# ------------------------------------------------------------------------ # RF-DETR # Copyright (c) 2025 Roboflow. All Rights Reserved. # Licensed under the Apache License, Version 2.0 [see LICENSE for details] # ------------------------------------------------------------------------ import json import os from collections import defaultdict from logging import getLogger from typing import Union, List from copy import deepcopy import numpy as np import supervision as sv import torch import torchvision.transforms.functional as F from PIL import Image try: torch.set_float32_matmul_precision('high') except: pass from rfdetr.config import RFDETRBaseConfig, RFDETRLargeConfig, TrainConfig, ModelConfig from rfdetr.main import Model, download_pretrain_weights from rfdetr.util.metrics import MetricsPlotSink, MetricsTensorBoardSink, MetricsWandBSink from rfdetr.util.coco_classes import COCO_CLASSES logger = getLogger(__name__) class RFDETR: means = [0.485, 0.456, 0.406] stds = [0.229, 0.224, 0.225] 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) 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 def maybe_download_pretrain_weights(self): download_pretrain_weights(self.model_config.pretrain_weights) def get_model_config(self, **kwargs): return ModelConfig(**kwargs) def train(self, **kwargs): config = self.get_train_config(**kwargs) self.train_from_config(config, **kwargs) def optimize_for_inference(self, compile=True, batch_size=1, dtype=torch.float32): self.remove_optimized_model() self.model.inference_model = deepcopy(self.model.model) self.model.inference_model.eval() self.model.inference_model.export() self._optimized_resolution = self.model.resolution self._is_optimized_for_inference = True self.model.inference_model = self.model.inference_model.to(dtype=dtype) self._optimized_dtype = dtype if compile: self.model.inference_model = torch.jit.trace( self.model.inference_model, torch.randn( batch_size, 3, self.model.resolution, self.model.resolution, device=self.model.device, dtype=dtype ) ) self._optimized_has_been_compiled = True self._optimized_batch_size = batch_size def remove_optimized_model(self): 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_half = False def export(self, **kwargs): self.model.export(**kwargs) def train_from_config(self, config: TrainConfig, **kwargs): with open( os.path.join(config.dataset_dir, "train", "_annotations.coco.json"), "r" ) as f: anns = json.load(f) num_classes = len(anns["categories"]) class_names = [c["name"] for c in anns["categories"] if c["supercategory"] != "none"] self.model.class_names = class_names if self.model_config.num_classes != num_classes: logger.warning( f"num_classes mismatch: model has {self.model_config.num_classes} classes, but your dataset has {num_classes} classes\n" f"reinitializing your detection head with {num_classes} classes." ) self.model.reinitialize_detection_head(num_classes) train_config = config.dict() model_config = self.model_config.dict() model_config.pop("num_classes") if "class_names" in model_config: model_config.pop("class_names") if "class_names" in train_config and train_config["class_names"] is None: train_config["class_names"] = class_names for k, v in train_config.items(): if k in model_config: model_config.pop(k) if k in kwargs: kwargs.pop(k) all_kwargs = {**model_config, **train_config, **kwargs, "num_classes": num_classes} metrics_plot_sink = MetricsPlotSink(output_dir=config.output_dir) self.callbacks["on_fit_epoch_end"].append(metrics_plot_sink.update) self.callbacks["on_train_end"].append(metrics_plot_sink.save) if config.tensorboard: metrics_tensor_board_sink = MetricsTensorBoardSink(output_dir=config.output_dir) self.callbacks["on_fit_epoch_end"].append(metrics_tensor_board_sink.update) self.callbacks["on_train_end"].append(metrics_tensor_board_sink.close) if config.wandb: metrics_wandb_sink = MetricsWandBSink( output_dir=config.output_dir, project=config.project, run=config.run, config=config.model_dump() ) self.callbacks["on_fit_epoch_end"].append(metrics_wandb_sink.update) self.callbacks["on_train_end"].append(metrics_wandb_sink.close) if config.early_stopping: from rfdetr.util.early_stopping import EarlyStoppingCallback early_stopping_callback = EarlyStoppingCallback( model=self.model, patience=config.early_stopping_patience, min_delta=config.early_stopping_min_delta, use_ema=config.early_stopping_use_ema ) self.callbacks["on_fit_epoch_end"].append(early_stopping_callback.update) self.model.train( **all_kwargs, callbacks=self.callbacks, ) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) def get_model(self, config: ModelConfig): return Model(**config.dict()) # Get class_names from the model @property def class_names(self): if hasattr(self.model, 'class_names') and self.model.class_names: return {i+1: name for i, name in enumerate(self.model.class_names)} return COCO_CLASSES def predict( self, images: Union[str, Image.Image, np.ndarray, torch.Tensor, List[Union[str, np.ndarray, Image.Image, torch.Tensor]]], threshold: float = 0.5, **kwargs, ) -> Union[sv.Detections, List[sv.Detections]]: """Performs object detection on the input images and returns bounding box predictions. This method accepts a single image or a list of images in various formats (file path, 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 (Union[str, Image.Image, np.ndarray, torch.Tensor, List[Union[str, np.ndarray, Image.Image, torch.Tensor]]]): 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 (float, optional): The minimum confidence score needed to consider a detected bounding box valid. **kwargs: Additional keyword arguments. Returns: Union[sv.Detections, List[sv.Detections]]: A single or multiple Detections objects, each containing bounding box coordinates, confidence scores, and class IDs. """ if not self._is_optimized_for_inference and not self._has_warned_about_not_being_optimized_for_inference: logger.warning( "Model is not optimized for inference. " "Latency may be higher than expected. " "You can optimize the model for inference by calling model.optimize_for_inference()." ) self._has_warned_about_not_being_optimized_for_inference = True self.model.model.eval() if not isinstance(images, list): images = [images] orig_sizes = [] processed_images = [] for img in images: if isinstance(img, str): img = Image.open(img) if not isinstance(img, torch.Tensor): img = F.to_tensor(img) if (img > 1).any(): raise ValueError( "Image has pixel values above 1. Please ensure the image is " "normalized (scaled to [0, 1])." ) if img.shape[0] != 3: raise ValueError( f"Invalid image shape. Expected 3 channels (RGB), but got " f"{img.shape[0]} channels." ) img_tensor = img h, w = img_tensor.shape[1:] orig_sizes.append((h, w)) img_tensor = img_tensor.to(self.model.device) img_tensor = F.normalize(img_tensor, self.means, self.stds) img_tensor = F.resize(img_tensor, (self.model.resolution, self.model.resolution)) processed_images.append(img_tensor) batch_tensor = torch.stack(processed_images) if self._is_optimized_for_inference: if self._optimized_resolution != batch_tensor.shape[2]: # this could happen if someone manually changes self.model.resolution after optimizing the model raise ValueError(f"Resolution mismatch. " f"Model was optimized for resolution {self._optimized_resolution}, " f"but got {batch_tensor.shape[2]}. " "You can explicitly remove the optimized model by calling model.remove_optimized_model().") if self._optimized_has_been_compiled: if self._optimized_batch_size != batch_tensor.shape[0]: 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]}. " "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=).") with torch.inference_mode(): 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): predictions = { "pred_logits": predictions[1], "pred_boxes": predictions[0] } target_sizes = torch.tensor(orig_sizes, device=self.model.device) results = self.model.postprocessors["bbox"](predictions, target_sizes=target_sizes) detections_list = [] for result in results: scores = result["scores"] labels = result["labels"] boxes = result["boxes"] keep = scores > threshold scores = scores[keep] labels = labels[keep] boxes = boxes[keep] detections = sv.Detections( xyxy=boxes.float().cpu().numpy(), confidence=scores.float().cpu().numpy(), class_id=labels.cpu().numpy(), ) detections_list.append(detections) return detections_list if len(detections_list) > 1 else detections_list[0] class RFDETRBase(RFDETR): def get_model_config(self, **kwargs): return RFDETRBaseConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs) class RFDETRLarge(RFDETR): def get_model_config(self, **kwargs): return RFDETRLargeConfig(**kwargs) def get_train_config(self, **kwargs): return TrainConfig(**kwargs)