ultralytics 8.0.30 Docker, rect, data=*.zip updates (#832)

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>

.github/workflows/docker.yaml

@@ -29,7 +29,7 @@ jobs:
           password: ${{ secrets.DOCKERHUB_TOKEN }}
 
       - name: Build and push arm64 image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v4
         continue-on-error: true
         with:
           context: .
@@ -39,7 +39,7 @@ jobs:
           tags: ultralytics/ultralytics:latest-arm64
 
       - name: Build and push CPU image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v4
         continue-on-error: true
         with:
           context: .
@@ -48,7 +48,7 @@ jobs:
           tags: ultralytics/ultralytics:latest-cpu
 
       - name: Build and push GPU image
-        uses: docker/build-push-action@v3
+        uses: docker/build-push-action@v4
         continue-on-error: true
         with:
           context: .

docker/Dockerfile-arm64

@@ -26,11 +26,9 @@ RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 # Install pip packages
 COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics gsutil notebook \
+RUN pip install --no-cache ultralytics albumentations gsutil notebook \
-    tensorflow-aarch64
+    coremltools onnx onnx-simplifier onnxruntime openvino-dev>=2022.3
-    # tensorflowjs \
+    # tensorflow-aarch64 tensorflowjs \
-    # onnx onnx-simplifier onnxruntime \
-    # coremltools openvino-dev>=2022.3 \
 
 # Cleanup
 ENV DEBIAN_FRONTEND teletype

docs/cfg.md

@@ -108,6 +108,7 @@ task.
 | overlap_mask    | True   | masks should overlap during training (segment train only)                   |
 | mask_ratio      | 4      | mask downsample ratio (segment train only)                                  |
 | dropout         | 0.0    | use dropout regularization (classify train only)                            |
+| val             | True   | validate/test during training                                               |
 
 ### Prediction
 
@@ -148,7 +149,6 @@ validation dataset and to detect and prevent overfitting.
 
 | Key         | Value | Description                                                                 |
 |-------------|-------|-----------------------------------------------------------------------------|
-| val         | True  | validate/test during training                                               |
 | save_json   | False | save results to JSON file                                                   |
 | save_hybrid | False | save hybrid version of labels (labels + additional predictions)             |
 | conf        | 0.001 | object confidence threshold for detection (default 0.25 predict, 0.001 val) |
@@ -157,6 +157,7 @@ validation dataset and to detect and prevent overfitting.
 | half        | True  | use half precision (FP16)                                                   |
 | dnn         | False | use OpenCV DNN for ONNX inference                                           |
 | plots       | False | show plots during training                                                  |
+| rect        | False | support rectangular evaluation                                              |
 
 ### Export
 

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.29"
+__version__ = "8.0.30"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/nn/tasks.py

@@ -338,8 +338,9 @@ def torch_safe_load(weight):
         if e.name == 'omegaconf':  # e.name is missing module name
             LOGGER.warning(f"WARNING ⚠️ {weight} requires {e.name}, which is not in ultralytics requirements."
                            f"\nAutoInstall will run now for {e.name} but this feature will be removed in the future."
-                           f"\nRecommend fixes are to train a new model using updated ultraltyics package or to "
+                           f"\nRecommend fixes are to train a new model using updated ultralytics package or to "
                            f"download updated models from https://github.com/ultralytics/assets/releases/tag/v0.0.0")
+        if e.name != 'models':
             check_requirements(e.name)  # install missing module
         return torch.load(file, map_location='cpu')  # load
 

ultralytics/yolo/cfg/default.yaml

@@ -25,7 +25,7 @@ seed: 0  # random seed for reproducibility
 deterministic: True  # whether to enable deterministic mode
 single_cls: False  # train multi-class data as single-class
 image_weights: False  # use weighted image selection for training
-rect: False  # support rectangular training
+rect: False  # support rectangular training if mode='train', support rectangular evaluation if mode='val'
 cos_lr: False  # use cosine learning rate scheduler
 close_mosaic: 10  # disable mosaic augmentation for final 10 epochs
 resume: False  # resume training from last checkpoint

ultralytics/yolo/data/build.py

@@ -61,7 +61,7 @@ def seed_worker(worker_id):
     random.seed(worker_seed)
 
