size ({self.model_config.patch_size}) * num_windows "
f"({self.model_config.num_windows}) = {block_size}. "
f"Choose a resolution that is a multiple of {block_size}."
)
# Smart PE update: only recompute positional_encoding_size when the
# current config derives it formulaically (PE == resolution // patch_size).
# Configs with a pretrained-specific PE (e.g. RFDETRBase uses DINOv2's
# PE=37 at 518 px, training at 560 px) must not have PE silently changed
# — doing so causes shape mismatches when loading pretrained checkpoints.
_current_pe = self.model_config.positional_encoding_size
_derived_pe = self.model_config.resolution // self.model_config.patch_size
if _current_pe == _derived_pe:
# Formula-derived: update PE proportionally to the new resolution.
new_pe = _resolution // self.model_config.patch_size
self.model_config.positional_encoding_size = new_pe
else:
# Pretrained-specific PE; leave it unchanged.
new_pe = _current_pe
self.model_config.resolution = _resolution
# Keep the cached inference/export context in sync with model_config so
# predict()/export()/deployment all see the same resolution metadata.
if hasattr(self, "model") and self.model is not None:
if hasattr(self.model, "resolution"):
self.model.resolution = _resolution
model_args = getattr(self.model, "args", None)
if model_args is not None:
if hasattr(model_args, "resolution"):
model_args.resolution = _resolution
if hasattr(model_args, "positional_encoding_size"):
model_args.positional_encoding_size = new_pe
config = self.get_train_config(**kwargs)
if config.batch_size == "auto":
# Auto-batch probing runs forward/backward on the actual model, which
# must be on the target device (typically CUDA). Lazy placement keeps
# the model on CPU until first use — move it now.
_move_model_context_to_device(self.model)
auto_batch = resolve_auto_batch_config(
model_context=self.model,
model_config=self.model_config,
train_config=config,
)
config.batch_size = auto_batch.safe_micro_batch
config.grad_accum_steps = auto_batch.recommended_grad_accum_steps
logger.info(
"[auto-batch] resolved train config: batch_size=%s grad_accum_steps=%s effective_batch_size=%s",
config.batch_size,
config.grad_accum_steps,
auto_batch.effective_batch_size,
)
self.model_config.model_name = type(self).__name__
# Auto-detect num_classes from the training dataset and align model_config.
# This must run before RFDETRModelModule is constructed so that weight loading
# inside the module uses the correct (dataset-derived) class count.
dataset_dir = getattr(config, "dataset_dir", None)
if dataset_dir:
self._align_keypoint_schema_from_dataset(config)
self._align_num_classes_from_dataset(dataset_dir)
module = RFDETRModelModule(self.model_config, config)
datamodule = RFDETRDataModule(self.model_config, config)
# Guard with LOCAL_RANK env var rather than is_main_process() because torch.distributed
# is not yet initialized here (it is set up inside trainer.fit()). In Lightning DDP
# subprocesses, LOCAL_RANK is set by the launcher before the subprocess calls train(),
# so this correctly identifies rank 0 even before dist.init_process_group() runs.
if config.save_dataset_grids and os.environ.get("LOCAL_RANK", "0") == "0":
try:
from rfdetr.datasets.save_grids import DatasetGridSaver
datamodule.setup("fit")
grids_output_dir = Path(config.output_dir) / "dataset_grids"
DatasetGridSaver(datamodule.train_dataloader(), grids_output_dir, dataset_type="train").save_grid()
DatasetGridSaver(datamodule.val_dataloader(), grids_output_dir, dataset_type="val").save_grid()
except Exception:
logger.warning(
"Failed to save dataset grids; training will continue without them.",
exc_info=True,
)
trainer_kwargs = {"accelerator": _accelerator}
if _devices is not None:
trainer_kwargs["devices"] = _devices
trainer = build_trainer(config, self.model_config, **trainer_kwargs)
trainer.fit(module, datamodule, ckpt_path=config.resume or None)
# Sync the trained weights back so predict() / export() see the updated model.
self.model.model = module.model
# Invalidate any compiled inference snapshot: it was built from the pre-training
# weights and must not survive the model reassignment above.
self.remove_optimized_model()
# Sync class names: prefer explicit config.class_names, otherwise fall back to dataset (#509).
config_class_names = getattr(config, "class_names", None)
if config_class_names is not None:
self.model.class_names = config_class_names
else:
dataset_class_names = getattr(datamodule, "class_names", None)
if dataset_class_names is not None:
self.model.class_names = dataset_class_names
# Save complete training configuration to disk for reproducibility.
# Guard to main process only to avoid races in distributed/multi-GPU training.
if is_main_process():
complete_config = {
"train_config": config.model_dump(),
"model_config": self.model_config.model_dump(),
"model_config_type": self.model_config.__class__.__name__,
"class_names": self.model.class_names,
"num_classes": len(self.model.class_names) if self.model.class_names else 0,
}
try:
os.makedirs(config.output_dir, exist_ok=True)
with open(os.path.join(config.output_dir, "training_config.json"), "w") as f:
json.dump(complete_config, f, indent=2, default=str)
except OSError as exc:
logger.warning("Could not save training_config.json to %s: %s", config.output_dir, exc)
@_ensure_model_on_device
def optimize_for_inference(
self,
compile: bool = True,
batch_size: int = 1,
dtype: torch.dtype | str = torch.float32,
*,
inplace: bool = False,
) -> None:
"""Optimize the model for inference with optional JIT compilation and dtype casting.
Operations are wrapped in the correct CUDA device context to prevent context leaks on multi-GPU setups. When
``compile=True`` the model is traced with ``torch.jit.trace`` using a dummy input of ``batch_size`` images at
the model's current resolution. By default, optimization deep-copies the loaded model before exporting it so the
original module remains available. Set ``inplace=True`` for memory-constrained inference-only deployments; this
exports the loaded module itself, may cast it to ``dtype``, and clears ``model.model`` after optimization
succeeds. In-place optimization is destructive: :meth:`remove_optimized_model` becomes a no-op (issues
:class:`UserWarning`), and :meth:`export` raises :class:`RuntimeError`. Create or reload a new ``RFDETR``
instance to recover the original model.
If ``inplace=True`` and the underlying ``export()`` call mutates the module before raising (e.g. setting
internal flags and swapping ``forward``), the exception handler resets RFDETR wrapper flags to the unoptimized
state but cannot undo changes made inside ``export()``. Create a new RFDETR instance for reliable inference
after such a failure.
Args:
compile: If ``True``, trace the model with ``torch.jit.trace`` to obtain
a JIT-compiled ``ScriptModule``. Set to ``False`` for broader compatibility (e.g. models with dynamic
control flow).
batch_size: Number of images the traced model will be optimized for. Ignored when ``compile=False``.
dtype: Target floating-point dtype for the inference model. Accepts a
``torch.dtype`` directly (e.g. ``torch.float16``) or its string name (e.g. ``"float16"``). Defaults to
``torch.float32``. When ``dtype`` differs from the model's current dtype, ``to()`` transiently
allocates both old and new parameter tensors simultaneously; peak memory during optimization is
approximately 1.5× the model weight size rather than 1×.
inplace: If ``True``, optimize ``model.model`` directly instead of deep-copying it. This is a destructive,
inference-only path because ``export()`` mutates the module and dtype casting mutates its parameters.
Requires ``compile=False``. With the default ``dtype=torch.float32``, the dtype cast is a no-op, so
memory savings come only from clearing the base model reference rather than from dtype reduction.
