`ultralytics 8.0.226` Validator Path and Tuner space (#6901)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <chr043416@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: DennisJ <106725464+DennisJcy@users.noreply.github.com>
Co-authored-by: Kirill Ionkin <56236621+kirill-ionkin@users.noreply.github.com>

docs/en/models/mobile-sam.md

@@ -22,7 +22,7 @@ This table presents the available models with their specific pre-trained weights
 
 | Model Type | Pre-trained Weights | Tasks Supported                              | Inference | Validation | Training | Export |
 |------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
-| MobileSAM  | `mobile_sam.pt`     | [Instance Segmentation](../tasks/segment.md) | ✅         | ❌          | ❌        | ✅      |
+| MobileSAM  | `mobile_sam.pt`     | [Instance Segmentation](../tasks/segment.md) | ✅         | ❌          | ❌        | ❌      |
 
 ## Adapting from SAM to MobileSAM
 

docs/en/models/sam.md

@@ -32,8 +32,8 @@ This table presents the available models with their specific pre-trained weights
 
 | Model Type | Pre-trained Weights | Tasks Supported                              | Inference | Validation | Training | Export |
 |------------|---------------------|----------------------------------------------|-----------|------------|----------|--------|
-| SAM base   | `sam_b.pt`          | [Instance Segmentation](../tasks/segment.md) | ✅         | ❌          | ❌        | ✅      |
-| SAM large  | `sam_l.pt`          | [Instance Segmentation](../tasks/segment.md) | ✅         | ❌          | ❌        | ✅      |
+| SAM base   | `sam_b.pt`          | [Instance Segmentation](../tasks/segment.md) | ✅         | ❌          | ❌        | ❌      |
+| SAM large  | `sam_l.pt`          | [Instance Segmentation](../tasks/segment.md) | ✅         | ❌          | ❌        | ❌      |
 
 ## How to Use SAM: Versatility and Power in Image Segmentation
 

docs/en/reference/solutions/heatmap.md

@@ -0,0 +1,16 @@
+---
+description: Explore Ultralytics YOLO's advanced Heatmaps feature designed to highlight areas of interest, providing an immediate, impactful way to interpret spatial information.
+keywords: Ultralytics, YOLO, heatmaps, object tracking, data visualization, real-time tracking, machine learning, object counting, computer vision, retail analytics, YOLOv8, artificial intelligence
+---
+
+# Reference for `ultralytics/solutions/heatmap.py`
+
+!!! Note
+
+    This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/heatmap.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/heatmap.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/solutions/heatmap.py) 🛠️. Thank you 🙏!
+
+<br><br>
+
+## ::: ultralytics.solutions.heatmap.Heatmap
+
+<br><br>

docs/mkdocs.yml

@@ -408,6 +408,7 @@ nav:
       - solutions:
           - ai_gym: reference/solutions/ai_gym.md
           - object_counter: reference/solutions/object_counter.md
+          - heatmap: reference/solutions/heatmap.md
       - trackers:
           - basetrack: reference/trackers/basetrack.md
           - bot_sort: reference/trackers/bot_sort.md

ultralytics/__init__.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 
-__version__ = '8.0.225'
+__version__ = '8.0.226'
 
 from ultralytics.models import RTDETR, SAM, YOLO
 from ultralytics.models.fastsam import FastSAM

ultralytics/engine/exporter.py

@@ -64,7 +64,7 @@ import torch
 from ultralytics.cfg import get_cfg
 from ultralytics.data.dataset import YOLODataset
 from ultralytics.data.utils import check_det_dataset
-from ultralytics.nn.autobackend import check_class_names
+from ultralytics.nn.autobackend import check_class_names, default_class_names
 from ultralytics.nn.modules import C2f, Detect, RTDETRDecoder
 from ultralytics.nn.tasks import DetectionModel, SegmentationModel
 from ultralytics.utils import (ARM64, DEFAULT_CFG, LINUX, LOGGER, MACOS, ROOT, WINDOWS, __version__, callbacks,
@@ -172,6 +172,8 @@ class Exporter:
         self.device = select_device('cpu' if self.args.device is None else self.args.device)
 
         # Checks
+        if not hasattr(model, 'names'):
+            model.names = default_class_names()
         model.names = check_class_names(model.names)
         if self.args.half and onnx and self.device.type == 'cpu':
             LOGGER.warning('WARNING ⚠️ half=True only compatible with GPU export, i.e. use device=0')

ultralytics/engine/tuner.py

@@ -56,6 +56,14 @@ class Tuner:
         model = YOLO('yolov8n.pt')
         model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
         ```
+
+        Tune with custom search space.
+        ```python
+        from ultralytics import YOLO
+
+        model = YOLO('yolov8n.pt')
+        model.tune(space={key1: val1, key2: val2})  # custom search space dictionary
+        ```
     """
 
     def __init__(self, args=DEFAULT_CFG, _callbacks=None):
@@ -65,10 +73,9 @@ class Tuner:
         Args:
             args (dict, optional): Configuration for hyperparameter evolution.
         """
-        self.args = get_cfg(overrides=args)
-        self.space = {  # key: (min, max, gain(optional))
+        self.space = args.pop('space', None) or {  # key: (min, max, gain(optional))
             # 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
-            'lr0': (1e-5, 1e-1),
+            'lr0': (1e-5, 1e-1),  # initial learning rate (i.e. SGD=1E-2, Adam=1E-3)
             'lrf': (0.0001, 0.1),  # final OneCycleLR learning rate (lr0 * lrf)
             'momentum': (0.7, 0.98, 0.3),  # SGD momentum/Adam beta1
             'weight_decay': (0.0, 0.001),  # optimizer weight decay 5e-4
@@ -90,6 +97,7 @@ class Tuner:
             'mosaic': (0.0, 1.0),  # image mixup (probability)
             'mixup': (0.0, 1.0),  # image mixup (probability)
             'copy_paste': (0.0, 1.0)}  # segment copy-paste (probability)
+        self.args = get_cfg(overrides=args)
         self.tune_dir = get_save_dir(self.args, name='tune')
         self.tune_csv = self.tune_dir / 'tune_results.csv'
         self.callbacks = _callbacks or callbacks.get_default_callbacks()