 
-def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank=-1, mode="train"):
+def build_dataloader(cfg, batch_size, img_path, stride=32, rect=False, label_path=None, rank=-1, mode="train"):
     assert mode in ["train", "val"]
     shuffle = mode == "train"
     if cfg.rect and shuffle:
@@ -75,7 +75,7 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
             batch_size=batch_size,
             augment=mode == "train",  # augmentation
             hyp=cfg,  # TODO: probably add a get_hyps_from_cfg function
-            rect=cfg.rect if mode == "train" else True,  # rectangular batches
+            rect=cfg.rect or rect,  # rectangular batches
             cache=cfg.cache or None,
             single_cls=cfg.single_cls or False,
             stride=int(stride),

ultralytics/yolo/data/dataset.py

@@ -113,13 +113,15 @@ class YOLODataset(BaseDataset):
             tqdm(None, desc=self.prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)  # display cache results
             if cache["msgs"]:
                 LOGGER.info("\n".join(cache["msgs"]))  # display warnings
-        assert nf > 0, f"{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}"
+        if nf == 0:  # number of labels found
+            raise FileNotFoundError(f"{self.prefix}No labels found in {cache_path}, can not start training. {HELP_URL}")
 
         # Read cache
         [cache.pop(k) for k in ("hash", "version", "msgs")]  # remove items
         labels = cache["labels"]
 
         # Check if the dataset is all boxes or all segments
+        len_cls = sum(len(lb["cls"]) for lb in labels)
         len_boxes = sum(len(lb["bboxes"]) for lb in labels)
         len_segments = sum(len(lb["segments"]) for lb in labels)
         if len_segments and len_boxes != len_segments:
@@ -129,8 +131,8 @@ class YOLODataset(BaseDataset):
                 "To avoid this please supply either a detect or segment dataset, not a detect-segment mixed dataset.")
             for lb in labels:
                 lb["segments"] = []
-        nl = len(np.concatenate([label["cls"] for label in labels], 0))  # number of labels
+        if len_cls == 0:
-        assert nl > 0, f"{self.prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}"
+            raise ValueError(f"{self.prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}")
         return labels
 
     # TODO: use hyp config to set all these augmentations

ultralytics/yolo/data/utils.py

@@ -192,7 +192,7 @@ def check_det_dataset(dataset, autodownload=True):
     # Download (optional)
     extract_dir = ''
     if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)):
-        download(data, dir=f'{DATASETS_DIR}/{Path(data).stem}', unzip=True, delete=False, curl=False, threads=1)
+        download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False, threads=1)
         data = next((DATASETS_DIR / Path(data).stem).rglob('*.yaml'))
         extract_dir, autodownload = data.parent, False
 
@@ -211,7 +211,8 @@ def check_det_dataset(dataset, autodownload=True):
     data['nc'] = len(data['names'])
 
     # Resolve paths
-    path = Path(extract_dir or data.get('path') or '')  # optional 'path' default to '.'
+    path = Path(extract_dir or data.get('path') or Path(data.get('yaml_file', '')).parent)  # dataset root
+
     if not path.is_absolute():
         path = (DATASETS_DIR / path).resolve()
         data['path'] = path  # download scripts

ultralytics/yolo/engine/model.py

@@ -156,6 +156,7 @@ class YOLO:
             **kwargs : Any other args accepted by the validators. To see all args check 'configuration' section in docs
         """
         overrides = self.overrides.copy()
+        overrides["rect"] = True  # rect batches as default
         overrides.update(kwargs)
         overrides["mode"] = "val"
         args = get_cfg(cfg=DEFAULT_CFG, overrides=overrides)

ultralytics/yolo/engine/trainer.py

@@ -116,13 +116,16 @@ class BaseTrainer:
 
