{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 21, "status": "ok", "timestamp": 1787619221155, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "yO5a2QDwuwx3", "outputId": "a8282fc7-1c7d-4bec-e2fd-4603b5156cc8" }, "outputs": [ { "data": { "text/plain": [ "129" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# #获取需要的库 \n", "# %%writefile /content/requirements.txt\n", "# # transformers==5.7.0\n", "# accelerate\n", "# safetensors\n", "# pillow\n", "# numpy<2\n", "# torchvision\n", "# opencv-python-headless<4.12\n", "# codec-video-prep\n", "from pathlib import Path\n", "\n", "Path(\"/content/requirements.txt\").write_text(\n", "\"\"\"transformers==5.7.0\n", "accelerate\n", "safetensors\n", "pillow\n", "# numpy<2\n", "numpy>=2,<3\n", "torchvision\n", "opencv-python-headless<4.12\n", "codec-video-prep\n", "\"\"\",\n", "encoding=\"utf-8\"\n", ")" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python 3.12.13\n" ] } ], "source": [ "!python --version" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 1820, "status": "ok", "timestamp": 1787619235573, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "_vF76oN3uk_e", "outputId": "7079eabd-43d2-403f-900e-7172736febe0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cloning into '/content/mage-vl-demo'...\n", "remote: Enumerating objects: 31, done.\u001b[K\n", "remote: Counting objects: 100% (27/27), done.\u001b[K\n", "remote: Compressing objects: 100% (25/25), done.\u001b[K\n", "remote: Total 31 (delta 8), reused 0 (delta 0), pack-reused 4 (from 1)\u001b[K\n", "Receiving objects: 100% (31/31), 22.13 KiB | 1.84 MiB/s, done.\n", "Resolving deltas: 100% (8/8), done.\n", "Filtering content: 100% (2/2), 4.23 MiB | 3.19 MiB/s, done.\n", "mamba_ssm 路径加载成功！\n" ] } ], "source": [ "# 1. 自动克隆官方 Demo 仓库（包含纯 PyTorch 版本的 mamba_ssm 文件夹）\n", "!git clone https://huggingface.co/spaces/microsoft/mage-vl-demo /content/mage-vl-demo\n", "\n", "# 2. 将包含 mamba_ssm 的目录加入 Python 模块搜索路径，确保 import mamba_ssm 永不报错\n", "import sys\n", "\n", "if \"/content/mage-vl-demo\" not in sys.path:\n", " sys.path.append(\"/content/mage-vl-demo\")\n", "\n", "print(\"mamba_ssm 路径加载成功！\")\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "executionInfo": { "elapsed": 233, "status": "ok", "timestamp": 1787619239776, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "LSKj4-71SO8H" }, "outputs": [], "source": [ "#移动目录\n", "!mv /content/mage-vl-demo/mamba_ssm /content/\n", "!mv /content/mage-vl-demo/examples/soccer-broadcast.mp4 /content/" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 581, "status": "ok", "timestamp": 1787619244608, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "IePDcWu59e1G", "outputId": "61107950-f0fa-4be8-a14f-e2bd8447046d" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "--2026-09-02 08:10:54-- http://172.245.243.238:8000/test02.mp4\n", "Connecting to 172.245.243.238:8000... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 30270635 (29M) [video/mp4]\n", "Saving to: ‘test02.mp4’\n", "\n", "test02.mp4 100%[===================>] 28.87M 7.92MB/s in 3.6s \n", "\n", "2026-09-02 08:10:58 (7.92 MB/s) - ‘test02.mp4’ saved [30270635/30270635]\n", "\n", "--2026-09-02 08:10:58-- http://172.245.243.238:8000/test03.mp4\n", "Connecting to 172.245.243.238:8000... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 13549203 (13M) [video/mp4]\n", "Saving to: ‘test03.mp4’\n", "\n", "test03.mp4 100%[===================>] 12.92M 5.33MB/s in 2.4s \n", "\n", "2026-09-02 08:11:01 (5.33 MB/s) - ‘test03.mp4’ saved [13549203/13549203]\n", "\n", "--2026-09-02 08:11:01-- http://172.245.243.238:8000/test04.mp4\n", "Connecting to 172.245.243.238:8000... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 27143071 (26M) [video/mp4]\n", "Saving to: ‘test04.mp4’\n", "\n", "test04.mp4 100%[===================>] 25.88M 7.69MB/s in 3.4s \n", "\n", "2026-09-02 08:11:05 (7.69 MB/s) - ‘test04.mp4’ saved [27143071/27143071]\n", "\n", "--2026-09-02 08:11:05-- http://172.245.243.238:8000/test05.mp4\n", "Connecting to 172.245.243.238:8000... connected.\n", "HTTP request sent, awaiting response... 200 OK\n", "Length: 36972551 (35M) [video/mp4]\n", "Saving to: ‘test05.mp4’\n", "\n", "test05.mp4 100%[===================>] 35.26M 11.4MB/s in 4.0s \n", "\n", "2026-09-02 08:11:09 (8.73 MB/s) - ‘test05.mp4’ saved [36972551/36972551]\n", "\n" ] } ], "source": [ "# !wget http://172.245.243.238:8000/test.mp4\n", "!wget http://172.245.243.238:8000/test02.mp4\n", "!wget http://172.245.243.238:8000/test03.mp4\n", "!wget http://172.245.243.238:8000/test04.mp4\n", "!wget http://172.245.243.238:8000/test05.mp4" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 17882, "status": "ok", "timestamp": 1787619266366, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "BBNwlkbfiu89", "outputId": "cf3a98a7-400f-40fe-e130-b7223e411286" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Get:1 https://cli.github.com/packages stable InRelease [4,685 B]\n", "Get:2 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ InRelease [3,632 B]\n", "Get:3 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 InRelease [1,578 B]\n", "Get:4 https://cli.github.com/packages stable/main amd64 Packages [356 B] \n", "Get:5 http://security.ubuntu.com/ubuntu jammy-security InRelease [129 kB] \n", "Get:6 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ Packages [112 kB]\n", "Hit:7 http://archive.ubuntu.com/ubuntu jammy InRelease \n", "Get:8 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [2,920 kB]\n", "Get:9 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", "Get:10 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease [18.1 kB]\n", "Get:11 https://r2u.stat.illinois.edu/ubuntu jammy InRelease [6,555 B] \n", "Get:12 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [4,265 kB]\n", "Get:13 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n", "Hit:14 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", "Get:15 https://r2u.stat.illinois.edu/ubuntu jammy/main amd64 Packages [3,182 kB]\n", "Get:16 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [4,598 kB]\n", "Get:17 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [84.1 kB]\n", "Get:18 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,316 kB]\n", "Get:19 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [7,674 kB]\n", "Hit:20 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease \n", "Get:21 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,620 kB]\n", "Get:22 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [92.7 kB]\n", "Get:23 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [7,965 kB]\n", "Get:24 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 Packages [44.3 kB]\n", "Get:25 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [82.8 kB]\n", "Get:26 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [35.6 kB]\n", "Get:27 https://r2u.stat.illinois.edu/ubuntu jammy/main all Packages [10.8 MB] \n", "Fetched 45.2 MB in 6s (8,011 kB/s) \n", "Reading package lists... Done\n", "W: Skipping acquire of configured file 'main/source/Sources' as repository 'https://r2u.stat.illinois.edu/ubuntu jammy InRelease' does not seem to provide it (sources.list entry misspelt?)\n", "Reading package lists... Done\n", "Building dependency tree... Done\n", "Reading state information... Done\n", "ffmpeg is already the newest version (7:4.4.2-0ubuntu0.22.04.1).\n", "0 upgraded, 0 newly installed, 0 to remove and 211 not upgraded.\n" ] } ], "source": [ "!apt-get update && apt-get install -y ffmpeg" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "executionInfo": { "elapsed": 379491, "status": "ok", "timestamp": 1787619653638, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "I3NLQ1Rvq5Ii", "outputId": "084d334b-66ef-4ee7-979c-5d3df5351c75" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting transformers==5.7.0 (from -r /content/requirements.txt (line 1))\n", " Downloading transformers-5.7.0-py3-none-any.whl.metadata (33 kB)\n", "Requirement already satisfied: accelerate in /usr/local/lib/python3.12/dist-packages (from -r /content/requirements.txt (line 2)) (1.14.0)\n", "Requirement already satisfied: safetensors in /usr/local/lib/python3.12/dist-packages (from -r /content/requirements.txt (line 3)) (0.8.0)\n", "Requirement already satisfied: pillow in /usr/local/lib/python3.12/dist-packages (from -r /content/requirements.txt (line 4)) (11.3.0)\n", "Requirement already satisfied: numpy<3,>=2 in /usr/local/lib/python3.12/dist-packages (from -r /content/requirements.txt (line 6)) (2.0.2)\n", "Requirement already satisfied: torchvision in /usr/local/lib/python3.12/dist-packages (from -r /content/requirements.txt (line 7)) (0.26.0+cu128)\n", "Collecting opencv-python-headless<4.12 (from -r /content/requirements.txt (line 8))\n", " Downloading opencv_python_headless-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n", "Collecting codec-video-prep (from -r /content/requirements.txt (line 9))\n", " Downloading codec_video_prep-0.2.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (11 kB)\n", "Requirement already satisfied: huggingface-hub<2.0,>=1.5.0 in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (1.23.0)\n", "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (26.2)\n", "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (6.0.3)\n", "Requirement already satisfied: regex>=2025.10.22 in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (2025.11.3)\n", "Requirement already satisfied: tokenizers<=0.23.0,>=0.22.0 in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (0.22.2)\n", "Requirement already satisfied: typer in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (0.26.8)\n", "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.12/dist-packages (from transformers==5.7.0->-r /content/requirements.txt (line 1)) (4.67.3)\n", "Requirement already satisfied: psutil in /usr/local/lib/python3.12/dist-packages (from accelerate->-r /content/requirements.txt (line 2)) (5.9.5)\n", "Requirement already satisfied: torch>=2.0.0 in /usr/local/lib/python3.12/dist-packages (from accelerate->-r /content/requirements.txt (line 2)) (2.11.0+cu128)\n", "Requirement already satisfied: filelock in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (3.29.7)\n", "Requirement already satisfied: typing-extensions>=4.10.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (4.16.0)\n", "Requirement already satisfied: setuptools<82 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (75.2.0)\n", "Requirement already satisfied: sympy>=1.13.3 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (1.14.0)\n", "Requirement already satisfied: networkx>=2.5.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (3.6.1)\n", "Requirement already satisfied: jinja2 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (3.1.6)\n", "Requirement already satisfied: fsspec>=0.8.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (2025.3.0)\n", "Requirement already satisfied: cuda-toolkit==12.8.1 in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.1)\n", "Requirement already satisfied: cuda-bindings<13,>=12.9.4 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.9.7)\n", "Requirement already satisfied: nvidia-cudnn-cu12==9.19.0.56 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (9.19.0.56)\n", "Requirement already satisfied: nvidia-cusparselt-cu12==0.7.1 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (0.7.1)\n", "Requirement already satisfied: nvidia-nccl-cu12==2.28.9 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (2.28.9)\n", "Requirement already satisfied: nvidia-nvshmem-cu12==3.4.5 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (3.4.5)\n", "Requirement already satisfied: triton==3.6.0 in /usr/local/lib/python3.12/dist-packages (from torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (3.6.0)\n", "Requirement already satisfied: nvidia-cublas-cu12==12.8.4.1.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.4.1)\n", "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.90)\n", "Requirement already satisfied: nvidia-cufft-cu12==11.3.3.83.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (11.3.3.83)\n", "Requirement already satisfied: nvidia-cufile-cu12==1.13.1.3.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (1.13.1.3)\n", "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.90)\n", "Requirement already satisfied: nvidia-curand-cu12==10.3.9.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (10.3.9.90)\n", "Requirement already satisfied: nvidia-cusolver-cu12==11.7.3.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (11.7.3.90)\n", "Requirement already satisfied: nvidia-cusparse-cu12==12.5.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.5.8.93)\n", "Requirement already satisfied: nvidia-nvjitlink-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.93)\n", "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.8.93.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.93)\n", "Requirement already satisfied: nvidia-nvtx-cu12==12.8.90.* in /usr/local/lib/python3.12/dist-packages (from cuda-toolkit[cublas,cudart,cufft,cufile,cupti,curand,cusolver,cusparse,nvjitlink,nvrtc,nvtx]==12.8.1; platform_system == \"Linux\"->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (12.8.90)\n", "INFO: pip is looking at multiple versions of codec-video-prep to determine which version is compatible with other requirements. This could take a while.\n", " Downloading codec_video_prep-0.2.4-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (11 kB)\n", " Downloading codec_video_prep-0.2.3-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (8.8 kB)\n", " Downloading codec_video_prep-0.2.2-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (8.8 kB)\n", " Downloading codec_video_prep-0.2.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (5.9 kB)\n", " Downloading codec_video_prep-0.2.0-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (5.9 kB)\n", " Downloading codec_video_prep-0.1.1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl.metadata (5.9 kB)\n", "Collecting opencv-python-headless<4.12 (from -r /content/requirements.txt (line 8))\n", " Downloading opencv_python_headless-4.10.0.84-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\n", "INFO: pip is still looking at multiple versions of codec-video-prep to determine which version is compatible with other requirements. This could take a while.\n", "INFO: This is taking longer than usual. You might need to provide the dependency resolver with stricter constraints to reduce runtime. See https://pip.pypa.io/warnings/backtracking for guidance. 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.8/89.8 MB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", "\u001b[?25h \u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n", " \n", " \u001b[31m×\u001b[0m \u001b[32mpip subprocess to install build dependencies\u001b[0m did not run successfully.\n", " \u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", " \u001b[31m╰─>\u001b[0m See above for output.\n", " \n", " \u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n", " Installing build dependencies ... \u001b[?25l\u001b[?25herror\n", "\u001b[1;31merror\u001b[0m: \u001b[1msubprocess-exited-with-error\u001b[0m\n", "\n", "\u001b[31m×\u001b[0m \u001b[32mpip subprocess to install build dependencies\u001b[0m did not run successfully.\n", "\u001b[31m│\u001b[0m exit code: \u001b[1;36m1\u001b[0m\n", "\u001b[31m╰─>\u001b[0m See above for output.\n", "\n", "\u001b[1;35mnote\u001b[0m: This error originates from a subprocess, and is likely not a problem with pip.