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ultralytics 8.0.174 Tuner plots and improvements (#4799)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher
GitHub
parent
dfe6dfb1d2
commit
16ce193d6e
@ -133,6 +133,10 @@ keywords: Ultralytics, Utils, utilitarian functions, colorstr, yaml_save, set_lo
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## ::: ultralytics.utils.colorstr
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<br><br>
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---
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## ::: ultralytics.utils.remove_colorstr
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<br><br>
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---
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## ::: ultralytics.utils.threaded
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<br><br>
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@ -33,6 +33,14 @@ keywords: Ultralytics, plotting, utils, color annotation, label plotting, image
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## ::: ultralytics.utils.plotting.plot_results
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<br><br>
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---
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## ::: ultralytics.utils.plotting.plt_color_scatter
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<br><br>
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---
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## ::: ultralytics.utils.plotting.plot_tune_results
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<br><br>
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---
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## ::: ultralytics.utils.plotting.output_to_target
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<br><br>
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@ -94,8 +94,8 @@ def test_model_ray_tune():
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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def test_model_tune():
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YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', imgsz=32, epochs=1, iterations=2, device='cpu')
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YOLO('yolov8n-cls.pt').tune(data='imagenet10', imgsz=32, epochs=1, iterations=2, device='cpu')
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YOLO('yolov8n-pose.pt').tune(data='coco8-pose.yaml', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
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YOLO('yolov8n-cls.pt').tune(data='imagenet10', plots=False, imgsz=32, epochs=1, iterations=2, device='cpu')
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@pytest.mark.skipif(not CUDA_IS_AVAILABLE, reason='CUDA is not available')
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@ -107,27 +107,22 @@ def test_pycocotools():
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# Download annotations after each dataset downloads first
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url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
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validator = DetectionValidator(args={'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64})
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args = {'model': 'yolov8n.pt', 'data': 'coco8.yaml', 'save_json': True, 'imgsz': 64}
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validator = DetectionValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8/annotations')
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_ = validator.eval_json(validator.stats)
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validator = SegmentationValidator(args={
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'model': 'yolov8n-seg.pt',
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'data': 'coco8-seg.yaml',
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'save_json': True,
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'imgsz': 64})
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args = {'model': 'yolov8n-seg.pt', 'data': 'coco8-seg.yaml', 'save_json': True, 'imgsz': 64}
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validator = SegmentationValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}instances_val2017.json', dir=DATASETS_DIR / 'coco8-seg/annotations')
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_ = validator.eval_json(validator.stats)
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validator = PoseValidator(args={
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'model': 'yolov8n-pose.pt',
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'data': 'coco8-pose.yaml',
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'save_json': True,
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'imgsz': 64})
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args = {'model': 'yolov8n-pose.pt', 'data': 'coco8-pose.yaml', 'save_json': True, 'imgsz': 64}
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validator = PoseValidator(args=args)
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validator()
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validator.is_coco = True
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download(f'{url}person_keypoints_val2017.json', dir=DATASETS_DIR / 'coco8-pose/annotations')
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@ -1,6 +1,6 @@
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# Ultralytics YOLO 🚀, AGPL-3.0 license
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__version__ = '8.0.173'
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__version__ = '8.0.174'
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from ultralytics.models import RTDETR, SAM, YOLO
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from ultralytics.models.fastsam import FastSAM
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@ -358,7 +358,7 @@ def entrypoint(debug=''):
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if '=' in a:
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try:
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k, v = parse_key_value_pair(a)
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if k == 'cfg': # custom.yaml passed
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if k == 'cfg' and v is not None: # custom.yaml passed
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LOGGER.info(f'Overriding {DEFAULT_CFG_PATH} with {v}')
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overrides = {k: val for k, val in yaml_load(checks.check_yaml(v)).items() if k != 'cfg'}
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else:
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@ -361,7 +361,7 @@ class Model:
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else:
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from .tuner import Tuner
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custom = {'plots': False, 'save': False} # method defaults
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custom = {} # method defaults
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args = {**self.overrides, **custom, **kwargs, 'mode': 'train'} # highest priority args on the right
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return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations)
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@ -423,7 +423,10 @@ class BaseTrainer:
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self.run_callbacks('teardown')
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def save_model(self):
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"""Save model checkpoints based on various conditions."""
