585 lines
27 KiB
Python
585 lines
27 KiB
Python
"""Utilities and tools for tracking runs with Weights & Biases."""
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import logging
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import os
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import sys
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from contextlib import contextmanager
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from pathlib import Path
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from typing import Dict
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import yaml
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from tqdm import tqdm
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[3] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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from app.yolov5.utils.dataloaders import LoadImagesAndLabels, img2label_paths
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from app.yolov5.utils.general import LOGGER, check_dataset, check_file
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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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except (ImportError, AssertionError):
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wandb = None
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RANK = int(os.getenv('RANK', -1))
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WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
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def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
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return from_string[len(prefix):]
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def check_wandb_config_file(data_config_file):
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wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1)) # updated data.yaml path
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if Path(wandb_config).is_file():
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return wandb_config
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return data_config_file
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def check_wandb_dataset(data_file):
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is_trainset_wandb_artifact = False
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is_valset_wandb_artifact = False
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if isinstance(data_file, dict):
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# In that case another dataset manager has already processed it and we don't have to
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return data_file
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if check_file(data_file) and data_file.endswith('.yaml'):
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with open(data_file, errors='ignore') as f:
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data_dict = yaml.safe_load(f)
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is_trainset_wandb_artifact = isinstance(data_dict['train'],
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str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX)
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is_valset_wandb_artifact = isinstance(data_dict['val'],
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str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX)
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if is_trainset_wandb_artifact or is_valset_wandb_artifact:
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return data_dict
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else:
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return check_dataset(data_file)
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def get_run_info(run_path):
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run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
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run_id = run_path.stem
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project = run_path.parent.stem
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entity = run_path.parent.parent.stem
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model_artifact_name = 'run_' + run_id + '_model'
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return entity, project, run_id, model_artifact_name
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def check_wandb_resume(opt):
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process_wandb_config_ddp_mode(opt) if RANK not in [-1, 0] else None
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if isinstance(opt.resume, str):
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if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
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if RANK not in [-1, 0]: # For resuming DDP runs
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entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
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api = wandb.Api()
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artifact = api.artifact(entity + '/' + project + '/' + model_artifact_name + ':latest')
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modeldir = artifact.download()
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opt.weights = str(Path(modeldir) / "last.pt")
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return True
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return None
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def process_wandb_config_ddp_mode(opt):
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with open(check_file(opt.data), errors='ignore') as f:
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data_dict = yaml.safe_load(f) # data dict
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train_dir, val_dir = None, None
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if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
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api = wandb.Api()
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train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
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train_dir = train_artifact.download()
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train_path = Path(train_dir) / 'data/images/'
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data_dict['train'] = str(train_path)
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if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
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api = wandb.Api()
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val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
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val_dir = val_artifact.download()
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val_path = Path(val_dir) / 'data/images/'
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data_dict['val'] = str(val_path)
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if train_dir or val_dir:
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ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
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with open(ddp_data_path, 'w') as f:
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yaml.safe_dump(data_dict, f)
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opt.data = ddp_data_path
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class WandbLogger():
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"""Log training runs, datasets, models, and predictions to Weights & Biases.
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This logger sends information to W&B at wandb.ai. By default, this information
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includes hyperparameters, system configuration and metrics, model metrics,
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and basic data metrics and analyses.
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By providing additional command line arguments to train.py, datasets,
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models and predictions can also be logged.
