ultralytics 8.0.51 add assets and CI actions (#1296)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Paul Kehrer <paulhkehrer@gmail.com>

ultralytics/nn/autobackend.py

@@ -14,7 +14,7 @@ import torch
 import torch.nn as nn
 from PIL import Image
 
-from ultralytics.yolo.utils import LOGGER, ROOT, yaml_load
+from ultralytics.yolo.utils import LINUX, LOGGER, ROOT, yaml_load
 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
@@ -143,7 +143,12 @@ class AutoBackend(nn.Module):
             metadata = w.parent / 'metadata.yaml'
         elif engine:  # TensorRT
             LOGGER.info(f'Loading {w} for TensorRT inference...')
-            import tensorrt as trt  # https://developer.nvidia.com/nvidia-tensorrt-download
+            try:
+                import tensorrt as trt  # noqa https://developer.nvidia.com/nvidia-tensorrt-download
+            except ImportError:
+                if LINUX:
+                    check_requirements('nvidia-tensorrt', cmds='-U --index-url https://pypi.ngc.nvidia.com')
+                import tensorrt as trt  # noqa
             check_version(trt.__version__, '7.0.0', hard=True)  # require tensorrt>=7.0.0
             if device.type == 'cpu':
                 device = torch.device('cuda:0')
@@ -230,7 +235,7 @@ class AutoBackend(nn.Module):
         elif paddle:  # PaddlePaddle
             LOGGER.info(f'Loading {w} for PaddlePaddle inference...')
             check_requirements('paddlepaddle-gpu' if cuda else 'paddlepaddle')
-            import paddle.inference as pdi
+            import paddle.inference as pdi  # noqa
             w = Path(w)
             if not w.is_file():  # if not *.pdmodel
                 w = next(w.rglob('*.pdmodel'))  # get *.pdmodel file from *_paddle_model dir
@@ -260,11 +265,16 @@ class AutoBackend(nn.Module):
         if isinstance(metadata, (str, Path)) and Path(metadata).exists():
             metadata = yaml_load(metadata)
         if metadata:
-            stride = int(metadata['stride'])
+            for k, v in metadata.items():
+                if k in ('stride', 'batch'):
+                    metadata[k] = int(v)
+                elif k in ('imgsz', 'names') and isinstance(v, str):
+                    metadata[k] = eval(v)
+            stride = metadata['stride']
             task = metadata['task']
-            batch = int(metadata['batch'])
-            imgsz = eval(metadata['imgsz']) if isinstance(metadata['imgsz'], str) else metadata['imgsz']
-            names = eval(metadata['names']) if isinstance(metadata['names'], str) else metadata['names']
+            batch = metadata['batch']
+            imgsz = metadata['imgsz']
+            names = metadata['names']
         elif not (pt or triton or nn_module):
             LOGGER.warning(f"WARNING ⚠️ Metadata not found for 'model={weights}'")
 
@@ -285,7 +295,7 @@ class AutoBackend(nn.Module):
             visualize (bool): whether to visualize the output predictions, defaults to False
 
         Returns:
-            (tuple): Tuple containing the raw output tensor, and the processed output for visualization (if visualize=True)
+            (tuple): Tuple containing the raw output tensor, and processed output for visualization (if visualize=True)
         """
         b, ch, h, w = im.shape  # batch, channel, height, width
         if self.fp16 and im.dtype != torch.float16:

ultralytics/nn/modules.py

@@ -67,7 +67,8 @@ class ConvTranspose(nn.Module):
 
 
 class DFL(nn.Module):
-    # Integral module of Distribution Focal Loss (DFL) proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
+    # Integral module of Distribution Focal Loss (DFL)
+    # Proposed in Generalized Focal Loss https://ieeexplore.ieee.org/document/9792391
     def __init__(self, c1=16):
         super().__init__()
         self.conv = nn.Conv2d(c1, 1, 1, bias=False).requires_grad_(False)

ultralytics/nn/tasks.py

@@ -8,7 +8,9 @@ import thop
 import torch
 import torch.nn as nn
 
-from ultralytics.nn.modules import *  # noqa: F403
+from ultralytics.nn.modules import (C1, C2, C3, C3TR, SPP, SPPF, Bottleneck, BottleneckCSP, C2f, C3Ghost, C3x, Classify,
+                                    Concat, Conv, ConvTranspose, Detect, DWConv, DWConvTranspose2d, Ensemble, Focus,
+                                    GhostBottleneck, GhostConv, Segment)
 from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, RANK, colorstr, emojis, yaml_load
 from ultralytics.yolo.utils.checks import check_requirements, check_yaml
 from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, fuse_deconv_and_bn, initialize_weights,
@@ -324,9 +326,9 @@ class ClassificationModel(BaseModel):
 
 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().
+    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.