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"""Save model training checkpoints with additional metadata."""
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import pandas as pd # scope for faster startup
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metrics = {**self.metrics, **{'fitness': self.fitness}}
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results = {k.strip(): v for k, v in pd.read_csv(self.save_dir / 'results.csv').to_dict(orient='list').items()}
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ckpt = {
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'epoch': self.epoch,
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'best_fitness': self.best_fitness,
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@ -432,22 +435,17 @@ class BaseTrainer:
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'updates': self.ema.updates,
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'optimizer': self.optimizer.state_dict(),
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'train_args': vars(self.args), # save as dict
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'train_metrics': metrics,
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'train_results': results,
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'date': datetime.now().isoformat(),
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'version': __version__}
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# Use dill (if exists) to serialize the lambda functions where pickle does not do this
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try:
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import dill as pickle
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except ImportError:
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import pickle
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# Save last, best and delete
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torch.save(ckpt, self.last, pickle_module=pickle)
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# Save last and best
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torch.save(ckpt, self.last)
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if self.best_fitness == self.fitness:
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torch.save(ckpt, self.best, pickle_module=pickle)
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if (self.epoch > 0) and (self.save_period > 0) and (self.epoch % self.save_period == 0):
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torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt', pickle_module=pickle)
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del ckpt
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torch.save(ckpt, self.best)
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if (self.save_period > 0) and (self.epoch > 0) and (self.epoch % self.save_period == 0):
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torch.save(ckpt, self.wdir / f'epoch{self.epoch}.pt')
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@staticmethod
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def get_dataset(data):
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@ -654,6 +652,9 @@ class BaseTrainer:
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g = [], [], [] # optimizer parameter groups
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bn = tuple(v for k, v in nn.__dict__.items() if 'Norm' in k) # normalization layers, i.e. BatchNorm2d()
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if name == 'auto':
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LOGGER.info(f"{colorstr('optimizer:')} 'optimizer=auto' found, "
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f"ignoring 'lr0={self.args.lr0}' and 'momentum={self.args.momentum}' and "
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f"determining best 'optimizer', 'lr0' and 'momentum' automatically... ")
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nc = getattr(model, 'nc', 10) # number of classes
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lr_fit = round(0.002 * 5 / (4 + nc), 6) # lr0 fit equation to 6 decimal places
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name, lr, momentum = ('SGD', 0.01, 0.9) if iterations > 10000 else ('AdamW', lr_fit, 0.9)
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@ -13,18 +13,20 @@ Example:
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from ultralytics import YOLO
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model = YOLO('yolov8n.pt')
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model.tune(data='coco8.yaml', imgsz=640, epochs=100, iterations=10)
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model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
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```
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"""
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import random
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import shutil
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import subprocess
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import time
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from copy import deepcopy
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import numpy as np
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import torch
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from ultralytics import YOLO
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from ultralytics.cfg import get_cfg, get_save_dir
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from ultralytics.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, yaml_print, yaml_save
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from ultralytics.utils import DEFAULT_CFG, LOGGER, callbacks, colorstr, remove_colorstr, yaml_print, yaml_save
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from ultralytics.utils.plotting import plot_tune_results
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class Tuner:
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@ -37,7 +39,7 @@ class Tuner:
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Attributes:
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space (dict): Hyperparameter search space containing bounds and scaling factors for mutation.
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tune_dir (Path): Directory where evolution logs and results will be saved.
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evolve_csv (Path): Path to the CSV file where evolution logs are saved.
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tune_csv (Path): Path to the CSV file where evolution logs are saved.
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Methods:
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_mutate(hyp: dict) -> dict:
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@ -52,7 +54,7 @@ class Tuner:
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from ultralytics import YOLO
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model = YOLO('yolov8n.pt')
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model.tune(data='coco8.yaml', imgsz=640, epochs=100, iterations=10, val=False, cache=True)
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model.tune(data='coco8.yaml', epochs=10, iterations=300, optimizer='AdamW', plots=False, save=False, val=False)
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```
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"""
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@ -64,22 +66,23 @@ class Tuner:
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args (dict, optional): Configuration for hyperparameter evolution.
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"""
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self.args = get_cfg(overrides=args)
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self.space = { # key: (min, max, gain(optionaL))
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self.space = { # key: (min, max, gain(optional))
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# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
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'lr0': (1e-5, 1e-1),
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'lrf': (0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
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'momentum': (0.6, 0.98, 0.3), # SGD momentum/Adam beta1
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'lrf': (0.0001, 0.1), # final OneCycleLR learning rate (lr0 * lrf)
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'momentum': (0.7, 0.98, 0.3), # SGD momentum/Adam beta1
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'weight_decay': (0.0, 0.001), # optimizer weight decay 5e-4
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'warmup_epochs': (0.0, 5.0), # warmup epochs (fractions ok)
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'warmup_momentum': (0.0, 0.95), # warmup initial momentum
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'box': (0.02, 0.2), # box loss gain
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'box': (1.0, 20.0), # box loss gain
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'cls': (0.2, 4.0), # cls loss gain (scale with pixels)
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'dfl': (0.4, 6.0), # dfl loss gain
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'hsv_h': (0.0, 0.1), # image HSV-Hue augmentation (fraction)
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'hsv_s': (0.0, 0.9), # image HSV-Saturation augmentation (fraction)
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'hsv_v': (0.0, 0.9), # image HSV-Value augmentation (fraction)
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'degrees': (0.0, 45.0), # image rotation (+/- deg)
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'translate': (0.0, 0.9), # image translation (+/- fraction)
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'scale': (0.0, 0.9), # image scale (+/- gain)
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'scale': (0.0, 0.95), # image scale (+/- gain)
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'shear': (0.0, 10.0), # image shear (+/- deg)
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'perspective': (0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
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'flipud': (0.0, 1.0), # image flip up-down (probability)
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@ -87,11 +90,13 @@ class Tuner:
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'mosaic': (0.0, 1.0), # image mixup (probability)
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'mixup': (0.0, 1.0), # image mixup (probability)
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'copy_paste': (0.0, 1.0)} # segment copy-paste (probability)
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self.tune_dir = get_save_dir(self.args, name='_tune')
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self.evolve_csv = self.tune_dir / 'evolve.csv'
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self.tune_dir = get_save_dir(self.args, name='tune')
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self.tune_csv = self.tune_dir / 'tune_results.csv'
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self.callbacks = _callbacks or callbacks.get_default_callbacks()
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self.prefix = colorstr('Tuner: ')
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callbacks.add_integration_callbacks(self)
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LOGGER.info(f"Initialized Tuner instance with 'tune_dir={self.tune_dir}'.")
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LOGGER.info(f"{self.prefix}Initialized Tuner instance with 'tune_dir={self.tune_dir}'\n"
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f'{self.prefix}💡 Learn about tuning at https://docs.ultralytics.com/guides/hyperparameter-tuning')
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def _mutate(self, parent='single', n=5, mutation=0.8, sigma=0.2):
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"""
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@ -106,9 +111,9 @@ class Tuner:
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Returns:
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(dict): A dictionary containing mutated hyperparameters.
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"""
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if self.evolve_csv.exists(): # if evolve.csv exists: select best hyps and mutate
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if self.tune_csv.exists(): # if CSV file exists: select best hyps and mutate
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# Select parent(s)
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x = np.loadtxt(self.evolve_csv, ndmin=2, delimiter=',', skiprows=1)
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x = np.loadtxt(self.tune_csv, ndmin=2, delimiter=',', skiprows=1)
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fitness = x[:, 0] # first column
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n = min(n, len(x)) # number of previous results to consider
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x = x[np.argsort(-fitness)][:n] # top n mutations
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@ -139,7 +144,7 @@ class Tuner:
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return hyp
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def __call__(self, model=None, iterations=10, prefix=colorstr('Tuner:')):
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def __call__(self, model=None, iterations=10, cleanup=True):
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"""
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Executes the hyperparameter evolution process when the Tuner instance is called.