\n" ] } ], "source": [ "%pip install -r /content/requirements.txt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "background_save": true, "base_uri": "https://localhost:8080/", "height": 666, "referenced_widgets": [ "2146c7055f0048ce8d21cb166f8e3d0f", "b14bc78b82d94246a335ed995ace3bc7", "01ba7d0ebc774b44a3b23fd2fa293e25", "77c543f3f01b4622a7c266699536befe", "790cc0377a3541dd8bb30020e8b81eb0", "f5045f2cdc7941d48d83ac977478ebcc", "655d5450206e46d2bbbb69c8337ace1d", "33e2524167ec464197adf82052ff1a28", "4f971939fd644d85b79eb9e5ac3fb254", "9c91f735ee154e3cb742e13cf250fbe7", "58a793c0c37c45c88cc7c28935dfd86e", "33d2f74a36cb4cff9b7c2a25f98fa9dc", "89f37aa8f21c40199e4311235fe50529", "e642be2c1e6a45bba3da59a2fcaa280c", "3654893da1a548bfab36c27c10ebb658", "c70aabc972a24fa5915b22f3dbea081e", "7113355aee024e53842940e7eaa4800b", "e369f44e6f8b4cd58bd06bbe968af7c0", "2e20d817d46947099cf317d70fde5cb4", "f7111982e4d445759be371c23a430637", "78a9e6fce42740f9be4c4d04936fb572", "01f8a89c1d0d4feab120b2e7be15ba7b", "003f3ae01dfc4a5dacbb3cd4f9c22b05", "fae6bbc264b9479a99f1255938686f06", "63f3d26dbd5045cfbd491395c7073a27", "d3756f253b8b4c1e9923828cfe03103c", "f4dedf2bc7bf40638056de24d895f10a", "fd6e57c3523b4527b257b30cafdfbc14", "8f9ce5dde93f4dbc843ac5ccb0d67a5e", "d6a6401cad6449c89085f4206d325bef", "3705670eef8645e38589f3e9f167f1c0", "91b455c557a045b0b7bf445588a9ee14", "9a7ea370e3d64b34bba25050cb454267", "4531fa06e24c41ad9e6ed6648a6dd9c8", "38adc087e2324488981e28a5c5aa9f8b", "9f141dc5f80f429d91fa4615748556c4", "c4e0a826cb1c4953974d98c1376f6f1f", "4f1ffbe0a4114c61b8c25a0515b97b78", "8cad1f9783534f669e2144f96fd22747", "dd9bdee5b3aa4ea6a7b4bbbd8cdca1d3", "7caccf6d8fda4dfaa92949c36f1fbcad", "3bf8b8434a6445f3a37a5412f02d9e3c", "4a716780f6da4ef8a4726723b546e41d", "8310c896c07248438f457c97fe49f0ed", "dd856863d5d24655b3ed46125d25ac06", "969428d2ec7b4034b683980c49a85967", "abb69e6625374498b6196f362325ab7e", "84777da5a0814f20b1991aec4443281c", "bd9b8a922acf43f4a04574615a4ff286", "26611c527e0d491e9948d3ec491b70a9", "0e9bbc59bfdf496b990b69ac13c339a0", "53cae6d330714ae1bfdc6da9070506f8", "8306364b2b5f42549e7a14068a25bd0a", "05c6bde7acdd43f2ab960cb4308b779d", "e5f527f1129948b3b5452cf8d39c6952" ] }, "id": "mLGjDxFjAPdb", "outputId": "84f41e77-faa3-4531-9bf1-4a3a484634a4" }, "outputs": [], "source": [ "# ============================================================\n", "# Mage-VL：洗衣机实时衣物状态识别\n", "# 只识别：数量变化 / 最终颜色 / 空间占用\n", "# ============================================================\n", "import os,gc,time,subprocess,importlib,warnings,json,sys\n", "from pathlib import Path\n", "\n", "\n", "\n", "# ---------- 环境 ----------\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\",\"expandable_segments:True,max_split_size_mb:128\")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\",\"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\",\"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"],exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"],exist_ok=True)\n", "\n", "\n", "\n", "# ---------- Import ----------\n", "import torch\n", "from transformers import AutoModelForCausalLM,AutoProcessor\n", "\n", "# ============================================================\n", "# 配置\n", "# ============================================================\n", "MODEL_ID=\"microsoft/Mage-VL\"\n", "VIDEO_FILE=\"/content/test02.mp4\"\n", "SEGMENT_SEC=4.0\n", "MAX_SEGMENTS=None\n", "GATE_THRESHOLD=0.50\n", "MAX_NEW_TOKENS=96\n", "FINAL_MAX_NEW_TOKENS=128\n", "CODEC_MAX_PIXELS=48_000\n", "CODEC_TARGET_CANVAS=20\n", "SIMULATE_REALTIME=True\n", "MAX_CHANGE_PER_SEGMENT=5\n", "ALWAYS_RUN_LLM=True\n", "\n", "# ============================================================\n", "# CUDA\n", "# ============================================================\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA GPU\")\n", "DEVICE=\"cuda\"\n", "GPU_NAME=torch.cuda.get_device_name(0)\n", "CUDA_VERSION=torch.version.cuda\n", "try:\n", " BF16_SUPPORTED=torch.cuda.is_bf16_supported()\n", "except Exception:\n", " BF16_SUPPORTED=False\n", "MODEL_DTYPE=torch.bfloat16 if BF16_SUPPORTED else torch.float16\n", "\n", "print(\"=\"*60)\n", "print(\"Mage-VL 实时洗衣机衣物状态识别\")\n", "print(\"=\"*60)\n", "print(\"[GPU]\",GPU_NAME)\n", "print(\"[CUDA]\",CUDA_VERSION)\n", "print(\"[BF16]\",BF16_SUPPORTED)\n", "print(\"[MODEL DTYPE]\",MODEL_DTYPE)\n", "print(\"[SEGMENT]\",SEGMENT_SEC)\n", "print(\"[CODEC]\",CODEC_MAX_PIXELS,CODEC_TARGET_CANVAS)\n", "print(\"=\"*60)\n", "\n", "# ============================================================\n", "# 实时识别 Prompt\n", "# ============================================================\n", "PROMPT_TEMPLATE=\"\"\"\n", "你是洗衣机滚筒实时视觉状态检测器。\n", "\n", "你只判断以下三个信息：\n", "\n", "1. 衣物数量发生了什么变化\n", "2. 当前滚筒中的衣物数量\n", "3. 当前能看到的主要衣物颜色\n", "4. 当前衣物占滚筒可见内部空间的大约比例\n", "\n", "绝对不要分析：\n", "- 材质\n", "- 衣物类型\n", "- 厚薄\n", "- 重量\n", "- 洗涤程序\n", "- 水温\n", "- 漂洗\n", "- 脱水\n", "- 洗涤建议\n", "\n", "==================================================\n", "上一时刻状态\n", "==================================================\n", "上一时刻衣物数量：\n", "{previous_count}\n", "\n", "上一时刻颜色：\n", "{previous_colors}\n", "\n", "上一时刻空间占用：\n", "{previous_occupancy}%\n", "\n", "==================================================\n", "当前视频片段\n", "==================================================\n", "\n", "这是洗衣机连续视频中的一个时间窗口。\n", "\n", "重点观察：\n", "- 有没有新的衣物被放入滚筒\n", "- 有没有衣物被从滚筒取出\n", "- 有没有衣物只是移动位置\n", "- 不要因为衣物移动而重复计数\n", "- 不要因为同一件衣服露出更多面积而重复计数\n", "\n", "==================================================\n", "数量判断原则\n", "==================================================\n", "\n", "只有明确看到衣物进入或离开滚筒时，才改变数量。\n", "\n", "放入一件：delta=+1\n", "放入两件：delta=+2\n", "取出一件：delta=-1\n", "取出两件：delta=-2\n", "\n", "只是移动衣物：delta=0\n", "看不清：delta=0\n", "\n", "不要因为看见一件已经存在的衣物而再次增加数量。\n", "\n", "current_count = 上一时刻数量 + 本片段明确发生的数量变化。\n", "\n", "如果无法确定变化：\n", "delta=0\n", "\n", "==================================================\n", "颜色\n", "==================================================\n", "\n", "只识别当前明显看到的主要颜色。\n", "\n", "允许：\n", "黑色、白色、灰色、蓝色、深蓝色、浅蓝色、\n", "红色、粉色、黄色、绿色、棕色、米色、紫色、橙色\n", "\n", "颜色无法确定时不要猜。\n", "\n", "==================================================\n", "空间占用\n", "==================================================\n", "\n", "估计衣物占据滚筒可见内部空间的比例。\n", "\n", "只允许：\n", "0、10、20、30、40、50、60、70、80、90、100\n", "\n", "这不是精确物理体积，只是视觉估计。\n", "\n", "==================================================\n", "非常重要\n", "==================================================\n", "\n", "如果没有明确放入或取出：\n", "delta=0\n", "\n", "如果只是重新堆叠：\n", "delta=0\n", "\n", "如果无法判断：\n", "delta=0\n", "\n", "==================================================\n", "输出\n", "==================================================\n", "\n", "只输出 JSON，不要解释：\n", "\n", "{{\n", " \"delta\":0,\n", " \"current_count\":{previous_count},\n", " \"colors\":[],\n", " \"occupancy\":0,\n", " \"confidence\":0.0\n", "}}\n", "\"\"\".strip()\n", "\n", "# ============================================================\n", "# 最终总结 Prompt\n", "# ============================================================\n", "FINAL_PROMPT=\"\"\"\n", "你是洗衣机最终衣物状态统计模块。\n", "\n", "下面是视频播放过程中每个时间窗口得到的状态：\n", "\n", "{history}\n", "\n", "只总结三个信息：\n", "\n", "1. 最终滚筒中的衣物数量\n", "2. 最终滚筒中的主要衣物颜色\n", "3. 最终滚筒中的空间占用情况\n", "\n", "不要分析：\n", "- 材质\n", "- 衣物类型\n", "- 洗涤程序\n", "- 水温\n", "- 漂洗\n", "- 脱水\n", "- 洗涤建议\n", "\n", "注意：\n", "1. 放入后又取出的衣物不能计入最终数量。\n", "2. 同一件衣物不能重复计算。\n", "3. 移动、翻转、重新堆叠不算新增。\n", "4. 最终颜色只统计视频结束时仍在滚筒中的颜色。\n", "5. 最终空间占用以视频结束时为准。\n", "\n", "只输出：\n", "\n", "【最终数量】\n", "X 件\n", "\n", "【最终颜色】\n", "黑色、白色、蓝色\n", "\n", "【空间占用】\n", "XX%\n", "\n", "【状态】\n", "空 / 少量 / 中等 / 较多 / 接近装满\n", "\n", "不要输出其它分析。\n", "\"\"\".strip()\n", "\n", "# ============================================================\n", "# Processor / Model\n", "# ============================================================\n", "print(\"[init] 加载 Processor...\")\n", "processor=AutoProcessor.from_pretrained(MODEL_ID,trust_remote_code=True)\n", "print(\"[init] Processor 加载完成\")\n", "\n", "print(\"[init] 加载 Mage-VL...\")\n", "model=AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=MODEL_DTYPE,\n", " attn_implementation=\"sdpa\",\n", " low_cpu_mem_usage=True\n", ").to(DEVICE).eval()\n", "print(\"[init] Mage-VL 加载完成\")\n", "\n", "# ============================================================\n", "# Codec\n", "# ============================================================\n", "_REMOTE_PKG=type(processor).__module__.rsplit(\".\",1)[0]\n", "codec_mod=importlib.import_module(_REMOTE_PKG+\".codec_video_processing_mage_vl\")\n", "CodecConfig=codec_mod.CodecConfig\n", "\n", "def codec_config():\n", " override={\"engine\":\"hevc\",\"target_canvas\":CODEC_TARGET_CANVAS,\"patch\":16}\n", " kwargs=dict(processor._codec_config_defaults)\n", " dcvc=dict(kwargs.get(\"dcvc\") or {})\n", " kwargs.update(override)\n", " if dcvc:\n", " kwargs[\"dcvc\"]=dcvc\n", " kwargs[\"max_pixels\"]=CODEC_MAX_PIXELS\n", " return CodecConfig(**kwargs),override\n", "\n", "# ============================================================\n", "# Gate\n", "# ============================================================\n", "print(\"[gate] 加载 Cognition Gate...\")\n", "try:\n", " gate=model.model._load_streammind_gate()\n", " print(\"[gate] Cognition Gate 加载成功\")\n", " try:\n", " print(\"[gate] 当前 dtype =\",next(gate.parameters()).dtype)\n", " except Exception:\n", " pass\n", "except Exception as e:\n", " gate=None\n", " print(\"[gate] 加载失败:\",type(e).__name__,e)\n", "\n", "if gate is not None and not BF16_SUPPORTED:\n", " try:\n", " gate=gate.float()\n", " model.model.streammind_gate=gate\n", " print(\"[gate] FP32 fallback 已启用\")\n", " except Exception as e:\n", " print(\"[gate] FP32 fallback 失败:\",e)\n", "\n", "# ============================================================\n", "# 工具\n", "# ============================================================\n", "def duration(path):\n", " try:\n", " r=subprocess.run(\n", " [\"ffprobe\",\"-v\",\"error\",\"-show_entries\",\"format=duration\",\n", " \"-of\",\"default=nw=1:nk=1\",str(path)],\n", " capture_output=True,text=True,check=True,timeout=60)\n", " return float(r.stdout.strip())\n", " except Exception:\n", " return 0.0\n", "\n", "def make_prompt(previous_count,previous_colors,previous_occupancy):\n", " text=PROMPT_TEMPLATE.format(\n", " previous_count=previous_count,\n", " previous_colors=\", \".join(previous_colors) if previous_colors else \"暂无\",\n", " previous_occupancy=previous_occupancy)\n", " messages=[{\"role\":\"user\",\"content\":[\n", " {\"type\":\"video\"},\n", " {\"type\":\"text\",\"text\":text}\n", " ]}]\n", " return processor.apply_chat_template(\n", " messages,tokenize=False,add_generation_prompt=True)\n", "\n", "def extract_clip(src,start,length,out):\n", " tmp=out.with_name(\".tmp_\"+out.name)\n", " if tmp.exists():\n", " try: tmp.unlink()\n", " except Exception: pass\n", " subprocess.run([\n", " \"ffmpeg\",\"-y\",\"-loglevel\",\"error\",\n", " \"-ss\",f\"{start:.3f}\",\"-i\",str(src),\n", " \"-t\",f\"{length:.3f}\",\n", " \"-c:v\",\"libx264\",\"-preset\",\"ultrafast\",\n", " \"-crf\",\"26\",\"-an\",\"-pix_fmt\",\"yuv420p\",str(tmp)\n", " ],check=True,capture_output=True,timeout=180)\n", " tmp.replace(out)\n", " return out\n", "\n", "def to_cuda(inputs):\n", " result={}\n", " for k,v in inputs.items():\n", " if not hasattr(v,\"to\"): continue\n", " v=v.to(DEVICE)\n", " if k==\"pixel_values\":\n", " v=v.to(MODEL_DTYPE)\n", " result[k]=v\n", " return result\n", "\n", "def cleanup(*objs):\n", " for x in objs:\n", " try: del x\n", " except Exception: pass\n", " gc.collect()\n", " try:\n", " torch.cuda.empty_cache()\n", " torch.cuda.ipc_collect()\n", " torch.cuda.synchronize()\n", " except Exception: pass\n", "\n", "def gpu_mem():\n", " a=torch.cuda.memory_allocated()/1024**3\n", " r=torch.cuda.memory_reserved()/1024**3\n", " p=torch.cuda.max_memory_allocated()/1024**3\n", " return f\"GPU={a:.2f}GB / reserved={r:.2f}GB / peak={p:.2f}GB\"\n", "\n", "# ============================================================\n", "# Gate\n", "# ============================================================\n", "@torch.inference_mode()\n", "def run_gate(inputs):\n", " if gate is None:\n", " return 1.0\n", " feat={}\n", " logits=None\n", " try:\n", " for k in (\"pixel_values\",\"image_grid_thw\",\"patch_positions\"):\n", " if k not in inputs: continue\n", " v=inputs[k].to(DEVICE)\n", " if k==\"pixel_values\":\n", " v=v.to(MODEL_DTYPE)\n", " feat[k]=v\n", "\n", " if BF16_SUPPORTED:\n", " logits=model.model.streammind_gate_forward_segments([feat])\n", " else:\n", " visual_tokens=model.model._streammind_vision_tokens(\n", " feat[\"pixel_values\"],\n", " feat[\"image_grid_thw\"],\n", " patch_positions=feat.get(\"patch_positions\"))\n", " if not torch.isfinite(visual_tokens).all():\n", " print(\"[gate] visual tokens NaN/Inf\")\n", " return 0.0\n", " logits=gate(torch.cat([visual_tokens.float()],dim=1))\n", "\n", " if not isinstance(logits,torch.Tensor) or logits.ndim!=3:\n", " print(\"[gate] Unexpected shape:\",getattr(logits,\"shape\",None))\n", " return 0.0\n", "\n", " gate_logits=logits[0]\n", " length=int(inputs[\"image_grid_thw\"][:,0].sum().item())\n", " if length<=0: return 0.0\n", "\n", " pos=min(length-1,gate_logits.shape[0]-1)\n", " last_logits=gate_logits[pos].float()\n", " if not torch.isfinite(last_logits).all():\n", " return 0.0\n", "\n", " prob=torch.exp(torch.log_softmax(last_logits,dim=-1)[1])\n", " if not torch.isfinite(prob):\n", " return 0.0\n", " return float(prob.item())\n", " except Exception as e:\n", " print(\"[gate] ERROR:\",type(e).__name__,e)\n", " return 0.0\n", " finally:\n", " cleanup(feat,logits)\n", "\n", "# ============================================================\n", "# Codec + Gate\n", "# ============================================================\n", "@torch.inference_mode()\n", "def analyze_clip(clip,prompt,codec_override):\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " inputs=processor(\n", " text=[prompt],\n", " videos=[str(clip)],\n", " video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS,\n", " codec_config=codec_override,\n", " return_tensors=\"pt\",\n", " padding=False)\n", " return inputs,run_gate(inputs)\n", "\n", "# ============================================================\n", "# LLM\n", "# ============================================================\n", "@torch.inference_mode()\n", "def generate(inputs,max_new_tokens=MAX_NEW_TOKENS):\n", " out=model.generate(\n", " **inputs,\n", " max_new_tokens=max_new_tokens,\n", " do_sample=False,\n", " use_cache=True)\n", " n=inputs[\"input_ids\"].shape[1]\n", " text=processor.tokenizer.decode(\n", " out[0,n:],skip_special_tokens=True).strip()\n", " del out\n", " return text\n", "\n", "# ============================================================\n", "# JSON\n", "# ============================================================\n", "def parse_json(text):\n", " if not text: return None\n", " text=text.strip().replace(\"```json\",\"\").replace(\"```\",\"\").strip()\n", " start=text.find(\"{\")\n", " end=text.rfind(\"}\")\n", " if start<0 or end<0: return None\n", " try:\n", " data=json.loads(text[start:end+1])\n", " return data if isinstance(data,dict) else None\n", " except Exception:\n", " return None\n", "\n", "VALID_COLORS={\n", " \"黑色\",\"白色\",\"灰色\",\"蓝色\",\"深蓝色\",\"浅蓝色\",\n", " \"红色\",\"粉色\",\"黄色\",\"绿色\",\"棕色\",\"米色\",\"紫色\",\"橙色\"\n", "}\n", "\n", "def normalize_colors(colors):\n", " if not isinstance(colors,list): return []\n", " result=[]\n", " for c in colors:\n", " if isinstance(c,str):\n", " c=c.strip()\n", " if c in VALID_COLORS and c not in result:\n", " result.append(c)\n", " return result\n", "\n", "def normalize_state(data,previous_count,previous_colors,previous_occupancy):\n", " if not isinstance(data,dict):\n", " return {\n", " \"delta\":0,\n", " \"current_count\":previous_count,\n", " \"colors\":previous_colors,\n", " \"occupancy\":previous_occupancy,\n", " \"confidence\":0.0\n", " }\n", "\n", " try:\n", " delta=int(round(float(data.get(\"delta\",0))))\n", " except Exception:\n", " delta=0\n", " delta=max(-MAX_CHANGE_PER_SEGMENT,min(MAX_CHANGE_PER_SEGMENT,delta))\n", "\n", " try:\n", " current_count=int(round(float(\n", " data.get(\"current_count\",previous_count+delta))))\n", " except Exception:\n", " current_count=previous_count+delta\n", "\n", " current_count=max(0,current_count)\n", " expected_count=max(0,previous_count+delta)\n", " if abs(current_count-expected_count)>1:\n", " current_count=expected_count\n", "\n", " colors=normalize_colors(data.get(\"colors\",previous_colors))\n", " if not colors:\n", " colors=previous_colors\n", "\n", " try:\n", " occupancy=int(round(float(\n", " data.get(\"occupancy\",previous_occupancy))))\n", " except Exception:\n", " occupancy=previous_occupancy\n", " occupancy=max(0,min(100,occupancy))\n", "\n", " try:\n", " confidence=float(data.get(\"confidence\",0.0))\n", " except Exception:\n", " confidence=0.0\n", " confidence=max(0.0,min(1.0,confidence))\n", "\n", " return {\n", " \"delta\":delta,\n", " \"current_count\":current_count,\n", " \"colors\":colors,\n", " \"occupancy\":occupancy,\n", " \"confidence\":confidence\n", " }\n", "\n", "def state_to_text(state):\n", " d=state[\"delta\"]\n", " if d>0: change=f\"放入 {d} 件\"\n", " elif d<0: change=f\"取出 {abs(d)} 件\"\n", " else: change=\"数量无变化\"\n", " colors=\"、\".join(state[\"colors\"]) if state[\"colors\"] else \"未知\"\n", " return f\"{change} | 当前 {state['current_count']} 件 | 颜色={colors} | 占用={state['occupancy']}% | 置信度={state['confidence']:.2f}\"\n", "\n", "# ============================================================\n", "# 最终总结\n", "# ============================================================\n", "@torch.inference_mode()\n", "def final_summary(history,final_state):\n", " if not history:\n", " return \"没有获得有效的衣物分析结果。\"\n", "\n", " prompt=FINAL_PROMPT.format(history=\"\\n\".join(history))\n", " prompt+=f\"\"\"\n", "程序计算出的最终状态：\n", "数量：{final_state[\"current_count\"]} 件\n", "颜色：{\", \".join(final_state[\"colors\"]) if final_state[\"colors\"] else \"未知\"}\n", "空间占用：{final_state[\"occupancy\"]}%\n", "请以这个程序最终状态为主要依据。