         # Model and Dataloaders.
         self.model = self.args.model
-        self.data = self.args.data
+        try:
-        if self.data.endswith(".yaml"):
+            if self.args.task == 'classify':
-            self.data = check_det_dataset(self.data)
+                self.data = check_cls_dataset(self.args.data)
-        elif self.args.task == 'classify':
+            elif self.args.data.endswith(".yaml") or self.args.task in ('detect', 'segment'):
-            self.data = check_cls_dataset(self.data)
+                self.data = check_det_dataset(self.args.data)
-        else:
+                if 'yaml_file' in self.data:
-            raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' not found ❌"))
+                    self.args.data = self.data['yaml_file']  # for validating 'yolo train data=url.zip' usage
+        except Exception as e:
+            raise FileNotFoundError(emojis(f"Dataset '{self.args.data}' error ❌ {e}")) from e
+
         self.trainset, self.testset = self.get_dataset(self.data)
         self.ema = None
 

ultralytics/yolo/engine/validator.py

@@ -117,6 +117,8 @@ class BaseValidator:
 
             if self.device.type == 'cpu':
                 self.args.workers = 0  # faster CPU val as time dominated by inference, not dataloading
+            if not pt:
+                self.args.rect = False
             self.dataloader = self.dataloader or \
                               self.get_dataloader(self.data.get("val") or self.data.set("test"), self.args.batch)
 

ultralytics/yolo/utils/__init__.py

@@ -491,6 +491,7 @@ def set_sentry():
             ((is_pip_package() and not is_git_dir()) or
              (get_git_origin_url() == "https://github.com/ultralytics/ultralytics.git" and get_git_branch() == "main")):
 
+        import hashlib
         import sentry_sdk  # noqa
         from ultralytics import __version__
 
@@ -502,13 +503,14 @@ def set_sentry():
             environment='production',  # 'dev' or 'production'
             before_send=before_send,
             ignore_errors=[KeyboardInterrupt, FileNotFoundError])
+        sentry_sdk.set_user({"id": SETTINGS['uuid']})
 
         # Disable all sentry logging
         for logger in "sentry_sdk", "sentry_sdk.errors":
             logging.getLogger(logger).setLevel(logging.CRITICAL)
 
 
-def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
+def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.2'):
     """
     Loads a global Ultralytics settings YAML file or creates one with default values if it does not exist.
 
@@ -519,6 +521,7 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
     Returns:
         dict: Dictionary of settings key-value pairs.
     """
+    import hashlib
     from ultralytics.yolo.utils.checks import check_version
     from ultralytics.yolo.utils.torch_utils import torch_distributed_zero_first
 
@@ -530,7 +533,7 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
         'weights_dir': str(root / 'weights'),  # default weights directory.
         'runs_dir': str(root / 'runs'),  # default runs directory.
         'sync': True,  # sync analytics to help with YOLO development
-        'uuid': uuid.getnode(),  # device UUID to align analytics
+        'uuid': hashlib.sha256(str(uuid.getnode()).encode()).hexdigest(),  # anonymized uuid hash
         'settings_version': version}  # Ultralytics settings version
 
     with torch_distributed_zero_first(RANK):
@@ -544,10 +547,9 @@ def get_settings(file=USER_CONFIG_DIR / 'settings.yaml', version='0.0.1'):
             and all(type(a) == type(b) for a, b in zip(settings.values(), defaults.values())) \
             and check_version(settings['settings_version'], version)
         if not correct:
-            LOGGER.warning('WARNING ⚠️ Ultralytics settings reset to defaults. '
+            LOGGER.warning('WARNING ⚠️ Ultralytics settings reset to defaults. This is normal and may be due to a '
-                           '\nThis is normal and may be due to a recent ultralytics package update, '
+                           'recent ultralytics package update, but may have overwritten previous settings. '
-                           'but may have overwritten previous settings. '
+                           f"\nView and update settings with 'yolo settings' or at '{file}'")
-                           f"\nYou may view and update settings directly in '{file}'")
             settings = defaults  # merge **defaults with **settings (prefer **settings)
             yaml_save(file, settings)  # save updated defaults
 