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For more on how this logger is used, see the Weights & Biases documentation:
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https://docs.wandb.com/guides/integrations/yolov5
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"""
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def __init__(self, opt, run_id=None, job_type='Training'):
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"""
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- Initialize WandbLogger instance
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- Upload dataset if opt.upload_dataset is True
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- Setup training processes if job_type is 'Training'
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arguments:
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opt (namespace) -- Commandline arguments for this run
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run_id (str) -- Run ID of W&B run to be resumed
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job_type (str) -- To set the job_type for this run
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"""
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# Pre-training routine --
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self.job_type = job_type
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self.wandb, self.wandb_run = wandb, None if not wandb else wandb.run
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self.val_artifact, self.train_artifact = None, None
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self.train_artifact_path, self.val_artifact_path = None, None
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self.result_artifact = None
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self.val_table, self.result_table = None, None
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self.bbox_media_panel_images = []
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self.val_table_path_map = None
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self.max_imgs_to_log = 16
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self.wandb_artifact_data_dict = None
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self.data_dict = None
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# It's more elegant to stick to 1 wandb.init call,
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# but useful config data is overwritten in the WandbLogger's wandb.init call
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if isinstance(opt.resume, str): # checks resume from artifact
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if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
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entity, project, run_id, model_artifact_name = get_run_info(opt.resume)
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model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
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assert wandb, 'install wandb to resume wandb runs'
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# Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
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self.wandb_run = wandb.init(id=run_id,
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project=project,
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entity=entity,
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resume='allow',
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allow_val_change=True)
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opt.resume = model_artifact_name
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elif self.wandb:
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self.wandb_run = wandb.init(config=opt,
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resume="allow",
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project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
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entity=opt.entity,
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name=opt.name if opt.name != 'exp' else None,
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job_type=job_type,
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id=run_id,
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allow_val_change=True) if not wandb.run else wandb.run
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if self.wandb_run:
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if self.job_type == 'Training':
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if opt.upload_dataset:
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if not opt.resume:
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self.wandb_artifact_data_dict = self.check_and_upload_dataset(opt)
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if isinstance(opt.data, dict):
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# This means another dataset manager has already processed the dataset info (e.g. ClearML)
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# and they will have stored the already processed dict in opt.data
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self.data_dict = opt.data
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elif opt.resume:
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# resume from artifact
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if isinstance(opt.resume, str) and opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
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self.data_dict = dict(self.wandb_run.config.data_dict)
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else: # local resume
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self.data_dict = check_wandb_dataset(opt.data)
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else:
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self.data_dict = check_wandb_dataset(opt.data)
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self.wandb_artifact_data_dict = self.wandb_artifact_data_dict or self.data_dict
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# write data_dict to config. useful for resuming from artifacts. Do this only when not resuming.
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self.wandb_run.config.update({'data_dict': self.wandb_artifact_data_dict}, allow_val_change=True)
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self.setup_training(opt)
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if self.job_type == 'Dataset Creation':
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self.wandb_run.config.update({"upload_dataset": True})
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self.data_dict = self.check_and_upload_dataset(opt)
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def check_and_upload_dataset(self, opt):
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"""
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Check if the dataset format is compatible and upload it as W&B artifact
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arguments:
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opt (namespace)-- Commandline arguments for current run
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returns:
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Updated dataset info dictionary where local dataset paths are replaced by WAND_ARFACT_PREFIX links.