\n", "\"\"\"\n", " inputs=processor.tokenizer(\n", " prompt,return_tensors=\"pt\",truncation=True,max_length=4096)\n", " inputs={k:v.to(DEVICE) for k,v in inputs.items()}\n", " out=None\n", " try:\n", " out=model.generate(\n", " **inputs,\n", " max_new_tokens=FINAL_MAX_NEW_TOKENS,\n", " do_sample=False,use_cache=True)\n", " n=inputs[\"input_ids\"].shape[1]\n", " return processor.tokenizer.decode(\n", " out[0,n:],skip_special_tokens=True).strip()\n", " finally:\n", " cleanup(inputs)\n", " try: del out\n", " except Exception: pass\n", "\n", "# ============================================================\n", "# 实时视频分析\n", "# ============================================================\n", "def stream_video_analysis(\n", " video_path,\n", " segment_sec=SEGMENT_SEC,\n", " gate_threshold=GATE_THRESHOLD,\n", " max_segments=MAX_SEGMENTS,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", " simulate_realtime=SIMULATE_REALTIME):\n", "\n", " path=Path(video_path)\n", " if not path.exists():\n", " raise FileNotFoundError(video_path)\n", "\n", " total=duration(path)\n", " if total<=0:\n", " raise RuntimeError(\"无法获取视频时长\")\n", "\n", " clip_dir=Path(os.environ[\"STREAMMIND_CLIP_CACHE\"])\n", " clip_dir.mkdir(parents=True,exist_ok=True)\n", "\n", " # 程序真正维护的状态\n", " previous_count=0\n", " previous_colors=[]\n", " previous_occupancy=0\n", " history=[]\n", " results=[]\n", " start=0.0\n", " index=0\n", " _,codec_override=codec_config()\n", "\n", " yield f\"开始实时分析 | 时长={total:.1f}s | 窗口={segment_sec:.1f}s | Gate={gate_threshold:.2f}\"\n", "\n", " while start=max_segments:\n", " break\n", "\n", " end=min(total,start+segment_sec)\n", " length=end-start\n", " index+=1\n", " t0=time.time()\n", "\n", " clip=clip_dir/f\"{path.stem}_{index:06d}_{start:.2f}_{end:.2f}.mp4\"\n", " inputs=None\n", " cuda_inputs=None\n", " said=None\n", "\n", " try:\n", " prompt=make_prompt(\n", " previous_count,\n", " previous_colors,\n", " previous_occupancy)\n", "\n", " extract_clip(path,start,length,clip)\n", " inputs,prob=analyze_clip(\n", " clip,prompt,codec_override)\n", "\n", " head=f\"**{start:.1f}–{end:.1f}s** Gate=`{prob:.2f}`\"\n", "\n", " # 保留原逻辑：ALWAYS_RUN_LLM=True 时 Gate 不阻止 LLM\n", " if not ALWAYS_RUN_LLM and prob0:\n", " change_text=f\"🟢 放入 {change} 件\"\n", " elif change<0:\n", " change_text=f\"🔴 取出 {abs(change)} 件\"\n", " else:\n", " change_text=\"⚪ 数量无变化\"\n", "\n", " colors_text=\"、\".join(state[\"colors\"]) if state[\"colors\"] else \"未知\"\n", " elapsed=time.time()-t0\n", " speed=length/max(elapsed,0.001)\n", "\n", " line=(\n", " f\"{head}\\n\"\n", " f\" {change_text} | 当前数量={state['current_count']}件\\n\"\n", " f\" 颜色={colors_text} | 占用={state['occupancy']}%\\n\"\n", " f\" 置信度={state['confidence']:.2f} | \"\n", " f\"Generate={gen_time:.2f}s | \"\n", " f\"Total={elapsed:.2f}s | Realtime={speed:.2f}×\"\n", " )\n", "\n", " results.append(line)\n", " history.append(\n", " f\"{start:.1f}-{end:.1f}s：{state_to_text(state)}\")\n", " yield line\n", "\n", " elapsed=time.time()-t0\n", " if simulate_realtime and elapsed 1:\n", "\n", " current_count = expected_count\n", "\n", "\n", " # --------------------------------------------------------\n", " # colors\n", " # --------------------------------------------------------\n", "\n", " colors = normalize_colors(\n", " data.get(\n", " \"colors\",\n", " previous_colors\n", " )\n", " )\n", "\n", "\n", " if not colors:\n", "\n", " colors = previous_colors\n", "\n", "\n", " # --------------------------------------------------------\n", " # occupancy\n", " # --------------------------------------------------------\n", "\n", " try:\n", "\n", " occupancy = int(\n", " round(\n", " float(\n", " data.get(\n", " \"occupancy\",\n", " previous_occupancy,\n", " )\n", " )\n", " )\n", " )\n", "\n", " except Exception:\n", "\n", " occupancy = previous_occupancy\n", "\n", "\n", " occupancy = max(\n", " 0,\n", " min(\n", " 100,\n", " occupancy\n", " )\n", " )\n", "\n", "\n", " # --------------------------------------------------------\n", " # confidence\n", " # --------------------------------------------------------\n", "\n", " try:\n", "\n", " confidence = float(\n", " data.get(\n", " \"confidence\",\n", " 0.0\n", " )\n", " )\n", "\n", " except Exception:\n", "\n", " confidence = 0.0\n", "\n", "\n", " confidence = max(\n", " 0.0,\n", " min(\n", " 1.0,\n", " confidence\n", " )\n", " )\n", "\n", "\n", " return {\n", " \"delta\": delta,\n", " \"current_count\": current_count,\n", " \"colors\": colors,\n", " \"occupancy\": occupancy,\n", " \"confidence\": confidence,\n", " }\n", "\n", "\n", "# ============================================================\n", "# 24. 状态格式化\n", "# ============================================================\n", "\n", "def state_to_text(state):\n", "\n", " delta = state[\"delta\"]\n", "\n", " if delta > 0:\n", "\n", " change = f\"放入 {delta} 件\"\n", "\n", " elif delta < 0:\n", "\n", " change = f\"取出 {abs(delta)} 件\"\n", "\n", " else:\n", "\n", " change = \"数量无变化\"\n", "\n", "\n", " colors = (\n", " \"、\".join(\n", " state[\"colors\"]\n", " )\n", " if state[\"colors\"]\n", " else \"未知\"\n", " )\n", "\n", "\n", " return (\n", " f\"{change} | \"\n", " f\"当前 {state['current_count']} 件 | \"\n", " f\"颜色={colors} | \"\n", " f\"占用={state['occupancy']}% | \"\n", " f\"置信度={state['confidence']:.2f}\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 25. 最终总结\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def final_summary(\n", " history,\n", " final_state,\n", "):\n", "\n", " if not history:\n", "\n", " return (\n", " \"没有获得有效的衣物分析结果。\"\n", " )\n", "\n", "\n", " history_text = \"\\n\".join(\n", " history\n", " )\n", "\n", "\n", " prompt = FINAL_PROMPT.format(\n", " history=history_text\n", " )\n", "\n", "\n", " # --------------------------------------------------------\n", " # 把程序最终状态直接放进 prompt\n", " # --------------------------------------------------------\n", "\n", " prompt += f\"\"\"\n", "\n", "==================================================\n", "程序计算出的最终状态\n", "==================================================\n", "\n", "数量：\n", "{final_state[\"current_count\"]} 件\n", "\n", "颜色：\n", "{\", \".join(final_state[\"colors\"]) if final_state[\"colors\"] else \"未知\"}\n", "\n", "空间占用：\n", "{final_state[\"occupancy\"]}%\n", "\n", "请以这个程序最终状态为主要依据。\n", "\"\"\"\n", "\n", "\n", " inputs = processor.tokenizer(\n", " prompt,\n", " return_tensors=\"pt\",\n", " truncation=True,\n", " max_length=4096,\n", " )\n", "\n", "\n", " inputs = {\n", " k: v.to(DEVICE)\n", " for k, v in inputs.items()\n", " }\n", "\n", "\n", " out = None\n", "\n", " try:\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=FINAL_MAX_NEW_TOKENS,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", "\n", " result = (\n", " processor.tokenizer.decode(\n", " out[0, n:],\n", " skip_special_tokens=True,\n", " )\n", " .strip()\n", " )\n", "\n", "\n", " return result\n", "\n", "\n", " finally:\n", "\n", " cleanup(\n", " inputs\n", " )\n", "\n", " try:\n", " del out\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 26. 实时视频分析\n", "# ============================================================\n", "\n", "def stream_video_analysis(\n", "\n", " video_path,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "):\n", "\n", " path = Path(\n", " video_path\n", " )\n", "\n", "\n", " if not path.exists():\n", "\n", " raise FileNotFoundError(\n", " video_path\n", " )\n", "\n", "\n", " total = duration(\n", " path\n", " )\n", "\n", "\n", " if total <= 0:\n", "\n", " raise RuntimeError(\n", " \"无法获取视频时长\"\n", " )\n", "\n", "\n", " clip_dir = Path(\n", " os.environ[\n", " \"STREAMMIND_CLIP_CACHE\"\n", " ]\n", " )\n", "\n", "\n", " clip_dir.mkdir(\n", " parents=True,\n", " exist_ok=True,\n", " )\n", "\n", "\n", " # ========================================================\n", " # 程序真正维护的状态\n", " # ========================================================\n", "\n", " previous_count = 0\n", "\n", " previous_colors = []\n", "\n", " previous_occupancy = 0\n", "\n", "\n", " # 所有状态记录\n", " history = []\n", "\n", " results = []\n", "\n", "\n", " start = 0.0\n", "\n", " index = 0\n", "\n", "\n", " # --------------------------------------------------------\n", " # Codec\n", " # --------------------------------------------------------\n", "\n", " _, codec_override = codec_config()\n", "\n", "\n", " yield (\n", " f\"开始实时分析 | \"\n", " f\"时长={total:.1f}s | \"\n", " f\"窗口={segment_sec:.1f}s | \"\n", " f\"Gate={gate_threshold:.2f}\"\n", " )\n", "\n", "\n", " # ========================================================\n", " # 主循环\n", " # ========================================================\n", "\n", " while start < total - 0.05:\n", "\n", "\n", " if (\n", " max_segments is not None\n", " and index >= max_segments\n", " ):\n", "\n", " break\n", "\n", "\n", " end = min(\n", " total,\n", " start + segment_sec,\n", " )\n", "\n", "\n", " length = (\n", " end - start\n", " )\n", "\n", "\n", " index += 1\n", "\n", "\n", " t0 = time.time()\n", "\n", "\n", " clip = clip_dir / (\n", "\n", " f\"{path.stem}_\"\n", " f\"{index:06d}_\"\n", " f\"{start:.2f}_\"\n", " f\"{end:.2f}.mp4\"\n", "\n", " )\n", "\n", "\n", " inputs = None\n", "\n", " cuda_inputs = None\n", "\n", " said = None\n", "\n", "\n", " try:\n", "\n", " # =================================================\n", " # 1. Prompt\n", " # =================================================\n", "\n", " prompt = make_prompt(\n", "\n", " previous_count,\n", "\n", " previous_colors,\n", "\n", " previous_occupancy,\n", "\n", " )\n", "\n", "\n", " # =================================================\n", " # 2. 视频切片\n", " # =================================================\n", "\n", " extract_clip(\n", "\n", " path,\n", "\n", " start,\n", "\n", " length,\n", "\n", " clip,\n", "\n", " )\n", "\n", "\n", " # =================================================\n", " # 3. Codec + Gate\n", " # =================================================\n", "\n", " inputs, prob = analyze_clip(\n", "\n", " clip,\n", "\n", " prompt,\n", "\n", " codec_override,\n", "\n", " )\n", "\n", "\n", " head = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** \"\n", " f\"Gate=`{prob:.2f}`\"\n", " )\n", "\n", "\n", " # =================================================\n", " # 4. Gate\n", " # =================================================\n", "\n", " if (\n", " not ALWAYS_RUN_LLM\n", " and prob < gate_threshold\n", " ):\n", "\n", " line = (\n", "\n", " f\"{head} → 🔇 \"\n", " f\"无明显变化\"\n", " )\n", "\n", " results.append(line)\n", "\n", " yield line\n", "\n", " start = end\n", "\n", " continue\n", "\n", "\n", " # =================================================\n", " # 5. Mage-VL\n", " # =================================================\n", "\n", " yield (\n", " f\"{head} → 🧠 \"\n", " f\"识别衣物状态...\"\n", " )\n", "\n", "\n", " gt = time.time()\n", "\n", "\n", " cuda_inputs = to_cuda(\n", " inputs\n", " )\n", "\n", "\n", " said = generate(\n", "\n", " cuda_inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", "\n", " )\n", "\n", "\n", " gen_time = (\n", " time.time()\n", " - gt\n", " )\n", "\n", "\n", " # =================================================\n", " # 6. JSON\n", " # =================================================\n", "\n", " raw_data = parse_json(\n", " said\n", " )\n", "\n", "\n", " # =================================================\n", " # 7. 状态\n", " # =================================================\n", "\n", " state = normalize_state(\n", "\n", " raw_data,\n", "\n", " previous_count,\n", "\n", " previous_colors,\n", "\n", " previous_occupancy,\n", "\n", " )\n", "\n", "\n", " # =================================================\n", " # 8. 更新程序状态\n", " # =================================================\n", "\n", " previous_count = (\n", " state[\"current_count\"]\n", " )\n", "\n", " previous_colors = (\n", " state[\"colors\"]\n", " )\n", "\n", " previous_occupancy = (\n", " state[\"occupancy\"]\n", " )\n", "\n", "\n", " # =================================================\n", " # 9. 输出\n", " # =================================================\n", "\n", " change = state[\"delta\"]\n", "\n", "\n", " if change > 0:\n", "\n", " change_text = (\n", " f\"🟢 放入 {change} 件\"\n", " )\n", "\n", " elif change < 0:\n", "\n", " change_text = (\n", " f\"🔴 取出 {abs(change)} 件\"\n", " )\n", "\n", " else:\n", "\n", " change_text = (\n", " \"⚪ 数量无变化\"\n", " )\n", "\n", "\n", " colors_text = (\n", "\n", " \"、\".join(\n", " state[\"colors\"]\n", " )\n", "\n", " if state[\"colors\"]\n", "\n", " else \"未知\"\n", " )\n", "\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", "\n", " speed = (\n", " length\n", " / max(\n", " elapsed,\n", " 0.001\n", " )\n", " )\n", "\n", "\n", " line = (\n", "\n", " f\"{head}\\n\"\n", "\n", " f\" {change_text} | \"\n", " f\"当前数量={state['current_count']}件\\n\"\n", "\n", " f\" 颜色={colors_text} | \"\n", " f\"占用={state['occupancy']}%\\n\"\n", "\n", " f\" 置信度={state['confidence']:.2f} | \"\n", "\n", " f\"Generate={gen_time:.2f}s | \"\n", "\n", " f\"Total={elapsed:.2f}s | \"\n", "\n", " f\"Realtime={speed:.2f}×\"\n", "\n", " )\n", "\n", "\n", " results.append(\n", " line\n", " )\n", "\n", "\n", " # =================================================\n", " # 10. 历史\n", " # =================================================\n", "\n", " history.append(\n", "\n", " f\"{start:.1f}-{end:.1f}s：\"\n", "\n", " f\"{state_to_text(state)}\"\n", "\n", " )\n", "\n", "\n", " yield line\n", "\n", "\n", " # =================================================\n", " # 11. 模拟实时\n", " # =================================================\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", "\n", " if (\n", " simulate_realtime\n", " and elapsed < length\n", " ):\n", "\n", " time.sleep(\n", " length - elapsed\n", " )\n", "\n", "\n", " except Exception as e:\n", "\n", "\n", " line = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** → \"\n", "\n", " f\"❌ \"\n", "\n", " f\"{type(e).__name__}: \"\n", " f\"{e}\"\n", "\n", " )\n", "\n", "\n", " results.append(\n", " line\n", " )\n", "\n", "\n", " yield line\n", "\n", "\n", " finally:\n", "\n", "\n", " # =================================================\n", " # GPU 清理\n", " # =================================================\n", "\n", " cleanup(\n", "\n", " cuda_inputs,\n", "\n", " inputs,\n", "\n", " said,\n", "\n", " )\n", "\n", "\n", " # =================================================\n", " # 删除视频片段\n", " # =================================================\n", "\n", " if clip.exists():\n", "\n", " try:\n", " clip.unlink()\n", "\n", " except Exception:\n", " pass\n", "\n", "\n", " start = end\n", "\n", "\n", " print(\n", "\n", " f\"[segment {index}] \"\n", "\n", " f\"{start:.1f}/\"\n", " f\"{total:.1f}s | \"\n", "\n", " f\"{gpu_mem()}\",\n", "\n", " flush=True,\n", "\n", " )\n", "\n", "\n", " try:\n", "\n", " torch.cuda.reset_peak_memory_stats()\n", "\n", " except Exception:\n", "\n", " pass\n", "\n", "\n", " # ========================================================\n", " # 视频结束\n", " # ========================================================\n", "\n", " final_state = {\n", "\n", " \"current_count\":\n", " previous_count,\n", "\n", " \"colors\":\n", " previous_colors,\n", "\n", " \"occupancy\":\n", " previous_occupancy,\n", "\n", " }\n", "\n", "\n", " yield (\n", " \"\\n\"\n", " \"========================================\\n\"\n", " \"🧺 视频播放完成\\n\"\n", " \"========================================\\n\"\n", " )\n", "\n", "\n", " # ========================================================\n", " # 最终状态直接输出\n", " # ========================================================\n", "\n", " colors_text = (\n", "\n", " \"、\".join(\n", " previous_colors\n", " )\n", "\n", " if previous_colors\n", "\n", " else \"未知\"\n", " )\n", "\n", "\n", " if previous_count == 0:\n", "\n", " occupancy_state = \"空\"\n", "\n", " elif previous_occupancy <= 20:\n", "\n", " occupancy_state = \"少量\"\n", "\n", " elif previous_occupancy <= 50:\n", "\n", " occupancy_state = \"中等\"\n", "\n", " elif previous_occupancy <= 80:\n", "\n", " occupancy_state = \"较多\"\n", "\n", " else:\n", "\n", " occupancy_state = \"接近装满\"\n", "\n", "\n", " final_direct = (\n", "\n", " \"========================================\\n\"\n", "\n", " \"🧺 最终衣物状态\\n\"\n", "\n", " \"========================================\\n\\n\"\n", "\n", " f\"【最终数量】\\n\"\n", " f\"{previous_count} 件\\n\\n\"\n", "\n", " f\"【最终颜色】\\n\"\n", " f\"{colors_text}\\n\\n\"\n", "\n", " f\"【空间占用】\\n\"\n", " f\"{previous_occupancy}%\\n\\n\"\n", "\n", " f\"【状态】\\n\"\n", " f\"{occupancy_state}\\n\\n\"\n", "\n", " )\n", "\n", "\n", " # ========================================================\n", " # Mage-VL 最终校验\n", " # ========================================================\n", "\n", " try:\n", "\n", " final = final_summary(\n", "\n", " history,\n", "\n", " final_state,\n", "\n", " )\n", "\n", "\n", " final_output = (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + final_direct\n", "\n", " + \"========================================\\n\"\n", "\n", " + \"🧠 最终模型校验\\n\"\n", "\n", " + \"========================================\\n\\n\"\n", "\n", " + final\n", "\n", " + \"\\n\\n\"\n", "\n", " + f\"✅ 完成，共 {index} 个片段\"\n", "\n", " )\n", "\n", "\n", " yield final_output\n", "\n", "\n", " except Exception as e:\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + final_direct\n", "\n", " + f\"❌ 最终模型校验失败：\"\n", " f\"{type(e).__name__}: {e}\"\n", "\n", " )\n", "\n", "\n", "# ============================================================\n", "# 27. Main\n", "# ============================================================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " for output in stream_video_analysis(\n", "\n", " VIDEO_FILE,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "\n", " ):\n", "\n", " print(output)\n", "\n", " print(\n", " \"\\n\"\n", " + \"-\" * 60\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 356 }, "executionInfo": { "elapsed": 16425, "status": "error", "timestamp": 1787561337513, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "PDrynEZBwIgp", "outputId": "9cf690a5-1b2d-453d-bc93-42c17a20f1d4" }, "outputs": [], "source": [ "# ============================================================\n", "# Mage-VL 第七版---2\n", "# 实时洗衣机衣物分析\n", "#\n", "# 功能：\n", "# 1. 4 秒窗口实时分析\n", "# 2. StreamMind Gate 数值稳定修复\n", "# 3. BF16 优先 / T4 自动 FP16 + FP32 Gate fallback\n", "# 4. 累计衣物状态\n", "# 5. 动态洗涤程序\n", "# 6. 动态漂洗次数\n", "# 7. 视频结束后的最终综合洗涤方案\n", "#\n", "# 显存优化：\n", "# - Codec max_pixels = 48000\n", "# - target_canvas = 20\n", "# - max_new_tokens = 48\n", "# - 不保存 GPU 上的历史 inputs\n", "# - 每段结束主动清理 CUDA cache\n", "# ============================================================\n", "\n", "\n", "import os\n", "import gc\n", "import time\n", "import subprocess\n", "import importlib\n", "import warnings\n", "import json\n", "import math\n", "\n", "from pathlib import Path\n", "\n", "\n", "# ============================================================\n", "# 0. 