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@ -152,54 +157,68 @@ class Tuner:
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Args:
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model (Model): A pre-initialized YOLO model to be used for training.
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iterations (int): The number of generations to run the evolution for.
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cleanup (bool): Whether to delete iteration weights to reduce storage space used during tuning.
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Note:
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The method utilizes the `self.evolve_csv` Path object to read and log hyperparameters and fitness scores.
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The method utilizes the `self.tune_csv` Path object to read and log hyperparameters and fitness scores.
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Ensure this path is set correctly in the Tuner instance.
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"""
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t0 = time.time()
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best_save_dir, best_metrics = None, None
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self.tune_dir.mkdir(parents=True, exist_ok=True)
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(self.tune_dir / 'weights').mkdir(parents=True, exist_ok=True)
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for i in range(iterations):
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# Mutate hyperparameters
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mutated_hyp = self._mutate()
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LOGGER.info(f'{prefix} Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}')
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LOGGER.info(f'{self.prefix}Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}')
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metrics = {}
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train_args = {**vars(self.args), **mutated_hyp}
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save_dir = get_save_dir(get_cfg(train_args))
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try:
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# Train YOLO model with mutated hyperparameters
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train_args = {**vars(self.args), **mutated_hyp}
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results = (deepcopy(model) or YOLO(self.args.model)).train(**train_args)
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fitness = results.fitness
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except Exception as e:
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LOGGER.warning(f'WARNING ❌️ training failure for hyperparameter tuning iteration {i}\n{e}')
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fitness = 0.0
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# Train YOLO model with mutated hyperparameters (run in subprocess to avoid dataloader hang)
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weights_dir = save_dir / 'weights'
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cmd = ['yolo', 'train', *(f'{k}={v}' for k, v in train_args.items())]
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assert subprocess.run(cmd, check=True).returncode == 0, 'training failed'
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ckpt_file = weights_dir / ('best.pt' if (weights_dir / 'best.pt').exists() else 'last.pt')
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metrics = torch.load(ckpt_file)['train_metrics']
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# Save results and mutated_hyp to evolve_csv
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except Exception as e:
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LOGGER.warning(f'WARNING ❌️ training failure for hyperparameter tuning iteration {i + 1}\n{e}')
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# Save results and mutated_hyp to CSV
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fitness = metrics.get('fitness', 0.0)
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log_row = [round(fitness, 5)] + [mutated_hyp[k] for k in self.space.keys()]
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headers = '' if self.evolve_csv.exists() else (','.join(['fitness_score'] + list(self.space.keys())) + '\n')
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with open(self.evolve_csv, 'a') as f:
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headers = '' if self.tune_csv.exists() else (','.join(['fitness'] + list(self.space.keys())) + '\n')
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with open(self.tune_csv, 'a') as f:
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f.write(headers + ','.join(map(str, log_row)) + '\n')
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# Print tuning results
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x = np.loadtxt(self.evolve_csv, ndmin=2, delimiter=',', skiprows=1)
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# Get best results
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x = np.loadtxt(self.tune_csv, ndmin=2, delimiter=',', skiprows=1)
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fitness = x[:, 0] # first column
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best_idx = fitness.argmax()
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best_is_current = best_idx == i
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if best_is_current:
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best_save_dir = results.save_dir
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best_metrics = {k: round(v, 5) for k, v in results.results_dict.items()}
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header = (f'{prefix} {i + 1} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
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f'{prefix} Results saved to {colorstr("bold", self.tune_dir)}\n'
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f'{prefix} Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
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f'{prefix} Best fitness metrics are {best_metrics}\n'
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f'{prefix} Best fitness model is {best_save_dir}\n'
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f'{prefix} Best fitness hyperparameters are printed below.\n')
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best_save_dir = save_dir
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best_metrics = {k: round(v, 5) for k, v in metrics.items()}
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for ckpt in weights_dir.glob('*.pt'):
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shutil.copy2(ckpt, self.tune_dir / 'weights')
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elif cleanup:
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shutil.rmtree(ckpt_file.parent) # remove iteration weights/ dir to reduce storage space
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# Plot tune results
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plot_tune_results(self.tune_csv)
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# Save and print tune results
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header = (f'{self.prefix}{i + 1}/{iterations} iterations complete ✅ ({time.time() - t0:.2f}s)\n'
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f'{self.prefix}Results saved to {colorstr("bold", self.tune_dir)}\n'
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f'{self.prefix}Best fitness={fitness[best_idx]} observed at iteration {best_idx + 1}\n'
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f'{self.prefix}Best fitness metrics are {best_metrics}\n'
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f'{self.prefix}Best fitness model is {best_save_dir}\n'
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f'{self.prefix}Best fitness hyperparameters are printed below.\n')
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LOGGER.info('\n' + header)
|
||||
|
||||
# Save turning results
|
||||
data = {k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())}
|
||||
header = header.replace(prefix, '#').replace('[1m/', '').replace('[0m', '') + '\n'
|
||||
yaml_save(self.tune_dir / 'best.yaml', data=data, header=header)
|
||||
yaml_print(self.tune_dir / 'best.yaml')
|
||||
data = {k: float(x[best_idx, i + 1]) for i, k in enumerate(self.space.keys())}
|
||||
yaml_save(self.tune_dir / 'best_hyperparameters.yaml',
|
||||
data=data,
|
||||
header=remove_colorstr(header.replace(self.prefix, '# ')) + '\n')
|
||||
yaml_print(self.tune_dir / 'best_hyperparameters.yaml')
|
||||
|
||||
@ -117,8 +117,7 @@ class HUBTrainingSession:
|
||||
|
||||
if data['status'] == 'new': # new model to start training
|
||||
self.train_args = {
|
||||
# TODO: deprecate 'batch_size' key for 'batch' in 3Q23
|
||||
'batch': data['batch' if ('batch' in data) else 'batch_size'],
|
||||
'batch': data['batch'],
|
||||
'epochs': data['epochs'],
|
||||
'imgsz': data['imgsz'],
|
||||
'patience': data['patience'],
|
||||
|
||||
@ -635,7 +635,33 @@ SETTINGS_YAML = USER_CONFIG_DIR / 'settings.yaml'
|
||||
|
||||
|
||||
def colorstr(*input):
|
||||
"""Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e. colorstr('blue', 'hello world')."""
|
||||
"""
|
||||
Colors a string based on the provided color and style arguments. Utilizes ANSI escape codes.
|
||||
See https://en.wikipedia.org/wiki/ANSI_escape_code for more details.
|
||||
|
||||
This function can be called in two ways:
|
||||
- colorstr('color', 'style', 'your string')
|
||||
- colorstr('your string')
|
||||
|
||||
In the second form, 'blue' and 'bold' will be applied by default.
|
||||
|
||||
Args:
|
||||
*input (str): A sequence of strings where the first n-1 strings are color and style arguments,
|
||||
and the last string is the one to be colored.
|
||||
|
||||
Supported Colors and Styles:
|
||||
Basic Colors: 'black', 'red', 'green', 'yellow', 'blue', 'magenta', 'cyan', 'white'
|
||||
Bright Colors: 'bright_black', 'bright_red', 'bright_green', 'bright_yellow',
|
||||
'bright_blue', 'bright_magenta', 'bright_cyan', 'bright_white'
|
||||
Misc: 'end', 'bold', 'underline'
|
||||
|
||||
Returns:
|
||||
(str): The input string wrapped with ANSI escape codes for the specified color and style.
|
||||
|
||||
Examples:
|
||||
>>> colorstr('blue', 'bold', 'hello world')
|
||||
>>> '\033[34m\033[1mhello world\033[0m'
|
||||
"""
|
||||
*args, string = input if len(input) > 1 else ('blue', 'bold', input[0]) # color arguments, string
|
||||
colors = {
|
||||
'black': '\033[30m', # basic colors
|
||||
@ -660,6 +686,24 @@ def colorstr(*input):
|
||||
return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
|
||||
|
||||
|
||||
def remove_colorstr(input_string):
|
||||
"""
|
||||
Removes ANSI escape codes from a string, effectively un-coloring it.
|
||||
|
||||
Args:
|
||||
input_string (str): The string to remove color and style from.
|
||||
|
||||
Returns:
|
||||
(str): A new string with all ANSI escape codes removed.
