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README.md

@@ -1,3 +1,102 @@
----
-license: apache-2.0
----
+# Android Control Dataset
+
+## Overview
+
+This directory contains two dataset files (`and_ctrl_train.json` and `and_ctrl_test.json`) derived from the [Android Control](https://github.com/google-research/google-research/tree/master/android_control) project by Google Research. These datasets have been formatted specifically for GUI grounding training in LLaMA-Factory.
+
+## Dataset Description
+
+The Android Control dataset consists of episodes where each episode contains multiple steps. Each step includes:
+- **Step instructions**: Natural language instructions for UI interactions
+- **Actions**: The type of action to perform (click, scroll, input text, etc.)
+- **Coordinates**: Precise x, y coordinates for the action
+
+The data has been extracted and formatted to train models for mobile UI understanding and interaction tasks.
+
+## Files
+
+- `and_ctrl_train.json`: Training dataset
+- `and_ctrl_test.json`: Test/evaluation dataset
+- `download_android_control.ipynb`: Jupyter notebook for downloading images and processing the original data
+
+## Data Format
+
+Each entry in the JSON files follows the LLaMA-Factory conversation format:
+
+```json
+{
+  "messages": [
+    {
+      "role": "system",
+      "content": "You are a helpful assistant that can identify what action to perform on mobile UI Screenshot given the user instruction."
+    },
+    {
+      "role": "user",
+      "content": "<image>Click on the Recording 2"
+    },
+    {
+      "role": "assistant",
+      "content": "{\"action_type\": \"click\", \"x\": 561, \"y\": 535}"
+    }
+  ],
+  "images": ["and_ctrl/out_episode_18557_step_001.png"]
+}
+```
+
+## Setup Instructions
+
+To use these datasets in LLaMA-Factory:
+
+1. **Create the image directory**:
+   ```bash
+   mkdir -p data/and_ctrl
+   ```
+
+2. **Download images**:
+   Run the provided `download_android_control.ipynb` notebook to download and process the original images. The notebook will:
+   - Download TFRecord files from Google Storage (`gs://gresearch/android_control/`)
+   - Extract images and save them directly to `and_ctrl/` directory
+   - Automatically organize images with the naming convention: `out_episode_{episode_id}_step_{step_number}.png`
+   - Generate an `and_ctrl.json` file with the processed data
+
+3. **Dataset files**:
+   - Images: Stored in `data/and_ctrl/` folder
+   - Training dataset: `and_ctrl_train.json` in `data/datasets/`
+   - Test dataset: `and_ctrl_test.json` in `data/datasets/`
+
+## Dataset Statistics
+
+**Total samples**: Train: 82,944 | Test: 904
+
+| Action Type | Train | Test |
+|-------------|-------|------|
+| click | 51,793 (62.44%) | 125 (13.83%) |
+| scroll | 11,005 (13.27%) | 125 (13.83%) |
+| input_text | 5,966 (7.19%) | 125 (13.83%) |
+| wait | 5,657 (6.82%) | 125 (13.83%) |
+| open_app | 5,572 (6.72%) | 125 (13.83%) |
+| navigate_back | 2,909 (3.51%) | 125 (13.83%) |
+| long_press | 42 (0.05%) | 125 (13.83%) |
+| navigate_home | 0 (0.00%) | 29 (3.21%) |
+
+**Note**: The training dataset shows a natural distribution with click actions being dominant (62.44%), while the test dataset is intentionally balanced with most action types having equal representation (~13.83% each). The `navigate_home` action appears only in the test set.
+
+## Training Usage
+
+These datasets are specifically formatted for training multimodal language models to:
+- Understand mobile UI screenshots
+- Ground natural language instructions to specific UI elements
+- Generate precise action coordinates for UI automation
+- Learn mobile app interaction patterns
+
+## Source and Attribution
+
+Original dataset: [Google Research Android Control](https://github.com/google-research/google-research/tree/master/android_control)
+
+The Android Control dataset was created by Google Research for advancing mobile UI understanding and automation research.
+
+## Notes
+
+- The images are referenced with relative paths starting with `and_ctrl/`
+- Each action includes the action type and necessary parameters (coordinates, text, direction, etc.)