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"""
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assert wandb, 'Install wandb to upload dataset'
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config_path = self.log_dataset_artifact(opt.data, opt.single_cls,
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'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
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with open(config_path, errors='ignore') as f:
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wandb_data_dict = yaml.safe_load(f)
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return wandb_data_dict
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def setup_training(self, opt):
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"""
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Setup the necessary processes for training YOLO models:
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- Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
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- Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
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- Setup log_dict, initialize bbox_interval
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arguments:
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opt (namespace) -- commandline arguments for this run
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"""
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self.log_dict, self.current_epoch = {}, 0
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self.bbox_interval = opt.bbox_interval
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if isinstance(opt.resume, str):
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modeldir, _ = self.download_model_artifact(opt)
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if modeldir:
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self.weights = Path(modeldir) / "last.pt"
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config = self.wandb_run.config
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opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = str(
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self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs,\
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config.hyp, config.imgsz
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data_dict = self.data_dict
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if self.val_artifact is None: # If --upload_dataset is set, use the existing artifact, don't download
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self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(
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data_dict.get('train'), opt.artifact_alias)
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self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(
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data_dict.get('val'), opt.artifact_alias)
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if self.train_artifact_path is not None:
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train_path = Path(self.train_artifact_path) / 'data/images/'
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data_dict['train'] = str(train_path)
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if self.val_artifact_path is not None:
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val_path = Path(self.val_artifact_path) / 'data/images/'
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data_dict['val'] = str(val_path)
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if self.val_artifact is not None:
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self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
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columns = ["epoch", "id", "ground truth", "prediction"]
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columns.extend(self.data_dict['names'])
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self.result_table = wandb.Table(columns)
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self.val_table = self.val_artifact.get("val")
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if self.val_table_path_map is None:
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self.map_val_table_path()
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if opt.bbox_interval == -1:
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self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
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if opt.evolve or opt.noplots:
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self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
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train_from_artifact = self.train_artifact_path is not None and self.val_artifact_path is not None
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# Update the the data_dict to point to local artifacts dir
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if train_from_artifact:
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self.data_dict = data_dict
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def download_dataset_artifact(self, path, alias):
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"""
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download the model checkpoint artifact if the path starts with WANDB_ARTIFACT_PREFIX
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arguments:
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path -- path of the dataset to be used for training
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alias (str)-- alias of the artifact to be download/used for training
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returns:
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(str, wandb.Artifact) -- path of the downladed dataset and it's corresponding artifact object if dataset
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is found otherwise returns (None, None)
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"""
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if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
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artifact_path = Path(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
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dataset_artifact = wandb.use_artifact(artifact_path.as_posix().replace("\\", "/"))
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assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
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datadir = dataset_artifact.download()
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return datadir, dataset_artifact
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return None, None
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def download_model_artifact(self, opt):
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"""
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download the model checkpoint artifact if the resume path starts with WANDB_ARTIFACT_PREFIX
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arguments:
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opt (namespace) -- Commandline arguments for this run
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"""
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if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
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model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
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assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
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modeldir = model_artifact.download()
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# epochs_trained = model_artifact.metadata.get('epochs_trained')
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total_epochs = model_artifact.metadata.get('total_epochs')
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is_finished = total_epochs is None
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assert not is_finished, 'training is finished, can only resume incomplete runs.'
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return modeldir, model_artifact
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return None, None
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def log_model(self, path, opt, epoch, fitness_score, best_model=False):
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"""
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Log the model checkpoint as W&B artifact
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arguments:
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path (Path) -- Path of directory containing the checkpoints
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opt (namespace) -- Command line arguments for this run
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epoch (int) -- Current epoch number
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fitness_score (float) -- fitness score for current epoch
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best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
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"""
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model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model',
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type='model',
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metadata={
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'original_url': str(path),
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'epochs_trained': epoch + 1,
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'save period': opt.save_period,
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'project': opt.project,
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'total_epochs': opt.epochs,
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'fitness_score': fitness_score})
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model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
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wandb.log_artifact(model_artifact,
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aliases=['latest', 'last', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
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LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
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def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
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"""
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Log the dataset as W&B artifact and return the new data file with W&B links
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arguments:
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data_file (str) -- the .yaml file with information about the dataset like - path, classes etc.
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single_class (boolean) -- train multi-class data as single-class
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project (str) -- project name. Used to construct the artifact path
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overwrite_config (boolean) -- overwrites the data.yaml file if set to true otherwise creates a new
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file with _wandb postfix. Eg -> data_wandb.yaml
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returns:
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the new .yaml file with artifact links. it can be used to start training directly from artifacts
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"""
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upload_dataset = self.wandb_run.config.upload_dataset
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log_val_only = isinstance(upload_dataset, str) and upload_dataset == 'val'
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self.data_dict = check_dataset(data_file) # parse and check
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data = dict(self.data_dict)
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nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
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names = {k: v for k, v in enumerate(names)} # to index dictionary
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# log train set
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if not log_val_only:
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self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(data['train'], rect=True, batch_size=1),
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names,
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name='train') if data.get('train') else None
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if data.get('train'):
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data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
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self.val_artifact = self.create_dataset_table(
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LoadImagesAndLabels(data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') else None
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if data.get('val'):
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data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
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path = Path(data_file)
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# create a _wandb.yaml file with artifacts links if both train and test set are logged
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if not log_val_only:
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path = (path.stem if overwrite_config else path.stem + '_wandb') + '.yaml' # updated data.yaml path
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path = ROOT / 'data' / path
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data.pop('download', None)
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data.pop('path', None)
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with open(path, 'w') as f:
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yaml.safe_dump(data, f)
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LOGGER.info(f"Created dataset config file {path}")
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if self.job_type == 'Training': # builds correct artifact pipeline graph
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if not log_val_only:
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self.wandb_run.log_artifact(
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self.train_artifact) # calling use_artifact downloads the dataset. NOT NEEDED!
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self.wandb_run.use_artifact(self.val_artifact)
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self.val_artifact.wait()
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self.val_table = self.val_artifact.get('val')
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self.map_val_table_path()
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else:
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self.wandb_run.log_artifact(self.train_artifact)
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self.wandb_run.log_artifact(self.val_artifact)
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return path
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def map_val_table_path(self):
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"""
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Map the validation dataset Table like name of file -> it's id in the W&B Table.
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Useful for - referencing artifacts for evaluation.
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"""
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self.val_table_path_map = {}
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LOGGER.info("Mapping dataset")
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for i, data in enumerate(tqdm(self.val_table.data)):
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self.val_table_path_map[data[3]] = data[0]
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def create_dataset_table(self, dataset: LoadImagesAndLabels, class_to_id: Dict[int, str], name: str = 'dataset'):
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"""
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Create and return W&B artifact containing W&B Table of the dataset.
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arguments:
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dataset -- instance of LoadImagesAndLabels class used to iterate over the data to build Table
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class_to_id -- hash map that maps class ids to labels
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name -- name of the artifact
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returns:
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dataset artifact to be logged or used
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"""
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# TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
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artifact = wandb.Artifact(name=name, type="dataset")
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img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
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img_files = tqdm(dataset.im_files) if not img_files else img_files
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for img_file in img_files:
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if Path(img_file).is_dir():
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artifact.add_dir(img_file, name='data/images')
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labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
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artifact.add_dir(labels_path, name='data/labels')
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else:
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artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
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|
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 = []
|
|
avg_conf_per_class = [0] * len(self.data_dict['names'])
|
|
pred_class_count = {}
|
|
for *xyxy, conf, cls in predn.tolist():
|
|
if conf >= 0.25:
|
|
cls = int(cls)
|
|
box_data.append({
|
|
"position": {
|
|
"minX": xyxy[0],
|
|
"minY": xyxy[1],
|
|
"maxX": xyxy[2],
|
|
"maxY": xyxy[3]},
|
|
"class_id": cls,
|
|
"box_caption": f"{names[cls]} {conf:.3f}",
|
|
"scores": {
|
|
"class_score": conf},
|
|
"domain": "pixel"})
|
|
avg_conf_per_class[cls] += conf
|
|
|
|
if cls in pred_class_count:
|
|
pred_class_count[cls] += 1
|
|
else:
|
|
pred_class_count[cls] = 1
|
|
|
|
for pred_class in pred_class_count.keys():
|
|
avg_conf_per_class[pred_class] = avg_conf_per_class[pred_class] / pred_class_count[pred_class]
|
|
|
|
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),
|
|
*avg_conf_per_class)
|
|
|
|
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[int(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})
|
|
columns = ["epoch", "id", "ground truth", "prediction"]
|
|
columns.extend(self.data_dict['names'])
|
|
self.result_table = wandb.Table(columns)
|
|
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)
|