ultralytics/yolo/utils/checks.py

@@ -247,7 +247,7 @@ def check_file(file, suffix=''):
         if Path(file).is_file():
             LOGGER.info(f'Found {url} locally at {file}')  # file already exists
         else:
-            downloads.safe_download(url=url, file=file)
+            downloads.safe_download(url=url, file=file, unzip=False)
         return file
     else:  # search
         files = []

ultralytics/yolo/utils/downloads.py

@@ -28,6 +28,19 @@ def is_url(url, check=True):
     return False
 
 
+def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX')):
+    """
+    Unzip a *.zip file to path/, excluding files containing strings in exclude list
+    Replaces: ZipFile(file).extractall(path=path)
+    """
+    if path is None:
+        path = Path(file).parent  # default path
+    with ZipFile(file) as zipObj:
+        for f in zipObj.namelist():  # list all archived filenames in the zip
+            if all(x not in f for x in exclude):
+                zipObj.extract(f, path=path)
+
+
 def safe_download(url,
                   file=None,
                   dir=None,
@@ -96,13 +109,14 @@ def safe_download(url,
                 LOGGER.warning(f'⚠️ Download failure, retrying {i + 1}/{retry} {url}...')
 
     if unzip and f.exists() and f.suffix in {'.zip', '.tar', '.gz'}:
-        LOGGER.info(f'Unzipping {f}...')
+        unzip_dir = dir or f.parent  # unzip to dir if provided else unzip in place
+        LOGGER.info(f'Unzipping {f} to {unzip_dir}...')
         if f.suffix == '.zip':
-            ZipFile(f).extractall(path=f.parent)  # unzip
+            unzip_file(file=f, path=unzip_dir)  # unzip
         elif f.suffix == '.tar':
-            subprocess.run(['tar', 'xf', f, '--directory', f.parent], check=True)  # unzip
+            subprocess.run(['tar', 'xf', f, '--directory', unzip_dir], check=True)  # unzip
         elif f.suffix == '.gz':
-            subprocess.run(['tar', 'xfz', f, '--directory', f.parent], check=True)  # unzip
+            subprocess.run(['tar', 'xfz', f, '--directory', unzip_dir], check=True)  # unzip
         if delete:
             f.unlink()  # remove zip
 

ultralytics/yolo/v8/detect/train.py

@@ -33,14 +33,14 @@ class DetectionTrainer(BaseTrainer):
                                  augment=mode == "train",
                                  cache=self.args.cache,
                                  pad=0 if mode == "train" else 0.5,
-                                 rect=self.args.rect,
+                                 rect=self.args.rect or mode=="val",
                                  rank=rank,
                                  workers=self.args.workers,
                                  close_mosaic=self.args.close_mosaic != 0,
                                  prefix=colorstr(f'{mode}: '),
                                  shuffle=mode == "train",
                                  seed=self.args.seed)[0] if self.args.v5loader else \
-            build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
+            build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode, rect=mode=="val")[0]
 
     def preprocess_batch(self, batch):
         batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255

ultralytics/yolo/v8/detect/val.py

@@ -22,7 +22,6 @@ class DetectionValidator(BaseValidator):
     def __init__(self, dataloader=None, save_dir=None, pbar=None, logger=None, args=None):
         super().__init__(dataloader, save_dir, pbar, logger, args)
         self.args.task = 'detect'
-        self.data_dict = yaml_load(check_file(self.args.data), append_filename=True) if self.args.data else None
         self.is_coco = False
         self.class_map = None
         self.metrics = DetMetrics(save_dir=self.save_dir)
@@ -172,7 +171,7 @@ class DetectionValidator(BaseValidator):
                                  hyp=vars(self.args),
                                  cache=False,
                                  pad=0.5,
-                                 rect=True,
+                                 rect=self.args.rect,
                                  workers=self.args.workers,
                                  prefix=colorstr(f'{self.args.mode}: '),
                                  shuffle=False,