ultralytics 8.0.12 - Hydra removal (#506)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pronoy Mandal <lukex9442@gmail.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>

ultralytics/yolo/v8/segment/predict.py

@@ -1,10 +1,9 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-import hydra
 import torch
 
 from ultralytics.yolo.engine.results import Results
-from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, is_git_directory, ops
+from ultralytics.yolo.utils import DEFAULT_CFG, ROOT, is_git_directory, ops
 from ultralytics.yolo.utils.plotting import colors, save_one_box
 from ultralytics.yolo.v8.detect.predict import DetectionPredictor
 
@@ -98,8 +97,7 @@ class SegmentationPredictor(DetectionPredictor):
         return log_string
 
 
-@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
-def predict(cfg):
+def predict(cfg=DEFAULT_CFG):
     cfg.model = cfg.model or "yolov8n-seg.pt"
     cfg.source = cfg.source if cfg.source is not None else ROOT / "assets" if is_git_directory() \
         else "https://ultralytics.com/images/bus.jpg"

ultralytics/yolo/v8/segment/train.py

@@ -2,13 +2,12 @@
 
 from copy import copy
 
-import hydra
 import torch
 import torch.nn.functional as F
 
 from ultralytics.nn.tasks import SegmentationModel
 from ultralytics.yolo import v8
-from ultralytics.yolo.utils import DEFAULT_CONFIG
+from ultralytics.yolo.utils import DEFAULT_CFG
 from ultralytics.yolo.utils.ops import crop_mask, xyxy2xywh
 from ultralytics.yolo.utils.plotting import plot_images, plot_results
 from ultralytics.yolo.utils.tal import make_anchors
@@ -19,7 +18,7 @@ from ultralytics.yolo.v8.detect.train import Loss
 # BaseTrainer python usage
 class SegmentationTrainer(v8.detect.DetectionTrainer):
 
-    def __init__(self, config=DEFAULT_CONFIG, overrides=None):
+    def __init__(self, config=DEFAULT_CFG, overrides=None):
         if overrides is None:
             overrides = {}
         overrides["task"] = "segment"
@@ -141,8 +140,7 @@ class SegLoss(Loss):
         return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
 
 
-@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
-def train(cfg):
+def train(cfg=DEFAULT_CFG):
     cfg.model = cfg.model or "yolov8n-seg.pt"
     cfg.data = cfg.data or "coco128-seg.yaml"  # or yolo.ClassificationDataset("mnist")
     cfg.device = cfg.device if cfg.device is not None else ''
@@ -150,7 +148,7 @@ def train(cfg):
     # trainer.train()
     from ultralytics import YOLO
     model = YOLO(cfg.model)
-    model.train(**cfg)
+    model.train(**vars(cfg))
 
 
 if __name__ == "__main__":

ultralytics/yolo/v8/segment/val.py

@@ -4,12 +4,11 @@ import os
 from multiprocessing.pool import ThreadPool
 from pathlib import Path
 
-import hydra
 import numpy as np
 import torch
 import torch.nn.functional as F
 
-from ultralytics.yolo.utils import DEFAULT_CONFIG, NUM_THREADS, ops
+from ultralytics.yolo.utils import DEFAULT_CFG, NUM_THREADS, ops
 from ultralytics.yolo.utils.checks import check_requirements
 from ultralytics.yolo.utils.metrics import ConfusionMatrix, SegmentMetrics, box_iou, mask_iou
 from ultralytics.yolo.utils.plotting import output_to_target, plot_images
@@ -243,8 +242,7 @@ class SegmentationValidator(DetectionValidator):
         return stats
 
 
-@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
-def val(cfg):
+def val(cfg=DEFAULT_CFG):
     cfg.data = cfg.data or "coco128-seg.yaml"
     validator = SegmentationValidator(args=cfg)
     validator(model=cfg.model)