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README.md
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README.md
@ -129,35 +129,35 @@ plugins = [
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在 nonebot2 项目的 `.env` 文件中添加下表中的必填配置
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| 配置项 | 必填 | 默认值 | 说明 |
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| :---------------------------: | :--: | :-----------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| **全局配置** | | | |
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| `PROXY` | 否 | `None` | 下载模型等文件时使用的代理地址 |
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| **响应配置** | | | |
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| `NAILONG_BYPASS_SUPERUSER` | 否 | `True` | 是否不检查超级用户发送的图片 |
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| `NAILONG_BYPASS_ADMIN` | 否 | `True` | 是否不检查群组管理员发送的图片 |
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| `NAILONG_NEED_ADMIN` | 否 | `False` | 当自身不为群组管理员时是否不检查群内所有图片 |
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| `NAILONG_LIST_SCENES` | 否 | `[]` | 聊天场景 ID 黑白名单列表<br />在单级聊天下为该聊天 ID,如 QQ 群号;<br />在多级聊天下为以 `_` 分割的各级聊天 ID,如频道下的子频道或频道下私聊 |
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| `NAILONG_BLACKLIST` | 否 | `True` | 是否使用黑名单模式 |
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| `NAILONG_PRIORITY` | 否 | `100` | Matcher 优先级 |
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| **行为配置** | | | |
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| `NAILONG_RECALL` | 否 | `True` | 是否撤回消息 |
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| `NAILONG_MUTE_SECONDS` | 否 | `0` | 设置禁言时间,默认为 0 即不禁言<br/>单位:秒 |
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| `NAILONG_TIP` | 否 | `本群禁止发奶龙!` | 发送的提示,使用 [Alconna 的消息模板](https://nonebot.dev/docs/best-practice/alconna/uniseg#%E4%BD%BF%E7%94%A8%E6%B6%88%E6%81%AF%E6%A8%A1%E6%9D%BF),可用变量见下 |
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| `NAILONG_FAILED_TIP` | 否 | `{:Reply($message_id)}呜,不要发奶龙了嘛 🥺 👉👈` | 撤回失败或禁用撤回时发送的提示,同上 |
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| `NAILONG_CHECK_ALL_FRAMES` | 否 | `False` | 使用模型 1 时是否检查图片中的所有帧,启用该项后消息模板中的 `$checked_result` 变量当原图为动图时会变为动图 |
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| **模型通用配置** | | | |
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| `NAILONG_MODEL_DIR` | 否 | `./data/nailongremove` | 模型的下载位置 |
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| `NAILONG_MODEL` | 否 | `0` | 选择需要加载的模型,可用模型见下 |
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| `NAILONG_AUTO_UPDATE_MODEL` | 否 | `True` | 是否自动更新模型 |
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| `NAILONG_CONCURRENCY` | 否 | `1` | 当图片为动图时,针对该图片并发识别图片帧的最大并发数 |
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| `NAILONG_ONNX_TRY_TO_USE_GPU` | 否 | `True` | 加载 onnx 模型时是否尝试使用 GPU,如果失败则会显示一串警告但是对插件并无影响,如果不想看见警告关闭此配置项即可 |
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| **模型 1 特定配置** | | | |
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| `NAILONG_MODEL1_TYPE` | 否 | `tiny` | 模型 1 使用的模型类型,可用 `tiny` / `m` |
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| `NAILONG_MODEL1_YOLOX_SIZE` | 否 | `None` | 针对模型 1,自定义模型输入可能会有尺寸更改 |
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| `NAILONG_MODEL1_SCORE` | 否 | `0.5` | 模型 1 置信度阈值,范围 `0` ~ `1` |
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| **杂项配置** | | | |
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| `NAILONG_GITHUB_TOKEN` | 否 | `None` | GitHub Access Token,遇到模型下载或更新问题时可尝试填写 |
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| 配置项 | 必填 | 默认值 | 说明 |
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| :---------------------------: | :--: | :--------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
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| **全局配置** | | | |
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| `PROXY` | 否 | `None` | 下载模型等文件时使用的代理地址 |
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| **响应配置** | | | |
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| `NAILONG_BYPASS_SUPERUSER` | 否 | `True` | 是否不检查超级用户发送的图片 |
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| `NAILONG_BYPASS_ADMIN` | 否 | `True` | 是否不检查群组管理员发送的图片 |
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| `NAILONG_NEED_ADMIN` | 否 | `False` | 当自身不为群组管理员时是否不检查群内所有图片 |
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| `NAILONG_LIST_SCENES` | 否 | `[]` | 聊天场景 ID 黑白名单列表<br />在单级聊天下为该聊天 ID,如 QQ 群号;<br />在多级聊天下为以 `_` 分割的各级聊天 ID,如频道下的子频道或频道下私聊 |
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| `NAILONG_BLACKLIST` | 否 | `True` | 是否使用黑名单模式 |
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| `NAILONG_PRIORITY` | 否 | `100` | Matcher 优先级 |
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| **行为配置** | | | |
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| `NAILONG_RECALL` | 否 | `True` | 是否撤回消息 |
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| `NAILONG_MUTE_SECONDS` | 否 | `0` | 设置禁言时间,默认为 0 即不禁言<br/>单位:秒 |
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| `NAILONG_TIP` | 否 | `{"nailong": "本群禁止发奶龙!"}` | 发送的提示,使用 [Alconna 的消息模板](https://nonebot.dev/docs/best-practice/alconna/uniseg#%E4%BD%BF%E7%94%A8%E6%B6%88%E6%81%AF%E6%A8%A1%E6%9D%BF),可用变量见下,可以根据标签自定义对应值,如遇其中没有的标签会回退到 `nailong` |
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| `NAILONG_FAILED_TIP` | 否 | `{"nailong": "{:Reply($message_id)}呜,不要发奶龙了嘛 🥺 👉👈"}` | 撤回失败或禁用撤回时发送的提示,同上 |
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| `NAILONG_CHECK_ALL_FRAMES` | 否 | `False` | 使用模型 1 时是否检查图片中的所有帧,启用该项后消息模板中的 `$checked_result` 变量当原图为动图时会变为动图 |
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| **模型通用配置** | | | |
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| `NAILONG_MODEL_DIR` | 否 | `./data/nailongremove` | 模型的下载位置 |
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| `NAILONG_MODEL` | 否 | `0` | 选择需要加载的模型,可用模型见下 |
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| `NAILONG_AUTO_UPDATE_MODEL` | 否 | `True` | 是否自动更新模型 |
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| `NAILONG_CONCURRENCY` | 否 | `1` | 当图片为动图时,针对该图片并发识别图片帧的最大并发数 |
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| `NAILONG_ONNX_TRY_TO_USE_GPU` | 否 | `True` | 加载 onnx 模型时是否尝试使用 GPU,如果失败则会显示一串警告但是对插件并无影响,如果不想看见警告关闭此配置项即可 |
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| **模型 1 特定配置** | | | |
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| `NAILONG_MODEL1_TYPE` | 否 | `tiny` | 模型 1 使用的模型类型,可用 `tiny` / `m` |
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| `NAILONG_MODEL1_YOLOX_SIZE` | 否 | `None` | 针对模型 1,自定义模型输入可能会有尺寸更改 |
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| `NAILONG_MODEL1_SCORE` | 否 | `{"nailong": 0.5}` | 模型 1 置信度阈值,范围 `0` ~ `1`,可以根据标签自定义对应值,设置对应标签的阈值以检测该标签,设为 `null` 或者不填可以忽略该标签 |
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| **杂项配置** | | | |
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| `NAILONG_GITHUB_TOKEN` | 否 | `None` | GitHub Access Token,遇到模型下载或更新问题时可尝试填写 |
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### 可用模型
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@ -193,6 +193,7 @@ plugins = [
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- 支持了检查 GIF 中的所有帧并将结果重新封成 GIF,默认禁用,同时弃用 `$checked_image` 变量,新增 `$checked_result` 变量
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- 现在模型 1 的输入大小可以根据模型类型自动配置了,但是如果配置项指定了那么会优先使用
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- 支持处理含有其他标签的图片了,部分配置项支持根据标签自定义对应值
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### 2.2.1
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BIN
docs/assets/17.png
Normal file
BIN
docs/assets/17.png
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Binary file not shown.
