update

ultralytics/engine/validator.py

@@ -0,0 +1,345 @@
+# Ultralytics YOLO 🚀, AGPL-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.mlpackage          # 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
+                          yolov8n_ncnn_model         # NCNN
+"""
+
+import json
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+
+from ultralytics.cfg import get_cfg, get_save_dir
+from ultralytics.data.utils import check_cls_dataset, check_det_dataset
+from ultralytics.nn.autobackend import AutoBackend
+from ultralytics.utils import LOGGER, TQDM, callbacks, colorstr, emojis
+from ultralytics.utils.checks import check_imgsz
+from ultralytics.utils.ops import Profile
+from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode
+
+
+class BaseValidator:
+    """
+    BaseValidator.
+
+    A base class for creating validators.
+
+    Attributes:
+        args (SimpleNamespace): Configuration for the validator.
+        dataloader (DataLoader): Dataloader to use for validation.
+        pbar (tqdm): Progress bar to update during validation.
+        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.
+        names (dict): Class names.
+        seen: Records the number of images seen so far during validation.
+        stats: Placeholder for statistics during validation.
+        confusion_matrix: Placeholder for a confusion matrix.
+        nc: Number of classes.
+        iouv: (torch.Tensor): IoU thresholds from 0.50 to 0.95 in spaces of 0.05.
+        jdict (dict): Dictionary to store JSON validation results.
+        speed (dict): Dictionary with keys 'preprocess', 'inference', 'loss', 'postprocess' and their respective
+                      batch processing times in milliseconds.
+        save_dir (Path): Directory to save results.
+        plots (dict): Dictionary to store plots for visualization.
+        callbacks (dict): Dictionary to store various callback functions.
+    """
+
+    def __init__(self, dataloader=None, save_dir=None, pbar=None, args=None, _callbacks=None):
+        """
+        Initializes a BaseValidator instance.
+
+        Args:
+            dataloader (torch.utils.data.DataLoader): Dataloader to be used for validation.
+            save_dir (Path, optional): Directory to save results.
+            pbar (tqdm.tqdm): Progress bar for displaying progress.
+            args (SimpleNamespace): Configuration for the validator.
+            _callbacks (dict): Dictionary to store various callback functions.
+        """
+        self.args = get_cfg(overrides=args)
+        self.dataloader = dataloader
+        self.pbar = pbar
+        self.stride = None
+        self.data = None
+        self.device = None
+        self.batch_i = None
+        self.training = True
+        self.names = None
+        self.seen = None
+        self.stats = None
+        self.confusion_matrix = None
+        self.nc = None
+        self.iouv = None
+        self.jdict = None
+        self.speed = {"preprocess": 0.0, "inference": 0.0, "loss": 0.0, "postprocess": 0.0}
+
+        self.save_dir = save_dir or get_save_dir(self.args)
+        (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.args.imgsz = check_imgsz(self.args.imgsz, max_dim=1)
+
+        self.plots = {}
+        self.callbacks = _callbacks or callbacks.get_default_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
+        augment = self.args.augment and (not self.training)
+        if self.training:
+            self.device = trainer.device
+            self.data = trainer.data
+            # self.args.half = self.device.type != "cpu"  # force FP16 val during training
+            model = trainer.ema.ema or trainer.model
+            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.stopper.possible_stop or (trainer.epoch == trainer.epochs - 1)
+            model.eval()
+        else:
+            callbacks.add_integration_callbacks(self)
+            model = AutoBackend(
+                weights=model or self.args.model,
+                device=select_device(self.args.device, self.args.batch),
+                dnn=self.args.dnn,
+                data=self.args.data,
+                fp16=self.args.half,
+            )
+            # self.model = model
+            self.device = model.device  # update device
+            self.args.half = model.fp16  # update half
+            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
+            elif 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 str(self.args.data).split(".")[-1] in ("yaml", "yml"):
+                self.data = check_det_dataset(self.args.data)
+            elif self.args.task == "classify":
+                self.data = check_cls_dataset(self.args.data, split=self.args.split)
+            else:
+                raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' for task={self.args.task} not found ❌"))
+
+            if self.device.type in ("cpu", "mps"):
+                self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
+            if not pt:
+                self.args.rect = False
+            self.stride = model.stride  # used in get_dataloader() for padding
+            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
+
+        self.run_callbacks("on_val_start")
+        dt = (
+            Profile(device=self.device),
+            Profile(device=self.device),
+            Profile(device=self.device),
+            Profile(device=self.device),
+        )
+        bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
+        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"], augment=augment)
+
+            # Loss
+            with dt[2]:
+                if self.training:
+                    self.loss += model.loss(batch, preds)[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.speed = dict(zip(self.speed.keys(), (x.t / len(self.dataloader.dataset) * 1e3 for x in dt)))
+        self.finalize_metrics()
+        if not (self.args.save_json and self.is_coco and len(self.jdict)):
+            self.print_results()
+        self.run_callbacks("on_val_end")
+        if self.training:
+            model.float()
+            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
+                stats['fitness'] = stats['metrics/mAP50-95(B)']
+            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 match_predictions(self, pred_classes, true_classes, iou, use_scipy=False):
+        """
+        Matches predictions to ground truth objects (pred_classes, true_classes) using IoU.
