退购1.1定位算法

ultralytics/yolo/v8/classify/train.py

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+# Ultralytics YOLO 🚀, AGPL-3.0 license
+
+import torch
+import torchvision
+
+from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight
+from ultralytics.yolo import v8
+from ultralytics.yolo.data import ClassificationDataset, build_dataloader
+from ultralytics.yolo.engine.trainer import BaseTrainer
+from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
+from ultralytics.yolo.utils.plotting import plot_images, plot_results
+from ultralytics.yolo.utils.torch_utils import is_parallel, strip_optimizer, torch_distributed_zero_first
+
+
+class ClassificationTrainer(BaseTrainer):
+
+    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
+        """Initialize a ClassificationTrainer object with optional configuration overrides and callbacks."""
+        if overrides is None:
+            overrides = {}
+        overrides['task'] = 'classify'
+        super().__init__(cfg, overrides, _callbacks)
+
+    def set_model_attributes(self):
+        """Set the YOLO model's class names from the loaded dataset."""
+        self.model.names = self.data['names']
+
+    def get_model(self, cfg=None, weights=None, verbose=True):
+        """Returns a modified PyTorch model configured for training YOLO."""
+        model = ClassificationModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
+        if weights:
+            model.load(weights)
+
+        pretrained = self.args.pretrained
+        for m in model.modules():
+            if not pretrained and hasattr(m, 'reset_parameters'):
+                m.reset_parameters()
+            if isinstance(m, torch.nn.Dropout) and self.args.dropout:
+                m.p = self.args.dropout  # set dropout
+        for p in model.parameters():
+            p.requires_grad = True  # for training
+
+        # Update defaults
+        if self.args.imgsz == 640:
+            self.args.imgsz = 224
+
+        return model
+
+    def setup_model(self):
+        """
+        load/create/download model for any task
+        """
+        # Classification models require special handling
+
+        if isinstance(self.model, torch.nn.Module):  # if model is loaded beforehand. No setup needed
+            return
+
+        model = str(self.model)
+        # Load a YOLO model locally, from torchvision, or from Ultralytics assets
+        if model.endswith('.pt'):
+            self.model, _ = attempt_load_one_weight(model, device='cpu')
+            for p in self.model.parameters():
+                p.requires_grad = True  # for training
+        elif model.endswith('.yaml'):
+            self.model = self.get_model(cfg=model)
+        elif model in torchvision.models.__dict__:
+            pretrained = True
+            self.model = torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
+        else:
+            FileNotFoundError(f'ERROR: model={model} not found locally or online. Please check model name.')
+        ClassificationModel.reshape_outputs(self.model, self.data['nc'])
+
+        return  # dont return ckpt. Classification doesn't support resume
+
+    def build_dataset(self, img_path, mode='train', batch=None):
+        return ClassificationDataset(root=img_path, imgsz=self.args.imgsz, augment=mode == 'train')
+
+    def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode='train'):
+        """Returns PyTorch DataLoader with transforms to preprocess images for inference."""
+        with torch_distributed_zero_first(rank):  # init dataset *.cache only once if DDP
+            dataset = self.build_dataset(dataset_path, mode)
+
+        loader = build_dataloader(dataset, batch_size, self.args.workers, rank=rank)
+        # Attach inference transforms
+        if mode != 'train':
+            if is_parallel(self.model):
+                self.model.module.transforms = loader.dataset.torch_transforms
+            else:
+                self.model.transforms = loader.dataset.torch_transforms
+        return loader
+
+    def preprocess_batch(self, batch):
+        """Preprocesses a batch of images and classes."""
+        batch['img'] = batch['img'].to(self.device)
+        batch['cls'] = batch['cls'].to(self.device)
+        return batch
+
+    def progress_string(self):
+        """Returns a formatted string showing training progress."""
+        return ('\n' + '%11s' * (4 + len(self.loss_names))) % \
+            ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
+
+    def get_validator(self):
+        """Returns an instance of ClassificationValidator for validation."""
+        self.loss_names = ['loss']
+        return v8.classify.ClassificationValidator(self.test_loader, self.save_dir)
+
+    def criterion(self, preds, batch):
+        """Compute the classification loss between predictions and true labels."""
+        loss = torch.nn.functional.cross_entropy(preds, batch['cls'], reduction='sum') / self.args.nbs
+        loss_items = loss.detach()
+        return loss, loss_items
+
+    def label_loss_items(self, loss_items=None, prefix='train'):
+        """
+        Returns a loss dict with labelled training loss items tensor
+        """
+        # Not needed for classification but necessary for segmentation & detection
+        keys = [f'{prefix}/{x}' for x in self.loss_names]
+        if loss_items is None:
+            return keys
+        loss_items = [round(float(loss_items), 5)]
+        return dict(zip(keys, loss_items))
+
+    def resume_training(self, ckpt):
+        """Resumes training from a given checkpoint."""
+        pass
+
+    def plot_metrics(self):
+        """Plots metrics from a CSV file."""
+        plot_results(file=self.csv, classify=True)  # save results.png
+
+    def final_eval(self):
+        """Evaluate trained model and save validation results."""
+        for f in self.last, self.best:
+            if f.exists():
+                strip_optimizer(f)  # strip optimizers
+                # TODO: validate best.pt after training completes
+                # if f is self.best:
+                #     LOGGER.info(f'\nValidating {f}...')
+                #     self.validator.args.save_json = True
+                #     self.metrics = self.validator(model=f)
+                #     self.metrics.pop('fitness', None)
+                #     self.run_callbacks('on_fit_epoch_end')
+        LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}")
+
+    def plot_training_samples(self, batch, ni):
+        """Plots training samples with their annotations."""
+        plot_images(images=batch['img'],
+                    batch_idx=torch.arange(len(batch['img'])),
+                    cls=batch['cls'].squeeze(-1),
+                    fname=self.save_dir / f'train_batch{ni}.jpg')
+
+
+def train(cfg=DEFAULT_CFG, use_python=False):
+    """Train the YOLO classification model."""
+    model = cfg.model or 'yolov8n-cls.pt'  # or "resnet18"
+    data = cfg.data or 'mnist160'  # or yolo.ClassificationDataset("mnist")
+    device = cfg.device if cfg.device is not None else ''
+
+    args = dict(model=model, data=data, device=device)
+    if use_python:
+        from ultralytics import YOLO
+        YOLO(model).train(**args)
+    else:
+        trainer = ClassificationTrainer(overrides=args)
+        trainer.train()
+
+
+if __name__ == '__main__':
+    train()