remove MVP specific code

model_zoo/DROID/echo_supervised_inference_recipe.py

@@ -210,7 +210,6 @@ def save_model_pred_as_df(pred, fname_suffix='', pred_col_names=[]):
         save_df.to_parquet(os.path.join(output_folder, f'prediction_{split_idx}' + fname_suffix + '.pq'))
 
     if extract_embeddings:
-        #embeddings = encoder.predict(io_inference_ds, steps=n_inference_steps, verbose=1, callbacks=[WandbMetricsLogger(log_freq=10)])
         embeddings = encoder.predict(io_inference_ds, steps=n_inference_steps, verbose=1)
         df = pd.DataFrame()
         df['sample_id'] = inference_ids_split

model_zoo/DROID/echo_supervised_training_recipe.py

@@ -456,7 +456,6 @@ def process_class_categories(df, cls_o_names, var_type='output_labels'):
     parser.add_argument('--es_loss2monitor', default='val_loss', type=str,
                         help='Loss on which the early stopping will be based, options are "val_loss", "val_echolab_loss" for regression loss, or "val_cls_COLUMN-NAME_loss" for classification loss.')
     parser.add_argument('--randomize_start_frame', action='store_true')
-    parser.add_argument('--sampling_ratio', default=9, type=int)
     # ---------- Adaptation for regression + classification ---------- #
     parser.add_argument('--output_labels_types', default='r', type=str,
                         help='A string indicating task types: r for regression, c for classification. Should be of length 1 or the same length of the specified output_labels variable, e.g. "r" or "rrcr".')
@@ -506,6 +505,5 @@ def process_class_categories(df, cls_o_names, var_type='output_labels'):
         add_separate_dense_cls=args.add_separate_dense_cls,
         loss_weights=args.loss_weights,
         # ---------------------------------------------------------------- #
-        sampling_ratio = args.sampling_ratio,
         randomize_start_frame=args.randomize_start_frame
     )