+- The test set can be used for evaluating model performance on unseen mobile UI interactions

download_android_control.ipynb

@@ -0,0 +1,119 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/google-research/google-research.git\n",
+    "!pip install tensorflow"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download images"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "CgoFvmqCtps3",
+    "outputId": "64381a1a-5e3d-4ce4-92dd-6a11e43f61ed"
+   },
+   "outputs": [],
+   "source": "# -*- coding: utf-8 -*-\nimport io\nimport json\nfrom pathlib import Path\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport tensorflow as tf\nfrom PIL import Image\n\n# =========================\n# TF.Feature -> Python 값 복원\n# =========================\ndef feature_to_list_bytes(f: tf.train.Feature) -> List[bytes]:\n    return list(f.bytes_list.value)\n\ndef feature_to_list_int(f: tf.train.Feature) -> List[int]:\n    return list(f.int64_list.value)\n\ndef feature_to_list_float(f: tf.train.Feature) -> List[float]:\n    return list(f.float_list.value)\n\ndef get_feature(example: tf.train.Example, key: str) -> Optional[tf.train.Feature]:\n    fmap = example.features.feature\n    return fmap[key] if key in fmap else None\n\n# =========================\n# 액션(dict)에서 좌표 추출(정규화/픽셀 모두 지원)\n# =========================\ndef extract_points_from_action(\n    action: Dict[str, Any],\n    img_w: int,\n    img_h: int\n) -> List[Tuple[int, int]]:\n    \"\"\"\n    반환: [(x_px, y_px), ...]\n    - 점 하나(탭)면 길이 1\n    - 드래그/스와이프면 시작/끝 2점\n    - 없으면 []\n    \"\"\"\n    pts: List[Tuple[int, int]] = []\n\n    def to_px(x: float, y: float, normalized: Optional[bool]=None) -> Tuple[int,int]:\n        if normalized is None:\n            normalized = (0.0 <= x <= 1.0 and 0.0 <= y <= 1.0)\n        if normalized:\n            return (int(round(x * img_w)), int(round(y * img_h)))\n        else:\n            return (int(round(x)), int(round(y)))\n\n    # 1) 최상위 x, y\n    if \"x\" in action and \"y\" in action:\n        pts.append(to_px(float(action[\"x\"]), float(action[\"y\"]), None))\n\n    # 2) point / click / tap / press / long_press / long_tap\n    for k in [\"point\", \"click\", \"tap\", \"press\", \"long_press\", \"long_tap\"]:\n        if k in action and isinstance(action[k], dict):\n            px = action[k]\n            if \"x\" in px and \"y\" in px:\n                pts.append(to_px(float(px[\"x\"]), float(px[\"y\"]), None))\n        if k in action and isinstance(action[k], list):\n            for px in action[k]:\n                if isinstance(px, dict) and \"x\" in px and \"y\" in px:\n                    pts.append(to_px(float(px[\"x\"]), float(px[\"y\"]), None))\n\n    # 3) from/to, start/end\n    for a, b in [(\"from\", \"to\"), (\"start\", \"end\")]:\n        if a in action and b in action and isinstance(action[a], dict) and isinstance(action[b], dict):\n            ax, ay = action[a].get(\"x\"), action[a].get(\"y\")\n            bx, by = action[b].get(\"x\"), action[b].get(\"y\")\n            if ax is not None and ay is not None and bx is not None and by is not None:\n                pts.append(to_px(float(ax), float(ay), None))\n                pts.append(to_px(float(bx), float(by), None))\n\n    # 4) start_x/start_y/end_x/end_y\n    cand = {\"start_x\": None, \"start_y\": None, \"end_x\": None, \"end_y\": None}\n    found = False\n    for ck in cand.keys():\n        if ck in action:\n            cand[ck] = float(action[ck])\n            found = True\n    if found and cand[\"start_x\"] is not None and cand[\"start_y\"] is not None:\n        pts.append(to_px(cand[\"start_x\"], cand[\"start_y\"], None))\n        if cand[\"end_x\"] is not None and cand[\"end_y\"] is not None:\n            pts.append(to_px(cand[\"end_x\"], cand[\"end_y\"], None))\n\n    # 중복 제거\n    uniq: List[Tuple[int,int]] = []\n    