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After Width: | Height: | Size: 348 KiB |
@ -162,7 +162,7 @@ nb plugin install nonebot-plugin-nailongremove
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如果想要使用模型 1,按照下面的操作来安装额外依赖
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如果想要使用模型 0,按照下面的操作来安装额外依赖
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先进入虚拟环境,进入虚拟环境后命令行左侧应该会多出来你虚拟环境的名字
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@ -173,7 +173,7 @@ nb sh
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再安装额外依赖
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```shell
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pip install "nonebot-plugin-nailongremove[model1]"
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pip install "nonebot-plugin-nailongremove[model0]"
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```
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安装过程如图
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@ -275,6 +275,25 @@ NAILONG_MODEL=1
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多行配置项例子:
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```properties
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NAILONG_MODEL1_SCORE='
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{
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"nailong": 0.75,
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"htgt": 0.75
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}
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'
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NAILONG_TIP='
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{
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"nailong": "本群禁止发奶龙!",
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"htgt": "疑似奶龙尸块"
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}
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'
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```
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### 更新插件
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在 Bot 项目目录下打开命令行,之后执行下面命令即可
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@ -9,7 +9,7 @@ require("nonebot_plugin_uninfo")
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from . import handler as handler
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from .config import Config
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__version__ = "2.2.1"
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__version__ = "2.3.0"
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__plugin_meta__ = PluginMetadata(
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name="自动撤回奶龙",
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description="一个基于图像分类模型的简单插件~",
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@ -1,11 +1,14 @@
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from enum import Enum, auto
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from pathlib import Path
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from typing import List, Optional, Tuple
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from typing import Any, Dict, List, Optional, Tuple
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from cookit import StrEnum
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from cookit.pyd import field_validator
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from nonebot import get_plugin_config
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from pydantic import BaseModel, Field
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DEFAULT_LABEL = "nailong"
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class ModelType(int, Enum):
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CLASSIFICATION = 0
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@ -36,23 +39,51 @@ class Config(BaseModel):
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nailong_recall: bool = True
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nailong_mute_seconds: int = 0
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nailong_tip: str = "本群禁止发送奶龙!"
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nailong_failed_tip: str = "{:Reply($message_id)}呜,不要发奶龙了嘛 🥺 👉👈"
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nailong_tip: Dict[str, str] = {
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DEFAULT_LABEL: "本群禁止发送奶龙!",
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}
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nailong_failed_tip: Dict[str, str] = {
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DEFAULT_LABEL: "{:Reply($message_id)}呜,不要发奶龙了嘛 🥺 👉👈",
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}
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nailong_check_all_frames: bool = False
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nailong_model_dir: Path = Field(
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default_factory=lambda: Path.cwd() / "data" / "nailongremove",
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)
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nailong_model: ModelType = ModelType.CLASSIFICATION
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nailong_model: ModelType = ModelType.TARGET_DETECTION
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nailong_auto_update_model: bool = True
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nailong_concurrency: int = 1
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nailong_onnx_try_to_use_gpu: bool = True
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nailong_model1_type: Model1Type = Model1Type.TINY
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nailong_model1_yolox_size: Optional[Tuple[int, int]] = None
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nailong_model1_score: float = 0.5
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nailong_model1_score: Dict[str, Optional[float]] = {
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DEFAULT_LABEL: 0.5,
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}
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nailong_github_token: Optional[str] = None
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@field_validator(
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"nailong_tip",
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"nailong_failed_tip",
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"nailong_model1_score",
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mode="before",
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)
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def transform_to_dict(cls, v: Any): # noqa: N805
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if not isinstance(v, dict):
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return {DEFAULT_LABEL: v}
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return v
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@field_validator(
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"nailong_tip",
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"nailong_failed_tip",
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# "nailong_model1_score",
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mode="after",
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)
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def check_default_label_exists(cls, v: Dict[str, Any]): # noqa: N805
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if DEFAULT_LABEL not in v:
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raise ValueError(f"Please ensure default label {DEFAULT_LABEL} in dict")
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return v
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config = get_plugin_config(Config)
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@ -10,7 +10,7 @@ from nonebot_plugin_uninfo import QryItrface, Uninfo
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from nonebot_plugin_nailongremove.frame_source import iter_sources_in_message
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from .config import config
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from .config import DEFAULT_LABEL, config
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from .model import check
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from .uniapi import mute, recall
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@ -100,7 +100,10 @@ async def handle_function(bot: BaseBot, ev: BaseEvent, msg: UniMsg, session: Uni
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functions.append(lambda: mute(bot, ev, config.nailong_mute_seconds))
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punish_ok = functions and (await execute_functions_any_ok(functions))
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template_str = config.nailong_tip if punish_ok else config.nailong_failed_tip
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template_dict = config.nailong_tip if punish_ok else config.nailong_failed_tip
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template_str = template_dict[
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check_res.label if (check_res.label in template_dict) else DEFAULT_LABEL
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]
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mapping = {
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"$event": ev,
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"$target": msg.get_target(),
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@ -16,13 +16,13 @@ def raise_extra_import_error(e: BaseException, group: str) -> NoReturn:
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check: Callable[[FrameSource], Awaitable[CheckResult]]
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if config.nailong_model is ModelType.CLASSIFICATION:
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from .classification import check as check
