Cli support (#50)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

.github/workflows/ci.yaml

@@ -94,8 +94,8 @@ jobs:
       - name: Test segmentation
         shell: bash  # for Windows compatibility
         run: |
-          python ultralytics/yolo/v8/segment/train.py model=yolov5n-seg.yaml data=coco128-seg.yaml epochs=1 img_size=64
+          yolo task=segment mode=train model=yolov5n-seg.yaml data=coco128-seg.yaml epochs=1 img_size=64
       - name: Test classification
         shell: bash  # for Windows compatibility
         run: |
-          python ultralytics/yolo/v8/classify/train.py model=resnet18 data=mnist160 epochs=1 img_size=32
+          yolo task=classify mode=train model=resnet18 data=mnist160 epochs=1 img_size=32

.gitignore

@@ -131,4 +131,6 @@ dmypy.json
 # datasets and projects
 datasets/
 ultralytics-yolo/
-runs/
+runs/
+
+.DS_Store

README.md

@@ -3,18 +3,32 @@
 ### Install
 
 ```bash
+pip install ultralytics
+```
+Development
+```
 git clone https://github.com/ultralytics/ultralytics
 cd ultralytics
-python -m pip install --upgrade pip wheel
-pip install .  # (dev)
-# pip install ultralytics (production)
+pip install -e .
 ```
 
-### Usage
-
+## Usage
+### 1. CLI
+To simply use the latest Ultralytics YOLO models
+```bash
+yolo task=detect    mode=train  model=s.yaml ...
+          classify       infer        s-cls.yaml
+          segment        val          s-seg.yaml
+```
+### 2. Python SDK
+To use pythonic interface of Ultralytics YOLO model
 ```python
 import ultralytics
-from ultralytics import HUB, YOLO
+from ultralytics import YOLO
 
-ultralytics.checks()
+model = YOLO()
+model.new("s-seg.yaml") # automatically detects task type
+model.load("s-seg.pt") # load checkpoint
+model.train(data="coco128-segments", epochs=1, lr0=0.01, ...)
 ```
+If you're looking to modify YOLO for R&D or to build on top of it, refer to [Using Trainer]() Guide on our docs.

setup.py

@@ -46,4 +46,7 @@ setup(
         "Topic :: Scientific/Engineering :: Artificial Intelligence",
         "Topic :: Scientific/Engineering :: Image Recognition", "Operating System :: POSIX :: Linux",
         "Operating System :: MacOS", "Operating System :: Microsoft :: Windows"],
-    keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics")
+    keywords="machine-learning, deep-learning, vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics",
+    entry_points={
+        'console_scripts': [
+            'yolo = ultralytics.yolo.__init__:cli',],})

ultralytics/__init__.py

@@ -1 +1 @@
-__version__ = "0.0.1.dev0"
+__version__ = "8.0.0.dev0"

ultralytics/yolo/__init__.py

@@ -1,7 +1,39 @@
-import ultralytics.yolo.v8 as v8
+import hydra
+
+import ultralytics
+import ultralytics.yolo.v8 as yolo
 
 from .engine.model import YOLO
-from .engine.trainer import BaseTrainer
+from .engine.trainer import DEFAULT_CONFIG, BaseTrainer
 from .engine.validator import BaseValidator
+from .utils import LOGGER
 
 __all__ = ["BaseTrainer", "BaseValidator", "YOLO"]  # allow simpler import
+
+
+@hydra.main(version_base=None, config_path="utils/configs", config_name="default")
+def cli(cfg):
+    LOGGER.info(f"using Ultralytics YOLO v{ultralytics.__version__}")
+    module_file = None
+    if cfg.task.lower() == "detect":
+        module_file = yolo.detect
+    elif cfg.task.lower() == "segment":
+        module_file = yolo.segment
+    elif cfg.task.lower() == "classify":
+        module_file = yolo.classify
+
+    if not module_file:
+        raise Exception("task not recognized. Choices are `'detect', 'segment', 'classify'`")
+
+    module_function = None
+
+    if cfg.mode.lower() == "train":
+        module_function = module_file.train
+    elif cfg.mode.lower() == "val":
+        module_function = module_file.val
+    elif cfg.mode.lower() == "infer":
+        module_function = module_file.infer
+
+    if not module_function:
+        raise Exception("mode not recognized. Choices are `'train', 'val', 'infer'`")
+    module_function(cfg)

ultralytics/yolo/utils/configs/default.yaml

@@ -1,6 +1,9 @@
 # YOLO 🚀 by Ultralytics, GPL-3.0 license
 # Default training settings and hyperparameters for medium-augmentation COCO training
 
+# Task and Mode
+task: "classify" # choices=['detect', 'segment', 'classify']
+mode: "train" # choice=['train', 'val', 'infer']
 
 # Train settings -------------------------------------------------------------------------------------------------------
 model: null  # i.e. yolov5s.pt, yolo.yaml
@@ -36,7 +39,6 @@ max_det: 300
 half: True
 plots: False
 save_txt: False
-task: 'val'
 
 # Hyperparameters ------------------------------------------------------------------------------------------------------
 lr0: 0.001  # initial learning rate (SGD=1E-2, Adam=1E-3)

ultralytics/yolo/v8/classify/__init__.py

@@ -1,4 +1,4 @@
-from ultralytics.yolo.v8.classify.train import ClassificationTrainer
+from ultralytics.yolo.v8.classify.train import ClassificationTrainer, train
 from ultralytics.yolo.v8.classify.val import ClassificationValidator
 
 __all__ = ["train"]

ultralytics/yolo/v8/segment/__init__.py

@@ -1,2 +1,2 @@
-from ultralytics.yolo.v8.segment.train import SegmentationTrainer
+from ultralytics.yolo.v8.segment.train import SegmentationTrainer, train
 from ultralytics.yolo.v8.segment.val import SegmentationValidator