|
|
@@ -57,14 +57,14 @@ 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 data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX): |
|
|
|
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 data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX): |
|
|
|
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() |
|
|
@@ -158,7 +158,7 @@ class WandbLogger(): |
|
|
|
return data_dict |
|
|
|
|
|
|
|
def download_dataset_artifact(self, path, alias): |
|
|
|
if path and path.startswith(WANDB_ARTIFACT_PREFIX): |
|
|
|
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() |
|
|
@@ -229,7 +229,9 @@ class WandbLogger(): |
|
|
|
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") |
|
|
|
for img_file in tqdm([dataset.path]) if Path(dataset.path).is_dir() else tqdm(dataset.img_files): |
|
|
|
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)) |