`ultralytics 8.0.26` new YOLOv5u models (#771)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Adrian Boguszewski <adrian.boguszewski@intel.com>

docker/Dockerfile

@@ -3,7 +3,7 @@
 # Image is CUDA-optimized for YOLOv8 single/multi-GPU training and inference
 
 # Start FROM NVIDIA PyTorch image https://ngc.nvidia.com/catalog/containers/nvidia:pytorch
-FROM nvcr.io/nvidia/pytorch:22.12-py3
+FROM nvcr.io/nvidia/pytorch:23.01-py3
 
 # Downloads to user config dir
 ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
@@ -26,7 +26,7 @@ RUN git clone https://github.com/ultralytics/ultralytics /usr/src/ultralytics
 
 # Install pip packages
 RUN python -m pip install --upgrade pip wheel
-RUN pip install --no-cache ultralytics albumentations comet gsutil notebook 'opencv-python<4.6.0.66'
+RUN pip install --no-cache ultralytics albumentations comet gsutil notebook
 
 # Set environment variables
 ENV OMP_NUM_THREADS=1

docker/Dockerfile-arm64

@@ -30,7 +30,7 @@ RUN pip install --no-cache ultralytics gsutil notebook \
     tensorflow-aarch64
     # tensorflowjs \
     # onnx onnx-simplifier onnxruntime \
-    # coremltools openvino-dev \
+    # coremltools openvino-dev>=2022.3 \
 
 # Cleanup
 ENV DEBIAN_FRONTEND teletype

docker/Dockerfile-cpu

@@ -28,7 +28,7 @@ COPY requirements.txt .
 RUN python3 -m pip install --upgrade pip wheel
 RUN pip install --no-cache ultralytics albumentations gsutil notebook \
     coremltools onnx onnx-simplifier onnxruntime tensorflow-cpu \
-    # openvino-dev tensorflowjs \
+    # openvino-dev>=2022.3 tensorflowjs \
     --extra-index-url https://download.pytorch.org/whl/cpu
 
 # Cleanup

examples/tutorial.ipynb

@@ -67,7 +67,7 @@
         "import ultralytics\n",
         "ultralytics.checks()"
       ],
-      "execution_count": 1,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -116,7 +116,7 @@
         "# Run inference on an image with YOLOv8n\n",
         "!yolo predict model=yolov8n.pt source='https://ultralytics.com/images/zidane.jpg'"
       ],
-      "execution_count": 2,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -183,7 +183,7 @@
         "# Validate YOLOv8n on COCO128 val\n",
         "!yolo val model=yolov8n.pt data=coco128.yaml"
       ],
-      "execution_count": 3,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -306,7 +306,7 @@
         "# Train YOLOv8n on COCO128 for 3 epochs\n",
         "!yolo train model=yolov8n.pt data=coco128.yaml epochs=3 imgsz=640"
       ],
-      "execution_count": 4,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -495,7 +495,7 @@
         "id": "CYIjW4igCjqD",
         "outputId": "69cab2fb-cbfa-4acf-8e29-9c4fb6f4a38f"
       },
-      "execution_count": 5,
+      "execution_count": null,
       "outputs": [
         {
           "output_type": "stream",
@@ -666,6 +666,19 @@
       ],
       "execution_count": null,
       "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# Validate multiple models\n",
+        "for x in 'nsmlx':\n",
+        "  !yolo val model=yolov8{x}.pt data=coco.yaml"
+      ],
+      "metadata": {
+        "id": "Wdc6t_bfzDDk"
+      },
+      "execution_count": null,
+      "outputs": []
     }
   ]
 }

requirements.txt

@@ -31,7 +31,7 @@ seaborn>=0.11.0
 # scikit-learn==0.19.2  # CoreML quantization
 # tensorflow>=2.4.1  # TF exports (-cpu, -aarch64, -macos)
 # tensorflowjs>=3.9.0  # TF.js export
-# openvino-dev>=2022.1  # OpenVINO export
+# openvino-dev>=2022.3  # OpenVINO export
 
 # Extras --------------------------------------
 ipython  # interactive notebook

tests/test_python.py

@@ -150,14 +150,14 @@ def test_predict_callback_and_setup():
         # results -> List[batch_size]
         path, _, im0s, _, _ = predictor.batch
         # print('on_predict_batch_end', im0s[0].shape)
-        bs = [predictor.bs for i in range(0, len(path))]
+        bs = [predictor.bs for _ in range(len(path))]
         predictor.results = zip(predictor.results, im0s, bs)
 
     model = YOLO("yolov8n.pt")
     model.add_callback("on_predict_batch_end", on_predict_batch_end)
 
     dataset = load_inference_source(source=SOURCE, transforms=model.transforms)
-    bs = dataset.bs  # access predictor properties
+    bs = dataset.bs  # noqa access predictor properties
     results = model.predict(dataset, stream=True)  # source already setup
     for _, (result, im0, bs) in enumerate(results):
         print('test_callback', im0.shape)

