diff --git a/pyproject.toml b/pyproject.toml index 6188b322aa1..4f22c6e8d7e 100644 --- a/pyproject.toml +++ b/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 diff --git a/ultralytics/__init__.py b/ultralytics/__init__.py index 51f7eb46ed6..3a91aec13e9 100644 --- a/ultralytics/__init__.py +++ b/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", +) diff --git a/ultralytics/cfg/datasets/Argoverse.yaml b/ultralytics/cfg/datasets/Argoverse.yaml index 138b575048e..43755f76870 100644 --- a/ultralytics/cfg/datasets/Argoverse.yaml +++ b/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/' diff --git a/ultralytics/engine/model.py b/ultralytics/engine/model.py index 745ec4a3fe8..a0783ced171 100644 --- a/ultralytics/engine/model.py +++ b/ultralytics/engine/model.py @@ -427,7 +427,9 @@ def _apply(self, fn): @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): diff --git a/ultralytics/nn/modules/head.py b/ultralytics/nn/modules/head.py index f35f19df2e2..33c724b518e 100644 --- a/ultralytics/nn/modules/head.py +++ b/ultralytics/nn/modules/head.py @@ -376,7 +376,7 @@ def _get_encoder_input(self, x): 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 diff --git a/ultralytics/nn/modules/transformer.py b/ultralytics/nn/modules/transformer.py index 465b7170e75..494c431aee6 100644 --- a/ultralytics/nn/modules/transformer.py +++ b/ultralytics/nn/modules/transformer.py @@ -101,10 +101,10 @@ def forward(self, x): @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) diff --git a/ultralytics/utils/callbacks/tensorboard.py b/ultralytics/utils/callbacks/tensorboard.py index b78412f7c1c..98107e8cad3 100644 --- a/ultralytics/utils/callbacks/tensorboard.py +++ b/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) + + with warnings.catch_warnings(): + warnings.simplefilter("ignore", category=UserWarning) # suppress jit trace warning + warnings.simplefilter("ignore", category=torch.jit.TracerWarning) # suppress jit trace warning - 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) - with warnings.catch_warnings(): - warnings.simplefilter("ignore", category=UserWarning) # suppress jit trace warning + # Try simple method first (YOLO) + with contextlib.suppress(Exception): WRITER.add_graph(torch.jit.trace(de_parallel(trainer.model), im, strict=False), []) - except Exception as e: - LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}") + LOGGER.info(f"{PREFIX}model graph visualization added ✅") + 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): diff --git a/ultralytics/utils/ops.py b/ultralytics/utils/ops.py index 53a1809023b..94f5b3b53b1 100644 --- a/ultralytics/utils/ops.py +++ b/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)