Raises:
TypeError: If ``dtype`` is not a ``torch.dtype``, or if ``dtype`` is a
string that does not correspond to a valid ``torch.dtype`` attribute.
ValueError: If ``dtype`` is not a floating-point dtype, or if ``inplace=True`` is used with
``compile=True``.
RuntimeError: If the base model has already been cleared by a previous inplace optimization.
Examples:
>>> from types import SimpleNamespace
>>> import torch
>>> class _TinyModel(torch.nn.Module):
... def __init__(self):
... super().__init__()
... self.linear = torch.nn.Linear(1, 1)
... def forward(self, x):
... return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
... def export(self):
... return None
>>> class _TinyContext:
... def __init__(self):
... self.device = torch.device("cpu")
... self.resolution = 28
... self.model = _TinyModel()
... self.inference_model = None
>>> model = object.__new__(RFDETR)
>>> model.model_config = SimpleNamespace(num_channels=3)
>>> model.model = _TinyContext()
>>> model._is_optimized_for_inference = False
>>> model._has_warned_about_not_being_optimized_for_inference = False
>>> model._optimized_has_been_compiled = False
>>> model._optimized_batch_size = None
>>> model._optimized_resolution = None
>>> model._optimized_dtype = None
>>> model._optimized_inplace = False
>>> # Standard (non-inplace) optimization — reversible:
>>> model.optimize_for_inference(compile=False)
>>> model._is_optimized_for_inference
True
>>> model._optimized_inplace
False
>>> model.remove_optimized_model()
>>> model._is_optimized_for_inference
False
>>> # Inplace optimization — destructive, cannot be reversed:
>>> model.optimize_for_inference(compile=False, dtype="float16", inplace=True)
>>> model._is_optimized_for_inference
True
>>> model._optimized_dtype
torch.float16
>>> model._optimized_inplace
True
"""
if isinstance(dtype, str):
try:
dtype = getattr(torch, dtype)
except AttributeError:
raise TypeError(f"dtype must be a torch.dtype or a string name of a dtype, got {dtype!r}") from None
if not isinstance(dtype, torch.dtype):
raise TypeError(f"dtype must be a torch.dtype or a string name of a dtype, got {type(dtype)!r}")
if not dtype.is_floating_point:
raise ValueError(f"dtype must be a floating-point torch.dtype or string name of one, got {dtype}")
if inplace and compile:
raise ValueError(
"optimize_for_inference(inplace=True) requires compile=False. "
"torch.jit.trace retains references to the original parameter storage in the returned "
"ScriptModule, so setting model.model=None would not free the weight tensors and "
"inplace=True would not reduce memory usage."
)
# Clear any previously optimized state before starting a new optimization run.
self.remove_optimized_model()
if self.model.model is None:
raise RuntimeError(
"Cannot optimize: the base model has been cleared by a previous inplace optimization. "
"Create or reload a new RFDETR instance."
)
device = self.model.device
cuda_ctx = torch.cuda.device(device) if device.type == "cuda" else contextlib.nullcontext()
try:
with cuda_ctx:
inference_model = self.model.model if inplace else deepcopy(self.model.model)
inference_model.eval()
inference_model.export()
inference_model = inference_model.to(dtype=dtype)
if compile:
inference_model = torch.jit.trace(
inference_model,
torch.randn(
batch_size,
self.model_config.num_channels,
self.model.resolution,
self.model.resolution,
device=self.model.device,
dtype=dtype,
),
)
self._optimized_has_been_compiled = True
self._optimized_batch_size = batch_size
# Set success flags only after all operations complete.
self.model.inference_model = inference_model
# _optimized_inplace must be set before the destructive clear so the cleanup
# guard in remove_optimized_model() sees the correct state if an exception fires
# between this assignment and the None clear (extremely unlikely in normal Python
# but eliminates a theoretical zombie-state window).
self._optimized_inplace = inplace
if inplace:
self.model.model = None
self._optimized_resolution = self.model.resolution
self._is_optimized_for_inference = True
self._optimized_dtype = dtype
except Exception:
# Ensure the object is left in a consistent, unoptimized state if optimization fails.
with contextlib.suppress(Exception):
self.remove_optimized_model()
raise
def remove_optimized_model(self) -> None:
"""Remove the optimized inference model and reset all optimization flags.
Clears ``model.inference_model`` and resets all internal state set by :meth:`optimize_for_inference`. Safe to
call even if the model has not been optimized. When the model was optimized with ``inplace=True``, this method
issues a :class:`UserWarning` and returns without modifying state — the original module cannot be restored
because ``export()`` and dtype casting mutate it; create or reload a new ``RFDETR`` instance instead.
Examples:
>>> from types import SimpleNamespace
>>> import torch
>>> class _TinyModel(torch.nn.Module):
... def __init__(self):
... super().__init__()
... self.linear = torch.nn.Linear(1, 1)
... def forward(self, x):
... return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
... def export(self):
... return None
>>> class _TinyContext:
... def __init__(self):
... self.device = torch.device("cpu")
... self.resolution = 28
... self.model = _TinyModel()
... self.inference_model = None
>>> model = object.__new__(RFDETR)
>>> model.model_config = SimpleNamespace(num_channels=3)
>>> model.model = _TinyContext()
>>> model._is_optimized_for_inference = False
>>> model._has_warned_about_not_being_optimized_for_inference = False
>>> model._optimized_has_been_compiled = False
>>> model._optimized_batch_size = None
>>> model._optimized_resolution = None
>>> model._optimized_dtype = None
>>> model._optimized_inplace = False
>>> model.optimize_for_inference(compile=False)
>>> model.remove_optimized_model()
>>> model._is_optimized_for_inference
False
"""
if getattr(self, "_optimized_inplace", False):
warnings.warn(
"remove_optimized_model() has no effect after inplace optimization — the original model "
"cannot be restored because export() and dtype casting mutate it. "
"Create or reload a new RFDETR instance instead.",
UserWarning,
stacklevel=2,
)
return
self.model.inference_model = None
self._is_optimized_for_inference = False
self._optimized_has_been_compiled = False
self._optimized_batch_size = None
self._optimized_resolution = None
self._optimized_dtype = None
self._optimized_inplace = False
@property
def is_optimized_inplace(self) -> bool:
"""Whether the model was optimized with ``inplace=True``.
Returns ``True`` after a successful :meth:`optimize_for_inference` call with ``inplace=True``,
meaning the base model has been cleared and :meth:`remove_optimized_model` is a no-op.
Examples:
>>> from types import SimpleNamespace
>>> import torch
>>> class _TinyModel(torch.nn.Module):
... def __init__(self):
... super().__init__()
... self.linear = torch.nn.Linear(1, 1)
... def forward(self, x):
... return {"pred_boxes": self.linear(x[:, :1, :1, :1].squeeze(-1).squeeze(-1))}
... def export(self):
... return None
>>> class _TinyContext:
... def __init__(self):
... self.device = torch.device("cpu")
... self.resolution = 28
... self.model = _TinyModel()
... self.inference_model = None
>>> model = object.__new__(RFDETR)
>>> model.model_config = SimpleNamespace(num_channels=3)
>>> model.model = _TinyContext()
>>> model._is_optimized_for_inference = False
>>> model._has_warned_about_not_being_optimized_for_inference = False
>>> model._optimized_has_been_compiled = False
>>> model._optimized_batch_size = None
>>> model._optimized_resolution = None
>>> model._optimized_dtype = None
>>> model._optimized_inplace = False
>>> model.is_optimized_inplace
False
>>> model.optimize_for_inference(compile=False, inplace=True)
>>> model.is_optimized_inplace
True
"""
return getattr(self, "_optimized_inplace", False)
def export(
self,
output_dir: str = "output",
infer_dir: str | None = None,
backbone_only: bool = False,
opset_version: int = 17,
verbose: bool = True,
shape: tuple[int, int] | None = None,
batch_size: int = 1,
dynamic_batch: bool = False,
patch_size: int | None = None,
format: str = "onnx",
quantization: str | None = None,
calibration_data: str | np.ndarray | None = None,
max_images: int = 100,
*,
notes: object = None,
) -> Path:
"""Export the trained model to ONNX or TFLite format.