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
-__version__ = "8.0.25"
+__version__ = "8.0.26"
 
 from ultralytics.yolo.engine.model import YOLO
 from ultralytics.yolo.utils import ops

ultralytics/yolo/cfg/__init__.py

@@ -148,7 +148,7 @@ def argument_error(arg):
     return SyntaxError(f"'{arg}' is not a valid YOLO argument.\n{CLI_HELP_MSG}")
 
 
-def entrypoint(debug=False):
+def entrypoint(debug=''):
     """
     This function is the ultralytics package entrypoint, it's responsible for parsing the command line arguments passed
     to the package.
@@ -163,7 +163,7 @@ def entrypoint(debug=False):
     It uses the package's default cfg and initializes it using the passed overrides.
     Then it calls the CLI function with the composed cfg
     """
-    args = ['train', 'model=yolov8n.pt', 'data=coco128.yaml', 'imgsz=32', 'epochs=1'] if debug else sys.argv[1:]
+    args = (debug.split(' ') if debug else sys.argv)[1:]
     if not args:  # no arguments passed
         LOGGER.info(CLI_HELP_MSG)
         return
@@ -275,4 +275,5 @@ def copy_default_cfg():
 
 
 if __name__ == '__main__':
-    entrypoint(debug=True)
+    # entrypoint(debug='yolo predict model=yolov8n.pt')
+    entrypoint(debug='')

ultralytics/yolo/data/dataloaders/stream_loaders.py

@@ -13,7 +13,7 @@ import cv2
 import numpy as np
 import requests
 import torch
-from PIL import Image, ImageOps
+from PIL import Image
 
 from ultralytics.yolo.data.augment import LetterBox
 from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS
@@ -61,9 +61,11 @@ class LoadStreams:
             self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float('inf')  # infinite stream fallback
             self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30  # 30 FPS fallback
 
-            _, self.imgs[i] = cap.read()  # guarantee first frame
+            success, self.imgs[i] = cap.read()  # guarantee first frame
+            if not success or self.imgs[i] is None:
+                raise ConnectionError(f'{st}Failed to read images from {s}')
             self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
-            LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
+            LOGGER.info(f"{st}Success ✅ ({self.frames[i]} frames of shape {w}x{h} at {self.fps[i]:.2f} FPS)")
             self.threads[i].start()
         LOGGER.info('')  # newline
 
@@ -221,15 +223,15 @@ class LoadImages:
             self.mode = 'video'
             for _ in range(self.vid_stride):
                 self.cap.grab()
-            ret_val, im0 = self.cap.retrieve()
+            success, im0 = self.cap.retrieve()
-            while not ret_val:
+            while not success:
                 self.count += 1
                 self.cap.release()
                 if self.count == self.nf:  # last video
                     raise StopIteration
                 path = self.files[self.count]
                 self._new_video(path)
-                ret_val, im0 = self.cap.read()
+                success, im0 = self.cap.read()
 
             self.frame += 1
             # im0 = self._cv2_rotate(im0)  # for use if cv2 autorotation is False
@@ -330,14 +332,14 @@ def autocast_list(source):
     Merges a list of source of different types into a list of numpy arrays or PIL images
     """
     files = []
-    for _, im in enumerate(source):
+    for im in source:
         if isinstance(im, (str, Path)):  # filename or uri
             files.append(Image.open(requests.get(im, stream=True).raw if str(im).startswith('http') else im))
         elif isinstance(im, (Image.Image, np.ndarray)):  # PIL or np Image
             files.append(im)
         else:
-            raise Exception(
+            raise TypeError(f"type {type(im).__name__} is not a supported Ultralytics prediction source type. \n"
-                "Unsupported type encountered! See docs for supported types https://docs.ultralytics.com/predict")
+                            f"See https://docs.ultralytics.com/predict for supported source types.")
 
     return files
 

ultralytics/yolo/data/scripts/download_weights.sh

@@ -1,22 +1,18 @@
 #!/bin/bash
 # Ultralytics YOLO 🚀, GPL-3.0 license
-# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Download latest models from https://github.com/ultralytics/assets/releases
-# Example usage: bash data/scripts/download_weights.sh
+# Example usage: bash ultralytics/yolo/data/scripts/download_weights.sh
 # parent
-# └── yolov5
+# └── weights
-#     ├── yolov5s.pt  ← downloads here
+#     ├── yolov8n.pt  ← downloads here
-#     ├── yolov5m.pt
+#     ├── yolov8s.pt
 #     └── ...
 
 python - <<EOF
-from utils.downloads import attempt_download
+from ultralytics.yolo.utils.downloads import attempt_download_asset
 
-p5 = list('nsmlx')  # P5 models
+assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '-cls', '-seg')]
-p6 = [f'{x}6' for x in p5]  # P6 models
+for x in assets:
-cls = [f'{x}-cls' for x in p5]  # classification models
+    attempt_download_asset(f'weights/{x}')
-seg = [f'{x}-seg' for x in p5]  # classification models
-
-for x in p5 + p6 + cls + seg:
-    attempt_download(f'weights/yolov5{x}.pt')
 
