yolov3 back to its original space
This commit is contained in:
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# YOLOv3 🚀 by Ultralytics, GPL-3.0 license
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"""
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Logging utils
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"""
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import os
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import warnings
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from threading import Thread
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import pkg_resources as pkg
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from utils.general import colorstr, emojis
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from utils.loggers.wandb.wandb_utils import WandbLogger
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from utils.plots import plot_images, plot_results
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from utils.torch_utils import de_parallel
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LOGGERS = ('csv', 'tb', 'wandb') # text-file, TensorBoard, Weights & Biases
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RANK = int(os.getenv('RANK', -1))
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try:
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import wandb
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assert hasattr(wandb, '__version__') # verify package import not local dir
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if pkg.parse_version(wandb.__version__) >= pkg.parse_version('0.12.2') and RANK in [0, -1]:
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try:
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wandb_login_success = wandb.login(timeout=30)
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except wandb.errors.UsageError: # known non-TTY terminal issue
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wandb_login_success = False
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if not wandb_login_success:
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wandb = None
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except (ImportError, AssertionError):
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wandb = None
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class Loggers():
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# Loggers class
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def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS):
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self.save_dir = save_dir
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self.weights = weights
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self.opt = opt
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self.hyp = hyp
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self.logger = logger # for printing results to console
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self.include = include
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self.keys = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
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'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95', # metrics
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'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
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'x/lr0', 'x/lr1', 'x/lr2'] # params
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for k in LOGGERS:
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setattr(self, k, None) # init empty logger dictionary
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self.csv = True # always log to csv
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# Message
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if not wandb:
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prefix = colorstr('Weights & Biases: ')
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s = f"{prefix}run 'pip install wandb' to automatically track and visualize YOLOv3 🚀 runs (RECOMMENDED)"
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print(emojis(s))
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# TensorBoard
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s = self.save_dir
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if 'tb' in self.include and not self.opt.evolve:
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prefix = colorstr('TensorBoard: ')
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self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/")
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self.tb = SummaryWriter(str(s))
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# W&B
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if wandb and 'wandb' in self.include:
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wandb_artifact_resume = isinstance(self.opt.resume, str) and self.opt.resume.startswith('wandb-artifact://')
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run_id = torch.load(self.weights).get('wandb_id') if self.opt.resume and not wandb_artifact_resume else None
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self.opt.hyp = self.hyp # add hyperparameters
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self.wandb = WandbLogger(self.opt, run_id)
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else:
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self.wandb = None
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def on_pretrain_routine_end(self):
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# Callback runs on pre-train routine end
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paths = self.save_dir.glob('*labels*.jpg') # training labels
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if self.wandb:
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self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]})
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def on_train_batch_end(self, ni, model, imgs, targets, paths, plots, sync_bn):
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# Callback runs on train batch end
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if plots:
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if ni == 0:
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if not sync_bn: # tb.add_graph() --sync known issue https://github.com/ultralytics/yolov5/issues/3754
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with warnings.catch_warnings():
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warnings.simplefilter('ignore') # suppress jit trace warning
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self.tb.add_graph(torch.jit.trace(de_parallel(model), imgs[0:1], strict=False), [])
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if ni < 3:
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f = self.save_dir / f'train_batch{ni}.jpg' # filename
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Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
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if self.wandb and ni == 10:
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files = sorted(self.save_dir.glob('train*.jpg'))
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self.wandb.log({'Mosaics': [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]})
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def on_train_epoch_end(self, epoch):
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# Callback runs on train epoch end
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if self.wandb:
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self.wandb.current_epoch = epoch + 1
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def on_val_image_end(self, pred, predn, path, names, im):
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# Callback runs on val image end
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if self.wandb:
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self.wandb.val_one_image(pred, predn, path, names, im)
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def on_val_end(self):
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# Callback runs on val end
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if self.wandb:
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files = sorted(self.save_dir.glob('val*.jpg'))
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self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]})
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def on_fit_epoch_end(self, vals, epoch, best_fitness, fi):
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# Callback runs at the end of each fit (train+val) epoch
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x = {k: v for k, v in zip(self.keys, vals)} # dict
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if self.csv:
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file = self.save_dir / 'results.csv'
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n = len(x) + 1 # number of cols
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s = '' if file.exists() else (('%20s,' * n % tuple(['epoch'] + self.keys)).rstrip(',') + '\n') # add header
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with open(file, 'a') as f:
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f.write(s + ('%20.5g,' * n % tuple([epoch] + vals)).rstrip(',') + '\n')
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if self.tb:
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for k, v in x.items():
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self.tb.add_scalar(k, v, epoch)
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if self.wandb:
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self.wandb.log(x)
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self.wandb.end_epoch(best_result=best_fitness == fi)
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def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
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# Callback runs on model save event
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if self.wandb:
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if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
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self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
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def on_train_end(self, last, best, plots, epoch, results):
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# Callback runs on training end
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if plots:
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plot_results(file=self.save_dir / 'results.csv') # save results.png
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files = ['results.png', 'confusion_matrix.png', *(f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R'))]
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files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter
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if self.tb:
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import cv2
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for f in files:
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self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats='HWC')
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if self.wandb:
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self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]})
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# Calling wandb.log. TODO: Refactor this into WandbLogger.log_model
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if not self.opt.evolve:
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wandb.log_artifact(str(best if best.exists() else last), type='model',
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name='run_' + self.wandb.wandb_run.id + '_model',
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aliases=['latest', 'best', 'stripped'])
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self.wandb.finish_run()
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else:
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self.wandb.finish_run()
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self.wandb = WandbLogger(self.opt)
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@@ -0,0 +1,271 @@
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# ClearML Integration
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<img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_dark.png#gh-light-mode-only" alt="Clear|ML"><img align="center" src="https://github.com/thepycoder/clearml_screenshots/raw/main/logos_light.png#gh-dark-mode-only" alt="Clear|ML">
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## About ClearML
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[ClearML](https://cutt.ly/yolov5-tutorial-clearml) is an [open-source](https://github.com/allegroai/clearml) toolbox
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designed to save you time ⏱️.
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🔨 Track every YOLOv5 training run in the <b>experiment manager</b>
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🔧 Version and easily access your custom training data with the integrated ClearML <b>Data Versioning Tool</b>
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🔦 <b>Remotely train and monitor</b> your YOLOv5 training runs using ClearML Agent
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🔬 Get the very best mAP using ClearML <b>Hyperparameter Optimization</b>
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🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
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<br />
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And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
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<br />
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<br />
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<br />
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<br />
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## 🦾 Setting Things Up
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To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
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Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-tutorial-clearml) or you can set up your
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own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is
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open-source, so even if you're dealing with sensitive data, you should be good to go!
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1. Install the `clearml` python package:
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```bash
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pip install clearml
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```
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1. Connect the ClearML SDK to the server
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by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings ->
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Workspace -> Create new credentials), then execute the command below and follow the instructions:
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```bash
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clearml-init
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```
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That's it! You're done 😎
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<br />
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## 🚀 Training YOLOv5 With ClearML
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To enable ClearML experiment tracking, simply install the ClearML pip package.
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```bash
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pip install clearml>=1.2.0
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```
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This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and
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stored by the ClearML experiment manager.
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If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py`
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script, by default the project will be called `YOLOv5` and the task `Training`.
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PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
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```bash
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python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
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```
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or with custom project and task name:
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```bash
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python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
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```
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This will capture:
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- Source code + uncommitted changes
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- Installed packages
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- (Hyper)parameters
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- Model files (use `--save-period n` to save a checkpoint every n epochs)
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- Console output
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- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
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- General info such as machine details, runtime, creation date etc.
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- All produced plots such as label correlogram and confusion matrix
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- Images with bounding boxes per epoch
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- Mosaic per epoch
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- Validation images per epoch
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- ...
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That's a lot right? 🤯
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Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom
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columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple
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experiments and directly compare them!
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There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep
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reading if you want to see how that works!
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<br />
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## 🔗 Dataset Version Management
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Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version
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too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there
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yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know
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for sure which data was used in which experiment!
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### Prepare Your Dataset
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The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By
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default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you
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downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder
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structure:
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```
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..
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|_ yolov5
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|_ datasets
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|_ coco128
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|_ images
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|_ labels
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|_ LICENSE
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|_ README.txt
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```
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But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
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Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the
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information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the
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structure of the example yamls.
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Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
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```
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..
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|_ yolov5
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|_ datasets
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|_ coco128
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|_ images
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|_ labels
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|_ coco128.yaml # <---- HERE!
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|_ LICENSE
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|_ README.txt
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```
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### Upload Your Dataset
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To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
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```bash
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cd coco128
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clearml-data sync --project YOLOv5 --name coco128 --folder .
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```
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The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
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```bash
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# Optionally add --parent <parent_dataset_id> if you want to base
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# this version on another dataset version, so no duplicate files are uploaded!
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clearml-data create --name coco128 --project YOLOv5
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clearml-data add --files .
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clearml-data close
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```
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### Run Training Using A ClearML Dataset
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||||
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Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models!
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```bash
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python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
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||||
```
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<br />
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## 👀 Hyperparameter Optimization
|
||||
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Now that we have our experiments and data versioned, it's time to take a look at what we can build on top!
|
||||
|
||||
Using the code information, installed packages and environment details, the experiment itself is now **completely
|
||||
reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just
|
||||
rerun it with these new parameters automatically, this is basically what HPO does!
|
||||
|
||||
To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task
|
||||
has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its
|
||||
hyperparameters.
|
||||
|
||||
You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then
|
||||
just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a
|
||||
remote agent work on it instead.
|
||||
|
||||
```bash
|
||||
# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch
|
||||
pip install optuna
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||||
python utils/loggers/clearml/hpo.py
|
||||
```
|
||||
|
||||
|
||||
|
||||
## 🤯 Remote Execution (advanced)
|
||||
|
||||
Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you
|
||||
have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs.
|
||||
This is where the ClearML Agent comes into play. Check out what the agent can do here:
|
||||
|
||||
- [YouTube video](https://youtu.be/MX3BrXnaULs)
|
||||
- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent)
|
||||
|
||||
In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different
|
||||
machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for
|
||||
incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc.
|
||||
to the experiment manager.
|
||||
|
||||
You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running:
|
||||
|
||||
```bash
|
||||
clearml-agent daemon --queue <queues_to_listen_to> [--docker]
|
||||
```
|
||||
|
||||
### Cloning, Editing And Enqueuing
|
||||
|
||||
With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the
|
||||
hyperparameters? We can do that from the interface too!
|
||||
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||||
🪄 Clone the experiment by right-clicking it
|
||||
|
||||
🎯 Edit the hyperparameters to what you wish them to be
|
||||
|
||||
⏳ Enqueue the task to any of the queues by right-clicking it
|
||||
|
||||
|
||||
|
||||
### Executing A Task Remotely
|
||||
|
||||
Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()`
|
||||
and on execution it will be put into a queue, for the agent to start working on!
|
||||
|
||||
To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the
|
||||
clearml logger has been instantiated:
|
||||
|
||||
```python
|
||||
# ...
|
||||
# Loggers
|
||||
data_dict = None
|
||||
if RANK in {-1, 0}:
|
||||
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
|
||||
if loggers.clearml:
|
||||
loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE
|
||||
# Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML
|
||||
data_dict = loggers.clearml.data_dict
|
||||
# ...
|
||||
```
|
||||
|
||||
When running the training script after this change, python will run the script up until that line, after which it will
|
||||
package the code and send it to the queue instead!
|
||||
|
||||
### Autoscaling workers
|
||||
|
||||
ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your
|
||||
choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue.
|
||||
Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying!
|
||||
|
||||
Check out the autoscalers getting started video below.
|
||||
|
||||
[](https://youtu.be/j4XVMAaUt3E)
|
||||
@@ -0,0 +1,164 @@
|
||||
"""Main Logger class for ClearML experiment tracking."""
|
||||
import glob
|
||||
import re
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import yaml
|
||||
|
||||
from utils.plots import Annotator, colors
|
||||
|
||||
try:
|
||||
import clearml
|
||||
from clearml import Dataset, Task
|
||||
|
||||
assert hasattr(clearml, '__version__') # verify package import not local dir
|
||||
except (ImportError, AssertionError):
|
||||
clearml = None
|
||||
|
||||
|
||||
def construct_dataset(clearml_info_string):
|
||||
"""Load in a clearml dataset and fill the internal data_dict with its contents.
|
||||
"""
|
||||
dataset_id = clearml_info_string.replace('clearml://', '')
|
||||
dataset = Dataset.get(dataset_id=dataset_id)
|
||||
dataset_root_path = Path(dataset.get_local_copy())
|
||||
|
||||
# We'll search for the yaml file definition in the dataset
|
||||
yaml_filenames = list(glob.glob(str(dataset_root_path / '*.yaml')) + glob.glob(str(dataset_root_path / '*.yml')))
|
||||
if len(yaml_filenames) > 1:
|
||||
raise ValueError('More than one yaml file was found in the dataset root, cannot determine which one contains '
|
||||
'the dataset definition this way.')