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try:
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from .classification import check as check
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except ImportError as e:
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raise_extra_import_error(e, "model0")
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elif config.nailong_model is ModelType.TARGET_DETECTION:
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try:
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from .target_detection import check as check
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except ImportError as e:
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raise_extra_import_error(e, "model1")
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from .target_detection import check as check
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else:
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raise ValueError("Invalid model type")
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@ -7,6 +7,7 @@ from nonebot.utils import run_sync
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from torch import nn
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from torchvision import transforms
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from ..config import DEFAULT_LABEL
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from ..frame_source import FrameSource
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from .utils.common import CheckResult, CheckSingleResult, race_check
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from .utils.update import GitHubRepoModelUpdater
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@ -36,7 +37,7 @@ SIZE = 224
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@run_sync
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def check_single(image: np.ndarray) -> CheckSingleResult[None]:
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if image.shape[0] < SIZE or image.shape[1] < SIZE:
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return CheckSingleResult(ok=False, extra=None)
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return CheckSingleResult.not_ok(None)
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image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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image = cv2.resize(image, (SIZE, SIZE))
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image = transform(image)
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@ -44,9 +45,9 @@ def check_single(image: np.ndarray) -> CheckSingleResult[None]:
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with torch.no_grad():
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output = model(image.to(device)) # type: ignore
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_, pred = torch.max(output, 1)
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return CheckSingleResult(ok=pred.item() == 1, extra=None)
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return CheckSingleResult(ok=pred.item() == 1, label=DEFAULT_LABEL, extra=None)
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async def check(source: FrameSource):
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res = await race_check(check_single, source)
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return CheckResult(ok=bool(res))
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return CheckResult(ok=bool(res), label=res.label if res else None)
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@ -102,40 +102,49 @@ def _check_single(frame: np.ndarray) -> CheckSingleResult[Optional[Detections]]:
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boxes_xyxy /= ratio
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dets = multiclass_nms(boxes_xyxy, scores, nms_thr=0.45, score_thr=0.1)
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if dets is None:
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return CheckSingleResult(ok=False, extra=None)
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return CheckSingleResult.not_ok(None)
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final_boxes, final_scores, final_cls_ids = (
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dets[:, :4], # type: ignore
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dets[:, 4], # type: ignore
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dets[:, 5], # type: ignore
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)
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has = any(
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True
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for c, s in zip(final_cls_ids, final_scores)
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if labels[int(c)] == "nailong" and s >= config.nailong_model1_score
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)
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if has:
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return CheckSingleResult(
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ok=True,
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extra=Detections(final_boxes, final_scores, final_cls_ids),
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)
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return CheckSingleResult(ok=False, extra=None)
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for c, s in zip(final_cls_ids, final_scores):
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label = labels[int(c)]
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expected = config.nailong_model1_score.get(label)
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if (expected is not None) and s >= expected:
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return CheckSingleResult(
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ok=True,
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label=label,
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extra=Detections(final_boxes, final_scores, final_cls_ids),
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)
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return CheckSingleResult.not_ok(None)
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async def check_single(frame: np.ndarray) -> CheckSingleResult[FrameInfo]:
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res = await _check_single(frame)
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return CheckSingleResult(ok=res.ok, extra=FrameInfo(frame, res.extra))
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return CheckSingleResult(
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ok=res.ok,
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label=res.label,
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extra=FrameInfo(frame, res.extra),
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)
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async def check(source: FrameSource) -> CheckResult:
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label = None
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extra_vars = {}
|
||||
if config.nailong_check_all_frames:
|
||||
sem = asyncio.Semaphore(config.nailong_concurrency)
|
||||
results = asyncio.gather(
|
||||
results = await asyncio.gather(
|
||||
*(with_semaphore(sem)(check_single)(frame) for frame in source),
|
||||
)
|
||||
ok = any(r.ok for r in results)
|
||||
if ok:
|
||||
all_labels = {r.label for r in results if r.label}
|
||||
label = next(
|
||||
(x for x in config.nailong_model1_score if x in all_labels),
|
||||
None,
|
||||
)
|
||||
extra_vars["$checked_result"] = await repack_save(
|
||||
source,
|
||||
(r.extra.vis() for r in results),
|
||||
@ -144,8 +153,9 @@ async def check(source: FrameSource) -> CheckResult:
|
||||
res = await race_check(check_single, source)
|
||||
ok = bool(res)
|
||||
if res:
|
||||
label = res.label
|
||||
extra_vars["$checked_result"] = await repack_save(
|
||||
source,
|
||||
iter((res.extra.vis(),)),
|
||||
)
|
||||
return CheckResult(ok, extra_vars)
|
||||
return CheckResult(ok, label, extra_vars)
|
||||
|
||||
@ -14,14 +14,24 @@ T = TypeVar("T")
|
||||
@dataclass
|
||||
class CheckSingleResult(Generic[T]):
|
||||
ok: bool
|
||||
label: Optional[str]
|
||||
extra: T
|
||||
|
||||
@classmethod
|
||||
def not_ok(cls, extra: T):
|
||||
return cls(ok=False, label=None, extra=extra)
|
||||
|
||||
|
||||
@dataclass
|
||||
class CheckResult:
|
||||
ok: bool
|
||||
label: Optional[str]
|
||||
extra_vars: Dict[str, Any] = field(default_factory=dict)
|
||||
|
||||
@classmethod
|
||||
def not_ok(cls):
|
||||
return cls(ok=False, label=None, extra_vars={})
|
||||
|
||||
|
||||
FrameChecker: TypeAlias = Callable[[np.ndarray], Awaitable[CheckSingleResult[T]]]
|
||||
|
||||
@ -38,7 +48,7 @@ async def race_check(
|
||||
try:
|
||||
frame = next(iterator)
|
||||
except StopIteration:
|
||||
return CheckSingleResult(ok=False, extra=None)
|
||||
return CheckSingleResult.not_ok(None)
|
||||
res = await checker(frame)
|
||||
if res.ok:
|
||||
return res
|
||||
|
||||
247
pdm.lock
generated
247
pdm.lock
generated
@ -2,10 +2,10 @@
|
||||
# It is not intended for manual editing.
|
||||
|
||||
[metadata]
|
||||
groups = ["default", "dev", "model1"]
|
||||
groups = ["default", "all", "dev", "model0", "model1"]
|
||||
strategy = ["inherit_metadata"]
|
||||
lock_version = "4.5.0"
|
||||
content_hash = "sha256:d860059b4caf6cf9e7e4d517bee3edfb2ee328d9ac9060de7c25198f5112ef5a"
|
||||
content_hash = "sha256:961c2e12bd8ba824d4943a23b06d0bbf3cd9df4e383867065b5a9ea1dcb2fd80"
|
||||
|
||||
[[metadata.targets]]
|
||||
requires_python = "~=3.9"
|
||||
@ -173,7 +173,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "arclet-alconna"
|
||||
version = "1.8.31"
|
||||
version = "1.8.32"
|
||||
requires_python = ">=3.9"
|
||||
summary = "A High-performance, Generality, Humane Command Line Arguments Parser Library."