环境\n", "# ============================================================\n", "\n", "os.environ.setdefault(\n", " \"PYTORCH_CUDA_ALLOC_CONF\",\n", " \"expandable_segments:True,max_split_size_mb:128\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"ONLINE_CODEC_CACHE_DIR\",\n", " \"/tmp/mage_vl_codec_cache\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"STREAMMIND_CLIP_CACHE\",\n", " \"/tmp/mage_vl_clips\"\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"ONLINE_CODEC_CACHE_DIR\"],\n", " exist_ok=True\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"STREAMMIND_CLIP_CACHE\"],\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ============================================================\n", "# 1. Import\n", "# ============================================================\n", "\n", "import torch\n", "\n", "from transformers import (\n", " AutoModelForCausalLM,\n", " AutoProcessor,\n", ")\n", "\n", "\n", "# ============================================================\n", "# 2. 配置\n", "# ============================================================\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "\n", "VIDEO_FILE = \"/content/test02.mp4\"\n", "\n", "\n", "# ---------- 实时窗口 ----------\n", "\n", "SEGMENT_SEC = 4.0\n", "\n", "MAX_SEGMENTS = None\n", "# 测试时可以改成：\n", "# MAX_SEGMENTS = 3\n", "\n", "\n", "# ---------- Gate ----------\n", "\n", "GATE_THRESHOLD = 0.50\n", "\n", "\n", "# ---------- LLM ----------\n", "\n", "MAX_NEW_TOKENS = 48\n", "\n", "\n", "# ---------- 最终总结 ----------\n", "\n", "FINAL_MAX_NEW_TOKENS = 192\n", "\n", "\n", "# ---------- Codec 显存 ----------\n", "\n", "# 第六版 64000\n", "# 第七版降低到 48000\n", "CODEC_MAX_PIXELS = 48_000\n", "\n", "# 第六版 24\n", "# 第七版降低到 20\n", "CODEC_TARGET_CANVAS = 20\n", "\n", "\n", "# ---------- 实时模拟 ----------\n", "\n", "SIMULATE_REALTIME = True\n", "\n", "\n", "# ============================================================\n", "# 3. CUDA\n", "# ============================================================\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA GPU\")\n", "\n", "\n", "DEVICE = \"cuda\"\n", "\n", "GPU_NAME = torch.cuda.get_device_name(0)\n", "\n", "CUDA_VERSION = torch.version.cuda\n", "\n", "\n", "# ============================================================\n", "# 4. 自动选择 dtype\n", "# ============================================================\n", "\n", "BF16_SUPPORTED = False\n", "\n", "try:\n", " BF16_SUPPORTED = torch.cuda.is_bf16_supported()\n", "except Exception:\n", " BF16_SUPPORTED = False\n", "\n", "\n", "if BF16_SUPPORTED:\n", " MODEL_DTYPE = torch.bfloat16\n", "\n", " print(\"[dtype] GPU 支持 BF16\")\n", " print(\"[dtype] Mage-VL = BF16\")\n", " print(\"[dtype] StreamMind Gate = BF16\")\n", "\n", "else:\n", " # T4 等旧 GPU：\n", " # 模型仍然使用 FP16 节省显存\n", " # Gate 单独尝试 FP32\n", " MODEL_DTYPE = torch.float16\n", "\n", " print(\"[dtype] GPU 不支持 BF16\")\n", " print(\"[dtype] Mage-VL = FP16\")\n", " print(\"[dtype] StreamMind Gate = FP32 fallback\")\n", "\n", "\n", "print()\n", "print(\"=\" * 60)\n", "print(\"Mage-VL 第七版\")\n", "print(\"=\" * 60)\n", "\n", "print(f\"[GPU] {GPU_NAME}\")\n", "print(f\"[CUDA] {CUDA_VERSION}\")\n", "print(f\"[BF16] {BF16_SUPPORTED}\")\n", "print(f\"[MODEL DTYPE] {MODEL_DTYPE}\")\n", "print(f\"[CODEC] max_pixels={CODEC_MAX_PIXELS}\")\n", "print(f\"[CODEC] target_canvas={CODEC_TARGET_CANVAS}\")\n", "print(\"=\" * 60)\n", "\n", "\n", "# ============================================================\n", "# 5. Prompt\n", "# ============================================================\n", "\n", "PROMPT_TEMPLATE = \"\"\"\n", "你是洗衣机的实时视觉分析助手。\n", "\n", "一个人正在不断把衣服放进洗衣机。\n", "\n", "你的任务不是简单描述画面，而是：\n", "\n", "【识别当前新增衣物】\n", "+\n", "【维护累计衣物】\n", "+\n", "【根据累计衣物动态选择最佳洗涤程序】。\n", "\n", "--------------------------------------------------\n", "已有累计状态\n", "--------------------------------------------------\n", "\n", "{history}\n", "\n", "--------------------------------------------------\n", "当前视频片段\n", "--------------------------------------------------\n", "\n", "请只重点判断当前这个视频片段中新出现或正在加入洗衣机的衣物。\n", "\n", "请分析：\n", "\n", "1. 当前新增衣物\n", "\n", "- 类型：\n", " 牛仔裤、T恤、衬衫、毛衣、外套、\n", " 运动服、裙子、裤子、床品、大件衣物等\n", "\n", "- 颜色\n", "\n", "- 厚薄\n", "\n", "- 重量感\n", "\n", "- 可能材质：\n", " 棉、牛仔、涤纶、尼龙、羊毛、混纺等\n", "\n", "如果无法确定材质，必须说“可能是”，不要假装确定。\n", "\n", "--------------------------------------------------\n", "2. 累计衣物变化\n", "--------------------------------------------------\n", "\n", "结合已有累计状态。\n", "\n", "不要重复计算已经出现过的同一件衣服。\n", "\n", "判断当前加入后，最终洗衣桶中的主要衣物组成。\n", "\n", "--------------------------------------------------\n", "3. 动态洗涤程序\n", "--------------------------------------------------\n", "\n", "根据目前累计衣物重新判断。\n", "\n", "不要机械沿用上一段程序。\n", "\n", "特别考虑：\n", "\n", "- 牛仔\n", "- 棉质\n", "- 化纤\n", "- 混纺\n", "- 毛衣\n", "- 针织\n", "- 外套\n", "- 厚重衣物\n", "- 大件衣物\n", "- 不同材质混洗\n", "- 深浅颜色混合\n", "\n", "如果新增衣物导致程序应该改变，请明确指出。\n", "\n", "例如：\n", "\n", "牛仔 → 混合\n", "\n", "混合 → 大物/混合\n", "\n", "漂洗 2 次 → 3 次\n", "\n", "--------------------------------------------------\n", "4. 当前推荐\n", "--------------------------------------------------\n", "\n", "程序：\n", "水温：\n", "漂洗：\n", "脱水：\n", "\n", "--------------------------------------------------\n", "5. 判断原则\n", "--------------------------------------------------\n", "\n", "最终程序优先保护最脆弱或最限制程序选择的衣物。\n", "\n", "如果存在明显不适合混洗的衣物，需要提醒。\n", "\n", "例如：\n", "\n", "羊毛/针织 → 不建议与普通衣物混洗\n", "\n", "深浅色混合 → 注意掉色\n", "\n", "厚外套 + 普通衣物 → 优先考虑厚重衣物的洗涤要求\n", "\n", "--------------------------------------------------\n", "输出要求\n", "--------------------------------------------------\n", "\n", "简洁。\n", "\n", "只输出：\n", "\n", "【新增衣物】\n", "【累计变化】\n", "【当前程序】\n", "【理由】\n", "\n", "不要长篇解释。\n", "\"\"\".strip()\n", "\n", "\n", "# ============================================================\n", "# 6. 最终总结 Prompt\n", "# ============================================================\n", "\n", "FINAL_PROMPT = \"\"\"\n", "你是洗衣机最终洗涤决策模块。\n", "\n", "下面是视频实时分析过程中得到的所有衣物状态：\n", "\n", "{history}\n", "\n", "请综合整个视频。\n", "\n", "不要只参考最后一个片段。\n", "\n", "--------------------------------------------------\n", "任务\n", "--------------------------------------------------\n", "\n", "1. 汇总最终实际放入洗衣机的衣物\n", "\n", "2. 删除重复描述\n", "\n", "3. 判断主要衣物类型\n", "\n", "4. 判断主要可能材质\n", "\n", "5. 找出最影响洗涤程序选择的衣物\n", "\n", "6. 判断是否存在不适合混洗的衣物\n", "\n", "7. 给出最终最佳洗涤程序\n", "\n", "--------------------------------------------------\n", "洗涤程序选择原则\n", "--------------------------------------------------\n", "\n", "如果只有牛仔类：\n", "优先考虑牛仔程序。\n", "\n", "如果普通棉质、T恤、衬衫等混合：\n", "优先考虑混合程序。\n", "\n", "如果出现较厚外套、大件或明显重量较大的衣物：\n", "考虑大物/混合或更适合厚重衣物的程序。\n", "\n", "如果存在羊毛、针织等特殊衣物：\n", "优先保护特殊衣物，并明确提示不建议混洗。\n", "\n", "漂洗次数：\n", "普通衣物一般较少。\n", "衣物较多、较厚、洗涤剂残留风险较高时增加漂洗。\n", "\n", "脱水：\n", "根据最终衣物厚薄和特殊材质判断。\n", "\n", "不要为了保持前面的建议而保持原程序。\n", "\n", "如果最终衣物组成发生变化，必须重新选择。\n", "\n", "--------------------------------------------------\n", "输出格式\n", "--------------------------------------------------\n", "\n", "【最终衣物】\n", "\n", "列出主要衣物。\n", "\n", "【主要材质】\n", "\n", "列出主要可能材质。\n", "\n", "【最终洗涤程序】\n", "\n", "程序：\n", "水温：\n", "漂洗：\n", "脱水：\n", "\n", "【为什么】\n", "\n", "用2～4句话说明最终选择。\n", "\n", "【注意事项】\n", "\n", "只列真正重要的问题。\n", "\"\"\".strip()\n", "\n", "\n", "# ============================================================\n", "# 7. 加载 Processor\n", "# ============================================================\n", "\n", "print()\n", "print(\"[init] 加载 Processor...\")\n", "\n", "processor = AutoProcessor.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", ")\n", "\n", "\n", "# ============================================================\n", "# 8. 加载 Mage-VL\n", "# ============================================================\n", "\n", "print(\"[init] 加载 Mage-VL...\")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=MODEL_DTYPE,\n", " attn_implementation=\"sdpa\",\n", " low_cpu_mem_usage=True,\n", ").to(DEVICE).eval()\n", "\n", "\n", "print(\"[init] 模型加载完成\")\n", "\n", "\n", "# ============================================================\n", "# 9. Codec\n", "# ============================================================\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "\n", "codec_mod = importlib.import_module(\n", " _REMOTE_PKG + \".codec_video_processing_mage_vl\"\n", ")\n", "\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "\n", "def codec_config():\n", "\n", " override = {\n", " \"engine\": \"hevc\",\n", " \"target_canvas\": CODEC_TARGET_CANVAS,\n", " \"patch\": 16,\n", " }\n", "\n", " kwargs = dict(\n", " processor._codec_config_defaults\n", " )\n", "\n", " dcvc = dict(\n", " kwargs.get(\"dcvc\") or {}\n", " )\n", "\n", " kwargs.update(override)\n", "\n", " if dcvc:\n", " kwargs[\"dcvc\"] = dcvc\n", "\n", " kwargs[\"max_pixels\"] = CODEC_MAX_PIXELS\n", "\n", " return CodecConfig(**kwargs), override\n", "\n", "\n", "# ============================================================\n", "# 10. 加载 Gate\n", "# ============================================================\n", "\n", "print(\"[gate] 加载 Cognition Gate...\")\n", "\n", "try:\n", "\n", " gate = model.model._load_streammind_gate()\n", "\n", " print(\"[gate] Cognition Gate 加载成功\")\n", "\n", " try:\n", " gate_dtype = next(\n", " gate.parameters()\n", " ).dtype\n", "\n", " print(\n", " f\"[gate] 当前 dtype = {gate_dtype}\"\n", " )\n", "\n", " except Exception:\n", " pass\n", "\n", "except Exception as e:\n", "\n", " gate = None\n", "\n", " print(\n", " f\"[gate] 加载失败: {type(e).__name__}: {e}\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 11. T4 / FP16 下：\n", "# 尝试把 Gate 单独提升到 FP32\n", "# ============================================================\n", "\n", "if (\n", " gate is not None\n", " and not BF16_SUPPORTED\n", "):\n", "\n", " try:\n", "\n", " gate = gate.float()\n", "\n", " model.model.streammind_gate = gate\n", "\n", " print(\n", " \"[gate] FP32 fallback 已启用\"\n", " )\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"[gate] FP32 fallback 失败:\",\n", " e\n", " )\n", "\n", "\n", "# ============================================================\n", "# 12. 工具\n", "# ============================================================\n", "\n", "def duration(path):\n", "\n", " try:\n", "\n", " r = subprocess.run(\n", " [\n", " \"ffprobe\",\n", " \"-v\",\n", " \"error\",\n", " \"-show_entries\",\n", " \"format=duration\",\n", " \"-of\",\n", " \"default=nw=1:nk=1\",\n", " str(path),\n", " ],\n", " capture_output=True,\n", " text=True,\n", " check=True,\n", " timeout=60,\n", " )\n", "\n", " return float(\n", " r.stdout.strip()\n", " )\n", "\n", " except Exception:\n", "\n", " return 0.0\n", "\n", "\n", "# ============================================================\n", "# 13. Prompt\n", "# ============================================================\n", "\n", "def make_prompt(history_text=\"\"):\n", "\n", " text = PROMPT_TEMPLATE.format(\n", " history=(\n", " history_text\n", " if history_text\n", " else \"暂无，这是第一个视频片段。\"\n", " )\n", " )\n", "\n", " messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\n", " \"type\": \"video\"\n", " },\n", " {\n", " \"type\": \"text\",\n", " \"text\": text,\n", " },\n", " ],\n", " }\n", " ]\n", "\n", " return processor.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 14. 视频切片\n", "# ============================================================\n", "\n", "def extract_clip(\n", " src,\n", " start,\n", " length,\n", " out,\n", "):\n", "\n", " tmp = out.with_name(\n", " \".tmp_\" + out.name\n", " )\n", "\n", " if tmp.exists():\n", "\n", " try:\n", " tmp.unlink()\n", " except Exception:\n", " pass\n", "\n", " subprocess.run(\n", " [\n", " \"ffmpeg\",\n", " \"-y\",\n", " \"-loglevel\",\n", " \"error\",\n", "\n", " \"-ss\",\n", " f\"{start:.3f}\",\n", "\n", " \"-i\",\n", " str(src),\n", "\n", " \"-t\",\n", " f\"{length:.3f}\",\n", "\n", " \"-c:v\",\n", " \"libx264\",\n", "\n", " \"-preset\",\n", " \"ultrafast\",\n", "\n", " \"-crf\",\n", " \"26\",\n", "\n", " \"-an\",\n", "\n", " \"-pix_fmt\",\n", " \"yuv420p\",\n", "\n", " str(tmp),\n", " ],\n", " check=True,\n", " capture_output=True,\n", " timeout=180,\n", " )\n", "\n", " tmp.replace(out)\n", "\n", " return out\n", "\n", "\n", "# ============================================================\n", "# 15. CPU → GPU\n", "# ============================================================\n", "\n", "def to_cuda(inputs):\n", "\n", " result = {}\n", "\n", " for k, v in inputs.items():\n", "\n", " if not hasattr(v, \"to\"):\n", " continue\n", "\n", " v = v.to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", "\n", " v = v.to(MODEL_DTYPE)\n", "\n", " result[k] = v\n", "\n", " return result\n", "\n", "\n", "# ============================================================\n", "# 16. 清理 GPU\n", "# ============================================================\n", "\n", "def cleanup(*objs):\n", "\n", " for x in objs:\n", "\n", " try:\n", " del x\n", "\n", " except Exception:\n", " pass\n", "\n", " gc.collect()\n", "\n", " try:\n", " torch.cuda.empty_cache()\n", " torch.cuda.ipc_collect()\n", " # 新增：更激进一点清理（T4 有时需要）\n", " torch.cuda.synchronize()\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 17. GPU 显存\n", "# ============================================================\n", "\n", "def gpu_mem():\n", "\n", " allocated = (\n", " torch.cuda.memory_allocated()\n", " / 1024**3\n", " )\n", "\n", " reserved = (\n", " torch.cuda.memory_reserved()\n", " / 1024**3\n", " )\n", "\n", " peak = (\n", " torch.cuda.max_memory_allocated()\n", " / 1024**3\n", " )\n", "\n", " return (\n", " f\"GPU={allocated:.2f}GB \"\n", " f\"/ reserved={reserved:.2f}GB \"\n", " f\"/ peak={peak:.2f}GB\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 18. Gate\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def run_gate(inputs):\n", "\n", " feat = {}\n", "\n", " for k in (\n", " \"pixel_values\",\n", " \"image_grid_thw\",\n", " \"patch_positions\",\n", " ):\n", "\n", " if k not in inputs:\n", " continue\n", "\n", " v = inputs[k].to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", "\n", " v = v.to(MODEL_DTYPE)\n", "\n", " feat[k] = v\n", "\n", " logits = None\n", "\n", " try:\n", "\n", " # ----------------------------------------------------\n", " # BF16 GPU\n", " # ----------------------------------------------------\n", "\n", " if BF16_SUPPORTED:\n", "\n", " logits = (\n", " model.model\n", " .streammind_gate_forward_segments(\n", " [feat]\n", " )\n", " )\n", "\n", " # ----------------------------------------------------\n", " # T4 / FP16 fallback\n", " #\n", " # 不直接使用官方 gate forward，\n", " # 而是：\n", " #\n", " # visual token → FP32 → Gate FP32\n", " #\n", " # 尽量避免 FP16 Gate overflow\n", " # ----------------------------------------------------\n", "\n", " else:\n", "\n", " visual_tokens = (\n", " model.model\n", " ._streammind_vision_tokens(\n", " feat[\"pixel_values\"],\n", " feat[\"image_grid_thw\"],\n", " patch_positions=feat.get(\n", " \"patch_positions\"\n", " ),\n", " )\n", " )\n", "\n", " if not torch.isfinite(\n", " visual_tokens\n", " ).all():\n", "\n", " print(\n", " \"[gate] visual tokens \"\n", " \"出现 NaN/Inf\"\n", " )\n", "\n", " return 0.0\n", "\n", " visual_tokens = (\n", " visual_tokens.float()\n", " )\n", "\n", " logits = gate(\n", " torch.cat(\n", " [visual_tokens],\n", " dim=1,\n", " )\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 官方 Gate 输出：\n", " #\n", " # [B,T,2]\n", " #\n", " # 0 = silence\n", " # 1 = response\n", " # ----------------------------------------------------\n", "\n", " if not isinstance(\n", " logits,\n", " torch.Tensor\n", " ):\n", "\n", " raise RuntimeError(\n", " \"Gate 输出不是 Tensor\"\n", " )\n", "\n", " if logits.ndim != 3:\n", "\n", " raise RuntimeError(\n", " \"Unexpected gate logits shape: \"\n", " + str(tuple(logits.shape))\n", " )\n", "\n", " gate_logits = logits[0]\n", "\n", " # Codec 时间长度\n", " length = int(\n", " inputs[\n", " \"image_grid_thw\"\n", " ][:, 0]\n", " .sum()\n", " .item()\n", " )\n", "\n", " if length <= 0:\n", "\n", " raise RuntimeError(\n", " f\"Invalid gate length={length}\"\n", " )\n", "\n", " pos = min(\n", " length - 1,\n", " gate_logits.shape[0] - 1,\n", " )\n", "\n", " last_logits = (\n", " gate_logits[pos]\n", " .float()\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 关键：\n", " # 在 softmax 之前检查\n", " # ----------------------------------------------------\n", "\n", " if not torch.isfinite(\n", " last_logits\n", " ).all():\n", "\n", " print(\n", " \"[gate] WARNING:\"\n", " \" logits =\",\n", " last_logits\n", " .detach()\n", " .cpu()\n", " .tolist(),\n", " )\n", "\n", " return 0.0\n", "\n", " # ----------------------------------------------------\n", " # 用 log_softmax 更稳定\n", " # ----------------------------------------------------\n", "\n", " log_prob = torch.log_softmax(\n", " last_logits,\n", " dim=-1,\n", " )\n", "\n", " prob = torch.exp(\n", " log_prob[1]\n", " )\n", "\n", " if not torch.isfinite(prob):\n", "\n", " print(\n", " \"[gate] WARNING:\"\n", " \" probability NaN/Inf\"\n", " )\n", "\n", " return 0.0\n", "\n", " return float(\n", " prob.item()\n", " )\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"[gate] ERROR:\",\n", " type(e).__name__,\n", " e,\n", " )\n", "\n", " return 0.0\n", "\n", " finally:\n", "\n", " cleanup(\n", " feat,\n", " logits,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 19. Codec + Gate\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def analyze_clip(\n", " clip,\n", " prompt,\n", " codec_override,\n", "):\n", "\n", " with warnings.catch_warnings():\n", "\n", " warnings.simplefilter(\n", " \"ignore\"\n", " )\n", "\n", " inputs = processor(\n", " text=[prompt],\n", "\n", " videos=[\n", " str(clip)\n", " ],\n", "\n", " video_backend=\"codec\",\n", "\n", " max_pixels=CODEC_MAX_PIXELS,\n", "\n", " codec_config=codec_override,\n", "\n", " return_tensors=\"pt\",\n", "\n", " padding=False,\n", " )\n", "\n", " prob = run_gate(\n", " inputs\n", " )\n", "\n", " return (\n", " inputs,\n", " prob,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 20. LLM\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def generate(\n", " inputs,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "):\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", " text = processor.tokenizer.decode(\n", "\n", " out[0, n:],\n", "\n", " skip_special_tokens=True,\n", " ).strip()\n", "\n", " del out\n", "\n", " return text\n", "\n", "\n", "# ============================================================\n", "# 21. 