|
||||
elif len(yaml_filenames) == 0:
|
||||
raise ValueError('No yaml definition found in dataset root path, check that there is a correct yaml file '
|
||||
'inside the dataset root path.')
|
||||
with open(yaml_filenames[0]) as f:
|
||||
dataset_definition = yaml.safe_load(f)
|
||||
|
||||
assert set(dataset_definition.keys()).issuperset(
|
||||
{'train', 'test', 'val', 'nc', 'names'}
|
||||
), "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')"
|
||||
|
||||
data_dict = dict()
|
||||
data_dict['train'] = str(
|
||||
(dataset_root_path / dataset_definition['train']).resolve()) if dataset_definition['train'] else None
|
||||
data_dict['test'] = str(
|
||||
(dataset_root_path / dataset_definition['test']).resolve()) if dataset_definition['test'] else None
|
||||
data_dict['val'] = str(
|
||||
(dataset_root_path / dataset_definition['val']).resolve()) if dataset_definition['val'] else None
|
||||
data_dict['nc'] = dataset_definition['nc']
|
||||
data_dict['names'] = dataset_definition['names']
|
||||
|
||||
return data_dict
|
||||
|
||||
|
||||
class ClearmlLogger:
|
||||
"""Log training runs, datasets, models, and predictions to ClearML.
|
||||
|
||||
This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default,
|
||||
this information includes hyperparameters, system configuration and metrics, model metrics, code information and
|
||||
basic data metrics and analyses.
|
||||
|
||||
By providing additional command line arguments to train.py, datasets,
|
||||
models and predictions can also be logged.
|
||||
"""
|
||||
|
||||
def __init__(self, opt, hyp):
|
||||
"""
|
||||
- Initialize ClearML Task, this object will capture the experiment
|
||||
- Upload dataset version to ClearML Data if opt.upload_dataset is True
|
||||
|
||||
arguments:
|
||||
opt (namespace) -- Commandline arguments for this run
|
||||
hyp (dict) -- Hyperparameters for this run
|
||||
|
||||
"""
|
||||
self.current_epoch = 0
|
||||
# Keep tracked of amount of logged images to enforce a limit
|
||||
self.current_epoch_logged_images = set()
|
||||
# Maximum number of images to log to clearML per epoch
|
||||
self.max_imgs_to_log_per_epoch = 16
|
||||
# Get the interval of epochs when bounding box images should be logged
|
||||
self.bbox_interval = opt.bbox_interval
|
||||
self.clearml = clearml
|
||||
self.task = None
|
||||
self.data_dict = None
|
||||
if self.clearml:
|
||||
self.task = Task.init(
|
||||
project_name=opt.project if opt.project != 'runs/train' else 'YOLOv5',
|
||||
task_name=opt.name if opt.name != 'exp' else 'Training',
|
||||
tags=['YOLOv5'],
|
||||
output_uri=True,
|
||||
reuse_last_task_id=opt.exist_ok,
|
||||
auto_connect_frameworks={'pytorch': False}
|
||||
# We disconnect pytorch auto-detection, because we added manual model save points in the code
|
||||
)
|
||||
# ClearML's hooks will already grab all general parameters
|
||||
# Only the hyperparameters coming from the yaml config file
|
||||
# will have to be added manually!
|
||||
self.task.connect(hyp, name='Hyperparameters')
|
||||
self.task.connect(opt, name='Args')
|
||||
|
||||
# Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent
|
||||
self.task.set_base_docker('ultralytics/yolov5:latest',
|
||||
docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"',
|
||||
docker_setup_bash_script='pip install clearml')
|
||||
|
||||
# Get ClearML Dataset Version if requested
|
||||
if opt.data.startswith('clearml://'):
|
||||
# data_dict should have the following keys:
|
||||
# names, nc (number of classes), test, train, val (all three relative paths to ../datasets)
|
||||
self.data_dict = construct_dataset(opt.data)
|
||||
# Set data to data_dict because wandb will crash without this information and opt is the best way
|
||||
# to give it to them
|
||||
opt.data = self.data_dict
|
||||
|
||||
def log_debug_samples(self, files, title='Debug Samples'):
|
||||
"""
|
||||
Log files (images) as debug samples in the ClearML task.
|
||||
|
||||
arguments:
|
||||
files (List(PosixPath)) a list of file paths in PosixPath format
|
||||
title (str) A title that groups together images with the same values
|
||||
"""
|
||||
for f in files:
|
||||
if f.exists():
|
||||
it = re.search(r'_batch(\d+)', f.name)
|
||||
iteration = int(it.groups()[0]) if it else 0
|
||||
self.task.get_logger().report_image(title=title,
|
||||
series=f.name.replace(it.group(), ''),
|
||||
local_path=str(f),
|
||||
iteration=iteration)
|
||||
|
||||
def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25):
|
||||
"""
|
||||
Draw the bounding boxes on a single image and report the result as a ClearML debug sample.
|
||||
|
||||
arguments:
|
||||
image_path (PosixPath) the path the original image file
|
||||
boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
class_names (dict): dict containing mapping of class int to class name
|
||||
image (Tensor): A torch tensor containing the actual image data
|
||||
"""
|
||||
if len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch and self.current_epoch >= 0:
|
||||
# Log every bbox_interval times and deduplicate for any intermittend extra eval runs
|
||||
if self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images:
|
||||
im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2))
|
||||
annotator = Annotator(im=im, pil=True)
|
||||
for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])):
|
||||
color = colors(i)
|
||||
|
||||
class_name = class_names[int(class_nr)]
|
||||
confidence_percentage = round(float(conf) * 100, 2)
|
||||
label = f'{class_name}: {confidence_percentage}%'
|
||||
|
||||
if conf > conf_threshold:
|
||||
annotator.rectangle(box.cpu().numpy(), outline=color)
|
||||
annotator.box_label(box.cpu().numpy(), label=label, color=color)
|
||||
|
||||
annotated_image = annotator.result()
|
||||
self.task.get_logger().report_image(title='Bounding Boxes',
|
||||
series=image_path.name,
|
||||
iteration=self.current_epoch,
|
||||
image=annotated_image)
|
||||
self.current_epoch_logged_images.add(image_path)
|
||||
@@ -0,0 +1,84 @@
|
||||
from clearml import Task
|
||||
# Connecting ClearML with the current process,
|
||||
# from here on everything is logged automatically
|
||||
from clearml.automation import HyperParameterOptimizer, UniformParameterRange
|
||||
from clearml.automation.optuna import OptimizerOptuna
|
||||
|
||||
task = Task.init(project_name='Hyper-Parameter Optimization',
|
||||
task_name='YOLOv5',
|
||||
task_type=Task.TaskTypes.optimizer,
|
||||
reuse_last_task_id=False)
|
||||
|
||||
# Example use case:
|
||||
optimizer = HyperParameterOptimizer(
|
||||
# This is the experiment we want to optimize
|
||||
base_task_id='<your_template_task_id>',
|
||||
# here we define the hyper-parameters to optimize
|
||||
# Notice: The parameter name should exactly match what you see in the UI: <section_name>/<parameter>
|
||||
# For Example, here we see in the base experiment a section Named: "General"
|
||||
# under it a parameter named "batch_size", this becomes "General/batch_size"
|
||||
# If you have `argparse` for example, then arguments will appear under the "Args" section,
|
||||
# and you should instead pass "Args/batch_size"
|
||||
hyper_parameters=[
|
||||
UniformParameterRange('Hyperparameters/lr0', min_value=1e-5, max_value=1e-1),
|
||||
UniformParameterRange('Hyperparameters/lrf', min_value=0.01, max_value=1.0),
|
||||
UniformParameterRange('Hyperparameters/momentum', min_value=0.6, max_value=0.98),
|
||||
UniformParameterRange('Hyperparameters/weight_decay', min_value=0.0, max_value=0.001),
|
||||
UniformParameterRange('Hyperparameters/warmup_epochs', min_value=0.0, max_value=5.0),
|
||||
UniformParameterRange('Hyperparameters/warmup_momentum', min_value=0.0, max_value=0.95),
|
||||
UniformParameterRange('Hyperparameters/warmup_bias_lr', min_value=0.0, max_value=0.2),
|
||||
UniformParameterRange('Hyperparameters/box', min_value=0.02, max_value=0.2),
|
||||
UniformParameterRange('Hyperparameters/cls', min_value=0.2, max_value=4.0),
|
||||
UniformParameterRange('Hyperparameters/cls_pw', min_value=0.5, max_value=2.0),
|
||||
UniformParameterRange('Hyperparameters/obj', min_value=0.2, max_value=4.0),
|
||||
UniformParameterRange('Hyperparameters/obj_pw', min_value=0.5, max_value=2.0),
|
||||
UniformParameterRange('Hyperparameters/iou_t', min_value=0.1, max_value=0.7),
|
||||
UniformParameterRange('Hyperparameters/anchor_t', min_value=2.0, max_value=8.0),
|
||||
UniformParameterRange('Hyperparameters/fl_gamma', min_value=0.0, max_value=4.0),
|
||||
UniformParameterRange('Hyperparameters/hsv_h', min_value=0.0, max_value=0.1),
|
||||
UniformParameterRange('Hyperparameters/hsv_s', min_value=0.0, max_value=0.9),
|
||||
UniformParameterRange('Hyperparameters/hsv_v', min_value=0.0, max_value=0.9),
|
||||
UniformParameterRange('Hyperparameters/degrees', min_value=0.0, max_value=45.0),
|
||||
UniformParameterRange('Hyperparameters/translate', min_value=0.0, max_value=0.9),
|
||||
UniformParameterRange('Hyperparameters/scale', min_value=0.0, max_value=0.9),
|
||||
UniformParameterRange('Hyperparameters/shear', min_value=0.0, max_value=10.0),
|
||||
UniformParameterRange('Hyperparameters/perspective', min_value=0.0, max_value=0.001),
|
||||
UniformParameterRange('Hyperparameters/flipud', min_value=0.0, max_value=1.0),
|
||||
UniformParameterRange('Hyperparameters/fliplr', min_value=0.0, max_value=1.0),
|
||||
UniformParameterRange('Hyperparameters/mosaic', min_value=0.0, max_value=1.0),
|
||||
UniformParameterRange('Hyperparameters/mixup', min_value=0.0, max_value=1.0),
|
||||
UniformParameterRange('Hyperparameters/copy_paste', min_value=0.0, max_value=1.0)],
|
||||
# this is the objective metric we want to maximize/minimize
|
||||
objective_metric_title='metrics',
|
||||
objective_metric_series='mAP_0.5',
|
||||
# now we decide if we want to maximize it or minimize it (accuracy we maximize)
|
||||
objective_metric_sign='max',
|
||||
# let us limit the number of concurrent experiments,
|
||||
# this in turn will make sure we do dont bombard the scheduler with experiments.
|
||||
# if we have an auto-scaler connected, this, by proxy, will limit the number of machine
|
||||
max_number_of_concurrent_tasks=1,
|
||||
# this is the optimizer class (actually doing the optimization)
|
||||
# Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band)
|
||||
optimizer_class=OptimizerOptuna,
|
||||
# If specified only the top K performing Tasks will be kept, the others will be automatically archived
|
||||
save_top_k_tasks_only=5, # 5,
|
||||
compute_time_limit=None,
|
||||
total_max_jobs=20,
|
||||
min_iteration_per_job=None,
|
||||
max_iteration_per_job=None,
|
||||
)
|
||||
|
||||
# report every 10 seconds, this is way too often, but we are testing here
|
||||
optimizer.set_report_period(10 / 60)
|
||||
# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent
|
||||
# an_optimizer.start_locally(job_complete_callback=job_complete_callback)
|
||||
# set the time limit for the optimization process (2 hours)
|
||||
optimizer.set_time_limit(in_minutes=120.0)
|
||||
# Start the optimization process in the local environment
|
||||
optimizer.start_locally()
|
||||
# wait until process is done (notice we are controlling the optimization process in the background)
|
||||
optimizer.wait()
|
||||
# make sure background optimization stopped
|
||||
optimizer.stop()
|
||||
|
||||
print('We are done, good bye')
|
||||
@@ -0,0 +1,284 @@
|
||||
<img src="https://cdn.comet.ml/img/notebook_logo.png">
|
||||
|
||||
# YOLOv5 with Comet
|
||||
|
||||
This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2)
|
||||
|
||||
# About Comet
|
||||
|
||||
Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and
|
||||
deep learning models.
|
||||
|
||||
Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and
|
||||
visualize your model predictions
|
||||
with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)!
