Classify training cleanup (#33)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

ultralytics/yolo/engine/trainer.py

@@ -24,13 +24,12 @@ from ultralytics.yolo.utils import LOGGER, ROOT
 from ultralytics.yolo.utils.files import increment_path, save_yaml
 from ultralytics.yolo.utils.modeling import get_model
 
-CONFIG_PATH_ABS = ROOT / "yolo/utils/configs"
-DEFAULT_CONFIG = "defaults.yaml"
+DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yml"
 
 
 class BaseTrainer:
 
-    def __init__(self, config=CONFIG_PATH_ABS / DEFAULT_CONFIG, overrides={}):
+    def __init__(self, config=DEFAULT_CONFIG, overrides={}):
         self.console = LOGGER
         self.args = self._get_config(config, overrides)
         self.validator = None

ultralytics/yolo/utils/configs/default.yml

@@ -1,25 +1,27 @@
-model: null
-data: null
+# YOLO 🚀 by Ultralytics, GPL-3.0 license
+# Default training settings and hyperparameters for medium-augmentation COCO training
 
-# Training options
+
+# Train settings -------------------------------------------------------------------------------------------------------
+model: null  # i.e. yolov5s.pt
+data: null  # i.e. coco128.yaml
 epochs: 300
 batch_size: 16
 img_size: 640
 nosave: False
-cache: False # True/ram for ram, or disc
-device: '' # cuda device, i.e. 0 or 0,1,2,3 or cpu
+cache: False  # True/ram, disk or False
+device: ''  # cuda device, i.e. 0 or 0,1,2,3 or cpu
 workers: 8
-project: "ultralytics-yolo"
-name: "exp" # TODO: make this informative, maybe exp{#number}_{datetime} ?
+project: 'runs'
+name: 'exp'
 exist_ok: False
 pretrained: False
-optimizer: "Adam" # choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
+optimizer: 'SGD'  # choices=['SGD', 'Adam', 'AdamW', 'RMSProp']
 verbose: False
 seed: 0
 local_rank: -1
-#-----------------------------------#
 
-# Hyper-parameters
+# Hyperparameters ------------------------------------------------------------------------------------------------------
 lr0: 0.001  # initial learning rate (SGD=1E-2, Adam=1E-3)
 lrf: 0.01  # final OneCycleLR learning rate (lr0 * lrf)
 momentum: 0.937  # SGD momentum/Adam beta1
@@ -50,9 +52,8 @@ mosaic: 1.0  # image mosaic (probability)
 mixup: 0.0  # image mixup (probability)
 copy_paste: 0.0  # segment copy-paste (probability)
 
-# Hydra configs -------------------------------------
-# to disable hydra directory creation
+# Hydra configs --------------------------------------------------------------------------------------------------------
 hydra:
-  output_subdir: null
+  output_subdir: null  # disable hydra directory creation
   run:
     dir: .

ultralytics/yolo/utils/modeling/__init__.py

@@ -107,18 +107,17 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
     return nn.Sequential(*layers), sorted(save)
 
 
-def get_model(model: str):
+def get_model(model='s.pt', pretrained=True):
+    # Load a YOLO model locally, from torchvision, or from Ultralytics assets
     if model.endswith(".pt"):
         model = model.split(".")[0]
 
-    if Path(model + ".pt").is_file():
-        trained_model = torch.load(model + ".pt", map_location='cpu')
-    elif model in torchvision.models.__dict__:  # try torch hub classifier models
-        trained_model = torch.hub.load("pytorch/vision", model, pretrained=True)
-    else:
-        model_ckpt = attempt_download(model + ".pt")  # try ultralytics assets
-        trained_model = torch.load(model_ckpt, map_location='cpu')
-    return trained_model
+    if Path(f"{model}.pt").is_file():  # local file
+        return torch.load(f"{model}.pt", map_location='cpu')
+    elif model in torchvision.models.__dict__:  # TorchVision models i.e. resnet50, efficientnet_b0
+        return torchvision.models.__dict__[model](weights='IMAGENET1K_V1' if pretrained else None)
+    else:  # Ultralytics assets
+        return torch.load(attempt_download(f"{model}.pt"), map_location='cpu')
 
 
 def yaml_load(file='data.yaml'):

ultralytics/yolo/v8/classify/train.py

@@ -4,13 +4,13 @@ from pathlib import Path
 
 import hydra
 import torch
-import torchvision
 
 from ultralytics.yolo import v8
 from ultralytics.yolo.data import build_classification_dataloader
-from ultralytics.yolo.engine.trainer import CONFIG_PATH_ABS, DEFAULT_CONFIG, BaseTrainer
+from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
 from ultralytics.yolo.utils.downloads import download
 from ultralytics.yolo.utils.files import WorkingDirectory
+from ultralytics.yolo.utils.loggers import colorstr
 from ultralytics.yolo.utils.torch_utils import LOCAL_RANK, torch_distributed_zero_first
 
 
@@ -30,8 +30,7 @@ class ClassificationTrainer(BaseTrainer):
                 else:
                     url = f'https://github.com/ultralytics/yolov5/releases/download/v1.0/{dataset}.zip'
                     download(url, dir=data_dir.parent)
-                # TODO: add colorstr
-                s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {'bold', data_dir}\n"
+                s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
                 self.console.info(s)
         train_set = data_dir / "train"
         test_set = data_dir / 'test' if (data_dir / 'test').exists() else data_dir / 'val'  # data/test or data/val
@@ -48,7 +47,7 @@ class ClassificationTrainer(BaseTrainer):
         return torch.nn.functional.cross_entropy(preds, targets)
 
 
-@hydra.main(version_base=None, config_path=CONFIG_PATH_ABS, config_name=str(DEFAULT_CONFIG).split(".")[0])
+@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.stem)
 def train(cfg):
     cfg.model = cfg.model or "resnet18"
     cfg.data = cfg.data or "imagenette160"  # or yolo.ClassificationDataset("mnist")
@@ -59,7 +58,7 @@ def train(cfg):
 if __name__ == "__main__":
     """
     CLI usage:
-    python ../path/to/train.py args.epochs=10 args.project="name" hyps.lr0=0.1
+    python path/to/train.py epochs=10 project=PROJECT lr0=0.1
 
     TODO:
     Direct cli support, i.e, yolov8 classify_train args.epochs 10