最终总结\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def final_summary(history):\n", "\n", " if not history:\n", "\n", " return (\n", " \"没有获得有效的衣物分析结果。\"\n", " )\n", "\n", " history_text = \"\\n\\n\".join(\n", " history\n", " )\n", "\n", " prompt = FINAL_PROMPT.format(\n", " history=history_text\n", " )\n", "\n", " inputs = processor.tokenizer(\n", " prompt,\n", " return_tensors=\"pt\",\n", " truncation=True,\n", " max_length=4096,\n", " )\n", "\n", " inputs = {\n", " k: v.to(DEVICE)\n", " for k, v in inputs.items()\n", " }\n", "\n", " try:\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=FINAL_MAX_NEW_TOKENS,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", " result = (\n", " processor.tokenizer.decode(\n", " out[0, n:],\n", " skip_special_tokens=True,\n", " )\n", " .strip()\n", " )\n", "\n", " return result\n", "\n", " finally:\n", "\n", " cleanup(\n", " inputs\n", " )\n", "\n", " try:\n", " del out\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 22. 实时视频分析\n", "# ============================================================\n", "\n", "def stream_video_analysis(\n", "\n", " video_path,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "):\n", "\n", " path = Path(\n", " video_path\n", " )\n", "\n", " if not path.exists():\n", "\n", " raise FileNotFoundError(\n", " video_path\n", " )\n", "\n", " total = duration(\n", " path\n", " )\n", "\n", " if total <= 0:\n", "\n", " raise RuntimeError(\n", " \"无法获取视频时长\"\n", " )\n", "\n", " clip_dir = Path(\n", " os.environ[\n", " \"STREAMMIND_CLIP_CACHE\"\n", " ]\n", " )\n", "\n", " clip_dir.mkdir(\n", " parents=True,\n", " exist_ok=True,\n", " )\n", "\n", " # --------------------------------------------------------\n", " # 两个历史：\n", " #\n", " # results：\n", " # 给用户看的完整输出\n", " #\n", " # history：\n", " # 给模型看的累计状态\n", " # --------------------------------------------------------\n", "\n", " results = []\n", "\n", " history = []\n", "\n", " start = 0.0\n", "\n", " index = 0\n", "\n", " yield (\n", " f\"开始实时分析 | \"\n", " f\"时长={total:.1f}s | \"\n", " f\"窗口={segment_sec:.1f}s | \"\n", " f\"Gate={gate_threshold:.2f} | \"\n", " f\"Codec={CODEC_MAX_PIXELS}px\"\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Codec\n", " # --------------------------------------------------------\n", "\n", " _, codec_override = codec_config()\n", "\n", "\n", " # ========================================================\n", " # 主循环\n", " # ========================================================\n", "\n", " while start < total - 0.05:\n", "\n", " if (\n", " max_segments is not None\n", " and index >= max_segments\n", " ):\n", " break\n", "\n", " end = min(\n", " total,\n", " start + segment_sec,\n", " )\n", "\n", " length = (\n", " end - start\n", " )\n", "\n", " index += 1\n", "\n", " t0 = time.time()\n", "\n", " clip = clip_dir / (\n", "\n", " f\"{path.stem}_\"\n", " f\"{index:06d}_\"\n", " f\"{start:.2f}_\"\n", " f\"{end:.2f}.mp4\"\n", "\n", " )\n", "\n", " inputs = None\n", "\n", " cuda_inputs = None\n", "\n", " said = None\n", "\n", " try:\n", "\n", " # ------------------------------------------------\n", " # 1. 当前累计历史\n", " # ------------------------------------------------\n", "\n", " history_text = \"\\n\".join(\n", " history[-6:]\n", " )\n", "\n", " prompt = make_prompt(\n", " history_text\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 2. 精确切片\n", " # ------------------------------------------------\n", "\n", " extract_clip(\n", "\n", " path,\n", "\n", " start,\n", "\n", " length,\n", "\n", " clip,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 3. Codec + Gate\n", " # ------------------------------------------------\n", "\n", " inputs, prob = analyze_clip(\n", "\n", " clip,\n", "\n", " prompt,\n", "\n", " codec_override,\n", " )\n", "\n", "\n", " head = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** \"\n", " f\"Gate=`{prob:.2f}`\"\n", "\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 4. Gate\n", " #\n", " # 注意：\n", " #\n", " # 本项目不让 Gate 决定“是否识别衣服”。\n", " #\n", " # Gate 只是告诉我们：\n", " # 这个片段是否具有明显的事件变化。\n", " #\n", " # 为避免漏掉新衣物：\n", " # 每个片段仍然进入 Mage-VL。\n", " # ------------------------------------------------\n", "\n", " if prob < gate_threshold:\n", "\n", " elapsed = time.time() - t0\n", " speed = length / max(elapsed, 0.001)\n", "\n", " line = (\n", " f\"{head} → 🔇 跳过\"\n", " f\"（Gate < {gate_threshold:.2f}）\"\n", " f\" | Total={elapsed:.2f}s\"\n", " )\n", "\n", " results.append(line)\n", "\n", " # yield \"\\n\\n\".join(results)\n", " yield line\n", "\n", " # --------------------------------------------\n", " # 关键：\n", " #\n", " # 不进入 Mage-VL\n", " # 不 generate\n", " # 不加入 history\n", " #\n", " # 直接进入下一个 4 秒片段\n", " # --------------------------------------------\n", "\n", " continue\n", "\n", "\n", " # ------------------------------------------------\n", " # Gate 通过\n", " # ------------------------------------------------\n", "\n", " # yield (\n", "\n", " # \"\\n\\n\".join(results)\n", " # + (\n", " # \"\\n\\n\"\n", " # if results\n", " # else \"\"\n", " # )\n", " # + f\"{head} → 🧠 分析衣物中...\"\n", " # )\n", "\n", " yield f\"{head} → 🧠 分析衣物中...\"\n", "\n", " gt = time.time()\n", "\n", "\n", " # ------------------------------------------------\n", " # GPU\n", " # ------------------------------------------------\n", "\n", " cuda_inputs = to_cuda(\n", " inputs\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # Mage-VL\n", " # ------------------------------------------------\n", "\n", " said = generate(\n", "\n", " cuda_inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", " )\n", "\n", "\n", " gen_time = (\n", " time.time()\n", " - gt\n", " )\n", "\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", "\n", " speed = (\n", " length\n", " / max(elapsed, 0.001)\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 保存结果\n", " # ------------------------------------------------\n", " line = (\n", " f\"{head} → 🗣️ \"\n", " f\"{said}\\n\"\n", " f\" Generate={gen_time:.2f}s | \"\n", " f\"Total={elapsed:.2f}s | \"\n", " f\"Realtime={speed:.2f}×\"\n", " )\n", " results.append(line)\n", " history.append(\n", " f\"{start:.1f}-{end:.1f}s：{said}\"\n", " )\n", " yield line # 只输出当前这一段\n", "\n", "\n", "\n", " # ------------------------------------------------\n", " # 9. 模拟真实播放速度\n", " # ------------------------------------------------\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", " if (\n", " simulate_realtime\n", " and elapsed < length\n", " ):\n", "\n", " time.sleep(\n", " length - elapsed\n", " )\n", "\n", "\n", " except Exception as e:\n", "\n", " line = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** → \"\n", "\n", " f\"❌ \"\n", " f\"{type(e).__name__}: \"\n", " f\"{e}\"\n", "\n", " )\n", "\n", " results.append(\n", " line\n", " )\n", " yield line\n", "\n", "\n", " finally:\n", "\n", " # ------------------------------------------------\n", " # 清理 GPU\n", " # ------------------------------------------------\n", "\n", " cleanup(\n", " cuda_inputs,\n", " inputs,\n", " said,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 删除临时视频\n", " # ------------------------------------------------\n", "\n", " if clip.exists():\n", "\n", " try:\n", " clip.unlink()\n", " except Exception:\n", " pass\n", "\n", "\n", " start = end\n", "\n", "\n", " # ------------------------------------------------\n", " # 显存统计\n", " # ------------------------------------------------\n", "\n", " print(\n", "\n", " f\"[segment {index}] \"\n", "\n", " f\"{start:.1f}/\"\n", " f\"{total:.1f}s | \"\n", "\n", " f\"{gpu_mem()}\",\n", "\n", " flush=True,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 重置 peak\n", " # ------------------------------------------------\n", "\n", " try:\n", "\n", " torch.cuda.reset_peak_memory_stats()\n", "\n", " except Exception:\n", "\n", " pass\n", "\n", "\n", " # ========================================================\n", " # 视频结束\n", " # ========================================================\n", "\n", "\n", " yield (\n", " \"========================================\\n\"\n", " \"🔄 视频播放完成，正在综合全部衣物...\\n\"\n", " \"========================================\"\n", " )\n", "\n", "\n", " # ========================================================\n", " # 最终综合\n", " # ========================================================\n", "\n", " try:\n", "\n", " final = final_summary(\n", " history\n", " )\n", "\n", " final_output = (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + \"========================================\\n\"\n", " + \"🧺 最终综合洗涤方案\\n\"\n", " + \"========================================\\n\\n\"\n", "\n", " + final\n", "\n", " + \"\\n\\n\"\n", "\n", " + f\"✅ 完成，共 {index} 个片段\"\n", "\n", " )\n", "\n", " yield final_output\n", "\n", "\n", " except Exception as e:\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + \"========================================\\n\"\n", " + \"🧺 最终综合洗涤方案\\n\"\n", " + \"========================================\\n\\n\"\n", "\n", " + f\"❌ 综合分析失败：\"\n", " + f\"{type(e).__name__}: {e}\"\n", "\n", " )\n", "\n", "\n", "# ============================================================\n", "# 23. Main\n", "# ============================================================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " for output in stream_video_analysis(\n", "\n", " VIDEO_FILE,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "\n", " ):\n", "\n", "\n", "\n", " print(output)\n", "\n", " print(\n", " \"\\n\"\n", " + \"-\" * 60\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "10f340be548f43f18c6b186b61270897", "c7d5360a53ec44038170519e4fc885ab", "08bd68cfb32843398b93d8d199b62e00", "fba85e09d6e24d5cad32e1e9bfa6ba10", "e188c6a94ae94b61bf8a8f9feb8a1036", "d6ec4202745a4722b32eedb3079d4a3a", "213b318627904dcd8450368a3869809b", "d7b48b5a5dbd4943bfa110e664b8019b", "88bc1bbdadca414583bee249d33a39b0", "b669ef28229946748c90243cb827ec9a", "575729d250144141b266d4cd7b008e6e" ] }, "executionInfo": { "elapsed": 192563, "status": "ok", "timestamp": 1787128962945, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "gQu9ORR-LLyF", "outputId": "c2ffaa15-7325-45c4-ceac-9bfc9a869fcc" }, "outputs": [], "source": [ "# ============================================================\n", "# Mage-VL 第七版\n", "# 实时洗衣机衣物分析\n", "#\n", "# 功能：\n", "# 1. 4 秒窗口实时分析\n", "# 2. StreamMind Gate 数值稳定修复\n", "# 3. BF16 优先 / T4 自动 FP16 + FP32 Gate fallback\n", "# 4. 累计衣物状态\n", "# 5. 动态洗涤程序\n", "# 6. 动态漂洗次数\n", "# 7. 视频结束后的最终综合洗涤方案\n", "#\n", "# 显存优化：\n", "# - Codec max_pixels = 48000\n", "# - target_canvas = 20\n", "# - max_new_tokens = 48\n", "# - 不保存 GPU 上的历史 inputs\n", "# - 每段结束主动清理 CUDA cache\n", "# ============================================================\n", "\n", "\n", "import os\n", "import gc\n", "import time\n", "import subprocess\n", "import importlib\n", "import warnings\n", "import json\n", "import math\n", "\n", "from pathlib import Path\n", "\n", "\n", "# ============================================================\n", "# 0. 环境\n", "# ============================================================\n", "\n", "os.environ.setdefault(\n", " \"PYTORCH_CUDA_ALLOC_CONF\",\n", " \"expandable_segments:True,max_split_size_mb:128\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"ONLINE_CODEC_CACHE_DIR\",\n", " \"/tmp/mage_vl_codec_cache\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"STREAMMIND_CLIP_CACHE\",\n", " \"/tmp/mage_vl_clips\"\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"ONLINE_CODEC_CACHE_DIR\"],\n", " exist_ok=True\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"STREAMMIND_CLIP_CACHE\"],\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ============================================================\n", "# 1. Import\n", "# ============================================================\n", "\n", "import torch\n", "\n", "from transformers import (\n", " AutoModelForCausalLM,\n", " AutoProcessor,\n", ")\n", "\n", "\n", "# ============================================================\n", "# 2. 配置\n", "# ============================================================\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "\n", "VIDEO_FILE = \"/content/test.mp4\"\n", "\n", "\n", "# ---------- 实时窗口 ----------\n", "\n", "SEGMENT_SEC = 4.0\n", "\n", "MAX_SEGMENTS = None\n", "# 测试时可以改成：\n", "# MAX_SEGMENTS = 3\n", "\n", "\n", "# ---------- Gate ----------\n", "\n", "GATE_THRESHOLD = 0.50\n", "\n", "\n", "# ---------- LLM ----------\n", "\n", "MAX_NEW_TOKENS = 48\n", "\n", "\n", "# ---------- 最终总结 ----------\n", "\n", "FINAL_MAX_NEW_TOKENS = 192\n", "\n", "\n", "# ---------- Codec 显存 ----------\n", "\n", "# 第六版 64000\n", "# 第七版降低到 48000\n", "CODEC_MAX_PIXELS = 48_000\n", "\n", "# 第六版 24\n", "# 第七版降低到 20\n", "CODEC_TARGET_CANVAS = 20\n", "\n", "\n", "# ---------- 实时模拟 ----------\n", "\n", "SIMULATE_REALTIME = True\n", "\n", "\n", "# ============================================================\n", "# 3. CUDA\n", "# ============================================================\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA GPU\")\n", "\n", "\n", "DEVICE = \"cuda\"\n", "\n", "GPU_NAME = torch.cuda.get_device_name(0)\n", "\n", "CUDA_VERSION = torch.version.cuda\n", "\n", "\n", "# ============================================================\n", "# 4. 自动选择 dtype\n", "# ============================================================\n", "\n", "BF16_SUPPORTED = False\n", "\n", "try:\n", " BF16_SUPPORTED = torch.cuda.is_bf16_supported()\n", "except Exception:\n", " BF16_SUPPORTED = False\n", "\n", "\n", "if BF16_SUPPORTED:\n", " MODEL_DTYPE = torch.bfloat16\n", "\n", " print(\"[dtype] GPU 支持 BF16\")\n", " print(\"[dtype] Mage-VL = BF16\")\n", " print(\"[dtype] StreamMind Gate = BF16\")\n", "\n", "else:\n", " # T4 等旧 GPU：\n", " # 模型仍然使用 FP16 节省显存\n", " # Gate 单独尝试 FP32\n", " MODEL_DTYPE = torch.float16\n", "\n", " print(\"[dtype] GPU 不支持 BF16\")\n", " print(\"[dtype] Mage-VL = FP16\")\n", " print(\"[dtype] StreamMind Gate = FP32 fallback\")\n", "\n", "\n", "print()\n", "print(\"=\" * 60)\n", "print(\"Mage-VL 第七版\")\n", "print(\"=\" * 60)\n", "\n", "print(f\"[GPU] {GPU_NAME}\")\n", "print(f\"[CUDA] {CUDA_VERSION}\")\n", "print(f\"[BF16] {BF16_SUPPORTED}\")\n", "print(f\"[MODEL DTYPE] {MODEL_DTYPE}\")\n", "print(f\"[CODEC] max_pixels={CODEC_MAX_PIXELS}\")\n", "print(f\"[CODEC] target_canvas={CODEC_TARGET_CANVAS}\")\n", "print(\"=\" * 60)\n", "\n", "\n", "# ============================================================\n", "# 5. Prompt\n", "# ============================================================\n", "\n", "PROMPT_TEMPLATE = \"\"\"\n", "你是洗衣机的实时视觉分析助手。\n", "\n", "一个人正在不断把衣服放进洗衣机。\n", "\n", "你的任务不是简单描述画面，而是：\n", "\n", "【识别当前新增衣物】\n", "+\n", "【维护累计衣物】\n", "+\n", "【根据累计衣物动态选择最佳洗涤程序】。\n", "\n", "--------------------------------------------------\n", "已有累计状态\n", "--------------------------------------------------\n", "\n", "{history}\n", "\n", "--------------------------------------------------\n", "当前视频片段\n", "--------------------------------------------------\n", "\n", "请只重点判断当前这个视频片段中新出现或正在加入洗衣机的衣物。\n", "\n", "请分析：\n", "\n", "1. 当前新增衣物\n", "\n", "- 类型：\n", " 牛仔裤、T恤、衬衫、毛衣、外套、\n", " 运动服、裙子、裤子、床品、大件衣物等\n", "\n", "- 颜色\n", "\n", "- 厚薄\n", "\n", "- 重量感\n", "\n", "- 可能材质：\n", " 棉、牛仔、涤纶、尼龙、羊毛、混纺等\n", "\n", "如果无法确定材质，必须说“可能是”，不要假装确定。\n", "\n", "--------------------------------------------------\n", "2. 累计衣物变化\n", "--------------------------------------------------\n", "\n", "结合已有累计状态。\n", "\n", "不要重复计算已经出现过的同一件衣服。\n", "\n", "判断当前加入后，最终洗衣桶中的主要衣物组成。\n", "\n", "--------------------------------------------------\n", "3. 动态洗涤程序\n", "--------------------------------------------------\n", "\n", "根据目前累计衣物重新判断。\n", "\n", "不要机械沿用上一段程序。\n", "\n", "特别考虑：\n", "\n", "- 牛仔\n", "- 棉质\n", "- 化纤\n", "- 混纺\n", "- 毛衣\n", "- 针织\n", "- 外套\n", "- 厚重衣物\n", "- 大件衣物\n", "- 不同材质混洗\n", "- 深浅颜色混合\n", "\n", "如果新增衣物导致程序应该改变，请明确指出。\n", "\n", "例如：\n", "\n", "牛仔 → 混合\n", "\n", "混合 → 大物/混合\n", "\n", "漂洗 2 次 → 3 次\n", "\n", "--------------------------------------------------\n", "4. 当前推荐\n", "--------------------------------------------------\n", "\n", "程序：\n", "水温：\n", "漂洗：\n", "脱水：\n", "\n", "--------------------------------------------------\n", "5. 判断原则\n", "--------------------------------------------------\n", "\n", "最终程序优先保护最脆弱或最限制程序选择的衣物。\n", "\n", "如果存在明显不适合混洗的衣物，需要提醒。\n", "\n", "例如：\n", "\n", "羊毛/针织 → 不建议与普通衣物混洗\n", "\n", "深浅色混合 → 注意掉色\n", "\n", "厚外套 + 普通衣物 → 优先考虑厚重衣物的洗涤要求\n", "\n", "--------------------------------------------------\n", "输出要求\n", "--------------------------------------------------\n", "\n", "简洁。\n", "\n", "只输出：\n", "\n", "【新增衣物】\n", "【累计变化】\n", "【当前程序】\n", "【理由】\n", "\n", "不要长篇解释。\n", "\"\"\".strip()\n", "\n", "\n", "# ============================================================\n", "# 6. 最终总结 Prompt\n", "# ============================================================\n", "\n", "FINAL_PROMPT = \"\"\"\n", "你是洗衣机最终洗涤决策模块。\n", "\n", "下面是视频实时分析过程中得到的所有衣物状态：\n", "\n", "{history}\n", "\n", "请综合整个视频。\n", "\n", "不要只参考最后一个片段。\n", "\n", "--------------------------------------------------\n", "任务\n", "--------------------------------------------------\n", "\n", "1. 汇总最终实际放入洗衣机的衣物\n", "\n", "2. 删除重复描述\n", "\n", "3. 判断主要衣物类型\n", "\n", "4. 判断主要可能材质\n", "\n", "5. 找出最影响洗涤程序选择的衣物\n", "\n", "6. 判断是否存在不适合混洗的衣物\n", "\n", "7. 