|
||||
|
||||
Examples:
|
||||
>>> remove_colorstr(colorstr('blue', 'bold', 'hello world'))
|
||||
>>> 'hello world'
|
||||
"""
|
||||
ansi_escape = re.compile(r'\x1B(?:[@-Z\\-_]|\[[0-?]*[ -/]*[@-~])')
|
||||
return ansi_escape.sub('', input_string)
|
||||
|
||||
|
||||
class TryExcept(contextlib.ContextDecorator):
|
||||
"""YOLOv8 TryExcept class. Usage: @TryExcept() decorator or 'with TryExcept():' context manager."""
|
||||
|
||||
|
||||
@ -498,13 +498,23 @@ def plot_images(images,
|
||||
@plt_settings()
|
||||
def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False, classify=False, on_plot=None):
|
||||
"""
|
||||
Plot training results from results CSV file.
|
||||
Plot training results from a results CSV file. The function supports various types of data including segmentation,
|
||||
pose estimation, and classification. Plots are saved as 'results.png' in the directory where the CSV is located.
|
||||
|
||||
Args:
|
||||
file (str, optional): Path to the CSV file containing the training results. Defaults to 'path/to/results.csv'.
|
||||
dir (str, optional): Directory where the CSV file is located if 'file' is not provided. Defaults to ''.
|
||||
segment (bool, optional): Flag to indicate if the data is for segmentation. Defaults to False.
|
||||
pose (bool, optional): Flag to indicate if the data is for pose estimation. Defaults to False.
|
||||
classify (bool, optional): Flag to indicate if the data is for classification. Defaults to False.
|
||||
on_plot (callable, optional): Callback function to be executed after plotting. Takes filename as an argument.
|
||||
Defaults to None.
|
||||
|
||||
Example:
|
||||
```python
|
||||
from ultralytics.utils.plotting import plot_results
|
||||
|
||||
plot_results('path/to/results.csv')
|
||||
plot_results('path/to/results.csv', segment=True)
|
||||
```
|
||||
"""
|
||||
import pandas as pd
|
||||
@ -548,6 +558,92 @@ def plot_results(file='path/to/results.csv', dir='', segment=False, pose=False,
|
||||
on_plot(fname)
|
||||
|
||||
|
||||
def plt_color_scatter(v, f, bins=20, cmap='viridis', alpha=0.8, edgecolors='none'):
|
||||
"""
|
||||
Plots a scatter plot with points colored based on a 2D histogram.
|
||||
|
||||
Args:
|
||||
v (array-like): Values for the x-axis.
|
||||
f (array-like): Values for the y-axis.
|
||||
bins (int, optional): Number of bins for the histogram. Defaults to 20.
|
||||
cmap (str, optional): Colormap for the scatter plot. Defaults to 'viridis'.
|
||||
alpha (float, optional): Alpha for the scatter plot. Defaults to 0.8.
|
||||
edgecolors (str, optional): Edge colors for the scatter plot. Defaults to 'none'.
|
||||
|
||||
Examples:
|
||||
>>> v = np.random.rand(100)
|
||||
>>> f = np.random.rand(100)
|
||||
>>> plt_color_scatter(v, f)
|
||||
"""
|
||||
|
||||
# Calculate 2D histogram and corresponding colors
|
||||
hist, xedges, yedges = np.histogram2d(v, f, bins=bins)
|
||||
colors = [
|
||||
hist[min(np.digitize(v[i], xedges, right=True) - 1, hist.shape[0] - 1),
|
||||
min(np.digitize(f[i], yedges, right=True) - 1, hist.shape[1] - 1)] for i in range(len(v))]
|
||||
|
||||
# Scatter plot
|
||||
plt.scatter(v, f, c=colors, cmap=cmap, alpha=alpha, edgecolors=edgecolors)
|
||||
|
||||
|
||||
def plot_tune_results(csv_file='tune_results.csv'):
|
||||
"""
|
||||
Plot the evolution results stored in an 'tune_results.csv' file. The function generates a scatter plot for each key
|
||||
in the CSV, color-coded based on fitness scores. The best-performing configurations are highlighted on the plots.
