rfdetr/detr.py

# ------------------------------------------------------------------------
# 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=<new_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)