seen = set()\n    for p in pts:\n        if p not in seen:\n            uniq.append(p)\n            seen.add(p)\n    return uniq\n\n# =========================\n# 에피소드 파싱\n# =========================\ndef load_episode_from_example(ex: tf.train.Example) -> Dict[str, Any]:\n    f = ex.features.feature\n\n    screenshots_bytes = feature_to_list_bytes(f[\"screenshots\"])\n    a11y_bytes_list = feature_to_list_bytes(f[\"accessibility_trees\"])\n    widths = feature_to_list_int(f[\"screenshot_widths\"])\n    heights = feature_to_list_int(f[\"screenshot_heights\"])\n\n    actions_json_list = [b.decode(\"utf-8\") for b in feature_to_list_bytes(f[\"actions\"])]\n    step_insts = [b.decode(\"utf-8\") for b in feature_to_list_bytes(f[\"step_instructions\"])]\n    actions = [json.loads(s) for s in actions_json_list]\n\n    goal = f[\"goal\"].bytes_list.value[0].decode(\"utf-8\")\n    episode_id = int(f[\"episode_id\"].int64_list.value[0]) if f[\"episode_id\"].int64_list.value else int(\n        f[\"episode_id\"].bytes_list.value[0].decode(\"utf-8\")\n    )\n\n    assert len(screenshots_bytes) == len(widths) == len(heights), \"screenshot/width/height 길이 불일치\"\n    assert len(actions) == len(step_insts) == (len(screenshots_bytes) - 1), \\\n        \"actions/step_instructions는 screenshots-1과 같아야 함\"\n\n    return {\n        \"episode_id\": episode_id,\n        \"goal\": goal,\n        \"screenshots\": screenshots_bytes,\n        \"a11y\": a11y_bytes_list,\n        \"widths\": widths,\n        \"heights\": heights,\n        \"actions\": actions,\n        \"step_instructions\": step_insts,\n    }\n\n# =========================\n# 액션 매핑 & 유틸\n# =========================\ndef _center_xy(w: int, h: int) -> Tuple[int,int]:\n    return (int(round(w/2)), int(round(h/2)))\n\ndef _norm_dir(d: Optional[str]) -> str:\n    if not d: return \"down\"\n    d = str(d).lower()\n    if d in [\"up\",\"down\",\"left\",\"right\"]:\n        return d\n    if d in [\"u\",\"top\"]: return \"up\"\n    if d in [\"d\",\"bottom\"]: return \"down\"\n    if d in [\"l\"]: return \"left\"\n    if d in [\"r\"]: return \"right\"\n    return \"down\"\n\ndef map_action(\n    action: Dict[str, Any],\n    w: int,\n    h: int,\n    pts: List[Tuple[int,int]],\n) -> Optional[Dict[str, Any]]:\n    \"\"\"\n    허용 매핑:\n    click -> {\"type\": \"touch\", \"x\": <x>, \"y\": <y>}\n    long_press -> {\"type\": \"long_touch\", \"x\": <x>, \"y\": <y>}\n    input_text -> {\"type\": \"set_text\", \"text\": \"...\", \"x\": <x>, \"y\": <y>}\n    scroll -> {\"type\": \"scroll\", \"direction\": \"up|down|left|right\", \"x\": <center_x>, \"y\": <center_y>}\n    navigate_home -> {\"type\": \"press\", \"key\": \"home\"}\n    navigate_back -> {\"type\": \"press\", \"key\": \"back\"}\n    \"\"\"\n    atype = (action.get(\"action_type\") or action.get(\"type\") or action.get(\"action\") or \"\").lower()\n    x, y = (pts[0] if pts else _center_xy(w, h))\n\n    if atype in [\"click\", \"tap\", \"press\", \"click_view\"]:\n        return {\"type\": \"touch\", \"x\": x, \"y\": y}\n\n    if atype in [\"long_press\", \"long_tap\", \"long_click\"]:\n        return {\"type\": \"long_touch\", \"x\": x, \"y\": y}\n\n    if atype in [\"input_text\", \"set_text\", \"type_text\", \"enter_text\", \"text\"]:\n        text = action.get(\"text\") or action.get(\"input_text\") or action.get(\"value\") or \"\"\n        return {\"type\": \"set_text\", \"text\": str(text), \"x\": x, \"y\": y}\n\n    if atype in [\"scroll\", \"swipe\"]:\n        if len(pts) >= 2:\n            cx = (pts[0][0] + pts[1][0]) // 2\n            cy = (pts[0][1] + pts[1][1]) // 2\n        else:\n            cx, cy = _center_xy(w, h)\n        return {\"type\": \"scroll\", \"direction\": _norm_dir(action.get(\"direction\")), \"x\": cx, \"y\": cy}\n\n    if atype in [\"navigate_home\", \"home\", \"press_home\"]:\n        