See the `export documentation `_ for more information.
Args:
output_dir: Directory to write the exported model to.
infer_dir: Optional directory of sample images for dynamic-axes inference.
backbone_only: Export only the backbone (feature extractor).
opset_version: ONNX opset version to target.
verbose: Print export progress information.
shape: ``(height, width)`` tuple; defaults to square at model resolution.
Both dimensions must be divisible by ``patch_size * num_windows``.
batch_size: Static batch size to bake into the ONNX graph.
dynamic_batch: If True, export with a dynamic batch dimension
so the ONNX model accepts variable batch sizes at runtime.
patch_size: Backbone patch size. Defaults to the value stored in
``model_config.patch_size`` (typically 14 or 16). When provided explicitly it must match the
instantiated model's patch size. Shape divisibility is validated against ``patch_size * num_windows``.
format: Export format — ``"onnx"`` (default) or ``"tflite"``.
When ``"tflite"`` is selected the model is first exported to ONNX then converted to TFLite via
``onnx2tf``. Requires ``pip install rfdetr[onnx,tflite]``.
.. warning::
TFLite export is experimental and subject to change; upstream dependency instabilities (``onnx2tf``,
``ai_edge_litert``) may affect results.
quantization: TFLite quantization mode (ignored when
``format="onnx"``). One of ``None``, ``"fp32"``, ``"fp16"``, ``"int8"``. ``None`` / ``"fp32"`` /
``"fp16"`` produce FP32 + FP16 ``.tflite`` files; ``"int8"`` additionally produces an INT8-quantized
model.
calibration_data: Representative images for INT8 calibration and ``onnx2tf`` output validation. Accepts:
* ``None`` — auto-generate random data (sufficient for fp32/fp16; warns for int8).
* A **directory path** (``str``) containing JPEG/PNG
images — the converter automatically loads, resizes, and prepares them. This is the simplest
approach.
* A path (``str``) to a ``.npy`` file of shape ``(N, H, W, 3)``, dtype float32, values in ``[0, 1]``.
* A :class:`numpy.ndarray` with the same format.
For INT8 quantization, provide 20–100 representative images from your training/validation set for best
accuracy.
max_images: Maximum number of images to load from a calibration directory. Defaults to ``100``. Only used
when *calibration_data* is a directory path.
notes: Optional user-defined metadata (string, dict, list, or
any JSON-serialisable value) to embed in the exported ONNX model under the ``"rfdetr_notes"`` metadata
property. When ``None`` no metadata entry is written. String values are stored verbatim; all other
types are JSON-encoded so consumers must call ``json.loads()`` to recover a dict or list. The same
value can be passed to :meth:`train` so the checkpoint and the ONNX file share the same provenance
information.
Returns:
Path to the exported model file (``.onnx`` or ``.tflite``).
"""
logger.info("Exporting model to ONNX format")
_valid_formats = ("onnx", "tflite")
if format not in _valid_formats:
raise ValueError(f"Unsupported export format {format!r}. Choose from: {_valid_formats}")
try:
from rfdetr.export.main import export_onnx, make_infer_image
except ImportError:
logger.error(
"It seems some dependencies for ONNX export are missing."
" Please run `pip install rfdetr[onnx]` and try again.",
)
raise
device = self.model.device
if getattr(self, "_optimized_inplace", False) or self.model.model is None:
raise RuntimeError(
"RFDETR.export() is not available after inplace optimization. "
"The original model has been cleared. Create a new RFDETR instance."
)
# Move the live model to CPU before deepcopying and keep it there during export. ``nn.Module.to(...)`` mutates
# in place, so this frees GPU memory for the local export copy, ONNX tracing, TFLite conversion, and any
# calibration tensors. The ``finally`` block restores the live model even if export or conversion raises.
self.model.model = self.model.model.to("cpu")
model = deepcopy(self.model.model)
model.to(device)
try:
os.makedirs(output_dir, exist_ok=True)
output_dir_path = Path(output_dir)
patch_size = _resolve_patch_size(patch_size, self.model_config, "export")
num_windows = getattr(self.model_config, "num_windows", 1)
if isinstance(num_windows, bool) or not isinstance(num_windows, int) or num_windows <= 0:
raise ValueError(f"num_windows must be a positive integer, got {num_windows!r}")
block_size = patch_size * num_windows
if shape is None:
shape = (self.model.resolution, self.model.resolution)
if shape[0] % block_size != 0:
raise ValueError(
f"Model's default resolution ({self.model.resolution}) is not divisible by "
f"block_size={block_size} (patch_size={patch_size} * num_windows={num_windows}). "
f"Provide an explicit shape divisible by {block_size}.",
)
else:
shape = _validate_shape_dims(shape, block_size, patch_size, num_windows)
input_tensors = make_infer_image(
infer_dir, shape, batch_size, device, num_channels=self.model_config.num_channels
).to(device)
input_names = ["input"]
if backbone_only:
output_names = ["features"]
elif self.model_config.segmentation_head:
output_names = ["dets", "labels", "masks"]
elif self.model_config.use_grouppose_keypoints:
output_names = ["dets", "labels", "keypoints"]
else:
output_names = ["dets", "labels"]
if dynamic_batch:
dynamic_axes = {name: {0: "batch"} for name in input_names + output_names}
else:
dynamic_axes = None
model.eval()
with torch.no_grad():
if backbone_only:
features = model(input_tensors)
logger.debug(f"PyTorch inference output shape: {features.shape}")
elif self.model_config.segmentation_head:
outputs = model(input_tensors)
dets = outputs["pred_boxes"]
labels = outputs["pred_logits"]
masks = outputs["pred_masks"]
if isinstance(masks, torch.Tensor):
logger.debug(
f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}, "
f"Masks: {masks.shape}",
)
else:
logger.debug(f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}")
elif self.model_config.use_grouppose_keypoints:
outputs = model(input_tensors)
dets = outputs["pred_boxes"]
labels = outputs["pred_logits"]
keypoints = outputs["pred_keypoints"]
logger.debug(
f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}, "
f"Keypoints: {keypoints.shape}",
)
else:
outputs = model(input_tensors)
dets = outputs["pred_boxes"]
labels = outputs["pred_logits"]
logger.debug(f"PyTorch inference output shapes - Boxes: {dets.shape}, Labels: {labels.shape}")
model.cpu()
input_tensors = input_tensors.cpu()
output_file = export_onnx(
output_dir=str(output_dir_path),
model=model,
input_names=input_names,
input_tensors=input_tensors,
output_names=output_names,
dynamic_axes=dynamic_axes,
backbone_only=backbone_only,
verbose=verbose,
opset_version=opset_version,
variant_name=getattr(self, "size", None),
notes=notes,
)
logger.info(f"Successfully exported ONNX model to: {output_file}")
if format == "tflite":
warnings.warn(
"TFLite export is experimental and work-in-progress. "
"Upstream dependency instabilities (onnx2tf, ai_edge_litert) may affect results.",
UserWarning,
stacklevel=2,
)
try:
from rfdetr.export._tflite.converter import export_tflite
except ImportError:
logger.error(
"It seems some dependencies for TFLite export are missing."
