ultralytics 8.1.4 RTDETR TensorBoard graph visualization fix (#7725)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

pyproject.toml

@@ -93,7 +93,7 @@ dev = [
     "mkdocstrings[python]",
     "mkdocs-jupyter", # for notebooks
     "mkdocs-redirects", # for 301 redirects
-    "mkdocs-ultralytics-plugin>=0.0.34", # for meta descriptions and images, dates and authors
+    "mkdocs-ultralytics-plugin>=0.0.38", # for meta descriptions and images, dates and authors
 ]
 export = [
     "onnx>=1.12.0", # ONNX export

ultralytics/__init__.py

@@ -1,13 +1,25 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = "8.1.3"
+__version__ = "8.1.4"
 
 from ultralytics.data.explorer.explorer import Explorer
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM
 from ultralytics.models.nas import NAS
-from ultralytics.utils import SETTINGS as settings
+from ultralytics.utils import ASSETS, SETTINGS as settings
 from ultralytics.utils.checks import check_yolo as checks
 from ultralytics.utils.downloads import download
 
-__all__ = "__version__", "YOLO", "NAS", "SAM", "FastSAM", "RTDETR", "checks", "download", "settings", "Explorer"
+__all__ = (
+    "__version__",
+    "ASSETS",
+    "YOLO",
+    "NAS",
+    "SAM",
+    "FastSAM",
+    "RTDETR",
+    "checks",
+    "download",
+    "settings",
+    "Explorer",
+)

ultralytics/cfg/datasets/Argoverse.yaml

@@ -63,7 +63,9 @@ download: |
   # Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
   dir = Path(yaml['path'])  # dataset root dir
   urls = ['https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link']
-  download(urls, dir=dir)
+  print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
+  print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
+  # download(urls, dir=dir)
 
   # Convert
   annotations_dir = 'Argoverse-HD/annotations/'

ultralytics/engine/model.py

@@ -427,7 +427,9 @@ class Model(nn.Module):
     @property
     def names(self):
         """Returns class names of the loaded model."""
-        return self.model.names if hasattr(self.model, "names") else None
+        from ultralytics.nn.autobackend import check_class_names
+
+        return check_class_names(self.model.names) if hasattr(self.model, "names") else None
 
     @property
     def device(self):

ultralytics/nn/modules/head.py

@@ -376,7 +376,7 @@ class RTDETRDecoder(nn.Module):
 
     def _get_decoder_input(self, feats, shapes, dn_embed=None, dn_bbox=None):
         """Generates and prepares the input required for the decoder from the provided features and shapes."""
-        bs = len(feats)
+        bs = feats.shape[0]
         # Prepare input for decoder
         anchors, valid_mask = self._generate_anchors(shapes, dtype=feats.dtype, device=feats.device)
         features = self.enc_output(valid_mask * feats)  # bs, h*w, 256

ultralytics/nn/modules/transformer.py

@@ -101,10 +101,10 @@ class AIFI(TransformerEncoderLayer):
     @staticmethod
     def build_2d_sincos_position_embedding(w, h, embed_dim=256, temperature=10000.0):
         """Builds 2D sine-cosine position embedding."""
-        grid_w = torch.arange(int(w), dtype=torch.float32)
-        grid_h = torch.arange(int(h), dtype=torch.float32)
-        grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
         assert embed_dim % 4 == 0, "Embed dimension must be divisible by 4 for 2D sin-cos position embedding"
+        grid_w = torch.arange(w, dtype=torch.float32)
+        grid_h = torch.arange(h, dtype=torch.float32)
+        grid_w, grid_h = torch.meshgrid(grid_w, grid_h, indexing="ij")
         pos_dim = embed_dim // 4
         omega = torch.arange(pos_dim, dtype=torch.float32) / pos_dim
         omega = 1.0 / (temperature**omega)

ultralytics/utils/callbacks/tensorboard.py

@@ -1,14 +1,21 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
+import contextlib
 
