Upload InternVideo2_6B_V5_ACT75_eval.py

InternVideo2_6B_V5_ACT75_eval.py

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+#!/usr/bin/env python
+# coding: utf-8
+# Cleaned and enhanced InternVideo2 6B evaluation script with structured logging
+# Source:
+
+import os
+import sys
+import subprocess
+import logging
+import json
+import argparse
+from pathlib import Path
+
+import numpy as np
+import cv2
+import torch
+from tqdm import tqdm
+from huggingface_hub import hf_hub_download, HfApi, login
+
+
+def setup_logging(log_level=logging.INFO, log_file=None):
+    handlers = []
+    fmt = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+    if log_file:
+        handlers.append(logging.FileHandler(log_file))
+    handlers.append(logging.StreamHandler(sys.stdout))
+    logging.basicConfig(level=log_level, format=fmt, handlers=handlers)
+    logging.info("Logging initialized.")
+
+
+def run_command(cmd, cwd=None):
+    logging.debug(f"Running command: {cmd} (cwd={cwd})")
+    result = subprocess.run(cmd, shell=True, cwd=cwd, capture_output=True, text=True)
+    if result.returncode != 0:
+        logging.error(f"Command failed: {cmd}\nSTDOUT: {result.stdout}\nSTDERR: {result.stderr}")
+        raise RuntimeError(f"Command '{cmd}' failed (exit code {result.returncode})")
+    logging.debug(f"Command succeeded, output: {result.stdout.strip()}")
+    return result.stdout.strip()
+
+
+def download_checkpoint(repo_id: str, filename: str) -> str:
+    logging.info(f"Downloading {filename} from {repo_id}...")
+    path = hf_hub_download(repo_id=repo_id, filename=filename)
+    logging.info(f"Downloaded vision checkpoint to {path}")
+    return path
+
+
+def load_config(config_path: str, vision_ckpt_path: str):
+    from demo.config import Config, eval_dict_leaf
+    logging.info(f"Loading config from {config_path}")
+    cfg = Config.from_file(config_path)
+    cfg = eval_dict_leaf(cfg)
+    cfg.model.vision_ckpt_path = vision_ckpt_path
+    cfg.model.vision_encoder.pretrained = vision_ckpt_path
+    cfg.pretrained_path = vision_ckpt_path
+    logging.debug(f"Config loaded: {cfg}")
+    return cfg
+
+
+def process_videos(
+    json_path: str,
+    model,
+    config,
+    output_prefix: str,
+    num_frames_override: int = None
+):
+    """
+    Run inference over each video, write outputs.
+    If num_frames_override is given, use it; otherwise use config.num_frames.
+    """
+    from demo.utils import retrieve_text, _frame_from_video
+
+    logging.info(f"Reading evaluation data from {json_path}")
+    data = json.loads(Path(json_path).read_text())
+    preds, logits = [], []
+
+    # choose frame window size
+    num_frames = num_frames_override if num_frames_override is not None else config.num_frames
+    logging.info(f"Using window size: {num_frames} frames")
+
+    for video_path, phrase, _ in data:
+        logging.info("\n--- Starting new video ---")
+        full_video = Path("photography-model") / video_path
+        logging.info(f"Processing {full_video} with phrase '{phrase}'")
+        frames = list(_frame_from_video(cv2.VideoCapture(str(full_video))))
+        scores = []
+
+        for j in tqdm(range(len(frames) - (num_frames - 1)), desc=Path(video_path).stem):
+            _, probs = retrieve_text(
+                frames[j : j + num_frames], [phrase],
+                model=model, topk=1, config=config
+            )
+            scores.append(probs[0])
+
+        best_idx = int(np.argmax(scores) + 1)
+        preds.append(best_idx)
+        logits.append(list(zip(map(float, scores), range(1, len(scores) + 1))))
+        logging.info(f"Video result: predicted frame {best_idx}\n")
+
+    preds_file = f"{output_prefix}-t{num_frames}.json"
+    logits_file = f"{output_prefix}-logits-t{num_frames}.json"
+    logging.info(f"Writing predictions to {preds_file}")
+    Path(preds_file).write_text(json.dumps(preds, indent=2))
+    logging.info(f"Writing logits to {logits_file}")
+    Path(logits_file).write_text(json.dumps(logits, indent=2))
+
+    return preds_file, logits_file
+
+
+def upload_results(token: str, upload_files: list, repo_id: str):
+    logging.info("Logging into Hugging Face Hub...")
+    login(token)
+    api = HfApi()
+    for file_path in upload_files:
+        logging.info(f"Uploading {file_path} to {repo_id}")
+        api.upload_file(
+            path_or_fileobj=file_path,
+            path_in_repo=Path(file_path).name,
+            repo_id=repo_id,
+            repo_type="dataset",
+        )
+    logging.info("Upload complete.")
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Evaluate InternVideo2 sliding-window retrieval."
+    )
+    parser.add_argument(
+        "--num_frames",
+        type=int,
+        default=None,
+        help="Manually set the number of frames per window."
+    )
+    args = parser.parse_args()
+
+    setup_logging()
+
+    # ensure IV2 repo
+    iv2_path = Path('~/IV2').expanduser()
+    if not iv2_path.exists():
+        logging.info("Cloning IV2 repository...")
+        run_command('git clone https://github.com/qingy1337/IV2.git ~/IV2')
+
+    os.chdir(iv2_path / 'InternVideo2' / 'multi_modality')
+    sys.path.append(os.getcwd())
+    run_command('git checkout fix-6b', cwd=os.getcwd())
+
+    MODEL_NAME = '6B'
+    vision_ckpt = download_checkpoint(
+        repo_id="OpenGVLab/InternVideo2-Stage2_6B-224p-f4",
+        filename="internvideo2-s2_6b-224p-f4.pt"
+    )
+    config = load_config('scripts/pretraining/stage2/6B/config.py', vision_ckpt)
+    from demo.utils import setup_internvideo2
+    model, tokenizer = setup_internvideo2(config)
+
+    if not Path('photography-model').exists():
+        run_command('git clone https://github.com/ruo2019/photography-model.git')
+
+    prefix = f"ACT75-V5-InternVideo-{MODEL_NAME}"
+    preds_file, logits_file = process_videos(
+        'photography-model/rustyjar/ACT75.json',
+        model, config, prefix,
+        num_frames_override=args.num_frames
+    )
+
+    upload_results(
+        os.getenv('HF_TOKEN', ''),
+        [preds_file, logits_file],
+        'qingy2024/InternVideo2-Data'
+    )
+
+
+if __name__ == '__main__':
+    main()