|
||||
groups = ["default"]
|
||||
@ -183,8 +183,8 @@ dependencies = [
|
||||
"typing-extensions>=4.5.0",
|
||||
]
|
||||
files = [
|
||||
{file = "arclet_alconna-1.8.31-py3-none-any.whl", hash = "sha256:94e015163649b41af7a880fef7165ad85df6e2c812129de7d680d87d7b5e7b52"},
|
||||
{file = "arclet_alconna-1.8.31.tar.gz", hash = "sha256:2d621068fc79e2febe0cece5512bd79bb207071b8fbb56e61b2a0565489c8551"},
|
||||
{file = "arclet_alconna-1.8.32-py3-none-any.whl", hash = "sha256:3cd0437d6cf42e4a85290aceffe72264010089af70041f16f78c820549fafa82"},
|
||||
{file = "arclet_alconna-1.8.32.tar.gz", hash = "sha256:51626e9d5a6e107139adcf060cda3d737c49459f8798bc91dab18817818b8865"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -298,7 +298,7 @@ name = "coloredlogs"
|
||||
version = "15.0.1"
|
||||
requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
summary = "Colored terminal output for Python's logging module"
|
||||
groups = ["model1"]
|
||||
groups = ["default"]
|
||||
dependencies = [
|
||||
"humanfriendly>=9.1",
|
||||
]
|
||||
@ -323,13 +323,28 @@ files = [
|
||||
{file = "cookit-0.8.1.tar.gz", hash = "sha256:0d4ac51db51ee2c306b442fa6493897387972739da7307a80d998d06911dc4f2"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "cookit"
|
||||
version = "0.8.1"
|
||||
extras = ["pydantic"]
|
||||
requires_python = "<4.0,>=3.9"
|
||||
summary = "A toolkit for self use."
|
||||
groups = ["default"]
|
||||
dependencies = [
|
||||
"cookit==0.8.1",
|
||||
"pydantic!=2.5.0,!=2.5.1,<3.0.0,>=1.10.0",
|
||||
]
|
||||
files = [
|
||||
{file = "cookit-0.8.1-py3-none-any.whl", hash = "sha256:382abe6604e0dc0444041a2ac416c0464172d2cad0e5c6ddfd0301a60fdaa357"},
|
||||
{file = "cookit-0.8.1.tar.gz", hash = "sha256:0d4ac51db51ee2c306b442fa6493897387972739da7307a80d998d06911dc4f2"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "exceptiongroup"
|
||||
version = "1.2.2"
|
||||
requires_python = ">=3.7"
|
||||
summary = "Backport of PEP 654 (exception groups)"
|
||||
groups = ["default", "dev"]
|
||||
marker = "python_version < \"3.11\""
|
||||
files = [
|
||||
{file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"},
|
||||
{file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"},
|
||||
@ -340,7 +355,7 @@ name = "filelock"
|
||||
version = "3.16.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "A platform independent file lock."
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
files = [
|
||||
{file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
|
||||
{file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
|
||||
@ -350,7 +365,7 @@ files = [
|
||||
name = "flatbuffers"
|
||||
version = "24.3.25"
|
||||
summary = "The FlatBuffers serialization format for Python"
|
||||
groups = ["model1"]
|
||||
groups = ["default"]
|
||||
files = [
|
||||
{file = "flatbuffers-24.3.25-py2.py3-none-any.whl", hash = "sha256:8dbdec58f935f3765e4f7f3cf635ac3a77f83568138d6a2311f524ec96364812"},
|
||||
{file = "flatbuffers-24.3.25.tar.gz", hash = "sha256:de2ec5b203f21441716617f38443e0a8ebf3d25bf0d9c0bb0ce68fa00ad546a4"},
|
||||
@ -457,7 +472,7 @@ name = "fsspec"
|
||||
version = "2024.10.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "File-system specification"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
files = [
|
||||
{file = "fsspec-2024.10.0-py3-none-any.whl", hash = "sha256:03b9a6785766a4de40368b88906366755e2819e758b83705c88cd7cb5fe81871"},
|
||||
{file = "fsspec-2024.10.0.tar.gz", hash = "sha256:eda2d8a4116d4f2429db8550f2457da57279247dd930bb12f821b58391359493"},
|
||||
@ -625,7 +640,7 @@ name = "humanfriendly"
|
||||
version = "10.0"
|
||||
requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
|
||||
summary = "Human friendly output for text interfaces using Python"
|
||||
groups = ["model1"]
|
||||
groups = ["default"]
|
||||
dependencies = [
|
||||
"monotonic; python_version == \"2.7\"",
|
||||
"pyreadline3; sys_platform == \"win32\" and python_version >= \"3.8\"",
|
||||
@ -678,7 +693,7 @@ name = "jinja2"
|
||||
version = "3.1.4"
|
||||
requires_python = ">=3.7"
|
||||
summary = "A very fast and expressive template engine."
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
dependencies = [
|
||||
"MarkupSafe>=2.0",
|
||||
]
|
||||
@ -743,7 +758,7 @@ name = "markupsafe"
|
||||
version = "3.0.2"
|
||||
requires_python = ">=3.9"
|
||||
summary = "Safely add untrusted strings to HTML/XML markup."
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
files = [
|
||||
{file = "MarkupSafe-3.0.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7e94c425039cde14257288fd61dcfb01963e658efbc0ff54f5306b06054700f8"},
|
||||
{file = "MarkupSafe-3.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e2d922824181480953426608b81967de705c3cef4d1af983af849d7bd619158"},
|
||||
@ -855,7 +870,7 @@ files = [
|
||||
name = "mpmath"
|
||||
version = "1.3.0"
|
||||
summary = "Python library for arbitrary-precision floating-point arithmetic"
|
||||
groups = ["default", "model1"]
|
||||
groups = ["default", "all", "model0"]
|
||||
files = [
|
||||
{file = "mpmath-1.3.0-py3-none-any.whl", hash = "sha256:a0b2b9fe80bbcd81a6647ff13108738cfb482d481d826cc0e02f5b35e5c88d2c"},
|
||||
{file = "mpmath-1.3.0.tar.gz", hash = "sha256:7a28eb2a9774d00c7bc92411c19a89209d5da7c4c9a9e227be8330a23a25b91f"},
|
||||
@ -1045,7 +1060,7 @@ name = "networkx"
|
||||
version = "3.2.1"
|
||||
requires_python = ">=3.9"
|
||||
summary = "Python package for creating and manipulating graphs and networks"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
files = [
|
||||
{file = "networkx-3.2.1-py3-none-any.whl", hash = "sha256:f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2"},
|
||||
{file = "networkx-3.2.1.tar.gz", hash = "sha256:9f1bb5cf3409bf324e0a722c20bdb4c20ee39bf1c30ce8ae499c8502b0b5e0c6"},
|
||||
@ -1286,11 +1301,13 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "nonebot2"
|
||||
version = "2.3.3"
|
||||
version = "2.4.0"
|
||||
requires_python = "<4.0,>=3.9"
|
||||
summary = "An asynchronous python bot framework."
