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- import json
- import sys
- from pathlib import Path
-
- import torch
- import yaml
- from tqdm import tqdm
-
- sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path
- from utils.datasets import LoadImagesAndLabels
- from utils.datasets import img2label_paths
- from utils.general import colorstr, xywh2xyxy, check_dataset
-
- try:
- import wandb
- from wandb import init, finish
- except ImportError:
- wandb = None
-
- 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 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
- model_artifact_name = 'run_' + run_id + '_model'
- return run_id, project, model_artifact_name
-
-
- def check_wandb_resume(opt):
- process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None
- if isinstance(opt.resume, str):
- if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
- if opt.global_rank not in [-1, 0]: # For resuming DDP runs
- run_id, project, model_artifact_name = get_run_info(opt.resume)
- api = wandb.Api()
- artifact = api.artifact(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(opt.data) as f:
- data_dict = yaml.load(f, Loader=yaml.SafeLoader) # 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.dump(data_dict, f)
- opt.data = ddp_data_path
-
-
- class WandbLogger():
- def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
- # Pre-training routine --
- self.job_type = job_type
- self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
- # 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):
- run_id, project, 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, resume='allow')
- opt.resume = model_artifact_name
- elif self.wandb:
- self.wandb_run = wandb.init(config=opt,
- resume="allow",
- project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
- name=name,
- job_type=job_type,
- id=run_id) if not wandb.run else wandb.run
- if self.wandb_run:
- if self.job_type == 'Training':
- if not opt.resume:
- wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
- # Info useful for resuming from artifacts
- self.wandb_run.config.opt = vars(opt)
- self.wandb_run.config.data_dict = wandb_data_dict
- self.data_dict = self.setup_training(opt, data_dict)
- if self.job_type == 'Dataset Creation':
- self.data_dict = self.check_and_upload_dataset(opt)
- else:
- prefix = colorstr('wandb: ')
- print(f"{prefix}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
-
- def check_and_upload_dataset(self, opt):
- assert wandb, 'Install wandb to upload dataset'
- check_dataset(self.data_dict)
- config_path = self.log_dataset_artifact(opt.data,
- opt.single_cls,
- 'YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem)
- print("Created dataset config file ", config_path)
- with open(config_path) as f:
- wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader)
- return wandb_data_dict
-
- def setup_training(self, opt, data_dict):
- self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16 # Logging Constants
- 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.total_batch_size, config.bbox_interval, config.epochs, \
- config.opt['hyp']
- data_dict = dict(self.wandb_run.config.data_dict) # eliminates the need for config file to resume
- if 'val_artifact' not in self.__dict__: # 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)
- self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None
- 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)
- self.val_table = self.val_artifact.get("val")
- self.map_val_table_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", "prediction", "avg_confidence"])
- if opt.bbox_interval == -1:
- self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
- return data_dict
-
- def download_dataset_artifact(self, path, alias):
- if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
- dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
- 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):
- 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')
- assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % (
- total_epochs)
- return modeldir, model_artifact
- return None, None
-
- def log_model(self, path, opt, epoch, fitness_score, best_model=False):
- 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', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
- print("Saving model artifact on epoch ", epoch + 1)
-
- def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
- with open(data_file) as f:
- data = yaml.load(f, Loader=yaml.SafeLoader) # 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']), names, name='train') if data.get('train') else None
- self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
- data['val']), 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 = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1)) # updated data.yaml path
- data.pop('download', None)
- with open(path, 'w') as f:
- yaml.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):
- self.val_table_map = {}
- print("Mapping dataset")
- for i, data in enumerate(tqdm(self.val_table.data)):
- self.val_table_map[data[3]] = data[0]
-
- def create_dataset_table(self, dataset, class_to_id, name='dataset'):
- # 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)):
- height, width = shapes[0]
- labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height])
- box_data, img_classes = [], {}
- for cls, *xyxy in labels[:, 1:].tolist():
- cls = int(cls)
- box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
- "class_id": cls,
- "box_caption": "%s" % (class_to_id[cls]),
- "scores": {"acc": 1},
- "domain": "pixel"})
- 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), json.dumps(img_classes),
- Path(paths).name)
- artifact.add(table, name)
- return artifact
-
- def log_training_progress(self, predn, path, names):
- if self.val_table and self.result_table:
- 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": "%s %.3f" % (names[cls], conf),
- "scores": {"class_score": conf},
- "domain": "pixel"})
- total_conf = total_conf + conf
- boxes = {"predictions": {"box_data": box_data, "class_labels": names}} # inference-space
- id = self.val_table_map[Path(path).name]
- self.result_table.add_data(self.current_epoch,
- id,
- wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
- total_conf / max(1, len(box_data))
- )
-
- def log(self, log_dict):
- if self.wandb_run:
- for key, value in log_dict.items():
- self.log_dict[key] = value
-
- def end_epoch(self, best_result=False):
- if self.wandb_run:
- wandb.log(self.log_dict)
- self.log_dict = {}
- if self.result_artifact:
- train_results = wandb.JoinedTable(self.val_table, self.result_table, "id")
- self.result_artifact.add(train_results, 'result')
- wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch),
- ('best' if best_result else '')])
- self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
- self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-
- def finish_run(self):
- if self.wandb_run:
- if self.log_dict:
- wandb.log(self.log_dict)
- wandb.run.finish()
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