|
||||
Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of
|
||||
all sizes!
|
||||
|
||||
# Getting Started
|
||||
|
||||
## Install Comet
|
||||
|
||||
```shell
|
||||
pip install comet_ml
|
||||
```
|
||||
|
||||
## Configure Comet Credentials
|
||||
|
||||
There are two ways to configure Comet with YOLOv5.
|
||||
|
||||
You can either set your credentials through environment variables
|
||||
|
||||
**Environment Variables**
|
||||
|
||||
```shell
|
||||
export COMET_API_KEY=<Your Comet API Key>
|
||||
export COMET_PROJECT_NAME=<Your Comet Project Name> # This will default to 'yolov5'
|
||||
```
|
||||
|
||||
Or create a `.comet.config` file in your working directory and set your credentials there.
|
||||
|
||||
**Comet Configuration File**
|
||||
|
||||
```
|
||||
[comet]
|
||||
api_key=<Your Comet API Key>
|
||||
project_name=<Your Comet Project Name> # This will default to 'yolov5'
|
||||
```
|
||||
|
||||
## Run the Training Script
|
||||
|
||||
```shell
|
||||
# Train YOLOv5s on COCO128 for 5 epochs
|
||||
python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt
|
||||
```
|
||||
|
||||
That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics.
|
||||
You can visualize and analyze your runs in the Comet UI
|
||||
|
||||
<img width="1920" alt="yolo-ui" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
|
||||
|
||||
# Try out an Example!
|
||||
|
||||
Check out an example of
|
||||
a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
|
||||
|
||||
Or better yet, try it out yourself in this Colab Notebook
|
||||
|
||||
[](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
|
||||
|
||||
# Log automatically
|
||||
|
||||
By default, Comet will log the following items
|
||||
|
||||
## Metrics
|
||||
|
||||
- Box Loss, Object Loss, Classification Loss for the training and validation data
|
||||
- mAP_0.5, mAP_0.5:0.95 metrics for the validation data.
|
||||
- Precision and Recall for the validation data
|
||||
|
||||
## Parameters
|
||||
|
||||
- Model Hyperparameters
|
||||
- All parameters passed through the command line options
|
||||
|
||||
## Visualizations
|
||||
|
||||
- Confusion Matrix of the model predictions on the validation data
|
||||
- Plots for the PR and F1 curves across all classes
|
||||
- Correlogram of the Class Labels
|
||||
|
||||
# Configure Comet Logging
|
||||
|
||||
Comet can be configured to log additional data either through command line flags passed to the training script
|
||||
or through environment variables.
|
||||
|
||||
```shell
|
||||
export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online
|
||||
export COMET_MODEL_NAME=<your model name> #Set the name for the saved model. Defaults to yolov5
|
||||
export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true
|
||||
export COMET_MAX_IMAGE_UPLOADS=<number of allowed images to upload to Comet> # Controls how many total image predictions to log to Comet. Defaults to 100.
|
||||
export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false
|
||||
export COMET_DEFAULT_CHECKPOINT_FILENAME=<your checkpoint filename> # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt'
|
||||
export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false.
|
||||
export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions
|
||||
```
|
||||
|
||||
## Logging Checkpoints with Comet
|
||||
|
||||
Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script.
|
||||
This will save the
|
||||
logged checkpoints to Comet based on the interval value provided by `save-period`
|
||||
|
||||
```shell
|
||||
python train.py \
|
||||
--img 640 \
|
||||
--batch 16 \
|
||||
--epochs 5 \
|
||||
--data coco128.yaml \
|
||||
--weights yolov5s.pt \
|
||||
--save-period 1
|
||||
```
|
||||
|
||||
## Logging Model Predictions
|
||||
|
||||
By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet.
|
||||
|
||||
You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command
|
||||
line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to
|
||||
every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch.
|
||||
|
||||
**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging
|
||||
frequency accordingly.
|
||||
|
||||
Here is
|
||||
an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
|
||||
|
||||
```shell
|
||||
python train.py \
|
||||
--img 640 \
|
||||
--batch 16 \
|
||||
--epochs 5 \
|
||||
--data coco128.yaml \
|
||||
--weights yolov5s.pt \
|
||||
--bbox_interval 2
|
||||
```
|
||||
|
||||
### Controlling the number of Prediction Images logged to Comet
|
||||
|
||||
When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a
|
||||
maximum of 100 validation images are logged. You can increase or decrease this number using
|
||||
the `COMET_MAX_IMAGE_UPLOADS` environment variable.
|
||||
|
||||
```shell
|
||||
env COMET_MAX_IMAGE_UPLOADS=200 python train.py \
|
||||
--img 640 \
|
||||
--batch 16 \
|
||||
--epochs 5 \
|
||||
--data coco128.yaml \
|
||||
--weights yolov5s.pt \
|
||||
--bbox_interval 1
|
||||
```
|
||||
|
||||
### Logging Class Level Metrics
|
||||
|
||||
Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class.
|
||||
|
||||
```shell
|
||||
env COMET_LOG_PER_CLASS_METRICS=true python train.py \
|
||||
--img 640 \
|
||||
--batch 16 \
|
||||
--epochs 5 \
|
||||
--data coco128.yaml \
|
||||
--weights yolov5s.pt
|
||||
```
|
||||
|
||||
## Uploading a Dataset to Comet Artifacts
|
||||
|
||||
If you would like to store your data
|
||||
using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github),
|
||||
you can do so using the `upload_dataset` flag.
|
||||
|
||||
The dataset be organized in the way described in
|
||||
the [YOLOv5 documentation](https://docs.ultralytics.com/tutorials/train-custom-datasets/#3-organize-directories). The
|
||||
dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file.
|
||||
|
||||
```shell
|
||||
python train.py \
|
||||
--img 640 \
|
||||
--batch 16 \
|
||||
--epochs 5 \
|
||||
--data coco128.yaml \
|
||||
--weights yolov5s.pt \
|
||||
--upload_dataset
|
||||
```
|
||||
|
||||
You can find the uploaded dataset in the Artifacts tab in your Comet Workspace
|
||||
<img width="1073" alt="artifact-1" src="https://user-images.githubusercontent.com/7529846/186929193-162718bf-ec7b-4eb9-8c3b-86b3763ef8ea.png">
|
||||
|
||||
You can preview the data directly in the Comet UI.
|
||||
<img width="1082" alt="artifact-2" src="https://user-images.githubusercontent.com/7529846/186929215-432c36a9-c109-4eb0-944b-84c2786590d6.png">
|
||||
|
||||
Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata
|
||||
from your dataset `yaml` file
|
||||
<img width="963" alt="artifact-3" src="https://user-images.githubusercontent.com/7529846/186929256-9d44d6eb-1a19-42de-889a-bcbca3018f2e.png">
|
||||
|
||||
### Using a saved Artifact
|
||||
|
||||
If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to
|
||||
the following Artifact resource URL.
|
||||
|
||||
```
|
||||
# contents of artifact.yaml file
|
||||
path: "comet://<workspace name>/<artifact name>:<artifact version or alias>"
|
||||
```
|
||||
|
||||
Then pass this file to your training script in the following way
|
||||
|
||||
```shell
|
||||
python train.py \
|
||||
--img 640 \
|
||||
--batch 16 \
|
||||
--epochs 5 \
|
||||
--data artifact.yaml \
|
||||
--weights yolov5s.pt
|
||||
```
|
||||
|
||||
Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can
|
||||
see a graph that shows you all the experiments that have used your uploaded dataset.
|
||||
<img width="1391" alt="artifact-4" src="https://user-images.githubusercontent.com/7529846/186929264-4c4014fa-fe51-4f3c-a5c5-f6d24649b1b4.png">
|
||||
|
||||
## Resuming a Training Run
|
||||
|
||||
If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using
|
||||
the `resume` flag and the Comet Run Path.
|
||||
|
||||
The Run Path has the following format `comet://<your workspace name>/<your project name>/<experiment id>`.
|
||||
|
||||
This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint,
|
||||
restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the
|
||||
original run. The resumed run will continue logging to the existing Experiment in the Comet UI
|
||||
|
||||
```shell
|
||||
python train.py \
|
||||
--resume "comet://<your run path>"
|
||||
```
|
||||
|
||||
## Hyperparameter Search with the Comet Optimizer
|
||||
|
||||
YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI.
|
||||
|
||||
### Configuring an Optimizer Sweep
|
||||
|
||||
To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example
|
||||
file has been provided in `utils/loggers/comet/optimizer_config.json`
|
||||
|
||||
```shell
|
||||
python utils/loggers/comet/hpo.py \
|
||||
--comet_optimizer_config "utils/loggers/comet/optimizer_config.json"
|
||||
```
|
||||
|
||||
The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep
|
||||
simply add them after
|
||||
the script.
|
||||
|
||||
```shell
|
||||
python utils/loggers/comet/hpo.py \
|
||||
--comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \
|
||||
--save-period 1 \
|
||||
--bbox_interval 1
|
||||
```
|
||||
|
||||
### Running a Sweep in Parallel
|
||||
|
||||
```shell
|
||||
comet optimizer -j <set number of workers> utils/loggers/comet/hpo.py \
|
||||
utils/loggers/comet/optimizer_config.json"
|
||||
```
|
||||
|
||||
### Visualizing Results
|
||||
|
||||
Comet provides a number of ways to visualize the results of your sweep. Take a look at
|
||||
a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
|
||||
|
||||
<img width="1626" alt="hyperparameter-yolo" src="https://user-images.githubusercontent.com/7529846/186914869-7dc1de14-583f-4323-967b-c9a66a29e495.png">
|
||||
@@ -0,0 +1,508 @@
|
||||
import glob
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[3] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
try:
|
||||
import comet_ml
|
||||
|
||||
# Project Configuration
|
||||
config = comet_ml.config.get_config()
|
||||
COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
|
||||
except (ModuleNotFoundError, ImportError):
|
||||
comet_ml = None
|
||||
COMET_PROJECT_NAME = None
|
||||
|
||||
import PIL
|
||||
import torch
|
||||
import torchvision.transforms as T
|
||||
import yaml
|
||||
|
||||
from utils.dataloaders import img2label_paths
|
||||
from utils.general import check_dataset, scale_boxes, xywh2xyxy
|
||||
from utils.metrics import box_iou
|
||||
|
||||
COMET_PREFIX = 'comet://'
|
||||
|
||||
COMET_MODE = os.getenv('COMET_MODE', 'online')