|
||||
groups = ["default", "dev"]
|
||||
dependencies = [
|
||||
"anyio<5.0.0,>=4.4.0",
|
||||
"exceptiongroup<2.0.0,>=1.2.2",
|
||||
"loguru<1.0.0,>=0.6.0",
|
||||
"pydantic!=2.5.0,!=2.5.1,<3.0.0,>=1.10.0",
|
||||
"pygtrie<3.0.0,>=2.4.1",
|
||||
@ -1300,8 +1317,8 @@ dependencies = [
|
||||
"yarl<2.0.0,>=1.7.2",
|
||||
]
|
||||
files = [
|
||||
{file = "nonebot2-2.3.3-py3-none-any.whl", hash = "sha256:5bc8d073091347f29c4a1a2f927c24a8941e5d286c77139376259318b9bbfc68"},
|
||||
{file = "nonebot2-2.3.3.tar.gz", hash = "sha256:4fa7707de5d708c27cc49493bc78a07fee2ba01f5516835a2ea5fbebb49b9dfa"},
|
||||
{file = "nonebot2-2.4.0-py3-none-any.whl", hash = "sha256:7c712e05561afa4795c9135a5b27a43d076220f4538ffec518e68c344e3e51d4"},
|
||||
{file = "nonebot2-2.4.0.tar.gz", hash = "sha256:4b10e33d389847500c9bde9ef3c5533b604a90ca1529750245f1aaf82b28f1e1"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -1309,7 +1326,7 @@ name = "numpy"
|
||||
version = "2.0.2"
|
||||
requires_python = ">=3.9"
|
||||
summary = "Fundamental package for array computing in Python"
|
||||
groups = ["default", "model1"]
|
||||
groups = ["default", "all", "model0"]
|
||||
files = [
|
||||
{file = "numpy-2.0.2-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:51129a29dbe56f9ca83438b706e2e69a39892b5eda6cedcb6b0c9fdc9b0d3ece"},
|
||||
{file = "numpy-2.0.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f15975dfec0cf2239224d80e32c3170b1d168335eaedee69da84fbe9f1f9cd04"},
|
||||
@ -1363,7 +1380,7 @@ name = "nvidia-cublas-cu12"
|
||||
version = "12.4.5.8"
|
||||
requires_python = ">=3"
|
||||
summary = "CUBLAS native runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cublas_cu12-12.4.5.8-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0f8aa1706812e00b9f19dfe0cdb3999b092ccb8ca168c0db5b8ea712456fd9b3"},
|
||||
@ -1376,7 +1393,7 @@ name = "nvidia-cuda-cupti-cu12"
|
||||
version = "12.4.127"
|
||||
requires_python = ">=3"
|
||||
summary = "CUDA profiling tools runtime libs."
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cuda_cupti_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:79279b35cf6f91da114182a5ce1864997fd52294a87a16179ce275773799458a"},
|
||||
@ -1389,7 +1406,7 @@ name = "nvidia-cuda-nvrtc-cu12"
|
||||
version = "12.4.127"
|
||||
requires_python = ">=3"
|
||||
summary = "NVRTC native runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cuda_nvrtc_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:0eedf14185e04b76aa05b1fea04133e59f465b6f960c0cbf4e37c3cb6b0ea198"},
|
||||
@ -1402,7 +1419,7 @@ name = "nvidia-cuda-runtime-cu12"
|
||||
version = "12.4.127"
|
||||
requires_python = ">=3"
|
||||
summary = "CUDA Runtime native Libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_cuda_runtime_cu12-12.4.127-py3-none-manylinux2014_aarch64.whl", hash = "sha256:961fe0e2e716a2a1d967aab7caee97512f71767f852f67432d572e36cb3a11f3"},
|
||||
@ -1415,7 +1432,7 @@ name = "nvidia-cudnn-cu12"
|
||||
version = "9.1.0.70"
|
||||
requires_python = ">=3"
|
||||
summary = "cuDNN runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
dependencies = [
|
||||
"nvidia-cublas-cu12",
|
||||
@ -1430,7 +1447,7 @@ name = "nvidia-cufft-cu12"
|
||||
version = "11.2.1.3"
|
||||
requires_python = ">=3"
|
||||
summary = "CUFFT native runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
dependencies = [
|
||||
"nvidia-nvjitlink-cu12",
|
||||
@ -1446,7 +1463,7 @@ name = "nvidia-curand-cu12"
|
||||
version = "10.3.5.147"
|
||||
requires_python = ">=3"
|
||||
summary = "CURAND native runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
{file = "nvidia_curand_cu12-10.3.5.147-py3-none-manylinux2014_aarch64.whl", hash = "sha256:1f173f09e3e3c76ab084aba0de819c49e56614feae5c12f69883f4ae9bb5fad9"},
|
||||
@ -1459,7 +1476,7 @@ name = "nvidia-cusolver-cu12"
|
||||
version = "11.6.1.9"
|
||||
requires_python = ">=3"
|
||||
summary = "CUDA solver native runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
dependencies = [
|
||||
"nvidia-cublas-cu12",
|
||||
@ -1477,7 +1494,7 @@ name = "nvidia-cusparse-cu12"
|
||||
version = "12.3.1.170"
|
||||
requires_python = ">=3"
|
||||
summary = "CUSPARSE native runtime libraries"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
dependencies = [
|
||||
"nvidia-nvjitlink-cu12",
|
||||
@ -1493,7 +1510,7 @@ name = "nvidia-nccl-cu12"
|
||||
version = "2.21.5"
|
||||
requires_python = ">=3"
|
||||
summary = "NVIDIA Collective Communication Library (NCCL) Runtime"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\""
|
||||
files = [
|
||||
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@ -1504,7 +1521,7 @@ name = "nvidia-nvjitlink-cu12"
|
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|
||||
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|
||||
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|
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|
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|
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|
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@ -1517,7 +1534,7 @@ name = "nvidia-nvtx-cu12"
|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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@ -1527,9 +1544,9 @@ files = [
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|
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[[package]]
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
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|
||||
"coloredlogs",
|
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"flatbuffers",
|
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@ -1539,33 +1556,34 @@ dependencies = [
|
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"sympy",
|
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|
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|
||||
{file = "onnxruntime-1.20.0-cp313-cp313t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:640ad9ea72d322f0325a51544eddb54f4fa843c4348573c88a9cb44f46678f3f"},
|
||||
{file = "onnxruntime-1.20.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:dc4e7c10c98c1f407835448c26a7e14ebff3234f131e1fbc53bd9500c828df89"},
|
||||
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|
||||
|
||||
[[package]]
|
||||
name = "onnxruntime-gpu"
|
||||
version = "1.19.2"
|
||||
version = "1.20.0"
|
||||
summary = "ONNX Runtime is a runtime accelerator for Machine Learning models"
|
||||
groups = ["model1"]
|
||||
groups = ["default"]
|
||||
dependencies = [
|
||||
"coloredlogs",
|
||||
"flatbuffers",
|
||||
@ -1575,14 +1593,15 @@ dependencies = [
|
||||
"sympy",
|
||||
]
|
||||
files = [
|
||||
{file = "onnxruntime_gpu-1.19.2-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:a49740e079e7c5215830d30cde3df792e903df007aa0b0fd7aa797937061b27a"},
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|
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|
||||
{file = "onnxruntime_gpu-1.20.0-cp313-cp313t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:cbe57d92e932ef9daf6f35508abf9f5ad8d28d5b6900770fac461a3b30b99466"},
|
||||
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|
||||
|
||||
[[package]]
|
||||
@ -1701,7 +1720,7 @@ name = "packaging"
|
||||
version = "24.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Core utilities for Python packages"
|
||||
groups = ["default", "dev", "model1"]
|
||||
groups = ["default", "dev"]
|
||||
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|
||||
{file = "packaging-24.1-py3-none-any.whl", hash = "sha256:5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124"},
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|
||||
@ -1712,7 +1731,7 @@ name = "pillow"
|
||||
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|
||||
requires_python = ">=3.9"
|
||||
summary = "Python Imaging Library (Fork)"
|
||||
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|
||||
groups = ["default", "all", "model0"]
|
||||
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|
||||
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|
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|
||||
@ -1887,7 +1906,7 @@ name = "protobuf"
|
||||
version = "5.28.3"
|
||||
requires_python = ">=3.8"
|
||||
summary = ""
|
||||
groups = ["model1"]
|
||||
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|
||||
files = [
|
||||
{file = "protobuf-5.28.3-cp310-abi3-win32.whl", hash = "sha256:0c4eec6f987338617072592b97943fdbe30d019c56126493111cf24344c1cc24"},
|
||||
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|
||||
@ -2060,7 +2079,7 @@ name = "pyreadline3"
|
||||
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|
||||
requires_python = ">=3.8"
|
||||
summary = "A python implementation of GNU readline."
|
||||
groups = ["model1"]
|
||||
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|
||||
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|
||||
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|
||||
@ -2139,7 +2158,7 @@ files = [
|
||||
|
||||
[[package]]
|
||||
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|
||||
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|
||||
version = "13.9.4"
|
||||
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|
||||
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|
||||
groups = ["default"]
|
||||
@ -2149,8 +2168,8 @@ dependencies = [
|
||||
"typing-extensions<5.0,>=4.0.0; python_version < \"3.11\"",
|
||||
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|
||||
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||||
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|
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|
||||
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|
||||
|
||||
[[package]]
|
||||
@ -2158,7 +2177,7 @@ name = "setuptools"
|
||||
version = "75.3.0"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Easily download, build, install, upgrade, and uninstall Python packages"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "python_version >= \"3.12\""
|
||||
files = [
|
||||
{file = "setuptools-75.3.0-py3-none-any.whl", hash = "sha256:f2504966861356aa38616760c0f66568e535562374995367b4e69c7143cf6bcd"},
|
||||
@ -2206,7 +2225,7 @@ name = "sympy"
|
||||
version = "1.13.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Computer algebra system (CAS) in Python"
|
||||
groups = ["default", "model1"]
|
||||
groups = ["default", "all", "model0"]
|
||||
dependencies = [
|
||||
"mpmath<1.4,>=1.1.0",
|
||||
]
|
||||
@ -2318,10 +2337,10 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.5.0"
|
||||
version = "2.5.1"
|
||||
requires_python = ">=3.8.0"
|
||||
summary = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
dependencies = [
|
||||
"filelock",
|
||||
"fsspec",
|
||||
@ -2346,57 +2365,53 @@ dependencies = [
|
||||
"typing-extensions>=4.8.0",
|
||||
]
|
||||
files = [
|
||||
{file = "torch-2.5.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:7f179373a047b947dec448243f4e6598a1c960fa3bb978a9a7eecd529fbc363f"},
|
||||
{file = "torch-2.5.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:15fbc95e38d330e5b0ef1593b7bc0a19f30e5bdad76895a5cffa1a6a044235e9"},
|
||||
{file = "torch-2.5.0-cp310-cp310-win_amd64.whl", hash = "sha256:f499212f1cffea5d587e5f06144630ed9aa9c399bba12ec8905798d833bd1404"},
|
||||
{file = "torch-2.5.0-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:c54db1fade17287aabbeed685d8e8ab3a56fea9dd8d46e71ced2da367f09a49f"},
|
||||
{file = "torch-2.5.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:499a68a756d3b30d10f7e0f6214dc3767b130b797265db3b1c02e9094e2a07be"},
|
||||
{file = "torch-2.5.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:9f3df8138a1126a851440b7d5a4869bfb7c9cc43563d64fd9d96d0465b581024"},
|
||||
