black ew

src/core/datasets.py

@@ -73,7 +73,7 @@ def _to_memory(self):
             n_bufs = int(len(idx) / buffer_size)
             idx = (
                 idx[: buffer_size * n_bufs].reshape(-1, buffer_size).tolist()
-                + idx[buffer_size * n_bufs:].reshape(1, -1).tolist()
+                + idx[buffer_size * n_bufs :].reshape(1, -1).tolist()
             )
             with multiprocessing.Pool(self.to_memory_workers) as pool:
                 mem_list = []

src/core/train.py

@@ -41,7 +41,9 @@ def train_dim(args):
 
     # Optimizer  -------------------------------------------------------------
     opt = optim.Adam(model.parameters(), lr=args["tr_lr"])
-    scheduler = optim.lr_scheduler.ReduceLROnPlateau(opt, "min", verbose=True, threshold=0.003, patience=args["tr_lr_patience"])
+    scheduler = optim.lr_scheduler.ReduceLROnPlateau(
+        opt, "min", verbose=True, threshold=0.003, patience=args["tr_lr_patience"]
+    )
     early_stop = EarlyStopperDim(args["tr_early_stop"])
 
     biasLosses = []
@@ -94,8 +96,12 @@ def train_dim(args):
             )
 
         directions = 2 if args["td_lstm_bidirectional"] else 1
-        h0 = torch.zeros(args["td_lstm_num_layers"] * directions * torch.cuda.device_count(), args["tr_bs"], args["td_lstm_h"])
-        c0 = torch.zeros(args["td_lstm_num_layers"] * directions * torch.cuda.device_count(), args["tr_bs"], args["td_lstm_h"])
+        h0 = torch.zeros(
+            args["td_lstm_num_layers"] * directions * torch.cuda.device_count(), args["tr_bs"], args["td_lstm_h"]
+        )
+        c0 = torch.zeros(
+            args["td_lstm_num_layers"] * directions * torch.cuda.device_count(), args["tr_bs"], args["td_lstm_h"]
+        )
         for xb_spec, yb_mos, (idx, n_wins) in dl_train:
             # Estimate batch ---------------------------------------------------
             xb_spec = xb_spec.to(dev)
@@ -294,7 +300,7 @@ def train_dim(args):
 
         # Scheduler update    ---------------------------------------------
         scheduler.step(loss)
-        earl_stp = early_stop.step(r)
+        early_stop = early_stop.step(r)
 
         # Print    --------------------------------------------------------
         ep_runtime = time.time() - tic_epoch
@@ -335,12 +341,16 @@ def train_dim(args):
         )
 
         # Early stopping    -----------------------------------------------
-        if earl_stp:
+        if earl_stop:
             logger.info(
-                "--> Early stopping. best_r_p {:0.2f} best_rmse {:0.2f}".format(early_stop.best_r_p, early_stop.best_rmse)
+                "--> Early stopping. best_r_p {:0.2f} best_rmse {:0.2f}".format(
+                    early_stop.best_r_p, early_stop.best_rmse
+                )
             )
             return
 
             # Training done --------------------------------------------------------
-    logger.info("--> Training done. best_r_p {:0.2f} best_rmse {:0.2f}".format(early_stop.best_r_p, early_stop.best_rmse))
+    logger.info("--> Training done. best_r_p {:0.2f} best_rmse {:0.2f}".format(
+        early_stop.best_r_p, early_stop.best_rmse)
+    )
     return

src/utils/train_utils.py

@@ -108,7 +108,9 @@ def get_loss(self, yb, yb_hat, idx):
         b = torch.tensor(self.b, dtype=torch.float).to(yb_hat.device)
         b = b[idx, :]
 
-        yb_hat_map = (b[:, 0] + b[:, 1] * yb_hat[:, 0] + b[:, 2] * yb_hat[:, 0] ** 2 + b[:, 3] * yb_hat[:, 0] ** 3).view(-1, 1)
+        yb_hat_map = (
+                b[:, 0] + b[:, 1] * yb_hat[:, 0] + b[:, 2] * yb_hat[:, 0] ** 2 + b[:, 3] * yb_hat[:, 0] ** 3
+        ).view(-1, 1)
 
         loss_bias = self._nan_mse(yb_hat_map, yb)
         loss_normal = self._nan_mse(yb_hat, yb)
@@ -282,7 +284,9 @@ def eval_results(
                     )
                 )
             else:
-                logger.info("%-30s r_p_file: %0.2f, rmse_map_file: %0.2f" % (db_name + ":", r["r_p_file"], r["rmse_map_file"]))
+                logger.info(
+                    "%-30s r_p_file: %0.2f, rmse_map_file: %0.2f" % (db_name + ":", r["r_p_file"], r["rmse_map_file"])
+                )
 
     # Save individual database results in DataFrame
     db_results_df = pd.DataFrame(db_results_df)