Fix black issues

exps/LFNA/basic-his.py

@@ -161,7 +161,10 @@ if __name__ == "__main__":
         help="The synthetic enviornment version.",
     )
     parser.add_argument(
-        "--hidden_dim", type=int, required=True, help="The hidden dimension.",
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
     )
     parser.add_argument(
         "--init_lr",
@@ -170,10 +173,16 @@ if __name__ == "__main__":
         help="The initial learning rate for the optimizer (default is Adam)",
     )
     parser.add_argument(
-        "--batch_size", type=int, default=512, help="The batch size",
+        "--batch_size",
+        type=int,
+        default=512,
+        help="The batch size",
     )
     parser.add_argument(
-        "--epochs", type=int, default=1000, help="The total number of epochs.",
+        "--epochs",
+        type=int,
+        default=1000,
+        help="The total number of epochs.",
     )
     parser.add_argument(
         "--srange", type=str, required=True, help="The range of models to be evaluated"

exps/LFNA/basic-maml.py

@@ -41,7 +41,10 @@ class MAML:
         )
         self.meta_lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
             self.meta_optimizer,
-            milestones=[int(epochs * 0.8), int(epochs * 0.9),],
+            milestones=[
+                int(epochs * 0.8),
+                int(epochs * 0.9),
+            ],
             gamma=0.1,
         )
         self.inner_lr = inner_lr
@@ -194,7 +197,10 @@ if __name__ == "__main__":
         help="The synthetic enviornment version.",
     )
     parser.add_argument(
-        "--hidden_dim", type=int, default=16, help="The hidden dimension.",
+        "--hidden_dim",
+        type=int,
+        default=16,
+        help="The hidden dimension.",
     )
     parser.add_argument(
         "--meta_lr",
@@ -224,10 +230,16 @@ if __name__ == "__main__":
         help="The gap between prev_time and current_timestamp",
     )
     parser.add_argument(
-        "--meta_batch", type=int, default=64, help="The batch size for the meta-model",
+        "--meta_batch",
+        type=int,
+        default=64,
+        help="The batch size for the meta-model",
     )
     parser.add_argument(
-        "--epochs", type=int, default=2000, help="The total number of epochs.",
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
     )
     parser.add_argument(
         "--early_stop_thresh",

exps/LFNA/basic-prev.py

@@ -149,7 +149,10 @@ if __name__ == "__main__":
         help="The synthetic enviornment version.",
     )
     parser.add_argument(
-        "--hidden_dim", type=int, required=True, help="The hidden dimension.",
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
     )
     parser.add_argument(
         "--init_lr",
@@ -164,10 +167,16 @@ if __name__ == "__main__":
         help="The gap between prev_time and current_timestamp",
     )
     parser.add_argument(
-        "--batch_size", type=int, default=512, help="The batch size",
+        "--batch_size",
+        type=int,
+        default=512,
+        help="The batch size",
     )
     parser.add_argument(
-        "--epochs", type=int, default=300, help="The total number of epochs.",
+        "--epochs",
+        type=int,
+        default=300,
+        help="The total number of epochs.",
     )
     parser.add_argument(
         "--workers",

exps/LFNA/basic-same.py

@@ -149,7 +149,10 @@ if __name__ == "__main__":
         help="The synthetic enviornment version.",
     )
     parser.add_argument(
-        "--hidden_dim", type=int, required=True, help="The hidden dimension.",
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
     )
     parser.add_argument(
         "--init_lr",
@@ -158,10 +161,16 @@ if __name__ == "__main__":
         help="The initial learning rate for the optimizer (default is Adam)",
     )
     parser.add_argument(
-        "--batch_size", type=int, default=512, help="The batch size",
+        "--batch_size",
+        type=int,
+        default=512,
+        help="The batch size",
     )
     parser.add_argument(
-        "--epochs", type=int, default=300, help="The total number of epochs.",
+        "--epochs",
+        type=int,
+        default=300,
+        help="The total number of epochs.",
     )
     parser.add_argument(
         "--workers",

exps/LFNA/lfna-debug-hpnet.py

@@ -62,7 +62,10 @@ def main(args):
     )
     lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
         optimizer,
-        milestones=[int(args.epochs * 0.8), int(args.epochs * 0.9),],
+        milestones=[
+            int(args.epochs * 0.8),
+            int(args.epochs * 0.9),
+        ],
         gamma=0.1,
     )
 