给出最终最佳洗涤程序\n", "\n", "--------------------------------------------------\n", "洗涤程序选择原则\n", "--------------------------------------------------\n", "\n", "如果只有牛仔类：\n", "优先考虑牛仔程序。\n", "\n", "如果普通棉质、T恤、衬衫等混合：\n", "优先考虑混合程序。\n", "\n", "如果出现较厚外套、大件或明显重量较大的衣物：\n", "考虑大物/混合或更适合厚重衣物的程序。\n", "\n", "如果存在羊毛、针织等特殊衣物：\n", "优先保护特殊衣物，并明确提示不建议混洗。\n", "\n", "漂洗次数：\n", "普通衣物一般较少。\n", "衣物较多、较厚、洗涤剂残留风险较高时增加漂洗。\n", "\n", "脱水：\n", "根据最终衣物厚薄和特殊材质判断。\n", "\n", "不要为了保持前面的建议而保持原程序。\n", "\n", "如果最终衣物组成发生变化，必须重新选择。\n", "\n", "--------------------------------------------------\n", "输出格式\n", "--------------------------------------------------\n", "\n", "【最终衣物】\n", "\n", "列出主要衣物。\n", "\n", "【主要材质】\n", "\n", "列出主要可能材质。\n", "\n", "【最终洗涤程序】\n", "\n", "程序：\n", "水温：\n", "漂洗：\n", "脱水：\n", "\n", "【为什么】\n", "\n", "用2～4句话说明最终选择。\n", "\n", "【注意事项】\n", "\n", "只列真正重要的问题。\n", "\"\"\".strip()\n", "\n", "\n", "# ============================================================\n", "# 7. 加载 Processor\n", "# ============================================================\n", "\n", "print()\n", "print(\"[init] 加载 Processor...\")\n", "\n", "processor = AutoProcessor.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", ")\n", "\n", "\n", "# ============================================================\n", "# 8. 加载 Mage-VL\n", "# ============================================================\n", "\n", "print(\"[init] 加载 Mage-VL...\")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=MODEL_DTYPE,\n", " attn_implementation=\"sdpa\",\n", " low_cpu_mem_usage=True,\n", ").to(DEVICE).eval()\n", "\n", "\n", "print(\"[init] 模型加载完成\")\n", "\n", "\n", "# ============================================================\n", "# 9. Codec\n", "# ============================================================\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "\n", "codec_mod = importlib.import_module(\n", " _REMOTE_PKG + \".codec_video_processing_mage_vl\"\n", ")\n", "\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "\n", "def codec_config():\n", "\n", " override = {\n", " \"engine\": \"hevc\",\n", " \"target_canvas\": CODEC_TARGET_CANVAS,\n", " \"patch\": 16,\n", " }\n", "\n", " kwargs = dict(\n", " processor._codec_config_defaults\n", " )\n", "\n", " dcvc = dict(\n", " kwargs.get(\"dcvc\") or {}\n", " )\n", "\n", " kwargs.update(override)\n", "\n", " if dcvc:\n", " kwargs[\"dcvc\"] = dcvc\n", "\n", " kwargs[\"max_pixels\"] = CODEC_MAX_PIXELS\n", "\n", " return CodecConfig(**kwargs), override\n", "\n", "\n", "# ============================================================\n", "# 10. 加载 Gate\n", "# ============================================================\n", "\n", "print(\"[gate] 加载 Cognition Gate...\")\n", "\n", "try:\n", "\n", " gate = model.model._load_streammind_gate()\n", "\n", " print(\"[gate] Cognition Gate 加载成功\")\n", "\n", " try:\n", " gate_dtype = next(\n", " gate.parameters()\n", " ).dtype\n", "\n", " print(\n", " f\"[gate] 当前 dtype = {gate_dtype}\"\n", " )\n", "\n", " except Exception:\n", " pass\n", "\n", "except Exception as e:\n", "\n", " gate = None\n", "\n", " print(\n", " f\"[gate] 加载失败: {type(e).__name__}: {e}\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 11. T4 / FP16 下：\n", "# 尝试把 Gate 单独提升到 FP32\n", "# ============================================================\n", "\n", "if (\n", " gate is not None\n", " and not BF16_SUPPORTED\n", "):\n", "\n", " try:\n", "\n", " gate = gate.float()\n", "\n", " model.model.streammind_gate = gate\n", "\n", " print(\n", " \"[gate] FP32 fallback 已启用\"\n", " )\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"[gate] FP32 fallback 失败:\",\n", " e\n", " )\n", "\n", "\n", "# ============================================================\n", "# 12. 工具\n", "# ============================================================\n", "\n", "def duration(path):\n", "\n", " try:\n", "\n", " r = subprocess.run(\n", " [\n", " \"ffprobe\",\n", " \"-v\",\n", " \"error\",\n", " \"-show_entries\",\n", " \"format=duration\",\n", " \"-of\",\n", " \"default=nw=1:nk=1\",\n", " str(path),\n", " ],\n", " capture_output=True,\n", " text=True,\n", " check=True,\n", " timeout=60,\n", " )\n", "\n", " return float(\n", " r.stdout.strip()\n", " )\n", "\n", " except Exception:\n", "\n", " return 0.0\n", "\n", "\n", "# ============================================================\n", "# 13. Prompt\n", "# ============================================================\n", "\n", "def make_prompt(history_text=\"\"):\n", "\n", " text = PROMPT_TEMPLATE.format(\n", " history=(\n", " history_text\n", " if history_text\n", " else \"暂无，这是第一个视频片段。\"\n", " )\n", " )\n", "\n", " messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\n", " \"type\": \"video\"\n", " },\n", " {\n", " \"type\": \"text\",\n", " \"text\": text,\n", " },\n", " ],\n", " }\n", " ]\n", "\n", " return processor.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 14. 视频切片\n", "# ============================================================\n", "\n", "def extract_clip(\n", " src,\n", " start,\n", " length,\n", " out,\n", "):\n", "\n", " tmp = out.with_name(\n", " \".tmp_\" + out.name\n", " )\n", "\n", " if tmp.exists():\n", "\n", " try:\n", " tmp.unlink()\n", " except Exception:\n", " pass\n", "\n", " subprocess.run(\n", " [\n", " \"ffmpeg\",\n", " \"-y\",\n", " \"-loglevel\",\n", " \"error\",\n", "\n", " \"-ss\",\n", " f\"{start:.3f}\",\n", "\n", " \"-i\",\n", " str(src),\n", "\n", " \"-t\",\n", " f\"{length:.3f}\",\n", "\n", " \"-c:v\",\n", " \"libx264\",\n", "\n", " \"-preset\",\n", " \"ultrafast\",\n", "\n", " \"-crf\",\n", " \"26\",\n", "\n", " \"-an\",\n", "\n", " \"-pix_fmt\",\n", " \"yuv420p\",\n", "\n", " str(tmp),\n", " ],\n", " check=True,\n", " capture_output=True,\n", " timeout=180,\n", " )\n", "\n", " tmp.replace(out)\n", "\n", " return out\n", "\n", "\n", "# ============================================================\n", "# 15. CPU → GPU\n", "# ============================================================\n", "\n", "def to_cuda(inputs):\n", "\n", " result = {}\n", "\n", " for k, v in inputs.items():\n", "\n", " if not hasattr(v, \"to\"):\n", " continue\n", "\n", " v = v.to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", "\n", " v = v.to(MODEL_DTYPE)\n", "\n", " result[k] = v\n", "\n", " return result\n", "\n", "\n", "# ============================================================\n", "# 16. 清理 GPU\n", "# ============================================================\n", "\n", "def cleanup(*objs):\n", "\n", " for x in objs:\n", "\n", " try:\n", " del x\n", "\n", " except Exception:\n", " pass\n", "\n", " gc.collect()\n", "\n", " try:\n", " torch.cuda.empty_cache()\n", " torch.cuda.ipc_collect()\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 17. GPU 显存\n", "# ============================================================\n", "\n", "def gpu_mem():\n", "\n", " allocated = (\n", " torch.cuda.memory_allocated()\n", " / 1024**3\n", " )\n", "\n", " reserved = (\n", " torch.cuda.memory_reserved()\n", " / 1024**3\n", " )\n", "\n", " peak = (\n", " torch.cuda.max_memory_allocated()\n", " / 1024**3\n", " )\n", "\n", " return (\n", " f\"GPU={allocated:.2f}GB \"\n", " f\"/ reserved={reserved:.2f}GB \"\n", " f\"/ peak={peak:.2f}GB\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 18. Gate\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def run_gate(inputs):\n", "\n", " feat = {}\n", "\n", " for k in (\n", " \"pixel_values\",\n", " \"image_grid_thw\",\n", " \"patch_positions\",\n", " ):\n", "\n", " if k not in inputs:\n", " continue\n", "\n", " v = inputs[k].to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", "\n", " v = v.to(MODEL_DTYPE)\n", "\n", " feat[k] = v\n", "\n", " logits = None\n", "\n", " try:\n", "\n", " # ----------------------------------------------------\n", " # BF16 GPU\n", " # ----------------------------------------------------\n", "\n", " if BF16_SUPPORTED:\n", "\n", " logits = (\n", " model.model\n", " .streammind_gate_forward_segments(\n", " [feat]\n", " )\n", " )\n", "\n", " # ----------------------------------------------------\n", " # T4 / FP16 fallback\n", " #\n", " # 不直接使用官方 gate forward，\n", " # 而是：\n", " #\n", " # visual token → FP32 → Gate FP32\n", " #\n", " # 尽量避免 FP16 Gate overflow\n", " # ----------------------------------------------------\n", "\n", " else:\n", "\n", " visual_tokens = (\n", " model.model\n", " ._streammind_vision_tokens(\n", " feat[\"pixel_values\"],\n", " feat[\"image_grid_thw\"],\n", " patch_positions=feat.get(\n", " \"patch_positions\"\n", " ),\n", " )\n", " )\n", "\n", " if not torch.isfinite(\n", " visual_tokens\n", " ).all():\n", "\n", " print(\n", " \"[gate] visual tokens \"\n", " \"出现 NaN/Inf\"\n", " )\n", "\n", " return 0.0\n", "\n", " visual_tokens = (\n", " visual_tokens.float()\n", " )\n", "\n", " logits = gate(\n", " torch.cat(\n", " [visual_tokens],\n", " dim=1,\n", " )\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 官方 Gate 输出：\n", " #\n", " # [B,T,2]\n", " #\n", " # 0 = silence\n", " # 1 = response\n", " # ----------------------------------------------------\n", "\n", " if not isinstance(\n", " logits,\n", " torch.Tensor\n", " ):\n", "\n", " raise RuntimeError(\n", " \"Gate 输出不是 Tensor\"\n", " )\n", "\n", " if logits.ndim != 3:\n", "\n", " raise RuntimeError(\n", " \"Unexpected gate logits shape: \"\n", " + str(tuple(logits.shape))\n", " )\n", "\n", " gate_logits = logits[0]\n", "\n", " # Codec 时间长度\n", " length = int(\n", " inputs[\n", " \"image_grid_thw\"\n", " ][:, 0]\n", " .sum()\n", " .item()\n", " )\n", "\n", " if length <= 0:\n", "\n", " raise RuntimeError(\n", " f\"Invalid gate length={length}\"\n", " )\n", "\n", " pos = min(\n", " length - 1,\n", " gate_logits.shape[0] - 1,\n", " )\n", "\n", " last_logits = (\n", " gate_logits[pos]\n", " .float()\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 关键：\n", " # 在 softmax 之前检查\n", " # ----------------------------------------------------\n", "\n", " if not torch.isfinite(\n", " last_logits\n", " ).all():\n", "\n", " print(\n", " \"[gate] WARNING:\"\n", " \" logits =\",\n", " last_logits\n", " .detach()\n", " .cpu()\n", " .tolist(),\n", " )\n", "\n", " return 0.0\n", "\n", " # ----------------------------------------------------\n", " # 用 log_softmax 更稳定\n", " # ----------------------------------------------------\n", "\n", " log_prob = torch.log_softmax(\n", " last_logits,\n", " dim=-1,\n", " )\n", "\n", " prob = torch.exp(\n", " log_prob[1]\n", " )\n", "\n", " if not torch.isfinite(prob):\n", "\n", " print(\n", " \"[gate] WARNING:\"\n", " \" probability NaN/Inf\"\n", " )\n", "\n", " return 0.0\n", "\n", " return float(\n", " prob.item()\n", " )\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"[gate] ERROR:\",\n", " type(e).__name__,\n", " e,\n", " )\n", "\n", " return 0.0\n", "\n", " finally:\n", "\n", " cleanup(\n", " feat,\n", " logits,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 19. Codec + Gate\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def analyze_clip(\n", " clip,\n", " prompt,\n", " codec_override,\n", "):\n", "\n", " with warnings.catch_warnings():\n", "\n", " warnings.simplefilter(\n", " \"ignore\"\n", " )\n", "\n", " inputs = processor(\n", " text=[prompt],\n", "\n", " videos=[\n", " str(clip)\n", " ],\n", "\n", " video_backend=\"codec\",\n", "\n", " max_pixels=CODEC_MAX_PIXELS,\n", "\n", " codec_config=codec_override,\n", "\n", " return_tensors=\"pt\",\n", "\n", " padding=False,\n", " )\n", "\n", " prob = run_gate(\n", " inputs\n", " )\n", "\n", " return (\n", " inputs,\n", " prob,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 20. LLM\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def generate(\n", " inputs,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "):\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", " text = processor.tokenizer.decode(\n", "\n", " out[0, n:],\n", "\n", " skip_special_tokens=True,\n", " ).strip()\n", "\n", " del out\n", "\n", " return text\n", "\n", "\n", "# ============================================================\n", "# 21. 最终总结\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def final_summary(history):\n", "\n", " if not history:\n", "\n", " return (\n", " \"没有获得有效的衣物分析结果。\"\n", " )\n", "\n", " history_text = \"\\n\\n\".join(\n", " history\n", " )\n", "\n", " prompt = FINAL_PROMPT.format(\n", " history=history_text\n", " )\n", "\n", " inputs = processor.tokenizer(\n", " prompt,\n", " return_tensors=\"pt\",\n", " truncation=True,\n", " max_length=4096,\n", " )\n", "\n", " inputs = {\n", " k: v.to(DEVICE)\n", " for k, v in inputs.items()\n", " }\n", "\n", " try:\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=FINAL_MAX_NEW_TOKENS,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", " result = (\n", " processor.tokenizer.decode(\n", " out[0, n:],\n", " skip_special_tokens=True,\n", " )\n", " .strip()\n", " )\n", "\n", " return result\n", "\n", " finally:\n", "\n", " cleanup(\n", " inputs\n", " )\n", "\n", " try:\n", " del out\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 22. 实时视频分析\n", "# ============================================================\n", "\n", "def stream_video_analysis(\n", "\n", " video_path,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "):\n", "\n", " path = Path(\n", " video_path\n", " )\n", "\n", " if not path.exists():\n", "\n", " raise FileNotFoundError(\n", " video_path\n", " )\n", "\n", " total = duration(\n", " path\n", " )\n", "\n", " if total <= 0:\n", "\n", " raise RuntimeError(\n", " \"无法获取视频时长\"\n", " )\n", "\n", " clip_dir = Path(\n", " os.environ[\n", " \"STREAMMIND_CLIP_CACHE\"\n", " ]\n", " )\n", "\n", " clip_dir.mkdir(\n", " parents=True,\n", " exist_ok=True,\n", " )\n", "\n", " # --------------------------------------------------------\n", " # 两个历史：\n", " #\n", " # results：\n", " # 给用户看的完整输出\n", " #\n", " # history：\n", " # 给模型看的累计状态\n", " # --------------------------------------------------------\n", "\n", " results = []\n", "\n", " history = []\n", "\n", " start = 0.0\n", "\n", " index = 0\n", "\n", " yield (\n", " f\"开始实时分析 | \"\n", " f\"时长={total:.1f}s | \"\n", " f\"窗口={segment_sec:.1f}s | \"\n", " f\"Gate={gate_threshold:.2f} | \"\n", " f\"Codec={CODEC_MAX_PIXELS}px\"\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Codec\n", " # --------------------------------------------------------\n", "\n", " _, codec_override = codec_config()\n", "\n", "\n", " # ========================================================\n", " # 主循环\n", " # ========================================================\n", "\n", " while start < total - 0.05:\n", "\n", " if (\n", " max_segments is not None\n", " and index >= max_segments\n", " ):\n", " break\n", "\n", " end = min(\n", " total,\n", " start + segment_sec,\n", " )\n", "\n", " length = (\n", " end - start\n", " )\n", "\n", " index += 1\n", "\n", " t0 = time.time()\n", "\n", " clip = clip_dir / (\n", "\n", " f\"{path.stem}_\"\n", " f\"{index:06d}_\"\n", " f\"{start:.2f}_\"\n", " f\"{end:.2f}.mp4\"\n", "\n", " )\n", "\n", " inputs = None\n", "\n", " cuda_inputs = None\n", "\n", " said = None\n", "\n", " try:\n", "\n", " # ------------------------------------------------\n", " # 1. 当前累计历史\n", " # ------------------------------------------------\n", "\n", " history_text = \"\\n\".join(\n", " history[-6:]\n", " )\n", "\n", " prompt = make_prompt(\n", " history_text\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 2. 精确切片\n", " # ------------------------------------------------\n", "\n", " extract_clip(\n", "\n", " path,\n", "\n", " start,\n", "\n", " length,\n", "\n", " clip,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 3. Codec + Gate\n", " # ------------------------------------------------\n", "\n", " inputs, prob = analyze_clip(\n", "\n", " clip,\n", "\n", " prompt,\n", "\n", " codec_override,\n", " )\n", "\n", "\n", " head = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** \"\n", " f\"Gate=`{prob:.2f}`\"\n", "\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 4. Gate\n", " #\n", " # 注意：\n", " #\n", " # 本项目不让 Gate 决定“是否识别衣服”。\n", " #\n", " # Gate 只是告诉我们：\n", " # 这个片段是否具有明显的事件变化。\n", " #\n", " # 为避免漏掉新衣物：\n", " # 每个片段仍然进入 Mage-VL。\n", " # ------------------------------------------------\n", "\n", " if prob < gate_threshold:\n", "\n", " elapsed = time.time() - t0\n", " speed = length / max(elapsed, 0.001)\n", "\n", " line = (\n", " f\"{head} → 🔇 跳过\"\n", " f\"（Gate < {gate_threshold:.2f}）\"\n", " f\" | Total={elapsed:.2f}s\"\n", " )\n", "\n", " results.append(line)\n", "\n", " yield \"\\n\\n\".join(results)\n", "\n", " # --------------------------------------------\n", " # 关键：\n", " #\n", " # 不进入 Mage-VL\n", " # 不 generate\n", " # 不加入 history\n", " #\n", " # 直接进入下一个 4 秒片段\n", " # --------------------------------------------\n", "\n", " continue\n", "\n", "\n", " # ------------------------------------------------\n", " # Gate 通过\n", " # ------------------------------------------------\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(results)\n", " + (\n", " \"\\n\\n\"\n", " if results\n", " else \"\"\n", " )\n", " + f\"{head} → 🧠 分析衣物中...