|
||||
|
||||
Args:
|
||||
csv_file (str, optional): Path to the CSV file containing the tuning results. Defaults to 'tune_results.csv'.
|
||||
|
||||
Examples:
|
||||
>>> plot_tune_results('path/to/tune_results.csv')
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
from scipy.ndimage import gaussian_filter1d
|
||||
|
||||
# Scatter plots for each hyperparameter
|
||||
csv_file = Path(csv_file)
|
||||
data = pd.read_csv(csv_file)
|
||||
num_metrics_columns = 1
|
||||
keys = [x.strip() for x in data.columns][num_metrics_columns:]
|
||||
x = data.values
|
||||
fitness = x[:, 0] # fitness
|
||||
j = np.argmax(fitness) # max fitness index
|
||||
n = math.ceil(len(keys) ** 0.5) # columns and rows in plot
|
||||
plt.figure(figsize=(10, 10), tight_layout=True)
|
||||
for i, k in enumerate(keys):
|
||||
v = x[:, i + num_metrics_columns]
|
||||
mu = v[j] # best single result
|
||||
plt.subplot(n, n, i + 1)
|
||||
plt_color_scatter(v, fitness, cmap='viridis', alpha=.8, edgecolors='none')
|
||||
plt.plot(mu, fitness.max(), 'k+', markersize=15)
|
||||
plt.title(f'{k} = {mu:.3g}', fontdict={'size': 9}) # limit to 40 characters
|
||||
plt.tick_params(axis='both', labelsize=8) # Set axis label size to 8
|
||||
if i % n != 0:
|
||||
plt.yticks([])
|
||||
|
||||
file = csv_file.with_name('tune_scatter_plots.png') # filename
|
||||
plt.savefig(file, dpi=200)
|
||||
plt.close()
|
||||
LOGGER.info(f'Saved {file}')
|
||||
|
||||
# Fitness vs iteration
|
||||
x = range(1, len(fitness) + 1)
|
||||
plt.figure(figsize=(10, 6), tight_layout=True)
|
||||
plt.plot(x, fitness, marker='o', linestyle='none', label='fitness')
|
||||
plt.plot(x, gaussian_filter1d(fitness, sigma=3), ':', label='smoothed', linewidth=2) # smoothing line
|
||||
plt.title('Fitness vs Iteration')
|
||||
plt.xlabel('Iteration')
|
||||
plt.ylabel('Fitness')
|
||||
plt.grid(True)
|
||||
plt.legend()
|
||||
|
||||
file = csv_file.with_name('tune_fitness.png') # filename
|
||||
plt.savefig(file, dpi=200)
|
||||
plt.close()
|
||||
LOGGER.info(f'Saved {file}')
|
||||
|
||||
|
||||
def output_to_target(output, max_det=300):
|
||||
"""Convert model output to target format [batch_id, class_id, x, y, w, h, conf] for plotting."""
|
||||
targets = []
|
||||
|
||||
@ -395,12 +395,6 @@ def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
|
||||
strip_optimizer(f)
|
||||
```
|
||||
"""
|
||||
# Use dill (if exists) to serialize the lambda functions where pickle does not do this
|
||||
try:
|
||||
import dill as pickle
|
||||
except ImportError:
|
||||
import pickle
|
||||
|
||||
x = torch.load(f, map_location=torch.device('cpu'))
|
||||
if 'model' not in x:
|
||||
LOGGER.info(f'Skipping {f}, not a valid Ultralytics model.')
|
||||
@ -419,8 +413,8 @@ def strip_optimizer(f: Union[str, Path] = 'best.pt', s: str = '') -> None:
|
||||
p.requires_grad = False
|
||||
x['train_args'] = {k: v for k, v in args.items() if k in DEFAULT_CFG_KEYS} # strip non-default keys
|
||||
# x['model'].args = x['train_args']
|
||||
torch.save(x, s or f, pickle_module=pickle)
|
||||
mb = os.path.getsize(s or f) / 1E6 # filesize
|
||||
torch.save(x, s or f)
|
||||
mb = os.path.getsize(s or f) / 1E6 # file size
|
||||
LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB")
|
||||
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user