Update metrics names (#85)

ultralytics/yolo/data/dataset.py

@@ -100,7 +100,7 @@ class YOLODataset(BaseDataset):
         self.label_files = img2label_paths(self.im_files)
         cache_path = Path(self.label_files[0]).parent.with_suffix(".cache")
         try:
-            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
+            cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True  # load dict
             assert cache["version"] == self.cache_version  # matches current version
             assert cache["hash"] == get_hash(self.label_files + self.im_files)  # identical hash
         except Exception:

ultralytics/yolo/engine/trainer.py

@@ -82,6 +82,7 @@ class BaseTrainer:
         self.fitness = None
         self.loss = None
         self.tloss = None
+        self.loss_names = None
         self.csv = self.save_dir / 'results.csv'
 
         for callback, func in callbacks.default_callbacks.items():
@@ -106,7 +107,7 @@ class BaseTrainer:
 
     def train(self):
         world_size = torch.cuda.device_count()
-        if world_size > 1 and not ("LOCAL_RANK" in os.environ):
+        if world_size > 1 and "LOCAL_RANK" not in os.environ:
             command = generate_ddp_command(world_size, self)
             subprocess.Popen(command)
             ddp_cleanup(command, self)
@@ -154,11 +155,9 @@ class BaseTrainer:
         self.train_loader = self.get_dataloader(self.trainset, batch_size=batch_size, rank=rank, mode="train")
         if rank in {0, -1}:
             self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode="val")
-            validator = self.get_validator()
-            # init metric, for plot_results
-            metric_keys = validator.metric_keys + self.label_loss_items(prefix="val")
-            self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))
-            self.validator = validator
+            self.validator = self.get_validator()
+            # metric_keys = self.validator.metric_keys + self.label_loss_items(prefix="val")
+            # self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))  # TODO: init metrics for plot_results()?
             self.ema = ModelEMA(self.model)
 
     def _do_train(self, rank=-1, world_size=1):

ultralytics/yolo/utils/callbacks/clearml.py

@@ -24,29 +24,22 @@ def before_train(trainer):
                      output_uri=True,
                      reuse_last_task_id=False,
                      auto_connect_frameworks={'pytorch': False})
-
-    task.connect(trainer.args, name='parameters')
+    task.connect(dict(trainer.args), name='General')
 
 
 def on_batch_end(trainer):
-    train_loss = trainer.tloss
-    _log_scalers(trainer.label_loss_items(train_loss), "train", trainer.epoch)
+    _log_scalers(trainer.label_loss_items(trainer.tloss, prefix="train"), "train", trainer.epoch)
 
 
 def on_val_end(trainer):
-    metrics = trainer.metrics
-    val_losses = trainer.validator.loss
-    val_loss_dict = trainer.label_loss_items(val_losses)
-    _log_scalers(val_loss_dict, "val", trainer.epoch)
-    _log_scalers(metrics, "metrics", trainer.epoch)
-
+    _log_scalers(trainer.label_loss_items(trainer.validator.loss, prefix="val"), "val", trainer.epoch)
+    _log_scalers({k: v for k, v in trainer.metrics.items() if k.startswith("metrics")}, "metrics", trainer.epoch)
     if trainer.epoch == 0:
-        infer_speed = trainer.validator.speed[1]
         model_info = {
-            "inference_speed": infer_speed,
+            "inference_speed": trainer.validator.speed[1],
             "flops@640": get_flops(trainer.model),
             "params": get_num_params(trainer.model)}
-        _log_scalers(model_info, "model")
+        Task.current_task().connect(model_info, 'Model')
 
 
 def on_train_end(trainer):

ultralytics/yolo/utils/configs/__init__.py

@@ -6,10 +6,11 @@ from omegaconf import DictConfig, OmegaConf
 from ultralytics.yolo.utils.configs.hydra_patch import check_config_mismatch
 
 
-def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict] = {}):
+def get_config(config: Union[str, DictConfig], overrides: Union[str, Dict]):
     """
     Accepts yaml file name or DictConfig containing experiment configuration.
     Returns training args namespace
+    :param overrides: Overrides str or Dict
     :param config: Optional file name or DictConfig object
     """
     if isinstance(config, (str, Path)):

ultralytics/yolo/utils/metrics.py

@@ -514,7 +514,7 @@ class DetMetrics:
 
