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@ -2,7 +2,7 @@ import hydra
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import torch
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import torch
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import torchvision
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import torchvision
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from ultralytics.nn.tasks import ClassificationModel, attempt_load_weights
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from ultralytics.nn.tasks import ClassificationModel, attempt_load_one_weight
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from ultralytics.yolo import v8
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from ultralytics.yolo import v8
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from ultralytics.yolo.data import build_classification_dataloader
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from ultralytics.yolo.data import build_classification_dataloader
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from ultralytics.yolo.engine.trainer import BaseTrainer
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from ultralytics.yolo.engine.trainer import BaseTrainer
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@ -20,8 +20,18 @@ class ClassificationTrainer(BaseTrainer):
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def set_model_attributes(self):
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def set_model_attributes(self):
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self.model.names = self.data["names"]
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self.model.names = self.data["names"]
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def get_model(self, cfg=None, weights=None):
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def get_model(self, cfg=None, weights=None, verbose=True):
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model = ClassificationModel(cfg, nc=self.data["nc"])
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model = ClassificationModel(cfg, nc=self.data["nc"])
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pretrained = False
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for m in model.modules():
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if not pretrained and hasattr(m, 'reset_parameters'):
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m.reset_parameters()
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if isinstance(m, torch.nn.Dropout) and self.args.dropout:
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m.p = self.args.dropout # set dropout
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for p in model.parameters():
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p.requires_grad = True # for training
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if weights:
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if weights:
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model.load(weights)
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model.load(weights)
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@ -43,7 +53,7 @@ class ClassificationTrainer(BaseTrainer):
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model = str(self.model)
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model = str(self.model)
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# Load a YOLO model locally, from torchvision, or from Ultralytics assets
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# Load a YOLO model locally, from torchvision, or from Ultralytics assets
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if model.endswith(".pt"):
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if model.endswith(".pt"):
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self.model = attempt_load_weights(model, device='cpu')
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self.model, _ = attempt_load_one_weight(model, device='cpu')
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elif model.endswith(".yaml"):
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elif model.endswith(".yaml"):
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self.model = self.get_model(cfg=model)
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self.model = self.get_model(cfg=model)
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elif model in torchvision.models.__dict__:
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elif model in torchvision.models.__dict__:
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@ -54,10 +64,11 @@ class ClassificationTrainer(BaseTrainer):
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return # dont return ckpt. Classification doesn't support resume
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return # dont return ckpt. Classification doesn't support resume
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def get_dataloader(self, dataset_path, batch_size, rank=0, mode="train"):
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def get_dataloader(self, dataset_path, batch_size=16, rank=0, mode="train"):
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return build_classification_dataloader(path=dataset_path,
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return build_classification_dataloader(path=dataset_path,
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imgsz=self.args.imgsz,
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imgsz=self.args.imgsz,
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batch_size=batch_size,
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batch_size=batch_size if mode == "train" else (batch_size * 2),
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augment=mode == "train",
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rank=rank)
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rank=rank)
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def preprocess_batch(self, batch):
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def preprocess_batch(self, batch):
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@ -66,15 +77,41 @@ class ClassificationTrainer(BaseTrainer):
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return batch
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return batch
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def progress_string(self):
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def progress_string(self):
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return ('\n' + '%11s' *
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return ('\n' + '%11s' * (4 + len(self.loss_names))) % \
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(4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
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('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
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def get_validator(self):
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def get_validator(self):
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self.loss_names = ['loss']
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return v8.classify.ClassificationValidator(self.test_loader, self.save_dir, logger=self.console)
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return v8.classify.ClassificationValidator(self.test_loader, self.save_dir, logger=self.console)
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def criterion(self, preds, batch):
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def criterion(self, preds, batch):
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loss = torch.nn.functional.cross_entropy(preds, batch["cls"])
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loss = torch.nn.functional.cross_entropy(preds, batch["cls"], reduction='sum') / self.args.nbs
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return loss, loss
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loss_items = loss.detach()
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return loss, loss_items
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# def label_loss_items(self, loss_items=None, prefix="train"):
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# """
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# Returns a loss dict with labelled training loss items tensor
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# """
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# # Not needed for classification but necessary for segmentation & detection
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# keys = [f"{prefix}/{x}" for x in self.loss_names]
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# if loss_items is not None:
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# loss_items = [round(float(x), 5) for x in loss_items] # convert tensors to 5 decimal place floats
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# return dict(zip(keys, loss_items))
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# else:
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# return keys
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def label_loss_items(self, loss_items=None, prefix="train"):
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"""
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Returns a loss dict with labelled training loss items tensor
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"""
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# Not needed for classification but necessary for segmentation & detection
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keys = [f"{prefix}/{x}" for x in self.loss_names]
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if loss_items is not None:
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loss_items = [round(float(loss_items), 5)]
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return dict(zip(keys, loss_items))
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else:
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return keys
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def resume_training(self, ckpt):
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def resume_training(self, ckpt):
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pass
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pass
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@ -86,12 +123,16 @@ class ClassificationTrainer(BaseTrainer):
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def train(cfg):
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def train(cfg):
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cfg.model = cfg.model or "yolov8n-cls.yaml" # or "resnet18"
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cfg.model = cfg.model or "yolov8n-cls.yaml" # or "resnet18"
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cfg.data = cfg.data or "imagenette160" # or yolo.ClassificationDataset("mnist")
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cfg.data = cfg.data or "mnist160" # or yolo.ClassificationDataset("mnist")
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# trainer = ClassificationTrainer(cfg)
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cfg.lr0 = 0.1
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# trainer.train()
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cfg.weight_decay = 5e-5
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from ultralytics import YOLO
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cfg.label_smoothing = 0.1
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model = YOLO(cfg.model)
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cfg.warmup_epochs = 0.0
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model.train(**cfg)
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trainer = ClassificationTrainer(cfg)
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trainer.train()
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# from ultralytics import YOLO
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# model = YOLO(cfg.model)
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# model.train(**cfg)
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if __name__ == "__main__":
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if __name__ == "__main__":
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Glenn Jocher
2 years ago
committed by
GitHub