" Please run `pip install rfdetr[onnx,tflite]` and try again.",
)
raise
tflite_path = export_tflite(
onnx_path=output_file,
output_dir=str(output_dir_path),
quantization=quantization,
calibration_data=calibration_data,
verbosity="info" if verbose else "error",
max_images=max_images,
verbose=verbose,
)
logger.info(f"Successfully exported TFLite model to: {tflite_path}")
return tflite_path
logger.info("Export completed successfully")
return Path(output_file)
finally:
self.model.model = self.model.model.to(device)
@staticmethod
def _load_classes(dataset_dir: str) -> list[str]:
"""Load class names from a COCO or YOLO dataset directory."""
if is_valid_coco_dataset(dataset_dir):
coco_path = os.path.join(dataset_dir, "train", "_annotations.coco.json")
with open(coco_path, encoding="utf-8") as f:
anns = json.load(f)
categories = sorted(anns["categories"], key=lambda category: category.get("id", float("inf")))
# Catch possible placeholders for no supercategory
placeholders = {"", "none", "null", None}
# If no meaningful supercategory exists anywhere, treat as flat dataset
has_any_sc = any(c.get("supercategory", "none") not in placeholders for c in categories)
if not has_any_sc:
return [c["name"] for c in categories]
# Mixed/Hierarchical: keep only categories that are not parents of other categories.
# Both leaves (with a real supercategory) and standalone top-level nodes (supercategory is a
# placeholder) satisfy this condition — neither appears as another category's supercategory.
parents = {c.get("supercategory") for c in categories if c.get("supercategory", "none") not in placeholders}
has_children = {c["name"] for c in categories if c["name"] in parents}
class_names = [c["name"] for c in categories if c["name"] not in has_children]
# Safety fallback for pathological inputs
return class_names or [c["name"] for c in categories]
# list all YAML files in the folder
if is_valid_yolo_dataset(dataset_dir):
yaml_paths = glob.glob(os.path.join(dataset_dir, "*.yaml")) + glob.glob(os.path.join(dataset_dir, "*.yml"))
# any YAML file starting with data e.g. data.yaml, dataset.yaml
yaml_data_files = [yp for yp in yaml_paths if os.path.basename(yp).startswith("data")]
yaml_path = yaml_data_files[0]
with open(yaml_path) as f:
data = yaml.safe_load(f)
if "names" in data:
if isinstance(data["names"], dict):
return [data["names"][i] for i in sorted(data["names"].keys())]
return data["names"]
raise ValueError(f"Found {yaml_path} but it does not contain 'names' field.")
raise FileNotFoundError(
f"Could not find class names in {dataset_dir}."
" Checked for COCO (train/_annotations.coco.json) and YOLO (data.yaml, data.yml) styles.",
)
@staticmethod
def _detect_num_classes_for_training(dataset_dir: str, *, use_grouppose_keypoints: bool = False) -> int:
"""Detect the class count using the same category basis as training labels.
For COCO-style datasets this counts all categories by ``id`` from ``train/_annotations.coco.json`` (matching the
remapping based on ``coco.cats`` used by the training datamodule). In keypoint mode it instead counts the
inferred RF-DETR keypoint label slots. In legacy background-first schemas (e.g. ``[0, 17]``) slot ``0`` is
reserved for classes without keypoints; active-first schemas (e.g. ``[17]``) use normal 0-based indices. For
YOLO-style datasets it falls back to ``_load_classes``.
"""
if is_valid_coco_dataset(dataset_dir):
coco_path = os.path.join(dataset_dir, "train", "_annotations.coco.json")
if use_grouppose_keypoints:
return len(infer_coco_keypoint_schema(coco_path).class_names)
with open(coco_path, encoding="utf-8") as f:
anns = json.load(f)
categories = anns["categories"]
cat_by_id = {category["id"]: category for category in categories}
return len(cat_by_id)
return len(RFDETR._load_classes(dataset_dir))
def _align_num_classes_from_dataset(self, dataset_dir: str) -> None:
"""Auto-detect the dataset class count and align ``model_config.num_classes`` in-place.
Must be called before ``RFDETRModelModule`` is constructed so that weight loading inside the module uses the
correct (dataset-derived) class count.
When the user did **not** explicitly set ``num_classes`` (it is left unset, e.g. inferred from a
checkpoint), ``model_config.num_classes`` and ``self.model.args.num_classes`` are updated to match the dataset.
When the user *did* set ``num_classes`` explicitly — to any value, including the class default — and it differs
from the dataset, the configured value is preserved and a warning is emitted.
Failures from ``_detect_num_classes_for_training`` are caught and logged at DEBUG level so that training is
never blocked by detection errors.
When ``model_config.use_grouppose_keypoints`` is True and
``model_config.num_keypoints_per_class`` is shorter than the adjusted
``num_classes``, the schema is zero-padded in-place so that
``len(num_keypoints_per_class) == num_classes``. Both ``model_config``
and ``model.args`` (if present) are updated. Appended classes receive
zero keypoints and contribute no class-logit boost.
Args:
dataset_dir: Path to the training dataset root directory.
"""
try:
dataset_num_classes = RFDETR._detect_num_classes_for_training(
dataset_dir,
use_grouppose_keypoints=self.model_config.use_grouppose_keypoints,
)
except (FileNotFoundError, ValueError, KeyError, OSError) as exc:
# Best-effort only; do not block training if detection fails.
logger.debug("Could not auto-detect num_classes from dataset '%s': %s", dataset_dir, exc)
return
# Hoist so both branches below can reference the schema without re-fetching.
keypoint_schema: list[int] = []
if self.model_config.use_grouppose_keypoints:
# Older configs may omit the schema; absence means no schema-based class-count expansion.
keypoint_schema = list(getattr(self.model_config, "num_keypoints_per_class", []) or [])
if keypoint_schema:
dataset_num_classes = max(dataset_num_classes, len(keypoint_schema))
model_num_classes = self.model_config.num_classes
if dataset_num_classes == model_num_classes:
return
# Determine whether the user explicitly set num_classes. "num_classes" in
# model_fields_set is True only when the field was explicitly provided at construction
# (or assigned afterwards); an explicit value is honored regardless of whether it equals
# the class default, so an intentional num_classes is never silently overridden by the
# dataset count. A checkpoint-derived num_classes is cleared from model_fields_set by
# ``from_checkpoint`` (see PR #1106 / issue #1092), so it correctly counts as "not set" here.
user_overrode = "num_classes" in getattr(self.model_config, "model_fields_set", set())
if not user_overrode:
logger.debug(
"Detected %d classes in dataset '%s'; auto-adjusting model num_classes from %d to %d.",
dataset_num_classes,
dataset_dir,
model_num_classes,
dataset_num_classes,
)
self.model_config.num_classes = dataset_num_classes
# Keep serialized checkpoint metadata in sync with the updated class count.
model_args = getattr(self.model, "args", None)
if model_args is not None:
