375 lines
19 KiB
Python
375 lines
19 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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import yaml
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from tqdm import tqdm
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sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path
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from utils.datasets import LoadImagesAndLabels
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from utils.datasets import img2label_paths
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from utils.general import colorstr, check_dataset, check_file
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try:
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import wandb
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from wandb import init, finish
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except ImportError:
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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 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)) 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, name, run_id, data_dict, job_type='Training'):
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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.data_dict = data_dict
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self.bbox_media_panel_images = []
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self.val_table_path_map = None
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# It's more elegant to stick to 1 wandb.init call, 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=name,
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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 not opt.resume:
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wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
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# Info useful for resuming from artifacts
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self.wandb_run.config.update({'opt': vars(opt), 'data_dict': data_dict}, allow_val_change=True)
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self.data_dict = self.setup_training(opt, data_dict)
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if self.job_type == 'Dataset Creation':
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self.data_dict = self.check_and_upload_dataset(opt)
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else:
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prefix = colorstr('wandb: ')
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print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
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def check_and_upload_dataset(self, opt):
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assert wandb, 'Install wandb to upload dataset'
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config_path = self.log_dataset_artifact(check_file(opt.data),
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opt.single_cls,
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'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
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print("Created dataset config file ", config_path)
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with open(config_path) 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, data_dict):
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self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants
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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 = str(
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self.weights), config.save_period, config.batch_size, config.bbox_interval, config.epochs, \
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config.opt['hyp']
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data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume
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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(data_dict.get('train'),
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opt.artifact_alias)
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self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
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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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self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
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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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wandb.log({"validation dataset": self.val_table})
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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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return data_dict
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def download_dataset_artifact(self, path, alias):
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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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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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model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', 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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})
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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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print("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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with open(data_file) as f:
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data = yaml.safe_load(f) # data dict
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check_dataset(data)
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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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self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
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data['train'], rect=True, batch_size=1), names, name='train') if data.get('train') else None
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self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
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data['val'], rect=True, batch_size=1), names, name='val') if data.get('val') 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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if data.get('val'):
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data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
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path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml 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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if self.job_type == 'Training': # builds correct artifact pipeline graph
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self.wandb_run.use_artifact(self.val_artifact)
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self.wandb_run.use_artifact(self.train_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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self.val_table_path_map = {}
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print("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, class_to_id, name='dataset'):
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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.img_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])
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artifact.add_file(str(label_file),
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name='data/labels/' + label_file.name) if label_file.exists() else None
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table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
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class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
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for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
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box_data, img_classes = [], {}
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for cls, *xywh in labels[:, 1:].tolist():
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cls = int(cls)
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box_data.append({"position": {"middle": [xywh[0], xywh[1]], "width": xywh[2], "height": xywh[3]},
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"class_id": cls,
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"box_caption": "%s" % (class_to_id[cls])})
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img_classes[cls] = class_to_id[cls]
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boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}} # inference-space
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table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), list(img_classes.values()),
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Path(paths).name)
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artifact.add(table, name)
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return artifact
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def log_training_progress(self, predn, path, names):
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class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
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box_data = []
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total_conf = 0
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for *xyxy, conf, cls in predn.tolist():
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if conf >= 0.25:
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box_data.append(
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{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
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"class_id": int(cls),
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"box_caption": "%s %.3f" % (names[cls], conf),
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"scores": {"class_score": conf},
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"domain": "pixel"})
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total_conf = total_conf + conf
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boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
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id = self.val_table_path_map[Path(path).name]
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self.result_table.add_data(self.current_epoch,
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id,
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self.val_table.data[id][1],
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wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
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total_conf / max(1, len(box_data))
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)
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def val_one_image(self, pred, predn, path, names, im):
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if self.val_table and self.result_table: # Log Table if Val dataset is uploaded as artifact
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self.log_training_progress(predn, path, names)
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else: # Default to bbox media panelif Val artifact not found
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log_imgs = min(self.log_imgs, 100)
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if len(self.bbox_media_panel_images) < log_imgs and self.current_epoch > 0:
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if self.current_epoch % self.bbox_interval == 0:
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box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
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"class_id": int(cls),
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"box_caption": "%s %.3f" % (names[cls], conf),
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"scores": {"class_score": conf},
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"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
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boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
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self.bbox_media_panel_images.append(wandb.Image(im, boxes=boxes, caption=path.name))
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def log(self, log_dict):
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if self.wandb_run:
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for key, value in log_dict.items():
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self.log_dict[key] = value
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def end_epoch(self, best_result=False):
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if self.wandb_run:
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with all_logging_disabled():
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if self.bbox_media_panel_images:
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self.log_dict["Bounding Box Debugger/Images"] = self.bbox_media_panel_images
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wandb.log(self.log_dict)
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self.log_dict = {}
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self.bbox_media_panel_images = []
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if self.result_artifact:
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self.result_artifact.add(self.result_table, 'result')
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wandb.log_artifact(self.result_artifact, aliases=['latest', 'last', 'epoch ' + str(self.current_epoch),
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('best' if best_result else '')])
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wandb.log({"evaluation": self.result_table})
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self.result_table = wandb.Table(["epoch", "id", "ground truth", "prediction", "avg_confidence"])
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self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
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def finish_run(self):
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if self.wandb_run:
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if self.log_dict:
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with all_logging_disabled():
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wandb.log(self.log_dict)
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wandb.run.finish()
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@contextmanager
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def all_logging_disabled(highest_level=logging.CRITICAL):
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""" source - https://gist.github.com/simon-weber/7853144
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A context manager that will prevent any logging messages triggered during the body from being processed.
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:param highest_level: the maximum logging level in use.
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This would only need to be changed if a custom level greater than CRITICAL is defined.
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"""
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previous_level = logging.root.manager.disable
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logging.disable(highest_level)
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try:
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yield
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finally:
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logging.disable(previous_level)
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