fixed paths

cikm_inter_dst_predrnn_run_taasss.py

@@ -139,7 +139,6 @@ def wrapper_train(model: Model):
         # imgs = padding_taasss(imgs, args)
         imgs = preprocess.reshape_patch(imgs, args.patch_size)
         eta, real_input_flag = schedule_sampling(eta, itr)
-        print(real_input_flag.shape)
         cost = trainer.train(model, imgs, real_input_flag, args)
         progress_bar.set_description(f"Loss: {cost}")
 

cikm_inter_dst_predrnn_run_taasss_utils.py

@@ -7,10 +7,10 @@ from data_provider.CIKM.taasss import infinite_batcher
 
 
 def get_batcher(args):
-    data_dir = Path("/") / "home" / "gabriele" / "Documents" / "dottorato" / "data" / "meteotn_data_new"
-    metadata_file = data_dir / "hdf_metadata.csv"
-    # data_dir = Path("/") / "data1" / "meteotn_data_2010_2016"
-    # metadata_file = data_dir / "run_metadata.csv"
+    # data_dir = Path("/") / "home" / "gabriele" / "Documents" / "dottorato" / "data" / "meteotn_data_new"
+    # metadata_file = data_dir / "hdf_metadata.csv"
+    data_dir = Path("/") / "data1" / "meteotn_data_2010_2016"
+    metadata_file = data_dir / "run_metadata.csv"
     all_data = h5py.File(
         data_dir / "hdf_archives" / "all_data.hdf5",
         "r",