|
||||
|
||||
# Model Saving Settings
|
||||
COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
|
||||
|
||||
# Dataset Artifact Settings
|
||||
COMET_UPLOAD_DATASET = os.getenv('COMET_UPLOAD_DATASET', 'false').lower() == 'true'
|
||||
|
||||
# Evaluation Settings
|
||||
COMET_LOG_CONFUSION_MATRIX = os.getenv('COMET_LOG_CONFUSION_MATRIX', 'true').lower() == 'true'
|
||||
COMET_LOG_PREDICTIONS = os.getenv('COMET_LOG_PREDICTIONS', 'true').lower() == 'true'
|
||||
COMET_MAX_IMAGE_UPLOADS = int(os.getenv('COMET_MAX_IMAGE_UPLOADS', 100))
|
||||
|
||||
# Confusion Matrix Settings
|
||||
CONF_THRES = float(os.getenv('CONF_THRES', 0.001))
|
||||
IOU_THRES = float(os.getenv('IOU_THRES', 0.6))
|
||||
|
||||
# Batch Logging Settings
|
||||
COMET_LOG_BATCH_METRICS = os.getenv('COMET_LOG_BATCH_METRICS', 'false').lower() == 'true'
|
||||
COMET_BATCH_LOGGING_INTERVAL = os.getenv('COMET_BATCH_LOGGING_INTERVAL', 1)
|
||||
COMET_PREDICTION_LOGGING_INTERVAL = os.getenv('COMET_PREDICTION_LOGGING_INTERVAL', 1)
|
||||
COMET_LOG_PER_CLASS_METRICS = os.getenv('COMET_LOG_PER_CLASS_METRICS', 'false').lower() == 'true'
|
||||
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
|
||||
to_pil = T.ToPILImage()
|
||||
|
||||
|
||||
class CometLogger:
|
||||
"""Log metrics, parameters, source code, models and much more
|
||||
with Comet
|
||||
"""
|
||||
|
||||
def __init__(self, opt, hyp, run_id=None, job_type='Training', **experiment_kwargs) -> None:
|
||||
self.job_type = job_type
|
||||
self.opt = opt
|
||||
self.hyp = hyp
|
||||
|
||||
# Comet Flags
|
||||
self.comet_mode = COMET_MODE
|
||||
|
||||
self.save_model = opt.save_period > -1
|
||||
self.model_name = COMET_MODEL_NAME
|
||||
|
||||
# Batch Logging Settings
|
||||
self.log_batch_metrics = COMET_LOG_BATCH_METRICS
|
||||
self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
|
||||
|
||||
# Dataset Artifact Settings
|
||||
self.upload_dataset = self.opt.upload_dataset if self.opt.upload_dataset else COMET_UPLOAD_DATASET
|
||||
self.resume = self.opt.resume
|
||||
|
||||
# Default parameters to pass to Experiment objects
|
||||
self.default_experiment_kwargs = {
|
||||
'log_code': False,
|
||||
'log_env_gpu': True,
|
||||
'log_env_cpu': True,
|
||||
'project_name': COMET_PROJECT_NAME,}
|
||||
self.default_experiment_kwargs.update(experiment_kwargs)
|
||||
self.experiment = self._get_experiment(self.comet_mode, run_id)
|
||||
|
||||
self.data_dict = self.check_dataset(self.opt.data)
|
||||
self.class_names = self.data_dict['names']
|
||||
self.num_classes = self.data_dict['nc']
|
||||
|
||||
self.logged_images_count = 0
|
||||
self.max_images = COMET_MAX_IMAGE_UPLOADS
|
||||
|
||||
if run_id is None:
|
||||
self.experiment.log_other('Created from', 'YOLOv5')
|
||||
if not isinstance(self.experiment, comet_ml.OfflineExperiment):
|
||||
workspace, project_name, experiment_id = self.experiment.url.split('/')[-3:]
|
||||
self.experiment.log_other(
|
||||
'Run Path',
|
||||
f'{workspace}/{project_name}/{experiment_id}',
|
||||
)
|
||||
self.log_parameters(vars(opt))
|
||||
self.log_parameters(self.opt.hyp)
|
||||
self.log_asset_data(
|
||||
self.opt.hyp,
|
||||
name='hyperparameters.json',
|
||||
metadata={'type': 'hyp-config-file'},
|
||||
)
|
||||
self.log_asset(
|
||||
f'{self.opt.save_dir}/opt.yaml',
|
||||
metadata={'type': 'opt-config-file'},
|
||||
)
|
||||
|
||||
self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
|
||||
|
||||
if hasattr(self.opt, 'conf_thres'):
|
||||
self.conf_thres = self.opt.conf_thres
|
||||
else:
|
||||
self.conf_thres = CONF_THRES
|
||||
if hasattr(self.opt, 'iou_thres'):
|
||||
self.iou_thres = self.opt.iou_thres
|
||||
else:
|
||||
self.iou_thres = IOU_THRES
|
||||
|
||||
self.log_parameters({'val_iou_threshold': self.iou_thres, 'val_conf_threshold': self.conf_thres})
|
||||
|
||||
self.comet_log_predictions = COMET_LOG_PREDICTIONS
|
||||
if self.opt.bbox_interval == -1:
|
||||
self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
|
||||
else:
|
||||
self.comet_log_prediction_interval = self.opt.bbox_interval
|
||||
|
||||
if self.comet_log_predictions:
|
||||
self.metadata_dict = {}
|
||||
self.logged_image_names = []
|
||||
|
||||
self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
|
||||
|
||||
self.experiment.log_others({
|
||||
'comet_mode': COMET_MODE,
|
||||
'comet_max_image_uploads': COMET_MAX_IMAGE_UPLOADS,
|
||||
'comet_log_per_class_metrics': COMET_LOG_PER_CLASS_METRICS,
|
||||
'comet_log_batch_metrics': COMET_LOG_BATCH_METRICS,
|
||||
'comet_log_confusion_matrix': COMET_LOG_CONFUSION_MATRIX,
|
||||
'comet_model_name': COMET_MODEL_NAME,})
|
||||
|
||||
# Check if running the Experiment with the Comet Optimizer
|
||||
if hasattr(self.opt, 'comet_optimizer_id'):
|
||||
self.experiment.log_other('optimizer_id', self.opt.comet_optimizer_id)
|
||||
self.experiment.log_other('optimizer_objective', self.opt.comet_optimizer_objective)
|
||||
self.experiment.log_other('optimizer_metric', self.opt.comet_optimizer_metric)
|
||||
self.experiment.log_other('optimizer_parameters', json.dumps(self.hyp))
|
||||
|
||||
def _get_experiment(self, mode, experiment_id=None):
|
||||
if mode == 'offline':
|
||||
if experiment_id is not None:
|
||||
return comet_ml.ExistingOfflineExperiment(
|
||||
previous_experiment=experiment_id,
|
||||
**self.default_experiment_kwargs,
|
||||
)
|
||||
|
||||
return comet_ml.OfflineExperiment(**self.default_experiment_kwargs,)
|
||||
|
||||
else:
|
||||
try:
|
||||
if experiment_id is not None:
|
||||
return comet_ml.ExistingExperiment(
|
||||
previous_experiment=experiment_id,
|
||||
**self.default_experiment_kwargs,
|
||||
)
|
||||
|
||||
return comet_ml.Experiment(**self.default_experiment_kwargs)
|
||||
|
||||
except ValueError:
|
||||
logger.warning('COMET WARNING: '
|
||||
'Comet credentials have not been set. '
|
||||
'Comet will default to offline logging. '
|
||||
'Please set your credentials to enable online logging.')
|
||||
return self._get_experiment('offline', experiment_id)
|
||||
|
||||
return
|
||||
|
||||
def log_metrics(self, log_dict, **kwargs):
|
||||
self.experiment.log_metrics(log_dict, **kwargs)
|
||||
|
||||
def log_parameters(self, log_dict, **kwargs):
|
||||
self.experiment.log_parameters(log_dict, **kwargs)
|
||||
|
||||
def log_asset(self, asset_path, **kwargs):
|
||||
self.experiment.log_asset(asset_path, **kwargs)
|
||||
|
||||
def log_asset_data(self, asset, **kwargs):
|
||||
self.experiment.log_asset_data(asset, **kwargs)
|
||||
|
||||
def log_image(self, img, **kwargs):
|
||||
self.experiment.log_image(img, **kwargs)
|
||||
|
||||
def log_model(self, path, opt, epoch, fitness_score, best_model=False):
|
||||
if not self.save_model:
|
||||
return
|
||||
|
||||
model_metadata = {
|
||||
'fitness_score': fitness_score[-1],
|
||||
'epochs_trained': epoch + 1,
|
||||
'save_period': opt.save_period,
|
||||
'total_epochs': opt.epochs,}
|
||||
|
||||
model_files = glob.glob(f'{path}/*.pt')
|
||||
for model_path in model_files:
|
||||
name = Path(model_path).name
|
||||
|
||||
self.experiment.log_model(
|
||||
self.model_name,
|
||||
file_or_folder=model_path,
|
||||
file_name=name,
|
||||
metadata=model_metadata,
|
||||
overwrite=True,
|
||||
)
|
||||
|
||||
def check_dataset(self, data_file):
|
||||
with open(data_file) as f:
|
||||
data_config = yaml.safe_load(f)
|
||||
|
||||
if data_config['path'].startswith(COMET_PREFIX):
|
||||
path = data_config['path'].replace(COMET_PREFIX, '')
|
||||
data_dict = self.download_dataset_artifact(path)
|
||||
|
||||
return data_dict
|
||||
|
||||
self.log_asset(self.opt.data, metadata={'type': 'data-config-file'})
|
||||
|
||||
return check_dataset(data_file)
|
||||
|
||||
def log_predictions(self, image, labelsn, path, shape, predn):
|
||||
if self.logged_images_count >= self.max_images:
|
||||
return
|
||||
detections = predn[predn[:, 4] > self.conf_thres]
|
||||
iou = box_iou(labelsn[:, 1:], detections[:, :4])
|
||||
mask, _ = torch.where(iou > self.iou_thres)
|
||||
if len(mask) == 0:
|
||||
return
|
||||
|
||||
filtered_detections = detections[mask]
|
||||
filtered_labels = labelsn[mask]
|
||||
|
||||
image_id = path.split('/')[-1].split('.')[0]
|
||||
image_name = f'{image_id}_curr_epoch_{self.experiment.curr_epoch}'
|
||||
if image_name not in self.logged_image_names:
|
||||
native_scale_image = PIL.Image.open(path)
|
||||
self.log_image(native_scale_image, name=image_name)
|
||||
self.logged_image_names.append(image_name)
|
||||
|
||||
metadata = []
|
||||
for cls, *xyxy in filtered_labels.tolist():
|
||||
metadata.append({
|
||||
'label': f'{self.class_names[int(cls)]}-gt',
|
||||
'score': 100,
|
||||
'box': {
|
||||
'x': xyxy[0],
|
||||
'y': xyxy[1],
|
||||
'x2': xyxy[2],
|
||||
'y2': xyxy[3]},})
|
||||
for *xyxy, conf, cls in filtered_detections.tolist():
|
||||
metadata.append({
|
||||
'label': f'{self.class_names[int(cls)]}',
|
||||
'score': conf * 100,
|
||||
'box': {
|
||||
'x': xyxy[0],
|
||||
'y': xyxy[1],
|
||||
'x2': xyxy[2],
|
||||
'y2': xyxy[3]},})
|
||||
|
||||
self.metadata_dict[image_name] = metadata
|
||||
self.logged_images_count += 1
|
||||
|
||||
return
|
||||
|
||||
def preprocess_prediction(self, image, labels, shape, pred):
|
||||
nl, _ = labels.shape[0], pred.shape[0]
|
||||
|
||||
# Predictions
|
||||
if self.opt.single_cls:
|
||||
pred[:, 5] = 0
|
||||
|
||||
predn = pred.clone()
|
||||
scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])
|
||||
|
||||
labelsn = None
|
||||
if nl:
|
||||
tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
|
||||
scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels
|
||||
labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
|
||||
scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred
|
||||
|
||||
return predn, labelsn
|
||||
|
||||
def add_assets_to_artifact(self, artifact, path, asset_path, split):
|
||||
img_paths = sorted(glob.glob(f'{asset_path}/*'))
|
||||
label_paths = img2label_paths(img_paths)
|
||||
|
||||
for image_file, label_file in zip(img_paths, label_paths):
|
||||
image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
|
||||
|
||||
try:
|
||||
artifact.add(image_file, logical_path=image_logical_path, metadata={'split': split})
|
||||
artifact.add(label_file, logical_path=label_logical_path, metadata={'split': split})
|
||||
except ValueError as e:
|
||||
logger.error('COMET ERROR: Error adding file to Artifact. Skipping file.')
