Cleaned up `precompute_embeddings.py`.

scripts/precompute_embeddings.py

@@ -95,8 +95,7 @@ def main(args):
         dataset, collate_fn=alphabet.get_batch_converter(), batch_sampler=batches
     )
     logging.info("Loaded all sequences")
-    assert all(-(model.num_layers + 1) <= i <= model.num_layers for i in args.repr_layers)
-    repr_layers = [(i + model.num_layers + 1) % (model.num_layers + 1) for i in args.repr_layers]
+    repr_layers = [33]
 
     with torch.no_grad():
         for batch_idx, (labels, strs, toks) in enumerate(data_loader):
@@ -118,7 +117,6 @@ def main(args):
                 os.makedirs(os.path.join(args.output_dir, label), exist_ok=True)
                 result = {"label": label}
 
-                if "per_tok" in args.include:
                 result["representations"] = {
                     layer: t[i, 1: len(strs[i]) + 1].clone()
                     for layer, t in representations.items()
@@ -146,15 +144,6 @@ if __name__ == "__main__":
         "--toks_per_batch", type=int, default=4096, 
         help="maximum tokens in a batch"
     )
-    parser.add_argument(
-        "--repr_layers", type=int, default=[-1], nargs="+",
-        help="Layer indices from which to extract representations (0 to num_layers, inclusive)"
-    )
-    parser.add_argument(
-        "--include", type=str, default=["per_tok"], nargs="+",
-        choices=["mean", "per_tok", "bos", "contacts"],
-        help="Specify which representations to return"
-    )
     parser.add_argument(
         "--truncate", action="store_true", default=True,
         help="Truncate sequences longer than 1022 (ESM restriction). Default: True"