204 lines
6.8 KiB
Python
204 lines
6.8 KiB
Python
import argparse
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import glob
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import json
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import os
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import re
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import clip
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import pandas as pd
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from tqdm import tqdm
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from utils.utils import clip_transform, load_video
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BATCH_SIZE = 32
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def get_aesthetic_model(path_to_model):
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"""Load the aesthetic predictor model"""
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m = nn.Linear(768, 1)
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s = torch.load(path_to_model, map_location="cpu", weights_only=False)
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m.load_state_dict(s)
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m.eval()
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return m
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def evaluate_aesthetic(aesthetic_model, clip_model, video_path, height=384, width=640, device="cuda"):
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"""Evaluate aesthetic quality for a single video"""
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aesthetic_model.eval()
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clip_model.eval()
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# Load video frames
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images = load_video(video_path, height=height, width=width)
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image_transform = clip_transform(224)
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aesthetic_scores_list = []
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# Process in batches
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for i in range(0, len(images), BATCH_SIZE):
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image_batch = images[i : i + BATCH_SIZE]
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image_batch = image_transform(image_batch)
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image_batch = image_batch.to(device)
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with torch.no_grad():
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image_feats = clip_model.encode_image(image_batch).to(torch.float32)
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image_feats = F.normalize(image_feats, dim=-1, p=2)
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aesthetic_scores = aesthetic_model(image_feats).squeeze(dim=-1)
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aesthetic_scores_list.append(aesthetic_scores)
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# Combine all scores
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aesthetic_scores = torch.cat(aesthetic_scores_list, dim=0)
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normalized_aesthetic_scores = aesthetic_scores / 10.0
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avg_score = torch.mean(normalized_aesthetic_scores, dim=0, keepdim=True)
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return avg_score.item()
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def main(args):
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baseline_name = os.path.basename(args.video_dir)
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output_path = os.path.join(args.output_path, baseline_name)
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output_json_path = os.path.join(output_path, "aesthetic_results.json")
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# Set device
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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print(f"Using device: {device}")
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# Load CSV file
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if not os.path.exists(args.input_csv):
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raise FileNotFoundError(f"CSV file not found: {args.input_csv}")
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df = pd.read_csv(args.input_csv)
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df_dict = df.set_index("id").to_dict("index")
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# Validate CSV columns
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required_columns = ["id", "duration"]
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for col in required_columns:
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if col not in df.columns:
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raise ValueError(f"CSV must contain '{col}' column. Found columns: {df.columns.tolist()}")
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# Load existing results if available
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existing_results = {}
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if os.path.exists(output_json_path):
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print(f"Found existing results at {output_json_path}, loading...")
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with open(output_json_path, "r") as f:
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existing_data = json.load(f)
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for item in existing_data.get("per_video_results", []):
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existing_results[item["id"]] = item
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print(f"Loaded {len(existing_results)} existing results")
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# Get all videos to process
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video_files = glob.glob(os.path.join(args.video_dir, "*_*_ori*.mp4"))
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video_files.sort(key=lambda x: int(re.search(r"(\d+)_", os.path.basename(x)).group(1)))
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print(f"\nFound {len(video_files)} videos in directory")
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# Check which videos need processing
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results = []
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scores = []
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videos_to_process = []
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for video_path in video_files:
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video_name = os.path.basename(video_path)
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parts = video_name.replace(".mp4", "").split("_")
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video_id = int(parts[0])
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if video_id not in df_dict:
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print(f"Warning: Video {video_name} (id={video_id}) not found in CSV, skipping")
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continue
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# Check if already processed
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if video_id in existing_results:
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# Use existing result
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results.append(existing_results[video_id])
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scores.append(existing_results[video_id]["aesthetic_score"])
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else:
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# Need to process
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videos_to_process.append((video_path, video_id, video_name))
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print(f"Already processed: {len(existing_results)} videos")
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print(f"Need to process: {len(videos_to_process)} videos")
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# Process remaining videos
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if videos_to_process:
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# Load models
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print("Loading CLIP model...")
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clip_model, preprocess = clip.load(args.clip_model_path, device=device)
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print("Loading aesthetic predictor model...")
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aesthetic_model = get_aesthetic_model(args.aesthetic_model_path).to(device)
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print("\nEvaluating remaining videos...")
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for video_path, video_id, video_name in tqdm(videos_to_process):
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try:
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score = evaluate_aesthetic(
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aesthetic_model,
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clip_model,
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video_path,
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height=args.height,
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width=args.width,
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device=device,
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)
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result_item = {"id": video_id, "video_name": video_name, "aesthetic_score": score}
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results.append(result_item)
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scores.append(score)
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except Exception as e:
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print(f"Error processing {video_name}: {str(e)}")
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continue
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else:
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print("No videos to process. Skipping evaluation.")
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return
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# Calculate overall metrics
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if scores:
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avg_score = sum(scores) / len(scores)
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# Sort results by video_id
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results_sorted = sorted(results, key=lambda x: x["id"])
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output = {
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"metric": "aesthetic",
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"average_score": avg_score,
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"num_videos": len(scores),
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"per_video_results": results_sorted,
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}
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# Save results
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os.makedirs(output_path, exist_ok=True)
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with open(output_json_path, "w") as f:
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json.dump(output, f, indent=2)
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print(f"\n{'=' * 60}")
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print("Results Summary:")
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print(f"{'=' * 60}")
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print(f"Average Aesthetic Score: {avg_score:.4f}")
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print(f"Number of videos evaluated: {len(scores)}")
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print(f"Results saved to: {output_json_path}")
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print(f"{'=' * 60}\n")
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else:
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print("No videos were successfully evaluated!")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Evaluate video aesthetic using CLIP + LAION aesthetic predictor")
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# Input/Output arguments
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parser.add_argument("--height", type=str, default=384)
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parser.add_argument("--width", type=str, default=640)
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parser.add_argument("--input_csv", type=str, default="playground/helios_t2v_prompts.csv")
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parser.add_argument("--video_dir", type=str, default="playground/toy-video")
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parser.add_argument("--output_path", type=str, default="playground/results")
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# Model arguments
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parser.add_argument("--clip_model_path", type=str, default="checkpoints/aesthetic_model/ViT-L-14.pt")
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parser.add_argument(
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"--aesthetic_model_path", type=str, default="checkpoints/aesthetic_model/sa_0_4_vit_l_14_linear.pth"
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)
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args = parser.parse_args()
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main(args)
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