|
||||
logger.error(f'COMET ERROR: {e}')
|
||||
continue
|
||||
|
||||
return artifact
|
||||
|
||||
def upload_dataset_artifact(self):
|
||||
dataset_name = self.data_dict.get('dataset_name', 'yolov5-dataset')
|
||||
path = str((ROOT / Path(self.data_dict['path'])).resolve())
|
||||
|
||||
metadata = self.data_dict.copy()
|
||||
for key in ['train', 'val', 'test']:
|
||||
split_path = metadata.get(key)
|
||||
if split_path is not None:
|
||||
metadata[key] = split_path.replace(path, '')
|
||||
|
||||
artifact = comet_ml.Artifact(name=dataset_name, artifact_type='dataset', metadata=metadata)
|
||||
for key in metadata.keys():
|
||||
if key in ['train', 'val', 'test']:
|
||||
if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
|
||||
continue
|
||||
|
||||
asset_path = self.data_dict.get(key)
|
||||
if asset_path is not None:
|
||||
artifact = self.add_assets_to_artifact(artifact, path, asset_path, key)
|
||||
|
||||
self.experiment.log_artifact(artifact)
|
||||
|
||||
return
|
||||
|
||||
def download_dataset_artifact(self, artifact_path):
|
||||
logged_artifact = self.experiment.get_artifact(artifact_path)
|
||||
artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name)
|
||||
logged_artifact.download(artifact_save_dir)
|
||||
|
||||
metadata = logged_artifact.metadata
|
||||
data_dict = metadata.copy()
|
||||
data_dict['path'] = artifact_save_dir
|
||||
|
||||
metadata_names = metadata.get('names')
|
||||
if type(metadata_names) == dict:
|
||||
data_dict['names'] = {int(k): v for k, v in metadata.get('names').items()}
|
||||
elif type(metadata_names) == list:
|
||||
data_dict['names'] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
|
||||
else:
|
||||
raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
|
||||
|
||||
data_dict = self.update_data_paths(data_dict)
|
||||
return data_dict
|
||||
|
||||
def update_data_paths(self, data_dict):
|
||||
path = data_dict.get('path', '')
|
||||
|
||||
for split in ['train', 'val', 'test']:
|
||||
if data_dict.get(split):
|
||||
split_path = data_dict.get(split)
|
||||
data_dict[split] = (f'{path}/{split_path}' if isinstance(split, str) else [
|
||||
f'{path}/{x}' for x in split_path])
|
||||
|
||||
return data_dict
|
||||
|
||||
def on_pretrain_routine_end(self, paths):
|
||||
if self.opt.resume:
|
||||
return
|
||||
|
||||
for path in paths:
|
||||
self.log_asset(str(path))
|
||||
|
||||
if self.upload_dataset:
|
||||
if not self.resume:
|
||||
self.upload_dataset_artifact()
|
||||
|
||||
return
|
||||
|
||||
def on_train_start(self):
|
||||
self.log_parameters(self.hyp)
|
||||
|
||||
def on_train_epoch_start(self):
|
||||
return
|
||||
|
||||
def on_train_epoch_end(self, epoch):
|
||||
self.experiment.curr_epoch = epoch
|
||||
|
||||
return
|
||||
|
||||
def on_train_batch_start(self):
|
||||
return
|
||||
|
||||
def on_train_batch_end(self, log_dict, step):
|
||||
self.experiment.curr_step = step
|
||||
if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0):
|
||||
self.log_metrics(log_dict, step=step)
|
||||
|
||||
return
|
||||
|
||||
def on_train_end(self, files, save_dir, last, best, epoch, results):
|
||||
if self.comet_log_predictions:
|
||||
curr_epoch = self.experiment.curr_epoch
|
||||
self.experiment.log_asset_data(self.metadata_dict, 'image-metadata.json', epoch=curr_epoch)
|
||||
|
||||
for f in files:
|
||||
self.log_asset(f, metadata={'epoch': epoch})
|
||||
self.log_asset(f'{save_dir}/results.csv', metadata={'epoch': epoch})
|
||||
|
||||
if not self.opt.evolve:
|
||||
model_path = str(best if best.exists() else last)
|
||||
name = Path(model_path).name
|
||||
if self.save_model:
|
||||
self.experiment.log_model(
|
||||
self.model_name,
|
||||
file_or_folder=model_path,
|
||||
file_name=name,
|
||||
overwrite=True,
|
||||
)
|
||||
|
||||
# Check if running Experiment with Comet Optimizer
|
||||
if hasattr(self.opt, 'comet_optimizer_id'):
|
||||
metric = results.get(self.opt.comet_optimizer_metric)
|
||||
self.experiment.log_other('optimizer_metric_value', metric)
|
||||
|
||||
self.finish_run()
|
||||
|
||||
def on_val_start(self):
|
||||
return
|
||||
|
||||
def on_val_batch_start(self):
|
||||
return
|
||||
|
||||
def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):
|
||||
if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)):
|
||||
return
|
||||
|
||||
for si, pred in enumerate(outputs):
|
||||
if len(pred) == 0:
|
||||
continue
|
||||
|
||||
image = images[si]
|
||||
labels = targets[targets[:, 0] == si, 1:]
|
||||
shape = shapes[si]
|
||||
path = paths[si]
|
||||
predn, labelsn = self.preprocess_prediction(image, labels, shape, pred)
|
||||
if labelsn is not None:
|
||||
self.log_predictions(image, labelsn, path, shape, predn)
|
||||
|
||||
return
|
||||
|
||||
def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
|
||||
if self.comet_log_per_class_metrics:
|
||||
if self.num_classes > 1:
|
||||
for i, c in enumerate(ap_class):
|
||||
class_name = self.class_names[c]
|
||||
self.experiment.log_metrics(
|
||||
{
|
||||
'mAP@.5': ap50[i],
|
||||
'mAP@.5:.95': ap[i],
|
||||
'precision': p[i],
|
||||
'recall': r[i],
|
||||
'f1': f1[i],
|
||||
'true_positives': tp[i],
|
||||
'false_positives': fp[i],
|
||||
'support': nt[c]},
|
||||
prefix=class_name)
|
||||
|
||||
if self.comet_log_confusion_matrix:
|
||||
epoch = self.experiment.curr_epoch
|
||||
class_names = list(self.class_names.values())
|
||||
class_names.append('background')
|
||||
num_classes = len(class_names)
|
||||
|
||||
self.experiment.log_confusion_matrix(
|
||||
matrix=confusion_matrix.matrix,
|
||||
max_categories=num_classes,
|
||||
labels=class_names,
|
||||
epoch=epoch,
|
||||
column_label='Actual Category',
|
||||
row_label='Predicted Category',
|
||||
file_name=f'confusion-matrix-epoch-{epoch}.json',
|
||||
)
|
||||
|
||||
def on_fit_epoch_end(self, result, epoch):
|
||||
self.log_metrics(result, epoch=epoch)
|
||||
|
||||
def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
|
||||
if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
|
||||
self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
|
||||
|
||||
def on_params_update(self, params):
|
||||
self.log_parameters(params)
|
||||
|
||||
def finish_run(self):
|
||||
self.experiment.end()
|
||||
@@ -0,0 +1,150 @@
|
||||
import logging
|
||||
import os
|
||||
from urllib.parse import urlparse
|
||||
|
||||
try:
|
||||
import comet_ml
|
||||
except (ModuleNotFoundError, ImportError):
|
||||
comet_ml = None
|
||||
|
||||
import yaml
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
COMET_PREFIX = 'comet://'
|
||||
COMET_MODEL_NAME = os.getenv('COMET_MODEL_NAME', 'yolov5')
|
||||
COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv('COMET_DEFAULT_CHECKPOINT_FILENAME', 'last.pt')
|
||||
|
||||
|
||||
def download_model_checkpoint(opt, experiment):
|
||||
model_dir = f'{opt.project}/{experiment.name}'
|
||||
os.makedirs(model_dir, exist_ok=True)
|
||||
|
||||
model_name = COMET_MODEL_NAME
|
||||
model_asset_list = experiment.get_model_asset_list(model_name)
|
||||
|
||||
if len(model_asset_list) == 0:
|
||||
logger.error(f'COMET ERROR: No checkpoints found for model name : {model_name}')
|
||||
return
|
||||
|
||||
model_asset_list = sorted(
|
||||
model_asset_list,
|
||||
key=lambda x: x['step'],
|
||||
reverse=True,
|
||||
)
|
||||
logged_checkpoint_map = {asset['fileName']: asset['assetId'] for asset in model_asset_list}
|
||||
|
||||
resource_url = urlparse(opt.weights)
|
||||
checkpoint_filename = resource_url.query
|
||||
|
||||
if checkpoint_filename:
|
||||
asset_id = logged_checkpoint_map.get(checkpoint_filename)
|
||||
else:
|
||||
asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME)
|
||||
checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
|
||||
|
||||
if asset_id is None:
|
||||
logger.error(f'COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment')
|
||||
return
|
||||
|
||||
try:
|
||||
logger.info(f'COMET INFO: Downloading checkpoint {checkpoint_filename}')
|
||||
asset_filename = checkpoint_filename
|
||||
|
||||
model_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
|
||||
model_download_path = f'{model_dir}/{asset_filename}'
|
||||
with open(model_download_path, 'wb') as f:
|
||||
f.write(model_binary)
|
||||
|
||||
opt.weights = model_download_path
|
||||
|
||||
except Exception as e:
|
||||
logger.warning('COMET WARNING: Unable to download checkpoint from Comet')
|
||||
logger.exception(e)
|
||||
|
||||
|
||||
def set_opt_parameters(opt, experiment):
|
||||
"""Update the opts Namespace with parameters
|
||||
from Comet's ExistingExperiment when resuming a run
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): Namespace of command line options
|
||||
experiment (comet_ml.APIExperiment): Comet API Experiment object
|
||||
"""
|
||||
asset_list = experiment.get_asset_list()
|
||||
resume_string = opt.resume
|
||||
|
||||
for asset in asset_list:
|
||||
if asset['fileName'] == 'opt.yaml':
|
||||
asset_id = asset['assetId']
|
||||
asset_binary = experiment.get_asset(asset_id, return_type='binary', stream=False)
|
||||
opt_dict = yaml.safe_load(asset_binary)
|
||||
for key, value in opt_dict.items():
|
||||
setattr(opt, key, value)
|
||||
opt.resume = resume_string
|
||||
|
||||
# Save hyperparameters to YAML file
|
||||
# Necessary to pass checks in training script
|
||||
save_dir = f'{opt.project}/{experiment.name}'
|
||||
os.makedirs(save_dir, exist_ok=True)
|
||||
|
||||
hyp_yaml_path = f'{save_dir}/hyp.yaml'
|
||||
with open(hyp_yaml_path, 'w') as f:
|
||||
yaml.dump(opt.hyp, f)
|
||||
opt.hyp = hyp_yaml_path
|
||||
|
||||
|
||||
def check_comet_weights(opt):
|
||||
"""Downloads model weights from Comet and updates the
|
||||
weights path to point to saved weights location
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): Command Line arguments passed
|
||||
to YOLOv5 training script
|
||||
|
||||
Returns:
|
||||
None/bool: Return True if weights are successfully downloaded
|
||||
else return None
|
||||
"""
|
||||
if comet_ml is None:
|
||||
return
|
||||
|
||||
if isinstance(opt.weights, str):
|
||||
if opt.weights.startswith(COMET_PREFIX):
|
||||
api = comet_ml.API()
|
||||
resource = urlparse(opt.weights)
|
||||
experiment_path = f'{resource.netloc}{resource.path}'
|
||||
experiment = api.get(experiment_path)
|
||||
download_model_checkpoint(opt, experiment)
|
||||
return True
|
||||
|
||||
return None
|
||||
|
||||
|
||||
def check_comet_resume(opt):
|
||||
"""Restores run parameters to its original state based on the model checkpoint
|
||||
and logged Experiment parameters.
|
||||
|
||||
Args:
|
||||
opt (argparse.Namespace): Command Line arguments passed
|
||||
to YOLOv5 training script
|
||||
|
||||
Returns:
|
||||
None/bool: Return True if the run is restored successfully
|
||||
else return None
|
||||
"""
|
||||
if comet_ml is None:
|
||||
return
|
||||
|
||||
if isinstance(opt.resume, str):
|
||||
if opt.resume.startswith(COMET_PREFIX):
|
||||
api = comet_ml.API()
|
||||
resource = urlparse(opt.resume)
|
||||
experiment_path = f'{resource.netloc}{resource.path}'
|
||||
experiment = api.get(experiment_path)
|
||||
set_opt_parameters(opt, experiment)
|
||||
download_model_checkpoint(opt, experiment)
|
||||
|
||||
return True
|
||||
|
||||
return None
|
||||
@@ -0,0 +1,118 @@
|
||||
import argparse
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import comet_ml
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[3] # YOLOv5 root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
from train import train
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.general import increment_path
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
# Project Configuration
|
||||
config = comet_ml.config.get_config()
|
||||
COMET_PROJECT_NAME = config.get_string(os.getenv('COMET_PROJECT_NAME'), 'comet.project_name', default='yolov5')
|
||||
|
||||
|
||||
def get_args(known=False):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--weights', type=str, default=ROOT / 'yolov5s.pt', help='initial weights path')
|
||||
parser.add_argument('--cfg', type=str, default='', help='model.yaml path')
|
||||
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='dataset.yaml path')
|
||||
parser.add_argument('--hyp', type=str, default=ROOT / 'data/hyps/hyp.scratch-low.yaml', help='hyperparameters path')
|
||||
parser.add_argument('--epochs', type=int, default=300, help='total training epochs')
|
||||
parser.add_argument('--batch-size', type=int, default=16, help='total batch size for all GPUs, -1 for autobatch')
|
||||
parser.add_argument('--imgsz', '--img', '--img-size', type=int, default=640, help='train, val image size (pixels)')
|
||||
parser.add_argument('--rect', action='store_true', help='rectangular training')
|
||||
parser.add_argument('--resume', nargs='?', const=True, default=False, help='resume most recent training')
|
||||
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
|
||||
parser.add_argument('--noval', action='store_true', help='only validate final epoch')
|
||||
parser.add_argument('--noautoanchor', action='store_true', help='disable AutoAnchor')
|
||||
parser.add_argument('--noplots', action='store_true', help='save no plot files')
|
||||
parser.add_argument('--evolve', type=int, nargs='?', const=300, help='evolve hyperparameters for x generations')
|
||||
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
|
||||
parser.add_argument('--cache', type=str, nargs='?', const='ram', help='--cache images in "ram" (default) or "disk"')
|
||||
parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
|
||||
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||||
parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
|
||||
parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='SGD', help='optimizer')
|
||||
parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
|
||||
parser.add_argument('--workers', type=int, default=8, help='max dataloader workers (per RANK in DDP mode)')
|
||||
parser.add_argument('--project', default=ROOT / 'runs/train', help='save to project/name')
|
||||
parser.add_argument('--name', default='exp', help='save to project/name')
|
||||
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||||
parser.add_argument('--quad', action='store_true', help='quad dataloader')
|
||||
parser.add_argument('--cos-lr', action='store_true', help='cosine LR scheduler')
|
||||
parser.add_argument('--label-smoothing', type=float, default=0.0, help='Label smoothing epsilon')
|
||||
parser.add_argument('--patience', type=int, default=100, help='EarlyStopping patience (epochs without improvement)')
|
||||
parser.add_argument('--freeze', nargs='+', type=int, default=[0], help='Freeze layers: backbone=10, first3=0 1 2')
|
||||
parser.add_argument('--save-period', type=int, default=-1, help='Save checkpoint every x epochs (disabled if < 1)')
|
||||
parser.add_argument('--seed', type=int, default=0, help='Global training seed')
|
||||
parser.add_argument('--local_rank', type=int, default=-1, help='Automatic DDP Multi-GPU argument, do not modify')
|
||||
|
||||
# Weights & Biases arguments
|
||||
parser.add_argument('--entity', default=None, help='W&B: Entity')
|
||||
parser.add_argument('--upload_dataset', nargs='?', const=True, default=False, help='W&B: Upload data, "val" option')
|
||||
parser.add_argument('--bbox_interval', type=int, default=-1, help='W&B: Set bounding-box image logging interval')
|
||||
parser.add_argument('--artifact_alias', type=str, default='latest', help='W&B: Version of dataset artifact to use')
|
||||
|
||||
# Comet Arguments
|
||||
parser.add_argument('--comet_optimizer_config', type=str, help='Comet: Path to a Comet Optimizer Config File.')
|
||||
parser.add_argument('--comet_optimizer_id', type=str, help='Comet: ID of the Comet Optimizer sweep.')
|
||||
parser.add_argument('--comet_optimizer_objective', type=str, help="Comet: Set to 'minimize' or 'maximize'.")
|
||||
parser.add_argument('--comet_optimizer_metric', type=str, help='Comet: Metric to Optimize.')
|
||||
parser.add_argument('--comet_optimizer_workers',
|
||||
type=int,
|
||||
default=1,
|
||||
help='Comet: Number of Parallel Workers to use with the Comet Optimizer.')
|
||||
|
||||
return parser.parse_known_args()[0] if known else parser.parse_args()
|
||||
|
||||
|
||||
def run(parameters, opt):
|
||||
hyp_dict = {k: v for k, v in parameters.items() if k not in ['epochs', 'batch_size']}
|
||||
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
|
||||
opt.batch_size = parameters.get('batch_size')
|
||||
opt.epochs = parameters.get('epochs')
|
||||
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
train(hyp_dict, opt, device, callbacks=Callbacks())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
opt = get_args(known=True)
|
||||
|
||||
opt.weights = str(opt.weights)
|
||||
opt.cfg = str(opt.cfg)
|
||||
opt.data = str(opt.data)
|
||||
opt.project = str(opt.project)
|
||||
|
||||
optimizer_id = os.getenv('COMET_OPTIMIZER_ID')
|
||||
if optimizer_id is None:
|
||||
with open(opt.comet_optimizer_config) as f:
|
||||
optimizer_config = json.load(f)
|
||||
optimizer = comet_ml.Optimizer(optimizer_config)
|
||||
else:
|
||||
optimizer = comet_ml.Optimizer(optimizer_id)
|
||||
|
||||
opt.comet_optimizer_id = optimizer.id
|
||||
status = optimizer.status()
|
||||
|
||||
opt.comet_optimizer_objective = status['spec']['objective']
|
||||
opt.comet_optimizer_metric = status['spec']['metric']
|
||||
|
||||
logger.info('COMET INFO: Starting Hyperparameter Sweep')
|
||||
for parameter in optimizer.get_parameters():
|
||||
run(parameter['parameters'], opt)
|
||||
@@ -0,0 +1,147 @@
|
||||
📚 This guide explains how to use **Weights & Biases** (W&B) with YOLOv3 🚀. UPDATED 29 September 2021.
|
||||
* [About Weights & Biases](#about-weights-&-biases)
|
||||
* [First-Time Setup](#first-time-setup)
|
||||
* [Viewing runs](#viewing-runs)
|
||||
* [Advanced Usage: Dataset Versioning and Evaluation](#advanced-usage)
|
||||
* [Reports: Share your work with the world!](#reports)
|
||||
|
||||
## About Weights & Biases
|
||||
Think of [W&B](https://wandb.ai/site?utm_campaign=repo_yolo_wandbtutorial) like GitHub for machine learning models. With a few lines of code, save everything you need to debug, compare and reproduce your models — architecture, hyperparameters, git commits, model weights, GPU usage, and even datasets and predictions.
|
||||
|
||||
Used by top researchers including teams at OpenAI, Lyft, Github, and MILA, W&B is part of the new standard of best practices for machine learning. How W&B can help you optimize your machine learning workflows:
|
||||
|
||||
* [Debug](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Free-2) model performance in real time
|
||||
* [GPU usage](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#System-4) visualized automatically
|
||||
* [Custom charts](https://wandb.ai/wandb/customizable-charts/reports/Powerful-Custom-Charts-To-Debug-Model-Peformance--VmlldzoyNzY4ODI) for powerful, extensible visualization
|
||||
* [Share insights](https://wandb.ai/wandb/getting-started/reports/Visualize-Debug-Machine-Learning-Models--VmlldzoyNzY5MDk#Share-8) interactively with collaborators
|
||||
* [Optimize hyperparameters](https://docs.wandb.com/sweeps) efficiently
|
||||
* [Track](https://docs.wandb.com/artifacts) datasets, pipelines, and production models
|
||||
|
||||
## First-Time Setup
|
||||
<details open>
|
||||
<summary> Toggle Details </summary>
|
||||
When you first train, W&B will prompt you to create a new account and will generate an **API key** for you. If you are an existing user you can retrieve your key from https://wandb.ai/authorize. This key is used to tell W&B where to log your data. You only need to supply your key once, and then it is remembered on the same device.
|
||||
|
||||
W&B will create a cloud **project** (default is 'YOLOv3') for your training runs, and each new training run will be provided a unique run **name** within that project as project/name. You can also manually set your project and run name as:
|
||||
|
||||
```shell
|
||||
$ python train.py --project ... --name ...
|
||||
```
|
||||
|
||||
YOLOv3 notebook example: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov3"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
<img width="960" alt="Screen Shot 2021-09-29 at 10 23 13 PM" src="https://user-images.githubusercontent.com/26833433/135392431-1ab7920a-c49d-450a-b0b0-0c86ec86100e.png">
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
## Viewing Runs
|
||||
<details open>
|
||||
<summary> Toggle Details </summary>
|
||||
Run information streams from your environment to the W&B cloud console as you train. This allows you to monitor and even cancel runs in <b>realtime</b> . All important information is logged:
|
||||
|
||||
* Training & Validation losses
|
||||
* Metrics: Precision, Recall, mAP@0.5, mAP@0.5:0.95
|
||||
* Learning Rate over time
|
||||
* A bounding box debugging panel, showing the training progress over time
|
||||
* GPU: Type, **GPU Utilization**, power, temperature, **CUDA memory usage**
|
||||
* System: Disk I/0, CPU utilization, RAM memory usage
|
||||
* Your trained model as W&B Artifact
|
||||
* Environment: OS and Python types, Git repository and state, **training command**
|
||||
|
||||
<p align="center"><img width="900" alt="Weights & Biases dashboard" src="https://user-images.githubusercontent.com/26833433/135390767-c28b050f-8455-4004-adb0-3b730386e2b2.png"></p>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
## Advanced Usage
|
||||
You can leverage W&B artifacts and Tables integration to easily visualize and manage your datasets, models and training evaluations. Here are some quick examples to get you started.
|
||||
<details open>
|
||||
<h3>1. Visualize and Version Datasets</h3>
|
||||
Log, visualize, dynamically query, and understand your data with <a href='https://docs.wandb.ai/guides/data-vis/tables'>W&B Tables</a>. You can use the following command to log your dataset as a W&B Table. This will generate a <code>{dataset}_wandb.yaml</code> file which can be used to train from dataset artifact.
|
||||
<details>
|
||||
<summary> <b>Usage</b> </summary>
|
||||
<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --project ... --name ... --data .. </code>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
<h3> 2: Train and Log Evaluation simultaneousy </h3>
|
||||
This is an extension of the previous section, but it'll also training after uploading the dataset. <b> This also evaluation Table</b>
|
||||
Evaluation table compares your predictions and ground truths across the validation set for each epoch. It uses the references to the already uploaded datasets,
|
||||
so no images will be uploaded from your system more than once.
|
||||
<details>
|
||||
<summary> <b>Usage</b> </summary>
|
||||
<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data .. --upload_data </code>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
<h3> 3: Train using dataset artifact </h3>
|
||||
When you upload a dataset as described in the first section, you get a new config file with an added `_wandb` to its name. This file contains the information that
|
||||
can be used to train a model directly from the dataset artifact. <b> This also logs evaluation </b>
|
||||
<details>
|
||||
<summary> <b>Usage</b> </summary>
|
||||
<b>Code</b> <code> $ python utils/logger/wandb/log_dataset.py --data {data}_wandb.yaml </code>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
<h3> 4: Save model checkpoints as artifacts </h3>
|
||||
To enable saving and versioning checkpoints of your experiment, pass `--save_period n` with the base cammand, where `n` represents checkpoint interval.
|
||||
You can also log both the dataset and model checkpoints simultaneously. If not passed, only the final model will be logged
|
||||
|
||||
<details>
|
||||
<summary> <b>Usage</b> </summary>
|
||||
<b>Code</b> <code> $ python train.py --save_period 1 </code>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
</details>
|
||||
|
||||
<h3> 5: Resume runs from checkpoint artifacts. </h3>
|
||||
Any run can be resumed using artifacts if the <code>--resume</code> argument starts with <code>wandb-artifact://</code> prefix followed by the run path, i.e, <code>wandb-artifact://username/project/runid </code>. This doesn't require the model checkpoint to be present on the local system.
|
||||
|
||||
<details>
|
||||
<summary> <b>Usage</b> </summary>
|
||||
<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
<h3> 6: Resume runs from dataset artifact & checkpoint artifacts. </h3>
|
||||
<b> Local dataset or model checkpoints are not required. This can be used to resume runs directly on a different device </b>
|
||||
The syntax is same as the previous section, but you'll need to lof both the dataset and model checkpoints as artifacts, i.e, set bot <code>--upload_dataset</code> or
|
||||
train from <code>_wandb.yaml</code> file and set <code>--save_period</code>
|
||||
|
||||
<details>
|
||||
<summary> <b>Usage</b> </summary>
|
||||
<b>Code</b> <code> $ python train.py --resume wandb-artifact://{run_path} </code>
|
||||
|
||||
|
||||
</details>
|
||||
|
||||
</details>
|
||||
|
||||
|
||||
<h3> Reports </h3>
|
||||
W&B Reports can be created from your saved runs for sharing online. Once a report is created you will receive a link you can use to publically share your results. Here is an example report created from the COCO128 tutorial trainings of all YOLOv5 models ([link](https://wandb.ai/glenn-jocher/yolov5_tutorial/reports/YOLOv5-COCO128-Tutorial-Results--VmlldzozMDI5OTY)).
