final_eval: resolve LoRA path via hf_hub_download, upload results to eval/final at end

final_eval.py

@@ -5,24 +5,27 @@ usage, matches training captions) and without (apples-to-apples with the
 baselines, which ran bare instructions). Reuses eval_edit.py's pipeline,
 metrics and seeding so numbers are directly comparable to the baseline run.
 
+Self-contained for job bootstrap: fetches its sibling eval_edit.py from the
+dataset repo, resolves the LoRA checkpoint from the model repo via
+hf_hub_download, and uploads results to the dataset repo at the end.
+
 Usage:
-  python final_eval.py --lora /ckpts/orbit_alpha_lora.safetensors --out /out/final
+  python final_eval.py --lora checkpoints/steps2000res768/orbit_alpha_lora/orbit_alpha_lora.safetensors --out /out/final
 """
 import argparse, json, os, sys
 
-# The job bootstrap only fetches this file; pull eval_edit.py (the module with
-# the pipeline/metrics/seeding shared with the baseline run) from the repo
-# itself so the sibling import works in a fresh container.
 _here = os.path.dirname(os.path.abspath(__file__))
 if _here not in sys.path:
     sys.path.insert(0, _here)
+
+# The job bootstrap only runs this file; pull eval_edit.py (pipeline/metrics/
+# seeding shared with the baseline run) from the dataset repo.
 if not os.path.exists(os.path.join(_here, 'eval_edit.py')):
     from huggingface_hub import hf_hub_download
+    import shutil
     p = hf_hub_download(repo_id='ysharma/gso-orbit-rgba', repo_type='dataset',
                         filename='eval_edit.py')
-    if os.path.abspath(os.path.dirname(p)) != _here:
-        import shutil
-        shutil.copy(p, os.path.join(_here, 'eval_edit.py'))
+    shutil.copy(p, os.path.join(_here, 'eval_edit.py'))
     print(f'fetched eval_edit.py -> {os.path.join(_here, "eval_edit.py")}', flush=True)
 
 import eval_edit as E
@@ -34,12 +37,20 @@ def main():
     ap.add_argument('--out', required=True)
     args = ap.parse_args()
 
+    # LoRA: local path if it exists, else a file path inside ysharma/orbit-alpha-lora.
+    lora_path = args.lora
+    if not os.path.exists(lora_path):
+        from huggingface_hub import hf_hub_download
+        lora_path = hf_hub_download(repo_id='ysharma/orbit-alpha-lora',
+                                    filename=args.lora)
+    print(f'using lora: {lora_path}', flush=True)
+
     pairs = [json.loads(l) for l in open(E.get_file('pairs_eval.jsonl'))]
     assert len(pairs) == 160, f'expected 160 eval pairs, got {len(pairs)}'
 
     pipe = E.load_pipe()
     m = E.make_metrics()
-    pipe.load_lora_weights(args.lora)
+    pipe.load_lora_weights(lora_path)
 
     for variant, suffix in (('no_suffix', ''), ('suffix', ' ' + E.ALPHA_SUFFIX)):
         pv = [dict(p, instruction=p['instruction'] + suffix) for p in pairs]
@@ -66,6 +77,12 @@ def main():
     except Exception as e:
         print(f'sheet failed (non-fatal): {e}', flush=True)
 
+    # results survive the container only if pushed from inside the job
+    from huggingface_hub import HfApi
+    HfApi().upload_folder(folder_path=args.out, repo_id='ysharma/gso-orbit-rgba',
+                          repo_type='dataset', path_in_repo='eval/final')
+    print('uploaded results -> ysharma/gso-orbit-rgba/eval/final', flush=True)
+
     print('DONE', flush=True)