model_args.num_classes = dataset_num_classes
# Pad keypoint schema with zeros so len(num_keypoints_per_class) == num_classes.
# Without this, _aggregate_keypoint_class_logits emits a one-time mismatch
# warning per model instance and the config state is inconsistent with the
# detection head width.
if keypoint_schema and len(keypoint_schema) < dataset_num_classes:
padded_schema = keypoint_schema + [0] * (dataset_num_classes - len(keypoint_schema))
self.model_config.num_keypoints_per_class = padded_schema
if model_args is not None:
model_args.num_keypoints_per_class = padded_schema
else:
logger.warning(
"Dataset '%s' has %d classes but model was initialized with num_classes=%d. "
"Using the model's configured value (%d). If this is unintentional, "
"reinitialize the model with num_classes=%d.",
dataset_dir,
dataset_num_classes,
model_num_classes,
model_num_classes,
dataset_num_classes,
)
# Also pad schema when the user-configured num_classes exceeds the schema length,
# to prevent the _aggregate_keypoint_class_logits mismatch warning in this path too.
if keypoint_schema and len(keypoint_schema) < model_num_classes:
padded_schema = keypoint_schema + [0] * (model_num_classes - len(keypoint_schema))
self.model_config.num_keypoints_per_class = padded_schema
model_args = getattr(self.model, "args", None)
if model_args is not None:
model_args.num_keypoints_per_class = padded_schema
@staticmethod
def _roboflow_keypoint_annotation_path(dataset_dir: str) -> Path | None:
"""Return the Roboflow COCO train annotation path when it exists.
Args:
dataset_dir: Path to the Roboflow dataset root.
Returns:
Train split annotation path, or ``None`` when the dataset is not Roboflow COCO style.
Raises:
This helper does not raise.
Example:
>>> RFDETR._roboflow_keypoint_annotation_path("/missing") is None
True
"""
if not is_valid_coco_dataset(dataset_dir):
return None
annotation_path = Path(dataset_dir) / "train" / "_annotations.coco.json"
return annotation_path if annotation_path.exists() else None
@staticmethod
def _coco_keypoint_annotation_path(dataset_dir: str) -> Path | None:
"""Return the native COCO train keypoint annotation path when it exists.
Args:
dataset_dir: Path to the COCO dataset root.
Returns:
Path to ``annotations/person_keypoints_train2017.json``, or ``None`` when it is absent.
Raises:
This helper does not raise.
Example:
>>> RFDETR._coco_keypoint_annotation_path("/missing") is None
True
"""
annotation_path = Path(dataset_dir) / "annotations" / "person_keypoints_train2017.json"
return annotation_path if annotation_path.exists() else None
@staticmethod
def _yolo_data_file_path(dataset_dir: str) -> Path | None:
"""Return the YOLO data file path when a dataset root has one.
Args:
dataset_dir: Path to the YOLO dataset root.
Returns:
Path to ``data.yaml`` or ``data.yml``, or ``None`` when neither exists.
Raises:
This helper does not raise.
Example:
>>> RFDETR._yolo_data_file_path("/missing") is None
True
"""
root = Path(dataset_dir)
for filename in REQUIRED_YOLO_YAML_FILES:
data_file = root / filename
if data_file.exists():
return data_file
return None
@staticmethod
def _flip_idx_to_pairs(flip_idx: list[int]) -> list[int]:
"""Convert Ultralytics ``flip_idx`` permutation metadata to flat swap pairs."""
pairs: list[int] = []
seen: set[int] = set()
for idx, mirror_idx in enumerate(flip_idx):
if idx in seen or mirror_idx in seen or idx == mirror_idx:
seen.add(idx)
continue
if mirror_idx < len(flip_idx) and flip_idx[mirror_idx] == idx:
pairs.extend([idx, mirror_idx])
seen.update({idx, mirror_idx})
return pairs
def _align_keypoint_schema_from_dataset(self, config: TrainConfig) -> None:
"""Infer or validate keypoint schema from COCO, Roboflow COCO, or YOLO pose metadata.
Args:
config: Training configuration containing dataset location and format.
Returns:
``None``. The model config is updated in-place when dataset metadata is available.
Raises:
This method does not raise for missing or malformed metadata; later dataset construction still validates
keypoint-mode requirements.
Example:
>>> from rfdetr.config import RFDETRKeypointPreviewConfig, TrainConfig
>>> model = object.__new__(RFDETR)
>>> model.model_config = RFDETRKeypointPreviewConfig(pretrain_weights=None)
>>> model.model = type("Context", (), {"args": None})()
>>> model._align_keypoint_schema_from_dataset(TrainConfig(dataset_dir="/missing", tensorboard=False))
"""
if not self.model_config.use_grouppose_keypoints:
return
dataset_file = getattr(config, "dataset_file", None)
if dataset_file not in ("coco", "roboflow", "yolo"):
return
dataset_dir = getattr(config, "dataset_dir", None)
if not dataset_dir:
return
if not hasattr(self, "_keypoint_schema_cache"):
self._keypoint_schema_cache: dict = {}
cache_key = (dataset_file, dataset_dir)
if cache_key in self._keypoint_schema_cache:
inferred, source_path, source_kind = self._keypoint_schema_cache[cache_key]
else:
try:
if dataset_file == "coco":
annotation_path = RFDETR._coco_keypoint_annotation_path(dataset_dir)
if annotation_path is None:
return
source_path = annotation_path
source_kind = "COCO"
inferred = infer_coco_keypoint_schema(annotation_path)
elif dataset_file == "roboflow":
annotation_path = RFDETR._roboflow_keypoint_annotation_path(dataset_dir)
if annotation_path is not None:
source_path = annotation_path
source_kind = "Roboflow COCO"
inferred = infer_coco_keypoint_schema(annotation_path)
else:
yolo_data_file = RFDETR._yolo_data_file_path(dataset_dir)
if yolo_data_file is None:
return
source_path = yolo_data_file
source_kind = "YOLO pose"
inferred = infer_yolo_keypoint_schema(yolo_data_file)
else:
yolo_data_file = RFDETR._yolo_data_file_path(dataset_dir)
if yolo_data_file is None:
return
source_path = yolo_data_file
source_kind = "YOLO pose"
inferred = infer_yolo_keypoint_schema(yolo_data_file)
except (FileNotFoundError, ValueError, KeyError, OSError) as exc:
logger.info("Could not infer keypoint schema from dataset '%s': %s", dataset_dir, exc)
return
self._keypoint_schema_cache[cache_key] = (inferred, source_path, source_kind)
inferred_schema = inferred.num_keypoints_per_class
if not getattr(config, "keypoint_flip_pairs", []):
config.keypoint_flip_pairs = list(inferred.keypoint_flip_pairs)
# Older configs may omit the schema; absence lets dataset inference populate it.
current_schema = list(getattr(self.model_config, "num_keypoints_per_class", []) or [])
user_set_schema = "num_keypoints_per_class" in getattr(self.model_config, "model_fields_set", set())
if user_set_schema and active_keypoint_counts(current_schema) == active_keypoint_counts(inferred_schema):
return
if current_schema != inferred_schema:
if user_set_schema:
logger.warning(
"Configured num_keypoints_per_class=%s does not match dataset keypoint metadata %s from '%s'. "
"Using dataset metadata as the source of truth.",
current_schema,
inferred_schema,
source_path,
)
else:
if _is_bg_first_schema(current_schema) and inferred_schema and not _is_bg_first_schema(inferred_schema):
warnings.warn(
f"Loaded checkpoint uses a legacy background-first keypoint schema "
f"num_keypoints_per_class={current_schema!r}, but the dataset infers "
f"active-first {inferred_schema!r}. Training will shift person from slot 1 "
f"to slot 0; checkpoint head weights are now misaligned. "
f"Pass num_keypoints_per_class={current_schema!r} to train() to keep the "
f"legacy schema.",
UserWarning,
stacklevel=2,
)
logger.info(
"Inferred num_keypoints_per_class=%s from %s keypoint metadata at '%s'.",
inferred_schema,
source_kind,
source_path,
)
self.model_config.num_keypoints_per_class = inferred_schema
model_args = getattr(self.model, "args", None)
if model_args is not None:
model_args.num_keypoints_per_class = inferred_schema
def get_train_config(self, **kwargs) -> TrainConfig:
"""Retrieve the configuration parameters that will be used for training."""
return self._train_config_class(**kwargs)
def get_model(self, config: ModelConfig) -> ModelContext:
"""Retrieve a model context from the provided architecture configuration.
