Upload scripts/training/critic-agent/train.py with huggingface_hub

scripts/training/critic-agent/train.py

@@ -571,7 +571,7 @@ def _split_rows(rows: list[dict]) -> tuple[list[dict], list[dict]]:
 
 def _format_scores_text(scores: dict) -> str:
     if _is_aesthetic_score_schema(scores):
-        return _format_aesthetic_scores_text(scores)
+        return _format_aesthetic_evaluator_target({"teacher_scores": scores})
     return "\n".join(
         [
             f"PROMPT_ALIGNMENT: {float(scores.get('prompt_alignment', 0.0) or 0.0):.3f}",
@@ -601,70 +601,137 @@ def _is_aesthetic_score_schema(scores: dict) -> bool:
 
 
 def _format_aesthetic_scores_text(scores: dict) -> str:
-    return "\n".join(
-        [
-            f"NARRATIVE_EMOTIONAL_FIT: {float(scores.get('narrative_emotional_fit', 0.0) or 0.0):.3f}",
-            f"STYLE_WORLD_CONSISTENCY: {float(scores.get('style_world_consistency', 0.0) or 0.0):.3f}",
-            f"COMPOSITION_LIGHTING_DESIGN: {float(scores.get('composition_lighting_design', 0.0) or 0.0):.3f}",
-            f"COLOR_TEXTURE_REFINEMENT: {float(scores.get('color_texture_refinement', 0.0) or 0.0):.3f}",
-            f"VISUAL_HIERARCHY_READABILITY: {float(scores.get('visual_hierarchy_readability', 0.0) or 0.0):.3f}",
-            f"OVERALL_AESTHETIC_SCORE: {float(scores.get('overall_aesthetic_score', 0.0) or 0.0):.3f}",
-        ]
-    )
+    return _format_aesthetic_evaluator_target({"teacher_scores": scores})
+
+
+def _clamp_aesthetic_dimension_score(value: object) -> int:
+    try:
+        score = int(round(float(value)))
+    except (TypeError, ValueError):
+        score = 1
+    return max(1, min(4, score))
+
+
+def _clamp_aesthetic_overall_score(value: object) -> float:
+    try:
+        score = float(value)
+    except (TypeError, ValueError):
+        score = 1.0
+    return max(1.0, min(4.0, score))
 
 
 def _build_aesthetic_evaluator_system_prompt() -> str:
-    return (
-        build_critic_identity_prefix()
-        + "你负责低质量到高质量视频编辑结果的美学质量评估。\n"
-        "你需要对比低质量视频帧和编辑后视频帧，判断编辑后结果是否形成高质量、连贯、有审美完成度的视觉效果。\n"
-        "仅以纯文本形式回复，不要输出JSON，不要添加markdown代码块标记。\n"
-        "分数使用1到5的整数或小数，1表示很差，5表示优秀。\n"
-        "不要包含思维链、逐步推理、自我修正或重复分析。\n"
-        "直接以NARRATIVE_EMOTIONAL_FIT:开头，不要在标题前添加任何前言。\n"
-        "每个分数标题必须在同一行只包含一个数值。\n"
-        "FAILURE_TAGS必须是单行的逗号分隔蛇形命名标签。\n"
-        "REFLECTION_HINTS必须是单行，包含1-3个简短建议，用逗号分隔。\n"
-        "NOTES必须是单行，最多3个简短事实观察，用逗号分隔。\n"
-    )
+    return """You are a strict cinematic/VFX aesthetic rater.
+You are doing pointwise standalone aesthetic scoring: you see sampled frames
+from the edited video, but you do not see the source video. Use the editing
+instruction only as weak context for the intended visual direction. Do not judge
+whether the edit accurately followed the instruction, because the source video is
+not provided.
+
+Scoring scale for every dimension:
+4 = excellent / strongly successful
+3 = good with minor issues
+2 = weak with clear issues
+1 = failed or harms the aesthetic goal
+
+Rules:
+- Score each dimension independently.
+- There is no neutral middle score. Choose 2 or 3 when uncertain between weak and good.
+- If a video matches multiple descriptions, assign the lowest applicable score.
+- For object removal, cleanup, denoising, de-watermarking, or other utility edits,
+  a seamless and visually natural result can be aesthetically successful even if
+  it is not dramatic or cinematic.
+- Do not penalize a candidate because the requested edit removes an interesting
+  object or makes the scene simpler.
+- Do not give high artistic scores just because the image is sharp or expensive-looking.
+- Do not give high color scores just because colors are saturated.
+- Penalize visible inpainting seams, visual clutter, incoherent style mixing,
+  cheap texture/filter look, and unclear focal hierarchy when they are visible in
+  the edited frames.
+- Return valid JSON only, with no markdown.
+"""
+
+
+def _infer_aesthetic_task_type(instruction: str) -> str:
+    text = instruction.lower()
+    if any(word in text for word in ["remove", "erase", "delete", "hide", "clean up", "de-watermark", "watermark", "logo"]):
+        return "removal_or_cleanup"
+    if any(word in text for word in ["add", "insert", "place", "put ", "introduce", "include"]):
+        return "addition_or_insertion"
+    if any(word in text for word in ["replace", "swap", "change into", "turn into", "transform", "convert"]):
+        return "replacement_or_transformation"
+    if any(word in text for word in ["style", "aesthetic", "cinematic", "film", "color", "lighting", "tone", "grain", "texture"]):
+        return "style_or_look_change"
+    if any(word in text for word in ["enhance", "restore", "sharpen", "denoise", "improve", "refine"]):
+        return "quality_refinement"
+    return "general_edit"
 
