ultralytics 8.0.35 TensorRT, ONNX and OpenVINO predict and val (#929)

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

ultralytics/nn/autobackend.py

@@ -13,7 +13,7 @@ import torch.nn as nn
 from PIL import Image
 
 from ultralytics.yolo.utils import LOGGER, ROOT, yaml_load
-from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_version
+from ultralytics.yolo.utils.checks import check_requirements, check_suffix, check_version, check_yaml
 from ultralytics.yolo.utils.downloads import attempt_download_asset, is_url
 from ultralytics.yolo.utils.ops import xywh2xyxy
 
@@ -38,7 +38,7 @@ class AutoBackend(nn.Module):
             weights (str): The path to the weights file. Default: 'yolov8n.pt'
             device (torch.device): The device to run the model on.
             dnn (bool): Use OpenCV's DNN module for inference if True, defaults to False.
-            data (dict): Additional data, optional
+            data (str), (Path): Additional data.yaml file for class names, optional
             fp16 (bool): If True, use half precision. Default: False
             fuse (bool): Whether to fuse the model or not. Default: True
 
@@ -237,7 +237,7 @@ class AutoBackend(nn.Module):
 
         # class names
         if 'names' not in locals():  # names missing
-            names = yaml_load(data)['names'] if data else {i: f'class{i}' for i in range(999)}  # assign default
+            names = yaml_load(check_yaml(data))['names'] if data else {i: f'class{i}' for i in range(999)}  # assign
         names = check_class_names(names)
 
         self.__dict__.update(locals())  # assign all variables to self

ultralytics/nn/tasks.py

@@ -2,6 +2,7 @@
 
 import contextlib
 from copy import deepcopy
+from pathlib import Path
 
 import thop
 import torch
@@ -490,6 +491,14 @@ def guess_model_task(model):
             with contextlib.suppress(Exception):
                 cfg = eval(x)
                 break
+    elif isinstance(model, (str, Path)):
+        model = str(model)
+        if '-seg' in model:
+            return "segment"
+        elif '-cls' in model:
+            return "classify"
+        else:
+            return "detect"
 
     # Guess from YAML dictionary
     if cfg: