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>
2025-05-23 05:24:22 +08:00 · 2023-12-10 18:15:59 +01:00 · 2023-12-10 18:15:59 +01:00 · 412eb57fca
commit 412eb57fca
parent 6e660dfaaf
12 changed files with 60 additions and 22 deletions
--- a/docs/en/models/mobile-sam.md
+++ b/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

--- a/docs/en/models/sam.md
+++ b/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

--- a/docs/en/reference/solutions/heatmap.md
+++ b/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>
--- a/docs/mkdocs.yml
+++ b/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
--- a/ultralytics/init.py
+++ b/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
--- a/ultralytics/engine/exporter.py
+++ b/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')
--- a/ultralytics/engine/tuner.py
+++ b/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()
--- a/ultralytics/engine/validator.py
+++ b/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)
--- a/ultralytics/nn/autobackend.py
+++ b/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'):
        """
--- a/ultralytics/solutions/ai_gym.py
+++ b/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
--- a/ultralytics/solutions/heatmap.py
+++ b/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
--- a/ultralytics/solutions/object_counter.py
+++ b/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)