🐛 [Fix] display emoji bugs, change it to shortcode

yolo/model/yolo.py

@@ -32,10 +32,10 @@ class YOLO(nn.Module):
     def build_model(self, model_arch: Dict[str, List[Dict[str, Dict[str, Dict]]]]):
         self.layer_index = {}
         output_dim, layer_idx = [3], 1
-        logger.info(f"🚜 Building YOLO")
+        logger.info(f":tractor: Building YOLO")
         for arch_name in model_arch:
             if model_arch[arch_name]:
-                logger.info(f"  🏗️  Building {arch_name}")
+                logger.info(f"  :building_construction:  Building {arch_name}")
             for layer_idx, layer_spec in enumerate(model_arch[arch_name], start=layer_idx):
                 layer_type, layer_info = next(iter(layer_spec.items()))
                 layer_args = layer_info.get("args", {})
@@ -123,7 +123,7 @@ class YOLO(nn.Module):
             weights: A OrderedDict containing the new weights.
         """
         if isinstance(weights, Path):
-            weights = torch.load(weights, map_location=torch.device("cpu"))
+            weights = torch.load(weights, map_location=torch.device("cpu"), weights_only=False)
         if "model_state_dict" in weights:
             weights = weights["model_state_dict"]
 
@@ -144,7 +144,7 @@ class YOLO(nn.Module):
 
         for error_name, error_set in error_dict.items():
             for weight_name in error_set:
-                logger.warning(f"⚠️ Weight {error_name} for key: {'.'.join(weight_name)}")
+                logger.warning(f":warning: Weight {error_name} for key: {'.'.join(weight_name)}")
 
         self.model.load_state_dict(model_state_dict)
 
@@ -171,7 +171,7 @@ def create_model(model_cfg: ModelConfig, weight_path: Union[bool, Path] = True,
             prepare_weight(weight_path=weight_path)
         if weight_path.exists():
             model.save_load_weights(weight_path)
-            logger.info("✅ Success load model & weight")
+            logger.info(":white_check_mark: Success load model & weight")
     else:
-        logger.info("✅ Success load model")
+        logger.info(":white_check_mark: Success load model")
     return model

yolo/tools/data_loader.py

@@ -48,12 +48,12 @@ class YoloDataset(Dataset):
         cache_path = dataset_path / f"{phase_name}.cache"
 
         if not cache_path.exists():
-            logger.info(f"🏭 Generating {phase_name} cache")
+            logger.info(f":factory: Generating {phase_name} cache")
             data = self.filter_data(dataset_path, phase_name)
             torch.save(data, cache_path)
         else:
             data = torch.load(cache_path, weights_only=False)
-            logger.info(f"📦 Loaded {phase_name} cache")
+            logger.info(f":package: Loaded {phase_name} cache")
         return data
 
     def filter_data(self, dataset_path: Path, phase_name: str) -> list:

yolo/tools/dataset_preparation.py

@@ -30,7 +30,7 @@ def download_file(url, destination: Path):
                 for data in response.iter_content(chunk_size=1024 * 1024):  # 1 MB chunks
                     file.write(data)
                     progress.update(task, advance=len(data))
-    logger.info("✅ Download completed.")
+    logger.info(":white_check_mark: Download completed.")
 
 
 def unzip_file(source: Path, destination: Path):
@@ -71,7 +71,7 @@ def prepare_dataset(dataset_cfg: DatasetConfig, task: str):
 
             final_place.mkdir(parents=True, exist_ok=True)
             if check_files(final_place, dataset_args.get("file_num")):
-                logger.info(f"✅ Dataset {dataset_type: <12} already verified.")
+                logger.info(f":white_check_mark: Dataset {dataset_type: <12} already verified.")
                 continue
 
             if not local_zip_path.exists():

yolo/tools/drawer.py

@@ -121,6 +121,6 @@ def draw_model(*, model_cfg: ModelConfig = None, model: YOLO = None, v7_base=Fal
                 dot.edge(str(idx), str(jdx))
     try:
         dot.render("Model-arch", format="png", cleanup=True)
-        logger.info("🎨 Drawing Model Architecture at Model-arch.png")
+        logger.info(":artist_palette: Drawing Model Architecture at Model-arch.png")
     except:
-        logger.warning("⚠️ Could not find graphviz backend, continue without drawing the model architecture")
+        logger.warning(":warning: Could not find graphviz backend, continue without drawing the model architecture")

