ultralytics 8.0.12 - Hydra removal (ultralytics#506)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Pronoy Mandal <[email protected]>
Co-authored-by: Ayush Chaurasia <[email protected]>

.github/workflows/ci.yaml

@@ -85,20 +85,23 @@ jobs:
         shell: bash  # for Windows compatibility
         run: |
           yolo task=detect mode=train data=coco8.yaml model=yolov8n.yaml epochs=1 imgsz=32
+          yolo task=detect mode=train data=coco8.yaml model=yolov8n.pt epochs=1 imgsz=32
           yolo task=detect mode=val data=coco8.yaml model=runs/detect/train/weights/last.pt imgsz=32
           yolo task=detect mode=predict model=runs/detect/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/detect/train/weights/last.pt imgsz=32 format=torchscript
       - name: Test segmentation
         shell: bash  # for Windows compatibility
         run: |
           yolo task=segment mode=train data=coco8-seg.yaml model=yolov8n-seg.yaml epochs=1 imgsz=32
+          yolo task=segment mode=train data=coco8-seg.yaml model=yolov8n-seg.pt epochs=1 imgsz=32
           yolo task=segment mode=val data=coco8-seg.yaml model=runs/segment/train/weights/last.pt imgsz=32
           yolo task=segment mode=predict model=runs/segment/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/segment/train/weights/last.pt imgsz=32 format=torchscript
       - name: Test classification
         shell: bash  # for Windows compatibility
         run: |
           yolo task=classify mode=train data=mnist160 model=yolov8n-cls.yaml epochs=1 imgsz=32
+          yolo task=classify mode=train data=mnist160 model=yolov8n-cls.pt epochs=1 imgsz=32
           yolo task=classify mode=val data=mnist160 model=runs/classify/train/weights/last.pt imgsz=32
           yolo task=classify mode=predict model=runs/classify/train/weights/last.pt imgsz=32 source=ultralytics/assets/bus.jpg
           yolo mode=export model=runs/classify/train/weights/last.pt imgsz=32 format=torchscript

.gitignore

@@ -136,6 +136,7 @@ wandb/
 .DS_Store
 
 # Neural Network weights -----------------------------------------------------------------------------------------------
+weights/
 *.weights
 *.pt
 *.pb

docker/Dockerfile

@@ -10,7 +10,7 @@ ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Aria
 
 # Remove torch nightly and install torch stable
 RUN rm -rf /opt/pytorch  # remove 1.2GB dir
-RUN pip uninstall -y torchtext torch torchvision
+RUN pip uninstall -y torchtext pillow torch torchvision
 RUN pip install --no-cache torch torchvision
 
 # Install linux packages

docs/predict.md

@@ -6,19 +6,19 @@ Inference or prediction of a task returns a list of `Results` objects. Alternati
         inputs = [img, img] # list of np arrays
         results = model(inputs) # List of Results objects
         for result in results:
-            boxes = results.boxes # Boxes object for bbox outputs
-            masks = results.masks # Masks object for segmenation masks outputs
-            probs = results.probs # Class probabilities for classification outputs
+            boxes = result.boxes # Boxes object for bbox outputs
+            masks = result.masks # Masks object for segmenation masks outputs
+            probs = result.probs # Class probabilities for classification outputs
             ...
         ```
     === "Getting a Generator"
         ```python
         inputs = [img, img] # list of np arrays
         results = model(inputs, stream=True) # Generator of Results objects
         for result in results:
-            boxes = results.boxes # Boxes object for bbox outputs
-            masks = results.masks # Masks object for segmenation masks outputs
-            probs = results.probs # Class probabilities for classification outputs
+            boxes = result.boxes # Boxes object for bbox outputs
+            masks = result.masks # Masks object for segmenation masks outputs
+            probs = result.probs # Class probabilities for classification outputs
             ...
         ```
 