ultralytics/engine/validator.py

@@ -135,7 +135,7 @@ class BaseValidator:
                 self.args.batch = 1  # export.py models default to batch-size 1
                 LOGGER.info(f'Forcing batch=1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models')
 
-            if isinstance(self.args.data, str) and self.args.data.split('.')[-1] in ('yaml', 'yml'):
+            if str(self.args.data).split('.')[-1] in ('yaml', 'yml'):
                 self.data = check_det_dataset(self.args.data)
             elif self.args.task == 'classify':
                 self.data = check_cls_dataset(self.args.data, split=self.args.split)

ultralytics/nn/autobackend.py

@@ -40,6 +40,14 @@ def check_class_names(names):
     return names
 
 
+def default_class_names(data=None):
+    """Applies default class names to an input YAML file or returns numerical class names."""
+    if data:
+        with contextlib.suppress(Exception):
+            return yaml_load(check_yaml(data))['names']
+    return {i: f'class{i}' for i in range(999)}  # return default if above errors
+
+
 class AutoBackend(nn.Module):
     """
     Handles dynamic backend selection for running inference using Ultralytics YOLO models.
@@ -315,7 +323,7 @@ class AutoBackend(nn.Module):
 
         # Check names
         if 'names' not in locals():  # names missing
-            names = self._apply_default_class_names(data)
+            names = default_class_names(data)
         names = check_class_names(names)
 
         # Disable gradients
@@ -479,13 +487,6 @@ class AutoBackend(nn.Module):
             for _ in range(2 if self.jit else 1):
                 self.forward(im)  # warmup
 
-    @staticmethod
-    def _apply_default_class_names(data):
-        """Applies default class names to an input YAML file or returns numerical class names."""
-        with contextlib.suppress(Exception):
-            return yaml_load(check_yaml(data))['names']
-        return {i: f'class{i}' for i in range(999)}  # return default if above errors
-
     @staticmethod
     def _model_type(p='path/to/model.pt'):
         """

ultralytics/solutions/ai_gym.py

@@ -2,6 +2,7 @@
 
 import cv2
 
+from ultralytics.utils.checks import check_imshow
 from ultralytics.utils.plotting import Annotator
 
 
@@ -32,6 +33,9 @@ class AIGym:
         self.view_img = False
         self.annotator = None
 
+        # Check if environment support imshow
+        self.env_check = check_imshow(warn=True)
+
     def set_args(self,
                  kpts_to_check,
                  line_thickness=2,
@@ -120,7 +124,7 @@ class AIGym:
 
             self.annotator.kpts(k, shape=(640, 640), radius=1, kpt_line=True)
 
-        if self.view_img:
+        if self.env_check and self.view_img:
             cv2.imshow('Ultralytics YOLOv8 AI GYM', self.im0)
             if cv2.waitKey(1) & 0xFF == ord('q'):
                 return

ultralytics/solutions/heatmap.py

@@ -5,7 +5,7 @@ from collections import defaultdict
 import cv2
 import numpy as np
 
-from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.checks import check_imshow, check_requirements
 from ultralytics.utils.plotting import Annotator
 
 check_requirements('shapely>=2.0.0')
@@ -50,6 +50,9 @@ class Heatmap:
         self.count_reg_color = (0, 255, 0)
         self.region_thickness = 5
 
+        # Check if environment support imshow
+        self.env_check = check_imshow(warn=True)
+
     def set_args(self,
                  imw,
                  imh,
@@ -155,7 +158,7 @@ class Heatmap:
 
         im0_with_heatmap = cv2.addWeighted(self.im0, 1 - self.heatmap_alpha, heatmap_colored, self.heatmap_alpha, 0)
 
-        if self.view_img:
+        if self.env_check and self.view_img:
             self.display_frames(im0_with_heatmap)
 
         return im0_with_heatmap

ultralytics/solutions/object_counter.py

@@ -4,7 +4,7 @@ from collections import defaultdict
 
 import cv2
 
-from ultralytics.utils.checks import check_requirements
+from ultralytics.utils.checks import check_imshow, check_requirements
 from ultralytics.utils.plotting import Annotator, colors
 
 check_requirements('shapely>=2.0.0')
@@ -46,6 +46,9 @@ class ObjectCounter:
         self.track_thickness = 2
         self.draw_tracks = False
 
+        # Check if environment support imshow
+        self.env_check = check_imshow(warn=True)
+
     def set_args(self,
                  classes_names,
                  reg_pts,
@@ -136,7 +139,7 @@ class ObjectCounter:
                     else:
                         self.in_counts += 1
 
-        if self.view_img:
+        if self.env_check and self.view_img:
             incount_label = 'InCount : ' + f'{self.in_counts}'
             outcount_label = 'OutCount : ' + f'{self.out_counts}'
             self.annotator.count_labels(in_count=incount_label, out_count=outcount_label)