 EOF

ultralytics/yolo/engine/exporter.py

@@ -319,15 +319,20 @@ class Exporter:
     @try_export
     def _export_openvino(self, prefix=colorstr('OpenVINO:')):
         # YOLOv8 OpenVINO export
-        check_requirements('openvino-dev')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
+        check_requirements('openvino-dev>=2022.3')  # requires openvino-dev: https://pypi.org/project/openvino-dev/
         import openvino.runtime as ov  # noqa
+        from openvino.tools import mo  # noqa
 
         LOGGER.info(f'\n{prefix} starting export with openvino {ov.__version__}...')
         f = str(self.file).replace(self.file.suffix, f'_openvino_model{os.sep}')
         f_onnx = self.file.with_suffix('.onnx')
+        f_ov = str(Path(f) / self.file.with_suffix('.xml').name)
 
-        cmd = f"mo --input_model {f_onnx} --output_dir {f} {'--compress_to_fp16' * self.args.half}"
+        ov_model = mo.convert_model(f_onnx,
-        subprocess.run(cmd.split(), check=True, env=os.environ)  # export
+                                    model_name=self.pretty_name,
+                                    framework="onnx",
+                                    compress_to_fp16=self.args.half)  # export
+        ov.serialize(ov_model, f_ov)  # save
         yaml_save(Path(f) / self.file.with_suffix('.yaml').name, self.metadata)  # add metadata.yaml
         return f, None
 

ultralytics/yolo/utils/checks.py

@@ -58,7 +58,13 @@ def check_imgsz(imgsz, stride=32, min_dim=1, floor=0):
     stride = int(stride.max() if isinstance(stride, torch.Tensor) else stride)
 
     # Convert image size to list if it is an integer
-    imgsz = [imgsz] if isinstance(imgsz, int) else list(imgsz)
+    if isinstance(imgsz, int):
+        imgsz = [imgsz]
+    elif isinstance(imgsz, (list, tuple)):
+        imgsz = list(imgsz)
+    else:
+        raise TypeError(f"'imgsz={imgsz}' is of invalid type {type(imgsz).__name__}. "
+                        f"Valid imgsz types are int i.e. 'imgsz=640' or list i.e. 'imgsz=[640,640]'")
 
     # Make image size a multiple of the stride
     sz = [max(math.ceil(x / stride) * stride, floor) for x in imgsz]

ultralytics/yolo/utils/downloads.py

@@ -1,6 +1,7 @@
 # Ultralytics YOLO 🚀, GPL-3.0 license
 
 import contextlib
+import re
 import subprocess
 from itertools import repeat
 from multiprocessing.pool import ThreadPool
@@ -118,7 +119,18 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
         response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api
         return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
 
-    file = Path(str(file).strip().replace("'", ''))
+    # YOLOv3/5u updates
+    file = str(file)
+    if 'yolov3' in file or 'yolov5' in file and 'u' not in file:
+        original_file = file
+        file = re.sub(r"(.*yolov5([nsmlx]))\.pt", "\\1u.pt", file)  # i.e. yolov5n.pt -> yolov5nu.pt
+        file = re.sub(r"(.*yolov3(|-tiny|-spp))\.pt", "\\1u.pt", file)  # i.e. yolov3-spp.pt -> yolov3-sppu.pt
+        if file != original_file:
+            LOGGER.info(f"PRO TIP 💡 Replace 'model={original_file}' with new 'model={file}'.\nYOLOv5 'u' models are "
+                        f"trained with https://github.com/ultralytics/ultralytics and feature improved performance vs "
+                        f"standard YOLOv5 models trained with https://github.com/ultralytics/yolov5.\n")
+
+    file = Path(file.strip().replace("'", ''))
     if file.exists():
         return str(file)
     elif (SETTINGS['weights_dir'] / file).exists():
@@ -136,7 +148,9 @@ def attempt_download_asset(file, repo='ultralytics/assets', release='v0.0.0'):
             return file
 
         # GitHub assets
-        assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
+        assets = [f'yolov8{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')] + \
+                 [f'yolov5{size}u.pt' for size in 'nsmlx'] + \
+                 [f'yolov3{size}u.pt' for size in ('', '-spp', '-tiny')]
         try:
             tag, assets = github_assets(repo, release)
         except Exception:

ultralytics/yolo/v8/segment/predict.py

@@ -72,15 +72,11 @@ class SegmentationPredictor(DetectionPredictor):
             im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(self.device).permute(2, 0, 1).flip(0).contiguous() /
             255 if self.args.retina_masks else im[idx])
 
-        # Segments
-        if self.args.save_txt:
-            segments = mask.segments
-
         # Write results
         for j, d in enumerate(reversed(det)):
             cls, conf = d.cls.squeeze(), d.conf.squeeze()
             if self.args.save_txt:  # Write to file
-                seg = segments[j].copy()
+                seg = mask.segments[len(det) - j - 1].copy()  # reversed mask.segments
                 seg = seg.reshape(-1)  # (n,2) to (n*2)
                 line = (cls, *seg, conf) if self.args.save_conf else (cls, *seg)  # label format
                 with open(f'{self.txt_path}.txt', 'a') as f: