Merge pull request #1175 from better629/feat_werewolf

Feat add werewolf game

examples/werewolf_game/evals/eval.py

@@ -0,0 +1,218 @@
+"""
+Filename: MetaGPT/examples/werewolf_game/evals/eval.py
+Created Date: Oct 18, 2023
+Updated Date: Oct 24, 2023
+Author: [Aria](https://github.com/ariafyy)
+Info: eval the Voting Accuracy Rate of non_werewolves and Vote Difficulity 
+"""
+
+import glob
+import os
+import re
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+from tqdm import tqdm
+from utils import Utils
+
+from metagpt.const import DEFAULT_WORKSPACE_ROOT, METAGPT_ROOT
+from metagpt.environment.werewolf.const import RoleType
+
+
+class Vote:
+    """Vote Evaluation"""
+
+    def __init__(self):
+        self.OUT_PATH = DEFAULT_WORKSPACE_ROOT / "outputs"
+        os.makedirs(self.OUT_PATH, exist_ok=True)
+        self.SUB_FOLDER_LIST = ["01-10", "11-20", "21-30"]
+
+    def _get_log_fileslist(self, IN_PATH) -> list[str]:
+        files_list = []
+        for SUB_FOLDER in self.SUB_FOLDER_LIST:
+            files_list.extend(glob.glob(str(IN_PATH / SUB_FOLDER / "*.txt")))
+        return files_list
+
+    def extract_votes_from_logs(self, files_list: list):
+        for in_logfile in tqdm(files_list):
+            SUB_FOLDER = (Path(in_logfile).parent).stem
+            out_txtfile = self.OUT_PATH / "# {0}_{1}.txt".format(SUB_FOLDER, Path(in_logfile).stem)
+            Utils().pick_vote_log(in_logfile, out_txtfile)
+        votefiles_list = Utils().get_file_list(self.OUT_PATH)
+        return votefiles_list
+
+    @staticmethod
+    def parse_vote_text2chunks(text: str):
+        """
+        parse each game vote log into text chunks
+
+        one chunk example:
+        ['Player1', 'Player2', 'Player3', 'Player5', 'Player6']. Say ONLY: I vote to eliminate ...
+        Player1(Witch): 49 | I vote to eliminate Player5
+        Player2(Villager): 49 | I vote to eliminate Player5
+        Player3(Villager): 49 | I vote to eliminate Player5
+        Player5(Werewolf): 49 | I vote to eliminate Player6
+        Player6(Seer): 49 | I vote to eliminate Player5
+        """
+        pattern = re.compile(r"""\[([^\]]+)\]. Say ONLY: I vote to eliminate ...""")
+        chunks = {}
+        chunk_id = 0
+        last_end = 0
+        for match in pattern.finditer(text):
+            start = match.start()
+            chunk = text[last_end:start]
+            chunks[f"vote_{chunk_id}"] = chunk.strip()
+            last_end = match.end()
+            chunk_id += 1
+        final_chunk = text[last_end:].strip()
+        if final_chunk:
+            chunks[f"vote_{chunk_id}"] = final_chunk
+        return chunks
+
+    def _vote_rate_players(self, text: str):
+        """
+        # calculate the rate of goodteam vote werewolves
+        :example:
+
+        input:
+        ['Player1', 'Player2', 'Player3', 'Player5', 'Player6']. Say ONLY: I vote to eliminate ...
+        Player1(Witch): 49 | I vote to eliminate Player5
+        Player2(Villager): 49 | I vote to eliminate Player5
+        Player3(Villager): 49 | I vote to eliminate Player5
+        Player5(Werewolf): 49 | I vote to eliminate Player6
+        Player6(Seer): 49 | I vote to eliminate Player5
+
+        output:
+        werewolves:  ['Player5']
+        non_werewolves: ['Player1', 'Player2', 'Player3', 'Player6']
+        as you can see :Player2(Villager) and   Player3(Villager) vote to eliminate Player5(Werewolf)
+        :return goodteam vote rateability: 100.00%
+        """
+        pattern = re.compile(r"(\w+)\(([^\)]+)\): \d+ \| I vote to eliminate (\w+)")
+        # find all werewolves
+        werewolves = []
+        for match in pattern.finditer(text):
+            if match.group(2) == RoleType.WEREWOLF.value:
+                werewolves.append(match.group(1))
+
+        # find all non_werewolves
+        non_werewolves = []
+        for match in pattern.finditer(text):
+            if match.group(2) != RoleType.WEREWOLF.value:
+                non_werewolves.append(match.group(1))
+        num_non_werewolves = len(non_werewolves)
+
+        # count players other than werewolves made the correct votes
+        correct_votes = 0
+        for match in pattern.finditer(text):
+            if match.group(2) != RoleType.WEREWOLF.value and match.group(3) in werewolves:
+                correct_votes += 1
+
+        # cal the rateability of non_werewolves
+        rate = correct_votes / num_non_werewolves
+        good_vote_rate = round(rate, 2)
+        return {"good_vote_rate": good_vote_rate, "werewolves": werewolves, "non_werewolves": non_werewolves}
+
+    def get_goodteam_vote_rate(self, text: str) -> float:
+        goodteam_vote_rate = self._vote_rate_players(text)["good_vote_rate"]
+        return goodteam_vote_rate
+
+    def get_werewolves(self, text: str) -> list:
+        werewolves_list = self._vote_rate_players(text)["werewolves"]
+        return werewolves_list
+
+    def get_non_werewolves(self, text: str) -> list:
+        non_werewolves_list = self._vote_rate_players(text)["non_werewolves"]
+        return non_werewolves_list
+
+    def get_votewolf_difficulty(self, werewolves: list, non_werewolves: list) -> str:
+        num_living_wolfs = len(werewolves)
+        num_living_players = len(werewolves) + len(non_werewolves)
+        votewolf_difficulty = "_{0} / {1}".format(num_living_wolfs, num_living_players)
+        return votewolf_difficulty
+
+    def get_result_df(self, out_txtfile: str) -> pd.DataFrame:
+        """
+        folder:  sub folders for evals
+        file: evaluation file, each file represents one game
+        votes: the number of votes, eg. vote_1 represents the first vote of this game,
+        good_vote_rate:the rateability of a good person voting against a werewolf,
+                   correct_votes / the total number of players other than werewolves
+        total_votes:the total number of votes cast
+        """
+        with open(out_txtfile, "r") as out_file:
+            text = out_file.read()
+            chunks = self.parse_vote_text2chunks(text)
+            res = []
+            for k, v in chunks.items():
+                if v != "":
+                    chunks_list = list(chunks.keys())
+                    total_votes = len(chunks_list) - 1
+                    werewolves = self.get_werewolves(v)
+                    non_werewolves = self.get_non_werewolves(v)
+                    good_vote_rate = self.get_goodteam_vote_rate(v)
+                    votewolf_difficulty = self.get_votewolf_difficulty(werewolves, non_werewolves)
+                    folder = Utils().filename_to_foldername(out_txtfile)
+                    result = {
+                        "folder": folder,
+                        "file": Path(out_txtfile).stem + ".txt",
+                        "vote_round": k,
+                        "good_vote_rate": good_vote_rate,
+                        "total_votes": total_votes,
+                        "votewolf_difficulty": votewolf_difficulty,
+                    }
+                    res.append(result)
+        df = pd.DataFrame(res)
+        return df
+
+    def calc_avg_rate(self, IN_PATH) -> pd.DataFrame:
+        """
+        get avg_rate for each game
+        avg_rate : the good_rate/total number of votes in the game
+        vote1_rate: First Round Voting Accuracy Rate
+        """
+        infiles_list = self._get_log_fileslist(IN_PATH)
+        votefiles_list = self.extract_votes_from_logs(infiles_list)
+        df_list = [self._load_df_from_file(file) for file in votefiles_list]
+        combined_df = pd.concat(df_list, ignore_index=True)
+        # calculate the average good_vote_rate for each file
+        mean_rates = self._calculate_mean_rates(combined_df)
+        combined_df["avg_rate"] = combined_df["file"].map(mean_rates)
+        # calculate vote1 rate
+        vote1_rates = self._calc_vote1_rates(combined_df)
+        combined_df["vote1_rate"] = combined_df["folder"].map(vote1_rates.set_index("folder")["good_vote_rate"])
+        combined_df.loc[combined_df["vote_round"] != "vote_1", "vote1_rate"] = np.nan
+        combined_df["vote1_rate"] = combined_df["vote1_rate"].apply(self._format_rates)
+        combined_df["good_vote_rate"] = combined_df["good_vote_rate"].apply(self._format_rates)
+        combined_df["avg_rate"] = combined_df["avg_rate"].apply(self._format_rates)
+        combined_df.sort_values(["file"], ascending=True, inplace=True)
+        return combined_df
+
+    def _calc_vote1_rates(self, df):
+        df_vote1 = df[df["vote_round"] == "vote_1"]
+        vote1_rates = df_vote1.groupby("folder")["good_vote_rate"].mean().reset_index()
+        return vote1_rates
+
+    def _load_df_from_file(self, file):
+        return self.get_result_df(file)
+
+    def _calculate_mean_rates(self, df):
+        return df.groupby("file")["good_vote_rate"].mean()
+
+    def _format_rates(self, s):
+        return Utils().float_to_percent(s)
+
+    def get_eval_csv(self, IN_PATH, EVAL_RESULT):
+        """
+        IN_PATH : parent folder of ["01-10", "11-20", "21-30"]
+        EVAL_RESULT : output csv file path
+        """
+        combined_df = self.calc_avg_rate(IN_PATH)
+        combined_df.to_csv(EVAL_RESULT, index=False)
+
+
+if __name__ == "__main__":
+    IN_PATH = METAGPT_ROOT / "examples/werewolf_game/evals"
+    EVAL_RESULT = DEFAULT_WORKSPACE_ROOT / "outputs" / "goodteam_vote_rate.csv"
+    Vote().get_eval_csv(IN_PATH, EVAL_RESULT)

examples/werewolf_game/evals/utils.py

@@ -0,0 +1,134 @@
+"""
+Filename: MetaGPT/examples/werewolf_game/evals/utils.py
+Created Date: Oct 11, 2023
+Revised Date: Oct 20, 2023
+Author: [Aria](https://github.com/ariafyy)
+"""
+import glob
+import os
+import re
+from pathlib import Path
+
+from metagpt.const import METAGPT_ROOT
+
+
+class Utils:
+    """Utils: utils of logs"""
+
+    @staticmethod
+    def polish_log(in_logfile, out_txtfile):
+        """polish logs for evaluation"""
+        pattern_text = r"(\d{4}-\d{2}-\d{2} \d{2}:\d{2}:\d{2}\.\d{3}) \| (\w+) +\| ([\w\.]+:\w+:\d+) - (.*\S)"
+        pattern_player = r"(Player(\d{1}): \w+)"
+        pattern_start = False
+        json_start = False
+
+        with open(in_logfile, "r") as f, open(out_txtfile, "w") as out:
+            for line in f.readlines():
+                matches = re.match(pattern_text, line)
+                if matches:
+                    message = matches.group(4).strip()
+                    pattern_start = True
+                    json_start = False
+
+                    if (
+                        "Moderator(Moderator) ready to InstructSpeak" not in message
+                        and "Moderator(Moderator) ready to ParseSpeak" not in message
+                        and "Total running cost:" not in message
+                    ):
+                        out.write("- " + message + "\n")
+                    else:
+                        out.write("\n")
+
+                elif pattern_start and not matches:
+                    if "gpt-4 may update over time" in line:
+                        line = ""
+                    out.write(line)
+
+                elif line.strip().startswith("{"):
+                    out.write(line.strip())
+                    json_start = True
+
+                elif json_start and not line.strip().endswith("}"):
+                    out.write(line.strip())
+
+                elif json_start and line.strip().endswith("}"):
+                    out.write(line.strip())
+                    json_start = False
+
+                elif (
+                    line.startswith("(User):") or line.startswith("********** STEP:") or re.search(pattern_player, line)
+                ):
+                    out.write(line)
+
+                else:
+                    out.write("\n")
+
+    @staticmethod
+    def pick_vote_log(in_logfile, out_txtfile):
+        """
+        pick the vote log from the log file.
+        ready to AnnounceGameResult serves as the 'HINT_TEXT ' which indicates the end of the game.
+        based on bservation and reflection, then discuss is not in vote session.
+        """
+        pattern_vote = r"(Player\d+)\(([A-Za-z]+)\): (\d+) \| (I vote to eliminate Player\d+)"
+        ignore_text = """reflection"""
+        HINT_TEXT = r"ready to AnnounceGameResult"
+        pattern_moderator = r"\[([^\]]+)\]\. Say ONLY: I vote to eliminate ..."
+        in_valid_block = False
+
+        with open(in_logfile, "r") as f:
+            lines = f.read()
+            split_lines = lines.split(HINT_TEXT)
+
+            if len(split_lines) < 2:
+                print(f"Key text :{HINT_TEXT} not found in {in_logfile}")
+                return
+
+            relevant_lines = split_lines[1].split("\n")
+            with open(out_txtfile, "w") as out:
+                for line in relevant_lines:
+                    if re.search(pattern_moderator, line):
+                        in_valid_block = True
+                        out.write(line.lstrip() + "\n")
+
+                    elif in_valid_block and re.search(pattern_vote, line):
+                        out.write(line + "\n")
+                    elif ignore_text in line:
+                        in_valid_block = False
+
+    @staticmethod
+    def get_file_list(path: str) -> list:
+        file_pattern = os.path.join(path, "*.txt")
+        files_list = glob.glob(file_pattern)
+        return files_list
+
+    @staticmethod
+    def filename_to_foldername(out_txtfile: str):
+        """
+        convert filename into its parent folder name
+        input:"....../# 01-10_10132100.txt"
+        output:# 01-10
+        """
+        s = Path(out_txtfile).stem
+        pattern_folder = r"([^_]*)_"
+        match = re.match(pattern_folder, s)
+        if match:
+            folder = match.group(1)
+            return folder
+
+    @staticmethod
+    def float_to_percent(decimal: float) -> str:
+        """
+        input:  1.00
+        output: 100.00%
+        """
+        percent = decimal * 100
+        return f"{percent:.2f}%"
+
+
+if __name__ == "__main__":
+    in_logfile = METAGPT_ROOT / "logs/log.txt"
+    out_txtfile = "input your wish path"
+    # Utils().polish_log(in_logfile, out_txtfile)
+    Utils().pick_vote_log(in_logfile, out_txtfile)

examples/werewolf_game/start_game.py

@@ -0,0 +1,66 @@
+import asyncio
+
+import fire
+
+from metagpt.ext.werewolf.roles import Guard, Moderator, Seer, Villager, Werewolf, Witch
+from metagpt.ext.werewolf.werewolf_game import WerewolfGame
+from metagpt.logs import logger
+
+
+async def start_game(
+    investment: float = 3.0,
+    n_round: int = 5,
+    shuffle: bool = True,
+    add_human: bool = False,
+    use_reflection: bool = True,
+    use_experience: bool = False,
+    use_memory_selection: bool = False,
+    new_experience_version: str = "",
+):
+    game = WerewolfGame()
+    game_setup, players = game.env.init_game_setup(
+        role_uniq_objs=[Villager, Werewolf, Guard, Seer, Witch],
+        num_werewolf=2,
+        num_villager=2,
+        shuffle=shuffle,
+        add_human=add_human,
+        use_reflection=use_reflection,
+        use_experience=use_experience,
+        use_memory_selection=use_memory_selection,
+        new_experience_version=new_experience_version,
+    )
+    logger.info(f"{game_setup}")
+
+    players = [Moderator()] + players
+    game.hire(players)
+    game.invest(investment)
+    game.run_project(game_setup)
+    await game.run(n_round=n_round)
+
+
+def main(
+    investment: float = 20.0,
+    n_round: int = 100,
+    shuffle: bool = True,
+    add_human: bool = False,
+    use_reflection: bool = True,
+    use_experience: bool = False,
+    use_memory_selection: bool = False,
+    new_experience_version: str = "",
+):
+    asyncio.run(
+        start_game(
+            investment,
+            n_round,
+            shuffle,
+            add_human,
+            use_reflection,
+            use_experience,
+            use_memory_selection,
+            new_experience_version,
+        )
+    )
+
+
+if __name__ == "__main__":
+    fire.Fire(main)

metagpt/environment/android/android_ext_env.py

@@ -6,7 +6,11 @@ import subprocess
 from pathlib import Path
 from typing import Any, Optional
 
+from modelscope.pipelines import pipeline
+from modelscope.utils.constant import Tasks
+from PIL import Image
 from pydantic import Field
+from text_icon_localization import ocr
 
 from metagpt.environment.android.const import ADB_EXEC_FAIL
 from metagpt.environment.android.env_space import (
@@ -17,6 +21,7 @@ from metagpt.environment.android.env_space import (
     EnvObsValType,
 )
 from metagpt.environment.base_env import ExtEnv, mark_as_readable, mark_as_writeable
+from metagpt.logs import logger
 
 
 class AndroidExtEnv(ExtEnv):
@@ -228,3 +233,39 @@ class AndroidExtEnv(ExtEnv):
         adb_cmd = f"{self.adb_prefix_si} swipe {start[0]} {start[1]} {end[0]} {end[1]} {duration}"
         swipe_res = self.execute_adb_with_cmd(adb_cmd)
         return swipe_res
+
+    @mark_as_writeable
+    def user_exit(self):
+        adb_cmd = "adb shell am start -a android.intent.action.MAIN -c android.intent.category.HOME"
+        exit_res = self.execute_adb_with_cmd(adb_cmd)
+        return exit_res
+
+    @mark_as_writeable
+    def user_openApp(self, app_name: str):
+        # openApp without xml
+        screenshot_path = self.get_screenshot("screenshot", "./screenshot")
+        image = screenshot_path
+        ocr_detection = pipeline(Tasks.ocr_detection, model="damo/cv_resnet18_ocr-detection-line-level_damo")
+        ocr_recognition = pipeline(Tasks.ocr_recognition, model="damo/cv_convnextTiny_ocr-recognition-document_damo")
+        iw, ih = Image.open(image).size
+        x, y = self.device_shape
+        if iw > ih:
+            x, y = y, x
+            iw, ih = ih, iw
+        in_coordinate, out_coordinate = ocr(image, app_name, ocr_detection, ocr_recognition, iw, ih)
+        if len(in_coordinate) == 0:
+            logger.info(f"No App named {app_name}.")
+            return "no"
+        else:
+            tap_coordinate = [
+                (in_coordinate[0][0] + in_coordinate[0][2]) / 2,
+                (in_coordinate[0][1] + in_coordinate[0][3]) / 2,
+            ]
+            tap_coordinate = [round(tap_coordinate[0] / iw, 2), round(tap_coordinate[1] / ih, 2)]
+            return self.system_tap(tap_coordinate[0] * x, (tap_coordinate[1] - round(50 / y, 2)) * y)
+
+    @mark_as_writeable
+    def user_stop(self):
+        logger.info("Successful execution of tasks")
+
+    # todo ： user_clickIcon

metagpt/environment/android/groundingdino/config/GroundingDINO_SwinT_OGC.py

@@ -0,0 +1,43 @@
+batch_size = 1
+modelname = "groundingdino"
+backbone = "swin_T_224_1k"
+position_embedding = "sine"
+pe_temperatureH = 20
+pe_temperatureW = 20
+return_interm_indices = [1, 2, 3]
+backbone_freeze_keywords = None
+enc_layers = 6
+dec_layers = 6
+pre_norm = False
+dim_feedforward = 2048
+hidden_dim = 256
+dropout = 0.0
+nheads = 8
+num_queries = 900
+query_dim = 4
+num_patterns = 0
+num_feature_levels = 4
+enc_n_points = 4
+dec_n_points = 4
+two_stage_type = "standard"
+two_stage_bbox_embed_share = False
+two_stage_class_embed_share = False
+transformer_activation = "relu"
+dec_pred_bbox_embed_share = True
+dn_box_noise_scale = 1.0
+dn_label_noise_ratio = 0.5
+dn_label_coef = 1.0
+dn_bbox_coef = 1.0
+embed_init_tgt = True
+dn_labelbook_size = 2000
+max_text_len = 256
+text_encoder_type = "bert-base-uncased"
+use_text_enhancer = True
+use_fusion_layer = True
+use_checkpoint = True
+use_transformer_ckpt = True
+use_text_cross_attention = True
+text_dropout = 0.0
+fusion_dropout = 0.0
+fusion_droppath = 0.1
+sub_sentence_present = True

metagpt/environment/android/groundingdino/datasets/transforms.py

@@ -0,0 +1,311 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Transforms and data augmentation for both image + bbox.
+"""
+import os
+import random
+
+import PIL
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as F
+
+from groundingdino.util.box_ops import box_xyxy_to_cxcywh
+from groundingdino.util.misc import interpolate
+
+
+def crop(image, target, region):
+    cropped_image = F.crop(image, *region)
+
+    target = target.copy()
+    i, j, h, w = region
+
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor([h, w])
+
+    fields = ["labels", "area", "iscrowd", "positive_map"]
+
+    if "boxes" in target:
+        boxes = target["boxes"]
+        max_size = torch.as_tensor([w, h], dtype=torch.float32)
+        cropped_boxes = boxes - torch.as_tensor([j, i, j, i])
+        cropped_boxes = torch.min(cropped_boxes.reshape(-1, 2, 2), max_size)
+        cropped_boxes = cropped_boxes.clamp(min=0)
+        area = (cropped_boxes[:, 1, :] - cropped_boxes[:, 0, :]).prod(dim=1)
+        target["boxes"] = cropped_boxes.reshape(-1, 4)
+        target["area"] = area
+        fields.append("boxes")
+
+    if "masks" in target:
+        # FIXME should we update the area here if there are no boxes?
+        target["masks"] = target["masks"][:, i : i + h, j : j + w]
+        fields.append("masks")
+
+    # remove elements for which the boxes or masks that have zero area
+    if "boxes" in target or "masks" in target:
+        # favor boxes selection when defining which elements to keep
+        # this is compatible with previous implementation
+        if "boxes" in target:
+            cropped_boxes = target["boxes"].reshape(-1, 2, 2)
+            keep = torch.all(cropped_boxes[:, 1, :] > cropped_boxes[:, 0, :], dim=1)
+        else:
+            keep = target["masks"].flatten(1).any(1)
+
+        for field in fields:
+            if field in target:
+                target[field] = target[field][keep]
+
+    if os.environ.get("IPDB_SHILONG_DEBUG", None) == "INFO":
+        # for debug and visualization only.
+        if "strings_positive" in target:
+            target["strings_positive"] = [
+                _i for _i, _j in zip(target["strings_positive"], keep) if _j
+            ]
+
+    return cropped_image, target
+
+
+def hflip(image, target):
+    flipped_image = F.hflip(image)
+
+    w, h = image.size
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor([-1, 1, -1, 1]) + torch.as_tensor(
+            [w, 0, w, 0]
+        )
+        target["boxes"] = boxes
+
+    if "masks" in target:
+        target["masks"] = target["masks"].flip(-1)
+
+    return flipped_image, target
+
+
+def resize(image, target, size, max_size=None):
+    # size can be min_size (scalar) or (w, h) tuple
+
+    def get_size_with_aspect_ratio(image_size, size, max_size=None):
+        w, h = image_size
+        if max_size is not None:
+            min_original_size = float(min((w, h)))
+            max_original_size = float(max((w, h)))
+            if max_original_size / min_original_size * size > max_size:
+                size = int(round(max_size * min_original_size / max_original_size))
+
+        if (w <= h and w == size) or (h <= w and h == size):
+            return (h, w)
+
+        if w < h:
+            ow = size
+            oh = int(size * h / w)
+        else:
+            oh = size
+            ow = int(size * w / h)
+
+        return (oh, ow)
+
+    def get_size(image_size, size, max_size=None):
+        if isinstance(size, (list, tuple)):
+            return size[::-1]
+        else:
+            return get_size_with_aspect_ratio(image_size, size, max_size)
+
+    size = get_size(image.size, size, max_size)
+    rescaled_image = F.resize(image, size)
+
+    if target is None:
+        return rescaled_image, None
+
+    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(rescaled_image.size, image.size))
+    ratio_width, ratio_height = ratios
+
+    target = target.copy()
+    if "boxes" in target:
+        boxes = target["boxes"]
+        scaled_boxes = boxes * torch.as_tensor(
+            [ratio_width, ratio_height, ratio_width, ratio_height]
+        )
+        target["boxes"] = scaled_boxes
+
+    if "area" in target:
+        area = target["area"]
+        scaled_area = area * (ratio_width * ratio_height)
+        target["area"] = scaled_area
+
+    h, w = size
+    target["size"] = torch.tensor([h, w])
+
+    if "masks" in target:
+        target["masks"] = (
+            interpolate(target["masks"][:, None].float(), size, mode="nearest")[:, 0] > 0.5
+        )
+
+    return rescaled_image, target
+
+
+def pad(image, target, padding):
+    # assumes that we only pad on the bottom right corners
+    padded_image = F.pad(image, (0, 0, padding[0], padding[1]))
+    if target is None:
+        return padded_image, None
+    target = target.copy()
+    # should we do something wrt the original size?
+    target["size"] = torch.tensor(padded_image.size[::-1])
+    if "masks" in target:
+        target["masks"] = torch.nn.functional.pad(target["masks"], (0, padding[0], 0, padding[1]))
+    return padded_image, target
+
+
+class ResizeDebug(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        return resize(img, target, self.size)
+
+
+class RandomCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        region = T.RandomCrop.get_params(img, self.size)
+        return crop(img, target, region)
+
+
+class RandomSizeCrop(object):
+    def __init__(self, min_size: int, max_size: int, respect_boxes: bool = False):
+        # respect_boxes:    True to keep all boxes
+        #                   False to tolerence box filter
+        self.min_size = min_size
+        self.max_size = max_size
+        self.respect_boxes = respect_boxes
+
+    def __call__(self, img: PIL.Image.Image, target: dict):
+        init_boxes = len(target["boxes"])
+        max_patience = 10
+        for i in range(max_patience):
+            w = random.randint(self.min_size, min(img.width, self.max_size))
+            h = random.randint(self.min_size, min(img.height, self.max_size))
+            region = T.RandomCrop.get_params(img, [h, w])
+            result_img, result_target = crop(img, target, region)
+            if (
+                not self.respect_boxes
+                or len(result_target["boxes"]) == init_boxes
+                or i == max_patience - 1
+            ):
+                return result_img, result_target
+        return result_img, result_target
+
+
+class CenterCrop(object):
+    def __init__(self, size):
+        self.size = size
+
+    def __call__(self, img, target):
+        image_width, image_height = img.size
+        crop_height, crop_width = self.size
+        crop_top = int(round((image_height - crop_height) / 2.0))
+        crop_left = int(round((image_width - crop_width) / 2.0))
+        return crop(img, target, (crop_top, crop_left, crop_height, crop_width))
+
+
+class RandomHorizontalFlip(object):
+    def __init__(self, p=0.5):
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return hflip(img, target)
+        return img, target
+
+
+class RandomResize(object):
+    def __init__(self, sizes, max_size=None):
+        assert isinstance(sizes, (list, tuple))
+        self.sizes = sizes
+        self.max_size = max_size
+
+    def __call__(self, img, target=None):
+        size = random.choice(self.sizes)
+        return resize(img, target, size, self.max_size)
+
+
+class RandomPad(object):
+    def __init__(self, max_pad):
+        self.max_pad = max_pad
+
+    def __call__(self, img, target):
+        pad_x = random.randint(0, self.max_pad)
+        pad_y = random.randint(0, self.max_pad)
+        return pad(img, target, (pad_x, pad_y))
+
+
+class RandomSelect(object):
+    """
+    Randomly selects between transforms1 and transforms2,
+    with probability p for transforms1 and (1 - p) for transforms2
+    """
+
+    def __init__(self, transforms1, transforms2, p=0.5):
+        self.transforms1 = transforms1
+        self.transforms2 = transforms2
+        self.p = p
+
+    def __call__(self, img, target):
+        if random.random() < self.p:
+            return self.transforms1(img, target)
+        return self.transforms2(img, target)
+
+
+class ToTensor(object):
+    def __call__(self, img, target):
+        return F.to_tensor(img), target
+
+
+class RandomErasing(object):
+    def __init__(self, *args, **kwargs):
+        self.eraser = T.RandomErasing(*args, **kwargs)
+
+    def __call__(self, img, target):
+        return self.eraser(img), target
+
+
+class Normalize(object):
+    def __init__(self, mean, std):
+        self.mean = mean
+        self.std = std
+
+    def __call__(self, image, target=None):
+        image = F.normalize(image, mean=self.mean, std=self.std)
+        if target is None:
+            return image, None
+        target = target.copy()
+        h, w = image.shape[-2:]
+        if "boxes" in target:
+            boxes = target["boxes"]
+            boxes = box_xyxy_to_cxcywh(boxes)
+            boxes = boxes / torch.tensor([w, h, w, h], dtype=torch.float32)
+            target["boxes"] = boxes
+        return image, target
+
+
+class Compose(object):
+    def __init__(self, transforms):
+        self.transforms = transforms
+
+    def __call__(self, image, target):
+        for t in self.transforms:
+            image, target = t(image, target)
+        return image, target
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + "("
+        for t in self.transforms:
+            format_string += "\n"
+            format_string += "    {0}".format(t)
+        format_string += "\n)"
+        return format_string

metagpt/environment/android/groundingdino/models/GroundingDINO/__init__.py

@@ -0,0 +1,15 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from .groundingdino import build_groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/backbone/__init__.py

@@ -0,0 +1 @@
+from .backbone import build_backbone

metagpt/environment/android/groundingdino/models/GroundingDINO/backbone/backbone.py

@@ -0,0 +1,221 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Backbone modules.
+"""
+
+from typing import Dict, List
+
+import torch
+import torch.nn.functional as F
+import torchvision
+from torch import nn
+from torchvision.models._utils import IntermediateLayerGetter
+
+from groundingdino.util.misc import NestedTensor, clean_state_dict, is_main_process
+
+from .position_encoding import build_position_encoding
+from .swin_transformer import build_swin_transformer
+
+
+class FrozenBatchNorm2d(torch.nn.Module):
+    """
+    BatchNorm2d where the batch statistics and the affine parameters are fixed.
+
+    Copy-paste from torchvision.misc.ops with added eps before rqsrt,
+    without which any other models than torchvision.models.resnet[18,34,50,101]
+    produce nans.
+    """
+
+    def __init__(self, n):
+        super(FrozenBatchNorm2d, self).__init__()
+        self.register_buffer("weight", torch.ones(n))
+        self.register_buffer("bias", torch.zeros(n))
+        self.register_buffer("running_mean", torch.zeros(n))
+        self.register_buffer("running_var", torch.ones(n))
+
+    def _load_from_state_dict(
+        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+    ):
+        num_batches_tracked_key = prefix + "num_batches_tracked"
+        if num_batches_tracked_key in state_dict:
+            del state_dict[num_batches_tracked_key]
+
+        super(FrozenBatchNorm2d, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
+        )
+
+    def forward(self, x):
+        # move reshapes to the beginning
+        # to make it fuser-friendly
+        w = self.weight.reshape(1, -1, 1, 1)
+        b = self.bias.reshape(1, -1, 1, 1)
+        rv = self.running_var.reshape(1, -1, 1, 1)
+        rm = self.running_mean.reshape(1, -1, 1, 1)
+        eps = 1e-5
+        scale = w * (rv + eps).rsqrt()
+        bias = b - rm * scale
+        return x * scale + bias
+
+
+class BackboneBase(nn.Module):
+    def __init__(
+        self,
+        backbone: nn.Module,
+        train_backbone: bool,
+        num_channels: int,
+        return_interm_indices: list,
+    ):
+        super().__init__()
+        for name, parameter in backbone.named_parameters():
+            if (
+                not train_backbone
+                or "layer2" not in name
+                and "layer3" not in name
+                and "layer4" not in name
+            ):
+                parameter.requires_grad_(False)
+
+        return_layers = {}
+        for idx, layer_index in enumerate(return_interm_indices):
+            return_layers.update(
+                {"layer{}".format(5 - len(return_interm_indices) + idx): "{}".format(layer_index)}
+            )
+
+        # if len:
+        #     if use_stage1_feature:
+        #         return_layers = {"layer1": "0", "layer2": "1", "layer3": "2", "layer4": "3"}
+        #     else:
+        #         return_layers = {"layer2": "0", "layer3": "1", "layer4": "2"}
+        # else:
+        #     return_layers = {'layer4': "0"}
+        self.body = IntermediateLayerGetter(backbone, return_layers=return_layers)
+        self.num_channels = num_channels
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self.body(tensor_list.tensors)
+        out: Dict[str, NestedTensor] = {}
+        for name, x in xs.items():
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=x.shape[-2:]).to(torch.bool)[0]
+            out[name] = NestedTensor(x, mask)
+        # import ipdb; ipdb.set_trace()
+        return out
+
+
+class Backbone(BackboneBase):
+    """ResNet backbone with frozen BatchNorm."""
+
+    def __init__(
+        self,
+        name: str,
+        train_backbone: bool,
+        dilation: bool,
+        return_interm_indices: list,
+        batch_norm=FrozenBatchNorm2d,
+    ):
+        if name in ["resnet18", "resnet34", "resnet50", "resnet101"]:
+            backbone = getattr(torchvision.models, name)(
+                replace_stride_with_dilation=[False, False, dilation],
+                pretrained=is_main_process(),
+                norm_layer=batch_norm,
+            )
+        else:
+            raise NotImplementedError("Why you can get here with name {}".format(name))
+        # num_channels = 512 if name in ('resnet18', 'resnet34') else 2048
+        assert name not in ("resnet18", "resnet34"), "Only resnet50 and resnet101 are available."
+        assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+        num_channels_all = [256, 512, 1024, 2048]
+        num_channels = num_channels_all[4 - len(return_interm_indices) :]
+        super().__init__(backbone, train_backbone, num_channels, return_interm_indices)
+
+
+class Joiner(nn.Sequential):
+    def __init__(self, backbone, position_embedding):
+        super().__init__(backbone, position_embedding)
+
+    def forward(self, tensor_list: NestedTensor):
+        xs = self[0](tensor_list)
+        out: List[NestedTensor] = []
+        pos = []
+        for name, x in xs.items():
+            out.append(x)
+            # position encoding
+            pos.append(self[1](x).to(x.tensors.dtype))
+
+        return out, pos
+
+
+def build_backbone(args):
+    """
+    Useful args:
+        - backbone: backbone name
+        - lr_backbone:
+        - dilation
+        - return_interm_indices: available: [0,1,2,3], [1,2,3], [3]
+        - backbone_freeze_keywords:
+        - use_checkpoint: for swin only for now
+
+    """
+    position_embedding = build_position_encoding(args)
+    train_backbone = True
+    if not train_backbone:
+        raise ValueError("Please set lr_backbone > 0")
+    return_interm_indices = args.return_interm_indices
+    assert return_interm_indices in [[0, 1, 2, 3], [1, 2, 3], [3]]
+    args.backbone_freeze_keywords
+    use_checkpoint = getattr(args, "use_checkpoint", False)
+
+    if args.backbone in ["resnet50", "resnet101"]:
+        backbone = Backbone(
+            args.backbone,
+            train_backbone,
+            args.dilation,
+            return_interm_indices,
+            batch_norm=FrozenBatchNorm2d,
+        )
+        bb_num_channels = backbone.num_channels
+    elif args.backbone in [
+        "swin_T_224_1k",
+        "swin_B_224_22k",
+        "swin_B_384_22k",
+        "swin_L_224_22k",
+        "swin_L_384_22k",
+    ]:
+        pretrain_img_size = int(args.backbone.split("_")[-2])
+        backbone = build_swin_transformer(
+            args.backbone,
+            pretrain_img_size=pretrain_img_size,
+            out_indices=tuple(return_interm_indices),
+            dilation=False,
+            use_checkpoint=use_checkpoint,
+        )
+
+        bb_num_channels = backbone.num_features[4 - len(return_interm_indices) :]
+    else:
+        raise NotImplementedError("Unknown backbone {}".format(args.backbone))
+
+    assert len(bb_num_channels) == len(
+        return_interm_indices
+    ), f"len(bb_num_channels) {len(bb_num_channels)} != len(return_interm_indices) {len(return_interm_indices)}"
+
+    model = Joiner(backbone, position_embedding)
+    model.num_channels = bb_num_channels
+    assert isinstance(
+        bb_num_channels, List
+    ), "bb_num_channels is expected to be a List but {}".format(type(bb_num_channels))
+    # import ipdb; ipdb.set_trace()
+    return model

metagpt/environment/android/groundingdino/models/GroundingDINO/backbone/position_encoding.py

@@ -0,0 +1,186 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copied from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+"""
+Various positional encodings for the transformer.
+"""
+import math
+
+import torch
+from torch import nn
+
+from groundingdino.util.misc import NestedTensor
+
+
+class PositionEmbeddingSine(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(self, num_pos_feats=64, temperature=10000, normalize=False, scale=None):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            eps = 1e-6
+            # if os.environ.get("SHILONG_AMP", None) == '1':
+            #     eps = 1e-4
+            # else:
+            #     eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_t = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_t = self.temperature ** (2 * (dim_t // 2) / self.num_pos_feats)
+
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+
+class PositionEmbeddingSineHW(nn.Module):
+    """
+    This is a more standard version of the position embedding, very similar to the one
+    used by the Attention is all you need paper, generalized to work on images.
+    """
+
+    def __init__(
+        self, num_pos_feats=64, temperatureH=10000, temperatureW=10000, normalize=False, scale=None
+    ):
+        super().__init__()
+        self.num_pos_feats = num_pos_feats
+        self.temperatureH = temperatureH
+        self.temperatureW = temperatureW
+        self.normalize = normalize
+        if scale is not None and normalize is False:
+            raise ValueError("normalize should be True if scale is passed")
+        if scale is None:
+            scale = 2 * math.pi
+        self.scale = scale
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        mask = tensor_list.mask
+        assert mask is not None
+        not_mask = ~mask
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+
+        # import ipdb; ipdb.set_trace()
+
+        if self.normalize:
+            eps = 1e-6
+            y_embed = y_embed / (y_embed[:, -1:, :] + eps) * self.scale
+            x_embed = x_embed / (x_embed[:, :, -1:] + eps) * self.scale
+
+        dim_tx = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_tx = self.temperatureW ** (2 * (torch.div(dim_tx, 2, rounding_mode='floor')) / self.num_pos_feats)
+        pos_x = x_embed[:, :, :, None] / dim_tx
+
+        dim_ty = torch.arange(self.num_pos_feats, dtype=torch.float32, device=x.device)
+        dim_ty = self.temperatureH ** (2 * (torch.div(dim_ty, 2, rounding_mode='floor')) / self.num_pos_feats)
+        pos_y = y_embed[:, :, :, None] / dim_ty
+
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()), dim=4
+        ).flatten(3)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+
+        # import ipdb; ipdb.set_trace()
+
+        return pos
+
+
+class PositionEmbeddingLearned(nn.Module):
+    """
+    Absolute pos embedding, learned.
+    """
+
+    def __init__(self, num_pos_feats=256):
+        super().__init__()
+        self.row_embed = nn.Embedding(50, num_pos_feats)
+        self.col_embed = nn.Embedding(50, num_pos_feats)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        nn.init.uniform_(self.row_embed.weight)
+        nn.init.uniform_(self.col_embed.weight)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+        h, w = x.shape[-2:]
+        i = torch.arange(w, device=x.device)
+        j = torch.arange(h, device=x.device)
+        x_emb = self.col_embed(i)
+        y_emb = self.row_embed(j)
+        pos = (
+            torch.cat(
+                [
+                    x_emb.unsqueeze(0).repeat(h, 1, 1),
+                    y_emb.unsqueeze(1).repeat(1, w, 1),
+                ],
+                dim=-1,
+            )
+            .permute(2, 0, 1)
+            .unsqueeze(0)
+            .repeat(x.shape[0], 1, 1, 1)
+        )
+        return pos
+
+
+def build_position_encoding(args):
+    N_steps = args.hidden_dim // 2
+    if args.position_embedding in ("v2", "sine"):
+        # TODO find a better way of exposing other arguments
+        position_embedding = PositionEmbeddingSineHW(
+            N_steps,
+            temperatureH=args.pe_temperatureH,
+            temperatureW=args.pe_temperatureW,
+            normalize=True,
+        )
+    elif args.position_embedding in ("v3", "learned"):
+        position_embedding = PositionEmbeddingLearned(N_steps)
+    else:
+        raise ValueError(f"not supported {args.position_embedding}")
+
+    return position_embedding

metagpt/environment/android/groundingdino/models/GroundingDINO/backbone/swin_transformer.py

@@ -0,0 +1,802 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# --------------------------------------------------------
+# modified from https://github.com/SwinTransformer/Swin-Transformer-Object-Detection/blob/master/mmdet/models/backbones/swin_transformer.py
+# --------------------------------------------------------
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+
+from groundingdino.util.misc import NestedTensor
+
+
+class Mlp(nn.Module):
+    """Multilayer perceptron."""
+
+    def __init__(
+        self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0
+    ):
+        super().__init__()
+        out_features = out_features or in_features
+        hidden_features = hidden_features or in_features
+        self.fc1 = nn.Linear(in_features, hidden_features)
+        self.act = act_layer()
+        self.fc2 = nn.Linear(hidden_features, out_features)
+        self.drop = nn.Dropout(drop)
+
+    def forward(self, x):
+        x = self.fc1(x)
+        x = self.act(x)
+        x = self.drop(x)
+        x = self.fc2(x)
+        x = self.drop(x)
+        return x
+
+
+def window_partition(x, window_size):
+    """
+    Args:
+        x: (B, H, W, C)
+        window_size (int): window size
+    Returns:
+        windows: (num_windows*B, window_size, window_size, C)
+    """
+    B, H, W, C = x.shape
+    x = x.view(B, H // window_size, window_size, W // window_size, window_size, C)
+    windows = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(-1, window_size, window_size, C)
+    return windows
+
+
+def window_reverse(windows, window_size, H, W):
+    """
+    Args:
+        windows: (num_windows*B, window_size, window_size, C)
+        window_size (int): Window size
+        H (int): Height of image
+        W (int): Width of image
+    Returns:
+        x: (B, H, W, C)
+    """
+    B = int(windows.shape[0] / (H * W / window_size / window_size))
+    x = windows.view(B, H // window_size, W // window_size, window_size, window_size, -1)
+    x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+    return x
+
+
+class WindowAttention(nn.Module):
+    """Window based multi-head self attention (W-MSA) module with relative position bias.
+    It supports both of shifted and non-shifted window.
+    Args:
+        dim (int): Number of input channels.
+        window_size (tuple[int]): The height and width of the window.
+        num_heads (int): Number of attention heads.
+        qkv_bias (bool, optional):  If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set
+        attn_drop (float, optional): Dropout ratio of attention weight. Default: 0.0
+        proj_drop (float, optional): Dropout ratio of output. Default: 0.0
+    """
+
+    def __init__(
+        self,
+        dim,
+        window_size,
+        num_heads,
+        qkv_bias=True,
+        qk_scale=None,
+        attn_drop=0.0,
+        proj_drop=0.0,
+    ):
+
+        super().__init__()
+        self.dim = dim
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_dim = dim // num_heads
+        self.scale = qk_scale or head_dim**-0.5
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)
+        )  # 2*Wh-1 * 2*Ww-1, nH
+
+        # get pair-wise relative position index for each token inside the window
+        coords_h = torch.arange(self.window_size[0])
+        coords_w = torch.arange(self.window_size[1])
+        coords = torch.stack(torch.meshgrid([coords_h, coords_w]))  # 2, Wh, Ww
+        coords_flatten = torch.flatten(coords, 1)  # 2, Wh*Ww
+        relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]  # 2, Wh*Ww, Wh*Ww
+        relative_coords = relative_coords.permute(1, 2, 0).contiguous()  # Wh*Ww, Wh*Ww, 2
+        relative_coords[:, :, 0] += self.window_size[0] - 1  # shift to start from 0
+        relative_coords[:, :, 1] += self.window_size[1] - 1
+        relative_coords[:, :, 0] *= 2 * self.window_size[1] - 1
+        relative_position_index = relative_coords.sum(-1)  # Wh*Ww, Wh*Ww
+        self.register_buffer("relative_position_index", relative_position_index)
+
+        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop)
+        self.proj = nn.Linear(dim, dim)
+        self.proj_drop = nn.Dropout(proj_drop)
+
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+        self.softmax = nn.Softmax(dim=-1)
+
+    def forward(self, x, mask=None):
+        """Forward function.
+        Args:
+            x: input features with shape of (num_windows*B, N, C)
+            mask: (0/-inf) mask with shape of (num_windows, Wh*Ww, Wh*Ww) or None
+        """
+        B_, N, C = x.shape
+        qkv = (
+            self.qkv(x)
+            .reshape(B_, N, 3, self.num_heads, C // self.num_heads)
+            .permute(2, 0, 3, 1, 4)
+        )
+        q, k, v = qkv[0], qkv[1], qkv[2]  # make torchscript happy (cannot use tensor as tuple)
+
+        q = q * self.scale
+        attn = q @ k.transpose(-2, -1)
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)
+        ].view(
+            self.window_size[0] * self.window_size[1], self.window_size[0] * self.window_size[1], -1
+        )  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1
+        ).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B_ // nW, nW, self.num_heads, N, N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+            attn = self.softmax(attn)
+        else:
+            attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B_, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+
+class SwinTransformerBlock(nn.Module):
+    """Swin Transformer Block.
+    Args:
+        dim (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): Window size.
+        shift_size (int): Shift size for SW-MSA.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float, optional): Stochastic depth rate. Default: 0.0
+        act_layer (nn.Module, optional): Activation layer. Default: nn.GELU
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(
+        self,
+        dim,
+        num_heads,
+        window_size=7,
+        shift_size=0,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        act_layer=nn.GELU,
+        norm_layer=nn.LayerNorm,
+    ):
+        super().__init__()
+        self.dim = dim
+        self.num_heads = num_heads
+        self.window_size = window_size
+        self.shift_size = shift_size
+        self.mlp_ratio = mlp_ratio
+        assert 0 <= self.shift_size < self.window_size, "shift_size must in 0-window_size"
+
+        self.norm1 = norm_layer(dim)
+        self.attn = WindowAttention(
+            dim,
+            window_size=to_2tuple(self.window_size),
+            num_heads=num_heads,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop=attn_drop,
+            proj_drop=drop,
+        )
+
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.norm2 = norm_layer(dim)
+        mlp_hidden_dim = int(dim * mlp_ratio)
+        self.mlp = Mlp(
+            in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop
+        )
+
+        self.H = None
+        self.W = None
+
+    def forward(self, x, mask_matrix):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+            mask_matrix: Attention mask for cyclic shift.
+        """
+        B, L, C = x.shape
+        H, W = self.H, self.W
+        assert L == H * W, "input feature has wrong size"
+
+        shortcut = x
+        x = self.norm1(x)
+        x = x.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_l = pad_t = 0
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        x = F.pad(x, (0, 0, pad_l, pad_r, pad_t, pad_b))
+        _, Hp, Wp, _ = x.shape
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_x = torch.roll(x, shifts=(-self.shift_size, -self.shift_size), dims=(1, 2))
+            attn_mask = mask_matrix
+        else:
+            shifted_x = x
+            attn_mask = None
+
+        # partition windows
+        x_windows = window_partition(
+            shifted_x, self.window_size
+        )  # nW*B, window_size, window_size, C
+        x_windows = x_windows.view(
+            -1, self.window_size * self.window_size, C
+        )  # nW*B, window_size*window_size, C
+
+        # W-MSA/SW-MSA
+        attn_windows = self.attn(x_windows, mask=attn_mask)  # nW*B, window_size*window_size, C
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size, self.window_size, C)
+        shifted_x = window_reverse(attn_windows, self.window_size, Hp, Wp)  # B H' W' C
+
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(shifted_x, shifts=(self.shift_size, self.shift_size), dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b > 0:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        # FFN
+        x = shortcut + self.drop_path(x)
+        x = x + self.drop_path(self.mlp(self.norm2(x)))
+
+        return x
+
+
+class PatchMerging(nn.Module):
+    """Patch Merging Layer
+    Args:
+        dim (int): Number of input channels.
+        norm_layer (nn.Module, optional): Normalization layer.  Default: nn.LayerNorm
+    """
+
+    def __init__(self, dim, norm_layer=nn.LayerNorm):
+        super().__init__()
+        self.dim = dim
+        self.reduction = nn.Linear(4 * dim, 2 * dim, bias=False)
+        self.norm = norm_layer(4 * dim)
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+        B, L, C = x.shape
+        assert L == H * W, "input feature has wrong size"
+
+        x = x.view(B, H, W, C)
+
+        # padding
+        pad_input = (H % 2 == 1) or (W % 2 == 1)
+        if pad_input:
+            x = F.pad(x, (0, 0, 0, W % 2, 0, H % 2))
+
+        x0 = x[:, 0::2, 0::2, :]  # B H/2 W/2 C
+        x1 = x[:, 1::2, 0::2, :]  # B H/2 W/2 C
+        x2 = x[:, 0::2, 1::2, :]  # B H/2 W/2 C
+        x3 = x[:, 1::2, 1::2, :]  # B H/2 W/2 C
+        x = torch.cat([x0, x1, x2, x3], -1)  # B H/2 W/2 4*C
+        x = x.view(B, -1, 4 * C)  # B H/2*W/2 4*C
+
+        x = self.norm(x)
+        x = self.reduction(x)
+
+        return x
+
+
+class BasicLayer(nn.Module):
+    """A basic Swin Transformer layer for one stage.
+    Args:
+        dim (int): Number of feature channels
+        depth (int): Depths of this stage.
+        num_heads (int): Number of attention head.
+        window_size (int): Local window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of head_dim ** -0.5 if set.
+        drop (float, optional): Dropout rate. Default: 0.0
+        attn_drop (float, optional): Attention dropout rate. Default: 0.0
+        drop_path (float | tuple[float], optional): Stochastic depth rate. Default: 0.0
+        norm_layer (nn.Module, optional): Normalization layer. Default: nn.LayerNorm
+        downsample (nn.Module | None, optional): Downsample layer at the end of the layer. Default: None
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+    """
+
+    def __init__(
+        self,
+        dim,
+        depth,
+        num_heads,
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop=0.0,
+        attn_drop=0.0,
+        drop_path=0.0,
+        norm_layer=nn.LayerNorm,
+        downsample=None,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+        self.window_size = window_size
+        self.shift_size = window_size // 2
+        self.depth = depth
+        self.use_checkpoint = use_checkpoint
+
+        # build blocks
+        self.blocks = nn.ModuleList(
+            [
+                SwinTransformerBlock(
+                    dim=dim,
+                    num_heads=num_heads,
+                    window_size=window_size,
+                    shift_size=0 if (i % 2 == 0) else window_size // 2,
+                    mlp_ratio=mlp_ratio,
+                    qkv_bias=qkv_bias,
+                    qk_scale=qk_scale,
+                    drop=drop,
+                    attn_drop=attn_drop,
+                    drop_path=drop_path[i] if isinstance(drop_path, list) else drop_path,
+                    norm_layer=norm_layer,
+                )
+                for i in range(depth)
+            ]
+        )
+
+        # patch merging layer
+        if downsample is not None:
+            self.downsample = downsample(dim=dim, norm_layer=norm_layer)
+        else:
+            self.downsample = None
+
+    def forward(self, x, H, W):
+        """Forward function.
+        Args:
+            x: Input feature, tensor size (B, H*W, C).
+            H, W: Spatial resolution of the input feature.
+        """
+
+        # calculate attention mask for SW-MSA
+        Hp = int(np.ceil(H / self.window_size)) * self.window_size
+        Wp = int(np.ceil(W / self.window_size)) * self.window_size
+        img_mask = torch.zeros((1, Hp, Wp, 1), device=x.device)  # 1 Hp Wp 1
+        h_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        w_slices = (
+            slice(0, -self.window_size),
+            slice(-self.window_size, -self.shift_size),
+            slice(-self.shift_size, None),
+        )
+        cnt = 0
+        for h in h_slices:
+            for w in w_slices:
+                img_mask[:, h, w, :] = cnt
+                cnt += 1
+
+        mask_windows = window_partition(
+            img_mask, self.window_size
+        )  # nW, window_size, window_size, 1
+        mask_windows = mask_windows.view(-1, self.window_size * self.window_size)
+        attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+        attn_mask = attn_mask.masked_fill(attn_mask != 0, float(-100.0)).masked_fill(
+            attn_mask == 0, float(0.0)
+        )
+
+        for blk in self.blocks:
+            blk.H, blk.W = H, W
+            if self.use_checkpoint:
+                x = checkpoint.checkpoint(blk, x, attn_mask)
+            else:
+                x = blk(x, attn_mask)
+        if self.downsample is not None:
+            x_down = self.downsample(x, H, W)
+            Wh, Ww = (H + 1) // 2, (W + 1) // 2
+            return x, H, W, x_down, Wh, Ww
+        else:
+            return x, H, W, x, H, W
+
+
+class PatchEmbed(nn.Module):
+    """Image to Patch Embedding
+    Args:
+        patch_size (int): Patch token size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        norm_layer (nn.Module, optional): Normalization layer. Default: None
+    """
+
+    def __init__(self, patch_size=4, in_chans=3, embed_dim=96, norm_layer=None):
+        super().__init__()
+        patch_size = to_2tuple(patch_size)
+        self.patch_size = patch_size
+
+        self.in_chans = in_chans
+        self.embed_dim = embed_dim
+
+        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+        if norm_layer is not None:
+            self.norm = norm_layer(embed_dim)
+        else:
+            self.norm = None
+
+    def forward(self, x):
+        """Forward function."""
+        # padding
+        _, _, H, W = x.size()
+        if W % self.patch_size[1] != 0:
+            x = F.pad(x, (0, self.patch_size[1] - W % self.patch_size[1]))
+        if H % self.patch_size[0] != 0:
+            x = F.pad(x, (0, 0, 0, self.patch_size[0] - H % self.patch_size[0]))
+
+        x = self.proj(x)  # B C Wh Ww
+        if self.norm is not None:
+            Wh, Ww = x.size(2), x.size(3)
+            x = x.flatten(2).transpose(1, 2)
+            x = self.norm(x)
+            x = x.transpose(1, 2).view(-1, self.embed_dim, Wh, Ww)
+
+        return x
+
+
+class SwinTransformer(nn.Module):
+    """Swin Transformer backbone.
+        A PyTorch impl of : `Swin Transformer: Hierarchical Vision Transformer using Shifted Windows`  -
+          https://arxiv.org/pdf/2103.14030
+    Args:
+        pretrain_img_size (int): Input image size for training the pretrained model,
+            used in absolute postion embedding. Default 224.
+        patch_size (int | tuple(int)): Patch size. Default: 4.
+        in_chans (int): Number of input image channels. Default: 3.
+        embed_dim (int): Number of linear projection output channels. Default: 96.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+        num_heads (tuple[int]): Number of attention head of each stage.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (float): Ratio of mlp hidden dim to embedding dim. Default: 4.
+        qkv_bias (bool): If True, add a learnable bias to query, key, value. Default: True
+        qk_scale (float): Override default qk scale of head_dim ** -0.5 if set.
+        drop_rate (float): Dropout rate.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Default: 0.2.
+        norm_layer (nn.Module): Normalization layer. Default: nn.LayerNorm.
+        ape (bool): If True, add absolute position embedding to the patch embedding. Default: False.
+        patch_norm (bool): If True, add normalization after patch embedding. Default: True.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        use_checkpoint (bool): Whether to use checkpointing to save memory. Default: False.
+        dilation (bool): if True, the output size if 16x downsample, ow 32x downsample.
+    """
+
+    def __init__(
+        self,
+        pretrain_img_size=224,
+        patch_size=4,
+        in_chans=3,
+        embed_dim=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4.0,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.0,
+        attn_drop_rate=0.0,
+        drop_path_rate=0.2,
+        norm_layer=nn.LayerNorm,
+        ape=False,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        dilation=False,
+        use_checkpoint=False,
+    ):
+        super().__init__()
+
+        self.pretrain_img_size = pretrain_img_size
+        self.num_layers = len(depths)
+        self.embed_dim = embed_dim
+        self.ape = ape
+        self.patch_norm = patch_norm
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.dilation = dilation
+
+        # if use_checkpoint:
+        #     print("use_checkpoint!!!!!!!!!!!!!!!!!!!!!!!!")
+
+        # split image into non-overlapping patches
+        self.patch_embed = PatchEmbed(
+            patch_size=patch_size,
+            in_chans=in_chans,
+            embed_dim=embed_dim,
+            norm_layer=norm_layer if self.patch_norm else None,
+        )
+
+        # absolute position embedding
+        if self.ape:
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+            patch_size = to_2tuple(patch_size)
+            patches_resolution = [
+                pretrain_img_size[0] // patch_size[0],
+                pretrain_img_size[1] // patch_size[1],
+            ]
+
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros(1, embed_dim, patches_resolution[0], patches_resolution[1])
+            )
+            trunc_normal_(self.absolute_pos_embed, std=0.02)
+
+        self.pos_drop = nn.Dropout(p=drop_rate)
+
+        # stochastic depth
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
+        ]  # stochastic depth decay rule
+
+        # build layers
+        self.layers = nn.ModuleList()
+        # prepare downsample list
+        downsamplelist = [PatchMerging for i in range(self.num_layers)]
+        downsamplelist[-1] = None
+        num_features = [int(embed_dim * 2**i) for i in range(self.num_layers)]
+        if self.dilation:
+            downsamplelist[-2] = None
+            num_features[-1] = int(embed_dim * 2 ** (self.num_layers - 1)) // 2
+        for i_layer in range(self.num_layers):
+            layer = BasicLayer(
+                # dim=int(embed_dim * 2 ** i_layer),
+                dim=num_features[i_layer],
+                depth=depths[i_layer],
+                num_heads=num_heads[i_layer],
+                window_size=window_size,
+                mlp_ratio=mlp_ratio,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop=drop_rate,
+                attn_drop=attn_drop_rate,
+                drop_path=dpr[sum(depths[:i_layer]) : sum(depths[: i_layer + 1])],
+                norm_layer=norm_layer,
+                # downsample=PatchMerging if (i_layer < self.num_layers - 1) else None,
+                downsample=downsamplelist[i_layer],
+                use_checkpoint=use_checkpoint,
+            )
+            self.layers.append(layer)
+
+        # num_features = [int(embed_dim * 2 ** i) for i in range(self.num_layers)]
+        self.num_features = num_features
+
+        # add a norm layer for each output
+        for i_layer in out_indices:
+            layer = norm_layer(num_features[i_layer])
+            layer_name = f"norm{i_layer}"
+            self.add_module(layer_name, layer)
+
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+
+        if self.frozen_stages >= 1 and self.ape:
+            self.absolute_pos_embed.requires_grad = False
+
+        if self.frozen_stages >= 2:
+            self.pos_drop.eval()
+            for i in range(0, self.frozen_stages - 1):
+                m = self.layers[i]
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    # def init_weights(self, pretrained=None):
+    #     """Initialize the weights in backbone.
+    #     Args:
+    #         pretrained (str, optional): Path to pre-trained weights.
+    #             Defaults to None.
+    #     """
+
+    #     def _init_weights(m):
+    #         if isinstance(m, nn.Linear):
+    #             trunc_normal_(m.weight, std=.02)
+    #             if isinstance(m, nn.Linear) and m.bias is not None:
+    #                 nn.init.constant_(m.bias, 0)
+    #         elif isinstance(m, nn.LayerNorm):
+    #             nn.init.constant_(m.bias, 0)
+    #             nn.init.constant_(m.weight, 1.0)
+
+    #     if isinstance(pretrained, str):
+    #         self.apply(_init_weights)
+    #         logger = get_root_logger()
+    #         load_checkpoint(self, pretrained, strict=False, logger=logger)
+    #     elif pretrained is None:
+    #         self.apply(_init_weights)
+    #     else:
+    #         raise TypeError('pretrained must be a str or None')
+
+    def forward_raw(self, x):
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+            # import ipdb; ipdb.set_trace()
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        # in:
+        #   torch.Size([2, 3, 1024, 1024])
+        # outs:
+        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+        return tuple(outs)
+
+    def forward(self, tensor_list: NestedTensor):
+        x = tensor_list.tensors
+
+        """Forward function."""
+        x = self.patch_embed(x)
+
+        Wh, Ww = x.size(2), x.size(3)
+        if self.ape:
+            # interpolate the position embedding to the corresponding size
+            absolute_pos_embed = F.interpolate(
+                self.absolute_pos_embed, size=(Wh, Ww), mode="bicubic"
+            )
+            x = (x + absolute_pos_embed).flatten(2).transpose(1, 2)  # B Wh*Ww C
+        else:
+            x = x.flatten(2).transpose(1, 2)
+        x = self.pos_drop(x)
+
+        outs = []
+        for i in range(self.num_layers):
+            layer = self.layers[i]
+            x_out, H, W, x, Wh, Ww = layer(x, Wh, Ww)
+
+            if i in self.out_indices:
+                norm_layer = getattr(self, f"norm{i}")
+                x_out = norm_layer(x_out)
+
+                out = x_out.view(-1, H, W, self.num_features[i]).permute(0, 3, 1, 2).contiguous()
+                outs.append(out)
+        # in:
+        #   torch.Size([2, 3, 1024, 1024])
+        # out:
+        #   [torch.Size([2, 192, 256, 256]), torch.Size([2, 384, 128, 128]), \
+        #       torch.Size([2, 768, 64, 64]), torch.Size([2, 1536, 32, 32])]
+
+        # collect for nesttensors
+        outs_dict = {}
+        for idx, out_i in enumerate(outs):
+            m = tensor_list.mask
+            assert m is not None
+            mask = F.interpolate(m[None].float(), size=out_i.shape[-2:]).to(torch.bool)[0]
+            outs_dict[idx] = NestedTensor(out_i, mask)
+
+        return outs_dict
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+
+def build_swin_transformer(modelname, pretrain_img_size, **kw):
+    assert modelname in [
+        "swin_T_224_1k",
+        "swin_B_224_22k",
+        "swin_B_384_22k",
+        "swin_L_224_22k",
+        "swin_L_384_22k",
+    ]
+
+    model_para_dict = {
+        "swin_T_224_1k": dict(
+            embed_dim=96, depths=[2, 2, 6, 2], num_heads=[3, 6, 12, 24], window_size=7
+        ),
+        "swin_B_224_22k": dict(
+            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=7
+        ),
+        "swin_B_384_22k": dict(
+            embed_dim=128, depths=[2, 2, 18, 2], num_heads=[4, 8, 16, 32], window_size=12
+        ),
+        "swin_L_224_22k": dict(
+            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=7
+        ),
+        "swin_L_384_22k": dict(
+            embed_dim=192, depths=[2, 2, 18, 2], num_heads=[6, 12, 24, 48], window_size=12
+        ),
+    }
+    kw_cgf = model_para_dict[modelname]
+    kw_cgf.update(kw)
+    model = SwinTransformer(pretrain_img_size=pretrain_img_size, **kw_cgf)
+    return model
+
+
+if __name__ == "__main__":
+    model = build_swin_transformer("swin_L_384_22k", 384, dilation=True)
+    x = torch.rand(2, 3, 1024, 1024)
+    y = model.forward_raw(x)
+    import ipdb
+
+    ipdb.set_trace()
+    x = torch.rand(2, 3, 384, 384)
+    y = model.forward_raw(x)

metagpt/environment/android/groundingdino/models/GroundingDINO/bertwarper.py

@@ -0,0 +1,273 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor, nn
+from torchvision.ops.boxes import nms
+from transformers import BertConfig, BertModel, BertPreTrainedModel
+from transformers.modeling_outputs import BaseModelOutputWithPoolingAndCrossAttentions
+
+
+class BertModelWarper(nn.Module):
+    def __init__(self, bert_model):
+        super().__init__()
+        # self.bert = bert_modelc
+
+        self.config = bert_model.config
+        self.embeddings = bert_model.embeddings
+        self.encoder = bert_model.encoder
+        self.pooler = bert_model.pooler
+
+        self.get_extended_attention_mask = bert_model.get_extended_attention_mask
+        self.invert_attention_mask = bert_model.invert_attention_mask
+        self.get_head_mask = bert_model.get_head_mask
+
+    def forward(
+        self,
+        input_ids=None,
+        attention_mask=None,
+        token_type_ids=None,
+        position_ids=None,
+        head_mask=None,
+        inputs_embeds=None,
+        encoder_hidden_states=None,
+        encoder_attention_mask=None,
+        past_key_values=None,
+        use_cache=None,
+        output_attentions=None,
+        output_hidden_states=None,
+        return_dict=None,
+    ):
+        r"""
+        encoder_hidden_states  (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length, hidden_size)`, `optional`):
+            Sequence of hidden-states at the output of the last layer of the encoder. Used in the cross-attention if
+            the model is configured as a decoder.
+        encoder_attention_mask (:obj:`torch.FloatTensor` of shape :obj:`(batch_size, sequence_length)`, `optional`):
+            Mask to avoid performing attention on the padding token indices of the encoder input. This mask is used in
+            the cross-attention if the model is configured as a decoder. Mask values selected in ``[0, 1]``:
+
+            - 1 for tokens that are **not masked**,
+            - 0 for tokens that are **masked**.
+        past_key_values (:obj:`tuple(tuple(torch.FloatTensor))` of length :obj:`config.n_layers` with each tuple having 4 tensors of shape :obj:`(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`):
+            Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up decoding.
+
+            If :obj:`past_key_values` are used, the user can optionally input only the last :obj:`decoder_input_ids`
+            (those that don't have their past key value states given to this model) of shape :obj:`(batch_size, 1)`
+            instead of all :obj:`decoder_input_ids` of shape :obj:`(batch_size, sequence_length)`.
+        use_cache (:obj:`bool`, `optional`):
+            If set to :obj:`True`, :obj:`past_key_values` key value states are returned and can be used to speed up
+            decoding (see :obj:`past_key_values`).
+        """
+        output_attentions = (
+            output_attentions if output_attentions is not None else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        if self.config.is_decoder:
+            use_cache = use_cache if use_cache is not None else self.config.use_cache
+        else:
+            use_cache = False
+
+        if input_ids is not None and inputs_embeds is not None:
+            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")
+        elif input_ids is not None:
+            input_shape = input_ids.size()
+            batch_size, seq_length = input_shape
+        elif inputs_embeds is not None:
+            input_shape = inputs_embeds.size()[:-1]
+            batch_size, seq_length = input_shape
+        else:
+            raise ValueError("You have to specify either input_ids or inputs_embeds")
+
+        device = input_ids.device if input_ids is not None else inputs_embeds.device
+
+        # past_key_values_length
+        past_key_values_length = (
+            past_key_values[0][0].shape[2] if past_key_values is not None else 0
+        )
+
+        if attention_mask is None:
+            attention_mask = torch.ones(
+                ((batch_size, seq_length + past_key_values_length)), device=device
+            )
+        if token_type_ids is None:
+            token_type_ids = torch.zeros(input_shape, dtype=torch.long, device=device)
+
+        # We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
+        # ourselves in which case we just need to make it broadcastable to all heads.
+        extended_attention_mask: torch.Tensor = self.get_extended_attention_mask(
+            attention_mask, input_shape, device
+        )
+
+        # If a 2D or 3D attention mask is provided for the cross-attention
+        # we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
+        if self.config.is_decoder and encoder_hidden_states is not None:
+            encoder_batch_size, encoder_sequence_length, _ = encoder_hidden_states.size()
+            encoder_hidden_shape = (encoder_batch_size, encoder_sequence_length)
+            if encoder_attention_mask is None:
+                encoder_attention_mask = torch.ones(encoder_hidden_shape, device=device)
+            encoder_extended_attention_mask = self.invert_attention_mask(encoder_attention_mask)
+        else:
+            encoder_extended_attention_mask = None
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+
+        # Prepare head mask if needed
+        # 1.0 in head_mask indicate we keep the head
+        # attention_probs has shape bsz x n_heads x N x N
+        # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
+        # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
+        head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers)
+
+        embedding_output = self.embeddings(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            token_type_ids=token_type_ids,
+            inputs_embeds=inputs_embeds,
+            past_key_values_length=past_key_values_length,
+        )
+
+        encoder_outputs = self.encoder(
+            embedding_output,
+            attention_mask=extended_attention_mask,
+            head_mask=head_mask,
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=encoder_extended_attention_mask,
+            past_key_values=past_key_values,
+            use_cache=use_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+        sequence_output = encoder_outputs[0]
+        pooled_output = self.pooler(sequence_output) if self.pooler is not None else None
+
+        if not return_dict:
+            return (sequence_output, pooled_output) + encoder_outputs[1:]
+
+        return BaseModelOutputWithPoolingAndCrossAttentions(
+            last_hidden_state=sequence_output,
+            pooler_output=pooled_output,
+            past_key_values=encoder_outputs.past_key_values,
+            hidden_states=encoder_outputs.hidden_states,
+            attentions=encoder_outputs.attentions,
+            cross_attentions=encoder_outputs.cross_attentions,
+        )
+
+
+class TextEncoderShell(nn.Module):
+    def __init__(self, text_encoder):
+        super().__init__()
+        self.text_encoder = text_encoder
+        self.config = self.text_encoder.config
+
+    def forward(self, **kw):
+        # feed into text encoder
+        return self.text_encoder(**kw)
+
+
+def generate_masks_with_special_tokens(tokenized, special_tokens_list, tokenizer):
+    """Generate attention mask between each pair of special tokens
+    Args:
+        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+        special_tokens_mask (list): special tokens mask.
+    Returns:
+        torch.Tensor: attention mask between each special tokens.
+    """
+    input_ids = tokenized["input_ids"]
+    bs, num_token = input_ids.shape
+    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+    for special_token in special_tokens_list:
+        special_tokens_mask |= input_ids == special_token
+
+    # idxs: each row is a list of indices of special tokens
+    idxs = torch.nonzero(special_tokens_mask)
+
+    # generate attention mask and positional ids
+    attention_mask = (
+        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+    )
+    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+    previous_col = 0
+    for i in range(idxs.shape[0]):
+        row, col = idxs[i]
+        if (col == 0) or (col == num_token - 1):
+            attention_mask[row, col, col] = True
+            position_ids[row, col] = 0
+        else:
+            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+                0, col - previous_col, device=input_ids.device
+            )
+
+        previous_col = col
+
+    # # padding mask
+    # padding_mask = tokenized['attention_mask']
+    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+    return attention_mask, position_ids.to(torch.long)
+
+
+def generate_masks_with_special_tokens_and_transfer_map(tokenized, special_tokens_list, tokenizer):
+    """Generate attention mask between each pair of special tokens
+    Args:
+        input_ids (torch.Tensor): input ids. Shape: [bs, num_token]
+        special_tokens_mask (list): special tokens mask.
+    Returns:
+        torch.Tensor: attention mask between each special tokens.
+    """
+    input_ids = tokenized["input_ids"]
+    bs, num_token = input_ids.shape
+    # special_tokens_mask: bs, num_token. 1 for special tokens. 0 for normal tokens
+    special_tokens_mask = torch.zeros((bs, num_token), device=input_ids.device).bool()
+    for special_token in special_tokens_list:
+        special_tokens_mask |= input_ids == special_token
+
+    # idxs: each row is a list of indices of special tokens
+    idxs = torch.nonzero(special_tokens_mask)
+
+    # generate attention mask and positional ids
+    attention_mask = (
+        torch.eye(num_token, device=input_ids.device).bool().unsqueeze(0).repeat(bs, 1, 1)
+    )
+    position_ids = torch.zeros((bs, num_token), device=input_ids.device)
+    cate_to_token_mask_list = [[] for _ in range(bs)]
+    previous_col = 0
+    for i in range(idxs.shape[0]):
+        row, col = idxs[i]
+        if (col == 0) or (col == num_token - 1):
+            attention_mask[row, col, col] = True
+            position_ids[row, col] = 0
+        else:
+            attention_mask[row, previous_col + 1 : col + 1, previous_col + 1 : col + 1] = True
+            position_ids[row, previous_col + 1 : col + 1] = torch.arange(
+                0, col - previous_col, device=input_ids.device
+            )
+            c2t_maski = torch.zeros((num_token), device=input_ids.device).bool()
+            c2t_maski[previous_col + 1 : col] = True
+            cate_to_token_mask_list[row].append(c2t_maski)
+        previous_col = col
+
+    cate_to_token_mask_list = [
+        torch.stack(cate_to_token_mask_listi, dim=0)
+        for cate_to_token_mask_listi in cate_to_token_mask_list
+    ]
+
+    # # padding mask
+    # padding_mask = tokenized['attention_mask']
+    # attention_mask = attention_mask & padding_mask.unsqueeze(1).bool() & padding_mask.unsqueeze(2).bool()
+
+    return attention_mask, position_ids.to(torch.long), cate_to_token_mask_list

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn.h

@@ -0,0 +1,64 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+
+#include "ms_deform_attn_cpu.h"
+
+#ifdef WITH_CUDA
+#include "ms_deform_attn_cuda.h"
+#endif
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    if (value.type().is_cuda())
+    {
+#ifdef WITH_CUDA
+        return ms_deform_attn_cuda_forward(
+            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, im2col_step);
+#else
+        AT_ERROR("Not compiled with GPU support");
+#endif
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    if (value.type().is_cuda())
+    {
+#ifdef WITH_CUDA
+        return ms_deform_attn_cuda_backward(
+            value, spatial_shapes, level_start_index, sampling_loc, attn_weight, grad_output, im2col_step);
+#else
+        AT_ERROR("Not compiled with GPU support");
+#endif
+    }
+    AT_ERROR("Not implemented on the CPU");
+}
+
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.cpp

@@ -0,0 +1,43 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+    AT_ERROR("Not implement on cpu");
+}
+
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cpu.h

@@ -0,0 +1,35 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor
+ms_deform_attn_cpu_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step);
+
+std::vector<at::Tensor>
+ms_deform_attn_cpu_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step);
+
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.cu

@@ -0,0 +1,156 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#include <vector>
+#include "ms_deform_im2col_cuda.cuh"
+
+#include <ATen/ATen.h>
+#include <ATen/cuda/CUDAContext.h>
+#include <cuda.h>
+#include <cuda_runtime.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step)
+{
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+    
+    auto output = at::zeros({batch, num_query, num_heads, channels}, value.options());
+
+    const int batch_n = im2col_step_;
+    auto output_n = output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto columns = output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_forward_cuda", ([&] {
+            ms_deformable_im2col_cuda(at::cuda::getCurrentCUDAStream(),
+                value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                spatial_shapes.data<int64_t>(),
+                level_start_index.data<int64_t>(),
+                sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                columns.data<scalar_t>());
+
+        }));
+    }
+
+    output = output.view({batch, num_query, num_heads*channels});
+
+    return output;
+}
+
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step)
+{
+
+    AT_ASSERTM(value.is_contiguous(), "value tensor has to be contiguous");
+    AT_ASSERTM(spatial_shapes.is_contiguous(), "spatial_shapes tensor has to be contiguous");
+    AT_ASSERTM(level_start_index.is_contiguous(), "level_start_index tensor has to be contiguous");
+    AT_ASSERTM(sampling_loc.is_contiguous(), "sampling_loc tensor has to be contiguous");
+    AT_ASSERTM(attn_weight.is_contiguous(), "attn_weight tensor has to be contiguous");
+    AT_ASSERTM(grad_output.is_contiguous(), "grad_output tensor has to be contiguous");
+
+    AT_ASSERTM(value.type().is_cuda(), "value must be a CUDA tensor");
+    AT_ASSERTM(spatial_shapes.type().is_cuda(), "spatial_shapes must be a CUDA tensor");
+    AT_ASSERTM(level_start_index.type().is_cuda(), "level_start_index must be a CUDA tensor");
+    AT_ASSERTM(sampling_loc.type().is_cuda(), "sampling_loc must be a CUDA tensor");
+    AT_ASSERTM(attn_weight.type().is_cuda(), "attn_weight must be a CUDA tensor");
+    AT_ASSERTM(grad_output.type().is_cuda(), "grad_output must be a CUDA tensor");
+
+    const int batch = value.size(0);
+    const int spatial_size = value.size(1);
+    const int num_heads = value.size(2);
+    const int channels = value.size(3);
+
+    const int num_levels = spatial_shapes.size(0);
+
+    const int num_query = sampling_loc.size(1);
+    const int num_point = sampling_loc.size(4);
+
+    const int im2col_step_ = std::min(batch, im2col_step);
+
+    AT_ASSERTM(batch % im2col_step_ == 0, "batch(%d) must divide im2col_step(%d)", batch, im2col_step_);
+
+    auto grad_value = at::zeros_like(value);
+    auto grad_sampling_loc = at::zeros_like(sampling_loc);
+    auto grad_attn_weight = at::zeros_like(attn_weight);
+
+    const int batch_n = im2col_step_;
+    auto per_value_size = spatial_size * num_heads * channels;
+    auto per_sample_loc_size = num_query * num_heads * num_levels * num_point * 2;
+    auto per_attn_weight_size = num_query * num_heads * num_levels * num_point;
+    auto grad_output_n = grad_output.view({batch/im2col_step_, batch_n, num_query, num_heads, channels});
+    
+    for (int n = 0; n < batch/im2col_step_; ++n)
+    {
+        auto grad_output_g = grad_output_n.select(0, n);
+        AT_DISPATCH_FLOATING_TYPES(value.type(), "ms_deform_attn_backward_cuda", ([&] {
+            ms_deformable_col2im_cuda(at::cuda::getCurrentCUDAStream(),
+                                    grad_output_g.data<scalar_t>(),
+                                    value.data<scalar_t>() + n * im2col_step_ * per_value_size,
+                                    spatial_shapes.data<int64_t>(),
+                                    level_start_index.data<int64_t>(),
+                                    sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size,
+                                    batch_n, spatial_size, num_heads, channels, num_levels, num_query, num_point,
+                                    grad_value.data<scalar_t>() +  n * im2col_step_ * per_value_size,
+                                    grad_sampling_loc.data<scalar_t>() + n * im2col_step_ * per_sample_loc_size,
+                                    grad_attn_weight.data<scalar_t>() + n * im2col_step_ * per_attn_weight_size);
+
+        }));
+    }
+
+    return {
+        grad_value, grad_sampling_loc, grad_attn_weight
+    };
+}
+
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/MsDeformAttn/ms_deform_attn_cuda.h

@@ -0,0 +1,33 @@
+/*!
+**************************************************************************************************
+* Deformable DETR
+* Copyright (c) 2020 SenseTime. All Rights Reserved.
+* Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+**************************************************************************************************
+* Modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0
+**************************************************************************************************
+*/
+
+#pragma once
+#include <torch/extension.h>
+
+namespace groundingdino {
+
+at::Tensor ms_deform_attn_cuda_forward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const int im2col_step);
+
+std::vector<at::Tensor> ms_deform_attn_cuda_backward(
+    const at::Tensor &value, 
+    const at::Tensor &spatial_shapes,
+    const at::Tensor &level_start_index,
+    const at::Tensor &sampling_loc,
+    const at::Tensor &attn_weight,
+    const at::Tensor &grad_output,
+    const int im2col_step);
+
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/cuda_version.cu

@@ -0,0 +1,7 @@
+#include <cuda_runtime_api.h>
+
+namespace groundingdino {
+int get_cudart_version() {
+  return CUDART_VERSION;
+}
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/csrc/vision.cpp

@@ -0,0 +1,58 @@
+// Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+#include "MsDeformAttn/ms_deform_attn.h"
+
+namespace groundingdino {
+
+#ifdef WITH_CUDA
+extern int get_cudart_version();
+#endif
+
+std::string get_cuda_version() {
+#ifdef WITH_CUDA
+  std::ostringstream oss;
+
+  // copied from
+  // https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/cuda/detail/CUDAHooks.cpp#L231
+  auto printCudaStyleVersion = [&](int v) {
+    oss << (v / 1000) << "." << (v / 10 % 100);
+    if (v % 10 != 0) {
+      oss << "." << (v % 10);
+    }
+  };
+  printCudaStyleVersion(get_cudart_version());
+  return oss.str();
+#else
+  return std::string("not available");
+#endif
+}
+
+// similar to
+// https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/Version.cpp
+std::string get_compiler_version() {
+  std::ostringstream ss;
+#if defined(__GNUC__)
+#ifndef __clang__
+  { ss << "GCC " << __GNUC__ << "." << __GNUC_MINOR__; }
+#endif
+#endif
+
+#if defined(__clang_major__)
+  {
+    ss << "clang " << __clang_major__ << "." << __clang_minor__ << "."
+       << __clang_patchlevel__;
+  }
+#endif
+
+#if defined(_MSC_VER)
+  { ss << "MSVC " << _MSC_FULL_VER; }
+#endif
+  return ss.str();
+}
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("ms_deform_attn_forward", &ms_deform_attn_forward, "ms_deform_attn_forward");
+  m.def("ms_deform_attn_backward", &ms_deform_attn_backward, "ms_deform_attn_backward");
+}
+
+} // namespace groundingdino

metagpt/environment/android/groundingdino/models/GroundingDINO/fuse_modules.py

@@ -0,0 +1,297 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from timm.models.layers import DropPath
+
+
+class FeatureResizer(nn.Module):
+    """
+    This class takes as input a set of embeddings of dimension C1 and outputs a set of
+    embedding of dimension C2, after a linear transformation, dropout and normalization (LN).
+    """
+
+    def __init__(self, input_feat_size, output_feat_size, dropout, do_ln=True):
+        super().__init__()
+        self.do_ln = do_ln
+        # Object feature encoding
+        self.fc = nn.Linear(input_feat_size, output_feat_size, bias=True)
+        self.layer_norm = nn.LayerNorm(output_feat_size, eps=1e-12)
+        self.dropout = nn.Dropout(dropout)
+
+    def forward(self, encoder_features):
+        x = self.fc(encoder_features)
+        if self.do_ln:
+            x = self.layer_norm(x)
+        output = self.dropout(x)
+        return output
+
+
+def l1norm(X, dim, eps=1e-8):
+    """L1-normalize columns of X"""
+    norm = torch.abs(X).sum(dim=dim, keepdim=True) + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def l2norm(X, dim, eps=1e-8):
+    """L2-normalize columns of X"""
+    norm = torch.pow(X, 2).sum(dim=dim, keepdim=True).sqrt() + eps
+    X = torch.div(X, norm)
+    return X
+
+
+def func_attention(query, context, smooth=1, raw_feature_norm="softmax", eps=1e-8):
+    """
+    query: (n_context, queryL, d)
+    context: (n_context, sourceL, d)
+    """
+    batch_size_q, queryL = query.size(0), query.size(1)
+    batch_size, sourceL = context.size(0), context.size(1)
+
+    # Get attention
+    # --> (batch, d, queryL)
+    queryT = torch.transpose(query, 1, 2)
+
+    # (batch, sourceL, d)(batch, d, queryL)
+    # --> (batch, sourceL, queryL)
+    attn = torch.bmm(context, queryT)
+    if raw_feature_norm == "softmax":
+        # --> (batch*sourceL, queryL)
+        attn = attn.view(batch_size * sourceL, queryL)
+        attn = nn.Softmax()(attn)
+        # --> (batch, sourceL, queryL)
+        attn = attn.view(batch_size, sourceL, queryL)
+    elif raw_feature_norm == "l2norm":
+        attn = l2norm(attn, 2)
+    elif raw_feature_norm == "clipped_l2norm":
+        attn = nn.LeakyReLU(0.1)(attn)
+        attn = l2norm(attn, 2)
+    else:
+        raise ValueError("unknown first norm type:", raw_feature_norm)
+    # --> (batch, queryL, sourceL)
+    attn = torch.transpose(attn, 1, 2).contiguous()
+    # --> (batch*queryL, sourceL)
+    attn = attn.view(batch_size * queryL, sourceL)
+    attn = nn.Softmax()(attn * smooth)
+    # --> (batch, queryL, sourceL)
+    attn = attn.view(batch_size, queryL, sourceL)
+    # --> (batch, sourceL, queryL)
+    attnT = torch.transpose(attn, 1, 2).contiguous()
+
+    # --> (batch, d, sourceL)
+    contextT = torch.transpose(context, 1, 2)
+    # (batch x d x sourceL)(batch x sourceL x queryL)
+    # --> (batch, d, queryL)
+    weightedContext = torch.bmm(contextT, attnT)
+    # --> (batch, queryL, d)
+    weightedContext = torch.transpose(weightedContext, 1, 2)
+
+    return weightedContext, attnT
+
+
+class BiMultiHeadAttention(nn.Module):
+    def __init__(self, v_dim, l_dim, embed_dim, num_heads, dropout=0.1, cfg=None):
+        super(BiMultiHeadAttention, self).__init__()
+
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.head_dim = embed_dim // num_heads
+        self.v_dim = v_dim
+        self.l_dim = l_dim
+
+        assert (
+            self.head_dim * self.num_heads == self.embed_dim
+        ), f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})."
+        self.scale = self.head_dim ** (-0.5)
+        self.dropout = dropout
+
+        self.v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.l_proj = nn.Linear(self.l_dim, self.embed_dim)
+        self.values_v_proj = nn.Linear(self.v_dim, self.embed_dim)
+        self.values_l_proj = nn.Linear(self.l_dim, self.embed_dim)
+
+        self.out_v_proj = nn.Linear(self.embed_dim, self.v_dim)
+        self.out_l_proj = nn.Linear(self.embed_dim, self.l_dim)
+
+        self.stable_softmax_2d = True
+        self.clamp_min_for_underflow = True
+        self.clamp_max_for_overflow = True
+
+        self._reset_parameters()
+
+    def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
+        return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+    def _reset_parameters(self):
+        nn.init.xavier_uniform_(self.v_proj.weight)
+        self.v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.l_proj.weight)
+        self.l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_v_proj.weight)
+        self.values_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.values_l_proj.weight)
+        self.values_l_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_v_proj.weight)
+        self.out_v_proj.bias.data.fill_(0)
+        nn.init.xavier_uniform_(self.out_l_proj.weight)
+        self.out_l_proj.bias.data.fill_(0)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        """_summary_
+
+        Args:
+            v (_type_): bs, n_img, dim
+            l (_type_): bs, n_text, dim
+            attention_mask_v (_type_, optional): _description_. bs, n_img
+            attention_mask_l (_type_, optional): _description_. bs, n_text
+
+        Returns:
+            _type_: _description_
+        """
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        bsz, tgt_len, _ = v.size()
+
+        query_states = self.v_proj(v) * self.scale
+        key_states = self._shape(self.l_proj(l), -1, bsz)
+        value_v_states = self._shape(self.values_v_proj(v), -1, bsz)
+        value_l_states = self._shape(self.values_l_proj(l), -1, bsz)
+
+        proj_shape = (bsz * self.num_heads, -1, self.head_dim)
+        query_states = self._shape(query_states, tgt_len, bsz).view(*proj_shape)
+        key_states = key_states.view(*proj_shape)
+        value_v_states = value_v_states.view(*proj_shape)
+        value_l_states = value_l_states.view(*proj_shape)
+
+        src_len = key_states.size(1)
+        attn_weights = torch.bmm(query_states, key_states.transpose(1, 2))  # bs*nhead, nimg, ntxt
+
+        if attn_weights.size() != (bsz * self.num_heads, tgt_len, src_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz * self.num_heads, tgt_len, src_len)}, but is {attn_weights.size()}"
+            )
+
+        if self.stable_softmax_2d:
+            attn_weights = attn_weights - attn_weights.max()
+
+        if self.clamp_min_for_underflow:
+            attn_weights = torch.clamp(
+                attn_weights, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights = torch.clamp(
+                attn_weights, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        attn_weights_T = attn_weights.transpose(1, 2)
+        attn_weights_l = attn_weights_T - torch.max(attn_weights_T, dim=-1, keepdim=True)[0]
+        if self.clamp_min_for_underflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, min=-50000
+            )  # Do not increase -50000, data type half has quite limited range
+        if self.clamp_max_for_overflow:
+            attn_weights_l = torch.clamp(
+                attn_weights_l, max=50000
+            )  # Do not increase 50000, data type half has quite limited range
+
+        # mask vison for language
+        if attention_mask_v is not None:
+            attention_mask_v = (
+                attention_mask_v[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights_l.masked_fill_(attention_mask_v, float("-inf"))
+
+        attn_weights_l = attn_weights_l.softmax(dim=-1)
+
+        # mask language for vision
+        if attention_mask_l is not None:
+            attention_mask_l = (
+                attention_mask_l[:, None, None, :].repeat(1, self.num_heads, 1, 1).flatten(0, 1)
+            )
+            attn_weights.masked_fill_(attention_mask_l, float("-inf"))
+        attn_weights_v = attn_weights.softmax(dim=-1)
+
+        attn_probs_v = F.dropout(attn_weights_v, p=self.dropout, training=self.training)
+        attn_probs_l = F.dropout(attn_weights_l, p=self.dropout, training=self.training)
+
+        attn_output_v = torch.bmm(attn_probs_v, value_l_states)
+        attn_output_l = torch.bmm(attn_probs_l, value_v_states)
+
+        if attn_output_v.size() != (bsz * self.num_heads, tgt_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_v` should be of size {(bsz, self.num_heads, tgt_len, self.head_dim)}, but is {attn_output_v.size()}"
+            )
+
+        if attn_output_l.size() != (bsz * self.num_heads, src_len, self.head_dim):
+            raise ValueError(
+                f"`attn_output_l` should be of size {(bsz, self.num_heads, src_len, self.head_dim)}, but is {attn_output_l.size()}"
+            )
+
+        attn_output_v = attn_output_v.view(bsz, self.num_heads, tgt_len, self.head_dim)
+        attn_output_v = attn_output_v.transpose(1, 2)
+        attn_output_v = attn_output_v.reshape(bsz, tgt_len, self.embed_dim)
+
+        attn_output_l = attn_output_l.view(bsz, self.num_heads, src_len, self.head_dim)
+        attn_output_l = attn_output_l.transpose(1, 2)
+        attn_output_l = attn_output_l.reshape(bsz, src_len, self.embed_dim)
+
+        attn_output_v = self.out_v_proj(attn_output_v)
+        attn_output_l = self.out_l_proj(attn_output_l)
+
+        return attn_output_v, attn_output_l
+
+
+# Bi-Direction MHA (text->image, image->text)
+class BiAttentionBlock(nn.Module):
+    def __init__(
+        self,
+        v_dim,
+        l_dim,
+        embed_dim,
+        num_heads,
+        dropout=0.1,
+        drop_path=0.0,
+        init_values=1e-4,
+        cfg=None,
+    ):
+        """
+        Inputs:
+            embed_dim - Dimensionality of input and attention feature vectors
+            hidden_dim - Dimensionality of hidden layer in feed-forward network
+                         (usually 2-4x larger than embed_dim)
+            num_heads - Number of heads to use in the Multi-Head Attention block
+            dropout - Amount of dropout to apply in the feed-forward network
+        """
+        super(BiAttentionBlock, self).__init__()
+
+        # pre layer norm
+        self.layer_norm_v = nn.LayerNorm(v_dim)
+        self.layer_norm_l = nn.LayerNorm(l_dim)
+        self.attn = BiMultiHeadAttention(
+            v_dim=v_dim, l_dim=l_dim, embed_dim=embed_dim, num_heads=num_heads, dropout=dropout
+        )
+
+        # add layer scale for training stability
+        self.drop_path = DropPath(drop_path) if drop_path > 0.0 else nn.Identity()
+        self.gamma_v = nn.Parameter(init_values * torch.ones((v_dim)), requires_grad=True)
+        self.gamma_l = nn.Parameter(init_values * torch.ones((l_dim)), requires_grad=True)
+
+    def forward(self, v, l, attention_mask_v=None, attention_mask_l=None):
+        v = self.layer_norm_v(v)
+        l = self.layer_norm_l(l)
+        delta_v, delta_l = self.attn(
+            v, l, attention_mask_v=attention_mask_v, attention_mask_l=attention_mask_l
+        )
+        # v, l = v + delta_v, l + delta_l
+        v = v + self.drop_path(self.gamma_v * delta_v)
+        l = l + self.drop_path(self.gamma_l * delta_l)
+        return v, l
+
+    # def forward(self, v:List[torch.Tensor], l, attention_mask_v=None, attention_mask_l=None)

metagpt/environment/android/groundingdino/models/GroundingDINO/groundingdino.py

@@ -0,0 +1,395 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR model and criterion classes.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+# Modified from Deformable DETR (https://github.com/fundamentalvision/Deformable-DETR)
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# ------------------------------------------------------------------------
+import copy
+from typing import List
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+from torchvision.ops.boxes import nms
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+
+from groundingdino.util import box_ops, get_tokenlizer
+from groundingdino.util.misc import (
+    NestedTensor,
+    accuracy,
+    get_world_size,
+    interpolate,
+    inverse_sigmoid,
+    is_dist_avail_and_initialized,
+    nested_tensor_from_tensor_list,
+)
+from groundingdino.util.utils import get_phrases_from_posmap
+from groundingdino.util.visualizer import COCOVisualizer
+from groundingdino.util.vl_utils import create_positive_map_from_span
+
+from ..registry import MODULE_BUILD_FUNCS
+from .backbone import build_backbone
+from .bertwarper import (
+    BertModelWarper,
+    generate_masks_with_special_tokens,
+    generate_masks_with_special_tokens_and_transfer_map,
+)
+from .transformer import build_transformer
+from .utils import MLP, ContrastiveEmbed, sigmoid_focal_loss
+
+
+class GroundingDINO(nn.Module):
+    """This is the Cross-Attention Detector module that performs object detection"""
+
+    def __init__(
+        self,
+        backbone,
+        transformer,
+        num_queries,
+        aux_loss=False,
+        iter_update=False,
+        query_dim=2,
+        num_feature_levels=1,
+        nheads=8,
+        # two stage
+        two_stage_type="no",  # ['no', 'standard']
+        dec_pred_bbox_embed_share=True,
+        two_stage_class_embed_share=True,
+        two_stage_bbox_embed_share=True,
+        num_patterns=0,
+        dn_number=100,
+        dn_box_noise_scale=0.4,
+        dn_label_noise_ratio=0.5,
+        dn_labelbook_size=100,
+        text_encoder_type="bert-base-uncased",
+        sub_sentence_present=True,
+        max_text_len=256,
+    ):
+        """Initializes the model.
+        Parameters:
+            backbone: torch module of the backbone to be used. See backbone.py
+            transformer: torch module of the transformer architecture. See transformer.py
+            num_queries: number of object queries, ie detection slot. This is the maximal number of objects
+                         Conditional DETR can detect in a single image. For COCO, we recommend 100 queries.
+            aux_loss: True if auxiliary decoding losses (loss at each decoder layer) are to be used.
+        """
+        super().__init__()
+        self.num_queries = num_queries
+        self.transformer = transformer
+        self.hidden_dim = hidden_dim = transformer.d_model
+        self.num_feature_levels = num_feature_levels
+        self.nheads = nheads
+        self.max_text_len = 256
+        self.sub_sentence_present = sub_sentence_present
+
+        # setting query dim
+        self.query_dim = query_dim
+        assert query_dim == 4
+
+        # for dn training
+        self.num_patterns = num_patterns
+        self.dn_number = dn_number
+        self.dn_box_noise_scale = dn_box_noise_scale
+        self.dn_label_noise_ratio = dn_label_noise_ratio
+        self.dn_labelbook_size = dn_labelbook_size
+
+        # bert
+        self.tokenizer = get_tokenlizer.get_tokenlizer(text_encoder_type)
+        self.bert = get_tokenlizer.get_pretrained_language_model(text_encoder_type)
+        self.bert.pooler.dense.weight.requires_grad_(False)
+        self.bert.pooler.dense.bias.requires_grad_(False)
+        self.bert = BertModelWarper(bert_model=self.bert)
+
+        self.feat_map = nn.Linear(self.bert.config.hidden_size, self.hidden_dim, bias=True)
+        nn.init.constant_(self.feat_map.bias.data, 0)
+        nn.init.xavier_uniform_(self.feat_map.weight.data)
+        # freeze
+
+        # special tokens
+        self.specical_tokens = self.tokenizer.convert_tokens_to_ids(["[CLS]", "[SEP]", ".", "?"])
+
+        # prepare input projection layers
+        if num_feature_levels > 1:
+            num_backbone_outs = len(backbone.num_channels)
+            input_proj_list = []
+            for _ in range(num_backbone_outs):
+                in_channels = backbone.num_channels[_]
+                input_proj_list.append(
+                    nn.Sequential(
+                        nn.Conv2d(in_channels, hidden_dim, kernel_size=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                )
+            for _ in range(num_feature_levels - num_backbone_outs):
+                input_proj_list.append(
+                    nn.Sequential(
+                        nn.Conv2d(in_channels, hidden_dim, kernel_size=3, stride=2, padding=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                )
+                in_channels = hidden_dim
+            self.input_proj = nn.ModuleList(input_proj_list)
+        else:
+            assert two_stage_type == "no", "two_stage_type should be no if num_feature_levels=1 !!!"
+            self.input_proj = nn.ModuleList(
+                [
+                    nn.Sequential(
+                        nn.Conv2d(backbone.num_channels[-1], hidden_dim, kernel_size=1),
+                        nn.GroupNorm(32, hidden_dim),
+                    )
+                ]
+            )
+
+        self.backbone = backbone
+        self.aux_loss = aux_loss
+        self.box_pred_damping = box_pred_damping = None
+
+        self.iter_update = iter_update
+        assert iter_update, "Why not iter_update?"
+
+        # prepare pred layers
+        self.dec_pred_bbox_embed_share = dec_pred_bbox_embed_share
+        # prepare class & box embed
+        _class_embed = ContrastiveEmbed()
+
+        _bbox_embed = MLP(hidden_dim, hidden_dim, 4, 3)
+        nn.init.constant_(_bbox_embed.layers[-1].weight.data, 0)
+        nn.init.constant_(_bbox_embed.layers[-1].bias.data, 0)
+
+        if dec_pred_bbox_embed_share:
+            box_embed_layerlist = [_bbox_embed for i in range(transformer.num_decoder_layers)]
+        else:
+            box_embed_layerlist = [
+                copy.deepcopy(_bbox_embed) for i in range(transformer.num_decoder_layers)
+            ]
+        class_embed_layerlist = [_class_embed for i in range(transformer.num_decoder_layers)]
+        self.bbox_embed = nn.ModuleList(box_embed_layerlist)
+        self.class_embed = nn.ModuleList(class_embed_layerlist)
+        self.transformer.decoder.bbox_embed = self.bbox_embed
+        self.transformer.decoder.class_embed = self.class_embed
+
+        # two stage
+        self.two_stage_type = two_stage_type
+        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+            two_stage_type
+        )
+        if two_stage_type != "no":
+            if two_stage_bbox_embed_share:
+                assert dec_pred_bbox_embed_share
+                self.transformer.enc_out_bbox_embed = _bbox_embed
+            else:
+                self.transformer.enc_out_bbox_embed = copy.deepcopy(_bbox_embed)
+
+            if two_stage_class_embed_share:
+                assert dec_pred_bbox_embed_share
+                self.transformer.enc_out_class_embed = _class_embed
+            else:
+                self.transformer.enc_out_class_embed = copy.deepcopy(_class_embed)
+
+            self.refpoint_embed = None
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        # init input_proj
+        for proj in self.input_proj:
+            nn.init.xavier_uniform_(proj[0].weight, gain=1)
+            nn.init.constant_(proj[0].bias, 0)
+
+    def init_ref_points(self, use_num_queries):
+        self.refpoint_embed = nn.Embedding(use_num_queries, self.query_dim)
+
+    def forward(self, samples: NestedTensor, targets: List = None, **kw):
+        """The forward expects a NestedTensor, which consists of:
+           - samples.tensor: batched images, of shape [batch_size x 3 x H x W]
+           - samples.mask: a binary mask of shape [batch_size x H x W], containing 1 on padded pixels
+
+        It returns a dict with the following elements:
+           - "pred_logits": the classification logits (including no-object) for all queries.
+                            Shape= [batch_size x num_queries x num_classes]
+           - "pred_boxes": The normalized boxes coordinates for all queries, represented as
+                           (center_x, center_y, width, height). These values are normalized in [0, 1],
+                           relative to the size of each individual image (disregarding possible padding).
+                           See PostProcess for information on how to retrieve the unnormalized bounding box.
+           - "aux_outputs": Optional, only returned when auxilary losses are activated. It is a list of
+                            dictionnaries containing the two above keys for each decoder layer.
+        """
+        if targets is None:
+            captions = kw["captions"]
+        else:
+            captions = [t["caption"] for t in targets]
+        len(captions)
+
+        # encoder texts
+        tokenized = self.tokenizer(captions, padding="longest", return_tensors="pt").to(
+            samples.device
+        )
+        (
+            text_self_attention_masks,
+            position_ids,
+            cate_to_token_mask_list,
+        ) = generate_masks_with_special_tokens_and_transfer_map(
+            tokenized, self.specical_tokens, self.tokenizer
+        )
+
+        if text_self_attention_masks.shape[1] > self.max_text_len:
+            text_self_attention_masks = text_self_attention_masks[
+                :, : self.max_text_len, : self.max_text_len
+            ]
+            position_ids = position_ids[:, : self.max_text_len]
+            tokenized["input_ids"] = tokenized["input_ids"][:, : self.max_text_len]
+            tokenized["attention_mask"] = tokenized["attention_mask"][:, : self.max_text_len]
+            tokenized["token_type_ids"] = tokenized["token_type_ids"][:, : self.max_text_len]
+
+        # extract text embeddings
+        if self.sub_sentence_present:
+            tokenized_for_encoder = {k: v for k, v in tokenized.items() if k != "attention_mask"}
+            tokenized_for_encoder["attention_mask"] = text_self_attention_masks
+            tokenized_for_encoder["position_ids"] = position_ids
+        else:
+            # import ipdb; ipdb.set_trace()
+            tokenized_for_encoder = tokenized
+
+        bert_output = self.bert(**tokenized_for_encoder)  # bs, 195, 768
+
+        encoded_text = self.feat_map(bert_output["last_hidden_state"])  # bs, 195, d_model
+        text_token_mask = tokenized.attention_mask.bool()  # bs, 195
+        # text_token_mask: True for nomask, False for mask
+        # text_self_attention_masks: True for nomask, False for mask
+
+        if encoded_text.shape[1] > self.max_text_len:
+            encoded_text = encoded_text[:, : self.max_text_len, :]
+            text_token_mask = text_token_mask[:, : self.max_text_len]
+            position_ids = position_ids[:, : self.max_text_len]
+            text_self_attention_masks = text_self_attention_masks[
+                :, : self.max_text_len, : self.max_text_len
+            ]
+
+        text_dict = {
+            "encoded_text": encoded_text,  # bs, 195, d_model
+            "text_token_mask": text_token_mask,  # bs, 195
+            "position_ids": position_ids,  # bs, 195
+            "text_self_attention_masks": text_self_attention_masks,  # bs, 195,195
+        }
+
+        # import ipdb; ipdb.set_trace()
+
+        if isinstance(samples, (list, torch.Tensor)):
+            samples = nested_tensor_from_tensor_list(samples)
+        features, poss = self.backbone(samples)
+
+        srcs = []
+        masks = []
+        for l, feat in enumerate(features):
+            src, mask = feat.decompose()
+            srcs.append(self.input_proj[l](src))
+            masks.append(mask)
+            assert mask is not None
+        if self.num_feature_levels > len(srcs):
+            _len_srcs = len(srcs)
+            for l in range(_len_srcs, self.num_feature_levels):
+                if l == _len_srcs:
+                    src = self.input_proj[l](features[-1].tensors)
+                else:
+                    src = self.input_proj[l](srcs[-1])
+                m = samples.mask
+                mask = F.interpolate(m[None].float(), size=src.shape[-2:]).to(torch.bool)[0]
+                pos_l = self.backbone[1](NestedTensor(src, mask)).to(src.dtype)
+                srcs.append(src)
+                masks.append(mask)
+                poss.append(pos_l)
+
+        input_query_bbox = input_query_label = attn_mask = dn_meta = None
+        hs, reference, hs_enc, ref_enc, init_box_proposal = self.transformer(
+            srcs, masks, input_query_bbox, poss, input_query_label, attn_mask, text_dict
+        )
+
+        # deformable-detr-like anchor update
+        outputs_coord_list = []
+        for dec_lid, (layer_ref_sig, layer_bbox_embed, layer_hs) in enumerate(
+            zip(reference[:-1], self.bbox_embed, hs)
+        ):
+            layer_delta_unsig = layer_bbox_embed(layer_hs)
+            layer_outputs_unsig = layer_delta_unsig + inverse_sigmoid(layer_ref_sig)
+            layer_outputs_unsig = layer_outputs_unsig.sigmoid()
+            outputs_coord_list.append(layer_outputs_unsig)
+        outputs_coord_list = torch.stack(outputs_coord_list)
+
+        # output
+        outputs_class = torch.stack(
+            [
+                layer_cls_embed(layer_hs, text_dict)
+                for layer_cls_embed, layer_hs in zip(self.class_embed, hs)
+            ]
+        )
+        out = {"pred_logits": outputs_class[-1], "pred_boxes": outputs_coord_list[-1]}
+
+        # # for intermediate outputs
+        # if self.aux_loss:
+        #     out['aux_outputs'] = self._set_aux_loss(outputs_class, outputs_coord_list)
+
+        # # for encoder output
+        # if hs_enc is not None:
+        #     # prepare intermediate outputs
+        #     interm_coord = ref_enc[-1]
+        #     interm_class = self.transformer.enc_out_class_embed(hs_enc[-1], text_dict)
+        #     out['interm_outputs'] = {'pred_logits': interm_class, 'pred_boxes': interm_coord}
+        #     out['interm_outputs_for_matching_pre'] = {'pred_logits': interm_class, 'pred_boxes': init_box_proposal}
+
+        return out
+
+    @torch.jit.unused
+    def _set_aux_loss(self, outputs_class, outputs_coord):
+        # this is a workaround to make torchscript happy, as torchscript
+        # doesn't support dictionary with non-homogeneous values, such
+        # as a dict having both a Tensor and a list.
+        return [
+            {"pred_logits": a, "pred_boxes": b}
+            for a, b in zip(outputs_class[:-1], outputs_coord[:-1])
+        ]
+
+
+@MODULE_BUILD_FUNCS.registe_with_name(module_name="groundingdino")
+def build_groundingdino(args):
+
+    backbone = build_backbone(args)
+    transformer = build_transformer(args)
+
+    dn_labelbook_size = args.dn_labelbook_size
+    dec_pred_bbox_embed_share = args.dec_pred_bbox_embed_share
+    sub_sentence_present = args.sub_sentence_present
+
+    model = GroundingDINO(
+        backbone,
+        transformer,
+        num_queries=args.num_queries,
+        aux_loss=True,
+        iter_update=True,
+        query_dim=4,
+        num_feature_levels=args.num_feature_levels,
+        nheads=args.nheads,
+        dec_pred_bbox_embed_share=dec_pred_bbox_embed_share,
+        two_stage_type=args.two_stage_type,
+        two_stage_bbox_embed_share=args.two_stage_bbox_embed_share,
+        two_stage_class_embed_share=args.two_stage_class_embed_share,
+        num_patterns=args.num_patterns,
+        dn_number=0,
+        dn_box_noise_scale=args.dn_box_noise_scale,
+        dn_label_noise_ratio=args.dn_label_noise_ratio,
+        dn_labelbook_size=dn_labelbook_size,
+        text_encoder_type=args.text_encoder_type,
+        sub_sentence_present=sub_sentence_present,
+        max_text_len=args.max_text_len,
+    )
+
+    return model

metagpt/environment/android/groundingdino/models/GroundingDINO/ms_deform_attn.py

@@ -0,0 +1,413 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Deformable DETR
+# Copyright (c) 2020 SenseTime. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------------------------------
+# Modified from:
+# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/functions/ms_deform_attn_func.py
+# https://github.com/fundamentalvision/Deformable-DETR/blob/main/models/ops/modules/ms_deform_attn.py
+# https://github.com/open-mmlab/mmcv/blob/master/mmcv/ops/multi_scale_deform_attn.py
+# ------------------------------------------------------------------------------------------------
+
+import math
+import warnings
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.autograd import Function
+from torch.autograd.function import once_differentiable
+from torch.nn.init import constant_, xavier_uniform_
+
+try:
+    from groundingdino import _C
+except:
+    warnings.warn("Failed to load custom C++ ops. Running on CPU mode Only!")
+
+
+# helpers
+def _is_power_of_2(n):
+    if (not isinstance(n, int)) or (n < 0):
+        raise ValueError("invalid input for _is_power_of_2: {} (type: {})".format(n, type(n)))
+    return (n & (n - 1) == 0) and n != 0
+
+
+class MultiScaleDeformableAttnFunction(Function):
+    @staticmethod
+    def forward(
+        ctx,
+        value,
+        value_spatial_shapes,
+        value_level_start_index,
+        sampling_locations,
+        attention_weights,
+        im2col_step,
+    ):
+        ctx.im2col_step = im2col_step
+        output = _C.ms_deform_attn_forward(
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+            ctx.im2col_step,
+        )
+        ctx.save_for_backward(
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+        )
+        return output
+
+    @staticmethod
+    @once_differentiable
+    def backward(ctx, grad_output):
+        (
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+        ) = ctx.saved_tensors
+        grad_value, grad_sampling_loc, grad_attn_weight = _C.ms_deform_attn_backward(
+            value,
+            value_spatial_shapes,
+            value_level_start_index,
+            sampling_locations,
+            attention_weights,
+            grad_output,
+            ctx.im2col_step,
+        )
+
+        return grad_value, None, None, grad_sampling_loc, grad_attn_weight, None
+
+
+def multi_scale_deformable_attn_pytorch(
+    value: torch.Tensor,
+    value_spatial_shapes: torch.Tensor,
+    sampling_locations: torch.Tensor,
+    attention_weights: torch.Tensor,
+) -> torch.Tensor:
+
+    bs, _, num_heads, embed_dims = value.shape
+    _, num_queries, num_heads, num_levels, num_points, _ = sampling_locations.shape
+    value_list = value.split([H_ * W_ for H_, W_ in value_spatial_shapes], dim=1)
+    sampling_grids = 2 * sampling_locations - 1
+    sampling_value_list = []
+    for level, (H_, W_) in enumerate(value_spatial_shapes):
+        # bs, H_*W_, num_heads, embed_dims ->
+        # bs, H_*W_, num_heads*embed_dims ->
+        # bs, num_heads*embed_dims, H_*W_ ->
+        # bs*num_heads, embed_dims, H_, W_
+        value_l_ = (
+            value_list[level].flatten(2).transpose(1, 2).reshape(bs * num_heads, embed_dims, H_, W_)
+        )
+        # bs, num_queries, num_heads, num_points, 2 ->
+        # bs, num_heads, num_queries, num_points, 2 ->
+        # bs*num_heads, num_queries, num_points, 2
+        sampling_grid_l_ = sampling_grids[:, :, :, level].transpose(1, 2).flatten(0, 1)
+        # bs*num_heads, embed_dims, num_queries, num_points
+        sampling_value_l_ = F.grid_sample(
+            value_l_, sampling_grid_l_, mode="bilinear", padding_mode="zeros", align_corners=False
+        )
+        sampling_value_list.append(sampling_value_l_)
+    # (bs, num_queries, num_heads, num_levels, num_points) ->
+    # (bs, num_heads, num_queries, num_levels, num_points) ->
+    # (bs, num_heads, 1, num_queries, num_levels*num_points)
+    attention_weights = attention_weights.transpose(1, 2).reshape(
+        bs * num_heads, 1, num_queries, num_levels * num_points
+    )
+    output = (
+        (torch.stack(sampling_value_list, dim=-2).flatten(-2) * attention_weights)
+        .sum(-1)
+        .view(bs, num_heads * embed_dims, num_queries)
+    )
+    return output.transpose(1, 2).contiguous()
+
+
+class MultiScaleDeformableAttention(nn.Module):
+    """Multi-Scale Deformable Attention Module used in Deformable-DETR
+
+    `Deformable DETR: Deformable Transformers for End-to-End Object Detection.
+    <https://arxiv.org/pdf/2010.04159.pdf>`_.
+
+    Args:
+        embed_dim (int): The embedding dimension of Attention. Default: 256.
+        num_heads (int): The number of attention heads. Default: 8.
+        num_levels (int): The number of feature map used in Attention. Default: 4.
+        num_points (int): The number of sampling points for each query
+            in each head. Default: 4.
+        img2col_steps (int): The step used in image_to_column. Defualt: 64.
+            dropout (float): Dropout layer used in output. Default: 0.1.
+        batch_first (bool): if ``True``, then the input and output tensor will be
+            provided as `(bs, n, embed_dim)`. Default: False. `(n, bs, embed_dim)`
+    """
+
+    def __init__(
+        self,
+        embed_dim: int = 256,
+        num_heads: int = 8,
+        num_levels: int = 4,
+        num_points: int = 4,
+        img2col_step: int = 64,
+        batch_first: bool = False,
+    ):
+        super().__init__()
+        if embed_dim % num_heads != 0:
+            raise ValueError(
+                "embed_dim must be divisible by num_heads, but got {} and {}".format(
+                    embed_dim, num_heads
+                )
+            )
+        head_dim = embed_dim // num_heads
+
+        self.batch_first = batch_first
+
+        if not _is_power_of_2(head_dim):
+            warnings.warn(
+                """
+                You'd better set d_model in MSDeformAttn to make sure that
+                each dim of the attention head a power of 2, which is more efficient.
+                """
+            )
+
+        self.im2col_step = img2col_step
+        self.embed_dim = embed_dim
+        self.num_heads = num_heads
+        self.num_levels = num_levels
+        self.num_points = num_points
+        self.sampling_offsets = nn.Linear(embed_dim, num_heads * num_levels * num_points * 2)
+        self.attention_weights = nn.Linear(embed_dim, num_heads * num_levels * num_points)
+        self.value_proj = nn.Linear(embed_dim, embed_dim)
+        self.output_proj = nn.Linear(embed_dim, embed_dim)
+
+        self.init_weights()
+
+    def _reset_parameters(self):
+        return self.init_weights()
+
+    def init_weights(self):
+        """
+        Default initialization for Parameters of Module.
+        """
+        constant_(self.sampling_offsets.weight.data, 0.0)
+        thetas = torch.arange(self.num_heads, dtype=torch.float32) * (
+            2.0 * math.pi / self.num_heads
+        )
+        grid_init = torch.stack([thetas.cos(), thetas.sin()], -1)
+        grid_init = (
+            (grid_init / grid_init.abs().max(-1, keepdim=True)[0])
+            .view(self.num_heads, 1, 1, 2)
+            .repeat(1, self.num_levels, self.num_points, 1)
+        )
+        for i in range(self.num_points):
+            grid_init[:, :, i, :] *= i + 1
+        with torch.no_grad():
+            self.sampling_offsets.bias = nn.Parameter(grid_init.view(-1))
+        constant_(self.attention_weights.weight.data, 0.0)
+        constant_(self.attention_weights.bias.data, 0.0)
+        xavier_uniform_(self.value_proj.weight.data)
+        constant_(self.value_proj.bias.data, 0.0)
+        xavier_uniform_(self.output_proj.weight.data)
+        constant_(self.output_proj.bias.data, 0.0)
+
+    def freeze_sampling_offsets(self):
+        print("Freeze sampling offsets")
+        self.sampling_offsets.weight.requires_grad = False
+        self.sampling_offsets.bias.requires_grad = False
+
+    def freeze_attention_weights(self):
+        print("Freeze attention weights")
+        self.attention_weights.weight.requires_grad = False
+        self.attention_weights.bias.requires_grad = False
+
+    def forward(
+        self,
+        query: torch.Tensor,
+        key: Optional[torch.Tensor] = None,
+        value: Optional[torch.Tensor] = None,
+        query_pos: Optional[torch.Tensor] = None,
+        key_padding_mask: Optional[torch.Tensor] = None,
+        reference_points: Optional[torch.Tensor] = None,
+        spatial_shapes: Optional[torch.Tensor] = None,
+        level_start_index: Optional[torch.Tensor] = None,
+        **kwargs
+    ) -> torch.Tensor:
+
+        """Forward Function of MultiScaleDeformableAttention
+
+        Args:
+            query (torch.Tensor): Query embeddings with shape
+                `(num_query, bs, embed_dim)`
+            key (torch.Tensor): Key embeddings with shape
+                `(num_key, bs, embed_dim)`
+            value (torch.Tensor): Value embeddings with shape
+                `(num_key, bs, embed_dim)`
+            query_pos (torch.Tensor): The position embedding for `query`. Default: None.
+            key_padding_mask (torch.Tensor): ByteTensor for `query`, with shape `(bs, num_key)`,
+                indicating which elements within `key` to be ignored in attention.
+            reference_points (torch.Tensor): The normalized reference points
+                with shape `(bs, num_query, num_levels, 2)`,
+                all elements is range in [0, 1], top-left (0, 0),
+                bottom-right (1, 1), including padding are.
+                or `(N, Length_{query}, num_levels, 4)`, add additional
+                two dimensions `(h, w)` to form reference boxes.
+            spatial_shapes (torch.Tensor): Spatial shape of features in different levels.
+                With shape `(num_levels, 2)`, last dimension represents `(h, w)`.
+            level_start_index (torch.Tensor): The start index of each level. A tensor with
+                shape `(num_levels, )` which can be represented as
+                `[0, h_0 * w_0, h_0 * w_0 + h_1 * w_1, ...]`.
+
+        Returns:
+            torch.Tensor: forward results with shape `(num_query, bs, embed_dim)`
+        """
+
+        if value is None:
+            value = query
+
+        if query_pos is not None:
+            query = query + query_pos
+
+        if not self.batch_first:
+            # change to (bs, num_query ,embed_dims)
+            query = query.permute(1, 0, 2)
+            value = value.permute(1, 0, 2)
+
+        bs, num_query, _ = query.shape
+        bs, num_value, _ = value.shape
+
+        assert (spatial_shapes[:, 0] * spatial_shapes[:, 1]).sum() == num_value
+
+        value = self.value_proj(value)
+        if key_padding_mask is not None:
+            value = value.masked_fill(key_padding_mask[..., None], float(0))
+        value = value.view(bs, num_value, self.num_heads, -1)
+        sampling_offsets = self.sampling_offsets(query).view(
+            bs, num_query, self.num_heads, self.num_levels, self.num_points, 2
+        )
+        attention_weights = self.attention_weights(query).view(
+            bs, num_query, self.num_heads, self.num_levels * self.num_points
+        )
+        attention_weights = attention_weights.softmax(-1)
+        attention_weights = attention_weights.view(
+            bs,
+            num_query,
+            self.num_heads,
+            self.num_levels,
+            self.num_points,
+        )
+
+        # bs, num_query, num_heads, num_levels, num_points, 2
+        if reference_points.shape[-1] == 2:
+            offset_normalizer = torch.stack([spatial_shapes[..., 1], spatial_shapes[..., 0]], -1)
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :]
+                + sampling_offsets / offset_normalizer[None, None, None, :, None, :]
+            )
+        elif reference_points.shape[-1] == 4:
+            sampling_locations = (
+                reference_points[:, :, None, :, None, :2]
+                + sampling_offsets
+                / self.num_points
+                * reference_points[:, :, None, :, None, 2:]
+                * 0.5
+            )
+        else:
+            raise ValueError(
+                "Last dim of reference_points must be 2 or 4, but get {} instead.".format(
+                    reference_points.shape[-1]
+                )
+            )
+    
+        if torch.cuda.is_available() and value.is_cuda:
+            halffloat = False
+            if value.dtype == torch.float16:
+                halffloat = True
+                value = value.float()
+                sampling_locations = sampling_locations.float()
+                attention_weights = attention_weights.float()
+
+            output = MultiScaleDeformableAttnFunction.apply(
+                value,
+                spatial_shapes,
+                level_start_index,
+                sampling_locations,
+                attention_weights,
+                self.im2col_step,
+            )
+
+            if halffloat:
+                output = output.half()
+        else:
+            output = multi_scale_deformable_attn_pytorch(
+                value, spatial_shapes, sampling_locations, attention_weights
+            )
+
+        output = self.output_proj(output)
+
+        if not self.batch_first:
+            output = output.permute(1, 0, 2)
+
+        return output
+
+
+def create_dummy_class(klass, dependency, message=""):
+    """
+    When a dependency of a class is not available, create a dummy class which throws ImportError
+    when used.
+
+    Args:
+        klass (str): name of the class.
+        dependency (str): name of the dependency.
+        message: extra message to print
+    Returns:
+        class: a class object
+    """
+    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, klass)
+    if message:
+        err = err + " " + message
+
+    class _DummyMetaClass(type):
+        # throw error on class attribute access
+        def __getattr__(_, __):  # noqa: B902
+            raise ImportError(err)
+
+    class _Dummy(object, metaclass=_DummyMetaClass):
+        # throw error on constructor
+        def __init__(self, *args, **kwargs):
+            raise ImportError(err)
+
+    return _Dummy
+
+
+def create_dummy_func(func, dependency, message=""):
+    """
+    When a dependency of a function is not available, create a dummy function which throws
+    ImportError when used.
+
+    Args:
+        func (str): name of the function.
+        dependency (str or list[str]): name(s) of the dependency.
+        message: extra message to print
+    Returns:
+        function: a function object
+    """
+    err = "Cannot import '{}', therefore '{}' is not available.".format(dependency, func)
+    if message:
+        err = err + " " + message
+
+    if isinstance(dependency, (list, tuple)):
+        dependency = ",".join(dependency)
+
+    def _dummy(*args, **kwargs):
+        raise ImportError(err)
+
+    return _dummy

metagpt/environment/android/groundingdino/models/GroundingDINO/transformer.py

@@ -0,0 +1,959 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# DINO
+# Copyright (c) 2022 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Conditional DETR Transformer class.
+# Copyright (c) 2021 Microsoft. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Modified from DETR (https://github.com/facebookresearch/detr)
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
+# ------------------------------------------------------------------------
+
+from typing import Optional
+
+import torch
+import torch.utils.checkpoint as checkpoint
+from torch import Tensor, nn
+
+from groundingdino.util.misc import inverse_sigmoid
+
+from .fuse_modules import BiAttentionBlock
+from .ms_deform_attn import MultiScaleDeformableAttention as MSDeformAttn
+from .transformer_vanilla import TransformerEncoderLayer
+from .utils import (
+    MLP,
+    _get_activation_fn,
+    _get_clones,
+    gen_encoder_output_proposals,
+    gen_sineembed_for_position,
+    get_sine_pos_embed,
+)
+
+
+class Transformer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        nhead=8,
+        num_queries=300,
+        num_encoder_layers=6,
+        num_unicoder_layers=0,
+        num_decoder_layers=6,
+        dim_feedforward=2048,
+        dropout=0.0,
+        activation="relu",
+        normalize_before=False,
+        return_intermediate_dec=False,
+        query_dim=4,
+        num_patterns=0,
+        # for deformable encoder
+        num_feature_levels=1,
+        enc_n_points=4,
+        dec_n_points=4,
+        # init query
+        learnable_tgt_init=False,
+        # two stage
+        two_stage_type="no",  # ['no', 'standard', 'early', 'combine', 'enceachlayer', 'enclayer1']
+        embed_init_tgt=False,
+        # for text
+        use_text_enhancer=False,
+        use_fusion_layer=False,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+        use_text_cross_attention=False,
+        text_dropout=0.1,
+        fusion_dropout=0.1,
+        fusion_droppath=0.0,
+    ):
+        super().__init__()
+        self.num_feature_levels = num_feature_levels
+        self.num_encoder_layers = num_encoder_layers
+        self.num_unicoder_layers = num_unicoder_layers
+        self.num_decoder_layers = num_decoder_layers
+        self.num_queries = num_queries
+        assert query_dim == 4
+
+        # choose encoder layer type
+        encoder_layer = DeformableTransformerEncoderLayer(
+            d_model, dim_feedforward, dropout, activation, num_feature_levels, nhead, enc_n_points
+        )
+
+        if use_text_enhancer:
+            text_enhance_layer = TransformerEncoderLayer(
+                d_model=d_model,
+                nhead=nhead // 2,
+                dim_feedforward=dim_feedforward // 2,
+                dropout=text_dropout,
+            )
+        else:
+            text_enhance_layer = None
+
+        if use_fusion_layer:
+            feature_fusion_layer = BiAttentionBlock(
+                v_dim=d_model,
+                l_dim=d_model,
+                embed_dim=dim_feedforward // 2,
+                num_heads=nhead // 2,
+                dropout=fusion_dropout,
+                drop_path=fusion_droppath,
+            )
+        else:
+            feature_fusion_layer = None
+
+        encoder_norm = nn.LayerNorm(d_model) if normalize_before else None
+        assert encoder_norm is None
+        self.encoder = TransformerEncoder(
+            encoder_layer,
+            num_encoder_layers,
+            d_model=d_model,
+            num_queries=num_queries,
+            text_enhance_layer=text_enhance_layer,
+            feature_fusion_layer=feature_fusion_layer,
+            use_checkpoint=use_checkpoint,
+            use_transformer_ckpt=use_transformer_ckpt,
+        )
+
+        # choose decoder layer type
+        decoder_layer = DeformableTransformerDecoderLayer(
+            d_model,
+            dim_feedforward,
+            dropout,
+            activation,
+            num_feature_levels,
+            nhead,
+            dec_n_points,
+            use_text_cross_attention=use_text_cross_attention,
+        )
+
+        decoder_norm = nn.LayerNorm(d_model)
+        self.decoder = TransformerDecoder(
+            decoder_layer,
+            num_decoder_layers,
+            decoder_norm,
+            return_intermediate=return_intermediate_dec,
+            d_model=d_model,
+            query_dim=query_dim,
+            num_feature_levels=num_feature_levels,
+        )
+
+        self.d_model = d_model
+        self.nhead = nhead
+        self.dec_layers = num_decoder_layers
+        self.num_queries = num_queries  # useful for single stage model only
+        self.num_patterns = num_patterns
+        if not isinstance(num_patterns, int):
+            Warning("num_patterns should be int but {}".format(type(num_patterns)))
+            self.num_patterns = 0
+
+        if num_feature_levels > 1:
+            if self.num_encoder_layers > 0:
+                self.level_embed = nn.Parameter(torch.Tensor(num_feature_levels, d_model))
+            else:
+                self.level_embed = None
+
+        self.learnable_tgt_init = learnable_tgt_init
+        assert learnable_tgt_init, "why not learnable_tgt_init"
+        self.embed_init_tgt = embed_init_tgt
+        if (two_stage_type != "no" and embed_init_tgt) or (two_stage_type == "no"):
+            self.tgt_embed = nn.Embedding(self.num_queries, d_model)
+            nn.init.normal_(self.tgt_embed.weight.data)
+        else:
+            self.tgt_embed = None
+
+        # for two stage
+        self.two_stage_type = two_stage_type
+        assert two_stage_type in ["no", "standard"], "unknown param {} of two_stage_type".format(
+            two_stage_type
+        )
+        if two_stage_type == "standard":
+            # anchor selection at the output of encoder
+            self.enc_output = nn.Linear(d_model, d_model)
+            self.enc_output_norm = nn.LayerNorm(d_model)
+            self.two_stage_wh_embedding = None
+
+        if two_stage_type == "no":
+            self.init_ref_points(num_queries)  # init self.refpoint_embed
+
+        self.enc_out_class_embed = None
+        self.enc_out_bbox_embed = None
+
+        self._reset_parameters()
+
+    def _reset_parameters(self):
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MSDeformAttn):
+                m._reset_parameters()
+        if self.num_feature_levels > 1 and self.level_embed is not None:
+            nn.init.normal_(self.level_embed)
+
+    def get_valid_ratio(self, mask):
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def init_ref_points(self, use_num_queries):
+        self.refpoint_embed = nn.Embedding(use_num_queries, 4)
+
+    def forward(self, srcs, masks, refpoint_embed, pos_embeds, tgt, attn_mask=None, text_dict=None):
+        """
+        Input:
+            - srcs: List of multi features [bs, ci, hi, wi]
+            - masks: List of multi masks [bs, hi, wi]
+            - refpoint_embed: [bs, num_dn, 4]. None in infer
+            - pos_embeds: List of multi pos embeds [bs, ci, hi, wi]
+            - tgt: [bs, num_dn, d_model]. None in infer
+
+        """
+        # prepare input for encoder
+        src_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        for lvl, (src, mask, pos_embed) in enumerate(zip(srcs, masks, pos_embeds)):
+            bs, c, h, w = src.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+
+            src = src.flatten(2).transpose(1, 2)  # bs, hw, c
+            mask = mask.flatten(1)  # bs, hw
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)  # bs, hw, c
+            if self.num_feature_levels > 1 and self.level_embed is not None:
+                lvl_pos_embed = pos_embed + self.level_embed[lvl].view(1, 1, -1)
+            else:
+                lvl_pos_embed = pos_embed
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            src_flatten.append(src)
+            mask_flatten.append(mask)
+        src_flatten = torch.cat(src_flatten, 1)  # bs, \sum{hxw}, c
+        mask_flatten = torch.cat(mask_flatten, 1)  # bs, \sum{hxw}
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)  # bs, \sum{hxw}, c
+        spatial_shapes = torch.as_tensor(
+            spatial_shapes, dtype=torch.long, device=src_flatten.device
+        )
+        level_start_index = torch.cat(
+            (spatial_shapes.new_zeros((1,)), spatial_shapes.prod(1).cumsum(0)[:-1])
+        )
+        valid_ratios = torch.stack([self.get_valid_ratio(m) for m in masks], 1)
+
+        # two stage
+        enc_topk_proposals = enc_refpoint_embed = None
+
+        #########################################################
+        # Begin Encoder
+        #########################################################
+        memory, memory_text = self.encoder(
+            src_flatten,
+            pos=lvl_pos_embed_flatten,
+            level_start_index=level_start_index,
+            spatial_shapes=spatial_shapes,
+            valid_ratios=valid_ratios,
+            key_padding_mask=mask_flatten,
+            memory_text=text_dict["encoded_text"],
+            text_attention_mask=~text_dict["text_token_mask"],
+            # we ~ the mask . False means use the token; True means pad the token
+            position_ids=text_dict["position_ids"],
+            text_self_attention_masks=text_dict["text_self_attention_masks"],
+        )
+        #########################################################
+        # End Encoder
+        # - memory: bs, \sum{hw}, c
+        # - mask_flatten: bs, \sum{hw}
+        # - lvl_pos_embed_flatten: bs, \sum{hw}, c
+        # - enc_intermediate_output: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
+        # - enc_intermediate_refpoints: None or (nenc+1, bs, nq, c) or (nenc, bs, nq, c)
+        #########################################################
+        text_dict["encoded_text"] = memory_text
+        # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+        #     if memory.isnan().any() | memory.isinf().any():
+        #         import ipdb; ipdb.set_trace()
+
+        if self.two_stage_type == "standard":
+            output_memory, output_proposals = gen_encoder_output_proposals(
+                memory, mask_flatten, spatial_shapes
+            )
+            output_memory = self.enc_output_norm(self.enc_output(output_memory))
+
+            if text_dict is not None:
+                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory, text_dict)
+            else:
+                enc_outputs_class_unselected = self.enc_out_class_embed(output_memory)
+
+            topk_logits = enc_outputs_class_unselected.max(-1)[0]
+            enc_outputs_coord_unselected = (
+                self.enc_out_bbox_embed(output_memory) + output_proposals
+            )  # (bs, \sum{hw}, 4) unsigmoid
+            topk = self.num_queries
+
+            topk_proposals = torch.topk(topk_logits, topk, dim=1)[1]  # bs, nq
+
+            # gather boxes
+            refpoint_embed_undetach = torch.gather(
+                enc_outputs_coord_unselected, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+            )  # unsigmoid
+            refpoint_embed_ = refpoint_embed_undetach.detach()
+            init_box_proposal = torch.gather(
+                output_proposals, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, 4)
+            ).sigmoid()  # sigmoid
+
+            # gather tgt
+            tgt_undetach = torch.gather(
+                output_memory, 1, topk_proposals.unsqueeze(-1).repeat(1, 1, self.d_model)
+            )
+            if self.embed_init_tgt:
+                tgt_ = (
+                    self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+                )  # nq, bs, d_model
+            else:
+                tgt_ = tgt_undetach.detach()
+
+            if refpoint_embed is not None:
+                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
+                tgt = torch.cat([tgt, tgt_], dim=1)
+            else:
+                refpoint_embed, tgt = refpoint_embed_, tgt_
+
+        elif self.two_stage_type == "no":
+            tgt_ = (
+                self.tgt_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+            )  # nq, bs, d_model
+            refpoint_embed_ = (
+                self.refpoint_embed.weight[:, None, :].repeat(1, bs, 1).transpose(0, 1)
+            )  # nq, bs, 4
+
+            if refpoint_embed is not None:
+                refpoint_embed = torch.cat([refpoint_embed, refpoint_embed_], dim=1)
+                tgt = torch.cat([tgt, tgt_], dim=1)
+            else:
+                refpoint_embed, tgt = refpoint_embed_, tgt_
+
+            if self.num_patterns > 0:
+                tgt_embed = tgt.repeat(1, self.num_patterns, 1)
+                refpoint_embed = refpoint_embed.repeat(1, self.num_patterns, 1)
+                tgt_pat = self.patterns.weight[None, :, :].repeat_interleave(
+                    self.num_queries, 1
+                )  # 1, n_q*n_pat, d_model
+                tgt = tgt_embed + tgt_pat
+
+            init_box_proposal = refpoint_embed_.sigmoid()
+
+        else:
+            raise NotImplementedError("unknown two_stage_type {}".format(self.two_stage_type))
+        #########################################################
+        # End preparing tgt
+        # - tgt: bs, NQ, d_model
+        # - refpoint_embed(unsigmoid): bs, NQ, d_model
+        #########################################################
+
+        #########################################################
+        # Begin Decoder
+        #########################################################
+        hs, references = self.decoder(
+            tgt=tgt.transpose(0, 1),
+            memory=memory.transpose(0, 1),
+            memory_key_padding_mask=mask_flatten,
+            pos=lvl_pos_embed_flatten.transpose(0, 1),
+            refpoints_unsigmoid=refpoint_embed.transpose(0, 1),
+            level_start_index=level_start_index,
+            spatial_shapes=spatial_shapes,
+            valid_ratios=valid_ratios,
+            tgt_mask=attn_mask,
+            memory_text=text_dict["encoded_text"],
+            text_attention_mask=~text_dict["text_token_mask"],
+            # we ~ the mask . False means use the token; True means pad the token
+        )
+        #########################################################
+        # End Decoder
+        # hs: n_dec, bs, nq, d_model
+        # references: n_dec+1, bs, nq, query_dim
+        #########################################################
+
+        #########################################################
+        # Begin postprocess
+        #########################################################
+        if self.two_stage_type == "standard":
+            hs_enc = tgt_undetach.unsqueeze(0)
+            ref_enc = refpoint_embed_undetach.sigmoid().unsqueeze(0)
+        else:
+            hs_enc = ref_enc = None
+        #########################################################
+        # End postprocess
+        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or (n_enc, bs, nq, d_model) or None
+        # ref_enc: (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or (n_enc, bs, nq, d_model) or None
+        #########################################################
+
+        return hs, references, hs_enc, ref_enc, init_box_proposal
+        # hs: (n_dec, bs, nq, d_model)
+        # references: sigmoid coordinates. (n_dec+1, bs, bq, 4)
+        # hs_enc: (n_enc+1, bs, nq, d_model) or (1, bs, nq, d_model) or None
+        # ref_enc: sigmoid coordinates. \
+        #           (n_enc+1, bs, nq, query_dim) or (1, bs, nq, query_dim) or None
+
+
+class TransformerEncoder(nn.Module):
+    def __init__(
+        self,
+        encoder_layer,
+        num_layers,
+        d_model=256,
+        num_queries=300,
+        enc_layer_share=False,
+        text_enhance_layer=None,
+        feature_fusion_layer=None,
+        use_checkpoint=False,
+        use_transformer_ckpt=False,
+    ):
+        """_summary_
+
+        Args:
+            encoder_layer (_type_): _description_
+            num_layers (_type_): _description_
+            norm (_type_, optional): _description_. Defaults to None.
+            d_model (int, optional): _description_. Defaults to 256.
+            num_queries (int, optional): _description_. Defaults to 300.
+            enc_layer_share (bool, optional): _description_. Defaults to False.
+
+        """
+        super().__init__()
+        # prepare layers
+        self.layers = []
+        self.text_layers = []
+        self.fusion_layers = []
+        if num_layers > 0:
+            self.layers = _get_clones(encoder_layer, num_layers, layer_share=enc_layer_share)
+
+            if text_enhance_layer is not None:
+                self.text_layers = _get_clones(
+                    text_enhance_layer, num_layers, layer_share=enc_layer_share
+                )
+            if feature_fusion_layer is not None:
+                self.fusion_layers = _get_clones(
+                    feature_fusion_layer, num_layers, layer_share=enc_layer_share
+                )
+        else:
+            self.layers = []
+            del encoder_layer
+
+            if text_enhance_layer is not None:
+                self.text_layers = []
+                del text_enhance_layer
+            if feature_fusion_layer is not None:
+                self.fusion_layers = []
+                del feature_fusion_layer
+
+        self.query_scale = None
+        self.num_queries = num_queries
+        self.num_layers = num_layers
+        self.d_model = d_model
+
+        self.use_checkpoint = use_checkpoint
+        self.use_transformer_ckpt = use_transformer_ckpt
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        reference_points_list = []
+        for lvl, (H_, W_) in enumerate(spatial_shapes):
+
+            ref_y, ref_x = torch.meshgrid(
+                torch.linspace(0.5, H_ - 0.5, H_, dtype=torch.float32, device=device),
+                torch.linspace(0.5, W_ - 0.5, W_, dtype=torch.float32, device=device),
+            )
+            ref_y = ref_y.reshape(-1)[None] / (valid_ratios[:, None, lvl, 1] * H_)
+            ref_x = ref_x.reshape(-1)[None] / (valid_ratios[:, None, lvl, 0] * W_)
+            ref = torch.stack((ref_x, ref_y), -1)
+            reference_points_list.append(ref)
+        reference_points = torch.cat(reference_points_list, 1)
+        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+        return reference_points
+
+    def forward(
+        self,
+        # for images
+        src: Tensor,
+        pos: Tensor,
+        spatial_shapes: Tensor,
+        level_start_index: Tensor,
+        valid_ratios: Tensor,
+        key_padding_mask: Tensor,
+        # for texts
+        memory_text: Tensor = None,
+        text_attention_mask: Tensor = None,
+        pos_text: Tensor = None,
+        text_self_attention_masks: Tensor = None,
+        position_ids: Tensor = None,
+    ):
+        """
+        Input:
+            - src: [bs, sum(hi*wi), 256]
+            - pos: pos embed for src. [bs, sum(hi*wi), 256]
+            - spatial_shapes: h,w of each level [num_level, 2]
+            - level_start_index: [num_level] start point of level in sum(hi*wi).
+            - valid_ratios: [bs, num_level, 2]
+            - key_padding_mask: [bs, sum(hi*wi)]
+
+            - memory_text: bs, n_text, 256
+            - text_attention_mask: bs, n_text
+                False for no padding; True for padding
+            - pos_text: bs, n_text, 256
+
+            - position_ids: bs, n_text
+        Intermedia:
+            - reference_points: [bs, sum(hi*wi), num_level, 2]
+        Outpus:
+            - output: [bs, sum(hi*wi), 256]
+        """
+
+        output = src
+
+        # preparation and reshape
+        if self.num_layers > 0:
+            reference_points = self.get_reference_points(
+                spatial_shapes, valid_ratios, device=src.device
+            )
+
+        if self.text_layers:
+            # generate pos_text
+            bs, n_text, text_dim = memory_text.shape
+            if pos_text is None and position_ids is None:
+                pos_text = (
+                    torch.arange(n_text, device=memory_text.device)
+                    .float()
+                    .unsqueeze(0)
+                    .unsqueeze(-1)
+                    .repeat(bs, 1, 1)
+                )
+                pos_text = get_sine_pos_embed(pos_text, num_pos_feats=256, exchange_xy=False)
+            if position_ids is not None:
+                pos_text = get_sine_pos_embed(
+                    position_ids[..., None], num_pos_feats=256, exchange_xy=False
+                )
+
+        # main process
+        for layer_id, layer in enumerate(self.layers):
+            # if output.isnan().any() or memory_text.isnan().any():
+            #     if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #         import ipdb; ipdb.set_trace()
+            if self.fusion_layers:
+                if self.use_checkpoint:
+                    output, memory_text = checkpoint.checkpoint(
+                        self.fusion_layers[layer_id],
+                        output,
+                        memory_text,
+                        key_padding_mask,
+                        text_attention_mask,
+                    )
+                else:
+                    output, memory_text = self.fusion_layers[layer_id](
+                        v=output,
+                        l=memory_text,
+                        attention_mask_v=key_padding_mask,
+                        attention_mask_l=text_attention_mask,
+                    )
+
+            if self.text_layers:
+                memory_text = self.text_layers[layer_id](
+                    src=memory_text.transpose(0, 1),
+                    src_mask=~text_self_attention_masks,  # note we use ~ for mask here
+                    src_key_padding_mask=text_attention_mask,
+                    pos=(pos_text.transpose(0, 1) if pos_text is not None else None),
+                ).transpose(0, 1)
+
+            # main process
+            if self.use_transformer_ckpt:
+                output = checkpoint.checkpoint(
+                    layer,
+                    output,
+                    pos,
+                    reference_points,
+                    spatial_shapes,
+                    level_start_index,
+                    key_padding_mask,
+                )
+            else:
+                output = layer(
+                    src=output,
+                    pos=pos,
+                    reference_points=reference_points,
+                    spatial_shapes=spatial_shapes,
+                    level_start_index=level_start_index,
+                    key_padding_mask=key_padding_mask,
+                )
+
+        return output, memory_text
+
+
+class TransformerDecoder(nn.Module):
+    def __init__(
+        self,
+        decoder_layer,
+        num_layers,
+        norm=None,
+        return_intermediate=False,
+        d_model=256,
+        query_dim=4,
+        num_feature_levels=1,
+    ):
+        super().__init__()
+        if num_layers > 0:
+            self.layers = _get_clones(decoder_layer, num_layers)
+        else:
+            self.layers = []
+        self.num_layers = num_layers
+        self.norm = norm
+        self.return_intermediate = return_intermediate
+        assert return_intermediate, "support return_intermediate only"
+        self.query_dim = query_dim
+        assert query_dim in [2, 4], "query_dim should be 2/4 but {}".format(query_dim)
+        self.num_feature_levels = num_feature_levels
+
+        self.ref_point_head = MLP(query_dim // 2 * d_model, d_model, d_model, 2)
+        self.query_pos_sine_scale = None
+
+        self.query_scale = None
+        self.bbox_embed = None
+        self.class_embed = None
+
+        self.d_model = d_model
+
+        self.ref_anchor_head = None
+
+    def forward(
+        self,
+        tgt,
+        memory,
+        tgt_mask: Optional[Tensor] = None,
+        memory_mask: Optional[Tensor] = None,
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        memory_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+        refpoints_unsigmoid: Optional[Tensor] = None,  # num_queries, bs, 2
+        # for memory
+        level_start_index: Optional[Tensor] = None,  # num_levels
+        spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        valid_ratios: Optional[Tensor] = None,
+        # for text
+        memory_text: Optional[Tensor] = None,
+        text_attention_mask: Optional[Tensor] = None,
+    ):
+        """
+        Input:
+            - tgt: nq, bs, d_model
+            - memory: hw, bs, d_model
+            - pos: hw, bs, d_model
+            - refpoints_unsigmoid: nq, bs, 2/4
+            - valid_ratios/spatial_shapes: bs, nlevel, 2
+        """
+        output = tgt
+
+        intermediate = []
+        reference_points = refpoints_unsigmoid.sigmoid()
+        ref_points = [reference_points]
+
+        for layer_id, layer in enumerate(self.layers):
+
+            if reference_points.shape[-1] == 4:
+                reference_points_input = (
+                    reference_points[:, :, None]
+                    * torch.cat([valid_ratios, valid_ratios], -1)[None, :]
+                )  # nq, bs, nlevel, 4
+            else:
+                assert reference_points.shape[-1] == 2
+                reference_points_input = reference_points[:, :, None] * valid_ratios[None, :]
+            query_sine_embed = gen_sineembed_for_position(
+                reference_points_input[:, :, 0, :]
+            )  # nq, bs, 256*2
+
+            # conditional query
+            raw_query_pos = self.ref_point_head(query_sine_embed)  # nq, bs, 256
+            pos_scale = self.query_scale(output) if self.query_scale is not None else 1
+            query_pos = pos_scale * raw_query_pos
+            # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+            #     if query_pos.isnan().any() | query_pos.isinf().any():
+            #         import ipdb; ipdb.set_trace()
+
+            # main process
+            output = layer(
+                tgt=output,
+                tgt_query_pos=query_pos,
+                tgt_query_sine_embed=query_sine_embed,
+                tgt_key_padding_mask=tgt_key_padding_mask,
+                tgt_reference_points=reference_points_input,
+                memory_text=memory_text,
+                text_attention_mask=text_attention_mask,
+                memory=memory,
+                memory_key_padding_mask=memory_key_padding_mask,
+                memory_level_start_index=level_start_index,
+                memory_spatial_shapes=spatial_shapes,
+                memory_pos=pos,
+                self_attn_mask=tgt_mask,
+                cross_attn_mask=memory_mask,
+            )
+            if output.isnan().any() | output.isinf().any():
+                print(f"output layer_id {layer_id} is nan")
+                try:
+                    num_nan = output.isnan().sum().item()
+                    num_inf = output.isinf().sum().item()
+                    print(f"num_nan {num_nan}, num_inf {num_inf}")
+                except Exception as e:
+                    print(e)
+                    # if os.environ.get("SHILONG_AMP_INFNAN_DEBUG") == '1':
+                    #     import ipdb; ipdb.set_trace()
+
+            # iter update
+            if self.bbox_embed is not None:
+                # box_holder = self.bbox_embed(output)
+                # box_holder[..., :self.query_dim] += inverse_sigmoid(reference_points)
+                # new_reference_points = box_holder[..., :self.query_dim].sigmoid()
+
+                reference_before_sigmoid = inverse_sigmoid(reference_points)
+                delta_unsig = self.bbox_embed[layer_id](output)
+                outputs_unsig = delta_unsig + reference_before_sigmoid
+                new_reference_points = outputs_unsig.sigmoid()
+
+                reference_points = new_reference_points.detach()
+                # if layer_id != self.num_layers - 1:
+                ref_points.append(new_reference_points)
+
+            intermediate.append(self.norm(output))
+
+        return [
+            [itm_out.transpose(0, 1) for itm_out in intermediate],
+            [itm_refpoint.transpose(0, 1) for itm_refpoint in ref_points],
+        ]
+
+
+class DeformableTransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        d_ffn=1024,
+        dropout=0.1,
+        activation="relu",
+        n_levels=4,
+        n_heads=8,
+        n_points=4,
+    ):
+        super().__init__()
+
+        # self attention
+        self.self_attn = MSDeformAttn(
+            embed_dim=d_model,
+            num_levels=n_levels,
+            num_heads=n_heads,
+            num_points=n_points,
+            batch_first=True,
+        )
+        self.dropout1 = nn.Dropout(dropout)
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation, d_model=d_ffn)
+        self.dropout2 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout3 = nn.Dropout(dropout)
+        self.norm2 = nn.LayerNorm(d_model)
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, src):
+        src2 = self.linear2(self.dropout2(self.activation(self.linear1(src))))
+        src = src + self.dropout3(src2)
+        src = self.norm2(src)
+        return src
+
+    def forward(
+        self, src, pos, reference_points, spatial_shapes, level_start_index, key_padding_mask=None
+    ):
+        # self attention
+        # import ipdb; ipdb.set_trace()
+        src2 = self.self_attn(
+            query=self.with_pos_embed(src, pos),
+            reference_points=reference_points,
+            value=src,
+            spatial_shapes=spatial_shapes,
+            level_start_index=level_start_index,
+            key_padding_mask=key_padding_mask,
+        )
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+
+        # ffn
+        src = self.forward_ffn(src)
+
+        return src
+
+
+class DeformableTransformerDecoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model=256,
+        d_ffn=1024,
+        dropout=0.1,
+        activation="relu",
+        n_levels=4,
+        n_heads=8,
+        n_points=4,
+        use_text_feat_guide=False,
+        use_text_cross_attention=False,
+    ):
+        super().__init__()
+
+        # cross attention
+        self.cross_attn = MSDeformAttn(
+            embed_dim=d_model,
+            num_levels=n_levels,
+            num_heads=n_heads,
+            num_points=n_points,
+            batch_first=True,
+        )
+        self.dropout1 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm1 = nn.LayerNorm(d_model)
+
+        # cross attention text
+        if use_text_cross_attention:
+            self.ca_text = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+            self.catext_dropout = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+            self.catext_norm = nn.LayerNorm(d_model)
+
+        # self attention
+        self.self_attn = nn.MultiheadAttention(d_model, n_heads, dropout=dropout)
+        self.dropout2 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm2 = nn.LayerNorm(d_model)
+
+        # ffn
+        self.linear1 = nn.Linear(d_model, d_ffn)
+        self.activation = _get_activation_fn(activation, d_model=d_ffn, batch_dim=1)
+        self.dropout3 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.linear2 = nn.Linear(d_ffn, d_model)
+        self.dropout4 = nn.Dropout(dropout) if dropout > 0 else nn.Identity()
+        self.norm3 = nn.LayerNorm(d_model)
+
+        self.key_aware_proj = None
+        self.use_text_feat_guide = use_text_feat_guide
+        assert not use_text_feat_guide
+        self.use_text_cross_attention = use_text_cross_attention
+
+    def rm_self_attn_modules(self):
+        self.self_attn = None
+        self.dropout2 = None
+        self.norm2 = None
+
+    @staticmethod
+    def with_pos_embed(tensor, pos):
+        return tensor if pos is None else tensor + pos
+
+    def forward_ffn(self, tgt):
+        with torch.cuda.amp.autocast(enabled=False):
+            tgt2 = self.linear2(self.dropout3(self.activation(self.linear1(tgt))))
+        tgt = tgt + self.dropout4(tgt2)
+        tgt = self.norm3(tgt)
+        return tgt
+
+    def forward(
+        self,
+        # for tgt
+        tgt: Optional[Tensor],  # nq, bs, d_model
+        tgt_query_pos: Optional[Tensor] = None,  # pos for query. MLP(Sine(pos))
+        tgt_query_sine_embed: Optional[Tensor] = None,  # pos for query. Sine(pos)
+        tgt_key_padding_mask: Optional[Tensor] = None,
+        tgt_reference_points: Optional[Tensor] = None,  # nq, bs, 4
+        memory_text: Optional[Tensor] = None,  # bs, num_token, d_model
+        text_attention_mask: Optional[Tensor] = None,  # bs, num_token
+        # for memory
+        memory: Optional[Tensor] = None,  # hw, bs, d_model
+        memory_key_padding_mask: Optional[Tensor] = None,
+        memory_level_start_index: Optional[Tensor] = None,  # num_levels
+        memory_spatial_shapes: Optional[Tensor] = None,  # bs, num_levels, 2
+        memory_pos: Optional[Tensor] = None,  # pos for memory
+        # sa
+        self_attn_mask: Optional[Tensor] = None,  # mask used for self-attention
+        cross_attn_mask: Optional[Tensor] = None,  # mask used for cross-attention
+    ):
+        """
+        Input:
+            - tgt/tgt_query_pos: nq, bs, d_model
+            -
+        """
+        assert cross_attn_mask is None
+
+        # self attention
+        if self.self_attn is not None:
+            # import ipdb; ipdb.set_trace()
+            q = k = self.with_pos_embed(tgt, tgt_query_pos)
+            tgt2 = self.self_attn(q, k, tgt, attn_mask=self_attn_mask)[0]
+            tgt = tgt + self.dropout2(tgt2)
+            tgt = self.norm2(tgt)
+
+        if self.use_text_cross_attention:
+            tgt2 = self.ca_text(
+                self.with_pos_embed(tgt, tgt_query_pos),
+                memory_text.transpose(0, 1),
+                memory_text.transpose(0, 1),
+                key_padding_mask=text_attention_mask,
+            )[0]
+            tgt = tgt + self.catext_dropout(tgt2)
+            tgt = self.catext_norm(tgt)
+
+        tgt2 = self.cross_attn(
+            query=self.with_pos_embed(tgt, tgt_query_pos).transpose(0, 1),
+            reference_points=tgt_reference_points.transpose(0, 1).contiguous(),
+            value=memory.transpose(0, 1),
+            spatial_shapes=memory_spatial_shapes,
+            level_start_index=memory_level_start_index,
+            key_padding_mask=memory_key_padding_mask,
+        ).transpose(0, 1)
+        tgt = tgt + self.dropout1(tgt2)
+        tgt = self.norm1(tgt)
+
+        # ffn
+        tgt = self.forward_ffn(tgt)
+
+        return tgt
+
+
+def build_transformer(args):
+    return Transformer(
+        d_model=args.hidden_dim,
+        dropout=args.dropout,
+        nhead=args.nheads,
+        num_queries=args.num_queries,
+        dim_feedforward=args.dim_feedforward,
+        num_encoder_layers=args.enc_layers,
+        num_decoder_layers=args.dec_layers,
+        normalize_before=args.pre_norm,
+        return_intermediate_dec=True,
+        query_dim=args.query_dim,
+        activation=args.transformer_activation,
+        num_patterns=args.num_patterns,
+        num_feature_levels=args.num_feature_levels,
+        enc_n_points=args.enc_n_points,
+        dec_n_points=args.dec_n_points,
+        learnable_tgt_init=True,
+        # two stage
+        two_stage_type=args.two_stage_type,  # ['no', 'standard', 'early']
+        embed_init_tgt=args.embed_init_tgt,
+        use_text_enhancer=args.use_text_enhancer,
+        use_fusion_layer=args.use_fusion_layer,
+        use_checkpoint=args.use_checkpoint,
+        use_transformer_ckpt=args.use_transformer_ckpt,
+        use_text_cross_attention=args.use_text_cross_attention,
+        text_dropout=args.text_dropout,
+        fusion_dropout=args.fusion_dropout,
+        fusion_droppath=args.fusion_droppath,
+    )

metagpt/environment/android/groundingdino/models/GroundingDINO/transformer_vanilla.py

@@ -0,0 +1,123 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+DETR Transformer class.
+
+Copy-paste from torch.nn.Transformer with modifications:
+    * positional encodings are passed in MHattention
+    * extra LN at the end of encoder is removed
+    * decoder returns a stack of activations from all decoding layers
+"""
+from typing import Optional
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+from .utils import (
+    MLP,
+    _get_activation_fn,
+    _get_clones,
+    gen_encoder_output_proposals,
+    gen_sineembed_for_position,
+    sigmoid_focal_loss,
+)
+
+
+class TextTransformer(nn.Module):
+    def __init__(self, num_layers, d_model=256, nheads=8, dim_feedforward=2048, dropout=0.1):
+        super().__init__()
+        self.num_layers = num_layers
+        self.d_model = d_model
+        self.nheads = nheads
+        self.dim_feedforward = dim_feedforward
+        self.norm = None
+
+        single_encoder_layer = TransformerEncoderLayer(
+            d_model=d_model, nhead=nheads, dim_feedforward=dim_feedforward, dropout=dropout
+        )
+        self.layers = _get_clones(single_encoder_layer, num_layers)
+
+    def forward(self, memory_text: torch.Tensor, text_attention_mask: torch.Tensor):
+        """
+
+        Args:
+            text_attention_mask: bs, num_token
+            memory_text: bs, num_token, d_model
+
+        Raises:
+            RuntimeError: _description_
+
+        Returns:
+            output: bs, num_token, d_model
+        """
+
+        output = memory_text.transpose(0, 1)
+
+        for layer in self.layers:
+            output = layer(output, src_key_padding_mask=text_attention_mask)
+
+        if self.norm is not None:
+            output = self.norm(output)
+
+        return output.transpose(0, 1)
+
+
+class TransformerEncoderLayer(nn.Module):
+    def __init__(
+        self,
+        d_model,
+        nhead,
+        dim_feedforward=2048,
+        dropout=0.1,
+        activation="relu",
+        normalize_before=False,
+    ):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
+        # Implementation of Feedforward model
+        self.linear1 = nn.Linear(d_model, dim_feedforward)
+        self.dropout = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(dim_feedforward, d_model)
+
+        self.norm1 = nn.LayerNorm(d_model)
+        self.norm2 = nn.LayerNorm(d_model)
+        self.dropout1 = nn.Dropout(dropout)
+        self.dropout2 = nn.Dropout(dropout)
+
+        self.activation = _get_activation_fn(activation)
+        self.normalize_before = normalize_before
+        self.nhead = nhead
+
+    def with_pos_embed(self, tensor, pos: Optional[Tensor]):
+        return tensor if pos is None else tensor + pos
+
+    def forward(
+        self,
+        src,
+        src_mask: Optional[Tensor] = None,
+        src_key_padding_mask: Optional[Tensor] = None,
+        pos: Optional[Tensor] = None,
+    ):
+        # repeat attn mask
+        if src_mask.dim() == 3 and src_mask.shape[0] == src.shape[1]:
+            # bs, num_q, num_k
+            src_mask = src_mask.repeat(self.nhead, 1, 1)
+
+        q = k = self.with_pos_embed(src, pos)
+
+        src2 = self.self_attn(q, k, value=src, attn_mask=src_mask)[0]
+
+        # src2 = self.self_attn(q, k, value=src, attn_mask=src_mask, key_padding_mask=src_key_padding_mask)[0]
+        src = src + self.dropout1(src2)
+        src = self.norm1(src)
+        src2 = self.linear2(self.dropout(self.activation(self.linear1(src))))
+        src = src + self.dropout2(src2)
+        src = self.norm2(src)
+        return src

metagpt/environment/android/groundingdino/models/GroundingDINO/utils.py

@@ -0,0 +1,268 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+
+import copy
+import math
+
+import torch
+import torch.nn.functional as F
+from torch import Tensor, nn
+
+
+def _get_clones(module, N, layer_share=False):
+    # import ipdb; ipdb.set_trace()
+    if layer_share:
+        return nn.ModuleList([module for i in range(N)])
+    else:
+        return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+
+def get_sine_pos_embed(
+    pos_tensor: torch.Tensor,
+    num_pos_feats: int = 128,
+    temperature: int = 10000,
+    exchange_xy: bool = True,
+):
+    """generate sine position embedding from a position tensor
+    Args:
+        pos_tensor (torch.Tensor): shape: [..., n].
+        num_pos_feats (int): projected shape for each float in the tensor.
+        temperature (int): temperature in the sine/cosine function.
+        exchange_xy (bool, optional): exchange pos x and pos y. \
+            For example, input tensor is [x,y], the results will be [pos(y), pos(x)]. Defaults to True.
+    Returns:
+        pos_embed (torch.Tensor): shape: [..., n*num_pos_feats].
+    """
+    scale = 2 * math.pi
+    dim_t = torch.arange(num_pos_feats, dtype=torch.float32, device=pos_tensor.device)
+    dim_t = temperature ** (2 * torch.div(dim_t, 2, rounding_mode="floor") / num_pos_feats)
+
+    def sine_func(x: torch.Tensor):
+        sin_x = x * scale / dim_t
+        sin_x = torch.stack((sin_x[..., 0::2].sin(), sin_x[..., 1::2].cos()), dim=3).flatten(2)
+        return sin_x
+
+    pos_res = [sine_func(x) for x in pos_tensor.split([1] * pos_tensor.shape[-1], dim=-1)]
+    if exchange_xy:
+        pos_res[0], pos_res[1] = pos_res[1], pos_res[0]
+    pos_res = torch.cat(pos_res, dim=-1)
+    return pos_res
+
+
+def gen_encoder_output_proposals(
+    memory: Tensor, memory_padding_mask: Tensor, spatial_shapes: Tensor, learnedwh=None
+):
+    """
+    Input:
+        - memory: bs, \sum{hw}, d_model
+        - memory_padding_mask: bs, \sum{hw}
+        - spatial_shapes: nlevel, 2
+        - learnedwh: 2
+    Output:
+        - output_memory: bs, \sum{hw}, d_model
+        - output_proposals: bs, \sum{hw}, 4
+    """
+    N_, S_, C_ = memory.shape
+    proposals = []
+    _cur = 0
+    for lvl, (H_, W_) in enumerate(spatial_shapes):
+        mask_flatten_ = memory_padding_mask[:, _cur : (_cur + H_ * W_)].view(N_, H_, W_, 1)
+        valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+        valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+        # import ipdb; ipdb.set_trace()
+
+        grid_y, grid_x = torch.meshgrid(
+            torch.linspace(0, H_ - 1, H_, dtype=torch.float32, device=memory.device),
+            torch.linspace(0, W_ - 1, W_, dtype=torch.float32, device=memory.device),
+        )
+        grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)  # H_, W_, 2
+
+        scale = torch.cat([valid_W.unsqueeze(-1), valid_H.unsqueeze(-1)], 1).view(N_, 1, 1, 2)
+        grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+
+        if learnedwh is not None:
+            # import ipdb; ipdb.set_trace()
+            wh = torch.ones_like(grid) * learnedwh.sigmoid() * (2.0**lvl)
+        else:
+            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
+
+        # scale = torch.cat([W_[None].unsqueeze(-1), H_[None].unsqueeze(-1)], 1).view(1, 1, 1, 2).repeat(N_, 1, 1, 1)
+        # grid = (grid.unsqueeze(0).expand(N_, -1, -1, -1) + 0.5) / scale
+        # wh = torch.ones_like(grid) / scale
+        proposal = torch.cat((grid, wh), -1).view(N_, -1, 4)
+        proposals.append(proposal)
+        _cur += H_ * W_
+    # import ipdb; ipdb.set_trace()
+    output_proposals = torch.cat(proposals, 1)
+    output_proposals_valid = ((output_proposals > 0.01) & (output_proposals < 0.99)).all(
+        -1, keepdim=True
+    )
+    output_proposals = torch.log(output_proposals / (1 - output_proposals))  # unsigmoid
+    output_proposals = output_proposals.masked_fill(memory_padding_mask.unsqueeze(-1), float("inf"))
+    output_proposals = output_proposals.masked_fill(~output_proposals_valid, float("inf"))
+
+    output_memory = memory
+    output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float(0))
+    output_memory = output_memory.masked_fill(~output_proposals_valid, float(0))
+
+    # output_memory = output_memory.masked_fill(memory_padding_mask.unsqueeze(-1), float('inf'))
+    # output_memory = output_memory.masked_fill(~output_proposals_valid, float('inf'))
+
+    return output_memory, output_proposals
+
+
+class RandomBoxPerturber:
+    def __init__(
+        self, x_noise_scale=0.2, y_noise_scale=0.2, w_noise_scale=0.2, h_noise_scale=0.2
+    ) -> None:
+        self.noise_scale = torch.Tensor(
+            [x_noise_scale, y_noise_scale, w_noise_scale, h_noise_scale]
+        )
+
+    def __call__(self, refanchors: Tensor) -> Tensor:
+        nq, bs, query_dim = refanchors.shape
+        device = refanchors.device
+
+        noise_raw = torch.rand_like(refanchors)
+        noise_scale = self.noise_scale.to(device)[:query_dim]
+
+        new_refanchors = refanchors * (1 + (noise_raw - 0.5) * noise_scale)
+        return new_refanchors.clamp_(0, 1)
+
+
+def sigmoid_focal_loss(
+    inputs, targets, num_boxes, alpha: float = 0.25, gamma: float = 2, no_reduction=False
+):
+    """
+    Loss used in RetinaNet for dense detection: https://arxiv.org/abs/1708.02002.
+    Args:
+        inputs: A float tensor of arbitrary shape.
+                The predictions for each example.
+        targets: A float tensor with the same shape as inputs. Stores the binary
+                 classification label for each element in inputs
+                (0 for the negative class and 1 for the positive class).
+        alpha: (optional) Weighting factor in range (0,1) to balance
+                positive vs negative examples. Default = -1 (no weighting).
+        gamma: Exponent of the modulating factor (1 - p_t) to
+               balance easy vs hard examples.
+    Returns:
+        Loss tensor
+    """
+    prob = inputs.sigmoid()
+    ce_loss = F.binary_cross_entropy_with_logits(inputs, targets, reduction="none")
+    p_t = prob * targets + (1 - prob) * (1 - targets)
+    loss = ce_loss * ((1 - p_t) ** gamma)
+
+    if alpha >= 0:
+        alpha_t = alpha * targets + (1 - alpha) * (1 - targets)
+        loss = alpha_t * loss
+
+    if no_reduction:
+        return loss
+
+    return loss.mean(1).sum() / num_boxes
+
+
+class MLP(nn.Module):
+    """Very simple multi-layer perceptron (also called FFN)"""
+
+    def __init__(self, input_dim, hidden_dim, output_dim, num_layers):
+        super().__init__()
+        self.num_layers = num_layers
+        h = [hidden_dim] * (num_layers - 1)
+        self.layers = nn.ModuleList(
+            nn.Linear(n, k) for n, k in zip([input_dim] + h, h + [output_dim])
+        )
+
+    def forward(self, x):
+        for i, layer in enumerate(self.layers):
+            x = F.relu(layer(x)) if i < self.num_layers - 1 else layer(x)
+        return x
+
+
+def _get_activation_fn(activation, d_model=256, batch_dim=0):
+    """Return an activation function given a string"""
+    if activation == "relu":
+        return F.relu
+    if activation == "gelu":
+        return F.gelu
+    if activation == "glu":
+        return F.glu
+    if activation == "prelu":
+        return nn.PReLU()
+    if activation == "selu":
+        return F.selu
+
+    raise RuntimeError(f"activation should be relu/gelu, not {activation}.")
+
+
+def gen_sineembed_for_position(pos_tensor):
+    # n_query, bs, _ = pos_tensor.size()
+    # sineembed_tensor = torch.zeros(n_query, bs, 256)
+    scale = 2 * math.pi
+    dim_t = torch.arange(128, dtype=torch.float32, device=pos_tensor.device)
+    dim_t = 10000 ** (2 * (torch.div(dim_t, 2, rounding_mode='floor')) / 128)
+    x_embed = pos_tensor[:, :, 0] * scale
+    y_embed = pos_tensor[:, :, 1] * scale
+    pos_x = x_embed[:, :, None] / dim_t
+    pos_y = y_embed[:, :, None] / dim_t
+    pos_x = torch.stack((pos_x[:, :, 0::2].sin(), pos_x[:, :, 1::2].cos()), dim=3).flatten(2)
+    pos_y = torch.stack((pos_y[:, :, 0::2].sin(), pos_y[:, :, 1::2].cos()), dim=3).flatten(2)
+    if pos_tensor.size(-1) == 2:
+        pos = torch.cat((pos_y, pos_x), dim=2)
+    elif pos_tensor.size(-1) == 4:
+        w_embed = pos_tensor[:, :, 2] * scale
+        pos_w = w_embed[:, :, None] / dim_t
+        pos_w = torch.stack((pos_w[:, :, 0::2].sin(), pos_w[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        h_embed = pos_tensor[:, :, 3] * scale
+        pos_h = h_embed[:, :, None] / dim_t
+        pos_h = torch.stack((pos_h[:, :, 0::2].sin(), pos_h[:, :, 1::2].cos()), dim=3).flatten(2)
+
+        pos = torch.cat((pos_y, pos_x, pos_w, pos_h), dim=2)
+    else:
+        raise ValueError("Unknown pos_tensor shape(-1):{}".format(pos_tensor.size(-1)))
+    return pos
+
+
+class ContrastiveEmbed(nn.Module):
+    def __init__(self, max_text_len=256):
+        """
+        Args:
+            max_text_len: max length of text.
+        """
+        super().__init__()
+        self.max_text_len = max_text_len
+
+    def forward(self, x, text_dict):
+        """_summary_
+
+        Args:
+            x (_type_): _description_
+            text_dict (_type_): _description_
+            {
+                'encoded_text': encoded_text, # bs, 195, d_model
+                'text_token_mask': text_token_mask, # bs, 195
+                        # True for used tokens. False for padding tokens
+            }
+        Returns:
+            _type_: _description_
+        """
+        assert isinstance(text_dict, dict)
+
+        y = text_dict["encoded_text"]
+        text_token_mask = text_dict["text_token_mask"]
+
+        res = x @ y.transpose(-1, -2)
+        res.masked_fill_(~text_token_mask[:, None, :], float("-inf"))
+
+        # padding to max_text_len
+        new_res = torch.full((*res.shape[:-1], self.max_text_len), float("-inf"), device=res.device)
+        new_res[..., : res.shape[-1]] = res
+
+        return new_res

metagpt/environment/android/groundingdino/models/__init__.py

@@ -0,0 +1,18 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+from .GroundingDINO import build_groundingdino
+
+
+def build_model(args):
+    # we use register to maintain models from catdet6 on.
+    from .registry import MODULE_BUILD_FUNCS
+
+    assert args.modelname in MODULE_BUILD_FUNCS._module_dict
+    build_func = MODULE_BUILD_FUNCS.get(args.modelname)
+    model = build_func(args)
+    return model

metagpt/environment/android/groundingdino/models/registry.py

@@ -0,0 +1,66 @@
+# ------------------------------------------------------------------------
+# Grounding DINO
+# url: https://github.com/IDEA-Research/GroundingDINO
+# Copyright (c) 2023 IDEA. All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
+# ------------------------------------------------------------------------
+# -*- coding: utf-8 -*-
+# @Author: Yihao Chen
+# @Date:   2021-08-16 16:03:17
+# @Last Modified by:   Shilong Liu
+# @Last Modified time: 2022-01-23 15:26
+# modified from mmcv
+
+import inspect
+from functools import partial
+
+
+class Registry(object):
+    def __init__(self, name):
+        self._name = name
+        self._module_dict = dict()
+
+    def __repr__(self):
+        format_str = self.__class__.__name__ + "(name={}, items={})".format(
+            self._name, list(self._module_dict.keys())
+        )
+        return format_str
+
+    def __len__(self):
+        return len(self._module_dict)
+
+    @property
+    def name(self):
+        return self._name
+
+    @property
+    def module_dict(self):
+        return self._module_dict
+
+    def get(self, key):
+        return self._module_dict.get(key, None)
+
+    def registe_with_name(self, module_name=None, force=False):
+        return partial(self.register, module_name=module_name, force=force)
+
+    def register(self, module_build_function, module_name=None, force=False):
+        """Register a module build function.
+        Args:
+            module (:obj:`nn.Module`): Module to be registered.
+        """
+        if not inspect.isfunction(module_build_function):
+            raise TypeError(
+                "module_build_function must be a function, but got {}".format(
+                    type(module_build_function)
+                )
+            )
+        if module_name is None:
+            module_name = module_build_function.__name__
+        if not force and module_name in self._module_dict:
+            raise KeyError("{} is already registered in {}".format(module_name, self.name))
+        self._module_dict[module_name] = module_build_function
+
+        return module_build_function
+
+
+MODULE_BUILD_FUNCS = Registry("model build functions")

metagpt/environment/android/groundingdino/requirements.txt

@@ -0,0 +1,14 @@
+torch
+torchvision
+transformers
+modelscope
+tensorflow-macos==2.9
+keras==2.9.0
+opencv-python
+matplotlib
+pycocotools
+SentencePiece
+tf_slim
+tf_keras
+pyclipper
+shapely

metagpt/environment/android/groundingdino/util/__init__.py

@@ -0,0 +1 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

metagpt/environment/android/groundingdino/util/box_ops.py

@@ -0,0 +1,140 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Utilities for bounding box manipulation and GIoU.
+"""
+import torch
+from torchvision.ops.boxes import box_area
+
+
+def box_cxcywh_to_xyxy(x):
+    x_c, y_c, w, h = x.unbind(-1)
+    b = [(x_c - 0.5 * w), (y_c - 0.5 * h), (x_c + 0.5 * w), (y_c + 0.5 * h)]
+    return torch.stack(b, dim=-1)
+
+
+def box_xyxy_to_cxcywh(x):
+    x0, y0, x1, y1 = x.unbind(-1)
+    b = [(x0 + x1) / 2, (y0 + y1) / 2, (x1 - x0), (y1 - y0)]
+    return torch.stack(b, dim=-1)
+
+
+# modified from torchvision to also return the union
+def box_iou(boxes1, boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    # import ipdb; ipdb.set_trace()
+    lt = torch.max(boxes1[:, None, :2], boxes2[:, :2])  # [N,M,2]
+    rb = torch.min(boxes1[:, None, 2:], boxes2[:, 2:])  # [N,M,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    inter = wh[:, :, 0] * wh[:, :, 1]  # [N,M]
+
+    union = area1[:, None] + area2 - inter
+
+    iou = inter / (union + 1e-6)
+    return iou, union
+
+
+def generalized_box_iou(boxes1, boxes2):
+    """
+    Generalized IoU from https://giou.stanford.edu/
+
+    The boxes should be in [x0, y0, x1, y1] format
+
+    Returns a [N, M] pairwise matrix, where N = len(boxes1)
+    and M = len(boxes2)
+    """
+    # degenerate boxes gives inf / nan results
+    # so do an early check
+    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    # except:
+    #     import ipdb; ipdb.set_trace()
+    iou, union = box_iou(boxes1, boxes2)
+
+    lt = torch.min(boxes1[:, None, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, None, 2:], boxes2[:, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # [N,M,2]
+    area = wh[:, :, 0] * wh[:, :, 1]
+
+    return iou - (area - union) / (area + 1e-6)
+
+
+# modified from torchvision to also return the union
+def box_iou_pairwise(boxes1, boxes2):
+    area1 = box_area(boxes1)
+    area2 = box_area(boxes2)
+
+    lt = torch.max(boxes1[:, :2], boxes2[:, :2])  # [N,2]
+    rb = torch.min(boxes1[:, 2:], boxes2[:, 2:])  # [N,2]
+
+    wh = (rb - lt).clamp(min=0)  # [N,2]
+    inter = wh[:, 0] * wh[:, 1]  # [N]
+
+    union = area1 + area2 - inter
+
+    iou = inter / union
+    return iou, union
+
+
+def generalized_box_iou_pairwise(boxes1, boxes2):
+    """
+    Generalized IoU from https://giou.stanford.edu/
+
+    Input:
+        - boxes1, boxes2: N,4
+    Output:
+        - giou: N, 4
+    """
+    # degenerate boxes gives inf / nan results
+    # so do an early check
+    assert (boxes1[:, 2:] >= boxes1[:, :2]).all()
+    assert (boxes2[:, 2:] >= boxes2[:, :2]).all()
+    assert boxes1.shape == boxes2.shape
+    iou, union = box_iou_pairwise(boxes1, boxes2)  # N, 4
+
+    lt = torch.min(boxes1[:, :2], boxes2[:, :2])
+    rb = torch.max(boxes1[:, 2:], boxes2[:, 2:])
+
+    wh = (rb - lt).clamp(min=0)  # [N,2]
+    area = wh[:, 0] * wh[:, 1]
+
+    return iou - (area - union) / area
+
+
+def masks_to_boxes(masks):
+    """Compute the bounding boxes around the provided masks
+
+    The masks should be in format [N, H, W] where N is the number of masks, (H, W) are the spatial dimensions.
+
+    Returns a [N, 4] tensors, with the boxes in xyxy format
+    """
+    if masks.numel() == 0:
+        return torch.zeros((0, 4), device=masks.device)
+
+    h, w = masks.shape[-2:]
+
+    y = torch.arange(0, h, dtype=torch.float)
+    x = torch.arange(0, w, dtype=torch.float)
+    y, x = torch.meshgrid(y, x)
+
+    x_mask = masks * x.unsqueeze(0)
+    x_max = x_mask.flatten(1).max(-1)[0]
+    x_min = x_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    y_mask = masks * y.unsqueeze(0)
+    y_max = y_mask.flatten(1).max(-1)[0]
+    y_min = y_mask.masked_fill(~(masks.bool()), 1e8).flatten(1).min(-1)[0]
+
+    return torch.stack([x_min, y_min, x_max, y_max], 1)
+
+
+if __name__ == "__main__":
+    x = torch.rand(5, 4)
+    y = torch.rand(3, 4)
+    iou, union = box_iou(x, y)
+    import ipdb
+
+    ipdb.set_trace()

metagpt/environment/android/groundingdino/util/get_tokenlizer.py

@@ -0,0 +1,26 @@
+from transformers import AutoTokenizer, BertModel, BertTokenizer, RobertaModel, RobertaTokenizerFast
+
+
+def get_tokenlizer(text_encoder_type):
+    if not isinstance(text_encoder_type, str):
+        # print("text_encoder_type is not a str")
+        if hasattr(text_encoder_type, "text_encoder_type"):
+            text_encoder_type = text_encoder_type.text_encoder_type
+        elif text_encoder_type.get("text_encoder_type", False):
+            text_encoder_type = text_encoder_type.get("text_encoder_type")
+        else:
+            raise ValueError(
+                "Unknown type of text_encoder_type: {}".format(type(text_encoder_type))
+            )
+    print("final text_encoder_type: {}".format(text_encoder_type))
+
+    tokenizer = AutoTokenizer.from_pretrained(text_encoder_type)
+    return tokenizer
+
+
+def get_pretrained_language_model(text_encoder_type):
+    if text_encoder_type == "bert-base-uncased":
+        return BertModel.from_pretrained(text_encoder_type)
+    if text_encoder_type == "roberta-base":
+        return RobertaModel.from_pretrained(text_encoder_type)
+    raise ValueError("Unknown text_encoder_type {}".format(text_encoder_type))

metagpt/environment/android/groundingdino/util/inference.py

@@ -0,0 +1,98 @@
+from typing import Tuple, List
+
+import cv2
+import numpy as np
+import supervision as sv
+import torch
+from PIL import Image
+from torchvision.ops import box_convert
+
+import groundingdino.datasets.transforms as T
+from groundingdino.models import build_model
+from groundingdino.util.misc import clean_state_dict
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import get_phrases_from_posmap
+
+
+def preprocess_caption(caption: str) -> str:
+    result = caption.lower().strip()
+    if result.endswith("."):
+        return result
+    return result + "."
+
+
+def load_model(model_config_path: str, model_checkpoint_path: str, device: str = "cuda"):
+    args = SLConfig.fromfile(model_config_path)
+    args.device = device
+    model = build_model(args)
+    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+    model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+    model.eval()
+    return model
+
+
+def load_image(image_path: str) -> Tuple[np.array, torch.Tensor]:
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    image_source = Image.open(image_path).convert("RGB")
+    image = np.asarray(image_source)
+    image_transformed, _ = transform(image_source, None)
+    return image, image_transformed
+
+
+def predict(
+        model,
+        image: torch.Tensor,
+        caption: str,
+        box_threshold: float,
+        text_threshold: float,
+        device: str = "cuda"
+) -> Tuple[torch.Tensor, torch.Tensor, List[str]]:
+    caption = preprocess_caption(caption=caption)
+
+    model = model.to(device)
+    image = image.to(device)
+
+    with torch.no_grad():
+        outputs = model(image[None], captions=[caption])
+
+    prediction_logits = outputs["pred_logits"].cpu().sigmoid()[0]  # prediction_logits.shape = (nq, 256)
+    prediction_boxes = outputs["pred_boxes"].cpu()[0]  # prediction_boxes.shape = (nq, 4)
+
+    mask = prediction_logits.max(dim=1)[0] > box_threshold
+    logits = prediction_logits[mask]  # logits.shape = (n, 256)
+    boxes = prediction_boxes[mask]  # boxes.shape = (n, 4)
+
+    tokenizer = model.tokenizer
+    tokenized = tokenizer(caption)
+
+    phrases = [
+        get_phrases_from_posmap(logit > text_threshold, tokenized, tokenizer).replace('.', '')
+        for logit
+        in logits
+    ]
+
+    return boxes, logits.max(dim=1)[0], phrases
+
+
+def annotate(image_source: np.ndarray, boxes: torch.Tensor, logits: torch.Tensor, phrases: List[str]) -> np.ndarray:
+    h, w, _ = image_source.shape
+    boxes = boxes * torch.Tensor([w, h, w, h])
+    xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
+    detections = sv.Detections(xyxy=xyxy)
+
+    labels = [
+        f"{phrase} {logit:.2f}"
+        for phrase, logit
+        in zip(phrases, logits)
+    ]
+
+    box_annotator = sv.BoxAnnotator()
+    annotated_frame = cv2.cvtColor(image_source, cv2.COLOR_RGB2BGR)
+    annotated_frame = box_annotator.annotate(scene=annotated_frame, detections=detections, labels=labels)
+    return annotated_frame

metagpt/environment/android/groundingdino/util/logger.py

@@ -0,0 +1,93 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+import functools
+import logging
+import os
+import sys
+
+from termcolor import colored
+
+
+class _ColorfulFormatter(logging.Formatter):
+    def __init__(self, *args, **kwargs):
+        self._root_name = kwargs.pop("root_name") + "."
+        self._abbrev_name = kwargs.pop("abbrev_name", "")
+        if len(self._abbrev_name):
+            self._abbrev_name = self._abbrev_name + "."
+        super(_ColorfulFormatter, self).__init__(*args, **kwargs)
+
+    def formatMessage(self, record):
+        record.name = record.name.replace(self._root_name, self._abbrev_name)
+        log = super(_ColorfulFormatter, self).formatMessage(record)
+        if record.levelno == logging.WARNING:
+            prefix = colored("WARNING", "red", attrs=["blink"])
+        elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL:
+            prefix = colored("ERROR", "red", attrs=["blink", "underline"])
+        else:
+            return log
+        return prefix + " " + log
+
+
+# so that calling setup_logger multiple times won't add many handlers
+@functools.lru_cache()
+def setup_logger(output=None, distributed_rank=0, *, color=True, name="imagenet", abbrev_name=None):
+    """
+    Initialize the detectron2 logger and set its verbosity level to "INFO".
+
+    Args:
+        output (str): a file name or a directory to save log. If None, will not save log file.
+            If ends with ".txt" or ".log", assumed to be a file name.
+            Otherwise, logs will be saved to `output/log.txt`.
+        name (str): the root module name of this logger
+
+    Returns:
+        logging.Logger: a logger
+    """
+    logger = logging.getLogger(name)
+    logger.setLevel(logging.DEBUG)
+    logger.propagate = False
+
+    if abbrev_name is None:
+        abbrev_name = name
+
+    plain_formatter = logging.Formatter(
+        "[%(asctime)s.%(msecs)03d]: %(message)s", datefmt="%m/%d %H:%M:%S"
+    )
+    # stdout logging: master only
+    if distributed_rank == 0:
+        ch = logging.StreamHandler(stream=sys.stdout)
+        ch.setLevel(logging.DEBUG)
+        if color:
+            formatter = _ColorfulFormatter(
+                colored("[%(asctime)s.%(msecs)03d]: ", "green") + "%(message)s",
+                datefmt="%m/%d %H:%M:%S",
+                root_name=name,
+                abbrev_name=str(abbrev_name),
+            )
+        else:
+            formatter = plain_formatter
+        ch.setFormatter(formatter)
+        logger.addHandler(ch)
+
+    # file logging: all workers
+    if output is not None:
+        if output.endswith(".txt") or output.endswith(".log"):
+            filename = output
+        else:
+            filename = os.path.join(output, "log.txt")
+        if distributed_rank > 0:
+            filename = filename + f".rank{distributed_rank}"
+        os.makedirs(os.path.dirname(filename), exist_ok=True)
+
+        fh = logging.StreamHandler(_cached_log_stream(filename))
+        fh.setLevel(logging.DEBUG)
+        fh.setFormatter(plain_formatter)
+        logger.addHandler(fh)
+
+    return logger
+
+
+# cache the opened file object, so that different calls to `setup_logger`
+# with the same file name can safely write to the same file.
+@functools.lru_cache(maxsize=None)
+def _cached_log_stream(filename):
+    return open(filename, "a")

metagpt/environment/android/groundingdino/util/misc.py

@@ -0,0 +1,717 @@
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+"""
+Misc functions, including distributed helpers.
+
+Mostly copy-paste from torchvision references.
+"""
+import colorsys
+import datetime
+import functools
+import io
+import json
+import os
+import pickle
+import subprocess
+import time
+from collections import OrderedDict, defaultdict, deque
+from typing import List, Optional
+
+import numpy as np
+import torch
+import torch.distributed as dist
+
+# needed due to empty tensor bug in pytorch and torchvision 0.5
+import torchvision
+from torch import Tensor
+
+__torchvision_need_compat_flag = float(torchvision.__version__.split(".")[1]) < 7
+if __torchvision_need_compat_flag:
+    from torchvision.ops import _new_empty_tensor
+    from torchvision.ops.misc import _output_size
+
+
+class SmoothedValue(object):
+    """Track a series of values and provide access to smoothed values over a
+    window or the global series average.
+    """
+
+    def __init__(self, window_size=20, fmt=None):
+        if fmt is None:
+            fmt = "{median:.4f} ({global_avg:.4f})"
+        self.deque = deque(maxlen=window_size)
+        self.total = 0.0
+        self.count = 0
+        self.fmt = fmt
+
+    def update(self, value, n=1):
+        self.deque.append(value)
+        self.count += n
+        self.total += value * n
+
+    def synchronize_between_processes(self):
+        """
+        Warning: does not synchronize the deque!
+        """
+        if not is_dist_avail_and_initialized():
+            return
+        t = torch.tensor([self.count, self.total], dtype=torch.float64, device="cuda")
+        dist.barrier()
+        dist.all_reduce(t)
+        t = t.tolist()
+        self.count = int(t[0])
+        self.total = t[1]
+
+    @property
+    def median(self):
+        d = torch.tensor(list(self.deque))
+        if d.shape[0] == 0:
+            return 0
+        return d.median().item()
+
+    @property
+    def avg(self):
+        d = torch.tensor(list(self.deque), dtype=torch.float32)
+        return d.mean().item()
+
+    @property
+    def global_avg(self):
+        if os.environ.get("SHILONG_AMP", None) == "1":
+            eps = 1e-4
+        else:
+            eps = 1e-6
+        return self.total / (self.count + eps)
+
+    @property
+    def max(self):
+        return max(self.deque)
+
+    @property
+    def value(self):
+        return self.deque[-1]
+
+    def __str__(self):
+        return self.fmt.format(
+            median=self.median,
+            avg=self.avg,
+            global_avg=self.global_avg,
+            max=self.max,
+            value=self.value,
+        )
+
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+    """
+    Return a process group based on gloo backend, containing all the ranks
+    The result is cached.
+    """
+
+    if dist.get_backend() == "nccl":
+        return dist.new_group(backend="gloo")
+
+    return dist.group.WORLD
+
+
+def all_gather_cpu(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    cpu_group = _get_global_gloo_group()
+
+    buffer = io.BytesIO()
+    torch.save(data, buffer)
+    data_view = buffer.getbuffer()
+    device = "cuda" if cpu_group is None else "cpu"
+    tensor = torch.ByteTensor(data_view).to(device)
+
+    # obtain Tensor size of each rank
+    local_size = torch.tensor([tensor.numel()], device=device, dtype=torch.long)
+    size_list = [torch.tensor([0], device=device, dtype=torch.long) for _ in range(world_size)]
+    if cpu_group is None:
+        dist.all_gather(size_list, local_size)
+    else:
+        print("gathering on cpu")
+        dist.all_gather(size_list, local_size, group=cpu_group)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+    assert isinstance(local_size.item(), int)
+    local_size = int(local_size.item())
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device=device))
+    if local_size != max_size:
+        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device=device)
+        tensor = torch.cat((tensor, padding), dim=0)
+    if cpu_group is None:
+        dist.all_gather(tensor_list, tensor)
+    else:
+        dist.all_gather(tensor_list, tensor, group=cpu_group)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        tensor = torch.split(tensor, [size, max_size - size], dim=0)[0]
+        buffer = io.BytesIO(tensor.cpu().numpy())
+        obj = torch.load(buffer)
+        data_list.append(obj)
+
+    return data_list
+
+
+def all_gather(data):
+    """
+    Run all_gather on arbitrary picklable data (not necessarily tensors)
+    Args:
+        data: any picklable object
+    Returns:
+        list[data]: list of data gathered from each rank
+    """
+
+    if os.getenv("CPU_REDUCE") == "1":
+        return all_gather_cpu(data)
+
+    world_size = get_world_size()
+    if world_size == 1:
+        return [data]
+
+    # serialized to a Tensor
+    buffer = pickle.dumps(data)
+    storage = torch.ByteStorage.from_buffer(buffer)
+    tensor = torch.ByteTensor(storage).to("cuda")
+
+    # obtain Tensor size of each rank
+    local_size = torch.tensor([tensor.numel()], device="cuda")
+    size_list = [torch.tensor([0], device="cuda") for _ in range(world_size)]
+    dist.all_gather(size_list, local_size)
+    size_list = [int(size.item()) for size in size_list]
+    max_size = max(size_list)
+
+    # receiving Tensor from all ranks
+    # we pad the tensor because torch all_gather does not support
+    # gathering tensors of different shapes
+    tensor_list = []
+    for _ in size_list:
+        tensor_list.append(torch.empty((max_size,), dtype=torch.uint8, device="cuda"))
+    if local_size != max_size:
+        padding = torch.empty(size=(max_size - local_size,), dtype=torch.uint8, device="cuda")
+        tensor = torch.cat((tensor, padding), dim=0)
+    dist.all_gather(tensor_list, tensor)
+
+    data_list = []
+    for size, tensor in zip(size_list, tensor_list):
+        buffer = tensor.cpu().numpy().tobytes()[:size]
+        data_list.append(pickle.loads(buffer))
+
+    return data_list
+
+
+def reduce_dict(input_dict, average=True):
+    """
+    Args:
+        input_dict (dict): all the values will be reduced
+        average (bool): whether to do average or sum
+    Reduce the values in the dictionary from all processes so that all processes
+    have the averaged results. Returns a dict with the same fields as
+    input_dict, after reduction.
+    """
+    world_size = get_world_size()
+    if world_size < 2:
+        return input_dict
+    with torch.no_grad():
+        names = []
+        values = []
+        # sort the keys so that they are consistent across processes
+        for k in sorted(input_dict.keys()):
+            names.append(k)
+            values.append(input_dict[k])
+        values = torch.stack(values, dim=0)
+        dist.all_reduce(values)
+        if average:
+            values /= world_size
+        reduced_dict = {k: v for k, v in zip(names, values)}
+    return reduced_dict
+
+
+class MetricLogger(object):
+    def __init__(self, delimiter="\t"):
+        self.meters = defaultdict(SmoothedValue)
+        self.delimiter = delimiter
+
+    def update(self, **kwargs):
+        for k, v in kwargs.items():
+            if isinstance(v, torch.Tensor):
+                v = v.item()
+            assert isinstance(v, (float, int))
+            self.meters[k].update(v)
+
+    def __getattr__(self, attr):
+        if attr in self.meters:
+            return self.meters[attr]
+        if attr in self.__dict__:
+            return self.__dict__[attr]
+        raise AttributeError("'{}' object has no attribute '{}'".format(type(self).__name__, attr))
+
+    def __str__(self):
+        loss_str = []
+        for name, meter in self.meters.items():
+            # print(name, str(meter))
+            # import ipdb;ipdb.set_trace()
+            if meter.count > 0:
+                loss_str.append("{}: {}".format(name, str(meter)))
+        return self.delimiter.join(loss_str)
+
+    def synchronize_between_processes(self):
+        for meter in self.meters.values():
+            meter.synchronize_between_processes()
+
+    def add_meter(self, name, meter):
+        self.meters[name] = meter
+
+    def log_every(self, iterable, print_freq, header=None, logger=None):
+        if logger is None:
+            print_func = print
+        else:
+            print_func = logger.info
+
+        i = 0
+        if not header:
+            header = ""
+        start_time = time.time()
+        end = time.time()
+        iter_time = SmoothedValue(fmt="{avg:.4f}")
+        data_time = SmoothedValue(fmt="{avg:.4f}")
+        space_fmt = ":" + str(len(str(len(iterable)))) + "d"
+        if torch.cuda.is_available():
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                    "max mem: {memory:.0f}",
+                ]
+            )
+        else:
+            log_msg = self.delimiter.join(
+                [
+                    header,
+                    "[{0" + space_fmt + "}/{1}]",
+                    "eta: {eta}",
+                    "{meters}",
+                    "time: {time}",
+                    "data: {data}",
+                ]
+            )
+        MB = 1024.0 * 1024.0
+        for obj in iterable:
+            data_time.update(time.time() - end)
+            yield obj
+            # import ipdb; ipdb.set_trace()
+            iter_time.update(time.time() - end)
+            if i % print_freq == 0 or i == len(iterable) - 1:
+                eta_seconds = iter_time.global_avg * (len(iterable) - i)
+                eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))
+                if torch.cuda.is_available():
+                    print_func(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                            memory=torch.cuda.max_memory_allocated() / MB,
+                        )
+                    )
+                else:
+                    print_func(
+                        log_msg.format(
+                            i,
+                            len(iterable),
+                            eta=eta_string,
+                            meters=str(self),
+                            time=str(iter_time),
+                            data=str(data_time),
+                        )
+                    )
+            i += 1
+            end = time.time()
+        total_time = time.time() - start_time
+        total_time_str = str(datetime.timedelta(seconds=int(total_time)))
+        print_func(
+            "{} Total time: {} ({:.4f} s / it)".format(
+                header, total_time_str, total_time / len(iterable)
+            )
+        )
+
+
+def get_sha():
+    cwd = os.path.dirname(os.path.abspath(__file__))
+
+    def _run(command):
+        return subprocess.check_output(command, cwd=cwd).decode("ascii").strip()
+
+    sha = "N/A"
+    diff = "clean"
+    branch = "N/A"
+    try:
+        sha = _run(["git", "rev-parse", "HEAD"])
+        subprocess.check_output(["git", "diff"], cwd=cwd)
+        diff = _run(["git", "diff-index", "HEAD"])
+        diff = "has uncommited changes" if diff else "clean"
+        branch = _run(["git", "rev-parse", "--abbrev-ref", "HEAD"])
+    except Exception:
+        pass
+    message = f"sha: {sha}, status: {diff}, branch: {branch}"
+    return message
+
+
+def collate_fn(batch):
+    # import ipdb; ipdb.set_trace()
+    batch = list(zip(*batch))
+    batch[0] = nested_tensor_from_tensor_list(batch[0])
+    return tuple(batch)
+
+
+def _max_by_axis(the_list):
+    # type: (List[List[int]]) -> List[int]
+    maxes = the_list[0]
+    for sublist in the_list[1:]:
+        for index, item in enumerate(sublist):
+            maxes[index] = max(maxes[index], item)
+    return maxes
+
+
+class NestedTensor(object):
+    def __init__(self, tensors, mask: Optional[Tensor]):
+        self.tensors = tensors
+        self.mask = mask
+        if mask == "auto":
+            self.mask = torch.zeros_like(tensors).to(tensors.device)
+            if self.mask.dim() == 3:
+                self.mask = self.mask.sum(0).to(bool)
+            elif self.mask.dim() == 4:
+                self.mask = self.mask.sum(1).to(bool)
+            else:
+                raise ValueError(
+                    "tensors dim must be 3 or 4 but {}({})".format(
+                        self.tensors.dim(), self.tensors.shape
+                    )
+                )
+
+    def imgsize(self):
+        res = []
+        for i in range(self.tensors.shape[0]):
+            mask = self.mask[i]
+            maxH = (~mask).sum(0).max()
+            maxW = (~mask).sum(1).max()
+            res.append(torch.Tensor([maxH, maxW]))
+        return res
+
+    def to(self, device):
+        # type: (Device) -> NestedTensor # noqa
+        cast_tensor = self.tensors.to(device)
+        mask = self.mask
+        if mask is not None:
+            assert mask is not None
+            cast_mask = mask.to(device)
+        else:
+            cast_mask = None
+        return NestedTensor(cast_tensor, cast_mask)
+
+    def to_img_list_single(self, tensor, mask):
+        assert tensor.dim() == 3, "dim of tensor should be 3 but {}".format(tensor.dim())
+        maxH = (~mask).sum(0).max()
+        maxW = (~mask).sum(1).max()
+        img = tensor[:, :maxH, :maxW]
+        return img
+
+    def to_img_list(self):
+        """remove the padding and convert to img list
+
+        Returns:
+            [type]: [description]
+        """
+        if self.tensors.dim() == 3:
+            return self.to_img_list_single(self.tensors, self.mask)
+        else:
+            res = []
+            for i in range(self.tensors.shape[0]):
+                tensor_i = self.tensors[i]
+                mask_i = self.mask[i]
+                res.append(self.to_img_list_single(tensor_i, mask_i))
+            return res
+
+    @property
+    def device(self):
+        return self.tensors.device
+
+    def decompose(self):
+        return self.tensors, self.mask
+
+    def __repr__(self):
+        return str(self.tensors)
+
+    @property
+    def shape(self):
+        return {"tensors.shape": self.tensors.shape, "mask.shape": self.mask.shape}
+
+
+def nested_tensor_from_tensor_list(tensor_list: List[Tensor]):
+    # TODO make this more general
+    if tensor_list[0].ndim == 3:
+        if torchvision._is_tracing():
+            # nested_tensor_from_tensor_list() does not export well to ONNX
+            # call _onnx_nested_tensor_from_tensor_list() instead
+            return _onnx_nested_tensor_from_tensor_list(tensor_list)
+
+        # TODO make it support different-sized images
+        max_size = _max_by_axis([list(img.shape) for img in tensor_list])
+        # min_size = tuple(min(s) for s in zip(*[img.shape for img in tensor_list]))
+        batch_shape = [len(tensor_list)] + max_size
+        b, c, h, w = batch_shape
+        dtype = tensor_list[0].dtype
+        device = tensor_list[0].device
+        tensor = torch.zeros(batch_shape, dtype=dtype, device=device)
+        mask = torch.ones((b, h, w), dtype=torch.bool, device=device)
+        for img, pad_img, m in zip(tensor_list, tensor, mask):
+            pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+            m[: img.shape[1], : img.shape[2]] = False
+    else:
+        raise ValueError("not supported")
+    return NestedTensor(tensor, mask)
+
+
+# _onnx_nested_tensor_from_tensor_list() is an implementation of
+# nested_tensor_from_tensor_list() that is supported by ONNX tracing.
+@torch.jit.unused
+def _onnx_nested_tensor_from_tensor_list(tensor_list: List[Tensor]) -> NestedTensor:
+    max_size = []
+    for i in range(tensor_list[0].dim()):
+        max_size_i = torch.max(
+            torch.stack([img.shape[i] for img in tensor_list]).to(torch.float32)
+        ).to(torch.int64)
+        max_size.append(max_size_i)
+    max_size = tuple(max_size)
+
+    # work around for
+    # pad_img[: img.shape[0], : img.shape[1], : img.shape[2]].copy_(img)
+    # m[: img.shape[1], :img.shape[2]] = False
+    # which is not yet supported in onnx
+    padded_imgs = []
+    padded_masks = []
+    for img in tensor_list:
+        padding = [(s1 - s2) for s1, s2 in zip(max_size, tuple(img.shape))]
+        padded_img = torch.nn.functional.pad(img, (0, padding[2], 0, padding[1], 0, padding[0]))
+        padded_imgs.append(padded_img)
+
+        m = torch.zeros_like(img[0], dtype=torch.int, device=img.device)
+        padded_mask = torch.nn.functional.pad(m, (0, padding[2], 0, padding[1]), "constant", 1)
+        padded_masks.append(padded_mask.to(torch.bool))
+
+    tensor = torch.stack(padded_imgs)
+    mask = torch.stack(padded_masks)
+
+    return NestedTensor(tensor, mask=mask)
+
+
+def setup_for_distributed(is_master):
+    """
+    This function disables printing when not in master process
+    """
+    import builtins as __builtin__
+
+    builtin_print = __builtin__.print
+
+    def print(*args, **kwargs):
+        force = kwargs.pop("force", False)
+        if is_master or force:
+            builtin_print(*args, **kwargs)
+
+    __builtin__.print = print
+
+
+def is_dist_avail_and_initialized():
+    if not dist.is_available():
+        return False
+    if not dist.is_initialized():
+        return False
+    return True
+
+
+def get_world_size():
+    if not is_dist_avail_and_initialized():
+        return 1
+    return dist.get_world_size()
+
+
+def get_rank():
+    if not is_dist_avail_and_initialized():
+        return 0
+    return dist.get_rank()
+
+
+def is_main_process():
+    return get_rank() == 0
+
+
+def save_on_master(*args, **kwargs):
+    if is_main_process():
+        torch.save(*args, **kwargs)
+
+
+def init_distributed_mode(args):
+    if "WORLD_SIZE" in os.environ and os.environ["WORLD_SIZE"] != "":  # 'RANK' in os.environ and
+        args.rank = int(os.environ["RANK"])
+        args.world_size = int(os.environ["WORLD_SIZE"])
+        args.gpu = args.local_rank = int(os.environ["LOCAL_RANK"])
+
+        # launch by torch.distributed.launch
+        # Single node
+        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 1 --rank 0 ...
+        # Multi nodes
+        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 0 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
+        #   python -m torch.distributed.launch --nproc_per_node=8 main.py --world-size 2 --rank 1 --dist-url 'tcp://IP_OF_NODE0:FREEPORT' ...
+        # args.rank = int(os.environ.get('OMPI_COMM_WORLD_RANK'))
+        # local_world_size = int(os.environ['GPU_PER_NODE_COUNT'])
+        # args.world_size = args.world_size * local_world_size
+        # args.gpu = args.local_rank = int(os.environ['LOCAL_RANK'])
+        # args.rank = args.rank * local_world_size + args.local_rank
+        print(
+            "world size: {}, rank: {}, local rank: {}".format(
+                args.world_size, args.rank, args.local_rank
+            )
+        )
+        print(json.dumps(dict(os.environ), indent=2))
+    elif "SLURM_PROCID" in os.environ:
+        args.rank = int(os.environ["SLURM_PROCID"])
+        args.gpu = args.local_rank = int(os.environ["SLURM_LOCALID"])
+        args.world_size = int(os.environ["SLURM_NPROCS"])
+
+        print(
+            "world size: {}, world rank: {}, local rank: {}, device_count: {}".format(
+                args.world_size, args.rank, args.local_rank, torch.cuda.device_count()
+            )
+        )
+    else:
+        print("Not using distributed mode")
+        args.distributed = False
+        args.world_size = 1
+        args.rank = 0
+        args.local_rank = 0
+        return
+
+    print("world_size:{} rank:{} local_rank:{}".format(args.world_size, args.rank, args.local_rank))
+    args.distributed = True
+    torch.cuda.set_device(args.local_rank)
+    args.dist_backend = "nccl"
+    print("| distributed init (rank {}): {}".format(args.rank, args.dist_url), flush=True)
+
+    torch.distributed.init_process_group(
+        backend=args.dist_backend,
+        world_size=args.world_size,
+        rank=args.rank,
+        init_method=args.dist_url,
+    )
+
+    print("Before torch.distributed.barrier()")
+    torch.distributed.barrier()
+    print("End torch.distributed.barrier()")
+    setup_for_distributed(args.rank == 0)
+
+
+@torch.no_grad()
+def accuracy(output, target, topk=(1,)):
+    """Computes the precision@k for the specified values of k"""
+    if target.numel() == 0:
+        return [torch.zeros([], device=output.device)]
+    maxk = max(topk)
+    batch_size = target.size(0)
+
+    _, pred = output.topk(maxk, 1, True, True)
+    pred = pred.t()
+    correct = pred.eq(target.view(1, -1).expand_as(pred))
+
+    res = []
+    for k in topk:
+        correct_k = correct[:k].view(-1).float().sum(0)
+        res.append(correct_k.mul_(100.0 / batch_size))
+    return res
+
+
+@torch.no_grad()
+def accuracy_onehot(pred, gt):
+    """_summary_
+
+    Args:
+        pred (_type_): n, c
+        gt (_type_): n, c
+    """
+    tp = ((pred - gt).abs().sum(-1) < 1e-4).float().sum()
+    acc = tp / gt.shape[0] * 100
+    return acc
+
+
+def interpolate(input, size=None, scale_factor=None, mode="nearest", align_corners=None):
+    # type: (Tensor, Optional[List[int]], Optional[float], str, Optional[bool]) -> Tensor
+    """
+    Equivalent to nn.functional.interpolate, but with support for empty batch sizes.
+    This will eventually be supported natively by PyTorch, and this
+    class can go away.
+    """
+    if __torchvision_need_compat_flag < 0.7:
+        if input.numel() > 0:
+            return torch.nn.functional.interpolate(input, size, scale_factor, mode, align_corners)
+
+        output_shape = _output_size(2, input, size, scale_factor)
+        output_shape = list(input.shape[:-2]) + list(output_shape)
+        return _new_empty_tensor(input, output_shape)
+    else:
+        return torchvision.ops.misc.interpolate(input, size, scale_factor, mode, align_corners)
+
+
+class color_sys:
+    def __init__(self, num_colors) -> None:
+        self.num_colors = num_colors
+        colors = []
+        for i in np.arange(0.0, 360.0, 360.0 / num_colors):
+            hue = i / 360.0
+            lightness = (50 + np.random.rand() * 10) / 100.0
+            saturation = (90 + np.random.rand() * 10) / 100.0
+            colors.append(
+                tuple([int(j * 255) for j in colorsys.hls_to_rgb(hue, lightness, saturation)])
+            )
+        self.colors = colors
+
+    def __call__(self, idx):
+        return self.colors[idx]
+
+
+def inverse_sigmoid(x, eps=1e-3):
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+def clean_state_dict(state_dict):
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        if k[:7] == "module.":
+            k = k[7:]  # remove `module.`
+        new_state_dict[k] = v
+    return new_state_dict

metagpt/environment/android/groundingdino/util/slconfig.py

@@ -0,0 +1,427 @@
+# ==========================================================
+# Modified from mmcv
+# ==========================================================
+import ast
+import os.path as osp
+import shutil
+import sys
+import tempfile
+from argparse import Action
+from importlib import import_module
+import platform
+
+from addict import Dict
+from yapf.yapflib.yapf_api import FormatCode
+
+BASE_KEY = "_base_"
+DELETE_KEY = "_delete_"
+RESERVED_KEYS = ["filename", "text", "pretty_text", "get", "dump", "merge_from_dict"]
+
+
+def check_file_exist(filename, msg_tmpl='file "{}" does not exist'):
+    if not osp.isfile(filename):
+        raise FileNotFoundError(msg_tmpl.format(filename))
+
+
+class ConfigDict(Dict):
+    def __missing__(self, name):
+        raise KeyError(name)
+
+    def __getattr__(self, name):
+        try:
+            value = super(ConfigDict, self).__getattr__(name)
+        except KeyError:
+            ex = AttributeError(f"'{self.__class__.__name__}' object has no " f"attribute '{name}'")
+        except Exception as e:
+            ex = e
+        else:
+            return value
+        raise ex
+
+
+class SLConfig(object):
+    """
+    config files.
+    only support .py file as config now.
+
+    ref: mmcv.utils.config
+
+    Example:
+        >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1])))
+        >>> cfg.a
+        1
+        >>> cfg.b
+        {'b1': [0, 1]}
+        >>> cfg.b.b1
+        [0, 1]
+        >>> cfg = Config.fromfile('tests/data/config/a.py')
+        >>> cfg.filename
+        "/home/kchen/projects/mmcv/tests/data/config/a.py"
+        >>> cfg.item4
+        'test'
+        >>> cfg
+        "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: "
+        "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}"
+    """
+
+    @staticmethod
+    def _validate_py_syntax(filename):
+        with open(filename) as f:
+            content = f.read()
+        try:
+            ast.parse(content)
+        except SyntaxError:
+            raise SyntaxError("There are syntax errors in config " f"file {filename}")
+
+    @staticmethod
+    def _file2dict(filename):
+        filename = osp.abspath(osp.expanduser(filename))
+        check_file_exist(filename)
+        if filename.lower().endswith(".py"):
+            with tempfile.TemporaryDirectory() as temp_config_dir:
+                temp_config_file = tempfile.NamedTemporaryFile(dir=temp_config_dir, suffix=".py")
+                temp_config_name = osp.basename(temp_config_file.name)
+                if platform.system() == 'Windows':
+                    temp_config_file.close()
+                shutil.copyfile(filename, osp.join(temp_config_dir, temp_config_name))
+                temp_module_name = osp.splitext(temp_config_name)[0]
+                sys.path.insert(0, temp_config_dir)
+                SLConfig._validate_py_syntax(filename)
+                mod = import_module(temp_module_name)
+                sys.path.pop(0)
+                cfg_dict = {
+                    name: value for name, value in mod.__dict__.items() if not name.startswith("__")
+                }
+                # delete imported module
+                del sys.modules[temp_module_name]
+                # close temp file
+                temp_config_file.close()
+        elif filename.lower().endswith((".yml", ".yaml", ".json")):
+            from .slio import slload
+
+            cfg_dict = slload(filename)
+        else:
+            raise IOError("Only py/yml/yaml/json type are supported now!")
+
+        cfg_text = filename + "\n"
+        with open(filename, "r") as f:
+            cfg_text += f.read()
+
+        # parse the base file
+        if BASE_KEY in cfg_dict:
+            cfg_dir = osp.dirname(filename)
+            base_filename = cfg_dict.pop(BASE_KEY)
+            base_filename = base_filename if isinstance(base_filename, list) else [base_filename]
+
+            cfg_dict_list = list()
+            cfg_text_list = list()
+            for f in base_filename:
+                _cfg_dict, _cfg_text = SLConfig._file2dict(osp.join(cfg_dir, f))
+                cfg_dict_list.append(_cfg_dict)
+                cfg_text_list.append(_cfg_text)
+
+            base_cfg_dict = dict()
+            for c in cfg_dict_list:
+                if len(base_cfg_dict.keys() & c.keys()) > 0:
+                    raise KeyError("Duplicate key is not allowed among bases")
+                    # TODO Allow the duplicate key while warnning user
+                base_cfg_dict.update(c)
+
+            base_cfg_dict = SLConfig._merge_a_into_b(cfg_dict, base_cfg_dict)
+            cfg_dict = base_cfg_dict
+
+            # merge cfg_text
+            cfg_text_list.append(cfg_text)
+            cfg_text = "\n".join(cfg_text_list)
+
+        return cfg_dict, cfg_text
+
+    @staticmethod
+    def _merge_a_into_b(a, b):
+        """merge dict `a` into dict `b` (non-inplace).
+            values in `a` will overwrite `b`.
+            copy first to avoid inplace modification
+
+        Args:
+            a ([type]): [description]
+            b ([type]): [description]
+
+        Returns:
+            [dict]: [description]
+        """
+        # import ipdb; ipdb.set_trace()
+        if not isinstance(a, dict):
+            return a
+
+        b = b.copy()
+        for k, v in a.items():
+            if isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False):
+
+                if not isinstance(b[k], dict) and not isinstance(b[k], list):
+                    # if :
+                    # import ipdb; ipdb.set_trace()
+                    raise TypeError(
+                        f"{k}={v} in child config cannot inherit from base "
+                        f"because {k} is a dict in the child config but is of "
+                        f"type {type(b[k])} in base config. You may set "
+                        f"`{DELETE_KEY}=True` to ignore the base config"
+                    )
+                b[k] = SLConfig._merge_a_into_b(v, b[k])
+            elif isinstance(b, list):
+                try:
+                    _ = int(k)
+                except:
+                    raise TypeError(
+                        f"b is a list, " f"index {k} should be an int when input but {type(k)}"
+                    )
+                b[int(k)] = SLConfig._merge_a_into_b(v, b[int(k)])
+            else:
+                b[k] = v
+
+        return b
+
+    @staticmethod
+    def fromfile(filename):
+        cfg_dict, cfg_text = SLConfig._file2dict(filename)
+        return SLConfig(cfg_dict, cfg_text=cfg_text, filename=filename)
+
+    def __init__(self, cfg_dict=None, cfg_text=None, filename=None):
+        if cfg_dict is None:
+            cfg_dict = dict()
+        elif not isinstance(cfg_dict, dict):
+            raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}")
+        for key in cfg_dict:
+            if key in RESERVED_KEYS:
+                raise KeyError(f"{key} is reserved for config file")
+
+        super(SLConfig, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict))
+        super(SLConfig, self).__setattr__("_filename", filename)
+        if cfg_text:
+            text = cfg_text
+        elif filename:
+            with open(filename, "r") as f:
+                text = f.read()
+        else:
+            text = ""
+        super(SLConfig, self).__setattr__("_text", text)
+
+    @property
+    def filename(self):
+        return self._filename
+
+    @property
+    def text(self):
+        return self._text
+
+    @property
+    def pretty_text(self):
+
+        indent = 4
+
+        def _indent(s_, num_spaces):
+            s = s_.split("\n")
+            if len(s) == 1:
+                return s_
+            first = s.pop(0)
+            s = [(num_spaces * " ") + line for line in s]
+            s = "\n".join(s)
+            s = first + "\n" + s
+            return s
+
+        def _format_basic_types(k, v, use_mapping=False):
+            if isinstance(v, str):
+                v_str = f"'{v}'"
+            else:
+                v_str = str(v)
+
+            if use_mapping:
+                k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                attr_str = f"{k_str}: {v_str}"
+            else:
+                attr_str = f"{str(k)}={v_str}"
+            attr_str = _indent(attr_str, indent)
+
+            return attr_str
+
+        def _format_list(k, v, use_mapping=False):
+            # check if all items in the list are dict
+            if all(isinstance(_, dict) for _ in v):
+                v_str = "[\n"
+                v_str += "\n".join(
+                    f"dict({_indent(_format_dict(v_), indent)})," for v_ in v
+                ).rstrip(",")
+                if use_mapping:
+                    k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                    attr_str = f"{k_str}: {v_str}"
+                else:
+                    attr_str = f"{str(k)}={v_str}"
+                attr_str = _indent(attr_str, indent) + "]"
+            else:
+                attr_str = _format_basic_types(k, v, use_mapping)
+            return attr_str
+
+        def _contain_invalid_identifier(dict_str):
+            contain_invalid_identifier = False
+            for key_name in dict_str:
+                contain_invalid_identifier |= not str(key_name).isidentifier()
+            return contain_invalid_identifier
+
+        def _format_dict(input_dict, outest_level=False):
+            r = ""
+            s = []
+
+            use_mapping = _contain_invalid_identifier(input_dict)
+            if use_mapping:
+                r += "{"
+            for idx, (k, v) in enumerate(input_dict.items()):
+                is_last = idx >= len(input_dict) - 1
+                end = "" if outest_level or is_last else ","
+                if isinstance(v, dict):
+                    v_str = "\n" + _format_dict(v)
+                    if use_mapping:
+                        k_str = f"'{k}'" if isinstance(k, str) else str(k)
+                        attr_str = f"{k_str}: dict({v_str}"
+                    else:
+                        attr_str = f"{str(k)}=dict({v_str}"
+                    attr_str = _indent(attr_str, indent) + ")" + end
+                elif isinstance(v, list):
+                    attr_str = _format_list(k, v, use_mapping) + end
+                else:
+                    attr_str = _format_basic_types(k, v, use_mapping) + end
+
+                s.append(attr_str)
+            r += "\n".join(s)
+            if use_mapping:
+                r += "}"
+            return r
+
+        cfg_dict = self._cfg_dict.to_dict()
+        text = _format_dict(cfg_dict, outest_level=True)
+        # copied from setup.cfg
+        yapf_style = dict(
+            based_on_style="pep8",
+            blank_line_before_nested_class_or_def=True,
+            split_before_expression_after_opening_paren=True,
+        )
+        text, _ = FormatCode(text, style_config=yapf_style, verify=True)
+
+        return text
+
+    def __repr__(self):
+        return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}"
+
+    def __len__(self):
+        return len(self._cfg_dict)
+
+    def __getattr__(self, name):
+        # # debug
+        # print('+'*15)
+        # print('name=%s' % name)
+        # print("addr:", id(self))
+        # # print('type(self):', type(self))
+        # print(self.__dict__)
+        # print('+'*15)
+        # if self.__dict__ == {}:
+        #     raise ValueError
+
+        return getattr(self._cfg_dict, name)
+
+    def __getitem__(self, name):
+        return self._cfg_dict.__getitem__(name)
+
+    def __setattr__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setattr__(name, value)
+
+    def __setitem__(self, name, value):
+        if isinstance(value, dict):
+            value = ConfigDict(value)
+        self._cfg_dict.__setitem__(name, value)
+
+    def __iter__(self):
+        return iter(self._cfg_dict)
+
+    def dump(self, file=None):
+        # import ipdb; ipdb.set_trace()
+        if file is None:
+            return self.pretty_text
+        else:
+            with open(file, "w") as f:
+                f.write(self.pretty_text)
+
+    def merge_from_dict(self, options):
+        """Merge list into cfg_dict
+
+        Merge the dict parsed by MultipleKVAction into this cfg.
+
+        Examples:
+            >>> options = {'model.backbone.depth': 50,
+            ...            'model.backbone.with_cp':True}
+            >>> cfg = Config(dict(model=dict(backbone=dict(type='ResNet'))))
+            >>> cfg.merge_from_dict(options)
+            >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict')
+            >>> assert cfg_dict == dict(
+            ...     model=dict(backbone=dict(depth=50, with_cp=True)))
+
+        Args:
+            options (dict): dict of configs to merge from.
+        """
+        option_cfg_dict = {}
+        for full_key, v in options.items():
+            d = option_cfg_dict
+            key_list = full_key.split(".")
+            for subkey in key_list[:-1]:
+                d.setdefault(subkey, ConfigDict())
+                d = d[subkey]
+            subkey = key_list[-1]
+            d[subkey] = v
+
+        cfg_dict = super(SLConfig, self).__getattribute__("_cfg_dict")
+        super(SLConfig, self).__setattr__(
+            "_cfg_dict", SLConfig._merge_a_into_b(option_cfg_dict, cfg_dict)
+        )
+
+    # for multiprocess
+    def __setstate__(self, state):
+        self.__init__(state)
+
+    def copy(self):
+        return SLConfig(self._cfg_dict.copy())
+
+    def deepcopy(self):
+        return SLConfig(self._cfg_dict.deepcopy())
+
+
+class DictAction(Action):
+    """
+    argparse action to split an argument into KEY=VALUE form
+    on the first = and append to a dictionary. List options should
+    be passed as comma separated values, i.e KEY=V1,V2,V3
+    """
+
+    @staticmethod
+    def _parse_int_float_bool(val):
+        try:
+            return int(val)
+        except ValueError:
+            pass
+        try:
+            return float(val)
+        except ValueError:
+            pass
+        if val.lower() in ["true", "false"]:
+            return True if val.lower() == "true" else False
+        if val.lower() in ["none", "null"]:
+            return None
+        return val
+
+    def __call__(self, parser, namespace, values, option_string=None):
+        options = {}
+        for kv in values:
+            key, val = kv.split("=", maxsplit=1)
+            val = [self._parse_int_float_bool(v) for v in val.split(",")]
+            if len(val) == 1:
+                val = val[0]
+            options[key] = val
+        setattr(namespace, self.dest, options)

metagpt/environment/android/groundingdino/util/slio.py

@@ -0,0 +1,177 @@
+# ==========================================================
+# Modified from mmcv
+# ==========================================================
+
+import json
+import pickle
+from abc import ABCMeta, abstractmethod
+from pathlib import Path
+
+import yaml
+
+try:
+    from yaml import CLoader as Loader, CDumper as Dumper
+except ImportError:
+    from yaml import Loader, Dumper
+
+
+# ===========================
+# Rigister handler
+# ===========================
+
+
+class BaseFileHandler(metaclass=ABCMeta):
+    @abstractmethod
+    def load_from_fileobj(self, file, **kwargs):
+        pass
+
+    @abstractmethod
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        pass
+
+    @abstractmethod
+    def dump_to_str(self, obj, **kwargs):
+        pass
+
+    def load_from_path(self, filepath, mode="r", **kwargs):
+        with open(filepath, mode) as f:
+            return self.load_from_fileobj(f, **kwargs)
+
+    def dump_to_path(self, obj, filepath, mode="w", **kwargs):
+        with open(filepath, mode) as f:
+            self.dump_to_fileobj(obj, f, **kwargs)
+
+
+class JsonHandler(BaseFileHandler):
+    def load_from_fileobj(self, file):
+        return json.load(file)
+
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        json.dump(obj, file, **kwargs)
+
+    def dump_to_str(self, obj, **kwargs):
+        return json.dumps(obj, **kwargs)
+
+
+class PickleHandler(BaseFileHandler):
+    def load_from_fileobj(self, file, **kwargs):
+        return pickle.load(file, **kwargs)
+
+    def load_from_path(self, filepath, **kwargs):
+        return super(PickleHandler, self).load_from_path(filepath, mode="rb", **kwargs)
+
+    def dump_to_str(self, obj, **kwargs):
+        kwargs.setdefault("protocol", 2)
+        return pickle.dumps(obj, **kwargs)
+
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        kwargs.setdefault("protocol", 2)
+        pickle.dump(obj, file, **kwargs)
+
+    def dump_to_path(self, obj, filepath, **kwargs):
+        super(PickleHandler, self).dump_to_path(obj, filepath, mode="wb", **kwargs)
+
+
+class YamlHandler(BaseFileHandler):
+    def load_from_fileobj(self, file, **kwargs):
+        kwargs.setdefault("Loader", Loader)
+        return yaml.load(file, **kwargs)
+
+    def dump_to_fileobj(self, obj, file, **kwargs):
+        kwargs.setdefault("Dumper", Dumper)
+        yaml.dump(obj, file, **kwargs)
+
+    def dump_to_str(self, obj, **kwargs):
+        kwargs.setdefault("Dumper", Dumper)
+        return yaml.dump(obj, **kwargs)
+
+
+file_handlers = {
+    "json": JsonHandler(),
+    "yaml": YamlHandler(),
+    "yml": YamlHandler(),
+    "pickle": PickleHandler(),
+    "pkl": PickleHandler(),
+}
+
+# ===========================
+# load and dump
+# ===========================
+
+
+def is_str(x):
+    """Whether the input is an string instance.
+
+    Note: This method is deprecated since python 2 is no longer supported.
+    """
+    return isinstance(x, str)
+
+
+def slload(file, file_format=None, **kwargs):
+    """Load data from json/yaml/pickle files.
+
+    This method provides a unified api for loading data from serialized files.
+
+    Args:
+        file (str or :obj:`Path` or file-like object): Filename or a file-like
+            object.
+        file_format (str, optional): If not specified, the file format will be
+            inferred from the file extension, otherwise use the specified one.
+            Currently supported formats include "json", "yaml/yml" and
+            "pickle/pkl".
+
+    Returns:
+        The content from the file.
+    """
+    if isinstance(file, Path):
+        file = str(file)
+    if file_format is None and is_str(file):
+        file_format = file.split(".")[-1]
+    if file_format not in file_handlers:
+        raise TypeError(f"Unsupported format: {file_format}")
+
+    handler = file_handlers[file_format]
+    if is_str(file):
+        obj = handler.load_from_path(file, **kwargs)
+    elif hasattr(file, "read"):
+        obj = handler.load_from_fileobj(file, **kwargs)
+    else:
+        raise TypeError('"file" must be a filepath str or a file-object')
+    return obj
+
+
+def sldump(obj, file=None, file_format=None, **kwargs):
+    """Dump data to json/yaml/pickle strings or files.
+
+    This method provides a unified api for dumping data as strings or to files,
+    and also supports custom arguments for each file format.
+
+    Args:
+        obj (any): The python object to be dumped.
+        file (str or :obj:`Path` or file-like object, optional): If not
+            specified, then the object is dump to a str, otherwise to a file
+            specified by the filename or file-like object.
+        file_format (str, optional): Same as :func:`load`.
+
+    Returns:
+        bool: True for success, False otherwise.
+    """
+    if isinstance(file, Path):
+        file = str(file)
+    if file_format is None:
+        if is_str(file):
+            file_format = file.split(".")[-1]
+        elif file is None:
+            raise ValueError("file_format must be specified since file is None")
+    if file_format not in file_handlers:
+        raise TypeError(f"Unsupported format: {file_format}")
+
+    handler = file_handlers[file_format]
+    if file is None:
+        return handler.dump_to_str(obj, **kwargs)
+    elif is_str(file):
+        handler.dump_to_path(obj, file, **kwargs)
+    elif hasattr(file, "write"):
+        handler.dump_to_fileobj(obj, file, **kwargs)
+    else:
+        raise TypeError('"file" must be a filename str or a file-object')

metagpt/environment/android/groundingdino/util/time_counter.py

@@ -0,0 +1,62 @@
+import json
+import time
+
+
+class TimeCounter:
+    def __init__(self) -> None:
+        pass
+
+    def clear(self):
+        self.timedict = {}
+        self.basetime = time.perf_counter()
+
+    def timeit(self, name):
+        nowtime = time.perf_counter() - self.basetime
+        self.timedict[name] = nowtime
+        self.basetime = time.perf_counter()
+
+
+class TimeHolder:
+    def __init__(self) -> None:
+        self.timedict = {}
+
+    def update(self, _timedict: dict):
+        for k, v in _timedict.items():
+            if k not in self.timedict:
+                self.timedict[k] = AverageMeter(name=k, val_only=True)
+            self.timedict[k].update(val=v)
+
+    def final_res(self):
+        return {k: v.avg for k, v in self.timedict.items()}
+
+    def __str__(self):
+        return json.dumps(self.final_res(), indent=2)
+
+
+class AverageMeter(object):
+    """Computes and stores the average and current value"""
+
+    def __init__(self, name, fmt=":f", val_only=False):
+        self.name = name
+        self.fmt = fmt
+        self.val_only = val_only
+        self.reset()
+
+    def reset(self):
+        self.val = 0
+        self.avg = 0
+        self.sum = 0
+        self.count = 0
+
+    def update(self, val, n=1):
+        self.val = val
+        self.sum += val * n
+        self.count += n
+        self.avg = self.sum / self.count
+
+    def __str__(self):
+        if self.val_only:
+            fmtstr = "{name} {val" + self.fmt + "}"
+        else:
+            fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})"
+        return fmtstr.format(**self.__dict__)

metagpt/environment/android/groundingdino/util/utils.py

@@ -0,0 +1,608 @@
+import argparse
+import json
+import warnings
+from collections import OrderedDict
+from copy import deepcopy
+from typing import Any, Dict, List
+
+import numpy as np
+import torch
+from transformers import AutoTokenizer
+
+from groundingdino.util.slconfig import SLConfig
+
+
+def slprint(x, name="x"):
+    if isinstance(x, (torch.Tensor, np.ndarray)):
+        print(f"{name}.shape:", x.shape)
+    elif isinstance(x, (tuple, list)):
+        print("type x:", type(x))
+        for i in range(min(10, len(x))):
+            slprint(x[i], f"{name}[{i}]")
+    elif isinstance(x, dict):
+        for k, v in x.items():
+            slprint(v, f"{name}[{k}]")
+    else:
+        print(f"{name}.type:", type(x))
+
+
+def clean_state_dict(state_dict):
+    new_state_dict = OrderedDict()
+    for k, v in state_dict.items():
+        if k[:7] == "module.":
+            k = k[7:]  # remove `module.`
+        new_state_dict[k] = v
+    return new_state_dict
+
+
+def renorm(
+    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+) -> torch.FloatTensor:
+    # img: tensor(3,H,W) or tensor(B,3,H,W)
+    # return: same as img
+    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+    if img.dim() == 3:
+        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
+            img.size(0),
+            str(img.size()),
+        )
+        img_perm = img.permute(1, 2, 0)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(2, 0, 1)
+    else:  # img.dim() == 4
+        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
+            img.size(1),
+            str(img.size()),
+        )
+        img_perm = img.permute(0, 2, 3, 1)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(0, 3, 1, 2)
+
+
+class CocoClassMapper:
+    def __init__(self) -> None:
+        self.category_map_str = {
+            "1": 1,
+            "2": 2,
+            "3": 3,
+            "4": 4,
+            "5": 5,
+            "6": 6,
+            "7": 7,
+            "8": 8,
+            "9": 9,
+            "10": 10,
+            "11": 11,
+            "13": 12,
+            "14": 13,
+            "15": 14,
+            "16": 15,
+            "17": 16,
+            "18": 17,
+            "19": 18,
+            "20": 19,
+            "21": 20,
+            "22": 21,
+            "23": 22,
+            "24": 23,
+            "25": 24,
+            "27": 25,
+            "28": 26,
+            "31": 27,
+            "32": 28,
+            "33": 29,
+            "34": 30,
+            "35": 31,
+            "36": 32,
+            "37": 33,
+            "38": 34,
+            "39": 35,
+            "40": 36,
+            "41": 37,
+            "42": 38,
+            "43": 39,
+            "44": 40,
+            "46": 41,
+            "47": 42,
+            "48": 43,
+            "49": 44,
+            "50": 45,
+            "51": 46,
+            "52": 47,
+            "53": 48,
+            "54": 49,
+            "55": 50,
+            "56": 51,
+            "57": 52,
+            "58": 53,
+            "59": 54,
+            "60": 55,
+            "61": 56,
+            "62": 57,
+            "63": 58,
+            "64": 59,
+            "65": 60,
+            "67": 61,
+            "70": 62,
+            "72": 63,
+            "73": 64,
+            "74": 65,
+            "75": 66,
+            "76": 67,
+            "77": 68,
+            "78": 69,
+            "79": 70,
+            "80": 71,
+            "81": 72,
+            "82": 73,
+            "84": 74,
+            "85": 75,
+            "86": 76,
+            "87": 77,
+            "88": 78,
+            "89": 79,
+            "90": 80,
+        }
+        self.origin2compact_mapper = {int(k): v - 1 for k, v in self.category_map_str.items()}
+        self.compact2origin_mapper = {int(v - 1): int(k) for k, v in self.category_map_str.items()}
+
+    def origin2compact(self, idx):
+        return self.origin2compact_mapper[int(idx)]
+
+    def compact2origin(self, idx):
+        return self.compact2origin_mapper[int(idx)]
+
+
+def to_device(item, device):
+    if isinstance(item, torch.Tensor):
+        return item.to(device)
+    elif isinstance(item, list):
+        return [to_device(i, device) for i in item]
+    elif isinstance(item, dict):
+        return {k: to_device(v, device) for k, v in item.items()}
+    else:
+        raise NotImplementedError(
+            "Call Shilong if you use other containers! type: {}".format(type(item))
+        )
+
+
+#
+def get_gaussian_mean(x, axis, other_axis, softmax=True):
+    """
+
+    Args:
+        x (float): Input images(BxCxHxW)
+        axis (int): The index for weighted mean
+        other_axis (int): The other index
+
+    Returns: weighted index for axis, BxC
+
+    """
+    mat2line = torch.sum(x, axis=other_axis)
+    # mat2line = mat2line / mat2line.mean() * 10
+    if softmax:
+        u = torch.softmax(mat2line, axis=2)
+    else:
+        u = mat2line / (mat2line.sum(2, keepdim=True) + 1e-6)
+    size = x.shape[axis]
+    ind = torch.linspace(0, 1, size).to(x.device)
+    batch = x.shape[0]
+    channel = x.shape[1]
+    index = ind.repeat([batch, channel, 1])
+    mean_position = torch.sum(index * u, dim=2)
+    return mean_position
+
+
+def get_expected_points_from_map(hm, softmax=True):
+    """get_gaussian_map_from_points
+        B,C,H,W -> B,N,2 float(0, 1) float(0, 1)
+        softargmax function
+
+    Args:
+        hm (float): Input images(BxCxHxW)
+
+    Returns:
+        weighted index for axis, BxCx2. float between 0 and 1.
+
+    """
+    # hm = 10*hm
+    B, C, H, W = hm.shape
+    y_mean = get_gaussian_mean(hm, 2, 3, softmax=softmax)  # B,C
+    x_mean = get_gaussian_mean(hm, 3, 2, softmax=softmax)  # B,C
+    # return torch.cat((x_mean.unsqueeze(-1), y_mean.unsqueeze(-1)), 2)
+    return torch.stack([x_mean, y_mean], dim=2)
+
+
+# Positional encoding (section 5.1)
+# borrow from nerf
+class Embedder:
+    def __init__(self, **kwargs):
+        self.kwargs = kwargs
+        self.create_embedding_fn()
+
+    def create_embedding_fn(self):
+        embed_fns = []
+        d = self.kwargs["input_dims"]
+        out_dim = 0
+        if self.kwargs["include_input"]:
+            embed_fns.append(lambda x: x)
+            out_dim += d
+
+        max_freq = self.kwargs["max_freq_log2"]
+        N_freqs = self.kwargs["num_freqs"]
+
+        if self.kwargs["log_sampling"]:
+            freq_bands = 2.0 ** torch.linspace(0.0, max_freq, steps=N_freqs)
+        else:
+            freq_bands = torch.linspace(2.0**0.0, 2.0**max_freq, steps=N_freqs)
+
+        for freq in freq_bands:
+            for p_fn in self.kwargs["periodic_fns"]:
+                embed_fns.append(lambda x, p_fn=p_fn, freq=freq: p_fn(x * freq))
+                out_dim += d
+
+        self.embed_fns = embed_fns
+        self.out_dim = out_dim
+
+    def embed(self, inputs):
+        return torch.cat([fn(inputs) for fn in self.embed_fns], -1)
+
+
+def get_embedder(multires, i=0):
+    import torch.nn as nn
+
+    if i == -1:
+        return nn.Identity(), 3
+
+    embed_kwargs = {
+        "include_input": True,
+        "input_dims": 3,
+        "max_freq_log2": multires - 1,
+        "num_freqs": multires,
+        "log_sampling": True,
+        "periodic_fns": [torch.sin, torch.cos],
+    }
+
+    embedder_obj = Embedder(**embed_kwargs)
+    embed = lambda x, eo=embedder_obj: eo.embed(x)
+    return embed, embedder_obj.out_dim
+
+
+class APOPMeter:
+    def __init__(self) -> None:
+        self.tp = 0
+        self.fp = 0
+        self.tn = 0
+        self.fn = 0
+
+    def update(self, pred, gt):
+        """
+        Input:
+            pred, gt: Tensor()
+        """
+        assert pred.shape == gt.shape
+        self.tp += torch.logical_and(pred == 1, gt == 1).sum().item()
+        self.fp += torch.logical_and(pred == 1, gt == 0).sum().item()
+        self.tn += torch.logical_and(pred == 0, gt == 0).sum().item()
+        self.tn += torch.logical_and(pred == 1, gt == 0).sum().item()
+
+    def update_cm(self, tp, fp, tn, fn):
+        self.tp += tp
+        self.fp += fp
+        self.tn += tn
+        self.tn += fn
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+def get_raw_dict(args):
+    """
+    return the dicf contained in args.
+
+    e.g:
+        >>> with open(path, 'w') as f:
+                json.dump(get_raw_dict(args), f, indent=2)
+    """
+    if isinstance(args, argparse.Namespace):
+        return vars(args)
+    elif isinstance(args, dict):
+        return args
+    elif isinstance(args, SLConfig):
+        return args._cfg_dict
+    else:
+        raise NotImplementedError("Unknown type {}".format(type(args)))
+
+
+def stat_tensors(tensor):
+    assert tensor.dim() == 1
+    tensor_sm = tensor.softmax(0)
+    entropy = (tensor_sm * torch.log(tensor_sm + 1e-9)).sum()
+
+    return {
+        "max": tensor.max(),
+        "min": tensor.min(),
+        "mean": tensor.mean(),
+        "var": tensor.var(),
+        "std": tensor.var() ** 0.5,
+        "entropy": entropy,
+    }
+
+
+class NiceRepr:
+    """Inherit from this class and define ``__nice__`` to "nicely" print your
+    objects.
+
+    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
+    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
+    If the inheriting class has a ``__len__``, method then the default
+    ``__nice__`` method will return its length.
+
+    Example:
+        >>> class Foo(NiceRepr):
+        ...    def __nice__(self):
+        ...        return 'info'
+        >>> foo = Foo()
+        >>> assert str(foo) == '<Foo(info)>'
+        >>> assert repr(foo).startswith('<Foo(info) at ')
+
+    Example:
+        >>> class Bar(NiceRepr):
+        ...    pass
+        >>> bar = Bar()
+        >>> import pytest
+        >>> with pytest.warns(None) as record:
+        >>>     assert 'object at' in str(bar)
+        >>>     assert 'object at' in repr(bar)
+
+    Example:
+        >>> class Baz(NiceRepr):
+        ...    def __len__(self):
+        ...        return 5
+        >>> baz = Baz()
+        >>> assert str(baz) == '<Baz(5)>'
+    """
+
+    def __nice__(self):
+        """str: a "nice" summary string describing this module"""
+        if hasattr(self, "__len__"):
+            # It is a common pattern for objects to use __len__ in __nice__
+            # As a convenience we define a default __nice__ for these objects
+            return str(len(self))
+        else:
+            # In all other cases force the subclass to overload __nice__
+            raise NotImplementedError(f"Define the __nice__ method for {self.__class__!r}")
+
+    def __repr__(self):
+        """str: the string of the module"""
+        try:
+            nice = self.__nice__()
+            classname = self.__class__.__name__
+            return f"<{classname}({nice}) at {hex(id(self))}>"
+        except NotImplementedError as ex:
+            warnings.warn(str(ex), category=RuntimeWarning)
+            return object.__repr__(self)
+
+    def __str__(self):
+        """str: the string of the module"""
+        try:
+            classname = self.__class__.__name__
+            nice = self.__nice__()
+            return f"<{classname}({nice})>"
+        except NotImplementedError as ex:
+            warnings.warn(str(ex), category=RuntimeWarning)
+            return object.__repr__(self)
+
+
+def ensure_rng(rng=None):
+    """Coerces input into a random number generator.
+
+    If the input is None, then a global random state is returned.
+
+    If the input is a numeric value, then that is used as a seed to construct a
+    random state. Otherwise the input is returned as-is.
+
+    Adapted from [1]_.
+
+    Args:
+        rng (int | numpy.random.RandomState | None):
+            if None, then defaults to the global rng. Otherwise this can be an
+            integer or a RandomState class
+    Returns:
+        (numpy.random.RandomState) : rng -
+            a numpy random number generator
+
+    References:
+        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501
+    """
+
+    if rng is None:
+        rng = np.random.mtrand._rand
+    elif isinstance(rng, int):
+        rng = np.random.RandomState(rng)
+    else:
+        rng = rng
+    return rng
+
+
+def random_boxes(num=1, scale=1, rng=None):
+    """Simple version of ``kwimage.Boxes.random``
+
+    Returns:
+        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
+
+    References:
+        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
+
+    Example:
+        >>> num = 3
+        >>> scale = 512
+        >>> rng = 0
+        >>> boxes = random_boxes(num, scale, rng)
+        >>> print(boxes)
+        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
+                [216.9113, 330.6978, 224.0446, 456.5878],
+                [405.3632, 196.3221, 493.3953, 270.7942]])
+    """
+    rng = ensure_rng(rng)
+
+    tlbr = rng.rand(num, 4).astype(np.float32)
+
+    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
+    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
+    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
+    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
+
+    tlbr[:, 0] = tl_x * scale
+    tlbr[:, 1] = tl_y * scale
+    tlbr[:, 2] = br_x * scale
+    tlbr[:, 3] = br_y * scale
+
+    boxes = torch.from_numpy(tlbr)
+    return boxes
+
+
+class ModelEma(torch.nn.Module):
+    def __init__(self, model, decay=0.9997, device=None):
+        super(ModelEma, self).__init__()
+        # make a copy of the model for accumulating moving average of weights
+        self.module = deepcopy(model)
+        self.module.eval()
+
+        # import ipdb; ipdb.set_trace()
+
+        self.decay = decay
+        self.device = device  # perform ema on different device from model if set
+        if self.device is not None:
+            self.module.to(device=device)
+
+    def _update(self, model, update_fn):
+        with torch.no_grad():
+            for ema_v, model_v in zip(
+                self.module.state_dict().values(), model.state_dict().values()
+            ):
+                if self.device is not None:
+                    model_v = model_v.to(device=self.device)
+                ema_v.copy_(update_fn(ema_v, model_v))
+
+    def update(self, model):
+        self._update(model, update_fn=lambda e, m: self.decay * e + (1.0 - self.decay) * m)
+
+    def set(self, model):
+        self._update(model, update_fn=lambda e, m: m)
+
+
+class BestMetricSingle:
+    def __init__(self, init_res=0.0, better="large") -> None:
+        self.init_res = init_res
+        self.best_res = init_res
+        self.best_ep = -1
+
+        self.better = better
+        assert better in ["large", "small"]
+
+    def isbetter(self, new_res, old_res):
+        if self.better == "large":
+            return new_res > old_res
+        if self.better == "small":
+            return new_res < old_res
+
+    def update(self, new_res, ep):
+        if self.isbetter(new_res, self.best_res):
+            self.best_res = new_res
+            self.best_ep = ep
+            return True
+        return False
+
+    def __str__(self) -> str:
+        return "best_res: {}\t best_ep: {}".format(self.best_res, self.best_ep)
+
+    def __repr__(self) -> str:
+        return self.__str__()
+
+    def summary(self) -> dict:
+        return {
+            "best_res": self.best_res,
+            "best_ep": self.best_ep,
+        }
+
+
+class BestMetricHolder:
+    def __init__(self, init_res=0.0, better="large", use_ema=False) -> None:
+        self.best_all = BestMetricSingle(init_res, better)
+        self.use_ema = use_ema
+        if use_ema:
+            self.best_ema = BestMetricSingle(init_res, better)
+            self.best_regular = BestMetricSingle(init_res, better)
+
+    def update(self, new_res, epoch, is_ema=False):
+        """
+        return if the results is the best.
+        """
+        if not self.use_ema:
+            return self.best_all.update(new_res, epoch)
+        else:
+            if is_ema:
+                self.best_ema.update(new_res, epoch)
+                return self.best_all.update(new_res, epoch)
+            else:
+                self.best_regular.update(new_res, epoch)
+                return self.best_all.update(new_res, epoch)
+
+    def summary(self):
+        if not self.use_ema:
+            return self.best_all.summary()
+
+        res = {}
+        res.update({f"all_{k}": v for k, v in self.best_all.summary().items()})
+        res.update({f"regular_{k}": v for k, v in self.best_regular.summary().items()})
+        res.update({f"ema_{k}": v for k, v in self.best_ema.summary().items()})
+        return res
+
+    def __repr__(self) -> str:
+        return json.dumps(self.summary(), indent=2)
+
+    def __str__(self) -> str:
+        return self.__repr__()
+
+
+def targets_to(targets: List[Dict[str, Any]], device):
+    """Moves the target dicts to the given device."""
+    excluded_keys = [
+        "questionId",
+        "tokens_positive",
+        "strings_positive",
+        "tokens",
+        "dataset_name",
+        "sentence_id",
+        "original_img_id",
+        "nb_eval",
+        "task_id",
+        "original_id",
+        "token_span",
+        "caption",
+        "dataset_type",
+    ]
+    return [
+        {k: v.to(device) if k not in excluded_keys else v for k, v in t.items()} for t in targets
+    ]
+
+
+def get_phrases_from_posmap(
+    posmap: torch.BoolTensor, tokenized: Dict, tokenizer: AutoTokenizer
+):
+    assert isinstance(posmap, torch.Tensor), "posmap must be torch.Tensor"
+    if posmap.dim() == 1:
+        non_zero_idx = posmap.nonzero(as_tuple=True)[0].tolist()
+        token_ids = [tokenized["input_ids"][i] for i in non_zero_idx]
+        return tokenizer.decode(token_ids)
+    else:
+        raise NotImplementedError("posmap must be 1-dim")

metagpt/environment/android/groundingdino/util/visualizer.py

@@ -0,0 +1,318 @@
+# -*- coding: utf-8 -*-
+"""
+@File    :   visualizer.py
+@Time    :   2022/04/05 11:39:33
+@Author  :   Shilong Liu 
+@Contact :   slongliu86@gmail.com
+"""
+
+import datetime
+import os
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from matplotlib import transforms
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+from pycocotools import mask as maskUtils
+
+
+def renorm(
+    img: torch.FloatTensor, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]
+) -> torch.FloatTensor:
+    # img: tensor(3,H,W) or tensor(B,3,H,W)
+    # return: same as img
+    assert img.dim() == 3 or img.dim() == 4, "img.dim() should be 3 or 4 but %d" % img.dim()
+    if img.dim() == 3:
+        assert img.size(0) == 3, 'img.size(0) shoule be 3 but "%d". (%s)' % (
+            img.size(0),
+            str(img.size()),
+        )
+        img_perm = img.permute(1, 2, 0)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(2, 0, 1)
+    else:  # img.dim() == 4
+        assert img.size(1) == 3, 'img.size(1) shoule be 3 but "%d". (%s)' % (
+            img.size(1),
+            str(img.size()),
+        )
+        img_perm = img.permute(0, 2, 3, 1)
+        mean = torch.Tensor(mean)
+        std = torch.Tensor(std)
+        img_res = img_perm * std + mean
+        return img_res.permute(0, 3, 1, 2)
+
+
+class ColorMap:
+    def __init__(self, basergb=[255, 255, 0]):
+        self.basergb = np.array(basergb)
+
+    def __call__(self, attnmap):
+        # attnmap: h, w. np.uint8.
+        # return: h, w, 4. np.uint8.
+        assert attnmap.dtype == np.uint8
+        h, w = attnmap.shape
+        res = self.basergb.copy()
+        res = res[None][None].repeat(h, 0).repeat(w, 1)  # h, w, 3
+        attn1 = attnmap.copy()[..., None]  # h, w, 1
+        res = np.concatenate((res, attn1), axis=-1).astype(np.uint8)
+        return res
+
+
+def rainbow_text(x, y, ls, lc, **kw):
+    """
+    Take a list of strings ``ls`` and colors ``lc`` and place them next to each
+    other, with text ls[i] being shown in color lc[i].
+
+    This example shows how to do both vertical and horizontal text, and will
+    pass all keyword arguments to plt.text, so you can set the font size,
+    family, etc.
+    """
+    t = plt.gca().transData
+    fig = plt.gcf()
+    plt.show()
+
+    # horizontal version
+    for s, c in zip(ls, lc):
+        text = plt.text(x, y, " " + s + " ", color=c, transform=t, **kw)
+        text.draw(fig.canvas.get_renderer())
+        ex = text.get_window_extent()
+        t = transforms.offset_copy(text._transform, x=ex.width, units="dots")
+
+    # #vertical version
+    # for s,c in zip(ls,lc):
+    #     text = plt.text(x,y," "+s+" ",color=c, transform=t,
+    #             rotation=90,va='bottom',ha='center',**kw)
+    #     text.draw(fig.canvas.get_renderer())
+    #     ex = text.get_window_extent()
+    #     t = transforms.offset_copy(text._transform, y=ex.height, units='dots')
+
+
+class COCOVisualizer:
+    def __init__(self, coco=None, tokenlizer=None) -> None:
+        self.coco = coco
+
+    def visualize(self, img, tgt, caption=None, dpi=180, savedir="vis"):
+        """
+        img: tensor(3, H, W)
+        tgt: make sure they are all on cpu.
+            must have items: 'image_id', 'boxes', 'size'
+        """
+        plt.figure(dpi=dpi)
+        plt.rcParams["font.size"] = "5"
+        ax = plt.gca()
+        img = renorm(img).permute(1, 2, 0)
+        # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+        #     import ipdb; ipdb.set_trace()
+        ax.imshow(img)
+
+        self.addtgt(tgt)
+
+        if tgt is None:
+            image_id = 0
+        elif "image_id" not in tgt:
+            image_id = 0
+        else:
+            image_id = tgt["image_id"]
+
+        if caption is None:
+            savename = "{}/{}-{}.png".format(
+                savedir, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+            )
+        else:
+            savename = "{}/{}-{}-{}.png".format(
+                savedir, caption, int(image_id), str(datetime.datetime.now()).replace(" ", "-")
+            )
+        print("savename: {}".format(savename))
+        os.makedirs(os.path.dirname(savename), exist_ok=True)
+        plt.savefig(savename)
+        plt.close()
+
+    def addtgt(self, tgt):
+        """ """
+        if tgt is None or not "boxes" in tgt:
+            ax = plt.gca()
+
+            if "caption" in tgt:
+                ax.set_title(tgt["caption"], wrap=True)
+
+            ax.set_axis_off()
+            return
+
+        ax = plt.gca()
+        H, W = tgt["size"]
+        numbox = tgt["boxes"].shape[0]
+
+        color = []
+        polygons = []
+        boxes = []
+        for box in tgt["boxes"].cpu():
+            unnormbbox = box * torch.Tensor([W, H, W, H])
+            unnormbbox[:2] -= unnormbbox[2:] / 2
+            [bbox_x, bbox_y, bbox_w, bbox_h] = unnormbbox.tolist()
+            boxes.append([bbox_x, bbox_y, bbox_w, bbox_h])
+            poly = [
+                [bbox_x, bbox_y],
+                [bbox_x, bbox_y + bbox_h],
+                [bbox_x + bbox_w, bbox_y + bbox_h],
+                [bbox_x + bbox_w, bbox_y],
+            ]
+            np_poly = np.array(poly).reshape((4, 2))
+            polygons.append(Polygon(np_poly))
+            c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+            color.append(c)
+
+        p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.1)
+        ax.add_collection(p)
+        p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+        ax.add_collection(p)
+
+        if "strings_positive" in tgt and len(tgt["strings_positive"]) > 0:
+            assert (
+                len(tgt["strings_positive"]) == numbox
+            ), f"{len(tgt['strings_positive'])} = {numbox}, "
+            for idx, strlist in enumerate(tgt["strings_positive"]):
+                cate_id = int(tgt["labels"][idx])
+                _string = str(cate_id) + ":" + " ".join(strlist)
+                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
+                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
+                ax.text(
+                    bbox_x,
+                    bbox_y,
+                    _string,
+                    color="black",
+                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
+                )
+
+        if "box_label" in tgt:
+            assert len(tgt["box_label"]) == numbox, f"{len(tgt['box_label'])} = {numbox}, "
+            for idx, bl in enumerate(tgt["box_label"]):
+                _string = str(bl)
+                bbox_x, bbox_y, bbox_w, bbox_h = boxes[idx]
+                # ax.text(bbox_x, bbox_y, _string, color='black', bbox={'facecolor': 'yellow', 'alpha': 1.0, 'pad': 1})
+                ax.text(
+                    bbox_x,
+                    bbox_y,
+                    _string,
+                    color="black",
+                    bbox={"facecolor": color[idx], "alpha": 0.6, "pad": 1},
+                )
+
+        if "caption" in tgt:
+            ax.set_title(tgt["caption"], wrap=True)
+            # plt.figure()
+            # rainbow_text(0.0,0.0,"all unicorns poop rainbows ! ! !".split(),
+            #         ['red', 'orange', 'brown', 'green', 'blue', 'purple', 'black'])
+
+        if "attn" in tgt:
+            # if os.environ.get('IPDB_SHILONG_DEBUG', None) == 'INFO':
+            #     import ipdb; ipdb.set_trace()
+            if isinstance(tgt["attn"], tuple):
+                tgt["attn"] = [tgt["attn"]]
+            for item in tgt["attn"]:
+                attn_map, basergb = item
+                attn_map = (attn_map - attn_map.min()) / (attn_map.max() - attn_map.min() + 1e-3)
+                attn_map = (attn_map * 255).astype(np.uint8)
+                cm = ColorMap(basergb)
+                heatmap = cm(attn_map)
+                ax.imshow(heatmap)
+        ax.set_axis_off()
+
+    def showAnns(self, anns, draw_bbox=False):
+        """
+        Display the specified annotations.
+        :param anns (array of object): annotations to display
+        :return: None
+        """
+        if len(anns) == 0:
+            return 0
+        if "segmentation" in anns[0] or "keypoints" in anns[0]:
+            datasetType = "instances"
+        elif "caption" in anns[0]:
+            datasetType = "captions"
+        else:
+            raise Exception("datasetType not supported")
+        if datasetType == "instances":
+            ax = plt.gca()
+            ax.set_autoscale_on(False)
+            polygons = []
+            color = []
+            for ann in anns:
+                c = (np.random.random((1, 3)) * 0.6 + 0.4).tolist()[0]
+                if "segmentation" in ann:
+                    if type(ann["segmentation"]) == list:
+                        # polygon
+                        for seg in ann["segmentation"]:
+                            poly = np.array(seg).reshape((int(len(seg) / 2), 2))
+                            polygons.append(Polygon(poly))
+                            color.append(c)
+                    else:
+                        # mask
+                        t = self.imgs[ann["image_id"]]
+                        if type(ann["segmentation"]["counts"]) == list:
+                            rle = maskUtils.frPyObjects(
+                                [ann["segmentation"]], t["height"], t["width"]
+                            )
+                        else:
+                            rle = [ann["segmentation"]]
+                        m = maskUtils.decode(rle)
+                        img = np.ones((m.shape[0], m.shape[1], 3))
+                        if ann["iscrowd"] == 1:
+                            color_mask = np.array([2.0, 166.0, 101.0]) / 255
+                        if ann["iscrowd"] == 0:
+                            color_mask = np.random.random((1, 3)).tolist()[0]
+                        for i in range(3):
+                            img[:, :, i] = color_mask[i]
+                        ax.imshow(np.dstack((img, m * 0.5)))
+                if "keypoints" in ann and type(ann["keypoints"]) == list:
+                    # turn skeleton into zero-based index
+                    sks = np.array(self.loadCats(ann["category_id"])[0]["skeleton"]) - 1
+                    kp = np.array(ann["keypoints"])
+                    x = kp[0::3]
+                    y = kp[1::3]
+                    v = kp[2::3]
+                    for sk in sks:
+                        if np.all(v[sk] > 0):
+                            plt.plot(x[sk], y[sk], linewidth=3, color=c)
+                    plt.plot(
+                        x[v > 0],
+                        y[v > 0],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor="k",
+                        markeredgewidth=2,
+                    )
+                    plt.plot(
+                        x[v > 1],
+                        y[v > 1],
+                        "o",
+                        markersize=8,
+                        markerfacecolor=c,
+                        markeredgecolor=c,
+                        markeredgewidth=2,
+                    )
+
+                if draw_bbox:
+                    [bbox_x, bbox_y, bbox_w, bbox_h] = ann["bbox"]
+                    poly = [
+                        [bbox_x, bbox_y],
+                        [bbox_x, bbox_y + bbox_h],
+                        [bbox_x + bbox_w, bbox_y + bbox_h],
+                        [bbox_x + bbox_w, bbox_y],
+                    ]
+                    np_poly = np.array(poly).reshape((4, 2))
+                    polygons.append(Polygon(np_poly))
+                    color.append(c)
+
+            # p = PatchCollection(polygons, facecolor=color, linewidths=0, alpha=0.4)
+            # ax.add_collection(p)
+            p = PatchCollection(polygons, facecolor="none", edgecolors=color, linewidths=2)
+            ax.add_collection(p)
+        elif datasetType == "captions":
+            for ann in anns:
+                print(ann["caption"])

metagpt/environment/android/groundingdino/util/vl_utils.py

@@ -0,0 +1,100 @@
+import os
+import random
+from typing import List
+
+import torch
+
+
+def create_positive_map_from_span(tokenized, token_span, max_text_len=256):
+    """construct a map such that positive_map[i,j] = True iff box i is associated to token j
+    Input:
+        - tokenized:
+            - input_ids: Tensor[1, ntokens]
+            - attention_mask: Tensor[1, ntokens]
+        - token_span: list with length num_boxes.
+            - each item: [start_idx, end_idx]
+    """
+    positive_map = torch.zeros((len(token_span), max_text_len), dtype=torch.float)
+    for j, tok_list in enumerate(token_span):
+        for (beg, end) in tok_list:
+            beg_pos = tokenized.char_to_token(beg)
+            end_pos = tokenized.char_to_token(end - 1)
+            if beg_pos is None:
+                try:
+                    beg_pos = tokenized.char_to_token(beg + 1)
+                    if beg_pos is None:
+                        beg_pos = tokenized.char_to_token(beg + 2)
+                except:
+                    beg_pos = None
+            if end_pos is None:
+                try:
+                    end_pos = tokenized.char_to_token(end - 2)
+                    if end_pos is None:
+                        end_pos = tokenized.char_to_token(end - 3)
+                except:
+                    end_pos = None
+            if beg_pos is None or end_pos is None:
+                continue
+
+            assert beg_pos is not None and end_pos is not None
+            if os.environ.get("SHILONG_DEBUG_ONLY_ONE_POS", None) == "TRUE":
+                positive_map[j, beg_pos] = 1
+                break
+            else:
+                positive_map[j, beg_pos : end_pos + 1].fill_(1)
+
+    return positive_map / (positive_map.sum(-1)[:, None] + 1e-6)
+
+
+def build_captions_and_token_span(cat_list, force_lowercase):
+    """
+    Return:
+        captions: str
+        cat2tokenspan: dict
+            {
+                'dog': [[0, 2]],
+                ...
+            }
+    """
+
+    cat2tokenspan = {}
+    captions = ""
+    for catname in cat_list:
+        class_name = catname
+        if force_lowercase:
+            class_name = class_name.lower()
+        if "/" in class_name:
+            class_name_list: List = class_name.strip().split("/")
+            class_name_list.append(class_name)
+            class_name: str = random.choice(class_name_list)
+
+        tokens_positive_i = []
+        subnamelist = [i.strip() for i in class_name.strip().split(" ")]
+        for subname in subnamelist:
+            if len(subname) == 0:
+                continue
+            if len(captions) > 0:
+                captions = captions + " "
+            strat_idx = len(captions)
+            end_idx = strat_idx + len(subname)
+            tokens_positive_i.append([strat_idx, end_idx])
+            captions = captions + subname
+
+        if len(tokens_positive_i) > 0:
+            captions = captions + " ."
+            cat2tokenspan[class_name] = tokens_positive_i
+
+    return captions, cat2tokenspan
+
+
+def build_id2posspan_and_caption(category_dict: dict):
+    """Build id2pos_span and caption from category_dict
+
+    Args:
+        category_dict (dict): category_dict
+    """
+    cat_list = [item["name"].lower() for item in category_dict]
+    id2catname = {item["id"]: item["name"].lower() for item in category_dict}
+    caption, cat2posspan = build_captions_and_token_span(cat_list, force_lowercase=True)
+    id2posspan = {catid: cat2posspan[catname] for catid, catname in id2catname.items()}
+    return id2posspan, caption

metagpt/environment/android/groundingdino/version.py

@@ -0,0 +1 @@
+__version__ = '0.1.0'

metagpt/environment/android/text_icon_localization.py

@@ -0,0 +1,401 @@
+import math
+import clip
+import cv2
+import groundingdino.datasets.transforms as T
+import numpy as np
+import torch
+from groundingdino.models import build_model
+from groundingdino.util.slconfig import SLConfig
+from groundingdino.util.utils import clean_state_dict, get_phrases_from_posmap
+from PIL import Image, ImageDraw
+
+
+################################## text_localization using ocr #######################
+def crop_image(img, position):
+    def distance(x1, y1, x2, y2):
+        return math.sqrt(pow(x1 - x2, 2) + pow(y1 - y2, 2))
+
+    position = position.tolist()
+    for i in range(4):
+        for j in range(i + 1, 4):
+            if position[i][0] > position[j][0]:
+                tmp = position[j]
+                position[j] = position[i]
+                position[i] = tmp
+    if position[0][1] > position[1][1]:
+        tmp = position[0]
+        position[0] = position[1]
+        position[1] = tmp
+
+    if position[2][1] > position[3][1]:
+        tmp = position[2]
+        position[2] = position[3]
+        position[3] = tmp
+
+    x1, y1 = position[0][0], position[0][1]
+    x2, y2 = position[2][0], position[2][1]
+    x3, y3 = position[3][0], position[3][1]
+    x4, y4 = position[1][0], position[1][1]
+
+    corners = np.zeros((4, 2), np.float32)
+    corners[0] = [x1, y1]
+    corners[1] = [x2, y2]
+    corners[2] = [x4, y4]
+    corners[3] = [x3, y3]
+
+    img_width = distance((x1 + x4) / 2, (y1 + y4) / 2, (x2 + x3) / 2, (y2 + y3) / 2)
+    img_height = distance((x1 + x2) / 2, (y1 + y2) / 2, (x4 + x3) / 2, (y4 + y3) / 2)
+
+    corners_trans = np.zeros((4, 2), np.float32)
+    corners_trans[0] = [0, 0]
+    corners_trans[1] = [img_width - 1, 0]
+    corners_trans[2] = [0, img_height - 1]
+    corners_trans[3] = [img_width - 1, img_height - 1]
+
+    transform = cv2.getPerspectiveTransform(corners, corners_trans)
+    dst = cv2.warpPerspective(img, transform, (int(img_width), int(img_height)))
+    return dst
+
+
+def calculate_size(box):
+    return (box[2] - box[0]) * (box[3] - box[1])
+
+
+def order_point(coor):
+    arr = np.array(coor).reshape([4, 2])
+    sum_ = np.sum(arr, 0)
+    centroid = sum_ / arr.shape[0]
+    theta = np.arctan2(arr[:, 1] - centroid[1], arr[:, 0] - centroid[0])
+    sort_points = arr[np.argsort(theta)]
+    sort_points = sort_points.reshape([4, -1])
+    if sort_points[0][0] > centroid[0]:
+        sort_points = np.concatenate([sort_points[3:], sort_points[:3]])
+    sort_points = sort_points.reshape([4, 2]).astype("float32")
+    return sort_points
+
+
+def longest_common_substring_length(str1, str2):
+    m = len(str1)
+    n = len(str2)
+    dp = [[0] * (n + 1) for _ in range(m + 1)]
+
+    for i in range(1, m + 1):
+        for j in range(1, n + 1):
+            if str1[i - 1] == str2[j - 1]:
+                dp[i][j] = dp[i - 1][j - 1] + 1
+            else:
+                dp[i][j] = max(dp[i - 1][j], dp[i][j - 1])
+
+    return dp[m][n]
+
+
+def ocr(image_path, prompt, ocr_detection, ocr_recognition, x, y):
+    text_data = []
+    coordinate = []
+    image = Image.open(image_path)
+    iw, ih = image.size
+
+    image_full = cv2.imread(image_path)
+    det_result = ocr_detection(image_full)
+    det_result = det_result["polygons"]
+    for i in range(det_result.shape[0]):
+        pts = order_point(det_result[i])
+        image_crop = crop_image(image_full, pts)
+        result = ocr_recognition(image_crop)["text"][0]
+
+        if result == prompt:
+            box = [int(e) for e in list(pts.reshape(-1))]
+            box = [box[0], box[1], box[4], box[5]]
+
+            if calculate_size(box) > 0.05 * iw * ih:
+                continue
+
+            text_data.append(
+                [
+                    int(max(0, box[0] - 10) * x / iw),
+                    int(max(0, box[1] - 10) * y / ih),
+                    int(min(box[2] + 10, iw) * x / iw),
+                    int(min(box[3] + 10, ih) * y / ih),
+                ]
+            )
+            coordinate.append(
+                [
+                    int(max(0, box[0] - 300) * x / iw),
+                    int(max(0, box[1] - 400) * y / ih),
+                    int(min(box[2] + 300, iw) * x / iw),
+                    int(min(box[3] + 400, ih) * y / ih),
+                ]
+            )
+
+    max_length = 0
+    if len(text_data) == 0:
+        for i in range(det_result.shape[0]):
+            pts = order_point(det_result[i])
+            image_crop = crop_image(image_full, pts)
+            result = ocr_recognition(image_crop)["text"][0]
+
+            if len(result) < 0.3 * len(prompt):
+                continue
+
+            if result in prompt:
+                now_length = len(result)
+            else:
+                now_length = longest_common_substring_length(result, prompt)
+
+            if now_length > max_length:
+                max_length = now_length
+                box = [int(e) for e in list(pts.reshape(-1))]
+                box = [box[0], box[1], box[4], box[5]]
+
+                text_data = [
+                    [
+                        int(max(0, box[0] - 10) * x / iw),
+                        int(max(0, box[1] - 10) * y / ih),
+                        int(min(box[2] + 10, iw) * x / iw),
+                        int(min(box[3] + 10, ih) * y / ih),
+                    ]
+                ]
+                coordinate = [
+                    [
+                        int(max(0, box[0] - 300) * x / iw),
+                        int(max(0, box[1] - 400) * y / ih),
+                        int(min(box[2] + 300, iw) * x / iw),
+                        int(min(box[3] + 400, ih) * y / ih),
+                    ]
+                ]
+
+        if len(prompt) <= 10:
+            if max_length >= 0.8 * len(prompt):
+                return text_data, coordinate
+            else:
+                return [], []
+        elif (len(prompt) > 10) and (len(prompt) <= 20):
+            if max_length >= 0.5 * len(prompt):
+                return text_data, coordinate
+            else:
+                return [], []
+        else:
+            if max_length >= 0.4 * len(prompt):
+                return text_data, coordinate
+            else:
+                return [], []
+
+    else:
+        return text_data, coordinate
+
+
+################################## icon_localization using clip #######################
+
+
+def calculate_iou(box1, box2):
+    xA = max(box1[0], box2[0])
+    yA = max(box1[1], box2[1])
+    xB = min(box1[2], box2[2])
+    yB = min(box1[3], box2[3])
+
+    interArea = max(0, xB - xA) * max(0, yB - yA)
+    box1Area = (box1[2] - box1[0]) * (box1[3] - box1[1])
+    box2Area = (box2[2] - box2[0]) * (box2[3] - box2[1])
+    unionArea = box1Area + box2Area - interArea
+    iou = interArea / unionArea
+
+    return iou
+
+
+def crop(image, box, i, text_data=None):
+    image = Image.open(image)
+
+    if text_data:
+        draw = ImageDraw.Draw(image)
+        draw.rectangle(((text_data[0], text_data[1]), (text_data[2], text_data[3])), outline="red", width=5)
+        # font_size = int((text_data[3] - text_data[1])*0.75)
+        # font = ImageFont.truetype("arial.ttf", font_size)
+        # draw.text((text_data[0]+5, text_data[1]+5), str(i), font=font, fill="red")
+
+    cropped_image = image.crop(box)
+    cropped_image.save(f"./temp/{i}.jpg")
+
+
+def in_box(box, target):
+    if (box[0] > target[0]) and (box[1] > target[1]) and (box[2] < target[2]) and (box[3] < target[3]):
+        return True
+    else:
+        return False
+
+
+def crop_for_clip(image, box, i, position):
+    image = Image.open(image)
+    w, h = image.size
+    if position == "left":
+        bound = [0, 0, w / 2, h]
+    elif position == "right":
+        bound = [w / 2, 0, w, h]
+    elif position == "top":
+        bound = [0, 0, w, h / 2]
+    elif position == "bottom":
+        bound = [0, h / 2, w, h]
+    elif position == "top left":
+        bound = [0, 0, w / 2, h / 2]
+    elif position == "top right":
+        bound = [w / 2, 0, w, h / 2]
+    elif position == "bottom left":
+        bound = [0, h / 2, w / 2, h]
+    elif position == "bottom right":
+        bound = [w / 2, h / 2, w, h]
+    else:
+        bound = [0, 0, w, h]
+
+    if in_box(box, bound):
+        cropped_image = image.crop(box)
+        cropped_image.save(f"./temp/{i}.jpg")
+        return True
+    else:
+        return False
+
+
+def clip_for_icon(clip_model, clip_preprocess, images, prompt):
+    image_features = []
+    for image_file in images:
+        image = clip_preprocess(Image.open(image_file)).unsqueeze(0).to(next(clip_model.parameters()).device)
+        image_feature = clip_model.encode_image(image)
+        image_features.append(image_feature)
+    image_features = torch.cat(image_features)
+
+    text = clip.tokenize([prompt]).to(next(clip_model.parameters()).device)
+    text_features = clip_model.encode_text(text)
+
+    image_features /= image_features.norm(dim=-1, keepdim=True)
+    text_features /= text_features.norm(dim=-1, keepdim=True)
+    similarity = (100.0 * image_features @ text_features.T).softmax(dim=0).squeeze(0)
+    _, max_pos = torch.max(similarity, dim=0)
+    pos = max_pos.item()
+
+    return pos
+
+
+def transform_image(image_pil):
+    transform = T.Compose(
+        [
+            T.RandomResize([800], max_size=1333),
+            T.ToTensor(),
+            T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
+        ]
+    )
+    image, _ = transform(image_pil, None)  # 3, h, w
+    return image
+
+
+def load_model(model_config_path, model_checkpoint_path, device):
+    args = SLConfig.fromfile(model_config_path)
+    args.device = device
+    model = build_model(args)
+    checkpoint = torch.load(model_checkpoint_path, map_location="cpu")
+    load_res = model.load_state_dict(clean_state_dict(checkpoint["model"]), strict=False)
+    print(load_res)
+    _ = model.eval()
+    return model
+
+
+def get_grounding_output(model, image, caption, box_threshold, text_threshold, with_logits=True):
+    caption = caption.lower()
+    caption = caption.strip()
+    if not caption.endswith("."):
+        caption = caption + "."
+
+    with torch.no_grad():
+        outputs = model(image[None], captions=[caption])
+    logits = outputs["pred_logits"].cpu().sigmoid()[0]  # (nq, 256)
+    boxes = outputs["pred_boxes"].cpu()[0]  # (nq, 4)
+    logits.shape[0]
+
+    logits_filt = logits.clone()
+    boxes_filt = boxes.clone()
+    filt_mask = logits_filt.max(dim=1)[0] > box_threshold
+    logits_filt = logits_filt[filt_mask]  # num_filt, 256
+    boxes_filt = boxes_filt[filt_mask]  # num_filt, 4
+    logits_filt.shape[0]
+
+    tokenlizer = model.tokenizer
+    tokenized = tokenlizer(caption)
+
+    pred_phrases = []
+    scores = []
+    for logit, box in zip(logits_filt, boxes_filt):
+        pred_phrase = get_phrases_from_posmap(logit > text_threshold, tokenized, tokenlizer)
+        if with_logits:
+            pred_phrases.append(pred_phrase + f"({str(logit.max().item())[:4]})")
+        else:
+            pred_phrases.append(pred_phrase)
+        scores.append(logit.max().item())
+
+    return boxes_filt, torch.Tensor(scores), pred_phrases
+
+
+def remove_boxes(boxes_filt, size, iou_threshold=0.5):
+    boxes_to_remove = set()
+
+    for i in range(len(boxes_filt)):
+        if calculate_size(boxes_filt[i]) > 0.05 * size[0] * size[1]:
+            boxes_to_remove.add(i)
+        for j in range(len(boxes_filt)):
+            if calculate_size(boxes_filt[j]) > 0.05 * size[0] * size[1]:
+                boxes_to_remove.add(j)
+            if i == j:
+                continue
+            if i in boxes_to_remove or j in boxes_to_remove:
+                continue
+            iou = calculate_iou(boxes_filt[i], boxes_filt[j])
+            if iou >= iou_threshold:
+                boxes_to_remove.add(j)
+
+    boxes_filt = [box for idx, box in enumerate(boxes_filt) if idx not in boxes_to_remove]
+
+    return boxes_filt
+
+
+def det(input_image, text_prompt, groundingdino_model, box_threshold=0.05, text_threshold=0.5):
+    image = Image.open(input_image)
+    size = image.size
+
+    image_pil = image.convert("RGB")
+    image = np.array(image_pil)
+
+    transformed_image = transform_image(image_pil)
+    boxes_filt, scores, pred_phrases = get_grounding_output(
+        groundingdino_model, transformed_image, text_prompt, box_threshold, text_threshold
+    )
+
+    H, W = size[1], size[0]
+    for i in range(boxes_filt.size(0)):
+        boxes_filt[i] = boxes_filt[i] * torch.Tensor([W, H, W, H])
+        boxes_filt[i][:2] -= boxes_filt[i][2:] / 2
+        boxes_filt[i][2:] += boxes_filt[i][:2]
+
+    boxes_filt = boxes_filt.cpu().int().tolist()
+    filtered_boxes = remove_boxes(boxes_filt, size)  # [:9]
+    coordinate = []
+    image_data = []
+    for box in filtered_boxes:
+        image_data.append(
+            [max(0, box[0] - 10), max(0, box[1] - 10), min(box[2] + 10, size[0]), min(box[3] + 10, size[1])]
+        )
+        coordinate.append(
+            [max(0, box[0] - 25), max(0, box[1] - 25), min(box[2] + 25, size[0]), min(box[3] + 25, size[1])]
+        )
+
+    return image_data, coordinate
+
+
+if __name__ == "__main__":
+    from modelscope.pipelines import pipeline
+    from modelscope.utils.constant import Tasks
+
+    image_ori = "./screenshot/screenshot.png"
+    image = "./screenshot/screenshot.png"
+    parameter = "抖音"
+    ocr_detection = pipeline(Tasks.ocr_detection, model="damo/cv_resnet18_ocr-detection-line-level_damo")
+    ocr_recognition = pipeline(Tasks.ocr_recognition, model="damo/cv_convnextTiny_ocr-recognition-document_damo")
+    iw, ih = Image.open(image).size
+    if iw > ih:
+        iw, ih = ih, iw
+    in_coordinate, out_coordinate = ocr(image_ori, parameter, ocr_detection, ocr_recognition, iw, ih)

metagpt/environment/minecraft/minecraft_env.py

@@ -19,7 +19,7 @@ from metagpt.logs import logger
 from metagpt.utils.common import load_mc_skills_code, read_json_file, write_json_file
 
 
-class MinecraftEnv(Environment, MinecraftExtEnv):
+class MinecraftEnv(MinecraftExtEnv, Environment):
     """MinecraftEnv, including shared memory of cache and information between roles"""
 
     model_config = ConfigDict(arbitrary_types_allowed=True)

metagpt/environment/werewolf/const.py

@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :
+
+from enum import Enum
+
+from metagpt.const import MESSAGE_ROUTE_TO_ALL
+
+
+class RoleType(Enum):
+    VILLAGER = "Villager"
+    WEREWOLF = "Werewolf"
+    GUARD = "Guard"
+    SEER = "Seer"
+    WITCH = "Witch"
+    MODERATOR = "Moderator"
+
+
+class RoleState(Enum):
+    ALIVE = "alive"  # the role is alive
+    DEAD = "dead"  # killed or poisoned
+    KILLED = "killed"  # killed by werewolf or voting
+    POISONED = "poisoned"  # killed by poison
+    SAVED = "saved"  # saved by antidote
+    PROTECTED = "projected"  # projected by guard
+
+
+class RoleActionRes(Enum):
+    SAVE = "save"
+    PASS = "pass"  # ignore current action output
+
+
+empty_set = set()
+
+# the ordered rules by the moderator to announce to everyone each step
+STEP_INSTRUCTIONS = {
+    0: {
+        "content": "It’s dark, everyone close your eyes. I will talk with you/your team secretly at night.",
+        "send_to": {RoleType.MODERATOR.value},  # for moderator to continue speaking
+        "restricted_to": empty_set,
+    },
+    1: {
+        "content": "Guard, please open your eyes!",
+        "send_to": {RoleType.MODERATOR.value},  # for moderator to continue speaking
+        "restricted_to": empty_set,
+    },
+    2: {
+        "content": """Guard, now tell me who you protect tonight?
+You only choose one from the following living options please: {living_players}.
+Or you can pass. For example: Protect ...""",
+        "send_to": {RoleType.GUARD.value},
+        "restricted_to": {RoleType.MODERATOR.value, RoleType.GUARD.value},
+    },
+    3: {"content": "Guard, close your eyes", "send_to": {RoleType.MODERATOR.value}, "restricted_to": empty_set},
+    4: {
+        "content": "Werewolves, please open your eyes!",
+        "send_to": {RoleType.MODERATOR.value},
+        "restricted_to": empty_set,
+    },
+    5: {
+        "content": """Werewolves, I secretly tell you that {werewolf_players} are
+all of the {werewolf_num} werewolves! Keep in mind you are teammates. The rest players are not werewolves.
+choose one from the following living options please:
+{living_players}. For example: Kill ...""",
+        "send_to": {RoleType.WEREWOLF.value},
+        "restricted_to": {RoleType.MODERATOR.value, RoleType.WEREWOLF.value},
+    },
+    6: {"content": "Werewolves, close your eyes", "send_to": {RoleType.MODERATOR.value}, "restricted_to": empty_set},
+    7: {"content": "Witch, please open your eyes!", "send_to": {RoleType.MODERATOR.value}, "restricted_to": empty_set},
+    8: {
+        "content": """Witch, tonight {player_hunted} has been killed by the werewolves.
+You have a bottle of antidote, would you like to save him/her? If so, say "Save", else, say "Pass".""",
+        "send_to": {RoleType.WITCH.value},
+        "restricted_to": {RoleType.MODERATOR.value, RoleType.WITCH.value},
+    },  # 要先判断女巫是否有解药，再去询问女巫是否使用解药救人
+    9: {
+        "content": """Witch, you also have a bottle of poison, would you like to use it to kill one of the living players?
+Choose one from the following living options: {living_players}.
+If so, say ONLY "Poison PlayerX", replace PlayerX with the actual player name, else, say "Pass".""",
+        "send_to": {RoleType.WITCH.value},
+        "restricted_to": {RoleType.MODERATOR.value, RoleType.WITCH.value},
+    },  #
+    10: {"content": "Witch, close your eyes", "send_to": {RoleType.MODERATOR.value}, "restricted_to": empty_set},
+    11: {"content": "Seer, please open your eyes!", "send_to": {RoleType.MODERATOR.value}, "restricted_to": empty_set},
+    12: {
+        "content": """Seer, you can check one player's identity. Who are you going to verify its identity tonight?
+Choose only one from the following living options:{living_players}.""",
+        "send_to": {RoleType.SEER.value},
+        "restricted_to": {RoleType.MODERATOR.value, RoleType.SEER.value},
+    },
+    13: {"content": "Seer, close your eyes", "send_to": {RoleType.MODERATOR.value}, "restricted_to": empty_set},
+    # The 1-st daytime
+    14: {
+        "content": """It's daytime. Everyone woke up except those who had been killed.""",
+        "send_to": {RoleType.MODERATOR.value},
+        "restricted_to": empty_set,
+    },
+    15: {
+        "content": "{player_current_dead} was killed last night!",
+        "send_to": {RoleType.MODERATOR.value},
+        "restricted_to": empty_set,
+    },
+    16: {
+        "content": """Living players: {living_players}, now freely talk about the current situation based on your observation and
+reflection with a few sentences. Decide whether to reveal your identity based on your reflection.""",
+        "send_to": {MESSAGE_ROUTE_TO_ALL},  # send to all to speak in daytime
+        "restricted_to": empty_set,
+    },
+    17: {
+        "content": """Now vote and tell me who you think is the werewolf. Don’t mention your role.
+You only choose one from the following living options please:
+{living_players}. Say ONLY: I vote to eliminate ...""",
+        "send_to": {MESSAGE_ROUTE_TO_ALL},
+        "restricted_to": empty_set,
+    },
+    18: {
+        "content": """{player_current_dead} was eliminated.""",
+        "send_to": {RoleType.MODERATOR.value},
+        "restricted_to": empty_set,
+    },
+}

metagpt/environment/werewolf/env_space.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   : werewolf observation/action space and its action definition
+
+from gymnasium import spaces
+from pydantic import ConfigDict, Field
+
+from metagpt.environment.base_env_space import BaseEnvAction, BaseEnvActionType
+from metagpt.environment.werewolf.const import STEP_INSTRUCTIONS
+
+
+class EnvActionType(BaseEnvActionType):
+    NONE = 0  # no action to run, just get observation
+    WOLF_KILL = 1  # wolf kill someone
+    VOTE_KILL = 2  # vote kill someone
+    WITCH_POISON = 3  # witch poison someone
+    WITCH_SAVE = 4  # witch save someone
+    GUARD_PROTECT = 5  # guard protect someone
+    PROGRESS_STEP = 6  # step increment
+
+
+class EnvAction(BaseEnvAction):
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    action_type: int = Field(default=EnvActionType.NONE, description="action type")
+    player_name: str = Field(default="", description="the name of the player to do the action")
+    target_player_name: str = Field(default="", description="the name of the player who take the action")
+
+
+def get_observation_space() -> spaces.Dict:
+    space = spaces.Dict(
+        {
+            "game_setup": spaces.Text(256),
+            "step_idx": spaces.Discrete(len(STEP_INSTRUCTIONS)),
+            "living_players": spaces.Tuple(
+                (spaces.Text(16), spaces.Text(16))
+            ),  # TODO should be tuple of variable length
+            "werewolf_players": spaces.Tuple(
+                (spaces.Text(16), spaces.Text(16))
+            ),  # TODO should be tuple of variable length
+            "player_hunted": spaces.Text(16),
+            "player_current_dead": spaces.Tuple((spaces.Text(16))),  # TODO should be tuple of variable length
+            "witch_poison_left": spaces.Discrete(2),
+            "witch_antidote_left": spaces.Discrete(2),
+            "winner": spaces.Text(16),
+            "win_reason": spaces.Text(64),
+        }
+    )
+    return space
+
+
+def get_action_space() -> spaces.Dict:
+    space = spaces.Dict(
+        {
+            "action_type": spaces.Discrete(len(EnvActionType)),
+            "player_name": spaces.Text(16),  # the player to do the action
+            "target_player_name": spaces.Text(16),  # the target player who take the action
+        }
+    )
+    return space

metagpt/environment/werewolf/werewolf_env.py

@@ -2,30 +2,40 @@
 # -*- coding: utf-8 -*-
 # @Desc   : MG Werewolf Env
 
+from typing import Iterable
+
 from pydantic import Field
 
 from metagpt.environment.base_env import Environment
 from metagpt.environment.werewolf.werewolf_ext_env import WerewolfExtEnv
-from metagpt.logs import logger
 from metagpt.schema import Message
 
 
-class WerewolfEnv(Environment, WerewolfExtEnv):
-    timestamp: int = Field(default=0)
+class WerewolfEnv(WerewolfExtEnv, Environment):
+    round_cnt: int = Field(default=0)
+
+    def add_roles(self, roles: Iterable["Role"]):
+        """增加一批在当前环境的角色
+        Add a batch of characters in the current environment
+        """
+        for role in roles:
+            self.roles[role.name] = role  # use name as key here, due to multi-player can have same profile
+
+        for role in roles:  # setup system message with roles
+            role.context = self.context
+            role.set_env(self)
 
     def publish_message(self, message: Message, add_timestamp: bool = True):
         """Post information to the current environment"""
-        logger.debug(f"publish_message: {message.dump()}")
         if add_timestamp:
             # Because the content of the message may be repeated, for example, killing the same person in two nights
-            # Therefore, a unique timestamp prefix needs to be added so that the same message will not be automatically deduplicated when added to the memory.
-            message.content = f"{self.timestamp} | " + message.content
-        self.memory.add(message)
-        self.history += f"\n{message}"
+            # Therefore, a unique round_cnt prefix needs to be added so that the same message will not be automatically deduplicated when added to the memory.
+            message.content = f"{self.round_cnt} | " + message.content
+        super().publish_message(message)
 
     async def run(self, k=1):
         """Process all Role runs by order"""
         for _ in range(k):
             for role in self.roles.values():
                 await role.run()
-            self.timestamp += 1
+            self.round_cnt += 1

metagpt/environment/werewolf/werewolf_ext_env.py

@@ -4,110 +4,27 @@
 
 import random
 from collections import Counter
-from enum import Enum
 from typing import Any, Callable, Optional
 
 from pydantic import ConfigDict, Field
 
 from metagpt.environment.base_env import ExtEnv, mark_as_readable, mark_as_writeable
-from metagpt.environment.base_env_space import BaseEnvAction, BaseEnvObsParams
+from metagpt.environment.base_env_space import BaseEnvObsParams
+from metagpt.environment.werewolf.const import STEP_INSTRUCTIONS, RoleState, RoleType
+from metagpt.environment.werewolf.env_space import EnvAction, EnvActionType
 from metagpt.logs import logger
 
 
-class RoleState(Enum):
-    ALIVE = "alive"  # the role is alive
-    KILLED = "killed"  # the role is killed by werewolf or voting
-    POISONED = "poisoned"  # the role is killed by posion
-    SAVED = "saved"  # the role is saved by antidote
-
-
-# the ordered rules by the moderator to announce to everyone each step
-STEP_INSTRUCTIONS = {
-    0: {
-        "content": "It’s dark, everyone close your eyes. I will talk with you/your team secretly at night.",
-        "send_to": "Moderator",  # for moderator to continuen speaking
-        "restricted_to": "",
-    },
-    1: {
-        "content": "Guard, please open your eyes!",
-        "send_to": "Moderator",  # for moderator to continuen speaking
-        "restricted_to": "",
-    },
-    2: {
-        "content": """Guard, now tell me who you protect tonight?
-                   You only choose one from the following living options please: {living_players}.
-                   Or you can pass. For example: Protect ...""",
-        "send_to": "Guard",
-        "restricted_to": "Moderator,Guard",
-    },
-    3: {"content": "Guard, close your eyes", "send_to": "Moderator", "restricted_to": ""},
-    4: {"content": "Werewolves, please open your eyes!", "send_to": "Moderator", "restricted_to": ""},
-    5: {
-        "content": """Werewolves, I secretly tell you that {werewolf_players} are
-                   all of the 2 werewolves! Keep in mind you are teammates. The rest players are not werewolves.
-                   choose one from the following living options please:
-                   {living_players}. For example: Kill ...""",
-        "send_to": "Werewolf",
-        "restricted_to": "Moderator,Werewolf",
-    },
-    6: {"content": "Werewolves, close your eyes", "send_to": "Moderator", "restricted_to": ""},
-    7: {"content": "Witch, please open your eyes!", "send_to": "Moderator", "restricted_to": ""},
-    8: {
-        "content": """Witch, tonight {player_hunted} has been killed by the werewolves.
-                   You have a bottle of antidote, would you like to save him/her? If so, say "Save", else, say "Pass".""",
-        "send_to": "Witch",
-        "restricted_to": "Moderator,Witch",
-    },  # 要先判断女巫是否有解药，再去询问女巫是否使用解药救人
-    9: {
-        "content": """Witch, you also have a bottle of poison, would you like to use it to kill one of the living players?
-                   Choose one from the following living options: {living_players}.
-                   If so, say ONLY "Poison PlayerX", replace PlayerX with the actual player name, else, say "Pass".""",
-        "send_to": "Witch",
-        "restricted_to": "Moderator,Witch",
-    },  #
-    10: {"content": "Witch, close your eyes", "send_to": "Moderator", "restricted_to": ""},
-    11: {"content": "Seer, please open your eyes!", "send_to": "Moderator", "restricted_to": ""},
-    12: {
-        "content": """Seer, you can check one player's identity. Who are you going to verify its identity tonight?
-                    Choose only one from the following living options:{living_players}.""",
-        "send_to": "Seer",
-        "restricted_to": "Moderator,Seer",
-    },
-    13: {"content": "Seer, close your eyes", "send_to": "Moderator", "restricted_to": ""},
-    # The 1-st daytime
-    14: {
-        "content": """It's daytime. Everyone woke up except those who had been killed.""",
-        "send_to": "Moderator",
-        "restricted_to": "",
-    },
-    15: {"content": "{player_current_dead} was killed last night!", "send_to": "Moderator", "restricted_to": ""},
-    16: {
-        "content": """Living players: {living_players}, now freely talk about the current situation based on your observation and
-                    reflection with a few sentences. Decide whether to reveal your identity based on your reflection.""",
-        "send_to": "",  # send to all to speak in daytime
-        "restricted_to": "",
-    },
-    17: {
-        "content": """Now vote and tell me who you think is the werewolf. Don’t mention your role.
-                    You only choose one from the following living options please:
-                    {living_players}. Say ONLY: I vote to eliminate ...""",
-        "send_to": "",
-        "restricted_to": "",
-    },
-    18: {"content": """{player_current_dead} was eliminated.""", "send_to": "Moderator", "restricted_to": ""},
-}
-
-
 class WerewolfExtEnv(ExtEnv):
     model_config = ConfigDict(arbitrary_types_allowed=True)
 
     players_state: dict[str, tuple[str, RoleState]] = Field(
-        default=dict(), description="the player's role type and state by player_name"
+        default_factory=dict, description="the player's role type and state by player_name"
     )
 
     round_idx: int = Field(default=0)  # the current round
     step_idx: int = Field(default=0)  # the current step of current round
-    eval_step_idx: int = Field(default=0)
+    eval_step_idx: list[int] = Field(default=[])
     per_round_steps: int = Field(default=len(STEP_INSTRUCTIONS))
 
     # game global states
@@ -115,13 +32,13 @@ class WerewolfExtEnv(ExtEnv):
     special_role_players: list[str] = Field(default=[])
     winner: Optional[str] = Field(default=None)
     win_reason: Optional[str] = Field(default=None)
-    witch_poison_left: int = Field(default=1)
-    witch_antidote_left: int = Field(default=1)
+    witch_poison_left: int = Field(default=1, description="should be 1 or 0")
+    witch_antidote_left: int = Field(default=1, description="should be 1 or 0")
 
     # game current round states, a round is from closing your eyes to the next time you close your eyes
-    round_hunts: dict[str, str] = Field(default=dict(), description="nighttime wolf hunt result")
+    round_hunts: dict[str, str] = Field(default_factory=dict, description="nighttime wolf hunt result")
     round_votes: dict[str, str] = Field(
-        default=dict(), description="daytime all players vote result, key=voteer, value=voted one"
+        default_factory=dict, description="daytime all players vote result, key=voter, value=voted one"
     )
     player_hunted: Optional[str] = Field(default=None)
     player_protected: Optional[str] = Field(default=None)
@@ -135,13 +52,69 @@ class WerewolfExtEnv(ExtEnv):
         seed: Optional[int] = None,
         options: Optional[dict[str, Any]] = None,
     ) -> tuple[dict[str, Any], dict[str, Any]]:
+        """currently unused"""
         pass
 
     def observe(self, obs_params: Optional[BaseEnvObsParams] = None) -> Any:
+        """currently unused"""
         pass
 
-    def step(self, action: BaseEnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
-        pass
+    def _get_obs(self):
+        return {
+            "game_setup": self.game_setup,
+            "step_idx": self.step_idx,
+            "living_players": self.living_players,
+            "werewolf_players": self.werewolf_players,  # currently, lack observation isolation
+            "player_hunted": self.player_hunted,
+            "player_current_dead": self.player_current_dead,
+            "witch_poison_left": self.witch_poison_left,
+            "witch_antidote_left": self.witch_antidote_left,
+            "winner": self.winner,
+            "win_reason": self.win_reason,
+        }
+
+    def step(self, action: EnvAction) -> tuple[dict[str, Any], float, bool, bool, dict[str, Any]]:
+        action_type = action.action_type
+        player_name = action.player_name
+        target_player_name = action.target_player_name
+        if action_type == EnvActionType.WOLF_KILL:
+            self.wolf_kill_someone(wolf_name=player_name, player_name=target_player_name)
+        elif action_type == EnvActionType.VOTE_KILL:
+            self.vote_kill_someone(voter_name=player_name, player_name=target_player_name)
+        elif action_type == EnvActionType.WITCH_POISON:
+            self.witch_poison_someone(witch_name=player_name, player_name=target_player_name)
+        elif action_type == EnvActionType.WITCH_SAVE:
+            self.witch_save_someone(witch_name=player_name, player_name=target_player_name)
+        elif action_type == EnvActionType.GUARD_PROTECT:
+            self.guard_protect_someone(guard_name=player_name, player_name=target_player_name)
+        elif action_type == EnvActionType.PROGRESS_STEP:
+            self.progress_step()
+        elif action_type == EnvActionType.NONE:
+            pass
+        else:
+            raise ValueError(f"not supported action_type: {action_type}")
+
+        self.update_game_states()
+        terminated = self._check_game_finish()
+        obs = self._get_obs()
+        return obs, 1.0, terminated, False, {}
+
+    def _check_game_finish(self) -> bool:
+        """return True if game finished else False"""
+        # game's termination condition
+        terminated = False
+        living_werewolf = [p for p in self.werewolf_players if p in self.living_players]
+        living_villagers = [p for p in self.villager_players if p in self.living_players]
+        living_special_roles = [p for p in self.special_role_players if p in self.living_players]
+        if not living_werewolf:
+            self.winner = "good guys"
+            self.win_reason = "werewolves all dead"
+            terminated = True
+        elif not living_villagers or not living_special_roles:
+            self.winner = "werewolf"
+            self.win_reason = "villagers all dead" if not living_villagers else "special roles all dead"
+            terminated = True
+        return terminated
 
     @property
     def living_players(self) -> list[str]:
@@ -161,12 +134,12 @@ class WerewolfExtEnv(ExtEnv):
 
     @property
     def werewolf_players(self) -> list[str]:
-        player_names = self._role_type_players(role_type="Werewolf")
+        player_names = self._role_type_players(role_type=RoleType.WEREWOLF.value)
         return player_names
 
     @property
     def villager_players(self) -> list[str]:
-        player_names = self._role_type_players(role_type="Villager")
+        player_names = self._role_type_players(role_type=RoleType.VILLAGER.value)
         return player_names
 
     def _init_players_state(self, players: list["Role"]):
@@ -193,14 +166,14 @@ class WerewolfExtEnv(ExtEnv):
         """init players using different roles' num"""
         role_objs = []
         for role_obj in role_uniq_objs:
-            if str(role_obj) == "Villager":
+            if RoleType.VILLAGER.value in str(role_obj):
                 role_objs.extend([role_obj] * num_villager)
-            elif str(role_obj) == "Werewolf":
+            elif RoleType.WEREWOLF.value in str(role_obj):
                 role_objs.extend([role_obj] * num_werewolf)
             else:
                 role_objs.append(role_obj)
         if shuffle:
-            random.shuffle(len(role_objs))
+            random.shuffle(role_objs)
         if add_human:
             assigned_role_idx = random.randint(0, len(role_objs) - 1)
             assigned_role = role_objs[assigned_role_idx]
@@ -233,10 +206,12 @@ class WerewolfExtEnv(ExtEnv):
                 roletype_state = self.players_state[player_name]
                 self.players_state[player_name] = (roletype_state[0], state)
 
-    def _check_valid_role(self, player: "Role", role_type: str) -> bool:
-        return True if role_type in str(player) else False
+    def _check_valid_role(self, player_name: str, role_type: str) -> bool:
+        roletype_state = self.players_state.get(player_name)
+        return True if roletype_state and role_type in roletype_state[0] else False
 
     def _check_player_continue(self, player_name: str, particular_step: int = -1) -> bool:
+        """to check if can do the operation to the player"""
         step_idx = self.step_idx % self.per_round_steps
         if particular_step > 0 and step_idx != particular_step:  # step no
             # particular_step = 18, not daytime vote time, ignore
@@ -253,6 +228,10 @@ class WerewolfExtEnv(ExtEnv):
         self.step_idx += 1
         return instruction
 
+    @mark_as_writeable
+    def progress_step(self):
+        self.step_idx += 1
+
     @mark_as_readable
     def get_players_state(self, player_names: list[str]) -> dict[str, RoleState]:
         players_state = {
@@ -263,57 +242,72 @@ class WerewolfExtEnv(ExtEnv):
         return players_state
 
     @mark_as_writeable
-    def vote_kill_someone(self, voteer: "Role", player_name: str = None):
+    def vote_kill_someone(self, voter_name: str, player_name: str = None):
         """player vote result at daytime
         player_name: if it's None, regard as abstaining from voting
         """
-        if not self._check_player_continue(voteer.name, particular_step=18):  # 18=step no
+        if not self._check_player_continue(voter_name, particular_step=18):  # 18=step no
             return
 
-        self.round_votes[voteer.name] = player_name
+        self.round_votes[voter_name] = player_name
         # check if all living players finish voting, then get the dead one
         if list(self.round_votes.keys()) == self.living_players:
             voted_all = list(self.round_votes.values())  # TODO in case of tie vote, check who was voted first
             voted_all = [item for item in voted_all if item]
-            self.player_current_dead = Counter(voted_all).most_common()[0][0]
-            self._update_players_state([self.player_current_dead])
+            self.player_current_dead = [Counter(voted_all).most_common()[0][0]]
+            self._update_players_state(self.player_current_dead)
 
     @mark_as_writeable
-    def wolf_kill_someone(self, wolf: "Role", player_name: str):
-        if not self._check_valid_role(wolf, "Werewolf"):
+    def wolf_kill_someone(self, wolf_name: str, player_name: str):
+        if not self._check_valid_role(wolf_name, RoleType.WEREWOLF.value):
             return
-        if not self._check_player_continue(wolf.name, particular_step=5):  # 5=step no
+        if not self._check_player_continue(wolf_name, particular_step=6):  # 5=step no
             return
 
-        self.round_hunts[wolf.name] = player_name
-        living_werewolf = [p for p in self.werewolf_players if p in self.living_players]
+        self.round_hunts[wolf_name] = player_name
+        # living_werewolf = [p for p in self.werewolf_players if p in self.living_players]
         # check if all living wolfs finish hunting, then get the hunted one
-        if list(self.round_hunts.keys()) == living_werewolf:
-            hunted_all = list(self.round_hunts.values())
-            self.player_hunted = Counter(hunted_all).most_common()[0][0]
+        # if list(self.round_hunts.keys()) == living_werewolf:
+        #     hunted_all = list(self.round_hunts.values())
+        #     self.player_hunted = Counter(hunted_all).most_common()[0][0]
+        self.player_hunted = player_name
 
-    @mark_as_writeable
-    def witch_poison_someone(self, witch: "Role", player_name: str = None):
-        if not self._check_valid_role(witch, "Witch"):
+    def _witch_poison_or_save_someone(
+        self, witch_name: str, player_name: str = None, state: RoleState = RoleState.POISONED
+    ):
+        if not self._check_valid_role(witch_name, RoleType.WITCH.value):
             return
         if not self._check_player_continue(player_name):
             return
 
-        self._update_players_state([player_name], RoleState.POISONED)
-        self.player_poisoned = player_name
+        assert state in [RoleState.POISONED, RoleState.SAVED]
+        self._update_players_state([player_name], state)
+        if state == RoleState.POISONED:
+            self.player_poisoned = player_name
+            self.witch_poison_left -= 1
+        else:
+            # self.player_protected = player_name
+            self.is_hunted_player_saved = True
+            self.witch_antidote_left -= 1
 
     @mark_as_writeable
-    def witch_save_someone(self, witch: "Role", player_name: str = None):
-        if not self._check_valid_role(witch, "Witch"):
+    def witch_poison_someone(self, witch_name: str, player_name: str = None):
+        self._witch_poison_or_save_someone(witch_name, player_name, RoleState.POISONED)
+
+    @mark_as_writeable
+    def witch_save_someone(self, witch_name: str, player_name: str = None):
+        self._witch_poison_or_save_someone(witch_name, player_name, RoleState.SAVED)
+
+    @mark_as_writeable
+    def guard_protect_someone(self, guard_name: str, player_name: str = None):
+        if not self._check_valid_role(guard_name, RoleType.GUARD.value):
             return
         if not self._check_player_continue(player_name):
             return
-
-        self._update_players_state([player_name], RoleState.SAVED)
         self.player_protected = player_name
 
     @mark_as_writeable
-    def update_game_states(self, memories: list):
+    def update_game_states(self):
         step_idx = self.step_idx % self.per_round_steps
         if step_idx not in [15, 18] or self.step_idx in self.eval_step_idx:
             return
@@ -329,22 +323,12 @@ class WerewolfExtEnv(ExtEnv):
             if self.player_poisoned:
                 self.player_current_dead.append(self.player_poisoned)
 
-            self._update_players_state([self.player_current_dead])
+            self._update_players_state(self.player_current_dead)
             # reset
             self.player_hunted = None
             self.player_protected = None
             self.is_hunted_player_saved = False
             self.player_poisoned = None
-
-        # game's termination condition
-        living_werewolf = [p for p in self.werewolf_players if p in self.living_players]
-        living_villagers = [p for p in self.villager_players if p in self.living_players]
-        living_special_roles = [p for p in self.special_role_players if p in self.living_players]
-        if not living_werewolf:
-            self.winner = "good guys"
-            self.win_reason = "werewolves all dead"
-        elif not living_villagers or not living_special_roles:
-            self.winner = "werewolf"
-            self.win_reason = "villagers all dead" if not living_villagers else "special roles all dead"
-        if self.winner is not None:
-            self._record_all_experiences()  # TODO
+        elif step_idx == 18:
+            # updated use vote_kill_someone
+            pass

metagpt/ext/stanford_town/utils/utils.py

@@ -49,6 +49,7 @@ def read_csv_to_list(curr_file: str, header=False, strip_trail=True):
 
 def get_embedding(text, model: str = "text-embedding-ada-002"):
     text = text.replace("\n", " ")
+    embedding = None
     if not text:
         text = "this is blank"
     for idx in range(3):
@@ -56,7 +57,8 @@ def get_embedding(text, model: str = "text-embedding-ada-002"):
             embedding = (
                 OpenAI(api_key=config.llm.api_key).embeddings.create(input=[text], model=model).data[0].embedding
             )
-        except Exception:
+        except Exception as exp:
+            logger.info(f"get_embedding failed, exp: {exp}, will retry.")
             time.sleep(5)
     if not embedding:
         raise ValueError("get_embedding failed")

metagpt/ext/werewolf/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

metagpt/ext/werewolf/actions/__init__.py

@@ -0,0 +1,23 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :
+
+from metagpt.ext.werewolf.actions.werewolf_actions import Hunt, Impersonate
+from metagpt.ext.werewolf.actions.guard_actions import Protect
+from metagpt.ext.werewolf.actions.seer_actions import Verify
+from metagpt.ext.werewolf.actions.witch_actions import Save, Poison
+from metagpt.ext.werewolf.actions.common_actions import Speak, NighttimeWhispers, Reflect
+from metagpt.ext.werewolf.actions.experience_operation import AddNewExperiences, RetrieveExperiences
+from metagpt.ext.werewolf.actions.moderator_actions import InstructSpeak
+
+ACTIONS = {
+    "Speak": Speak,
+    "Hunt": Hunt,
+    "Protect": Protect,
+    "Verify": Verify,
+    "Save": Save,
+    "Poison": Poison,
+    "Impersonate": Impersonate,
+}
+
+__all__ = ["NighttimeWhispers", "Reflect", "AddNewExperiences", "RetrieveExperiences", "InstructSpeak"]

metagpt/ext/werewolf/actions/common_actions.py

@@ -0,0 +1,240 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :
+
+import json
+
+from tenacity import retry, stop_after_attempt, wait_fixed
+
+from metagpt.actions import Action
+from metagpt.logs import logger
+from metagpt.utils.common import parse_json_code_block
+
+
+def log_and_parse_json(name: str, rsp: str) -> dict:
+    rsp = rsp.replace("\n", " ")
+    logger.debug(f"{name} result: {rsp}")
+    json_blocks = parse_json_code_block(rsp)
+    rsp_json = json.loads(json_blocks[0])
+    return rsp_json
+
+
+class Speak(Action):
+    """Action: Any speak action in a game"""
+
+    PROMPT_TEMPLATE: str = """
+    {
+    "BACKGROUND": "It's a Werewolf game, in this game, we have 2 werewolves, 2 villagers, 1 guard, 1 witch, 1 seer. You are __profile__. Note that villager, seer, guard and witch are all in villager side, they have the same objective. Werewolves can collectively hunt ONE player at night."
+    ,"HISTORY": "You have knowledge to the following conversation: __context__"
+    ,"ATTENTION": "You can NOT VOTE a player who is NOT ALIVE now!"
+    ,"REFLECTION": "__reflection__"
+    ,"STRATEGY": __strategy__
+    ,"PAST_EXPERIENCES": "__experiences__"
+    ,"MODERATOR_INSTRUCTION": __latest_instruction__,
+    ,"RULE": "Please follow the moderator's latest instruction, figure out if you need to speak your opinion or directly to vote:
+              1. If the instruction is to SPEAK, speak in 200 words. Remember the goal of your role and try to achieve it using your speech;
+              2. If the instruction is to VOTE, you MUST vote and ONLY say 'I vote to eliminate PlayerX', replace PlayerX with the actual player name, DO NOT include any other words."
+    ,"OUTPUT_FORMAT":
+        {
+        "ROLE": "Your role, in this case, __profile__"
+        ,"PLAYER_NAME": "Your name, in this case, __name__"
+        ,"LIVING_PLAYERS": "List living players based on MODERATOR_INSTRUCTION. Return a json LIST datatype."
+        ,"THOUGHTS": "Based on `MODERATOR_INSTRUCTION` and `RULE`, carefully think about what to say or vote so that your chance of win as __profile__ maximizes.
+                      If you find similar situation in `PAST_EXPERIENCES`, you may draw lessons from them to refine your strategy, take better vote action, or improve your speech.
+                      Give your step-by-step thought process, you should think no more than 3 steps. For example: My step-by-step thought process:..."
+        ,"RESPONSE": "Based on `MODERATOR_INSTRUCTION`, `RULE`, and the 'THOUGHTS' you had, express your opinion or cast a vote."
+        }
+    }
+    """
+    STRATEGY: str = """
+    Decide whether to reveal your identity based on benefits vs. risks, provide useful information, and vote to eliminate the most suspicious.
+    If you have special abilities, pay attention to those who falsely claims your role, for they are probably werewolves.
+    """
+
+    name: str = "Speak"
+
+    @retry(stop=stop_after_attempt(2), wait=wait_fixed(1))
+    async def run(
+        self,
+        profile: str,
+        name: str,
+        context: str,
+        latest_instruction: str,
+        reflection: str = "",
+        experiences: str = "",
+    ):
+        prompt = (
+            self.PROMPT_TEMPLATE.replace("__context__", context)
+            .replace("__profile__", profile)
+            .replace("__name__", name)
+            .replace("__latest_instruction__", latest_instruction)
+            .replace("__strategy__", self.STRATEGY)
+            .replace("__reflection__", reflection)
+            .replace("__experiences__", experiences)
+        )
+
+        rsp = await self._aask(prompt)
+        rsp_json = log_and_parse_json(self.name, rsp)
+
+        return rsp_json["RESPONSE"]
+
+
+class NighttimeWhispers(Action):
+    """
+
+    Action: nighttime whispers with thinking processes
+
+    Usage Example:
+
+        class Hunt(NighttimeWhispers):
+            def __init__(self, name="Hunt", context=None, llm=None):
+                super().__init__(name, context, llm)
+
+        class Protect(NighttimeWhispers):
+            def __init__(self, name="Protect", context=None, llm=None):
+                super().__init__(name, context, llm)
+
+        class Verify(NighttimeWhispers):
+            def __init__(self, name="Verify", context=None, llm=None):
+                super().__init__(name, context, llm)
+
+        class Save(NighttimeWhispers):
+            def __init__(self, name="Save", context=None, llm=None):
+                super().__init__(name, context, llm)
+
+            def _update_prompt_json(self, prompt_json: dict, profile: str, name: str, context: str, **kwargs):
+                del prompt_json['ACTION']
+                del prompt_json['ATTENTION']
+                prompt_json["OUTPUT_FORMAT"]["THOUGHTS"] = "It is night time. Return the thinking steps of your decision of whether to save the player JUST be killed at this night."
+                prompt_json["OUTPUT_FORMAT"]["RESPONSE"] = "Follow the Moderator's instruction, decide whether you want to save that person or not. Return SAVE or PASS."
+                return prompt_json
+
+        class Poison(NighttimeWhispers):
+            def __init__(self, name="Poison", context=None, llm=None):
+                super().__init__(name, context, llm)
+
+            def _update_prompt_json(self, prompt_json: dict, profile: str, name: str, context: str, **kwargs):
+                prompt_json["OUTPUT_FORMAT"]["RESPONSE"] += "Or if you want to PASS, return PASS."
+                return prompt_json
+    """
+
+    PROMPT_TEMPLATE: str = """
+    {
+    "BACKGROUND": "It's a Werewolf game, in this game, we have 2 werewolves, 2 villagers, 1 guard, 1 witch, 1 seer. You are __profile__. Note that villager, seer, guard and witch are all in villager side, they have the same objective. Werewolves can collectively hunt ONE player at night."
+    ,"HISTORY": "You have knowledge to the following conversation: __context__"
+    ,"ACTION": "Choose one living player to __action__."
+    ,"ATTENTION": "1. You can only __action__ a player who is alive this night! And you can not __action__ a player who is dead this night!  2. `HISTORY` is all the information you observed, DONT hallucinate other player actions!"
+    ,"REFLECTION": "__reflection__"
+    ,"STRATEGY": "__strategy__"
+    ,"PAST_EXPERIENCES": "__experiences__"
+    ,"OUTPUT_FORMAT":
+        {
+        "ROLE": "Your role, in this case, __profile__"
+        ,"PLAYER_NAME": "Your name, in this case, __name__"
+        ,"LIVING_PLAYERS": "List the players who is alive based on moderator's latest instruction. Return a json LIST datatype."
+        ,"THOUGHTS": "Choose one living player from `LIVING_PLAYERS` to __action__ this night. Return the reason why you choose to __action__ this player. If you observe nothing at first night, DONT imagine unexisting player actions! If you find similar situation in `PAST_EXPERIENCES`, you may draw lessons from them to refine your strategy and take better actions. Give your step-by-step thought process, you should think no more than 3 steps. For example: My step-by-step thought process:..."
+        ,"RESPONSE": "As a __profile__, you should choose one living player from `LIVING_PLAYERS` to __action__ this night according to the THOUGHTS you have just now. Return the player name ONLY."
+        }
+    }
+    """
+    STRATEGY: str = """
+    Decide which player is most threatening to you or most needs your support, take your action correspondingly.
+    """
+
+    name: str = "NightTimeWhispers"
+
+    def _construct_prompt_json(
+        self, role_profile: str, role_name: str, context: str, reflection: str, experiences: str, **kwargs
+    ):
+        prompt_template = self.PROMPT_TEMPLATE
+
+        def replace_string(prompt_json: dict):
+            k: str
+            for k in prompt_json.keys():
+                if isinstance(prompt_json[k], dict):
+                    prompt_json[k] = replace_string(prompt_json[k])
+                    continue
+                prompt_json[k] = prompt_json[k].replace("__profile__", role_profile)
+                prompt_json[k] = prompt_json[k].replace("__name__", role_name)
+                prompt_json[k] = prompt_json[k].replace("__context__", context)
+                prompt_json[k] = prompt_json[k].replace("__action__", self.name)
+                prompt_json[k] = prompt_json[k].replace("__strategy__", self.STRATEGY)
+                prompt_json[k] = prompt_json[k].replace("__reflection__", reflection)
+                prompt_json[k] = prompt_json[k].replace("__experiences__", experiences)
+
+            return prompt_json
+
+        prompt_json: dict = json.loads(prompt_template)
+
+        prompt_json = replace_string(prompt_json)
+
+        prompt_json: dict = self._update_prompt_json(
+            prompt_json, role_profile, role_name, context, reflection, experiences, **kwargs
+        )
+        assert isinstance(prompt_json, dict)
+
+        prompt: str = json.dumps(prompt_json, indent=4, ensure_ascii=False)
+
+        return prompt
+
+    def _update_prompt_json(
+        self, prompt_json: dict, role_profile: str, role_name: str, context: str, reflection: str, experiences: str
+    ) -> dict:
+        # one can modify the prompt_json dictionary here
+        return prompt_json
+
+    @retry(stop=stop_after_attempt(2), wait=wait_fixed(1))
+    async def run(self, context: str, profile: str, name: str, reflection: str = "", experiences: str = ""):
+        prompt = self._construct_prompt_json(
+            role_profile=profile, role_name=name, context=context, reflection=reflection, experiences=experiences
+        )
+
+        rsp = await self._aask(prompt)
+        rsp_json = log_and_parse_json(self.name, rsp)
+
+        return f"{self.name} " + rsp_json["RESPONSE"]
+
+
+class Reflect(Action):
+    PROMPT_TEMPLATE: str = """
+    {
+    "BACKGROUND": "It's a Werewolf game, in this game, we have 2 werewolves, 2 villagers, 1 guard, 1 witch, 1 seer. You are __profile__. Note that villager, seer, guard and witch are all in villager side, they have the same objective. Werewolves can collectively hunt ONE player at night."
+    ,"HISTORY": "You have knowledge to the following conversation: __context__"
+    ,"MODERATOR_INSTRUCTION": __latest_instruction__,
+    ,"OUTPUT_FORMAT" (a json):
+        {
+        "ROLE": "Your role, in this case, __profile__"
+        ,"PLAYER_NAME": "Your name, in this case, __name__"
+        "GAME_STATES": "You are about to follow `MODERATOR_INSTRUCTION`, but before taking any action, analyze each player, including the living and the dead, and summarize the game states.
+                        For each player, your reflection should be a ONE-LINE json covering the following dimension, return a LIST of jsons (return an empty LIST for the first night):
+                        [
+                            {"TARGET": "the player you will analyze, if the player is yourself or your werewolf partner, indicate it" ,"STATUS": "living or dead, if dead, how was he/she possibly killed?", "CLAIMED_ROLE": "claims a role or not, if so, what role, any contradiction to others? If there is no claim, return 'None'", "SIDE_WITH": "sides with which players? If none, return 'None'", "ACCUSE": "accuses which players? If none, return 'None'"}
+                            ,{...}
+                            ,...
+                        ]"
+        ,"REFLECTION": "Based on the whole `GAME_STATES`, return a json (return an empty string for the first night):
+                       {
+                            "Player1": "the true role (werewolf / special role / villager, living or dead) you infer about him/her, and why is this role? If the player is yourself or your werewolf partner, indicate it."
+                            ,...
+                            ,"Player7": "the true role (werewolf / special role / villager, living or dead) you infer about him/her, and why is this role? If the player is yourself or your werewolf partner, indicate it."
+                            ,"GAME_STATE_SUMMARIZATION": "summarize the current situation from your standpoint in one sentence, your summarization should catch the most important information from your reflection, such as conflicts, number of living werewolves, special roles, and villagers."
+                       }"
+        }
+    }
+    """
+
+    name: str = "Reflect"
+
+    @retry(stop=stop_after_attempt(2), wait=wait_fixed(1))
+    async def run(self, profile: str, name: str, context: str, latest_instruction: str):
+        prompt = (
+            self.PROMPT_TEMPLATE.replace("__context__", context)
+            .replace("__profile__", profile)
+            .replace("__name__", name)
+            .replace("__latest_instruction__", latest_instruction)
+        )
+
+        rsp = await self._aask(prompt)
+        rsp_json = log_and_parse_json(self.name, rsp)
+
+        return json.dumps(rsp_json["REFLECTION"])

metagpt/ext/werewolf/actions/experience_operation.py

@@ -0,0 +1,162 @@
+import json
+from typing import Optional
+
+import chromadb
+from pydantic import model_validator
+
+from metagpt.actions import Action
+from metagpt.const import DEFAULT_WORKSPACE_ROOT
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.schema import RoleExperience
+from metagpt.logs import logger
+from metagpt.rag.engines.simple import SimpleEngine
+from metagpt.rag.schema import ChromaIndexConfig, ChromaRetrieverConfig
+from metagpt.utils.common import read_json_file, write_json_file
+
+DEFAULT_COLLECTION_NAME = "role_reflection"  # FIXME: some hard code for now
+PERSIST_PATH = DEFAULT_WORKSPACE_ROOT.joinpath("werewolf_game/chroma")
+PERSIST_PATH.mkdir(parents=True, exist_ok=True)
+
+
+class AddNewExperiences(Action):
+    name: str = "AddNewExperience"
+    collection_name: str = DEFAULT_COLLECTION_NAME
+    delete_existing: bool = False
+    engine: Optional[SimpleEngine] = None
+
+    @model_validator(mode="after")
+    def validate_collection(self):
+        if self.engine:
+            return
+        if self.delete_existing:
+            try:
+                # implement engine `DELETE` method later
+                chromadb.PersistentClient(PERSIST_PATH.as_posix()).delete_collection(self.collection_name)
+            except Exception as exp:
+                logger.error(f"delete chroma collection: {self.collection_name} failed, exp: {exp}")
+
+        self.engine = SimpleEngine.from_objs(
+            retriever_configs=[
+                ChromaRetrieverConfig(
+                    persist_path=PERSIST_PATH, collection_name=self.collection_name, metadata={"hnsw:space": "cosine"}
+                )
+            ]
+        )
+
+    def run(self, experiences: list[RoleExperience]):
+        if not experiences:
+            return
+        for i, exp in enumerate(experiences):
+            exp.id = f"{exp.profile}-{exp.name}-step{i}-round_{exp.round_id}"
+
+        AddNewExperiences._record_experiences_local(experiences)
+
+        self.engine.add_objs(experiences)
+
+    def add_from_file(self, file_path):
+        experiences = read_json_file(file_path)
+        experiences = [RoleExperience.model_validate(item) for item in experiences]
+        experiences = [exp for exp in experiences if len(exp.reflection) > 2]  # not "" or not '""'
+
+        self.engine.add_objs(experiences)
+
+    @staticmethod
+    def _record_experiences_local(experiences: list[RoleExperience]):
+        round_id = experiences[0].round_id
+        version = experiences[0].version
+        version = "test" if not version else version
+        experiences = [exp.model_dump() for exp in experiences]
+
+        experience_path = DEFAULT_WORKSPACE_ROOT.joinpath(f"werewolf_game/experiences/{version}")
+        experience_path.mkdir(parents=True, exist_ok=True)
+        save_path = f"{experience_path}/{round_id}.json"
+        write_json_file(save_path, experiences)
+        logger.info(f"experiences saved to {save_path}")
+
+
+class RetrieveExperiences(Action):
+    name: str = "RetrieveExperiences"
+    collection_name: str = DEFAULT_COLLECTION_NAME
+    has_experiences: bool = True
+    engine: Optional[SimpleEngine] = None
+    topk: int = 10
+
+    @model_validator(mode="after")
+    def validate_collection(self):
+        if self.engine:
+            return
+        try:
+            self.engine = SimpleEngine.from_index(
+                index_config=ChromaIndexConfig(
+                    persist_path=PERSIST_PATH, collection_name=self.collection_name, metadata={"hnsw:space": "cosine"}
+                ),
+                retriever_configs=[
+                    ChromaRetrieverConfig(
+                        similarity_top_k=self.topk,
+                        persist_path=PERSIST_PATH,
+                        collection_name=self.collection_name,
+                        metadata={"hnsw:space": "cosine"},
+                    )
+                ],
+            )
+        except Exception as exp:
+            logger.warning(f"No experience pool: {self.collection_name}, exp: {exp}")
+
+    def run(self, query: str, profile: str, excluded_version: str = "", verbose: bool = False) -> str:
+        """_summary_
+
+        Args:
+            query (str): 用当前的reflection作为query去检索过去相似的reflection
+            profile (str): _description_
+
+        Returns:
+            _type_: _description_
+        """
+        if not self.engine or len(query) <= 2:  # not "" or not '""'
+            logger.warning("engine is None or query too short")
+            return ""
+
+        # ablation experiment logic
+        if profile == RoleType.WEREWOLF.value:  # role werewolf as baseline, don't use experiences
+            logger.warning("Disable werewolves' experiences")
+            return ""
+
+        results = self.engine.retrieve(query)
+
+        logger.info(f"retrieve {profile}'s experiences")
+        experiences = [res.metadata["obj"] for res in results]
+
+        past_experiences = []  # currently use post-process to filter, and later add `filters` in rag
+        for exp in experiences:
+            if exp.profile == profile and exp.version != excluded_version:
+                past_experiences.append(exp)
+
+        if verbose and results:
+            logger.info("past_experiences: {}".format("\n\n".join(past_experiences)))
+            distances = results[0].score
+            logger.info(f"distances: {distances}")
+
+        template = """
+        {
+            "Situation __i__": "__situation__"
+            ,"Moderator's instruction": "__instruction__"
+            ,"Your action or speech during that time": "__response__"
+            ,"Reality": "In fact, it turned out the true roles are __game_step__",
+            ,"Outcome": "You __outcome__ in the end"
+        }
+        """
+        past_experiences = [
+            (
+                template.replace("__i__", str(i))
+                .replace("__situation__", exp.reflection)
+                .replace("__instruction__", exp.instruction)
+                .replace("__response__", exp.response)
+                .replace("__game_step__", exp.game_setup.replace("0 | Game setup:\n", "").replace("\n", " "))
+                .replace("__outcome__", exp.outcome)
+            )
+            for i, exp in enumerate(past_experiences)
+        ]
+        logger.info("past_experiences: {}".format("\n".join(past_experiences)))
+        logger.info("retrieval done")
+
+        return json.dumps(past_experiences)

metagpt/ext/werewolf/actions/guard_actions.py

@@ -0,0 +1,9 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :
+
+from metagpt.ext.werewolf.actions.common_actions import NighttimeWhispers
+
+
+class Protect(NighttimeWhispers):
+    name: str = "Protect"

metagpt/ext/werewolf/actions/moderator_actions.py

@@ -0,0 +1,39 @@
+from metagpt.actions import Action
+from metagpt.environment.werewolf.const import STEP_INSTRUCTIONS
+
+
+class InstructSpeak(Action):
+    name: str = "InstructSpeak"
+
+    async def run(self, step_idx, living_players, werewolf_players, player_hunted, player_current_dead):
+        instruction_info = STEP_INSTRUCTIONS.get(
+            step_idx, {"content": "Unknown instruction.", "send_to": {}, "restricted_to": {}}
+        )
+        content = instruction_info["content"]
+        if "{living_players}" in content and "{werewolf_players}" in content:
+            content = content.format(
+                living_players=living_players, werewolf_players=werewolf_players, werewolf_num=len(werewolf_players)
+            )
+        if "{living_players}" in content:
+            content = content.format(living_players=living_players)
+        if "{werewolf_players}" in content:
+            content = content.format(werewolf_players=werewolf_players)
+        if "{player_hunted}" in content:
+            content = content.format(player_hunted=player_hunted)
+        if "{player_current_dead}" in content:
+            player_current_dead = "No one" if not player_current_dead else player_current_dead
+            content = content.format(player_current_dead=player_current_dead)
+
+        return content, instruction_info["send_to"], instruction_info["restricted_to"]
+
+
+class ParseSpeak(Action):
+    name: str = "ParseSpeak"
+
+    async def run(self):
+        pass
+
+
+class AnnounceGameResult(Action):
+    async def run(self, winner: str, win_reason: str):
+        return f"Game over! {win_reason}. The winner is the {winner}"

metagpt/ext/werewolf/actions/seer_actions.py

@@ -0,0 +1,5 @@
+from metagpt.ext.werewolf.actions.common_actions import NighttimeWhispers
+
+
+class Verify(NighttimeWhispers):
+    name: str = "Verify"

metagpt/ext/werewolf/actions/werewolf_actions.py

@@ -0,0 +1,17 @@
+from metagpt.ext.werewolf.actions.common_actions import NighttimeWhispers, Speak
+
+
+class Hunt(NighttimeWhispers):
+    name: str = "Hunt"
+
+
+class Impersonate(Speak):
+    """Action: werewolf impersonating a good guy in daytime speak"""
+
+    STRATEGY: str = """
+    Try continuously impersonating a role, such as Seer, Guard, Villager, etc., in order to mislead
+    other players, make them trust you, and thus hiding your werewolf identity. However, pay attention to what your werewolf partner said, 
+    DONT claim the same role as your werewolf partner. Remmber NOT to reveal your real identity as a werewolf!
+    """
+
+    name: str = "Impersonate"

metagpt/ext/werewolf/actions/witch_actions.py

@@ -0,0 +1,47 @@
+from metagpt.environment.werewolf.const import RoleActionRes
+from metagpt.ext.werewolf.actions.common_actions import NighttimeWhispers
+
+
+class Save(NighttimeWhispers):
+    name: str = "Save"
+
+    def _update_prompt_json(
+        self, prompt_json: dict, role_profile: str, role_name: str, context: str, reflection: str, experiences: str
+    ) -> dict:
+        del prompt_json["ACTION"]
+        del prompt_json["ATTENTION"]
+
+        prompt_json["OUTPUT_FORMAT"][
+            "THOUGHTS"
+        ] = "It is night time. Return the thinking steps of your decision of whether to save the player JUST killed this night."
+        prompt_json["OUTPUT_FORMAT"][
+            "RESPONSE"
+        ] = "Follow the Moderator's instruction, decide whether you want to save that person or not. Return SAVE or PASS."
+
+        return prompt_json
+
+    async def run(self, *args, **kwargs):
+        rsp = await super().run(*args, **kwargs)
+        action_name, rsp = rsp.split()
+        return rsp  # 只需回复SAVE或PASS，不需要带上action名
+
+
+class Poison(NighttimeWhispers):
+    STRATEGY: str = """
+    Only poison a player if you are confident he/she is a werewolf. Don't poison a player randomly or at first night.
+    If someone claims to be the witch, poison him/her, because you are the only witch, he/she can only be a werewolf.
+    """
+
+    name: str = "Poison"
+
+    def _update_prompt_json(
+        self, prompt_json: dict, role_profile: str, role_name: str, context: str, reflection: str, experiences: str
+    ) -> dict:
+        prompt_json["OUTPUT_FORMAT"]["RESPONSE"] += "Or if you want to PASS, return PASS."
+        return prompt_json
+
+    async def run(self, *args, **kwargs):
+        rsp = await super().run(*args, **kwargs)
+        if RoleActionRes.PASS.value in rsp.lower():
+            action_name, rsp = rsp.split()  # 带PASS，只需回复PASS，不需要带上action名，否则是Poison PlayerX，无需改动
+        return rsp

metagpt/ext/werewolf/roles/__init__.py

@@ -0,0 +1,13 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :
+
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+from metagpt.ext.werewolf.roles.guard import Guard
+from metagpt.ext.werewolf.roles.seer import Seer
+from metagpt.ext.werewolf.roles.villager import Villager
+from metagpt.ext.werewolf.roles.werewolf import Werewolf
+from metagpt.ext.werewolf.roles.witch import Witch
+from metagpt.ext.werewolf.roles.moderator import Moderator
+
+__all__ = ["BasePlayer", "Guard", "Moderator", "Seer", "Villager", "Witch", "Werewolf"]

metagpt/ext/werewolf/roles/base_player.py

@@ -0,0 +1,176 @@
+import re
+
+from pydantic import Field, SerializeAsAny, model_validator
+
+from metagpt.actions.action import Action
+from metagpt.environment.werewolf.const import RoleState, RoleType
+from metagpt.ext.werewolf.actions import (
+    ACTIONS,
+    AddNewExperiences,
+    InstructSpeak,
+    NighttimeWhispers,
+    Reflect,
+    RetrieveExperiences,
+    Speak,
+)
+from metagpt.ext.werewolf.schema import RoleExperience, WwMessage
+from metagpt.logs import logger
+from metagpt.roles import Role
+from metagpt.utils.common import any_to_str
+
+
+class BasePlayer(Role):
+    name: str = "PlayerXYZ"
+    profile: str = "BasePlayer"
+    special_action_names: list[str] = []
+    use_reflection: bool = True
+    use_experience: bool = False
+    use_memory_selection: bool = False
+    new_experience_version: str = ""
+    status: RoleState = RoleState.ALIVE
+
+    special_actions: list[SerializeAsAny[Action]] = Field(default=[], validate_default=True)
+    experiences: list[RoleExperience] = []
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        # 技能和监听配置
+        self._watch([InstructSpeak])  # 监听Moderator的指令以做行动
+        special_actions = [ACTIONS[action_name] for action_name in self.special_action_names]
+        capable_actions = [Speak] + special_actions
+        self.set_actions(capable_actions)  # 给角色赋予行动技能
+        self.special_actions = special_actions
+
+        if not self.use_reflection and self.use_experience:
+            logger.warning("You must enable use_reflection before using experience")
+            self.use_experience = False
+
+    @model_validator(mode="after")
+    def check_addresses(self):
+        if not self.addresses:
+            self.addresses = {any_to_str(self), self.name, self.profile} if self.name else {any_to_str(self)}
+        return self
+
+    async def _observe(self, ignore_memory=False) -> int:
+        if self.status != RoleState.ALIVE:
+            # 死者不再参与游戏
+            return 0
+        news = []
+        if not news:
+            news = self.rc.msg_buffer.pop_all()
+        old_messages = [] if ignore_memory else self.rc.memory.get()
+        for m in news:
+            if len(m.restricted_to) and self.profile not in m.restricted_to and self.name not in m.restricted_to:
+                # if the msg is not send to the whole audience ("") nor this role (self.profile or self.name),
+                # then this role should not be able to receive it and record it into its memory
+                continue
+            self.rc.memory.add(m)
+        self.rc.news = [
+            n for n in news if (n.cause_by in self.rc.watch or self.profile in n.send_to) and n not in old_messages
+        ]
+
+        # TODO to delete
+        # await super()._observe()
+        # # 只有发给全体的（""）或发给自己的（self.profile）消息需要走下面的_react流程，
+        # # 其他的收听到即可，不用做动作
+        # self.rc.news = [msg for msg in self.rc.news if msg.send_to in ["", self.profile]]
+        return len(self.rc.news)
+
+    async def _think(self):
+        news = self.rc.news[0]
+        assert news.cause_by == any_to_str(InstructSpeak)  # 消息为来自Moderator的指令时，才去做动作
+        if not news.restricted_to:
+            # 消息接收范围为全体角色的，做公开发言（发表投票观点也算发言）
+            self.rc.todo = Speak()
+        elif self.profile in news.restricted_to:
+            # FIXME: hard code to split, restricted为"Moderator"或"Moderator, 角色profile"
+            # Moderator加密发给自己的，意味着要执行角色的特殊动作
+            self.rc.todo = self.special_actions[0]()
+
+    async def _act(self):
+        # todo为_think时确定的，有两种情况，Speak或Protect
+        todo = self.rc.todo
+        logger.info(f"{self._setting}: ready to {str(todo)}")
+
+        # 可以用这个函数获取该角色的全部记忆和最新的instruction
+        memories = self.get_all_memories()
+        latest_instruction = self.get_latest_instruction()
+
+        reflection = (
+            await Reflect().run(
+                profile=self.profile, name=self.name, context=memories, latest_instruction=latest_instruction
+            )
+            if self.use_reflection
+            else ""
+        )
+
+        experiences = (
+            RetrieveExperiences().run(
+                query=reflection, profile=self.profile, excluded_version=self.new_experience_version
+            )
+            if self.use_experience
+            else ""
+        )
+
+        # 根据自己定义的角色Action，对应地去run，run的入参可能不同
+        if isinstance(todo, Speak):
+            rsp = await todo.run(
+                profile=self.profile,
+                name=self.name,
+                context=memories,
+                latest_instruction=latest_instruction,
+                reflection=reflection,
+                experiences=experiences,
+            )
+            restricted_to = set()
+
+        elif isinstance(todo, NighttimeWhispers):
+            rsp = await todo.run(
+                profile=self.profile, name=self.name, context=memories, reflection=reflection, experiences=experiences
+            )
+            restricted_to = {RoleType.MODERATOR.value, self.profile}  # 给Moderator发送使用特殊技能的加密消息
+        msg = WwMessage(
+            content=rsp,
+            role=self.profile,
+            sent_from=self.name,
+            cause_by=type(todo),
+            send_to={},
+            restricted_to=restricted_to,
+        )
+
+        self.experiences.append(
+            RoleExperience(
+                name=self.name,
+                profile=self.profile,
+                reflection=reflection,
+                instruction=latest_instruction,
+                response=rsp,
+                version=self.new_experience_version,
+            )
+        )
+
+        logger.info(f"{self._setting}: {rsp}")
+
+        return msg
+
+    def get_all_memories(self) -> str:
+        memories = self.rc.memory.get()
+        time_stamp_pattern = r"[0-9]+ \| "
+        # NOTE: 除Moderator外，其他角色使用memory，只能用m.sent_from（玩家名）不能用m.role（玩家角色），因为他们不知道说话者的身份
+        memories = [f"{m.sent_from}: {re.sub(time_stamp_pattern, '', m.content)}" for m in memories]  # regex去掉时间戳
+        memories = "\n".join(memories)
+        return memories
+
+    def get_latest_instruction(self) -> str:
+        return self.rc.important_memory[-1].content  # 角色监听着Moderator的InstructSpeak，是其重要记忆，直接获取即可
+
+    def set_status(self, new_status: RoleState):
+        self.status = new_status
+
+    def record_experiences(self, round_id: str, outcome: str, game_setup: str):
+        experiences = [exp for exp in self.experiences if len(exp.reflection) > 2]  # not "" or not '""'
+        for exp in experiences:
+            exp.round_id = round_id
+            exp.outcome = outcome
+            exp.game_setup = game_setup
+        AddNewExperiences().run(experiences)

metagpt/ext/werewolf/roles/guard.py

@@ -0,0 +1,8 @@
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+
+
+class Guard(BasePlayer):
+    name: str = RoleType.GUARD.value
+    profile: str = RoleType.GUARD.value
+    special_action_names: list[str] = ["Protect"]

metagpt/ext/werewolf/roles/human_player.py

@@ -0,0 +1,45 @@
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.actions import Speak
+from metagpt.ext.werewolf.roles import BasePlayer
+from metagpt.ext.werewolf.schema import WwMessage
+from metagpt.logs import logger
+
+
+async def _act(self):
+    todo = self.rc.todo
+
+    memories = self.get_all_memories()
+
+    input_instruction = f"""
+    ## As a reminder, you have access to the following game history:
+    {memories}
+    ## You are {self.name}({self.profile})
+    ## Guidance:
+    1. If you are performing a special action or exercising a vote,
+    end your response with "PlayerX", replace PlayerX with the actual player name, e.g., "..., kill/protect/poison/.../vote Player1".
+    2. If it is a daytime free speech, you can speak in whatever format.
+    Now, please speak:
+    """
+    rsp = input(input_instruction)  # wait for human input
+
+    msg_cause_by = type(todo)
+    msg_restricted_to = {} if isinstance(todo, Speak) else {RoleType.MODERATOR.value, self.profile}
+
+    msg = WwMessage(
+        content=rsp,
+        role=self.profile,
+        sent_from=self.name,
+        cause_by=msg_cause_by,
+        send_to={},
+        restricted_to=msg_restricted_to,  # 给Moderator及自身阵营发送加密消息
+    )
+
+    logger.info(f"{self._setting}: {rsp}")
+
+    return msg
+
+
+def prepare_human_player(player_class: BasePlayer):
+    # Dynamically define a human player class that inherits from a certain role class
+    HumanPlayer = type("HumanPlayer", (player_class,), {"_act": _act})
+    return HumanPlayer

metagpt/ext/werewolf/roles/moderator.py

@@ -0,0 +1,251 @@
+import re
+from datetime import datetime
+from typing import Union
+
+from metagpt.actions.add_requirement import UserRequirement
+from metagpt.const import DEFAULT_WORKSPACE_ROOT, MESSAGE_ROUTE_TO_ALL
+from metagpt.environment.werewolf.const import (
+    STEP_INSTRUCTIONS,
+    RoleActionRes,
+    RoleState,
+    RoleType,
+)
+from metagpt.environment.werewolf.env_space import EnvAction, EnvActionType
+from metagpt.ext.werewolf.actions import Hunt, Poison, Protect, Save, Verify
+from metagpt.ext.werewolf.actions.moderator_actions import (
+    AnnounceGameResult,
+    InstructSpeak,
+    ParseSpeak,
+)
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+from metagpt.ext.werewolf.schema import WwMessage
+from metagpt.logs import logger
+from metagpt.utils.common import any_to_str
+
+
+class Moderator(BasePlayer):
+    name: str = RoleType.MODERATOR.value
+    profile: str = RoleType.MODERATOR.value
+
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self._watch([UserRequirement, InstructSpeak, ParseSpeak])
+        self.set_actions([InstructSpeak, ParseSpeak, AnnounceGameResult])
+
+        # game states
+        self.step_idx = 0
+        self.game_setup = ""
+        self.werewolf_players = []
+        self.winner = None
+        self.win_reason = None
+        self.witch_poison_left = 1
+        self.witch_antidote_left = 1
+
+    def update_player_status(self, player_names: list[str]):
+        if not player_names:
+            return
+        roles_in_env = self.rc.env.get_roles()
+        for role_setting, role in roles_in_env.items():
+            for player_name in player_names:
+                if player_name in role_setting:
+                    role.set_status(new_status=RoleState.DEAD)  # 更新为死亡
+
+    def _record_all_experiences(self):
+        logger.info(f"The winner of the game: {self.winner}, start to record roles' experiences")
+        roles_in_env = self.rc.env.get_roles()
+        timestamp = datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
+        for _, role in roles_in_env.items():
+            if role == self:
+                continue
+            if self.winner == "werewolf":
+                outcome = "won" if role.profile in RoleType.WEREWOLF.value else "lost"
+            else:
+                outcome = "won" if role.profile not in RoleType.WEREWOLF.value else "lost"
+            role.record_experiences(round_id=timestamp, outcome=outcome, game_setup=self.game_setup)
+
+    async def _parse_speak(self, memories):
+        latest_msg = memories[-1]
+        latest_msg_content = latest_msg.content
+
+        match = re.search(r"Player[0-9]+", latest_msg_content[-10:])  # FIXME: hard code truncation
+        target = match.group(0) if match else ""
+
+        # default return
+        msg_content = "Understood"
+        restricted_to = set()
+
+        msg_cause_by = latest_msg.cause_by
+        if msg_cause_by == any_to_str(Hunt):
+            self.rc.env.step(
+                EnvAction(
+                    action_type=EnvActionType.WOLF_KILL, player_name=latest_msg.sent_from, target_player_name=target
+                )
+            )
+        elif msg_cause_by == any_to_str(Protect):
+            self.rc.env.step(
+                EnvAction(
+                    action_type=EnvActionType.GUARD_PROTECT, player_name=latest_msg.sent_from, target_player_name=target
+                )
+            )
+        elif msg_cause_by == any_to_str(Verify):
+            if target in self.werewolf_players:
+                msg_content = f"{target} is a werewolf"
+            else:
+                msg_content = f"{target} is a good guy"
+            restricted_to = {RoleType.MODERATOR.value, RoleType.SEER.value}
+        elif msg_cause_by == any_to_str(Save):
+            if RoleActionRes.PASS.value in latest_msg_content.lower():
+                # the role ignore to response, answer `pass`
+                pass
+            elif not self.witch_antidote_left:
+                msg_content = "You have no antidote left and thus can not save the player"
+                restricted_to = {RoleType.MODERATOR.value, RoleType.WITCH.value}
+            else:
+                self.rc.env.step(
+                    EnvAction(
+                        action_type=EnvActionType.WITCH_SAVE,
+                        player_name=latest_msg.sent_from,
+                        target_player_name=target,
+                    )
+                )
+        elif msg_cause_by == any_to_str(Poison):
+            if RoleActionRes.PASS.value in latest_msg_content.lower():
+                pass
+            elif not self.witch_poison_left:
+                msg_content = "You have no poison left and thus can not poison the player"
+                restricted_to = {RoleType.MODERATOR.value, RoleType.WITCH.value}
+            else:
+                self.rc.env.step(
+                    EnvAction(
+                        action_type=EnvActionType.WITCH_POISON,
+                        player_name=latest_msg.sent_from,
+                        target_player_name=target,
+                    )
+                )
+
+        return msg_content, restricted_to
+
+    def _update_player_status(self, step_idx: int, player_current_dead: list[str]):
+        """update dead player's status"""
+        if step_idx in [15, 18]:
+            self.update_player_status(player_current_dead)
+
+    def _record_game_history(self, step_idx: int):
+        if step_idx and step_idx % len(STEP_INSTRUCTIONS) == 0 or self.winner:
+            logger.info("a night and day cycle completed, examine all history")
+            logger.debug(f"all_memories: {self.get_all_memories()}")
+            with open(DEFAULT_WORKSPACE_ROOT / "werewolf_transcript.txt", "w") as f:
+                f.write(self.get_all_memories())
+
+    async def _observe(self, ignore_memory=False) -> int:
+        news = []
+        if not news:
+            news = self.rc.msg_buffer.pop_all()
+        old_messages = [] if ignore_memory else self.rc.memory.get()
+        for m in news:
+            if len(m.restricted_to) and self.profile not in m.restricted_to and self.name not in m.restricted_to:
+                # if the msg is not send to the whole audience ("") nor this role (self.profile or self.name),
+                # then this role should not be able to receive it and record it into its memory
+                continue
+            self.rc.memory.add(m)
+        # add `MESSAGE_ROUTE_TO_ALL in n.send_to` make it to run `ParseSpeak`
+        self.rc.news = [
+            n
+            for n in news
+            if (n.cause_by in self.rc.watch or self.profile in n.send_to or MESSAGE_ROUTE_TO_ALL in n.send_to)
+            and n not in old_messages
+        ]
+        return len(self.rc.news)
+
+    async def _think(self):
+        if self.winner:
+            self.rc.todo = AnnounceGameResult()
+            return
+
+        latest_msg = self.rc.memory.get()[-1]
+        if latest_msg.role in ["User", "Human", self.profile]:
+            # 1. 上一轮消息是用户指令，解析用户指令，开始游戏
+            # 2.1. 上一轮消息是Moderator自己的指令，继续发出指令，一个事情可以分几条消息来说
+            # 2.2. 上一轮消息是Moderator自己的解析消息，一个阶段结束，发出新一个阶段的指令
+            self.rc.todo = InstructSpeak()
+        else:
+            # 上一轮消息是游戏角色的发言，解析角色的发言
+            self.rc.todo = ParseSpeak()
+
+    def _init_fields_from_obj(self, obs: dict[str, Union[int, str, list[str]]]):
+        self.game_setup = obs.get("game_setup", "")
+        self.step_idx = obs.get("step_idx", 0)
+        self.winner = obs.get("winner")
+        self.win_reason = obs.get("win_reason")
+        self.werewolf_players = obs.get("werewolf_players", [])
+        self.witch_poison_left = obs.get("witch_poison_left", 0)
+        self.witch_antidote_left = obs.get("witch_antidote_left", 0)
+
+    async def _act(self):
+        todo = self.rc.todo
+        logger.info(f"{self._setting} ready to {todo}")
+
+        memories = self.get_all_memories(mode="msg")
+
+        obs, _, _, _, _ = self.rc.env.step(action=EnvAction(action_type=EnvActionType.NONE))
+        living_players = obs["living_players"]
+        werewolf_players = obs["werewolf_players"]
+        player_hunted = obs["player_hunted"]
+        player_current_dead = obs["player_current_dead"]
+        self._init_fields_from_obj(obs)
+
+        # 若进行完一夜一日的循环，打印和记录一次完整发言历史
+        self._record_game_history(self.step_idx)
+
+        # 若一晚或一日周期结束，对当晚或当日的死者进行总结，并更新玩家状态
+        self._update_player_status(self.step_idx, player_current_dead)
+        if self.winner:
+            self._record_all_experiences()
+
+        # 根据_think的结果，执行InstructSpeak还是ParseSpeak, 并将结果返回
+        if isinstance(todo, InstructSpeak):
+            msg_content, msg_to_send_to, msg_restricted_to = await InstructSpeak().run(
+                self.step_idx,
+                living_players=living_players,
+                werewolf_players=werewolf_players,
+                player_hunted=player_hunted,
+                player_current_dead=player_current_dead,
+            )
+            # msg_content = f"Step {self.step_idx}: {msg_content}" # HACK: 加一个unique的step_idx避免记忆的自动去重
+            msg = WwMessage(
+                content=msg_content,
+                role=self.profile,
+                sent_from=self.name,
+                cause_by=InstructSpeak,
+                send_to=msg_to_send_to,
+                restricted_to=msg_restricted_to,
+            )
+            logger.info(f"current step_idx: {self.step_idx}")
+            self.rc.env.step(EnvAction(action_type=EnvActionType.PROGRESS_STEP))  # to update step_idx
+
+        elif isinstance(todo, ParseSpeak):
+            msg_content, msg_restricted_to = await self._parse_speak(memories)
+            # msg_content = f"Step {self.step_idx}: {msg_content}" # HACK: 加一个unique的step_idx避免记忆的自动去重
+            msg = WwMessage(
+                content=msg_content,
+                role=self.profile,
+                sent_from=self.name,
+                cause_by=ParseSpeak,
+                send_to={},
+                restricted_to=msg_restricted_to,
+            )
+
+        elif isinstance(todo, AnnounceGameResult):
+            msg_content = await AnnounceGameResult().run(winner=self.winner, win_reason=self.win_reason)
+            msg = WwMessage(content=msg_content, role=self.profile, sent_from=self.name, cause_by=AnnounceGameResult)
+
+        logger.info(f"{self._setting}: {msg_content}")
+
+        return msg
+
+    def get_all_memories(self, mode="str") -> str:
+        memories = self.rc.memory.get()
+        if mode == "str":
+            memories = [f"{m.sent_from}({m.role}): {m.content}" for m in memories]
+            memories = "\n".join(memories)
+        return memories

metagpt/ext/werewolf/roles/seer.py

@@ -0,0 +1,8 @@
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+
+
+class Seer(BasePlayer):
+    name: str = RoleType.SEER.value
+    profile: str = RoleType.SEER.value
+    special_action_names: list[str] = ["Verify"]

metagpt/ext/werewolf/roles/villager.py

@@ -0,0 +1,8 @@
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+
+
+class Villager(BasePlayer):
+    name: str = RoleType.VILLAGER.value
+    profile: str = RoleType.VILLAGER.value
+    special_action_names: list[str] = []

metagpt/ext/werewolf/roles/werewolf.py

@@ -0,0 +1,15 @@
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.actions import Impersonate, Speak
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+
+
+class Werewolf(BasePlayer):
+    name: str = RoleType.WEREWOLF.value
+    profile: str = RoleType.WEREWOLF.value
+    special_action_names: list[str] = ["Hunt"]
+
+    async def _think(self):
+        """狼人白天发言时需要伪装，与其他角色不同，因此需要重写_think"""
+        await super()._think()
+        if isinstance(self.rc.todo, Speak):
+            self.rc.todo = Impersonate()

metagpt/ext/werewolf/roles/witch.py

@@ -0,0 +1,28 @@
+from metagpt.environment.werewolf.const import RoleType
+from metagpt.ext.werewolf.actions import InstructSpeak, Poison, Save, Speak
+from metagpt.ext.werewolf.roles.base_player import BasePlayer
+from metagpt.utils.common import any_to_str
+
+
+class Witch(BasePlayer):
+    name: str = RoleType.WITCH.value
+    profile: str = RoleType.WITCH.value
+    special_action_names: list[str] = ["Save", "Poison"]
+
+    async def _think(self):
+        """女巫涉及两个特殊技能，因此在此需要改写_think进行路由"""
+        news = self.rc.news[0]
+        assert news.cause_by == any_to_str(InstructSpeak)  # 消息为来自Moderator的指令时，才去做动作
+        if not news.restricted_to:
+            # 消息接收范围为全体角色的，做公开发言（发表投票观点也算发言）
+            self.rc.todo = Speak()
+        elif self.profile in news.restricted_to:
+            # FIXME: hard code to split, restricted为"Moderator"或"Moderator,角色profile"
+            # Moderator加密发给自己的，意味着要执行角色的特殊动作
+            # 这里用关键词进行动作的选择，需要Moderator侧的指令进行配合
+            if "save" in news.content.lower():
+                self.rc.todo = Save()
+            elif "poison" in news.content.lower():
+                self.rc.todo = Poison()
+            else:
+                raise ValueError("Moderator's instructions must include save or poison keyword")

metagpt/ext/werewolf/schema.py

@@ -0,0 +1,33 @@
+from typing import Any
+
+from pydantic import BaseModel, Field, field_validator
+
+from metagpt.schema import Message
+from metagpt.utils.common import any_to_str_set
+
+
+class RoleExperience(BaseModel):
+    id: str = ""
+    name: str = ""
+    profile: str
+    reflection: str
+    instruction: str = ""
+    response: str
+    outcome: str = ""
+    round_id: str = ""
+    game_setup: str = ""
+    version: str = ""
+
+    def rag_key(self) -> str:
+        """For search"""
+        return self.reflection
+
+
+class WwMessage(Message):
+    # Werewolf Message
+    restricted_to: set[str] = Field(default=set(), validate_default=True)
+
+    @field_validator("restricted_to", mode="before")
+    @classmethod
+    def check_restricted_to(cls, restricted_to: Any):
+        return any_to_str_set(restricted_to if restricted_to else set())

metagpt/ext/werewolf/werewolf_game.py

@@ -0,0 +1,28 @@
+from typing import Any, Optional
+
+from metagpt.actions.add_requirement import UserRequirement
+from metagpt.context import Context
+from metagpt.environment.werewolf.werewolf_env import WerewolfEnv
+from metagpt.ext.werewolf.schema import WwMessage
+from metagpt.team import Team
+
+
+class WerewolfGame(Team):
+    """Use the "software company paradigm" to hold a werewolf game"""
+
+    env: Optional[WerewolfEnv] = None
+
+    def __init__(self, context: Context = None, **data: Any):
+        super(Team, self).__init__(**data)
+        ctx = context or Context()
+        if not self.env:
+            self.env = WerewolfEnv(context=ctx)
+        else:
+            self.env.context = ctx  # The `env` object is allocated by deserialization
+
+    def run_project(self, idea):
+        """Run a project from user instruction."""
+        self.idea = idea
+        self.env.publish_message(
+            WwMessage(role="User", content=idea, cause_by=UserRequirement, restricted_to={"Moderator"})
+        )

metagpt/rag/engines/simple.py

@@ -161,6 +161,13 @@ class SimpleEngine(RetrieverQueryEngine):
         """Inplement tools.SearchInterface"""
         return await self.aquery(content)
 
+    def retrieve(self, query: QueryType) -> list[NodeWithScore]:
+        query_bundle = QueryBundle(query) if isinstance(query, str) else query
+
+        nodes = super().retrieve(query_bundle)
+        self._try_reconstruct_obj(nodes)
+        return nodes
+
     async def aretrieve(self, query: QueryType) -> list[NodeWithScore]:
         """Allow query to be str."""
         query_bundle = QueryBundle(query) if isinstance(query, str) else query

metagpt/rag/factories/index.py

@@ -48,7 +48,7 @@ class RAGIndexFactory(ConfigBasedFactory):
 
     def _create_chroma(self, config: ChromaIndexConfig, **kwargs) -> VectorStoreIndex:
         db = chromadb.PersistentClient(str(config.persist_path))
-        chroma_collection = db.get_or_create_collection(config.collection_name)
+        chroma_collection = db.get_or_create_collection(config.collection_name, metadata=config.metadata)
         vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
 
         return self._index_from_vector_store(vector_store=vector_store, config=config, **kwargs)

metagpt/rag/factories/retriever.py

@@ -69,7 +69,7 @@ class RetrieverFactory(ConfigBasedFactory):
 
     def _create_chroma_retriever(self, config: ChromaRetrieverConfig, **kwargs) -> ChromaRetriever:
         db = chromadb.PersistentClient(path=str(config.persist_path))
-        chroma_collection = db.get_or_create_collection(config.collection_name)
+        chroma_collection = db.get_or_create_collection(config.collection_name, metadata=config.metadata)
 
         vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
         config.index = self._build_index_from_vector_store(config, vector_store, **kwargs)

metagpt/rag/schema.py

@@ -1,8 +1,9 @@
 """RAG schemas."""
 
 from pathlib import Path
-from typing import Any, ClassVar, Literal, Union
+from typing import Any, ClassVar, Literal, Optional, Union
 
+from chromadb.api.types import CollectionMetadata
 from llama_index.core.embeddings import BaseEmbedding
 from llama_index.core.indices.base import BaseIndex
 from llama_index.core.schema import TextNode
@@ -59,6 +60,9 @@ class ChromaRetrieverConfig(IndexRetrieverConfig):
 
     persist_path: Union[str, Path] = Field(default="./chroma_db", description="The directory to save data.")
     collection_name: str = Field(default="metagpt", description="The name of the collection.")
+    metadata: Optional[CollectionMetadata] = Field(
+        default=None, description="Optional metadata to associate with the collection"
+    )
 
 
 class ElasticsearchStoreConfig(BaseModel):
@@ -144,6 +148,9 @@ class ChromaIndexConfig(VectorIndexConfig):
     """Config for chroma-based index."""
 
     collection_name: str = Field(default="metagpt", description="The name of the collection.")
+    metadata: Optional[CollectionMetadata] = Field(
+        default=None, description="Optional metadata to associate with the collection"
+    )
 
 
 class BM25IndexConfig(BaseIndexConfig):

metagpt/roles/role.py

@@ -335,6 +335,11 @@ class Role(SerializationMixin, ContextMixin, BaseModel):
             self.llm.cost_manager = self.context.cost_manager
             self.set_actions(self.actions)  # reset actions to update llm and prefix
 
+    @property
+    def name(self):
+        """Get the role name"""
+        return self._setting.name
+
     def _get_prefix(self):
         """Get the role prefix"""
         if self.desc:

metagpt/utils/common.py

@@ -722,7 +722,10 @@ def list_files(root: str | Path) -> List[Path]:
 
 
 def parse_json_code_block(markdown_text: str) -> List[str]:
-    json_blocks = re.findall(r"```json(.*?)```", markdown_text, re.DOTALL)
+    json_blocks = (
+        re.findall(r"```json(.*?)```", markdown_text, re.DOTALL) if "```json" in markdown_text else [markdown_text]
+    )
+
     return [v.strip() for v in json_blocks]
 
 

tests/metagpt/environment/werewolf_env/test_werewolf_ext_env.py

@@ -2,33 +2,34 @@
 # -*- coding: utf-8 -*-
 # @Desc   : the unittest of WerewolfExtEnv
 
-from metagpt.environment.werewolf.werewolf_ext_env import RoleState, WerewolfExtEnv
+from metagpt.environment.werewolf.const import RoleState, RoleType
+from metagpt.environment.werewolf.werewolf_ext_env import WerewolfExtEnv
 from metagpt.roles.role import Role
 
 
 class Werewolf(Role):
-    profile: str = "Werewolf"
+    profile: str = RoleType.WEREWOLF.value
 
 
 class Villager(Role):
-    profile: str = "Villager"
+    profile: str = RoleType.VILLAGER.value
 
 
 class Witch(Role):
-    profile: str = "Witch"
+    profile: str = RoleType.WITCH.value
 
 
 class Guard(Role):
-    profile: str = "Guard"
+    profile: str = RoleType.GUARD.value
 
 
 def test_werewolf_ext_env():
     players_state = {
-        "Player0": ("Werewolf", RoleState.ALIVE),
-        "Player1": ("Werewolf", RoleState.ALIVE),
-        "Player2": ("Villager", RoleState.ALIVE),
-        "Player3": ("Witch", RoleState.ALIVE),
-        "Player4": ("Guard", RoleState.ALIVE),
+        "Player0": (RoleType.WEREWOLF.value, RoleState.ALIVE),
+        "Player1": (RoleType.WEREWOLF.value, RoleState.ALIVE),
+        "Player2": (RoleType.VILLAGER.value, RoleState.ALIVE),
+        "Player3": (RoleType.WITCH.value, RoleState.ALIVE),
+        "Player4": (RoleType.GUARD.value, RoleState.ALIVE),
     }
     ext_env = WerewolfExtEnv(players_state=players_state, step_idx=4, special_role_players=["Player3", "Player4"])
 
@@ -41,9 +42,9 @@ def test_werewolf_ext_env():
     assert "Werewolves, please open your eyes" in curr_instr["content"]
 
     # current step_idx = 5
-    ext_env.wolf_kill_someone(wolf=Role(name="Player10"), player_name="Player4")
-    ext_env.wolf_kill_someone(wolf=Werewolf(name="Player0"), player_name="Player4")
-    ext_env.wolf_kill_someone(wolf=Werewolf(name="Player1"), player_name="Player4")
+    ext_env.wolf_kill_someone(wolf_name="Player10", player_name="Player4")
+    ext_env.wolf_kill_someone(wolf_name="Player0", player_name="Player4")
+    ext_env.wolf_kill_someone(wolf_name="Player1", player_name="Player4")
     assert ext_env.player_hunted == "Player4"
     assert len(ext_env.living_players) == 5  # hunted but can be saved by witch
 
@@ -52,11 +53,11 @@ def test_werewolf_ext_env():
 
     # current step_idx = 18
     assert ext_env.step_idx == 18
-    ext_env.vote_kill_someone(voteer=Werewolf(name="Player0"), player_name="Player2")
-    ext_env.vote_kill_someone(voteer=Werewolf(name="Player1"), player_name="Player3")
-    ext_env.vote_kill_someone(voteer=Villager(name="Player2"), player_name="Player3")
-    ext_env.vote_kill_someone(voteer=Witch(name="Player3"), player_name="Player4")
-    ext_env.vote_kill_someone(voteer=Guard(name="Player4"), player_name="Player2")
+    ext_env.vote_kill_someone(voter_name="Player0", player_name="Player2")
+    ext_env.vote_kill_someone(voter_name="Player1", player_name="Player3")
+    ext_env.vote_kill_someone(voter_name="Player2", player_name="Player3")
+    ext_env.vote_kill_someone(voter_name="Player3", player_name="Player4")
+    ext_env.vote_kill_someone(voter_name="Player4", player_name="Player2")
     assert ext_env.player_current_dead == "Player2"
     assert len(ext_env.living_players) == 4
 

tests/metagpt/ext/werewolf/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

tests/metagpt/ext/werewolf/actions/__init__.py

@@ -0,0 +1,3 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# @Desc   :

tests/metagpt/ext/werewolf/actions/test_experience_operation.py

@@ -0,0 +1,164 @@
+import json
+
+import pytest
+
+from metagpt.const import DEFAULT_WORKSPACE_ROOT
+from metagpt.ext.werewolf.actions import AddNewExperiences, RetrieveExperiences
+from metagpt.ext.werewolf.schema import RoleExperience
+from metagpt.logs import logger
+
+
+class TestExperiencesOperation:
+    collection_name = "test"
+    test_round_id = "test_01"
+    version = "test"
+    samples_to_add = [
+        RoleExperience(
+            profile="Witch",
+            reflection="The game is intense with two players claiming to be the Witch and one claiming to be the Seer. "
+            "Player4's behavior is suspicious.",
+            response="",
+            outcome="",
+            round_id=test_round_id,
+            version=version,
+        ),
+        RoleExperience(
+            profile="Witch",
+            reflection="The game is in a critical state with only three players left, "
+            "and I need to make a wise decision to save Player7 or not.",
+            response="",
+            outcome="",
+            round_id=test_round_id,
+            version=version,
+        ),
+        RoleExperience(
+            profile="Seer",
+            reflection="Player1, who is a werewolf, falsely claimed to be a Seer, and Player6, who might be a Witch, "
+            "sided with him. I, as the real Seer, am under suspicion.",
+            response="",
+            outcome="",
+            round_id=test_round_id,
+            version=version,
+        ),
+        RoleExperience(
+            profile="TestRole",
+            reflection="Some test reflection1",
+            response="",
+            outcome="",
+            round_id=test_round_id,
+            version=version + "_01-10",
+        ),
+        RoleExperience(
+            profile="TestRole",
+            reflection="Some test reflection2",
+            response="",
+            outcome="",
+            round_id=test_round_id,
+            version=version + "_11-20",
+        ),
+        RoleExperience(
+            profile="TestRole",
+            reflection="Some test reflection3",
+            response="",
+            outcome="",
+            round_id=test_round_id,
+            version=version + "_21-30",
+        ),
+    ]
+
+    @pytest.mark.asyncio
+    async def test_add(self):
+        saved_file = DEFAULT_WORKSPACE_ROOT.joinpath(
+            f"werewolf_game/experiences/{self.version}/{self.test_round_id}.json"
+        )
+        if saved_file.exists():
+            saved_file.unlink()
+
+        action = AddNewExperiences(collection_name=self.collection_name, delete_existing=True)
+        action.run(self.samples_to_add)
+
+        # test insertion
+        inserted = action.engine.retriever._index._vector_store._collection.get()
+        assert len(inserted["documents"]) == len(self.samples_to_add)
+
+        # test if we record the samples correctly to local file
+        # & test if we could recover a embedding db from the file
+        action = AddNewExperiences(collection_name=self.collection_name, delete_existing=True)
+        action.add_from_file(saved_file)
+        inserted = action.engine.retriever._index._vector_store._collection.get()
+        assert len(inserted["documents"]) == len(self.samples_to_add)
+
+    @pytest.mark.asyncio
+    async def test_retrieve(self):
+        action = RetrieveExperiences(collection_name=self.collection_name)
+
+        query = "one player claimed to be Seer and the other Witch"
+        results = action.run(query, profile="Witch")
+        results = json.loads(results)
+
+        assert len(results) == 2, "Witch should have 2 experiences"
+        assert "The game is intense with two players" in results[0]
+
+    @pytest.mark.asyncio
+    async def test_retrieve_filtering(self):
+        action = RetrieveExperiences(collection_name=self.collection_name)
+
+        query = "some test query"
+        profile = "TestRole"
+
+        excluded_version = ""
+        results = action.run(query, profile=profile, excluded_version=excluded_version)
+        results = json.loads(results)
+        assert len(results) == 3
+
+        excluded_version = self.version + "_21-30"
+        results = action.run(query, profile=profile, excluded_version=excluded_version)
+        results = json.loads(results)
+        assert len(results) == 2
+
+
+class TestActualRetrieve:
+    collection_name = "role_reflection"
+
+    @pytest.mark.asyncio
+    async def test_check_experience_pool(self):
+        logger.info("check experience pool")
+        action = RetrieveExperiences(collection_name=self.collection_name)
+        if action.engine:
+            all_experiences = action.engine.retriever._index._vector_store._collection.get()
+            logger.info(f"{len(all_experiences['metadatas'])=}")
+
+    @pytest.mark.asyncio
+    async def test_retrieve_werewolf_experience(self):
+        action = RetrieveExperiences(collection_name=self.collection_name)
+
+        query = "there are conflicts"
+
+        logger.info(f"test retrieval with {query=}")
+        action.run(query, "Werewolf")
+
+    @pytest.mark.asyncio
+    async def test_retrieve_villager_experience(self):
+        action = RetrieveExperiences(collection_name=self.collection_name)
+
+        query = "there are conflicts"
+
+        logger.info(f"test retrieval with {query=}")
+        results = action.run(query, "Seer")
+        assert "conflict" not in results  # 相似局面应该需要包含conflict关键词
+
+    @pytest.mark.asyncio
+    async def test_retrieve_villager_experience_filtering(self):
+        action = RetrieveExperiences(collection_name=self.collection_name)
+
+        query = "there are conflicts"
+
+        excluded_version = "01-10"
+        logger.info(f"test retrieval with {excluded_version=}")
+        results_01_10 = action.run(query, profile="Seer", excluded_version=excluded_version, verbose=True)
+
+        excluded_version = "11-20"
+        logger.info(f"test retrieval with {excluded_version=}")
+        results_11_20 = action.run(query, profile="Seer", excluded_version=excluded_version, verbose=True)
+
+        assert results_01_10 == results_11_20

tests/metagpt/test_environment.py

@@ -29,7 +29,7 @@ def test_add_role(env: Environment):
         name="Alice", profile="product manager", goal="create a new product", constraints="limited resources"
     )
     env.add_role(role)
-    assert env.get_role(role.profile) == role
+    assert env.get_role(str(role._setting)) == role
 
 
 def test_get_roles(env: Environment):