\"\n", " )\n", "\n", "\n", " gt = time.time()\n", "\n", "\n", " # ------------------------------------------------\n", " # GPU\n", " # ------------------------------------------------\n", "\n", " cuda_inputs = to_cuda(\n", " inputs\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # Mage-VL\n", " # ------------------------------------------------\n", "\n", " said = generate(\n", "\n", " cuda_inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", " )\n", "\n", "\n", " gen_time = (\n", " time.time()\n", " - gt\n", " )\n", "\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", "\n", " speed = (\n", " length\n", " / max(elapsed, 0.001)\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 保存结果\n", " # ------------------------------------------------\n", "\n", " line = (\n", "\n", " f\"{head} → 🗣️ \"\n", " f\"{said}\\n\"\n", "\n", " f\" Generate=\"\n", " f\"{gen_time:.2f}s | \"\n", "\n", " f\"Total=\"\n", " f\"{elapsed:.2f}s | \"\n", "\n", " f\"Realtime=\"\n", " f\"{speed:.2f}×\"\n", "\n", " )\n", "\n", "\n", " results.append(\n", " line\n", " )\n", "\n", "\n", " history.append(\n", "\n", " f\"{start:.1f}-\"\n", " f\"{end:.1f}s：\"\n", " f\"{said}\"\n", "\n", " )\n", "\n", "\n", " yield \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " # if prob < gate_threshold:\n", "\n", " # gate_state = (\n", " # \"Gate较低\"\n", " # )\n", "\n", " # else:\n", "\n", " # gate_state = (\n", " # \"Gate触发\"\n", " # )\n", "\n", "\n", " # # ------------------------------------------------\n", " # # 5. 进入 Mage-VL\n", " # # ------------------------------------------------\n", "\n", " # yield (\n", "\n", " # \"\\n\\n\".join(\n", " # results\n", " # )\n", " # + (\n", " # \"\\n\\n\"\n", " # if results\n", " # else \"\"\n", " # )\n", " # + f\"{head} \"\n", " # + f\"→ 🧠 {gate_state}，\"\n", " # + \"分析衣物中...\"\n", " # )\n", "\n", "\n", " # gt = time.time()\n", "\n", "\n", " # # ------------------------------------------------\n", " # # GPU\n", " # # ------------------------------------------------\n", "\n", " # cuda_inputs = to_cuda(\n", " # inputs\n", " # )\n", "\n", "\n", " # # ------------------------------------------------\n", " # # Generate\n", " # # ------------------------------------------------\n", "\n", " # said = generate(\n", "\n", " # cuda_inputs,\n", "\n", " # max_new_tokens=max_new_tokens,\n", " # )\n", "\n", "\n", " # gen_time = (\n", " # time.time()\n", " # - gt\n", " # )\n", "\n", " # elapsed = (\n", " # time.time()\n", " # - t0\n", " # )\n", "\n", " # speed = (\n", " # length\n", " # / max(elapsed, 0.001)\n", " # )\n", "\n", "\n", " # # ------------------------------------------------\n", " # # 6. 当前结果\n", " # # ------------------------------------------------\n", "\n", " # line = (\n", "\n", " # f\"{head} → 🗣️ \"\n", " # f\"{said}\\n\"\n", "\n", " # f\" Generate=\"\n", " # f\"{gen_time:.2f}s | \"\n", "\n", " # f\"Total=\"\n", " # f\"{elapsed:.2f}s | \"\n", "\n", " # f\"Realtime=\"\n", " # f\"{speed:.2f}×\"\n", "\n", " # )\n", "\n", "\n", " # results.append(\n", " # line\n", " # )\n", "\n", "\n", " # # ------------------------------------------------\n", " # # 7. 只把模型结果加入累计历史\n", " # # ------------------------------------------------\n", "\n", " # history.append(\n", "\n", " # f\"{start:.1f}-\"\n", " # f\"{end:.1f}s：\"\n", " # f\"{said}\"\n", "\n", " # )\n", "\n", "\n", " # # ------------------------------------------------\n", " # # 8. 实时输出\n", " # # ------------------------------------------------\n", "\n", " # yield \"\\n\\n\".join(\n", " # results\n", " # )\n", "\n", "\n", " # ------------------------------------------------\n", " # 9. 模拟真实播放速度\n", " # ------------------------------------------------\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", " if (\n", " simulate_realtime\n", " and elapsed < length\n", " ):\n", "\n", " time.sleep(\n", " length - elapsed\n", " )\n", "\n", "\n", " except Exception as e:\n", "\n", " line = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** → \"\n", "\n", " f\"❌ \"\n", " f\"{type(e).__name__}: \"\n", " f\"{e}\"\n", "\n", " )\n", "\n", " results.append(\n", " line\n", " )\n", "\n", " yield \"\\n\\n\".join(\n", " results\n", " )\n", "\n", "\n", " finally:\n", "\n", " # ------------------------------------------------\n", " # 清理 GPU\n", " # ------------------------------------------------\n", "\n", " cleanup(\n", " cuda_inputs,\n", " inputs,\n", " said,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 删除临时视频\n", " # ------------------------------------------------\n", "\n", " if clip.exists():\n", "\n", " try:\n", " clip.unlink()\n", " except Exception:\n", " pass\n", "\n", "\n", " start = end\n", "\n", "\n", " # ------------------------------------------------\n", " # 显存统计\n", " # ------------------------------------------------\n", "\n", " print(\n", "\n", " f\"[segment {index}] \"\n", "\n", " f\"{start:.1f}/\"\n", " f\"{total:.1f}s | \"\n", "\n", " f\"{gpu_mem()}\",\n", "\n", " flush=True,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 重置 peak\n", " # ------------------------------------------------\n", "\n", " try:\n", "\n", " torch.cuda.reset_peak_memory_stats()\n", "\n", " except Exception:\n", "\n", " pass\n", "\n", "\n", " # ========================================================\n", " # 视频结束\n", " # ========================================================\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", " + \"========================================\\n\"\n", " + \"🔄 视频播放完成，正在综合全部衣物...\\n\"\n", " + \"========================================\"\n", " )\n", "\n", "\n", " # ========================================================\n", " # 最终综合\n", " # ========================================================\n", "\n", " try:\n", "\n", " final = final_summary(\n", " history\n", " )\n", "\n", " final_output = (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + \"========================================\\n\"\n", " + \"🧺 最终综合洗涤方案\\n\"\n", " + \"========================================\\n\\n\"\n", "\n", " + final\n", "\n", " + \"\\n\\n\"\n", "\n", " + f\"✅ 完成，共 {index} 个片段\"\n", "\n", " )\n", "\n", " yield final_output\n", "\n", "\n", " except Exception as e:\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + \"========================================\\n\"\n", " + \"🧺 最终综合洗涤方案\\n\"\n", " + \"========================================\\n\\n\"\n", "\n", " + f\"❌ 综合分析失败：\"\n", " + f\"{type(e).__name__}: {e}\"\n", "\n", " )\n", "\n", "\n", "# ============================================================\n", "# 23. Main\n", "# ============================================================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " for output in stream_video_analysis(\n", "\n", " VIDEO_FILE,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "\n", " ):\n", "\n", " # 清屏\n", " print(\n", " \"\\033[H\\033[J\",\n", " end=\"\"\n", " )\n", "\n", " print(\n", " \"=== Mage-VL 第七版：\"\n", " \"洗衣机实时衣物分析 ===\\n\"\n", " )\n", "\n", " print(output)\n", "\n", " print(\n", " \"\\n\"\n", " + \"-\" * 60\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "ExVwjuGCEBRh" }, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "697be85de85b4e549aed337747d47b82", "325f208604fa4bc59b86342425c65a98", "927b14b6b6eb4cebb380e74a7de28637", "f6b419181b5a4a1684c5578ae008599a", "259fed6559164ad8b0f39e2d88725888", "219bfd88839344369ef5b9a39f039b68", "d926421442744cdc88f632094f606641", "e3705ce54f0946299da16d260baf1584", "69294401700a47208072d2fc756eacb6", "f449fe5baafa484e8dfbf927bf30510b", "c7b5d59a4ab34508963f3d7e2def7fbf" ] }, "executionInfo": { "elapsed": 228874, "status": "ok", "timestamp": 1787126414472, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "m6aDr8PABO4w", "outputId": "74ad1b96-2bde-456a-970a-36f30ccaad5d" }, "outputs": [], "source": [ "# ============================================================\n", "# Mage-VL 第七版\n", "# 实时洗衣机衣物分析\n", "#\n", "# 功能：\n", "# 1. 4 秒窗口实时分析\n", "# 2. StreamMind Gate 数值稳定修复\n", "# 3. BF16 优先 / T4 自动 FP16 + FP32 Gate fallback\n", "# 4. 累计衣物状态\n", "# 5. 动态洗涤程序\n", "# 6. 动态漂洗次数\n", "# 7. 视频结束后的最终综合洗涤方案\n", "#\n", "# 显存优化：\n", "# - Codec max_pixels = 48000\n", "# - target_canvas = 20\n", "# - max_new_tokens = 48\n", "# - 不保存 GPU 上的历史 inputs\n", "# - 每段结束主动清理 CUDA cache\n", "# ============================================================\n", "\n", "\n", "import os\n", "import gc\n", "import time\n", "import subprocess\n", "import importlib\n", "import warnings\n", "import json\n", "import math\n", "\n", "from pathlib import Path\n", "\n", "\n", "# ============================================================\n", "# 0. 环境\n", "# ============================================================\n", "\n", "os.environ.setdefault(\n", " \"PYTORCH_CUDA_ALLOC_CONF\",\n", " \"expandable_segments:True,max_split_size_mb:128\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"ONLINE_CODEC_CACHE_DIR\",\n", " \"/tmp/mage_vl_codec_cache\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"STREAMMIND_CLIP_CACHE\",\n", " \"/tmp/mage_vl_clips\"\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"ONLINE_CODEC_CACHE_DIR\"],\n", " exist_ok=True\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"STREAMMIND_CLIP_CACHE\"],\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ============================================================\n", "# 1. Import\n", "# ============================================================\n", "\n", "import torch\n", "\n", "from transformers import (\n", " AutoModelForCausalLM,\n", " AutoProcessor,\n", ")\n", "\n", "\n", "# ============================================================\n", "# 2. 配置\n", "# ============================================================\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "\n", "VIDEO_FILE = \"/content/test.mp4\"\n", "\n", "\n", "# ---------- 实时窗口 ----------\n", "\n", "SEGMENT_SEC = 4.0\n", "\n", "MAX_SEGMENTS = None\n", "# 测试时可以改成：\n", "# MAX_SEGMENTS = 3\n", "\n", "\n", "# ---------- Gate ----------\n", "\n", "GATE_THRESHOLD = 0.50\n", "\n", "\n", "# ---------- LLM ----------\n", "\n", "MAX_NEW_TOKENS = 48\n", "\n", "\n", "# ---------- 最终总结 ----------\n", "\n", "FINAL_MAX_NEW_TOKENS = 192\n", "\n", "\n", "# ---------- Codec 显存 ----------\n", "\n", "# 第六版 64000\n", "# 第七版降低到 48000\n", "CODEC_MAX_PIXELS = 48_000\n", "\n", "# 第六版 24\n", "# 第七版降低到 20\n", "CODEC_TARGET_CANVAS = 20\n", "\n", "\n", "# ---------- 实时模拟 ----------\n", "\n", "SIMULATE_REALTIME = True\n", "\n", "\n", "# ============================================================\n", "# 3. CUDA\n", "# ============================================================\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA GPU\")\n", "\n", "\n", "DEVICE = \"cuda\"\n", "\n", "GPU_NAME = torch.cuda.get_device_name(0)\n", "\n", "CUDA_VERSION = torch.version.cuda\n", "\n", "\n", "# ============================================================\n", "# 4. 自动选择 dtype\n", "# ============================================================\n", "\n", "BF16_SUPPORTED = False\n", "\n", "try:\n", " BF16_SUPPORTED = torch.cuda.is_bf16_supported()\n", "except Exception:\n", " BF16_SUPPORTED = False\n", "\n", "\n", "if BF16_SUPPORTED:\n", " MODEL_DTYPE = torch.bfloat16\n", "\n", " print(\"[dtype] GPU 支持 BF16\")\n", " print(\"[dtype] Mage-VL = BF16\")\n", " print(\"[dtype] StreamMind Gate = BF16\")\n", "\n", "else:\n", " # T4 等旧 GPU：\n", " # 模型仍然使用 FP16 节省显存\n", " # Gate 单独尝试 FP32\n", " MODEL_DTYPE = torch.float16\n", "\n", " print(\"[dtype] GPU 不支持 BF16\")\n", " print(\"[dtype] Mage-VL = FP16\")\n", " print(\"[dtype] StreamMind Gate = FP32 fallback\")\n", "\n", "\n", "print()\n", "print(\"=\" * 60)\n", "print(\"Mage-VL 第七版\")\n", "print(\"=\" * 60)\n", "\n", "print(f\"[GPU] {GPU_NAME}\")\n", "print(f\"[CUDA] {CUDA_VERSION}\")\n", "print(f\"[BF16] {BF16_SUPPORTED}\")\n", "print(f\"[MODEL DTYPE] {MODEL_DTYPE}\")\n", "print(f\"[CODEC] max_pixels={CODEC_MAX_PIXELS}\")\n", "print(f\"[CODEC] target_canvas={CODEC_TARGET_CANVAS}\")\n", "print(\"=\" * 60)\n", "\n", "\n", "# ============================================================\n", "# 5. Prompt\n", "# ============================================================\n", "\n", "PROMPT_TEMPLATE = \"\"\"\n", "你是洗衣机的实时视觉分析助手。\n", "\n", "一个人正在不断把衣服放进洗衣机。\n", "\n", "你的任务不是简单描述画面，而是：\n", "\n", "【识别当前新增衣物】\n", "+\n", "【维护累计衣物】\n", "+\n", "【根据累计衣物动态选择最佳洗涤程序】。\n", "\n", "--------------------------------------------------\n", "已有累计状态\n", "--------------------------------------------------\n", "\n", "{history}\n", "\n", "--------------------------------------------------\n", "当前视频片段\n", "--------------------------------------------------\n", "\n", "请只重点判断当前这个视频片段中新出现或正在加入洗衣机的衣物。\n", "\n", "请分析：\n", "\n", "1. 当前新增衣物\n", "\n", "- 类型：\n", " 牛仔裤、T恤、衬衫、毛衣、外套、\n", " 运动服、裙子、裤子、床品、大件衣物等\n", "\n", "- 颜色\n", "\n", "- 厚薄\n", "\n", "- 重量感\n", "\n", "- 可能材质：\n", " 棉、牛仔、涤纶、尼龙、羊毛、混纺等\n", "\n", "如果无法确定材质，必须说“可能是”，不要假装确定。\n", "\n", "--------------------------------------------------\n", "2. 累计衣物变化\n", "--------------------------------------------------\n", "\n", "结合已有累计状态。\n", "\n", "不要重复计算已经出现过的同一件衣服。\n", "\n", "判断当前加入后，最终洗衣桶中的主要衣物组成。\n", "\n", "--------------------------------------------------\n", "3. 动态洗涤程序\n", "--------------------------------------------------\n", "\n", "根据目前累计衣物重新判断。\n", "\n", "不要机械沿用上一段程序。\n", "\n", "特别考虑：\n", "\n", "- 牛仔\n", "- 棉质\n", "- 化纤\n", "- 混纺\n", "- 毛衣\n", "- 针织\n", "- 外套\n", "- 厚重衣物\n", "- 大件衣物\n", "- 不同材质混洗\n", "- 深浅颜色混合\n", "\n", "如果新增衣物导致程序应该改变，请明确指出。\n", "\n", "例如：\n", "\n", "牛仔 → 混合\n", "\n", "混合 → 大物/混合\n", "\n", "漂洗 2 次 → 3 次\n", "\n", "--------------------------------------------------\n", "4. 当前推荐\n", "--------------------------------------------------\n", "\n", "程序：\n", "水温：\n", "漂洗：\n", "脱水：\n", "\n", "--------------------------------------------------\n", "5. 判断原则\n", "--------------------------------------------------\n", "\n", "最终程序优先保护最脆弱或最限制程序选择的衣物。\n", "\n", "如果存在明显不适合混洗的衣物，需要提醒。\n", "\n", "例如：\n", "\n", "羊毛/针织 → 不建议与普通衣物混洗\n", "\n", "深浅色混合 → 注意掉色\n", "\n", "厚外套 + 普通衣物 → 优先考虑厚重衣物的洗涤要求\n", "\n", "--------------------------------------------------\n", "输出要求\n", "--------------------------------------------------\n", "\n", "简洁。\n", "\n", "只输出：\n", "\n", "【新增衣物】\n", "【累计变化】\n", "【当前程序】\n", "【理由】\n", "\n", "不要长篇解释。\n", "\"\"\".strip()\n", "\n", "\n", "# ============================================================\n", "# 6. 最终总结 Prompt\n", "# ============================================================\n", "\n", "FINAL_PROMPT = \"\"\"\n", "你是洗衣机最终洗涤决策模块。\n", "\n", "下面是视频实时分析过程中得到的所有衣物状态：\n", "\n", "{history}\n", "\n", "请综合整个视频。\n", "\n", "不要只参考最后一个片段。\n", "\n", "--------------------------------------------------\n", "任务\n", "--------------------------------------------------\n", "\n", "1. 汇总最终实际放入洗衣机的衣物\n", "\n", "2. 删除重复描述\n", "\n", "3. 判断主要衣物类型\n", "\n", "4. 判断主要可能材质\n", "\n", "5. 找出最影响洗涤程序选择的衣物\n", "\n", "6. 判断是否存在不适合混洗的衣物\n", "\n", "7. 给出最终最佳洗涤程序\n", "\n", "--------------------------------------------------\n", "洗涤程序选择原则\n", "--------------------------------------------------\n", "\n", "如果只有牛仔类：\n", "优先考虑牛仔程序。\n", "\n", "如果普通棉质、T恤、衬衫等混合：\n", "优先考虑混合程序。\n", "\n", "如果出现较厚外套、大件或明显重量较大的衣物：\n", "考虑大物/混合或更适合厚重衣物的程序。\n", "\n", "如果存在羊毛、针织等特殊衣物：\n", "优先保护特殊衣物，并明确提示不建议混洗。\n", "\n", "漂洗次数：\n", "普通衣物一般较少。\n", "衣物较多、较厚、洗涤剂残留风险较高时增加漂洗。\n", "\n", "脱水：\n", "根据最终衣物厚薄和特殊材质判断。\n", "\n", "不要为了保持前面的建议而保持原程序。\n", "\n", "如果最终衣物组成发生变化，必须重新选择。\n", "\n", "--------------------------------------------------\n", "输出格式\n", "--------------------------------------------------\n", "\n", "【最终衣物】\n", "\n", "列出主要衣物。\n", "\n", "【主要材质】\n", "\n", "列出主要可能材质。\n", "\n", "【最终洗涤程序】\n", "\n", "程序：\n", "水温：\n", "漂洗：\n", "脱水：\n", "\n", "【为什么】\n", "\n", "用2～4句话说明最终选择。\n", "\n", "【注意事项】\n", "\n", "只列真正重要的问题。\n", "\"\"\".strip()\n", "\n", "\n", "# ============================================================\n", "# 7. 加载 Processor\n", "# ============================================================\n", "\n", "print()\n", "print(\"[init] 加载 Processor...\")\n", "\n", "processor = AutoProcessor.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", ")\n", "\n", "\n", "# ============================================================\n", "# 8. 加载 Mage-VL\n", "# ============================================================\n", "\n", "print(\"[init] 加载 Mage-VL...\")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=MODEL_DTYPE,\n", " attn_implementation=\"sdpa\",\n", " low_cpu_mem_usage=True,\n", ").to(DEVICE).eval()\n", "\n", "\n", "print(\"[init] 模型加载完成\")\n", "\n", "\n", "# ============================================================\n", "# 9. Codec\n", "# ============================================================\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "\n", "codec_mod = importlib.import_module(\n", " _REMOTE_PKG + \".codec_video_processing_mage_vl\"\n", ")\n", "\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "\n", "def codec_config():\n", "\n", " override = {\n", " \"engine\": \"hevc\",\n", " \"target_canvas\": CODEC_TARGET_CANVAS,\n", " \"patch\": 16,\n", " }\n", "\n", " kwargs = dict(\n", " processor._codec_config_defaults\n", " )\n", "\n", " dcvc = dict(\n", " kwargs.get(\"dcvc\") or {}\n", " )\n", "\n", " kwargs.update(override)\n", "\n", " if dcvc:\n", " kwargs[\"dcvc\"] = dcvc\n", "\n", " kwargs[\"max_pixels\"] = CODEC_MAX_PIXELS\n", "\n", " return CodecConfig(**kwargs), override\n", "\n", "\n", "# ============================================================\n", "# 10. 加载 Gate\n", "# ============================================================\n", "\n", "print(\"[gate] 加载 Cognition Gate...\")\n", "\n", "try:\n", "\n", " gate = model.model._load_streammind_gate()\n", "\n", " print(\"[gate] Cognition Gate 加载成功\")\n", "\n", " try:\n", " gate_dtype = next(\n", " gate.parameters()\n", " ).dtype\n", "\n", " print(\n", " f\"[gate] 当前 dtype = {gate_dtype}\"\n", " )\n", "\n", " except Exception:\n", " pass\n", "\n", "except Exception as e:\n", "\n", " gate = None\n", "\n", " print(\n", " f\"[gate] 加载失败: {type(e).