 
 def _build_aesthetic_evaluator_user_prompt(row: dict) -> str:
-    return (
-        f"sample_id: {row.get('sample_id', '')}\n"
-        f"target_edit_prompt: {row.get('prompt', '')}\n"
-        "图像顺序:\n"
-        "- 前半部分是 low/source video 抽帧。\n"
-        "- 后半部分是 edited video 抽帧。\n"
-        "请按照以下5个美学维度评估 edited video:\n"
-        "- narrative_emotional_fit: 视觉结果是否服务于提示词的叙事、情绪和审美意图。\n"
-        "- style_world_consistency: 风格、世界观、材质和场景设定是否统一可信。\n"
-        "- composition_lighting_design: 构图、光照、景深、镜头观感是否有设计感。\n"
-        "- color_texture_refinement: 色彩、纹理、细节、材质是否精致且不廉价。\n"
-        "- visual_hierarchy_readability: 主体层次、可读性、视觉焦点是否清晰。\n"
-        "overall_aesthetic_score 是综合美学评分。\n"
-        "失败标签应使用简短蛇形命名标签，如: artifact_heavy, incoherent_style, weak_composition, poor_lighting, muddy_texture, unreadable_focus。\n"
-        "格式规则:\n"
-        "- 立即以NARRATIVE_EMOTIONAL_FIT:开头，不要引言、不要推理。\n"
-        "- 每个标题后只放一个数字在同一行。\n"
-        "- FAILURE_TAGS: 仅限逗号分隔蛇形命名标签。\n"
-        "- REFLECTION_HINTS: 仅限1到3个简短改进建议。\n"
-        "- NOTES: 最多3个简短事实观察。\n"
-        "返回以下精确的标题:\n"
-        "NARRATIVE_EMOTIONAL_FIT:\n"
-        "STYLE_WORLD_CONSISTENCY:\n"
-        "COMPOSITION_LIGHTING_DESIGN:\n"
-        "COLOR_TEXTURE_REFINEMENT:\n"
-        "VISUAL_HIERARCHY_READABILITY:\n"
-        "OVERALL_AESTHETIC_SCORE:\n"
-        "FAILURE_TAGS:\n"
-        "REFLECTION_HINTS:\n"
-        "NOTES:\n"
-    )
+    instruction = str(row.get("prompt", "") or "")
+    task_type = _infer_aesthetic_task_type(instruction)
+    return f"""Editing instruction / intended effect:
+{instruction}
+
+Inferred task type:
+{task_type}
+
+Important: you do not see the source video. Do not evaluate whether the edit was
+completed relative to the source. Evaluate the final edited video as a standalone
+visual result. For removal or cleanup tasks, invisible/seamless blending is a
+positive aesthetic outcome.
+
+Evaluate the edited video frames on these five dimensions:
+1. narrative_emotional_fit: whether the final edited result naturally integrates with the scene mood, emotional tone, and visual context without artificial or distracting anomalies.
+2. style_world_consistency: whether the final visual style fits the world, era, genre, and style language such as classical, wuxia, sci-fi, realistic, fantasy, or cinematic.
+3. composition_lighting_design: composition, contrast, lighting hierarchy, lens/cinematic design, and shot-level visual arrangement.
+4. color_texture_refinement: color harmony, saturation control, material/texture/filter refinement, and whether it avoids cheap or generic looks.
+5. visual_hierarchy_readability: whether the main visual intent is clear, focal hierarchy is readable, and important content is not obscured.
+
+"overall_aesthetic_score" should be a holistic assessment of final visual
+quality from 1.0 to 4.0, not a simple mathematical average of the five
+dimensions.
+
+Return this exact JSON schema:
+{{
+  "scores": {{
+    "narrative_emotional_fit": 1,
+    "style_world_consistency": 1,
+    "composition_lighting_design": 1,
+    "color_texture_refinement": 1,
+    "visual_hierarchy_readability": 1
+  }},
+  "overall_aesthetic_score": 1.0,
+  "uncertain": false,
+  "reason": "one concise sentence"
+}}
+"""
+
+
+def _format_aesthetic_evaluator_target(row: dict) -> str:
+    scores = row.get("teacher_scores", {}) or {}
+    raw_label = row.get("raw_label", {}) or {}
+    hints = row.get("reflection_hints", []) or []
+    reason = str(raw_label.get("reason", "") or (hints[0] if hints else "") or "")
+    payload = {
+        "scores": {
+            "narrative_emotional_fit": _clamp_aesthetic_dimension_score(scores.get("narrative_emotional_fit", 1)),
+            "style_world_consistency": _clamp_aesthetic_dimension_score(scores.get("style_world_consistency", 1)),
+            "composition_lighting_design": _clamp_aesthetic_dimension_score(scores.get("composition_lighting_design", 1)),
+            "color_texture_refinement": _clamp_aesthetic_dimension_score(scores.get("color_texture_refinement", 1)),
+            "visual_hierarchy_readability": _clamp_aesthetic_dimension_score(scores.get("visual_hierarchy_readability", 1)),
+        },
+        "overall_aesthetic_score": _clamp_aesthetic_overall_score(scores.get("overall_aesthetic_score", raw_label.get("overall_aesthetic_score", 1.0))),
+        "uncertain": bool(raw_label.get("uncertain", False)),
+        "reason": reason,
+    }
+    return json.dumps(payload, ensure_ascii=False, indent=2)
 