@@ -170,7 +173,10 @@ if __name__ == "__main__":
         help="The synthetic enviornment version.",
     )
     parser.add_argument(
-        "--hidden_dim", type=int, required=True, help="The hidden dimension.",
+        "--hidden_dim",
+        type=int,
+        required=True,
+        help="The hidden dimension.",
     )
     #####
     parser.add_argument(
@@ -180,7 +186,10 @@ if __name__ == "__main__":
         help="The initial learning rate for the optimizer (default is Adam)",
     )
     parser.add_argument(
-        "--meta_batch", type=int, default=64, help="The batch size for the meta-model",
+        "--meta_batch",
+        type=int,
+        default=64,
+        help="The batch size for the meta-model",
     )
     parser.add_argument(
         "--early_stop_thresh",
@@ -189,13 +198,22 @@ if __name__ == "__main__":
         help="The maximum epochs for early stop.",
     )
     parser.add_argument(
-        "--epochs", type=int, default=2000, help="The total number of epochs.",
+        "--epochs",
+        type=int,
+        default=2000,
+        help="The total number of epochs.",
     )
     parser.add_argument(
-        "--per_epoch_step", type=int, default=20, help="The total number of epochs.",
+        "--per_epoch_step",
+        type=int,
+        default=20,
+        help="The total number of epochs.",
     )
     parser.add_argument(
-        "--device", type=str, default="cpu", help="",
+        "--device",
+        type=str,
+        default="cpu",
+        help="",
     )
     # Random Seed
     parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")

exps/LFNA/lfna.py

@@ -101,7 +101,10 @@ def main(args):
     )
     lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(
         optimizer,
-        milestones=[int(args.epochs * 0.8), int(args.epochs * 0.9),],
+        milestones=[
+            int(args.epochs * 0.8),
+            int(args.epochs * 0.9),
+        ],
         gamma=0.1,
     )
     logger.log("The base-model is\n{:}".format(base_model))
@@ -240,13 +243,22 @@ if __name__ == "__main__":
         help="The synthetic enviornment version.",
     )
     parser.add_argument(
-        "--hidden_dim", type=int, default=16, help="The hidden dimension.",
+        "--hidden_dim",
+        type=int,
+        default=16,
+        help="The hidden dimension.",
     )
     parser.add_argument(
-        "--layer_dim", type=int, default=16, help="The layer chunk dimension.",
+        "--layer_dim",
+        type=int,
+        default=16,
+        help="The layer chunk dimension.",
     )
     parser.add_argument(
-        "--time_dim", type=int, default=16, help="The timestamp dimension.",
+        "--time_dim",
+        type=int,
+        default=16,
+        help="The timestamp dimension.",
     )
     #####
     parser.add_argument(
@@ -262,7 +274,10 @@ if __name__ == "__main__":
         help="The weight decay for the optimizer (default is Adam)",
     )
     parser.add_argument(
-        "--meta_batch", type=int, default=64, help="The batch size for the meta-model",
+        "--meta_batch",
+        type=int,
+        default=64,
+        help="The batch size for the meta-model",
     )
     parser.add_argument(
         "--sampler_enlarge",
@@ -284,7 +299,10 @@ if __name__ == "__main__":
         "--workers", type=int, default=4, help="The number of workers in parallel."
     )
     parser.add_argument(
-        "--device", type=str, default="cpu", help="",
+        "--device",
+        type=str,
+        default="cpu",
+        help="",
     )
     # Random Seed
     parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")

exps/LFNA/lfna_meta_model.py

@@ -75,7 +75,8 @@ class LFNA_Meta(super_core.SuperModule):
 
         # unknown token
         self.register_parameter(
-            "_unknown_token", torch.nn.Parameter(torch.Tensor(1, time_embedding)),
+            "_unknown_token",
+            torch.nn.Parameter(torch.Tensor(1, time_embedding)),
         )
 
         # initialization

exps/LFNA/vis-synthetic.py

@@ -164,9 +164,11 @@ def compare_cl(save_dir):
         )
     print("Save all figures into {:}".format(save_dir))
     save_dir = save_dir.resolve()
-    base_cmd = "ffmpeg -y -i {xdir}/%04d.png -vf fps=1 -vf scale=2200:1800 -vb 5000k".format(
+    base_cmd = (
+        "ffmpeg -y -i {xdir}/%04d.png -vf fps=1 -vf scale=2200:1800 -vb 5000k".format(
             xdir=save_dir
         )
+    )
     video_cmd = "{:} -pix_fmt yuv420p {xdir}/compare-cl.mp4".format(
         base_cmd, xdir=save_dir
     )