     @property
     def keys(self):
-        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP_0.5(B)", "metrics/mAP_0.5:0.95(B)"]
+        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
 
     def mean_results(self):
         return self.metric.mean_results()
@@ -567,12 +567,12 @@ class SegmentMetrics:
         return [
             "metrics/precision(B)",
             "metrics/recall(B)",
-            "metrics/mAP_0.5(B)",
-            "metrics/mAP_0.5:0.95(B)",  # metrics
+            "metrics/mAP50(B)",
+            "metrics/mAP50-95(B)",  # metrics
             "metrics/precision(M)",
             "metrics/recall(M)",
-            "metrics/mAP_0.5(M)",
-            "metrics/mAP_0.5:0.95(M)"]
+            "metrics/mAP50(M)",
+            "metrics/mAP50-95(M)"]
 
     def mean_results(self):
         return self.metric_box.mean_results() + self.metric_mask.mean_results()

ultralytics/yolo/v8/detect/train.py

@@ -46,6 +46,7 @@ class DetectionTrainer(BaseTrainer):
         return model
 
     def get_validator(self):
+        self.loss_names = 'box_loss', 'obj_loss', 'cls_loss'
         return v8.detect.DetectionValidator(self.test_loader,
                                             save_dir=self.save_dir,
                                             logger=self.console,
@@ -190,15 +191,14 @@ class DetectionTrainer(BaseTrainer):
         loss = lbox + lobj + lcls
         return loss * bs, torch.cat((lbox, lobj, lcls)).detach()
 
-    # TODO: improve from API users perspective
     def label_loss_items(self, loss_items=None, prefix="train"):
         # We should just use named tensors here in future
-        keys = [f"{prefix}/lbox", f"{prefix}/lobj", f"{prefix}/lcls"]
+        keys = [f"{prefix}/{x}" for x in self.loss_names]
         return dict(zip(keys, loss_items)) if loss_items is not None else keys
 
     def progress_string(self):
         return ('\n' + '%11s' * 6) % \
-               ('Epoch', 'GPU_mem', 'box_loss', 'obj_loss', 'cls_loss', 'Size')
+               ('Epoch', 'GPU_mem', *self.loss_names, 'Size')
 
     def plot_training_samples(self, batch, ni):
         images = batch["img"]

ultralytics/yolo/v8/detect/val.py

@@ -173,7 +173,7 @@ class DetectionValidator(BaseValidator):
     # TODO: align with train loss metrics
     @property
     def metric_keys(self):
-        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP_0.5(B)", "metrics/mAP_0.5:0.95(B)"]
+        return ["metrics/precision(B)", "metrics/recall(B)", "metrics/mAP50(B)", "metrics/mAP50-95(B)"]
 
     def plot_val_samples(self, batch, ni):
         images = batch["img"]

ultralytics/yolo/v8/segment/train.py

@@ -29,6 +29,7 @@ class SegmentationTrainer(DetectionTrainer):
         return model
 
     def get_validator(self):
+        self.loss_names = 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss'
         return v8.segment.SegmentationValidator(self.test_loader,
                                                 save_dir=self.save_dir,
                                                 logger=self.console,
@@ -212,12 +213,12 @@ class SegmentationTrainer(DetectionTrainer):
 
     def label_loss_items(self, loss_items=None, prefix="train"):
         # We should just use named tensors here in future
-        keys = [f"{prefix}/lbox", f"{prefix}/lseg", f"{prefix}/lobj", f"{prefix}/lcls"]
+        keys = [f"{prefix}/{x}" for x in self.loss_names]
         return dict(zip(keys, loss_items)) if loss_items is not None else keys
 
     def progress_string(self):
         return ('\n' + '%11s' * 7) % \
-               ('Epoch', 'GPU_mem', 'box_loss', 'seg_loss', 'obj_loss', 'cls_loss', 'Size')
+               ('Epoch', 'GPU_mem', *self.loss_names, 'Size')
 
     def plot_training_samples(self, batch, ni):
         images = batch["img"]

ultralytics/yolo/v8/segment/val.py

@@ -178,12 +178,12 @@ class SegmentationValidator(DetectionValidator):
         return [
             "metrics/precision(B)",
             "metrics/recall(B)",
-            "metrics/mAP_0.5(B)",
-            "metrics/mAP_0.5:0.95(B)",  # metrics
+            "metrics/mAP50(B)",
+            "metrics/mAP50-95(B)",  # metrics
             "metrics/precision(M)",
             "metrics/recall(M)",
-            "metrics/mAP_0.5(M)",
-            "metrics/mAP_0.5:0.95(M)",]
+            "metrics/mAP50(M)",
+            "metrics/mAP50-95(M)",]
 
     def plot_val_samples(self, batch, ni):
         images = batch["img"]