 
 def _format_evaluator_target(row: dict) -> str:
     scores = row.get("teacher_scores", {}) or {}
+    if _is_aesthetic_score_schema(scores):
+        return _format_aesthetic_evaluator_target(row)
     failure_tags = row.get("failure_tags", []) or []
     reflection_hints = row.get("reflection_hints", []) or []
     notes = row.get("teacher_replan_directives", []) or []
@@ -926,6 +993,13 @@ def _extract_training_frames(row: dict, frames_per_video: int, *, include_high:
     return image_paths
 
 
+def _extract_aesthetic_training_frames(row: dict, frames_per_video: int) -> list[str]:
+    value = str(row.get("edited_video_path", "") or "").strip()
+    if not value:
+        return []
+    return [str(p) for p in extract_frames_jpg(value, frame_count=frames_per_video)]
+
+
 def _extract_pairwise_frames(row: dict, frames_per_video: int) -> list[str]:
     image_paths: list[str] = []
     for key in ["low_video_path", "high_video_path", "candidate_a_video_path", "candidate_b_video_path"]:
@@ -1577,11 +1651,15 @@ def _run_real_sft(
                     )
                     loss = regression_criterion(preds, target)
                 else:
-                    image_paths = _extract_training_frames(
-                        row,
-                        args.frames_per_video,
-                        include_high=canonical_stage != "evaluator_sft",
-                    )
+                    scores = row.get("teacher_scores", {}) or {}
+                    if canonical_stage == "evaluator_sft" and _is_aesthetic_score_schema(scores):
+                        image_paths = _extract_aesthetic_training_frames(row, args.frames_per_video)
+                    else:
+                        image_paths = _extract_training_frames(
+                            row,
+                            args.frames_per_video,
+                            include_high=canonical_stage != "evaluator_sft",
+                        )
                     messages, _ = _build_multimodal_messages(stage, row, image_paths)
                     prompt_messages = messages[:-1]
                     full_text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)