Start setup for improved W&B integration (#1948)
* Add helper functions for wandb and artifacts * cleanup * Reorganize files * Update wandb_utils.py * Update log_dataset.py We can remove this code, as the giou hyp has been deprecated for a while now. * Reorganize and update dataloader call * yaml.SafeLoader * PEP8 reformat * remove redundant checks * Add helper functions for wandb and artifacts * cleanup * Reorganize files * Update wandb_utils.py * Update log_dataset.py We can remove this code, as the giou hyp has been deprecated for a while now. * Reorganize and update dataloader call * yaml.SafeLoader * PEP8 reformat * remove redundant checks * Update util files * Update wandb_utils.py * Remove word size * Change path of labels.zip * remove unused imports * remove --rect * log_dataset.py cleanup * log_dataset.py cleanup2 * wandb_utils.py cleanup * remove redundant id_count * wandb_utils.py cleanup2 * rename cls * use pathlib for zip * rename dataloader to dataset * Change import order * Remove redundant code * remove unused import * remove unused imports Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>pull/2140/head
Ayush Chaurasia
GitHub
parent
9646ca438a
commit
73a0669930
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@ -348,6 +348,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
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self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
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self.mosaic_border = [-img_size // 2, -img_size // 2]
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self.stride = stride
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self.path = path
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try:
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f = [] # image files
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@ -0,0 +1,39 @@
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import argparse
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from pathlib import Path
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import yaml
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from wandb_utils import WandbLogger
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from utils.datasets import LoadImagesAndLabels
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WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
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def create_dataset_artifact(opt):
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with open(opt.data) as f:
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data = yaml.load(f, Loader=yaml.SafeLoader) # data dict
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logger = WandbLogger(opt, '', None, data, job_type='create_dataset')
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nc, names = (1, ['item']) if opt.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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logger.log_dataset_artifact(LoadImagesAndLabels(data['train']), names, name='train') # trainset
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logger.log_dataset_artifact(LoadImagesAndLabels(data['val']), names, name='val') # valset
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# Update data.yaml with artifact links
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data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(opt.project) / 'train')
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data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(opt.project) / 'val')
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path = opt.data if opt.overwrite_config else opt.data.replace('.', '_wandb.') # updated data.yaml path
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data.pop('download', None) # download via artifact instead of predefined field 'download:'
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with open(path, 'w') as f:
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yaml.dump(data, f)
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print("New Config file => ", path)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--data', type=str, default='data/coco128.yaml', help='data.yaml path')
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parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
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parser.add_argument('--project', type=str, default='YOLOv5', help='name of W&B Project')
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parser.add_argument('--overwrite_config', action='store_true', help='overwrite data.yaml')
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opt = parser.parse_args()
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create_dataset_artifact(opt)
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@ -0,0 +1,145 @@
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import json
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import shutil
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import sys
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from datetime import datetime
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from pathlib import Path
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import torch
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sys.path.append(str(Path(__file__).parent.parent.parent)) # add utils/ to path
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from utils.general import colorstr, xywh2xyxy
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try:
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import wandb
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except ImportError:
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wandb = None
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print(f"{colorstr('wandb: ')}Install Weights & Biases for YOLOv5 logging with 'pip install wandb' (recommended)")
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WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
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def remove_prefix(from_string, prefix):
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return from_string[len(prefix):]
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class WandbLogger():
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def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
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self.wandb = wandb
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self.wandb_run = wandb.init(config=opt, resume="allow",
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project='YOLOv5' if opt.project == 'runs/train' else Path(opt.project).stem,
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name=name,
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job_type=job_type,
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id=run_id) if self.wandb else None
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if job_type == 'Training':
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self.setup_training(opt, data_dict)
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if opt.bbox_interval == -1:
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opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs
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if opt.save_period == -1:
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opt.save_period = (opt.epochs // 10) if opt.epochs > 10 else opt.epochs
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def setup_training(self, opt, data_dict):
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self.log_dict = {}
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self.train_artifact_path, self.trainset_artifact = \
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self.download_dataset_artifact(data_dict['train'], opt.artifact_alias)
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self.test_artifact_path, self.testset_artifact = \
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self.download_dataset_artifact(data_dict['val'], opt.artifact_alias)
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self.result_artifact, self.result_table, self.weights = None, None, None
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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.test_artifact_path is not None:
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test_path = Path(self.test_artifact_path) / 'data/images/'
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data_dict['val'] = str(test_path)
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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", "prediction", "avg_confidence"])
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if opt.resume_from_artifact:
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modeldir, _ = self.download_model_artifact(opt.resume_from_artifact)
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if modeldir:
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self.weights = Path(modeldir) / "best.pt"
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opt.weights = self.weights
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def download_dataset_artifact(self, path, alias):
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if path.startswith(WANDB_ARTIFACT_PREFIX):
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dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
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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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labels_zip = Path(datadir) / "data/labels.zip"
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shutil.unpack_archive(labels_zip, Path(datadir) / 'data/labels', 'zip')
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print("Downloaded dataset to : ", datadir)
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return datadir, dataset_artifact
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return None, None
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def download_model_artifact(self, name):
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model_artifact = wandb.use_artifact(name + ":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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print("Downloaded model to : ", modeldir)
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return modeldir, model_artifact
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def log_model(self, path, opt, epoch):
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datetime_suffix = datetime.today().strftime('%Y-%m-%d-%H-%M-%S')
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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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'epoch': epoch + 1,
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'save period': opt.save_period,
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'project': opt.project,
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'datetime': datetime_suffix
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})
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model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
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model_artifact.add_file(str(path / 'best.pt'), name='best.pt')
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wandb.log_artifact(model_artifact)
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print("Saving model artifact on epoch ", epoch + 1)
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def log_dataset_artifact(self, dataset, class_to_id, name='dataset'):
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artifact = wandb.Artifact(name=name, type="dataset")
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image_path = dataset.path
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artifact.add_dir(image_path, name='data/images')
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table = wandb.Table(columns=["id", "train_image", "Classes"])
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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(dataset):
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height, width = shapes[0]
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labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4)))
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labels[:, 2:] *= torch.Tensor([width, height, width, height])
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box_data = []
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img_classes = {}
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for cls, *xyxy in labels[:, 1:].tolist():
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cls = int(cls)
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box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
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"class_id": cls,
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"box_caption": "%s" % (class_to_id[cls]),
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"scores": {"acc": 1},
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"domain": "pixel"})
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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), json.dumps(img_classes))
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artifact.add(table, name)
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labels_path = 'labels'.join(image_path.rsplit('images', 1))
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zip_path = Path(labels_path).parent / (name + '_labels.zip')
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if not zip_path.is_file(): # make_archive won't check if file exists
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shutil.make_archive(zip_path.with_suffix(''), 'zip', labels_path)
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artifact.add_file(str(zip_path), name='data/labels.zip')
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wandb.log_artifact(artifact)
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print("Saving data to W&B...")
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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):
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if self.wandb_run and self.log_dict:
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wandb.log(self.log_dict)
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self.log_dict = {}
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def finish_run(self):
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if self.wandb_run:
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if self.result_artifact:
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print("Add Training Progress Artifact")
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self.result_artifact.add(self.result_table, 'result')
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train_results = wandb.JoinedTable(self.testset_artifact.get("val"), self.result_table, "id")
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self.result_artifact.add(train_results, 'joined_result')
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wandb.log_artifact(self.result_artifact)
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if self.log_dict:
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wandb.log(self.log_dict)
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wandb.run.finish()
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