Implement yaml.safe_load() (#2876)

* Implement yaml.safe_load()

* yaml.safe_dump()

train.py

@@ -41,7 +41,7 @@ logger = logging.getLogger(__name__)
 def train(hyp, opt, device, tb_writer=None):
     logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
     save_dir, epochs, batch_size, total_batch_size, weights, rank = \
-        opt.save_dir, opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
+        Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
 
     # Directories
     wdir = save_dir / 'weights'
@@ -52,16 +52,16 @@ def train(hyp, opt, device, tb_writer=None):
 
     # Save run settings
     with open(save_dir / 'hyp.yaml', 'w') as f:
-        yaml.dump(hyp, f, sort_keys=False)
+        yaml.safe_dump(hyp, f, sort_keys=False)
     with open(save_dir / 'opt.yaml', 'w') as f:
-        yaml.dump(vars(opt), f, sort_keys=False)
+        yaml.safe_dump(vars(opt), f, sort_keys=False)
 
     # Configure
     plots = not opt.evolve  # create plots
     cuda = device.type != 'cpu'
     init_seeds(2 + rank)
     with open(opt.data) as f:
-        data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
+        data_dict = yaml.safe_load(f)  # data dict
     is_coco = opt.data.endswith('coco.yaml')
 
     # Logging- Doing this before checking the dataset. Might update data_dict
@@ -506,8 +506,9 @@ if __name__ == '__main__':
         assert os.path.isfile(ckpt), 'ERROR: --resume checkpoint does not exist'
         apriori = opt.global_rank, opt.local_rank
         with open(Path(ckpt).parent.parent / 'opt.yaml') as f:
-            opt = argparse.Namespace(**yaml.load(f, Loader=yaml.SafeLoader))  # replace
-        opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = '', ckpt, True, opt.total_batch_size, *apriori  # reinstate
+            opt = argparse.Namespace(**yaml.safe_load(f))  # replace
+        opt.cfg, opt.weights, opt.resume, opt.batch_size, opt.global_rank, opt.local_rank = \
+            '', ckpt, True, opt.total_batch_size, *apriori  # reinstate
         logger.info('Resuming training from %s' % ckpt)
     else:
         # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
@@ -515,7 +516,7 @@ if __name__ == '__main__':
         assert len(opt.cfg) or len(opt.weights), 'either --cfg or --weights must be specified'
         opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
         opt.name = 'evolve' if opt.evolve else opt.name
-        opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)  # increment run
+        opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve))
 
     # DDP mode
     opt.total_batch_size = opt.batch_size
@@ -530,7 +531,7 @@ if __name__ == '__main__':
 
     # Hyperparameters
     with open(opt.hyp) as f:
-        hyp = yaml.load(f, Loader=yaml.SafeLoader)  # load hyps
+        hyp = yaml.safe_load(f)  # load hyps
 
     # Train
     logger.info(opt)
@@ -577,7 +578,7 @@ if __name__ == '__main__':
         assert opt.local_rank == -1, 'DDP mode not implemented for --evolve'
         opt.notest, opt.nosave = True, True  # only test/save final epoch
         # ei = [isinstance(x, (int, float)) for x in hyp.values()]  # evolvable indices
-        yaml_file = opt.save_dir / 'hyp_evolved.yaml'  # save best result here
+        yaml_file = Path(opt.save_dir) / 'hyp_evolved.yaml'  # save best result here
         if opt.bucket:
             os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket)  # download evolve.txt if exists