 from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
 
 try:
-    # WARNING: do not move import due to protobuf issue in https://github.com/ultralytics/ultralytics/pull/4674
+    # WARNING: do not move SummaryWriter import due to protobuf bug https://github.com/ultralytics/ultralytics/pull/4674
     from torch.utils.tensorboard import SummaryWriter
 
     assert not TESTS_RUNNING  # do not log pytest
     assert SETTINGS["tensorboard"] is True  # verify integration is enabled
     WRITER = None  # TensorBoard SummaryWriter instance
+    PREFIX = colorstr("TensorBoard: ")
+
+    # Imports below only required if TensorBoard enabled
+    import warnings
+    from copy import deepcopy
+    from ultralytics.utils.torch_utils import de_parallel, torch
 
 except (ImportError, AssertionError, TypeError, AttributeError):
     # TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
@@ -25,20 +32,37 @@ def _log_scalars(scalars, step=0):
 
 def _log_tensorboard_graph(trainer):
     """Log model graph to TensorBoard."""
-    try:
-        import warnings
 
-        from ultralytics.utils.torch_utils import de_parallel, torch
+    # Input image
+    imgsz = trainer.args.imgsz
+    imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz
+    p = next(trainer.model.parameters())  # for device, type
+    im = torch.zeros((1, 3, *imgsz), device=p.device, dtype=p.dtype)  # input image (must be zeros, not empty)
 
-        imgsz = trainer.args.imgsz
+    with warnings.catch_warnings():
-        imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz
+        warnings.simplefilter("ignore", category=UserWarning)  # suppress jit trace warning
-        p = next(trainer.model.parameters())  # for device, type
+        warnings.simplefilter("ignore", category=torch.jit.TracerWarning)  # suppress jit trace warning
-        im = torch.zeros((1, 3, *imgsz), device=p.device, dtype=p.dtype)  # input image (must be zeros, not empty)
+
-        with warnings.catch_warnings():
+        # Try simple method first (YOLO)
-            warnings.simplefilter("ignore", category=UserWarning)  # suppress jit trace warning
+        with contextlib.suppress(Exception):
             WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), [])
-    except Exception as e:
+            LOGGER.info(f"{PREFIX}model graph visualization added ✅")
-        LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}")
+            return
+
+        # Fallback to TorchScript export steps (RTDETR)
+        try:
+            model = deepcopy(de_parallel(trainer.model))
+            model.eval()
+            model = model.fuse(verbose=False)
+            for m in model.modules():
+                if hasattr(m, "export"):  # Detect, RTDETRDecoder (Segment and Pose use Detect base class)
+                    m.export = True
+                    m.format = "torchscript"
+            model(im)  # dry run
+            WRITER.add_graph(torch.jit.trace(model, im, strict=False), [])
+            LOGGER.info(f"{PREFIX}model graph visualization added ✅")
+        except Exception as e:
+            LOGGER.warning(f"{PREFIX}WARNING ⚠️ TensorBoard graph visualization failure {e}")
 
 
 def on_pretrain_routine_start(trainer):
@@ -47,10 +71,9 @@ def on_pretrain_routine_start(trainer):
         try:
             global WRITER
             WRITER = SummaryWriter(str(trainer.save_dir))
-            prefix = colorstr("TensorBoard: ")
+            LOGGER.info(f"{PREFIX}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
-            LOGGER.info(f"{prefix}Start with 'tensorboard --logdir {trainer.save_dir}', view at http://localhost:6006/")
         except Exception as e:
-            LOGGER.warning(f"WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}")
+            LOGGER.warning(f"{PREFIX}WARNING ⚠️ TensorBoard not initialized correctly, not logging this run. {e}")
 
 
 def on_train_start(trainer):

ultralytics/utils/ops.py

@@ -220,7 +220,7 @@ def non_max_suppression(
 
     # Settings
     # min_wh = 2  # (pixels) minimum box width and height
-    time_limit = 0.5 + max_time_img * bs  # seconds to quit after
+    time_limit = 2.0 + max_time_img * bs  # seconds to quit after
     multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
 
     prediction = prediction.transpose(-1, -2)  # shape(1,84,6300) to shape(1,6300,84)