Args:
config: Architecture configuration.
Returns:
ModelContext with model, postprocess, device, resolution, args, and class_names attributes.
"""
return _build_model_context(config)
@property
def class_names(self) -> list[str]:
"""Retrieve the class names supported by the loaded model.
Returns:
A list of class name strings, 0-indexed. When no custom class names are embedded in the checkpoint, returns
the standard 80 COCO class names.
"""
if hasattr(self.model, "class_names") and self.model.class_names is not None:
return list(self.model.class_names)
return list(COCO_CLASS_NAMES)
def _ensure_eval_mode_for_unoptimized_inference(self) -> None:
"""Put the underlying module in eval mode before unoptimized inference.
Inference must never run with dropout / batch-norm in training mode. The warning that the model is not optimized
is emitted at most once, but eval mode is (re)asserted on every call: ``train()`` reassigns ``self.model.model``
to a module that PyTorch Lightning leaves in training mode (see ``train()``), so gating ``eval()`` behind the
once-only warning would let a later ``predict()`` silently run with dropout active.
When ``_is_optimized_for_inference`` is ``True``, the method returns immediately — the compiled
``inference_model`` snapshot is already in eval mode and ``self.model.model`` is not used for inference.
"""
if self._is_optimized_for_inference:
return
if not self._has_warned_about_not_being_optimized_for_inference:
logger.warning(
"Model is not optimized for inference. Latency may be higher than expected."
" For full GPU throughput (e.g. ~8x on T4 via FP16 Tensor Cores),"
" call model.optimize_for_inference(dtype=torch.float16).",
)
self._has_warned_about_not_being_optimized_for_inference = True
self.model.model.eval()
@torch.inference_mode()
@_ensure_model_on_device
def predict(
self,
images: str | Image.Image | np.ndarray | torch.Tensor | list[str | np.ndarray | Image.Image | torch.Tensor],
threshold: float = 0.5,
shape: tuple[int, int] | None = None,
patch_size: int | None = None,
include_source_image: bool = True,
**kwargs: Any,
) -> Detections | KeyPoints | list[Detections | KeyPoints]:
"""Performs model inference on the input images.
This method accepts a single image or a list of images in various formats (file path, image url, PIL Image,
NumPy array, or torch.Tensor). The images should be in RGB channel order. If a torch.Tensor is provided, it must
already be normalized to values in the [0, 1] range and have the shape (C, H, W).
Args:
images:
A single image or a list of images to process. Images can be provided
as file paths, PIL Images, NumPy arrays, or torch.Tensors.
threshold:
The minimum confidence score needed to consider a detected bounding box valid.
shape:
Optional ``(height, width)`` tuple to resize images to before inference. When provided, overrides the
model's default inference resolution. The tuple should match the resolution used when exporting the
model (typically a square shape). Both dimensions must be positive integers divisible by ``patch_size *
num_windows``. Defaults to ``(model.resolution, model.resolution)`` when not set.
patch_size:
Backbone patch size used for shape divisibility validation. Defaults to ``model_config.patch_size``
(typically 14 for large models, 16 for smaller ones). Divisibility is checked against ``patch_size *
num_windows``.
include_source_image:
Whether to attach the original image to the returned prediction. Detection and segmentation outputs use
``detections.metadata["source_image"]``. Keypoint outputs use per-object
``key_points.data["source_image"]`` because Supervision ``KeyPoints`` currently has no collection-level
metadata field. Defaults to ``True``. Set to ``False`` to reduce memory use when source images are not
needed.
**kwargs:
Additional keyword arguments.
Returns:
A single or multiple Supervision prediction objects. Detection and segmentation models return
:class:`~supervision.Detections`. Keypoint models return :class:`~supervision.KeyPoints`, with keypoint
coordinates in ``xy``. Keypoint predictions preserve the detection-level fields produced by RF-DETR:
``key_points.detection_confidence`` is the per-object score used by ``threshold``. For keypoint models this
is the postprocessed detection score and, by default, includes keypoint uncertainty fusion controlled by
``model_config.postprocess_trace_alpha``. ``key_points.keypoint_confidence`` is separate: it is a
``(num_detections, num_keypoints)`` array of per-keypoint findability scores decoded from the keypoint head,
not a repeated copy of the detection score. When RF-DETR emits keypoint precision parameters,
``key_points.data["covariance"]`` stores per-keypoint pixel-space covariance matrices with shape
``(num_detections, num_keypoints, 2, 2)``. ``key_points.data["xyxy"]`` stores the corresponding detection
boxes as a ``(num_detections, 4)`` array in the same row order as ``key_points.xy`` because Supervision
``KeyPoints`` does not have a native bounding-box field. The ``data`` dict also contains ``class_name`` and
``source_shape`` as per-object arrays. When ``include_source_image=True`` for keypoint models,
``source_image`` is stored as per-object data until Supervision exposes collection-level metadata for
``KeyPoints``.
Note:
For ``Detections`` outputs, ``source_image`` moved from ``detections.data`` to ``detections.metadata``.
Update detection callers reading ``detections.data["source_image"]`` to use
``detections.metadata["source_image"]``.
Note:
``class_name`` mapping uses one of three modes depending on the checkpoint. For pretrained COCO checkpoints
(detected when ``model.args.num_classes > len(class_names)`` and ``class_names`` matches
``COCO_CLASS_NAMES``), raw COCO category IDs (1–90, sparse) are looked up by category ID rather than by
position — so ``class_id=18`` yields ``"dog"``, not ``class_names[18]``. For fine-tuned detection and
segmentation models and active-first keypoint models, ``class_id`` is a 0-based index into ``class_names``.
Legacy keypoint checkpoints with ``args.num_keypoints_per_class[0] == 0`` use a background-first layout:
slot 0 maps to ``"__background__"`` and foreground slots map to ``class_names`` in order.
Raises:
ValueError: If ``shape`` cannot be unpacked as a two-element sequence,
if either dimension does not support the ``__index__`` protocol (e.g. ``float``) or is a ``bool``, if
either dimension is zero or negative, if either dimension is not divisible by ``patch_size *
num_windows``, or if ``patch_size`` is not a positive integer.