return {\"type\": \"press\", \"key\": \"home\"}\n\n    if atype in [\"navigate_back\", \"back\", \"press_back\"]:\n        return {\"type\": \"press\", \"key\": \"back\"}\n\n    # 그 외(open_app, wait 등) → 저장하지 않음\n    return None\n\ndef save_clean_image(img_bytes: bytes, episode_id: int, step_idx: int, base_dir: str = \"and_ctrl\") -> str:\n    \"\"\"\n    out_episode_{EP}_step_{STEP:03d}.png  (오버레이 없음)\n    \"\"\"\n    Path(base_dir).mkdir(parents=True, exist_ok=True)\n    fname = f\"out_episode_{episode_id}_step_{step_idx:03d}.png\"\n    fpath = Path(base_dir) / fname\n    Image.open(io.BytesIO(img_bytes)).convert(\"RGB\").save(fpath)\n    # Return just the relative path from base_dir\n    return f\"{base_dir}/{fname}\"\n\n# =========================\n# 메시지 JSON 내보내기\n# =========================\ndef export_messages(ds, limit_episodes: int = 5, out_json: str = \"and_ctrl.json\", image_dir: str = \"and_ctrl\"):\n    \"\"\"\n    지정한 TFRecordDataset에서 앞 N개 에피소드의 스텝 중\n    허용 액션만 모아 요청 포맷으로 and_ctrl.json 저장.\n    \"\"\"\n    all_items: List[Dict[str, Any]] = []\n    ep_cnt = 0\n\n    for raw in ds:\n        ex = tf.train.Example()\n        ex.ParseFromString(raw.numpy())\n        ep = load_episode_from_example(ex)\n\n        ep_id = ep[\"episode_id\"]\n        for i, (action, inst) in enumerate(zip(ep[\"actions\"], ep[\"step_instructions\"])):\n            w, h = ep[\"widths\"][i], ep[\"heights\"][i]\n            img_bytes = ep[\"screenshots\"][i]\n            pts = extract_points_from_action(action, w, h)\n            mapped = map_action(action, w, h, pts)\n            if not mapped:\n                continue  # 스킵\n\n            img_path = save_clean_image(img_bytes, ep_id, i, base_dir=image_dir)\n\n            all_items.append({\n                \"messages\": [\n                    {\"role\": \"user\", \"content\": f\"<image>\\n{inst}\"},\n                    # JSON이 아닌, 파이썬 dict 문자열(single quote)로 저장\n                    {\"role\": \"assistant\", \"content\": str(mapped)}\n                ],\n                \"images\": [img_path]\n            })\n\n        ep_cnt += 1\n        if ep_cnt >= limit_episodes:\n            break\n\n    with open(out_json, \"w\", encoding=\"utf-8\") as f:\n        json.dump(all_items, f, ensure_ascii=False, indent=2)\n\n    print(f\"[DONE] episodes processed: {ep_cnt}, items saved: {len(all_items)} → {out_json}\")\n\n# =========================\n# 실행 진입점\n# =========================\ndef main():\n    # 필요시 경로 패턴 조정\n    filenames = tf.io.gfile.glob('gs://gresearch/android_control/android_control*')\n    ds = tf.data.TFRecordDataset(filenames, compression_type='GZIP')\n    export_messages(ds, limit_episodes=50, out_json=\"and_ctrl.json\", image_dir=\"and_ctrl\")\n\nif __name__ == \"__main__\":\n    main()"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
+    "id": "9wgmlG4CxlkL",
+    "outputId": "8dfce9bd-7003-4637-98ad-6843f2aeb9f9"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "46.5 GiB     gs://gresearch/android_control/android_control*\n"
+     ]
+    }
+   ],
+   "source": [
+    "!gsutil du -sh gs://gresearch/android_control/android_control*\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Download Images and episodes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "eP8n5YdK8Mns"
+   },
+   "outputs": [
+    {
+     "ename": "",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31mRunning cells with 'and_ctrl (Python 3.12.11)' requires the ipykernel package.\n",
+      "\u001b[1;31mInstall 'ipykernel' into the Python environment. \n",
+      "\u001b[1;31mCommand: '/home/work/kyochul/and_ctrl/bin/python -m pip install ipykernel -U --force-reinstall'"
+     ]
+    }
+   ],
+   "source": [
+    "import os"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "and_ctrl",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}