{file = "torch-2.5.0-cp311-cp311-win_amd64.whl", hash = "sha256:b81da3bdb58c9de29d0e1361e52f12fcf10a89673f17a11a5c6c7da1cb1a8376"},
|
||||
{file = "torch-2.5.0-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:ba135923295d564355326dc409b6b7f5bd6edc80f764cdaef1fb0a1b23ff2f9c"},
|
||||
{file = "torch-2.5.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:2dd40c885a05ef7fe29356cca81be1435a893096ceb984441d6e2c27aff8c6f4"},
|
||||
{file = "torch-2.5.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:bc52d603d87fe1da24439c0d5fdbbb14e0ae4874451d53f0120ffb1f6c192727"},
|
||||
{file = "torch-2.5.0-cp312-cp312-win_amd64.whl", hash = "sha256:ea718746469246cc63b3353afd75698a288344adb55e29b7f814a5d3c0a7c78d"},
|
||||
{file = "torch-2.5.0-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:6de1fd253e27e7f01f05cd7c37929ae521ca23ca4620cfc7c485299941679112"},
|
||||
{file = "torch-2.5.0-cp313-cp313-manylinux1_x86_64.whl", hash = "sha256:83dcf518685db20912b71fc49cbddcc8849438cdb0e9dcc919b02a849e2cd9e8"},
|
||||
{file = "torch-2.5.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:65e0a60894435608334d68c8811e55fd8f73e5bf8ee6f9ccedb0064486a7b418"},
|
||||
{file = "torch-2.5.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:38c21ff1bd39f076d72ab06e3c88c2ea6874f2e6f235c9450816b6c8e7627094"},
|
||||
{file = "torch-2.5.0-cp39-cp39-win_amd64.whl", hash = "sha256:ce4baeba9804da5a346e210b3b70826f5811330c343e4fe1582200359ee77fe5"},
|
||||
{file = "torch-2.5.0-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:03e53f577a96e4d41aca472da8faa40e55df89d2273664af390ce1f570e885bd"},
|
||||
{file = "torch-2.5.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:71328e1bbe39d213b8721678f9dcac30dfc452a46d586f1d514a6aa0a99d4744"},
|
||||
{file = "torch-2.5.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:34bfa1a852e5714cbfa17f27c49d8ce35e1b7af5608c4bc6e81392c352dbc601"},
|
||||
{file = "torch-2.5.1-cp310-cp310-win_amd64.whl", hash = "sha256:32a037bd98a241df6c93e4c789b683335da76a2ac142c0973675b715102dc5fa"},
|
||||
{file = "torch-2.5.1-cp310-none-macosx_11_0_arm64.whl", hash = "sha256:23d062bf70776a3d04dbe74db950db2a5245e1ba4f27208a87f0d743b0d06e86"},
|
||||
{file = "torch-2.5.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:de5b7d6740c4b636ef4db92be922f0edc425b65ed78c5076c43c42d362a45457"},
|
||||
{file = "torch-2.5.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:340ce0432cad0d37f5a31be666896e16788f1adf8ad7be481196b503dad675b9"},
|
||||
{file = "torch-2.5.1-cp311-cp311-win_amd64.whl", hash = "sha256:603c52d2fe06433c18b747d25f5c333f9c1d58615620578c326d66f258686f9a"},
|
||||
{file = "torch-2.5.1-cp311-none-macosx_11_0_arm64.whl", hash = "sha256:31f8c39660962f9ae4eeec995e3049b5492eb7360dd4f07377658ef4d728fa4c"},
|
||||
{file = "torch-2.5.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:ed231a4b3a5952177fafb661213d690a72caaad97d5824dd4fc17ab9e15cec03"},
|
||||
{file = "torch-2.5.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:3f4b7f10a247e0dcd7ea97dc2d3bfbfc90302ed36d7f3952b0008d0df264e697"},
|
||||
{file = "torch-2.5.1-cp312-cp312-win_amd64.whl", hash = "sha256:73e58e78f7d220917c5dbfad1a40e09df9929d3b95d25e57d9f8558f84c9a11c"},
|
||||
{file = "torch-2.5.1-cp312-none-macosx_11_0_arm64.whl", hash = "sha256:8c712df61101964eb11910a846514011f0b6f5920c55dbf567bff8a34163d5b1"},
|
||||
{file = "torch-2.5.1-cp313-cp313-manylinux1_x86_64.whl", hash = "sha256:9b61edf3b4f6e3b0e0adda8b3960266b9009d02b37555971f4d1c8f7a05afed7"},
|
||||
{file = "torch-2.5.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:1f3b7fb3cf7ab97fae52161423f81be8c6b8afac8d9760823fd623994581e1a3"},
|
||||
{file = "torch-2.5.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:7974e3dce28b5a21fb554b73e1bc9072c25dde873fa00d54280861e7a009d7dc"},
|
||||
{file = "torch-2.5.1-cp39-cp39-win_amd64.whl", hash = "sha256:46c817d3ea33696ad3b9df5e774dba2257e9a4cd3c4a3afbf92f6bb13ac5ce2d"},
|
||||
{file = "torch-2.5.1-cp39-none-macosx_11_0_arm64.whl", hash = "sha256:8046768b7f6d35b85d101b4b38cba8aa2f3cd51952bc4c06a49580f2ce682291"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "torchvision"
|
||||
version = "0.20.0"
|
||||
version = "0.20.1"
|
||||
requires_python = ">=3.8"
|
||||
summary = "image and video datasets and models for torch deep learning"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
dependencies = [
|
||||
"numpy",
|
||||
"pillow!=8.3.*,>=5.3.0",
|
||||
"torch==2.5.0",
|
||||
"torch==2.5.1",
|
||||
]
|
||||
files = [
|
||||
{file = "torchvision-0.20.0-1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:e084f50ecbdbe7a9cc2fc51ea0367ae35fde46e84a964bf4046cb1c7feb7e3e6"},
|
||||
{file = "torchvision-0.20.0-1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:55d7f43ef912ebc4da4bba73a0bbf387d38a6be9cd521679c0f4056f9564b698"},
|
||||
{file = "torchvision-0.20.0-1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:f8d0213489acfb138369f2455a6893880c194a8195e381c19f872b277f2654c3"},
|
||||
{file = "torchvision-0.20.0-1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8d6cea8ab0bf72ecb71b07cd0fe836eacf5a5fa98f6629d2261212e90977b963"},
|
||||
{file = "torchvision-0.20.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f164d545965186ffd66014e34a966706d12c84198302dd46748cae45984609a4"},
|
||||
{file = "torchvision-0.20.0-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:9c18208575d60b96e7d53a09c453781afea4a81487c9ebc501dfc2bc88daa308"},
|
||||
{file = "torchvision-0.20.0-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:09080359be90314fc4fdd64b11a4d231c1999018f19d58bf7764f5e15f8e9fb3"},
|
||||
{file = "torchvision-0.20.0-cp310-cp310-win_amd64.whl", hash = "sha256:a7d46cf096007b7e8df1bddad7375427664a064bc05d9cbff5d506b73c1ab8ca"},
|
||||
{file = "torchvision-0.20.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:a15de6266a36bcd10d89f6f3d7ba4e2dd567a7a0add616ebc6e65aea20790e5d"},
|
||||