"""
from supervision import Detections, KeyPoints
patch_size = _resolve_patch_size(patch_size, self.model_config, "predict")
num_windows = getattr(self.model_config, "num_windows", 1)
if isinstance(num_windows, bool) or not isinstance(num_windows, int) or num_windows <= 0:
raise ValueError(f"model_config.num_windows must be a positive integer, got {num_windows!r}")
block_size = patch_size * num_windows
if shape is None:
default_res = self.model.resolution
if default_res % block_size != 0:
raise ValueError(
f"Model's default resolution ({default_res}) is not divisible by "
f"block_size={block_size} (patch_size={patch_size} * num_windows={num_windows}). "
f"Provide an explicit shape divisible by {block_size}.",
)
else:
shape = _validate_shape_dims(shape, block_size, patch_size, num_windows)
self._ensure_eval_mode_for_unoptimized_inference()
if not isinstance(images, list):
images = [images]
orig_sizes = []
processed_images = []
source_images = [] if include_source_image else None
for img in images:
if isinstance(img, str):
if img.startswith("http"):
img = requests.get(img, stream=True).raw
img = Image.open(img)
if not isinstance(img, torch.Tensor):
if include_source_image:
src = np.array(img)
if src.dtype != np.uint8:
src = (src * 255).clip(0, 255).astype(np.uint8)
source_images.append(src)
img = F.to_tensor(img)
elif include_source_image:
source_images.append((img.permute(1, 2, 0).cpu().numpy() * 255).astype(np.uint8))
if (img > 1).any():
raise ValueError(
"Image has pixel values above 1. Please ensure the image is normalized (scaled to [0, 1]).",
)
if (img < 0).any():
raise ValueError(
"Image has pixel values below 0. Please ensure the image is normalized (scaled to [0, 1]).",
)
if img.shape[0] != self.model_config.num_channels:
raise ValueError(
"Invalid tensor image shape. Tensor inputs to `predict()` must be in (C, H, W) format "
f"with C matching the model configuration ({self.model_config.num_channels} channels). "
f"Received tensor with shape {tuple(img.shape)}."
)
img_tensor = img
h, w = img_tensor.shape[1:]
orig_sizes.append((h, w))
processed_images.append(img_tensor.to(self.model.device))
resize_to = list(shape) if shape is not None else [self.model.resolution, self.model.resolution]
batch_tensor = torch.stack([F.resize(t, resize_to) for t in processed_images])
batch_tensor = F.normalize(batch_tensor, self.means, self.stds)
if self._is_optimized_for_inference:
if (
self._optimized_resolution != batch_tensor.shape[2]
or self._optimized_resolution != batch_tensor.shape[3]
):
# this could happen if someone manually changes self.model.resolution after optimizing the model,
# or if predict(shape=...) is used with a shape that doesn't match the compiled square resolution.
_restore_hint = (
" Create a new RFDETR instance to use a different resolution."
if getattr(self, "_optimized_inplace", False)
else " You can explicitly remove the optimized model by calling model.remove_optimized_model()."
)
raise ValueError(
f"Resolution mismatch. "
f"Model was optimized for resolution {self._optimized_resolution}x{self._optimized_resolution}, "
f"but got {batch_tensor.shape[2]}x{batch_tensor.shape[3]}." + _restore_hint,
)
if self._optimized_has_been_compiled:
if self._optimized_batch_size != batch_tensor.shape[0]:
_restore_hint = (
" Create a new RFDETR instance to recompile for a different batch size."
if getattr(self, "_optimized_inplace", False)
else (
" You can explicitly remove the optimized model by calling model.remove_optimized_model()."
" Alternatively, you can recompile the optimized model for a different batch size"
" by calling model.optimize_for_inference(batch_size=)."
)
)
raise ValueError(
f"Batch size mismatch. "
f"Optimized model was compiled for batch size {self._optimized_batch_size}, "
f"but got {batch_tensor.shape[0]}." + _restore_hint,
)
if self._is_optimized_for_inference:
predictions = self.model.inference_model(batch_tensor.to(dtype=self._optimized_dtype))
else:
predictions = self.model.model(batch_tensor)
if isinstance(predictions, tuple):
return_predictions = {
"pred_logits": predictions[1],
"pred_boxes": predictions[0],
}
if len(predictions) == 3:
# Distinguish optional keypoint vs mask tuple output for legacy compiled/export shims.
if getattr(getattr(self.model, "model_config", None), "use_grouppose_keypoints", False):
return_predictions["pred_keypoints"] = predictions[2]
else:
return_predictions["pred_masks"] = predictions[2]
predictions = return_predictions
target_sizes = torch.tensor(orig_sizes, device=self.model.device)
results = self.model.postprocess(predictions, target_sizes=target_sizes)
model_class_names = self.class_names
n = len(model_class_names)
# Pretrained COCO models use COCO category IDs (1–90, with gaps) as class_ids,
# while class_names is a flat 0-indexed list of 80 entries. Detected when
# args.num_classes > len(class_names) AND class_names == COCO_CLASS_NAMES.
# Fine-tuned models remap category IDs to 0-based contiguous indices, so
# class_id i maps directly to class_names[i].
_model_args = getattr(self.model, "args", None)
if _model_args is None and model_class_names == list(COCO_CLASS_NAMES):
logger.warning_once(
"predict(): model has no 'args' attribute — COCO sparse-ID mapping cannot activate; "
"class_ids are treated as 0-indexed (may be wrong for pretrained COCO checkpoints)"
)
num_logit_slots: int = getattr(_model_args, "num_classes", n)
_is_coco_pretrained = num_logit_slots > n and model_class_names == list(COCO_CLASS_NAMES)
# Legacy keypoint models may use a shifted class scheme: slot 0 = background
# (0 keypoints), real classes start at slot 1. Active-first schemas such as
# [17] use normal 0-based class IDs and fall through to the default mapping.
_num_keypoints_per_class: list[int] = getattr(_model_args, "num_keypoints_per_class", []) or []
_is_legacy_bgfirst_keypoint = _is_bg_first_schema(_num_keypoints_per_class)
if _is_coco_pretrained:
_class_id_to_name: dict[int, str] = {
coco_id: model_class_names[i] for i, coco_id in enumerate(COCO_CLASSES) if i < n
}
elif _is_legacy_bgfirst_keypoint:
# Map foreground keypoint slots (slots where num_keypoints > 0) to class names.
# Slot 0 is background and is skipped. Slot 1 → class_names[0], slot 2 → class_names[1], …
# Note: slots where num_keypoints == 0 but slot != 0 (detect-only classes in a mixed schema
# such as [0, 17, 0, 4]) are not present in _kp_foreground_slots and will map to an empty
# string with a one-time warning. Mixed keypoint+detection schemas are not a supported
# configuration for the shipped models.
_kp_foreground_slots = [idx for idx, k in enumerate(_num_keypoints_per_class) if k > 0]
_class_id_to_name = {slot: model_class_names[i] for i, slot in enumerate(_kp_foreground_slots) if i < n}
else:
_class_id_to_name = dict(enumerate(model_class_names))
predictions_list: list[Detections | KeyPoints] = []
for i, result in enumerate(results):
scores = result["scores"]
labels = result["labels"]
boxes = result["boxes"]
keep = scores > threshold
scores = scores[keep]
labels = labels[keep]
boxes = boxes[keep]
keypoints_array = None
if "keypoints" in result:
keypoints = result["keypoints"][keep]
keypoints_array = keypoints.float().cpu().numpy()
has_keypoints = keypoints_array is not None
if "masks" in result:
masks = result["masks"]
masks = masks[keep]
detections = Detections(
xyxy=boxes.float().cpu().numpy(),
confidence=scores.float().cpu().numpy(),
class_id=labels.cpu().numpy(),
mask=masks.squeeze(1).cpu().numpy(),
)
else:
detections = Detections(
xyxy=boxes.float().cpu().numpy(),
confidence=scores.float().cpu().numpy(),
class_id=labels.cpu().numpy(),
)
if "keypoint_precision_cholesky" in result:
keypoint_precision = result["keypoint_precision_cholesky"][keep]
detections.data["keypoint_precision_cholesky"] = keypoint_precision.float().cpu().numpy()
if include_source_image:
detections.metadata["source_image"] = source_images[i]
detections.data["source_shape"] = np.tile(np.array(orig_sizes[i], dtype=np.int64), (len(detections), 1))