yolo/tools/loss_functions.py

@@ -136,5 +136,5 @@ class DualLoss:
 def create_loss_function(cfg: Config, vec2box) -> DualLoss:
     # TODO: make it flexible, if cfg doesn't contain aux, only use SingleLoss
     loss_function = DualLoss(cfg, vec2box)
-    logger.info("✅ Success load loss function")
+    logger.info(":white_check_mark: Success load loss function")
     return loss_function

yolo/utils/bounding_box_utils.py

@@ -270,7 +270,7 @@ class Vec2Box:
         self.device = device
 
         if hasattr(anchor_cfg, "strides"):
-            logger.info(f"🈶 Found stride of model {anchor_cfg.strides}")
+            logger.info(f":japanese_not_free_of_charge_button: Found stride of model {anchor_cfg.strides}")
             self.strides = anchor_cfg.strides
         else:
             logger.info("🧸 Found no stride of model, performed a dummy test for auto-anchor size")
@@ -314,7 +314,7 @@ class Anc2Box:
         self.device = device
 
         if hasattr(anchor_cfg, "strides"):
-            logger.info(f"🈶 Found stride of model {anchor_cfg.strides}")
+            logger.info(f":japanese_not_free_of_charge_button: Found stride of model {anchor_cfg.strides}")
             self.strides = anchor_cfg.strides
         else:
             logger.info("🧸 Found no stride of model, performed a dummy test for auto-anchor size")

yolo/utils/deploy_utils.py

@@ -21,10 +21,10 @@ class FastModelLoader:
 
     def _validate_compiler(self):
         if self.compiler not in ["onnx", "trt", "deploy"]:
-            logger.warning(f"⚠️ Compiler '{self.compiler}' is not supported. Using original model.")
+            logger.warning(f":warning: Compiler '{self.compiler}' is not supported. Using original model.")
             self.compiler = None
         if self.cfg.device == "mps" and self.compiler == "trt":
-            logger.warning("🍎 TensorRT does not support MPS devices. Using original model.")
+            logger.warning(":red_apple: TensorRT does not support MPS devices. Using original model.")
             self.compiler = None
 
     def load_model(self, device):
@@ -59,7 +59,7 @@ class FastModelLoader:
             providers = ["CUDAExecutionProvider"]
         try:
             ort_session = InferenceSession(self.model_path, providers=providers)
-            logger.info("🚀 Using ONNX as MODEL frameworks!")
+            logger.info(":rocket: Using ONNX as MODEL frameworks!")
         except Exception as e:
             logger.warning(f"🈳 Error loading ONNX model: {e}")
             ort_session = self._create_onnx_model(providers)
@@ -79,7 +79,7 @@ class FastModelLoader:
             output_names=["output"],
             dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},
         )
-        logger.info(f"📥 ONNX model saved to {self.model_path}")
+        logger.info(f":inbox_tray: ONNX model saved to {self.model_path}")
         return InferenceSession(self.model_path, providers=providers)
 
     def _load_trt_model(self):
@@ -88,7 +88,7 @@ class FastModelLoader:
         try:
             model_trt = TRTModule()
             model_trt.load_state_dict(torch.load(self.model_path))
-            logger.info("🚀 Using TensorRT as MODEL frameworks!")
+            logger.info(":rocket: Using TensorRT as MODEL frameworks!")
         except FileNotFoundError:
             logger.warning(f"🈳 No found model weight at {self.model_path}")
             model_trt = self._create_trt_model()
@@ -102,5 +102,5 @@ class FastModelLoader:
         logger.info(f"♻️ Creating TensorRT model")
         model_trt = torch2trt(model.cuda(), [dummy_input])
         torch.save(model_trt.state_dict(), self.model_path)
-        logger.info(f"📥 TensorRT model saved to {self.model_path}")
+        logger.info(f":inbox_tray: TensorRT model saved to {self.model_path}")
         return model_trt

yolo/utils/logger.py

@@ -3,8 +3,8 @@ import logging
 from rich.console import Console
 from rich.logging import RichHandler
 
-logger = logging.getLogger("YOLO_logger")
+logger = logging.getLogger(__name__)
 logger.setLevel(logging.DEBUG)
 logger.propagate = False
 if not logger.hasHandlers():
-    logger.addHandler(RichHandler(console=Console(), show_level=True, show_path=True, show_time=True))
+    logger.addHandler(RichHandler(console=Console(), show_level=True, show_path=True, show_time=True, markup=True))