# Ultralytics YOLO 🚀, GPL-3.0 license """ Check a model's accuracy on a test or val split of a dataset Usage: $ yolo mode=val model=yolov8n.pt data=coco128.yaml imgsz=640 Usage - formats: $ yolo mode=val model=yolov8n.pt # PyTorch yolov8n.torchscript # TorchScript yolov8n.onnx # ONNX Runtime or OpenCV DNN with dnn=True yolov8n_openvino_model # OpenVINO yolov8n.engine # TensorRT yolov8n.mlmodel # CoreML (macOS-only) yolov8n_saved_model # TensorFlow SavedModel yolov8n.pb # TensorFlow GraphDef yolov8n.tflite # TensorFlow Lite yolov8n_edgetpu.tflite # TensorFlow Edge TPU yolov8n_paddle_model # PaddlePaddle """ import json from collections import defaultdict from pathlib import Path import torch from tqdm import tqdm from ultralytics.nn.autobackend import AutoBackend from ultralytics.yolo.cfg import get_cfg from ultralytics.yolo.data.utils import check_cls_dataset, check_det_dataset from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, SETTINGS, TQDM_BAR_FORMAT, callbacks, colorstr, emojis from ultralytics.yolo.utils.checks import check_imgsz from ultralytics.yolo.utils.files import increment_path from ultralytics.yolo.utils.ops import Profile from ultralytics.yolo.utils.torch_utils import de_parallel, select_device, smart_inference_mode class BaseValidator: """ BaseValidator A base class for creating validators. Attributes: dataloader (DataLoader): Dataloader to use for validation. pbar (tqdm): Progress bar to update during validation. args (SimpleNamespace): Configuration for the validator. model (nn.Module): Model to validate. data (dict): Data dictionary. device (torch.device): Device to use for validation. batch_i (int): Current batch index. training (bool): Whether the model is in training mode. speed (float): Batch processing speed in seconds. jdict (dict): Dictionary to store validation results. save_dir (Path): Directory to save results. """ def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None): """ Initializes a BaseValidator instance. Args: dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation. save_dir (Path): Directory to save results. pbar (tqdm.tqdm): Progress bar for displaying progress. logger (logging.Logger): Logger to log messages. args (SimpleNamespace): Configuration for the validator. """ self.dataloader = dataloader self.pbar = pbar self.args = args or get_cfg(DEFAULT_CFG) self.model = None self.data = None self.device = None self.batch_i = None self.training = True self.speed = {'preprocess': 0.0, 'inference': 0.0, 'loss': 0.0, 'postprocess': 0.0} self.jdict = None project = self.args.project or Path(SETTINGS['runs_dir']) / self.args.task name = self.args.name or f'{self.args.mode}' self.save_dir = save_dir or increment_path(Path(project) / name, exist_ok=self.args.exist_ok if RANK in {-1, 0} else True) (self.save_dir / 'labels' if self.args.save_txt else self.save_dir).mkdir(parents=True, exist_ok=True) if self.args.conf is None: self.args.conf = 0.001 # default conf=0.001 self.callbacks = defaultdict(list, callbacks.default_callbacks) # add callbacks @smart_inference_mode() def __call__(self, trainer=None, model=None): """ Supports validation of a pre-trained model if passed or a model being trained if trainer is passed (trainer gets priority). """ self.training = trainer is not None if self.training: self.device = trainer.device self.data = trainer.data model = trainer.ema.ema or trainer.model self.args.half = self.device.type != 'cpu' # force FP16 val during training model = model.half() if self.args.half else model.float() self.model = model self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device) self.args.plots = trainer.epoch == trainer.epochs - 1 # always plot final epoch model.eval() else: callbacks.add_integration_callbacks(self) self.run_callbacks('on_val_start') assert model is not None, 'Either trainer or model is needed for validation' self.device = select_device(self.args.device, self.args.batch) self.args.half &= self.device.type != 'cpu' model = AutoBackend(model, device=self.device, dnn=self.args.dnn, data=self.args.data, fp16=self.args.half) self.model = model stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine imgsz = check_imgsz(self.args.imgsz, stride=stride) if engine: self.args.batch = model.batch_size else: self.device = model.device if not pt and not jit: self.args.batch = 1 # export.py models default to batch-size 1 LOGGER.info(f'Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models') if isinstance(self.args.data, str) and self.args.data.endswith('.yaml'): self.data = check_det_dataset(self.args.data) elif self.args.task == 'classify': self.data = check_cls_dataset(self.args.data) else: raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌")) if self.device.type == 'cpu': self.args.workers = 0 # faster CPU val as time dominated by inference, not dataloading if not pt: self.args.rect = False self.dataloader = self.dataloader or self.get_dataloader(self.data.get(self.args.split), self.args.batch) model.eval() model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup dt = Profile(), Profile(), Profile(), Profile() n_batches = len(self.dataloader) desc = self.get_desc() # NOTE: keeping `not self.training` in tqdm will eliminate pbar after segmentation evaluation during training, # which may affect classification task since this arg is in yolov5/classify/val.py. # bar = tqdm(self.dataloader, desc, n_batches, not self.training, bar_format=TQDM_BAR_FORMAT) bar = tqdm(self.dataloader, desc, n_batches, bar_format=TQDM_BAR_FORMAT) self.init_metrics(de_parallel(model)) self.jdict = [] # empty before each val for batch_i, batch in enumerate(bar): self.run_callbacks('on_val_batch_start') self.batch_i = batch_i # preprocess with dt[0]: batch = self.preprocess(batch) # inference with dt[1]: preds = model(batch['img']) # loss with dt[2]: if self.training: self.loss += trainer.criterion(preds, batch)[1] # postprocess with dt[3]: preds = self.postprocess(preds) self.update_metrics(preds, batch) if self.args.plots and batch_i < 3: self.plot_val_samples(batch, batch_i) self.plot_predictions(batch, preds, batch_i) self.run_callbacks('on_val_batch_end') stats = self.get_stats() self.check_stats(stats) self.print_results() self.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1E3 for x in dt))) self.finalize_metrics() self.run_callbacks('on_val_end') if self.training: model.float() results = {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix='val')} return {k: round(float(v), 5) for k, v in results.items()} # return results as 5 decimal place floats else: LOGGER.info('Speed: %.1fms preprocess, %.1fms inference, %.1fms loss, %.1fms postprocess per image' % tuple(self.speed.values())) if self.args.save_json and self.jdict: with open(str(self.save_dir / 'predictions.json'), 'w') as f: LOGGER.info(f'Saving {f.name}...') json.dump(self.jdict, f) # flatten and save stats = self.eval_json(stats) # update stats if self.args.plots or self.args.save_json: LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}") return stats def run_callbacks(self, event: str): for callback in self.callbacks.get(event, []): callback(self) def get_dataloader(self, dataset_path, batch_size): raise NotImplementedError('get_dataloader function not implemented for this validator') def preprocess(self, batch): return batch def postprocess(self, preds): return preds def init_metrics(self, model): pass def update_metrics(self, preds, batch): pass def finalize_metrics(self, *args, **kwargs): pass def get_stats(self): return {} def check_stats(self, stats): pass def print_results(self): pass def get_desc(self): pass @property def metric_keys(self): return [] # TODO: may need to put these following functions into callback def plot_val_samples(self, batch, ni): pass def plot_predictions(self, batch, preds, ni): pass def pred_to_json(self, preds, batch): pass def eval_json(self, stats): pass