@@ -69,4 +69,4 @@ results.masks.segments  # bounding coordinates of masks, List[segment] * N
 results.probs     # cls prob, (num_class, )
 ```
 
-Class reference documentation for `Results` module and its components can be found [here](reference/results.md)
+Class reference documentation for `Results` module and its components can be found [here](reference/results.md)

requirements.txt

@@ -2,7 +2,6 @@
 # Usage: pip install -r requirements.txt
 
 # Base ----------------------------------------
-hydra-core>=1.2.0
 matplotlib>=3.2.2
 numpy>=1.18.5
 opencv-python>=4.1.1

setup.py

@@ -51,4 +51,5 @@ def get_version():
         "Operating System :: MacOS", "Operating System :: Microsoft :: Windows"],
     keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics",
     entry_points={
-        'console_scripts': ['yolo = ultralytics.yolo.cli:entrypoint', 'ultralytics = ultralytics.yolo.cli:entrypoint']})
+        'console_scripts':
+        ['yolo = ultralytics.yolo.configs:entrypoint', 'ultralytics = ultralytics.yolo.configs:entrypoint']})

tests/test_engine.py

@@ -3,13 +3,13 @@
 from pathlib import Path
 
 from ultralytics.yolo.configs import get_config
-from ultralytics.yolo.utils import DEFAULT_CONFIG, ROOT, SETTINGS
+from ultralytics.yolo.utils import DEFAULT_CFG_PATH, ROOT, SETTINGS
 from ultralytics.yolo.v8 import classify, detect, segment
 
 CFG_DET = 'yolov8n.yaml'
 CFG_SEG = 'yolov8n-seg.yaml'
 CFG_CLS = 'squeezenet1_0'
-CFG = get_config(DEFAULT_CONFIG)
+CFG = get_config(DEFAULT_CFG_PATH)
 MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
 SOURCE = ROOT / "assets"
 

tests/test_python.py

@@ -49,6 +49,8 @@ def test_predict_img():
     assert len(output) == 1, "predict test failed"
     output = model(source=[img, img], save=True, save_txt=True)  # batch
     assert len(output) == 2, "predict test failed"
+    output = model(source=[img, img], save=True, stream=True)  # stream
+    assert len(list(output)) == 2, "predict test failed"
     tens = torch.zeros(320, 640, 3)
     output = model(tens.numpy())
     assert len(output) == 1, "predict test failed"

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.11"
+__version__ = "8.0.12"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/hub/utils.py

@@ -7,7 +7,7 @@
 
 import requests
 
-from ultralytics.yolo.utils import DEFAULT_CONFIG_DICT, LOGGER, RANK, SETTINGS, TryExcept, colorstr, emojis
+from ultralytics.yolo.utils import DEFAULT_CFG_DICT, LOGGER, RANK, SETTINGS, TryExcept, colorstr, emojis
 
 PREFIX = colorstr('Ultralytics: ')
 HELP_MSG = 'If this issue persists please visit https://github.com/ultralytics/hub/issues for assistance.'
@@ -143,7 +143,7 @@ def sync_analytics(cfg, all_keys=False, enabled=False):
     if SETTINGS['sync'] and RANK in {-1, 0} and enabled:
         cfg = dict(cfg)  # convert type from DictConfig to dict
         if not all_keys:
-            cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CONFIG_DICT.get(k, None)}  # retain non-default values
+            cfg = {k: v for k, v in cfg.items() if v != DEFAULT_CFG_DICT.get(k, None)}  # retain non-default values
         cfg['uuid'] = SETTINGS['uuid']  # add the device UUID to the configuration data
 
         # Send a request to the HUB API to sync analytics

ultralytics/nn/tasks.py

@@ -10,7 +10,7 @@
 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_CONFIG_DICT, DEFAULT_CONFIG_KEYS, LOGGER, colorstr, yaml_load
+from ultralytics.yolo.utils import DEFAULT_CFG_DICT, DEFAULT_CFG_KEYS, LOGGER, colorstr, yaml_load
 from ultralytics.yolo.utils.checks import check_yaml
 from ultralytics.yolo.utils.torch_utils import (fuse_conv_and_bn, initialize_weights, intersect_dicts, make_divisible,
                                                 model_info, scale_img, time_sync)
@@ -113,7 +113,7 @@ def is_fused(self, thresh=10):
             thresh (int, optional): The threshold number of BatchNorm layers. Default is 10.
 
         Returns:
-            bool: True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
+            (bool): True if the number of BatchNorm layers in the model is less than the threshold, False otherwise.
         """
         bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k)  # normalization layers, i.e. BatchNorm2d()
         return sum(isinstance(v, bn) for v in self.modules()) < thresh  # True if < 'thresh' BatchNorm layers in model
@@ -321,11 +321,11 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=False):
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:
         ckpt = torch.load(attempt_download(w), map_location='cpu')  # load
-        args = {**DEFAULT_CONFIG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
+        args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
         ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
 
         # Model compatibility updates
-        ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CONFIG_KEYS}  # attach args to model
+        ckpt.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # attach args to model
         ckpt.pt_path = weights  # attach *.pt file path to model
         if not hasattr(ckpt, 'stride'):
             ckpt.stride = torch.tensor([32.])
@@ -359,11 +359,11 @@ def attempt_load_one_weight(weight, device=None, inplace=True, fuse=False):
     from ultralytics.yolo.utils.downloads import attempt_download
 
     ckpt = torch.load(attempt_download(weight), map_location='cpu')  # load
-    args = {**DEFAULT_CONFIG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
+    args = {**DEFAULT_CFG_DICT, **ckpt['train_args']}  # combine model and default args, preferring model args
     model = (ckpt.get('ema') or ckpt['model']).to(device).float()  # FP32 model
 
     # Model compatibility updates
-    model.args = {k: v for k, v in args.items() if k in DEFAULT_CONFIG_KEYS}  # attach args to model
+    model.args = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS}  # attach args to model
     model.pt_path = weight  # attach *.pt file path to model
     if not hasattr(model, 'stride'):
         model.stride = torch.tensor([32.])