+
+        Args:
+            pred_classes (torch.Tensor): Predicted class indices of shape(N,).
+            true_classes (torch.Tensor): Target class indices of shape(M,).
+            iou (torch.Tensor): An NxM tensor containing the pairwise IoU values for predictions and ground of truth
+            use_scipy (bool): Whether to use scipy for matching (more precise).
+
+        Returns:
+            (torch.Tensor): Correct tensor of shape(N,10) for 10 IoU thresholds.
+        """
+        # Dx10 matrix, where D - detections, 10 - IoU thresholds
+        correct = np.zeros((pred_classes.shape[0], self.iouv.shape[0])).astype(bool)
+        # LxD matrix where L - labels (rows), D - detections (columns)
+        correct_class = true_classes[:, None] == pred_classes
+        iou = iou * correct_class  # zero out the wrong classes
+        iou = iou.cpu().numpy()
+        for i, threshold in enumerate(self.iouv.cpu().tolist()):
+            if use_scipy:
+                # WARNING: known issue that reduces mAP in https://github.com/ultralytics/ultralytics/pull/4708
+                import scipy  # scope import to avoid importing for all commands
+
+                cost_matrix = iou * (iou >= threshold)
+                if cost_matrix.any():
+                    labels_idx, detections_idx = scipy.optimize.linear_sum_assignment(cost_matrix, maximize=True)
+                    valid = cost_matrix[labels_idx, detections_idx] > 0
+                    if valid.any():
+                        correct[detections_idx[valid], i] = True
+            else:
+                matches = np.nonzero(iou >= threshold)  # IoU > threshold and classes match
+                matches = np.array(matches).T
+                if matches.shape[0]:
+                    if matches.shape[0] > 1:
+                        matches = matches[iou[matches[:, 0], matches[:, 1]].argsort()[::-1]]
+                        matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+                        # matches = matches[matches[:, 2].argsort()[::-1]]
+                        matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+                    correct[matches[:, 1].astype(int), i] = True
+        return torch.tensor(correct, dtype=torch.bool, device=pred_classes.device)
+
+    def add_callback(self, event: str, callback):
+        """Appends the given callback."""
+        self.callbacks[event].append(callback)
+
+    def run_callbacks(self, event: str):
+        """Runs all callbacks associated with a specified event."""
+        for callback in self.callbacks.get(event, []):
+            callback(self)
+
+    def get_dataloader(self, dataset_path, batch_size):
+        """Get data loader from dataset path and batch size."""
+        raise NotImplementedError("get_dataloader function not implemented for this validator")
+
+    def build_dataset(self, img_path):
+        """Build dataset."""
+        raise NotImplementedError("build_dataset function not implemented in validator")
+
+    def preprocess(self, batch):
+        """Preprocesses an input batch."""
+        return batch
+
+    def postprocess(self, preds):
+        """Describes and summarizes the purpose of 'postprocess()' but no details mentioned."""
+        return preds
+
+    def init_metrics(self, model):
+        """Initialize performance metrics for the YOLO model."""
+        pass
+
+    def update_metrics(self, preds, batch):
+        """Updates metrics based on predictions and batch."""
+        pass
+
+    def finalize_metrics(self, *args, **kwargs):
+        """Finalizes and returns all metrics."""
+        pass
+
+    def get_stats(self):
+        """Returns statistics about the model's performance."""
+        return {}
+
+    def check_stats(self, stats):
+        """Checks statistics."""
+        pass
+
+    def print_results(self):
+        """Prints the results of the model's predictions."""
+        pass
+
+    def get_desc(self):
+        """Get description of the YOLO model."""
+        pass
+
+    @property
+    def metric_keys(self):
+        """Returns the metric keys used in YOLO training/validation."""
+        return []
+
+    def on_plot(self, name, data=None):
+        """Registers plots (e.g. to be consumed in callbacks)"""
+        self.plots[Path(name)] = {"data": data, "timestamp": time.time()}
+
+    # TODO: may need to put these following functions into callback
+    def plot_val_samples(self, batch, ni):
+        """Plots validation samples during training."""
+        pass
+
+    def plot_predictions(self, batch, preds, ni):
+        """Plots YOLO model predictions on batch images."""
+        pass
+
+    def pred_to_json(self, preds, batch):
+        """Convert predictions to JSON format."""
+        pass
+
+    def eval_json(self, stats):
+        """Evaluate and return JSON format of prediction statistics."""
+        pass