{file = "torchvision-0.20.0-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:b64d9f83cf201ebda4f6b03533e4918fa0b4223b28b0ee3cbede15b8174c7cbd"},
|
||||
{file = "torchvision-0.20.0-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:d80eb740810804bac4b8e6b6411946ab286a1ee1d731db36af2f885333254802"},
|
||||
{file = "torchvision-0.20.0-cp311-cp311-win_amd64.whl", hash = "sha256:1fd045757335d34969d176fc5688b643d201860cb45b48ce8d5d8fb90868f746"},
|
||||
{file = "torchvision-0.20.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:ac0edba534fb071b2b03a2fd5cbbf9b7c259896d17a1d0d830b3c5b7dfae0782"},
|
||||
{file = "torchvision-0.20.0-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:c8f3bc399d9c3e4ba05d74ca6dd5e63fed08ad5c5b302a946c8fcaa56216220f"},
|
||||
{file = "torchvision-0.20.0-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:a78c99ebe1a62857b68e97ff9417b92f299f2ee61f009491a114ddad050c493d"},
|
||||
{file = "torchvision-0.20.0-cp312-cp312-win_amd64.whl", hash = "sha256:bb0da0950d2034a0412c251a3a9117ff9612157f45177d37ba1b20b472c0864b"},
|
||||
{file = "torchvision-0.20.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6a70c81ea5068dd7b1e340ebeabb65364576d8b9819454cfdf812290cf03e45a"},
|
||||
{file = "torchvision-0.20.0-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:95d8c817681a4c2156f66ef83cafc4c5c4b97e4694956d54d7dc554804ee510d"},
|
||||
{file = "torchvision-0.20.0-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:1ab53244701eab897e5c65026ba178c0abbc5bd08629c3d20f737d618e9e5a37"},
|
||||
{file = "torchvision-0.20.0-cp39-cp39-win_amd64.whl", hash = "sha256:47d0751aeaa7057ee6a5973d35e7acad3ad7c17b8e57a2c4304d13e001e330ae"},
|
||||
{file = "torchvision-0.20.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:4878fefb96ef293d06c27210918adc83c399d9faaf34cda5a63e129f772328f1"},
|
||||
{file = "torchvision-0.20.1-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:8ffbdf8bf5b30eade22d459f5a313329eeadb20dc75efa142987b53c007098c3"},
|
||||
{file = "torchvision-0.20.1-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:75f8a4d51a593c4bab6c9bf7d75bdd88691b00a53b07656678bc55a3a753dd73"},
|
||||
{file = "torchvision-0.20.1-cp310-cp310-win_amd64.whl", hash = "sha256:22c2fa44e20eb404b85e42b22b453863a14b0927d25e550fd4f84eea97fa5b39"},
|
||||
{file = "torchvision-0.20.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:344b339e15e6bbb59ee0700772616d0afefd209920c762b1604368d8c3458322"},
|
||||
{file = "torchvision-0.20.1-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:86f6523dee420000fe14c3527f6c8e0175139fda7d995b187f54a0b0ebec7eb6"},
|
||||
{file = "torchvision-0.20.1-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:a40d766345927639da322c693934e5f91b1ba2218846c7104b868dea2314ce8e"},
|
||||
{file = "torchvision-0.20.1-cp311-cp311-win_amd64.whl", hash = "sha256:5b501d5c04b034d2ecda96a31ed050e383cf8201352e4c9276ca249cbecfded0"},
|
||||
{file = "torchvision-0.20.1-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:1a31256ff945d64f006bb306813a7c95a531fe16bfb2535c837dd4c104533d7a"},
|
||||
{file = "torchvision-0.20.1-cp312-cp312-manylinux1_x86_64.whl", hash = "sha256:17cd78adddf81dac57d7dccc9277a4d686425b1c55715f308769770cb26cad5c"},
|
||||
{file = "torchvision-0.20.1-cp312-cp312-manylinux2014_aarch64.whl", hash = "sha256:9f853ba4497ac4691815ad41b523ee23cf5ba4f87b1ce869d704052e233ca8b7"},
|
||||
{file = "torchvision-0.20.1-cp312-cp312-win_amd64.whl", hash = "sha256:4a330422c36dbfc946d3a6c1caec3489db07ecdf3675d83369adb2e5a0ca17c4"},
|
||||
{file = "torchvision-0.20.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:2cd58406978b813188cf4e9135b218775b57e0bb86d4a88f0339874b8a224819"},
|
||||
{file = "torchvision-0.20.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:408766b2f0ada9e1bc880d12346cec9638535af5df6459ba9ac4ce5c46402f91"},
|
||||
{file = "torchvision-0.20.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:abcb8005de8dc393dbd1310ecb669dc68ab664b9107af6d698a6341d1d3f2c3c"},
|
||||
{file = "torchvision-0.20.1-cp39-cp39-win_amd64.whl", hash = "sha256:ea9678163bbf19568f4f959d927f3751eeb833cc8eac949de507edde38c1fc9f"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
@ -2417,7 +2432,7 @@ files = [
|
||||
name = "triton"
|
||||
version = "3.1.0"
|
||||
summary = "A language and compiler for custom Deep Learning operations"
|
||||
groups = ["default"]
|
||||
groups = ["all", "model0"]
|
||||
marker = "platform_system == \"Linux\" and platform_machine == \"x86_64\" and python_version < \"3.13\""
|
||||
dependencies = [
|
||||
"filelock",
|
||||
@ -2434,7 +2449,7 @@ name = "typing-extensions"
|
||||
version = "4.12.2"
|
||||
requires_python = ">=3.8"
|
||||
summary = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
groups = ["default", "dev"]
|
||||
groups = ["default", "all", "dev", "model0"]
|
||||
files = [
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
|
||||
@ -15,13 +15,14 @@ dependencies = [
|
||||
"numpy>=1.19",
|
||||
"keras>=2.4",
|
||||
"pillow>=9",
|
||||
"torch>=2.4",
|
||||
"torchvision>=0.19",
|
||||
"cookit>=0.8.1",
|
||||
"cookit[pydantic]>=0.8.1",
|
||||
"httpx>=0.27.2",
|
||||
"githubkit>=0.11.14",
|
||||
"yarl>=1.17.1",
|
||||
"tqdm>=4.66.6",
|
||||
# model1
|
||||
"onnxruntime>=1.19.2",
|
||||
"onnxruntime-gpu>=1.19.2"
|
||||
]
|
||||
license = { text = "MIT" }
|
||||
readme = "README.md"
|
||||
@ -32,7 +33,9 @@ homepage = "https://github.com/Refound-445/nonebot-plugin-nailongremove"
|
||||
repository = "https://github.com/Refound-445/nonebot-plugin-nailongremove"
|
||||
|
||||
[project.optional-dependencies]
|
||||
model1 = ["onnxruntime>=1.19.2", "onnxruntime-gpu>=1.19.2"]
|
||||
model0 = ["torch>=2.4", "torchvision>=0.19"]
|
||||
model1 = []
|
||||
all = ["nonebot-plugin-nailongremove[model0,model1]"]
|
||||
|
||||
[build-system]
|
||||
requires = ["pdm-backend"]
|
||||
|
||||
Loading…
Reference in New Issue
Block a user