|
||||
|
||||
<img width="900" alt="Weights & Biases Reports" src="https://user-images.githubusercontent.com/26833433/135394029-a17eaf86-c6c1-4b1d-bb80-b90e83aaffa7.png">
|
||||
|
||||
|
||||
## Environments
|
||||
|
||||
YOLOv3 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):
|
||||
|
||||
- **Google Colab and Kaggle** notebooks with free GPU: <a href="https://colab.research.google.com/github/ultralytics/yolov3/blob/master/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> <a href="https://www.kaggle.com/ultralytics/yolov3"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
|
||||
- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/GCP-Quickstart)
|
||||
- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/AWS-Quickstart)
|
||||
- **Docker Image**. See [Docker Quickstart Guide](https://github.com/ultralytics/yolov3/wiki/Docker-Quickstart) <a href="https://hub.docker.com/r/ultralytics/yolov3"><img src="https://img.shields.io/docker/pulls/ultralytics/yolov3?logo=docker" alt="Docker Pulls"></a>
|
||||
|
||||
|
||||
## Status
|
||||
|
||||
|
||||
|
||||
If this badge is green, all [YOLOv3 GitHub Actions](https://github.com/ultralytics/yolov3/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv3 training ([train.py](https://github.com/ultralytics/yolov3/blob/master/train.py)), validation ([val.py](https://github.com/ultralytics/yolov3/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov3/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov3/blob/master/export.py)) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
|
||||
@@ -0,0 +1,27 @@
|
||||
import argparse
|
||||
|
||||
from wandb_utils import WandbLogger
|
||||
|
||||
from utils.general import LOGGER
|
||||
|
||||
WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
|
||||
|
||||
|
||||
def create_dataset_artifact(opt):
|
||||
logger = WandbLogger(opt, None, job_type='Dataset Creation') # TODO: return value unused
|
||||
if not logger.wandb:
|
||||
LOGGER.info("install wandb using `pip install wandb` to log the dataset")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
|
||||
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
|
||||
parser.add_argument('--project', type=str, default='YOLOv3', help='name of W&B Project')
|
||||
parser.add_argument('--entity', default=None, help='W&B entity')
|
||||
parser.add_argument('--name', type=str, default='log dataset', help='name of W&B run')
|
||||
|
||||
opt = parser.parse_args()
|
||||
opt.resume = False # Explicitly disallow resume check for dataset upload job
|
||||
|
||||
create_dataset_artifact(opt)
|
||||
@@ -0,0 +1,41 @@
|
||||
import sys
|
||||
from pathlib import Path
|
||||
|
||||
import wandb
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[3] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
from train import parse_opt, train
|
||||
from utils.callbacks import Callbacks
|
||||
from utils.general import increment_path
|
||||
from utils.torch_utils import select_device
|
||||
|
||||
|
||||
def sweep():
|
||||
wandb.init()
|
||||
# Get hyp dict from sweep agent
|
||||
hyp_dict = vars(wandb.config).get("_items")
|
||||
|
||||
# Workaround: get necessary opt args
|
||||
opt = parse_opt(known=True)
|
||||
opt.batch_size = hyp_dict.get("batch_size")
|
||||
opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
|
||||
opt.epochs = hyp_dict.get("epochs")
|
||||
opt.nosave = True
|
||||
opt.data = hyp_dict.get("data")
|
||||
opt.weights = str(opt.weights)
|
||||
opt.cfg = str(opt.cfg)
|
||||
opt.data = str(opt.data)
|
||||
opt.hyp = str(opt.hyp)
|
||||
opt.project = str(opt.project)
|
||||
device = select_device(opt.device, batch_size=opt.batch_size)
|
||||
|
||||
# train
|
||||
train(hyp_dict, opt, device, callbacks=Callbacks())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
sweep()
|
||||
@@ -0,0 +1,143 @@
|
||||
# Hyperparameters for training
|
||||
# To set range-
|
||||
# Provide min and max values as:
|
||||
# parameter:
|
||||
#
|
||||
# min: scalar
|
||||
# max: scalar
|
||||
# OR
|
||||
#
|
||||
# Set a specific list of search space-
|
||||
# parameter:
|
||||
# values: [scalar1, scalar2, scalar3...]
|
||||
#
|
||||
# You can use grid, bayesian and hyperopt search strategy
|
||||
# For more info on configuring sweeps visit - https://docs.wandb.ai/guides/sweeps/configuration
|
||||
|
||||
program: utils/loggers/wandb/sweep.py
|
||||
method: random
|
||||
metric:
|
||||
name: metrics/mAP_0.5
|
||||
goal: maximize
|
||||
|
||||
parameters:
|
||||
# hyperparameters: set either min, max range or values list
|
||||
data:
|
||||
value: "data/coco128.yaml"
|
||||
batch_size:
|
||||
values: [64]
|
||||
epochs:
|
||||
values: [10]
|
||||
|
||||
lr0:
|
||||
distribution: uniform
|
||||
min: 1e-5
|
||||
max: 1e-1
|
||||
lrf:
|
||||
distribution: uniform
|
||||
min: 0.01
|
||||
max: 1.0
|
||||
momentum:
|
||||
distribution: uniform
|
||||
min: 0.6
|
||||
max: 0.98
|
||||
weight_decay:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.001
|
||||
warmup_epochs:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 5.0
|
||||
warmup_momentum:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.95
|
||||
warmup_bias_lr:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.2
|
||||
box:
|
||||
distribution: uniform
|
||||
min: 0.02
|
||||
max: 0.2
|
||||
cls:
|
||||
distribution: uniform
|
||||
min: 0.2
|
||||
max: 4.0
|
||||
cls_pw:
|
||||
distribution: uniform
|
||||
min: 0.5
|
||||
max: 2.0
|
||||
obj:
|
||||
distribution: uniform
|
||||
min: 0.2
|
||||
max: 4.0
|
||||
obj_pw:
|
||||
distribution: uniform
|
||||
min: 0.5
|
||||
max: 2.0
|
||||
iou_t:
|
||||
distribution: uniform
|
||||
min: 0.1
|
||||
max: 0.7
|
||||
anchor_t:
|
||||
distribution: uniform
|
||||
min: 2.0
|
||||
max: 8.0
|
||||
fl_gamma:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.1
|
||||
hsv_h:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.1
|
||||
hsv_s:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.9
|
||||
hsv_v:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.9
|
||||
degrees:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 45.0
|
||||
translate:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.9
|
||||
scale:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.9
|
||||
shear:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 10.0
|
||||
perspective:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 0.001
|
||||
flipud:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 1.0
|
||||
fliplr:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 1.0
|
||||
mosaic:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 1.0
|
||||
mixup:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 1.0
|
||||
copy_paste:
|
||||
distribution: uniform
|
||||
min: 0.0
|
||||
max: 1.0
|
||||
@@ -0,0 +1,532 @@
|
||||
"""Utilities and tools for tracking runs with Weights & Biases."""
|
||||
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
from contextlib import contextmanager
|
||||
from pathlib import Path
|
||||
from typing import Dict
|
||||
|
||||
import pkg_resources as pkg
|
||||
import yaml
|
||||
from tqdm import tqdm
|
||||
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[3] # root directory
|
||||
if str(ROOT) not in sys.path:
|
||||
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||||
|
||||
from utils.datasets import LoadImagesAndLabels, img2label_paths
|
||||
from utils.general import LOGGER, check_dataset, check_file
|
||||
|
||||
try:
|
||||
import wandb
|
||||
|
||||
assert hasattr(wandb, '__version__') # verify package import not local dir
|
||||
except (ImportError, AssertionError):
|
||||
wandb = None
|
||||
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
|
||||
|
||||
|
||||
def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
|
||||
return from_string[len(prefix):]
|
||||
|
||||
|
||||
def check_wandb_config_file(data_config_file):
|
||||
wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path
|
||||
if Path(wandb_config).is_file():
|
||||
return wandb_config
|
||||
return data_config_file
|
||||
|
||||
|
||||
def check_wandb_dataset(data_file):
|
||||
is_trainset_wandb_artifact = False
|
||||
is_valset_wandb_artifact = False
|
||||
if check_file(data_file) and data_file.endswith('.yaml'):
|
||||
with open(data_file, errors='ignore') as f:
|
||||
data_dict = yaml.safe_load(f)
|
||||
is_trainset_wandb_artifact = (isinstance(data_dict['train'], str) and
|
||||
data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX))
|
||||
is_valset_wandb_artifact = (isinstance(data_dict['val'], str) and
|
||||
data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX))
|
||||
if is_trainset_wandb_artifact or is_valset_wandb_artifact:
|
||||
return data_dict
|
||||
else:
|
||||
return check_dataset(data_file)
|
||||
|
||||
|
||||
def get_run_info(run_path):
|
||||
run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
|
||||
run_id = run_path.stem
|
||||
project = run_path.parent.stem
|
||||
entity = run_path.parent.parent.stem
|
||||
model_artifact_name = 'run_' + run_id + '_model'
|
||||
return entity, project, run_id, model_artifact_name
|
||||
|
||||
|
||||
def check_wandb_resume(opt):
|
||||
process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
|
||||
if isinstance(opt.resume, str):
|
||||
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
if RANK not in [-1, 0]: # For resuming DDP runs
|
||||
entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
|
||||
api = wandb.Api()
|
||||
artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
|
||||
modeldir = artifact.download()
|
||||
opt.weights = str(Path(modeldir) / "last.pt")
|
||||
return True
|
||||
return None
|
||||
|
||||
|
||||
def process_wandb_config_ddp_mode(opt):
|
||||
with open(check_file(opt.data), errors='ignore') as f:
|
||||
data_dict = yaml.safe_load(f) # data dict
|
||||
train_dir, val_dir = None, None
|
||||
if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
|
||||
api = wandb.Api()
|
||||
train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
|
||||
train_dir = train_artifact.download()
|
||||
train_path = Path(train_dir) / 'data/images/'
|
||||
data_dict['train'] = str(train_path)
|
||||
|
||||
if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
|
||||
api = wandb.Api()
|
||||
val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
|
||||
val_dir = val_artifact.download()
|
||||
val_path = Path(val_dir) / 'data/images/'
|
||||
data_dict['val'] = str(val_path)
|
||||
if train_dir or val_dir:
|
||||
ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
|
||||
with open(ddp_data_path, 'w') as f:
|
||||
yaml.safe_dump(data_dict, f)
|
||||
opt.data = ddp_data_path
|
||||
|
||||
|
||||
class WandbLogger():
|
||||
"""Log training runs, datasets, models, and predictions to Weights & Biases.
|
||||
|
||||
This logger sends information to W&B at wandb.ai. By default, this information
|
||||
includes hyperparameters, system configuration and metrics, model metrics,
|
||||
and basic data metrics and analyses.
|
||||
|
||||
By providing additional command line arguments to train.py, datasets,
|
||||
models and predictions can also be logged.