# Attach class names so callers can map class_id → name without a
# separate lookup. Always set data["class_name"] for a consistent interface.
#
# For fine-tuned models, logit index num_logit_slots is the no-object slot —
# map it to "__background__" without warning. For COCO-pretrained models,
# background is implicit (filtered by threshold); class ID 90 is "toothbrush".
# IDs not in _class_id_to_name are genuinely unexpected and produce an empty
# string with a one-time warning.
class_ids = detections.class_id if detections.class_id is not None else np.array([], dtype=int)
# Sentinel for the no-object / background class differs by model type.
# Legacy background-first keypoint models: slot 0 is background in the keypoint schema.
# Detection/segmentation models: the no-object slot is at index num_logit_slots.
_bg_sentinel = 0 if _is_legacy_bgfirst_keypoint else num_logit_slots
truly_oob = [cid for cid in class_ids if cid not in _class_id_to_name and cid != _bg_sentinel]
if truly_oob:
logger.warning_once(
"predict() encountered unmapped class_id(s): %s — mapping to empty string",
truly_oob[:5],
)
if _is_coco_pretrained:
class_names = [_class_id_to_name.get(cid, "") for cid in class_ids]
else:
class_names = [
"__background__" if cid == _bg_sentinel else _class_id_to_name.get(cid, "") for cid in class_ids
]
detections.data["class_name"] = np.array(class_names, dtype=object)
if has_keypoints and keypoints_array is not None:
keypoint_data = dict(detections.data)
keypoint_data["xyxy"] = detections.xyxy.astype(np.float32)
if include_source_image:
keypoint_data["source_image"] = [source_images[i] for _ in range(len(detections))]
raw_precision = keypoint_data.get("keypoint_precision_cholesky")
raw_source_shape = keypoint_data.get("source_shape")
if raw_precision is not None and raw_source_shape is not None and len(detections) > 0:
precision = np.asarray(raw_precision, dtype=np.float32)
source_shape = np.asarray(raw_source_shape, dtype=np.float32)
if precision.shape[:2] == keypoints_array.shape[:2] and source_shape.shape == (len(detections), 2):
keypoint_data["covariance"] = precision_cholesky_to_pixel_covariance(
precision_cholesky=precision, source_shape=source_shape
)
keypoints_array = keypoints_array.astype(np.float32, copy=False)
keypoint_confidence = keypoints_array[:, :, 2]
key_points = KeyPoints(
xy=keypoints_array[:, :, :2],
keypoint_confidence=keypoint_confidence,
detection_confidence=detections.confidence.astype(np.float32)
if detections.confidence is not None
else None,
class_id=detections.class_id.astype(int) if detections.class_id is not None else None,
visible=keypoint_confidence > 0,
data=keypoint_data,
)
predictions_list.append(key_points)
else:
predictions_list.append(detections)
return predictions_list if len(predictions_list) > 1 else predictions_list[0]
def deploy_to_roboflow(
self,
workspace: str,
project_id: str,
version: int | str,
api_key: str | None = None,
size: str | None = None,
) -> None:
"""Deploy the trained RF-DETR model to Roboflow.
Deploying with Roboflow will create a Serverless API to which you can make requests.
You can also download weights into a Roboflow Inference deployment for use in Roboflow Workflows and on-device
deployment.
Args:
workspace: The name of the Roboflow workspace to deploy to.
project_id: The project ID to which the model will be deployed.
version: The project version to which the model will be deployed.
api_key: Your Roboflow API key. If not provided,
it will be read from the environment variable `ROBOFLOW_API_KEY`.
size: The size of the model to deploy. If not provided,
it will default to the size of the model being trained (e.g., "rfdetr-base", "rfdetr-large", etc.).
Raises:
ValueError: If the `api_key` is not provided and not found in the
environment variable `ROBOFLOW_API_KEY`, or if the `size` is not set for custom architectures.
Note:
Bundle creation is delegated to :meth:`export_for_roboflow`, which can be called independently
to write ``weights.pt`` and ``class_names.txt`` without a network round-trip.
"""
from roboflow import Roboflow
if api_key is None:
api_key = os.getenv("ROBOFLOW_API_KEY")
if api_key is None:
raise ValueError("Set api_key= in deploy_to_roboflow or export ROBOFLOW_API_KEY=")
rf = Roboflow(api_key=api_key)
workspace = rf.workspace(workspace)
if self.size is None and size is None:
raise ValueError("Must set size for custom architectures")
size = self.size or size
with tempfile.TemporaryDirectory(prefix="roboflow_upload_") as tmp_out_dir:
self.export_for_roboflow(tmp_out_dir)
project = workspace.project(project_id)
project_version = project.version(version)
project_version.deploy(model_type=size, model_path=tmp_out_dir, filename="weights.pt")
def export_for_roboflow(self, output_dir: str | os.PathLike[str]) -> None:
"""Write a Roboflow upload bundle (``weights.pt`` + ``class_names.txt``) into *output_dir*.
This is the network-free core of :meth:`deploy_to_roboflow`: it serialises the model state and
training args into ``weights.pt``, always embedding ``class_names`` into a copy of the args so
the bundle is self-contained, and writes the class labels to ``class_names.txt``. The Roboflow
SDK uses this format to adapt raw PyTorch-Lightning checkpoints into a deploy-ready bundle.
Args:
output_dir: Directory into which ``weights.pt`` and ``class_names.txt`` are written. Created
if it does not exist. Existing files are silently overwritten.
Raises:
PermissionError: If the process lacks write access to *output_dir* or its parent directory.
OSError: On disk-full, invalid path, or other filesystem failure during directory creation,
file write, or ``torch.save``.
"""
os.makedirs(output_dir, exist_ok=True)
# Write class_names.txt so the Roboflow upload pipeline can discover
# the class labels without relying on args.class_names in the checkpoint.
class_names_path = os.path.join(output_dir, "class_names.txt")
with open(class_names_path, "w", encoding="utf-8", newline="\n") as f:
f.write("\n".join(self.class_names))
# Embed class_names in a shallow copy of args so the saved bundle is
# self-contained (roboflow-python's second fallback reads args.class_names
# directly from the checkpoint). Using a copy leaves self.model.args
# unmodified — each export call is independent regardless of call order.
args = copy(self.model.args)
if not hasattr(args, "class_names") or args.class_names is None:
args.class_names = self.class_names
outpath = os.path.join(output_dir, "weights.pt")
torch.save({"model": self.model.model.state_dict(), "args": args}, outpath)
def __getattr__(name: str):
"""Lazily resolve legacy re-exports without creating import-order cycles."""
if name in _VARIANT_EXPORTS:
module = importlib.import_module("rfdetr.variants")
value = getattr(module, name)
globals()[name] = value
return value
raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
def __dir__() -> list[str]:
"""Include lazy re-exports in interactive discovery."""
return sorted(set(globals()) | set(_VARIANT_EXPORTS))