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- import json
- import shutil
- import sys
- from datetime import datetime
- from pathlib import Path
-
- import torch
-
- sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path
- from utils.general import colorstr, xywh2xyxy
-
- try:
- import wandb
- except ImportError:
- wandb = None
- print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
-
- WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
- def remove_prefix(from_string, prefix):
- return from_string[len(prefix):]
-
-
- class WandbLogger():
- def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
- self.wandb = 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 self.wandb else None
-
- if job_type == 'Training':
- self.setup_training(opt, data_dict)
- if opt.bbox_interval == -1:
- opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs
- if opt.save_period == -1:
- opt.save_period = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs
-
- def setup_training(self, opt, data_dict):
- self.log_dict = {}
- self.train_artifact_path, self.trainset_artifact = \
- self.download_dataset_artifact(data_dict['train'], opt.artifact_alias)
- self.test_artifact_path, self.testset_artifact = \
- self.download_dataset_artifact(data_dict['val'], opt.artifact_alias)
- self.result_artifact, self.result_table, self.weights = 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.test_artifact_path is not None:
- test_path = Path(self.test_artifact_path) / 'data/images/'
- data_dict['val'] = str(test_path)
- self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
- self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
- if opt.resume_from_artifact:
- modeldir, _ = self.download_model_artifact(opt.resume_from_artifact)
- if modeldir:
- self.weights = Path(modeldir) / "best.pt"
- opt.weights = self.weights
-
- def download_dataset_artifact(self, path, alias):
- if 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()
- labels_zip = Path(datadir) / "data/labels.zip"
- shutil.unpack_archive(labels_zip, Path(datadir) / 'data/labels', 'zip')
- print("Downloaded dataset to : ", datadir)
- return datadir, dataset_artifact
- return None, None
-
- def download_model_artifact(self, name):
- model_artifact = wandb.use_artifact(name + ":latest")
- assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
- modeldir = model_artifact.download()
- print("Downloaded model to : ", modeldir)
- return modeldir, model_artifact
-
- def log_model(self, path, opt, epoch):
- datetime_suffix = datetime.today().strftime('%Y-%m-%d-%H-%M-%S')
- model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
- 'original_url': str(path),
- 'epoch': epoch + 1,
- 'save period': opt.save_period,
- 'project': opt.project,
- 'datetime': datetime_suffix
- })
- model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
- model_artifact.add_file(str(path / 'best.pt'), name='best.pt')
- wandb.log_artifact(model_artifact)
- print("Saving model artifact on epoch ", epoch + 1)
-
- def log_dataset_artifact(self, dataset, class_to_id, name='dataset'):
- artifact = wandb.Artifact(name=name, type="dataset")
- image_path = dataset.path
- artifact.add_dir(image_path, name='data/images')
- table = wandb.Table(columns=["id", "train_image", "Classes"])
- class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
- for si, (img, labels, paths, shapes) in enumerate(dataset):
- height, width = shapes[0]
- labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4)))
- labels[:, 2:] *= 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))
- artifact.add(table, name)
- labels_path = 'labels'.join(image_path.rsplit('images', 1))
- zip_path = Path(labels_path).parent / (name + '_labels.zip')
- if not zip_path.is_file(): # make_archive won't check if file exists
- shutil.make_archive(zip_path.with_suffix(''), 'zip', labels_path)
- artifact.add_file(str(zip_path), name='data/labels.zip')
- wandb.log_artifact(artifact)
- print("Saving data to W&B...")
-
- 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):
- if self.wandb_run and self.log_dict:
- wandb.log(self.log_dict)
- self.log_dict = {}
-
- def finish_run(self):
- if self.wandb_run:
- if self.result_artifact:
- print("Add Training Progress Artifact")
- self.result_artifact.add(self.result_table, 'result')
- train_results = wandb.JoinedTable(self.testset_artifact.get("val"), self.result_table, "id")
- self.result_artifact.add(train_results, 'joined_result')
- wandb.log_artifact(self.result_artifact)
- if self.log_dict:
- wandb.log(self.log_dict)
- wandb.run.finish()
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