|
||||
|
||||
For more on how this logger is used, see the Weights & Biases documentation:
|
||||
https://docs.wandb.com/guides/integrations/yolov5
|
||||
"""
|
||||
|
||||
def __init__(self, opt, run_id=None, job_type='Training'):
|
||||
"""
|
||||
- Initialize WandbLogger instance
|
||||
- Upload dataset if opt.upload_dataset is True
|
||||
- Setup trainig processes if job_type is 'Training'
|
||||
|
||||
arguments:
|
||||
opt (namespace) -- Commandline arguments for this run
|
||||
run_id (str) -- Run ID of W&B run to be resumed
|
||||
job_type (str) -- To set the job_type for this run
|
||||
|
||||
"""
|
||||
# Pre-training routine --
|
||||
self.job_type = job_type
|
||||
self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
|
||||
self.val_artifact, self.train_artifact = None, None
|
||||
self.train_artifact_path, self.val_artifact_path = None, None
|
||||
self.result_artifact = None
|
||||
self.val_table, self.result_table = None, None
|
||||
self.bbox_media_panel_images = []
|
||||
self.val_table_path_map = None
|
||||
self.max_imgs_to_log = 16
|
||||
self.wandb_artifact_data_dict = None
|
||||
self.data_dict = None
|
||||
# It's more elegant to stick to 1 wandb.init call,
|
||||
# but useful config data is overwritten in the WandbLogger's wandb.init call
|
||||
if isinstance(opt.resume, str): # checks resume from artifact
|
||||
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
|
||||
model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
|
||||
assert wandb, 'install wandb to resume wandb runs'
|
||||
# Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
|
||||
self.wandb_run = wandb.init(id=run_id,
|
||||
project=project,
|
||||
entity=entity,
|
||||
resume='allow',
|
||||
allow_val_change=True)
|
||||
opt.resume = model_artifact_name
|
||||
elif self.wandb:
|
||||
self.wandb_run = wandb.init(config=opt,
|
||||
resume="allow",
|
||||
project='YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem,
|
||||
entity=opt.entity,
|
||||
name=opt.name if opt.name != 'exp' else None,
|
||||
job_type=job_type,
|
||||
id=run_id,
|
||||
allow_val_change=True) if not wandb.run else wandb.run
|
||||
if self.wandb_run:
|
||||
if self.job_type == 'Training':
|
||||
if opt.upload_dataset:
|
||||
if not opt.resume:
|
||||
self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
|
||||
|
||||
if opt.resume:
|
||||
# resume from artifact
|
||||
if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
self.data_dict = dict(self.wandb_run.config.data_dict)
|
||||
else: # local resume
|
||||
self.data_dict = check_wandb_dataset(opt.data)
|
||||
else:
|
||||
self.data_dict = check_wandb_dataset(opt.data)
|
||||
self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
|
||||
|
||||
# write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
|
||||
self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict},
|
||||
allow_val_change=True)
|
||||
self.setup_training(opt)
|
||||
|
||||
if self.job_type == 'Dataset Creation':
|
||||
self.data_dict = self.check_and_upload_dataset(opt)
|
||||
|
||||
def check_and_upload_dataset(self, opt):
|
||||
"""
|
||||
Check if the dataset format is compatible and upload it as W&B artifact
|
||||
|
||||
arguments:
|
||||
opt (namespace)-- Commandline arguments for current run
|
||||
|
||||
returns:
|
||||
Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
|
||||
"""
|
||||
assert wandb, 'Install wandb to upload dataset'
|
||||
config_path = self.log_dataset_artifact(opt.data,
|
||||
opt.single_cls,
|
||||
'YOLOv3' if opt.project == 'runs/train' else Path(opt.project).stem)
|
||||
LOGGER.info(f"Created dataset config file {config_path}")
|
||||
with open(config_path, errors='ignore') as f:
|
||||
wandb_data_dict = yaml.safe_load(f)
|
||||
return wandb_data_dict
|
||||
|
||||
def setup_training(self, opt):
|
||||
"""
|
||||
Setup the necessary processes for training YOLO models:
|
||||
- Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
|
||||
- Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
|
||||
- Setup log_dict, initialize bbox_interval
|
||||
|
||||
arguments:
|
||||
opt (namespace) -- commandline arguments for this run
|
||||
|
||||
"""
|
||||
self.log_dict, self.current_epoch = {}, 0
|
||||
self.bbox_interval = opt.bbox_interval
|
||||
if isinstance(opt.resume, str):
|
||||
modeldir, _ = self.download_model_artifact(opt)
|
||||
if modeldir:
|
||||
self.weights = Path(modeldir) / "last.pt"
|
||||
config = self.wandb_run.config
|
||||
opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
|
||||
self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
|
||||
config.hyp
|
||||
data_dict = self.data_dict
|
||||
if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download
|
||||
self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
|
||||
opt.artifact_alias)
|
||||
self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
|
||||
opt.artifact_alias)
|
||||
|
||||
if self.train_artifact_path is not None:
|
||||
train_path = Path(self.train_artifact_path) / 'data/images/'
|
||||
data_dict['train'] = str(train_path)
|
||||
if self.val_artifact_path is not None:
|
||||
val_path = Path(self.val_artifact_path) / 'data/images/'
|
||||
data_dict['val'] = str(val_path)
|
||||
|
||||
if self.val_artifact is not None:
|
||||
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
|
||||
self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
|
||||
self.val_table = self.val_artifact.get("val")
|
||||
if self.val_table_path_map is None:
|
||||
self.map_val_table_path()
|
||||
if opt.bbox_interval == -1:
|
||||
self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
|
||||
train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
|
||||
# Update the the data_dict to point to local artifacts dir
|
||||
if train_from_artifact:
|
||||
self.data_dict = data_dict
|
||||
|
||||
def download_dataset_artifact(self, path, alias):
|
||||
"""
|
||||
download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
|
||||
|
||||
arguments:
|
||||
path -- path of the dataset to be used for training
|
||||
alias (str)-- alias of the artifact to be download/used for training
|
||||
|
||||
returns:
|
||||
(str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
|
||||
is found otherwise returns (None, None)
|
||||
"""
|
||||
if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
|
||||
dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
|
||||
assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
|
||||
datadir = dataset_artifact.download()
|
||||
return datadir, dataset_artifact
|
||||
return None, None
|
||||
|
||||
def download_model_artifact(self, opt):
|
||||
"""
|
||||
download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
|
||||
|
||||
arguments:
|
||||
opt (namespace) -- Commandline arguments for this run
|
||||
"""
|
||||
if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
|
||||
model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
|
||||
assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
|
||||
modeldir = model_artifact.download()
|
||||
epochs_trained = model_artifact.metadata.get('epochs_trained')
|
||||
total_epochs = model_artifact.metadata.get('total_epochs')
|
||||
is_finished = total_epochs is None
|
||||
assert not is_finished, 'training is finished, can only resume incomplete runs.'
|
||||
return modeldir, model_artifact
|
||||
return None, None
|
||||
|
||||
def log_model(self, path, opt, epoch, fitness_score, best_model=False):
|
||||
"""
|
||||
Log the model checkpoint as W&B artifact
|
||||
|
||||
arguments:
|
||||
path (Path) -- Path of directory containing the checkpoints
|
||||
opt (namespace) -- Command line arguments for this run
|
||||
epoch (int) -- Current epoch number
|
||||
fitness_score (float) -- fitness score for current epoch
|
||||
best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
|
||||
"""
|
||||
model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
|
||||
'original_url': str(path),
|
||||
'epochs_trained': epoch + 1,
|
||||
'save period': opt.save_period,
|
||||
'project': opt.project,
|
||||
'total_epochs': opt.epochs,
|
||||
'fitness_score': fitness_score
|
||||
})
|
||||
model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
|
||||
wandb.log_artifact(model_artifact,
|
||||
aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
|
||||
LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
|
||||
|
||||
def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
|
||||
"""
|
||||
Log the dataset as W&B artifact and return the new data file with W&B links
|
||||
|
||||
arguments:
|
||||
data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
|
||||
single_class (boolean) -- train multi-class data as single-class
|
||||
project (str) -- project name. Used to construct the artifact path
|
||||
overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
|
||||
file with _wandb postfix. Eg -> data_wandb.yaml
|
||||
|
||||
returns:
|
||||
the new .yaml file with artifact links. it can be used to start training directly from artifacts
|
||||
"""
|
||||
self.data_dict = check_dataset(data_file) # parse and check
|
||||
data = dict(self.data_dict)
|
||||
nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
|
||||
names = {k: v for k, v in enumerate(names)} # to index dictionary
|
||||
self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
|
||||
data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None
|
||||
self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
|
||||
data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
|
||||
if data.get('train'):
|
||||
data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
|
||||
if data.get('val'):
|
||||
data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
|
||||
path = Path(data_file).stem
|
||||
path = (path if overwrite_config else path + '_wandb') + '.yaml' # updated data.yaml path
|
||||
data.pop('download', None)
|
||||
data.pop('path', None)
|
||||
with open(path, 'w') as f:
|
||||
yaml.safe_dump(data, f)
|
||||
|
||||
if self.job_type == 'Training': # builds correct artifact pipeline graph
|
||||
self.wandb_run.use_artifact(self.val_artifact)
|
||||
self.wandb_run.use_artifact(self.train_artifact)
|
||||
self.val_artifact.wait()
|
||||
self.val_table = self.val_artifact.get('val')
|
||||
self.map_val_table_path()
|
||||
else:
|
||||
self.wandb_run.log_artifact(self.train_artifact)
|
||||
self.wandb_run.log_artifact(self.val_artifact)
|
||||
return path
|
||||
|
||||
def map_val_table_path(self):
|
||||
"""
|
||||
Map the validation dataset Table like name of file -> it's id in the W&B Table.
|
||||
Useful for - referencing artifacts for evaluation.
|
||||
"""
|
||||
self.val_table_path_map = {}
|
||||
LOGGER.info("Mapping dataset")
|
||||
for i, data in enumerate(tqdm(self.val_table.data)):
|
||||
self.val_table_path_map[data[3]] = data[0]
|
||||
|
||||
def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int,str], name: str = 'dataset'):
|
||||
"""
|
||||
Create and return W&B artifact containing W&B Table of the dataset.
|
||||
|
||||
arguments:
|
||||
dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
|
||||
class_to_id -- hash map that maps class ids to labels
|
||||
name -- name of the artifact
|
||||
|
||||
returns:
|
||||
dataset artifact to be logged or used
|
||||
"""
|
||||
# TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
|
||||
artifact = wandb.Artifact(name=name, type="dataset")
|
||||
img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
|
||||
img_files = tqdm(dataset.img_files) if not img_files else img_files
|
||||
for img_file in img_files:
|
||||
if Path(img_file).is_dir():
|
||||
artifact.add_dir(img_file, name='data/images')
|
||||
labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
|
||||
artifact.add_dir(labels_path, name='data/labels')
|
||||
else:
|
||||
artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
|
||||
label_file = Path(img2label_paths([img_file])[0])
|
||||
artifact.add_file(str(label_file),
|
||||
name='data/labels/' + label_file.name) if label_file.exists() else None
|
||||
table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
|
||||
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
|
||||
for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
|
||||
box_data, img_classes = [], {}
|
||||
for cls, *xywh in labels[:, 1:].tolist():
|
||||
cls = int(cls)
|
||||
box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]},
|
||||
"class_id": cls,
|
||||
"box_caption": "%s" % (class_to_id[cls])})
|
||||
img_classes[cls] = class_to_id[cls]
|
||||
boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space
|
||||
table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
|
||||
Path(paths).name)
|
||||
artifact.add(table, name)
|
||||
return artifact
|
||||
|
||||
def log_training_progress(self, predn, path, names):
|
||||
"""
|
||||
Build evaluation Table. Uses reference from validation dataset table.
|
||||
|
||||
arguments:
|
||||
predn (list): list of predictions in the native space in the format - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
path (str): local path of the current evaluation image
|
||||
names (dict(int, str)): hash map that maps class ids to labels
|
||||
"""
|
||||
class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
|
||||
box_data = []
|
||||
total_conf = 0
|
||||
for *xyxy, conf, cls in predn.tolist():
|
||||
if conf >= 0.25:
|
||||
box_data.append(
|
||||
{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
|
||||
"class_id": int(cls),
|
||||
"box_caption": f"{names[cls]} {conf:.3f}",
|
||||
"scores": {"class_score": conf},
|
||||
"domain": "pixel"})
|
||||
total_conf += conf
|
||||
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
|
||||
id = self.val_table_path_map[Path(path).name]
|
||||
self.result_table.add_data(self.current_epoch,
|
||||
id,
|
||||
self.val_table.data[id][1],
|
||||
wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
|
||||
total_conf / max(1, len(box_data))
|
||||
)
|
||||
|
||||
def val_one_image(self, pred, predn, path, names, im):
|
||||
"""
|
||||
Log validation data for one image. updates the result Table if validation dataset is uploaded and log bbox media panel
|
||||
|
||||
arguments:
|
||||
pred (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
predn (list): list of predictions in the native space - [xmin, ymin, xmax, ymax, confidence, class]
|
||||
path (str): local path of the current evaluation image
|
||||
"""
|
||||
if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
|
||||
self.log_training_progress(predn, path, names)
|
||||
|
||||
if len(self.bbox_media_panel_images) < self.max_imgs_to_log and self.current_epoch > 0:
|
||||
if self.current_epoch % self.bbox_interval == 0:
|
||||
box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
|
||||
"class_id": int(cls),
|
||||
"box_caption": f"{names[cls]} {conf:.3f}",
|
||||
"scores": {"class_score": conf},
|
||||
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
|
||||
boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
|
||||
self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
|
||||
|
||||
def log(self, log_dict):
|
||||
"""
|
||||
save the metrics to the logging dictionary
|
||||
|
||||
arguments:
|
||||
log_dict (Dict) -- metrics/media to be logged in current step
|
||||
"""
|
||||
if self.wandb_run:
|
||||
for key, value in log_dict.items():
|
||||
self.log_dict[key] = value
|
||||
|
||||
def end_epoch(self, best_result=False):
|
||||
"""
|
||||
commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
|
||||
|
||||
arguments:
|
||||
best_result (boolean): Boolean representing if the result of this evaluation is best or not
|
||||
"""
|
||||
if self.wandb_run:
|
||||
with all_logging_disabled():
|
||||
if self.bbox_media_panel_images:
|
||||
self.log_dict["BoundingBoxDebugger"] = self.bbox_media_panel_images
|
||||
try:
|
||||
wandb.log(self.log_dict)
|
||||
except BaseException as e:
|
||||
LOGGER.info(f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}")
|
||||
self.wandb_run.finish()
|
||||
self.wandb_run = None
|
||||
|
||||
self.log_dict = {}
|
||||
self.bbox_media_panel_images = []
|
||||
if self.result_artifact:
|
||||
self.result_artifact.add(self.result_table, 'result')
|
||||
wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
|
||||
('best' if best_result else '')])
|
||||
|
||||
wandb.log({"evaluation": self.result_table})
|
||||
self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
|
||||
self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
|
||||
|
||||
def finish_run(self):
|
||||
"""
|
||||
Log metrics if any and finish the current W&B run
|
||||
"""
|
||||
if self.wandb_run:
|
||||
if self.log_dict:
|
||||
with all_logging_disabled():
|
||||
wandb.log(self.log_dict)
|
||||
wandb.run.finish()
|
||||
|
||||
|
||||
@contextmanager
|
||||
def all_logging_disabled(highest_level=logging.CRITICAL):
|
||||
""" source - https://gist.github.com/simon-weber/7853144
|
||||
A context manager that will prevent any logging messages triggered during the body from being processed.
|
||||
:param highest_level: the maximum logging level in use.
|
||||
This would only need to be changed if a custom level greater than CRITICAL is defined.
|
||||
"""
|
||||
previous_level = logging.root.manager.disable
|
||||
logging.disable(highest_level)
|
||||
try:
|
||||
yield
|
||||
finally:
|
||||
logging.disable(previous_level)
|
||||
Reference in New Issue
Block a user