new

Dockerfile

@@ -28,4 +28,4 @@ USER user
 COPY --chown=user . /app
 
 # Set the command to run the app with Gunicorn
-CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app"]
+CMD ["gunicorn", "-b", "0.0.0.0:7860", "app:app","--timeout", "9600"]

app.py

@@ -102,7 +102,10 @@ def process_image():
     # Check contents in the root directory
     print("Current directory contents:", os.listdir('/'))
 
-    model = YOLOv10(f'./runs/detect/train3/weights/best (1).pt')
+    model = YOLOv10(f'./runs/detect/train3/weights/best.pt')
+    #device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    #model.to(device)
+
     dataset = sv.DetectionDataset.from_yolo(
         images_directory_path=f"./data/MyNewVersion5.0Dataset/valid/images",
         annotations_directory_path=f"./data/MyNewVersion5.0Dataset/valid/labels",
@@ -239,7 +242,3 @@ def download_image(filename):
 @app.route('/uploads/<filename>')
 def serve_uploaded_file(filename):
     return send_from_directory('./app/data1', filename)
-
-
-
-

ultralytics/nn/tasks.py

@@ -82,10 +82,8 @@ class BaseModel(nn.Module):
     def forward(self, x, *args, **kwargs):
         """
         Forward pass of the model on a single scale. Wrapper for `_forward_once` method.
-
         Args:
             x (torch.Tensor | dict): The input image tensor or a dict including image tensor and gt labels.
-
         Returns:
             (torch.Tensor): The output of the network.
         """
@@ -96,14 +94,12 @@ class BaseModel(nn.Module):
     def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
         """
         Perform a forward pass through the network.
-
         Args:
             x (torch.Tensor): The input tensor to the model.
             profile (bool):  Print the computation time of each layer if True, defaults to False.
             visualize (bool): Save the feature maps of the model if True, defaults to False.
             augment (bool): Augment image during prediction, defaults to False.
             embed (list, optional): A list of feature vectors/embeddings to return.
-
         Returns:
             (torch.Tensor): The last output of the model.
         """
@@ -114,13 +110,11 @@ class BaseModel(nn.Module):
     def _predict_once(self, x, profile=False, visualize=False, embed=None):
         """
         Perform a forward pass through the network.
-
         Args:
             x (torch.Tensor): The input tensor to the model.
             profile (bool):  Print the computation time of each layer if True, defaults to False.
             visualize (bool): Save the feature maps of the model if True, defaults to False.
             embed (list, optional): A list of feature vectors/embeddings to return.
-
         Returns:
             (torch.Tensor): The last output of the model.
         """
@@ -152,12 +146,10 @@ class BaseModel(nn.Module):
         """
         Profile the computation time and FLOPs of a single layer of the model on a given input. Appends the results to
         the provided list.
-
         Args:
             m (nn.Module): The layer to be profiled.
             x (torch.Tensor): The input data to the layer.
             dt (list): A list to store the computation time of the layer.
-
         Returns:
             None
         """
@@ -177,7 +169,6 @@ class BaseModel(nn.Module):
         """
         Fuse the `Conv2d()` and `BatchNorm2d()` layers of the model into a single layer, in order to improve the
         computation efficiency.
-
         Returns:
             (nn.Module): The fused model is returned.
         """
@@ -206,10 +197,8 @@ class BaseModel(nn.Module):
     def is_fused(self, thresh=10):
         """
         Check if the model has less than a certain threshold of BatchNorm layers.
-
         Args:
             thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.
-
         Returns:
             (bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
         """
@@ -219,7 +208,6 @@ class BaseModel(nn.Module):
     def info(self, detailed=False, verbose=True, imgsz=640):
         """
         Prints model information.
-
         Args:
             detailed (bool): if True, prints out detailed information about the model. Defaults to False
             verbose (bool): if True, prints out the model information. Defaults to False
@@ -230,10 +218,8 @@ class BaseModel(nn.Module):
     def _apply(self, fn):
         """
         Applies a function to all the tensors in the model that are not parameters or registered buffers.
-
         Args:
             fn (function): the function to apply to the model
-
         Returns:
             (BaseModel): An updated BaseModel object.
         """
@@ -248,7 +234,6 @@ class BaseModel(nn.Module):
     def load(self, weights, verbose=True):
         """
         Load the weights into the model.
-
         Args:
             weights (dict | torch.nn.Module): The pre-trained weights to be loaded.
             verbose (bool, optional): Whether to log the transfer progress. Defaults to True.
@@ -263,7 +248,6 @@ class BaseModel(nn.Module):
     def loss(self, batch, preds=None):
         """
         Compute loss.
-
         Args:
             batch (dict): Batch to compute loss on
             preds (torch.Tensor | List[torch.Tensor]): Predictions.
@@ -455,17 +439,14 @@ class ClassificationModel(BaseModel):
 class RTDETRDetectionModel(DetectionModel):
     """
     RTDETR (Real-time DEtection and Tracking using Transformers) Detection Model class.
-
     This class is responsible for constructing the RTDETR architecture, defining loss functions, and facilitating both
     the training and inference processes. RTDETR is an object detection and tracking model that extends from the
     DetectionModel base class.
-
     Attributes:
         cfg (str): The configuration file path or preset string. Default is 'rtdetr-l.yaml'.
         ch (int): Number of input channels. Default is 3 (RGB).
         nc (int, optional): Number of classes for object detection. Default is None.
         verbose (bool): Specifies if summary statistics are shown during initialization. Default is True.
-
     Methods:
         init_criterion: Initializes the criterion used for loss calculation.
         loss: Computes and returns the loss during training.
@@ -475,7 +456,6 @@ class RTDETRDetectionModel(DetectionModel):
     def __init__(self, cfg="rtdetr-l.yaml", ch=3, nc=None, verbose=True):
         """
         Initialize the RTDETRDetectionModel.
-
         Args:
             cfg (str): Configuration file name or path.
             ch (int): Number of input channels.
@@ -493,11 +473,9 @@ class RTDETRDetectionModel(DetectionModel):
     def loss(self, batch, preds=None):
         """
         Compute the loss for the given batch of data.
-
         Args:
             batch (dict): Dictionary containing image and label data.
             preds (torch.Tensor, optional): Precomputed model predictions. Defaults to None.
-
         Returns:
             (tuple): A tuple containing the total loss and main three losses in a tensor.
         """
@@ -538,7 +516,6 @@ class RTDETRDetectionModel(DetectionModel):
     def predict(self, x, profile=False, visualize=False, batch=None, augment=False, embed=None):
         """
         Perform a forward pass through the model.
-
         Args:
             x (torch.Tensor): The input tensor.
             profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
@@ -546,7 +523,6 @@ class RTDETRDetectionModel(DetectionModel):
             batch (dict, optional): Ground truth data for evaluation. Defaults to None.
             augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
             embed (list, optional): A list of feature vectors/embeddings to return.
-
         Returns:
             (torch.Tensor): Model's output tensor.
         """
@@ -602,14 +578,12 @@ class WorldModel(DetectionModel):
     def predict(self, x, profile=False, visualize=False, augment=False, embed=None):
         """
         Perform a forward pass through the model.
-
         Args:
             x (torch.Tensor): The input tensor.
             profile (bool, optional): If True, profile the computation time for each layer. Defaults to False.
             visualize (bool, optional): If True, save feature maps for visualization. Defaults to False.
             augment (bool, optional): If True, perform data augmentation during inference. Defaults to False.
             embed (list, optional): A list of feature vectors/embeddings to return.
-
         Returns:
             (torch.Tensor): Model's output tensor.
         """
@@ -668,20 +642,16 @@ class Ensemble(nn.ModuleList):
 def temporary_modules(modules=None):
     """
     Context manager for temporarily adding or modifying modules in Python's module cache (`sys.modules`).
-
     This function can be used to change the module paths during runtime. It's useful when refactoring code,
     where you've moved a module from one location to another, but you still want to support the old import
     paths for backwards compatibility.
-
     Args:
         modules (dict, optional): A dictionary mapping old module paths to new module paths.
-
     Example:
         ```python
         with temporary_modules({'old.module.path': 'new.module.path'}):
             import old.module.path  # this will now import new.module.path
         ```
-
     Note:
         The changes are only in effect inside the context manager and are undone once the context manager exits.
         Be aware that directly manipulating `sys.modules` can lead to unpredictable results, especially in larger
@@ -711,10 +681,8 @@ def torch_safe_load(weight):
     This function attempts to load a PyTorch model with the torch.load() function. If a ModuleNotFoundError is raised,
     it catches the error, logs a warning message, and attempts to install the missing module via the
     check_requirements() function. After installation, the function again attempts to load the model using torch.load().
-
     Args:
         weight (str): The file path of the PyTorch model.
-
     Returns:
         (dict): The loaded PyTorch model.
     """
@@ -730,7 +698,7 @@ def torch_safe_load(weight):
                 "ultralytics.yolo.data": "ultralytics.data",
             }
         ):  # for legacy 8.0 Classify and Pose models
-            ckpt = torch.load(file, map_location="cpu")
+            ckpt = torch.load(file, map_location="cpu",weights_only=False)
 
     except ModuleNotFoundError as e:  # e.name is missing module name
         if e.name == "models":
@@ -972,10 +940,8 @@ def guess_model_scale(model_path):
     Takes a path to a YOLO model's YAML file as input and extracts the size character of the model's scale. The function
     uses regular expression matching to find the pattern of the model scale in the YAML file name, which is denoted by
     n, s, m, l, or x. The function returns the size character of the model scale as a string.
-
     Args:
         model_path (str | Path): The path to the YOLO model's YAML file.
-
     Returns:
         (str): The size character of the model's scale, which can be n, s, m, l, or x.
     """
@@ -989,13 +955,10 @@ def guess_model_scale(model_path):
 def guess_model_task(model):
     """
     Guess the task of a PyTorch model from its architecture or configuration.
-
     Args:
         model (nn.Module | dict): PyTorch model or model configuration in YAML format.
-
     Returns:
         (str): Task of the model ('detect', 'segment', 'classify', 'pose').
-
     Raises:
         SyntaxError: If the task of the model could not be determined.
     """