Implement yaml.safe_load() (#2876)

* Implement yaml.safe_load()

* yaml.safe_dump()

utils/autoanchor.py

@@ -102,7 +102,7 @@ def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=10
 
     if isinstance(path, str):  # *.yaml file
         with open(path) as f:
-            data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # model dict
+            data_dict = yaml.safe_load(f)  # model dict
         from utils.datasets import LoadImagesAndLabels
         dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
     else:

utils/aws/resume.py

@@ -19,7 +19,7 @@ for last in path.rglob('*/**/last.pt'):
 
     # Load opt.yaml
     with open(last.parent.parent / 'opt.yaml') as f:
-        opt = yaml.load(f, Loader=yaml.SafeLoader)
+        opt = yaml.safe_load(f)
 
     # Get device count
     d = opt['device'].split(',')  # devices

utils/general.py

@@ -550,7 +550,7 @@ def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
         results = tuple(x[0, :7])
         c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
         f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
-        yaml.dump(hyp, f, sort_keys=False)
+        yaml.safe_dump(hyp, f, sort_keys=False)
 
     if bucket:
         os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket))  # upload

utils/plots.py

@@ -323,7 +323,7 @@ def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
 def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots import *; plot_evolution()
     # Plot hyperparameter evolution results in evolve.txt
     with open(yaml_file) as f:
-        hyp = yaml.load(f, Loader=yaml.SafeLoader)
+        hyp = yaml.safe_load(f)
     x = np.loadtxt('evolve.txt', ndmin=2)
     f = fitness(x)
     # weights = (f - f.min()) ** 2  # for weighted results

utils/wandb_logging/log_dataset.py

@@ -9,7 +9,7 @@ WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
 
 def create_dataset_artifact(opt):
     with open(opt.data) as f:
-        data = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
+        data = yaml.safe_load(f)  # data dict
     logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')
 
 

utils/wandb_logging/wandb_utils.py

@@ -55,7 +55,7 @@ def check_wandb_resume(opt):
 
 def process_wandb_config_ddp_mode(opt):
     with open(opt.data) as f:
-        data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
+        data_dict = yaml.safe_load(f)  # 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()
@@ -73,7 +73,7 @@ def process_wandb_config_ddp_mode(opt):
     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)
+            yaml.safe_dump(data_dict, f)
         opt.data = ddp_data_path
 
 
@@ -120,7 +120,7 @@ class WandbLogger():
                                                 '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)
+            wandb_data_dict = yaml.safe_load(f)
         return wandb_data_dict
 
     def setup_training(self, opt, data_dict):
@@ -192,7 +192,7 @@ class WandbLogger():
 
     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
+            data = yaml.safe_load(f)  # 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(
@@ -206,7 +206,7 @@ class WandbLogger():
         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)
+            yaml.safe_dump(data, f)
 
         if self.job_type == 'Training':  # builds correct artifact pipeline graph
             self.wandb_run.use_artifact(self.val_artifact)