update requirements (#64)

.github/workflows/ci.yml

@@ -13,7 +13,7 @@ jobs:
     strategy:
       matrix:
         operating-system: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: [3.7, 3.8, 3.9, "3.10"]
+        python-version: [3.8, 3.9, "3.10", "3.11"]
       fail-fast: false
 
     steps:
@@ -48,7 +48,7 @@ jobs:
               pip install -r requirements.txt --extra-index-url https://download.pytorch.org/whl/cpu
           fi
           pip install .[tests]
-        shell: bash  # for Windows compatibility
+        shell: bash # for Windows compatibility
 
       - name: Check environment
         run: |
@@ -66,4 +66,4 @@ jobs:
         env:
           HF_TOKEN: ${{ secrets.HF_TOKEN }}
         run: |
-          pytest -s
+          pytest -s

requirements.txt

@@ -1,6 +1,7 @@
 huggingface_hub>=0.12.0
 fire
-ultralytics>=8.0.43,<8.0.44
+ultralytics>=8.0.225,<8.1.0
 sahi>=0.11.11,<0.12.0
 pandas
 roboflow>= 0.2.32
+protobuf>=3.20,<3.21

tests/test_cli.py

@@ -3,7 +3,8 @@
 import subprocess
 from pathlib import Path
 
-from ultralytics.yolo.utils import SETTINGS
+from ultralytics.utils import SETTINGS
+
 
 MODEL = Path(SETTINGS['weights_dir']) / 'yolov8n'
 CFG = 'yolov8n'

tests/test_hf.py

@@ -74,7 +74,7 @@ def test_detection_upload():
     # run following lines if linux and python major == 3 and python minor == 10 (python micor can be anything)
     if platform.system() == 'Linux' and Version(platform.python_version()) >= Version("3.10"):
         print('training started')
-        run('yolo train detect model=yolov8n.pt data=coco8.yaml imgsz=32 epochs=1')
+        run(f'yolo train detect exist_ok=True model=yolov8n.pt data=coco8.yaml imgsz=32 epochs=1 --name={os.getcwd()}/runs/detect/train')
         print('training ended')
         hf_token = os.getenv('HF_TOKEN')
         if hf_token is None:

tests/test_python.py

@@ -6,7 +6,7 @@
 import torch
 from PIL import Image
 from ultralytics import YOLO
-from ultralytics.yolo.utils import ROOT, SETTINGS
+from ultralytics.utils import ROOT, SETTINGS
 from sahi.utils.cv import read_image_as_pil
 import numpy as np
 
@@ -83,7 +83,7 @@ def test_export_torchscript():
     10          TensorFlow.js         tfjs       _web_model  False  False
     11           PaddlePaddle       paddle    _paddle_model   True   True
     """
-    from ultralytics.yolo.engine.exporter import export_formats
+    from ultralytics.engine.exporter import export_formats
     print(export_formats())
 
     model = YOLO(MODEL)

ultralyticsplus/hf_utils.py

@@ -534,7 +534,6 @@ def push_to_hfhub(
         score_top1_acc=score_top1_acc,
         score_top5_acc=score_top5_acc,
         task=task,
-        model_type=model.type,
         thumbnail_text=thumbnail_text,
         custom_tags=custom_tags,
     )

ultralyticsplus/ultralytics_utils.py

@@ -12,7 +12,7 @@
 )
 from ultralytics import YOLO as YOLOBase
 from ultralytics.nn.tasks import attempt_load_one_weight, guess_model_task
-from ultralytics.yolo.utils.downloads import GITHUB_ASSET_STEMS
+from ultralytics.utils.downloads import GITHUB_ASSETS_STEMS
 from ultralyticsplus.hf_utils import download_from_hub
 
 LOGLEVEL = os.environ.get("LOGLEVEL", "INFO").upper()
@@ -43,10 +43,13 @@ def __init__(self, model="yolov8n.yaml", type="v8", hf_token=None) -> None:
         self.cfg = None  # if loaded from *.yaml
         self.ckpt_path = None
         self.overrides = {}  # overrides for trainer object
+
+        # needed so torch can load models
+        super().__init__()
 
         # Load or create new YOLO model
         suffix = Path(model).suffix
-        if not suffix and Path(model).stem in GITHUB_ASSET_STEMS:
+        if not suffix and Path(model).stem in GITHUB_ASSETS_STEMS:
             model, suffix = (
                 Path(model).with_suffix(".pt"),
                 ".pt",
@@ -91,7 +94,7 @@ def _load_from_hf_hub(self, weights: str, hf_token=None):
 def render_result(
     image, 
     model: YOLO,
-    result: "ultralytics.yolo.engine.result.Result",
+    result: "ultralytics.engine.result.Result",
     rect_th: int = 2,
     text_th: int = 2,
 ) -> Image.Image:
@@ -101,7 +104,7 @@ def render_result(
     Args:
         image (str, URL, Image.Image): image to be rendered
         model (YOLO): YOLO model
-        result (ultralytics.yolo.engine.result.Result): output of the model. This is the output of the model.predict() method.
+        result (ultralytics.engine.result.Result): output of the model. This is the output of the model.predict() method.
 
     Returns:
         Image.Image: Image with predictions
@@ -126,7 +129,7 @@ def render_result(
             if masks:
                 img_height = np_image.shape[0]
                 img_width = np_image.shape[1]
-                segments = masks.segments
+                segments = masks.xyn
                 segments = segments[det_ind]  # segments: np.array([[x1, y1], [x2, y2]])
                 # convert segments into full shape
                 segments[:, 0] = segments[:, 0] * img_width
@@ -166,7 +169,7 @@ def render_result(
 
 
 def postprocess_classify_output(
-    model: YOLO, result: "ultralytics.yolo.engine.result.Result"
+    model: YOLO, result: "ultralytics.engine.result.Result"
 ) -> dict:
     """
     Postprocesses the output of classification models
@@ -187,5 +190,5 @@ def postprocess_classify_output(
         raise ValueError("Model names must be either a list or a dict")
 
     for i, label in enumerate(names):
-        output[label] = result.probs[i].item()
+        output[label] = result.probs[i].top1
     return output