146 lines
6.7 KiB
Python
146 lines
6.7 KiB
Python
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()
|