__name__}: {e}\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 11. T4 / FP16 下：\n", "# 尝试把 Gate 单独提升到 FP32\n", "# ============================================================\n", "\n", "if (\n", " gate is not None\n", " and not BF16_SUPPORTED\n", "):\n", "\n", " try:\n", "\n", " gate = gate.float()\n", "\n", " model.model.streammind_gate = gate\n", "\n", " print(\n", " \"[gate] FP32 fallback 已启用\"\n", " )\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"[gate] FP32 fallback 失败:\",\n", " e\n", " )\n", "\n", "\n", "# ============================================================\n", "# 12. 工具\n", "# ============================================================\n", "\n", "def duration(path):\n", "\n", " try:\n", "\n", " r = subprocess.run(\n", " [\n", " \"ffprobe\",\n", " \"-v\",\n", " \"error\",\n", " \"-show_entries\",\n", " \"format=duration\",\n", " \"-of\",\n", " \"default=nw=1:nk=1\",\n", " str(path),\n", " ],\n", " capture_output=True,\n", " text=True,\n", " check=True,\n", " timeout=60,\n", " )\n", "\n", " return float(\n", " r.stdout.strip()\n", " )\n", "\n", " except Exception:\n", "\n", " return 0.0\n", "\n", "\n", "# ============================================================\n", "# 13. Prompt\n", "# ============================================================\n", "\n", "def make_prompt(history_text=\"\"):\n", "\n", " text = PROMPT_TEMPLATE.format(\n", " history=(\n", " history_text\n", " if history_text\n", " else \"暂无，这是第一个视频片段。\"\n", " )\n", " )\n", "\n", " messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\n", " \"type\": \"video\"\n", " },\n", " {\n", " \"type\": \"text\",\n", " \"text\": text,\n", " },\n", " ],\n", " }\n", " ]\n", "\n", " return processor.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 14. 视频切片\n", "# ============================================================\n", "\n", "def extract_clip(\n", " src,\n", " start,\n", " length,\n", " out,\n", "):\n", "\n", " tmp = out.with_name(\n", " \".tmp_\" + out.name\n", " )\n", "\n", " if tmp.exists():\n", "\n", " try:\n", " tmp.unlink()\n", " except Exception:\n", " pass\n", "\n", " subprocess.run(\n", " [\n", " \"ffmpeg\",\n", " \"-y\",\n", " \"-loglevel\",\n", " \"error\",\n", "\n", " \"-ss\",\n", " f\"{start:.3f}\",\n", "\n", " \"-i\",\n", " str(src),\n", "\n", " \"-t\",\n", " f\"{length:.3f}\",\n", "\n", " \"-c:v\",\n", " \"libx264\",\n", "\n", " \"-preset\",\n", " \"ultrafast\",\n", "\n", " \"-crf\",\n", " \"26\",\n", "\n", " \"-an\",\n", "\n", " \"-pix_fmt\",\n", " \"yuv420p\",\n", "\n", " str(tmp),\n", " ],\n", " check=True,\n", " capture_output=True,\n", " timeout=180,\n", " )\n", "\n", " tmp.replace(out)\n", "\n", " return out\n", "\n", "\n", "# ============================================================\n", "# 15. CPU → GPU\n", "# ============================================================\n", "\n", "def to_cuda(inputs):\n", "\n", " result = {}\n", "\n", " for k, v in inputs.items():\n", "\n", " if not hasattr(v, \"to\"):\n", " continue\n", "\n", " v = v.to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", "\n", " v = v.to(MODEL_DTYPE)\n", "\n", " result[k] = v\n", "\n", " return result\n", "\n", "\n", "# ============================================================\n", "# 16. 清理 GPU\n", "# ============================================================\n", "\n", "def cleanup(*objs):\n", "\n", " for x in objs:\n", "\n", " try:\n", " del x\n", "\n", " except Exception:\n", " pass\n", "\n", " gc.collect()\n", "\n", " try:\n", " torch.cuda.empty_cache()\n", " torch.cuda.ipc_collect()\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 17. GPU 显存\n", "# ============================================================\n", "\n", "def gpu_mem():\n", "\n", " allocated = (\n", " torch.cuda.memory_allocated()\n", " / 1024**3\n", " )\n", "\n", " reserved = (\n", " torch.cuda.memory_reserved()\n", " / 1024**3\n", " )\n", "\n", " peak = (\n", " torch.cuda.max_memory_allocated()\n", " / 1024**3\n", " )\n", "\n", " return (\n", " f\"GPU={allocated:.2f}GB \"\n", " f\"/ reserved={reserved:.2f}GB \"\n", " f\"/ peak={peak:.2f}GB\"\n", " )\n", "\n", "\n", "# ============================================================\n", "# 18. Gate\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def run_gate(inputs):\n", "\n", " feat = {}\n", "\n", " for k in (\n", " \"pixel_values\",\n", " \"image_grid_thw\",\n", " \"patch_positions\",\n", " ):\n", "\n", " if k not in inputs:\n", " continue\n", "\n", " v = inputs[k].to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", "\n", " v = v.to(MODEL_DTYPE)\n", "\n", " feat[k] = v\n", "\n", " logits = None\n", "\n", " try:\n", "\n", " # ----------------------------------------------------\n", " # BF16 GPU\n", " # ----------------------------------------------------\n", "\n", " if BF16_SUPPORTED:\n", "\n", " logits = (\n", " model.model\n", " .streammind_gate_forward_segments(\n", " [feat]\n", " )\n", " )\n", "\n", " # ----------------------------------------------------\n", " # T4 / FP16 fallback\n", " #\n", " # 不直接使用官方 gate forward，\n", " # 而是：\n", " #\n", " # visual token → FP32 → Gate FP32\n", " #\n", " # 尽量避免 FP16 Gate overflow\n", " # ----------------------------------------------------\n", "\n", " else:\n", "\n", " visual_tokens = (\n", " model.model\n", " ._streammind_vision_tokens(\n", " feat[\"pixel_values\"],\n", " feat[\"image_grid_thw\"],\n", " patch_positions=feat.get(\n", " \"patch_positions\"\n", " ),\n", " )\n", " )\n", "\n", " if not torch.isfinite(\n", " visual_tokens\n", " ).all():\n", "\n", " print(\n", " \"[gate] visual tokens \"\n", " \"出现 NaN/Inf\"\n", " )\n", "\n", " return 0.0\n", "\n", " visual_tokens = (\n", " visual_tokens.float()\n", " )\n", "\n", " logits = gate(\n", " torch.cat(\n", " [visual_tokens],\n", " dim=1,\n", " )\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 官方 Gate 输出：\n", " #\n", " # [B,T,2]\n", " #\n", " # 0 = silence\n", " # 1 = response\n", " # ----------------------------------------------------\n", "\n", " if not isinstance(\n", " logits,\n", " torch.Tensor\n", " ):\n", "\n", " raise RuntimeError(\n", " \"Gate 输出不是 Tensor\"\n", " )\n", "\n", " if logits.ndim != 3:\n", "\n", " raise RuntimeError(\n", " \"Unexpected gate logits shape: \"\n", " + str(tuple(logits.shape))\n", " )\n", "\n", " gate_logits = logits[0]\n", "\n", " # Codec 时间长度\n", " length = int(\n", " inputs[\n", " \"image_grid_thw\"\n", " ][:, 0]\n", " .sum()\n", " .item()\n", " )\n", "\n", " if length <= 0:\n", "\n", " raise RuntimeError(\n", " f\"Invalid gate length={length}\"\n", " )\n", "\n", " pos = min(\n", " length - 1,\n", " gate_logits.shape[0] - 1,\n", " )\n", "\n", " last_logits = (\n", " gate_logits[pos]\n", " .float()\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 关键：\n", " # 在 softmax 之前检查\n", " # ----------------------------------------------------\n", "\n", " if not torch.isfinite(\n", " last_logits\n", " ).all():\n", "\n", " print(\n", " \"[gate] WARNING:\"\n", " \" logits =\",\n", " last_logits\n", " .detach()\n", " .cpu()\n", " .tolist(),\n", " )\n", "\n", " return 0.0\n", "\n", " # ----------------------------------------------------\n", " # 用 log_softmax 更稳定\n", " # ----------------------------------------------------\n", "\n", " log_prob = torch.log_softmax(\n", " last_logits,\n", " dim=-1,\n", " )\n", "\n", " prob = torch.exp(\n", " log_prob[1]\n", " )\n", "\n", " if not torch.isfinite(prob):\n", "\n", " print(\n", " \"[gate] WARNING:\"\n", " \" probability NaN/Inf\"\n", " )\n", "\n", " return 0.0\n", "\n", " return float(\n", " prob.item()\n", " )\n", "\n", " except Exception as e:\n", "\n", " print(\n", " \"[gate] ERROR:\",\n", " type(e).__name__,\n", " e,\n", " )\n", "\n", " return 0.0\n", "\n", " finally:\n", "\n", " cleanup(\n", " feat,\n", " logits,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 19. Codec + Gate\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def analyze_clip(\n", " clip,\n", " prompt,\n", " codec_override,\n", "):\n", "\n", " with warnings.catch_warnings():\n", "\n", " warnings.simplefilter(\n", " \"ignore\"\n", " )\n", "\n", " inputs = processor(\n", " text=[prompt],\n", "\n", " videos=[\n", " str(clip)\n", " ],\n", "\n", " video_backend=\"codec\",\n", "\n", " max_pixels=CODEC_MAX_PIXELS,\n", "\n", " codec_config=codec_override,\n", "\n", " return_tensors=\"pt\",\n", "\n", " padding=False,\n", " )\n", "\n", " prob = run_gate(\n", " inputs\n", " )\n", "\n", " return (\n", " inputs,\n", " prob,\n", " )\n", "\n", "\n", "# ============================================================\n", "# 20. LLM\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def generate(\n", " inputs,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "):\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", " text = processor.tokenizer.decode(\n", "\n", " out[0, n:],\n", "\n", " skip_special_tokens=True,\n", " ).strip()\n", "\n", " del out\n", "\n", " return text\n", "\n", "\n", "# ============================================================\n", "# 21. 最终总结\n", "# ============================================================\n", "\n", "@torch.inference_mode()\n", "def final_summary(history):\n", "\n", " if not history:\n", "\n", " return (\n", " \"没有获得有效的衣物分析结果。\"\n", " )\n", "\n", " history_text = \"\\n\\n\".join(\n", " history\n", " )\n", "\n", " prompt = FINAL_PROMPT.format(\n", " history=history_text\n", " )\n", "\n", " inputs = processor.tokenizer(\n", " prompt,\n", " return_tensors=\"pt\",\n", " truncation=True,\n", " max_length=4096,\n", " )\n", "\n", " inputs = {\n", " k: v.to(DEVICE)\n", " for k, v in inputs.items()\n", " }\n", "\n", " try:\n", "\n", " out = model.generate(\n", "\n", " **inputs,\n", "\n", " max_new_tokens=FINAL_MAX_NEW_TOKENS,\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " n = (\n", " inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", " )\n", "\n", " result = (\n", " processor.tokenizer.decode(\n", " out[0, n:],\n", " skip_special_tokens=True,\n", " )\n", " .strip()\n", " )\n", "\n", " return result\n", "\n", " finally:\n", "\n", " cleanup(\n", " inputs\n", " )\n", "\n", " try:\n", " del out\n", " except Exception:\n", " pass\n", "\n", "\n", "# ============================================================\n", "# 22. 实时视频分析\n", "# ============================================================\n", "\n", "def stream_video_analysis(\n", "\n", " video_path,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "):\n", "\n", " path = Path(\n", " video_path\n", " )\n", "\n", " if not path.exists():\n", "\n", " raise FileNotFoundError(\n", " video_path\n", " )\n", "\n", " total = duration(\n", " path\n", " )\n", "\n", " if total <= 0:\n", "\n", " raise RuntimeError(\n", " \"无法获取视频时长\"\n", " )\n", "\n", " clip_dir = Path(\n", " os.environ[\n", " \"STREAMMIND_CLIP_CACHE\"\n", " ]\n", " )\n", "\n", " clip_dir.mkdir(\n", " parents=True,\n", " exist_ok=True,\n", " )\n", "\n", " # --------------------------------------------------------\n", " # 两个历史：\n", " #\n", " # results：\n", " # 给用户看的完整输出\n", " #\n", " # history：\n", " # 给模型看的累计状态\n", " # --------------------------------------------------------\n", "\n", " results = []\n", "\n", " history = []\n", "\n", " start = 0.0\n", "\n", " index = 0\n", "\n", " yield (\n", " f\"开始实时分析 | \"\n", " f\"时长={total:.1f}s | \"\n", " f\"窗口={segment_sec:.1f}s | \"\n", " f\"Gate={gate_threshold:.2f} | \"\n", " f\"Codec={CODEC_MAX_PIXELS}px\"\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Codec\n", " # --------------------------------------------------------\n", "\n", " _, codec_override = codec_config()\n", "\n", "\n", " # ========================================================\n", " # 主循环\n", " # ========================================================\n", "\n", " while start < total - 0.05:\n", "\n", " if (\n", " max_segments is not None\n", " and index >= max_segments\n", " ):\n", " break\n", "\n", " end = min(\n", " total,\n", " start + segment_sec,\n", " )\n", "\n", " length = (\n", " end - start\n", " )\n", "\n", " index += 1\n", "\n", " t0 = time.time()\n", "\n", " clip = clip_dir / (\n", "\n", " f\"{path.stem}_\"\n", " f\"{index:06d}_\"\n", " f\"{start:.2f}_\"\n", " f\"{end:.2f}.mp4\"\n", "\n", " )\n", "\n", " inputs = None\n", "\n", " cuda_inputs = None\n", "\n", " said = None\n", "\n", " try:\n", "\n", " # ------------------------------------------------\n", " # 1. 当前累计历史\n", " # ------------------------------------------------\n", "\n", " history_text = \"\\n\".join(\n", " history[-6:]\n", " )\n", "\n", " prompt = make_prompt(\n", " history_text\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 2. 精确切片\n", " # ------------------------------------------------\n", "\n", " extract_clip(\n", "\n", " path,\n", "\n", " start,\n", "\n", " length,\n", "\n", " clip,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 3. Codec + Gate\n", " # ------------------------------------------------\n", "\n", " inputs, prob = analyze_clip(\n", "\n", " clip,\n", "\n", " prompt,\n", "\n", " codec_override,\n", " )\n", "\n", "\n", " head = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** \"\n", " f\"Gate=`{prob:.2f}`\"\n", "\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 4. Gate\n", " #\n", " # 注意：\n", " #\n", " # 本项目不让 Gate 决定“是否识别衣服”。\n", " #\n", " # Gate 只是告诉我们：\n", " # 这个片段是否具有明显的事件变化。\n", " #\n", " # 为避免漏掉新衣物：\n", " # 每个片段仍然进入 Mage-VL。\n", " # ------------------------------------------------\n", "\n", " if prob < gate_threshold:\n", "\n", " gate_state = (\n", " \"Gate较低\"\n", " )\n", "\n", " else:\n", "\n", " gate_state = (\n", " \"Gate触发\"\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 5. 进入 Mage-VL\n", " # ------------------------------------------------\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", " + (\n", " \"\\n\\n\"\n", " if results\n", " else \"\"\n", " )\n", " + f\"{head} \"\n", " + f\"→ 🧠 {gate_state}，\"\n", " + \"分析衣物中...\"\n", " )\n", "\n", "\n", " gt = time.time()\n", "\n", "\n", " # ------------------------------------------------\n", " # GPU\n", " # ------------------------------------------------\n", "\n", " cuda_inputs = to_cuda(\n", " inputs\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # Generate\n", " # ------------------------------------------------\n", "\n", " said = generate(\n", "\n", " cuda_inputs,\n", "\n", " max_new_tokens=max_new_tokens,\n", " )\n", "\n", "\n", " gen_time = (\n", " time.time()\n", " - gt\n", " )\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", " speed = (\n", " length\n", " / max(elapsed, 0.001)\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 6. 当前结果\n", " # ------------------------------------------------\n", "\n", " line = (\n", "\n", " f\"{head} → 🗣️ \"\n", " f\"{said}\\n\"\n", "\n", " f\" Generate=\"\n", " f\"{gen_time:.2f}s | \"\n", "\n", " f\"Total=\"\n", " f\"{elapsed:.2f}s | \"\n", "\n", " f\"Realtime=\"\n", " f\"{speed:.2f}×\"\n", "\n", " )\n", "\n", "\n", " results.append(\n", " line\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 7. 只把模型结果加入累计历史\n", " # ------------------------------------------------\n", "\n", " history.append(\n", "\n", " f\"{start:.1f}-\"\n", " f\"{end:.1f}s：\"\n", " f\"{said}\"\n", "\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 8. 实时输出\n", " # ------------------------------------------------\n", "\n", " yield \"\\n\\n\".join(\n", " results\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 9. 模拟真实播放速度\n", " # ------------------------------------------------\n", "\n", " elapsed = (\n", " time.time()\n", " - t0\n", " )\n", "\n", " if (\n", " simulate_realtime\n", " and elapsed < length\n", " ):\n", "\n", " time.sleep(\n", " length - elapsed\n", " )\n", "\n", "\n", " except Exception as e:\n", "\n", " line = (\n", "\n", " f\"**{start:.1f}–\"\n", " f\"{end:.1f}s** → \"\n", "\n", " f\"❌ \"\n", " f\"{type(e).__name__}: \"\n", " f\"{e}\"\n", "\n", " )\n", "\n", " results.append(\n", " line\n", " )\n", "\n", " yield \"\\n\\n\".join(\n", " results\n", " )\n", "\n", "\n", " finally:\n", "\n", " # ------------------------------------------------\n", " # 清理 GPU\n", " # ------------------------------------------------\n", "\n", " cleanup(\n", " cuda_inputs,\n", " inputs,\n", " said,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 删除临时视频\n", " # ------------------------------------------------\n", "\n", " if clip.exists():\n", "\n", " try:\n", " clip.unlink()\n", " except Exception:\n", " pass\n", "\n", "\n", " start = end\n", "\n", "\n", " # ------------------------------------------------\n", " # 显存统计\n", " # ------------------------------------------------\n", "\n", " print(\n", "\n", " f\"[segment {index}] \"\n", "\n", " f\"{start:.1f}/\"\n", " f\"{total:.1f}s | \"\n", "\n", " f\"{gpu_mem()}\",\n", "\n", " flush=True,\n", " )\n", "\n", "\n", " # ------------------------------------------------\n", " # 重置 peak\n", " # ------------------------------------------------\n", "\n", " try:\n", "\n", " torch.cuda.reset_peak_memory_stats()\n", "\n", " except Exception:\n", "\n", " pass\n", "\n", "\n", " # ========================================================\n", " # 视频结束\n", " # ========================================================\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", " + \"========================================\\n\"\n", " + \"🔄 视频播放完成，正在综合全部衣物...\\n\"\n", " + \"========================================\"\n", " )\n", "\n", "\n", " # ========================================================\n", " # 最终综合\n", " # ========================================================\n", "\n", " try:\n", "\n", " final = final_summary(\n", " history\n", " )\n", "\n", " final_output = (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + \"========================================\\n\"\n", " + \"🧺 最终综合洗涤方案\\n\"\n", " + \"========================================\\n\\n\"\n", "\n", " + final\n", "\n", " + \"\\n\\n\"\n", "\n", " + f\"✅ 完成，共 {index} 个片段\"\n", "\n", " )\n", "\n", " yield final_output\n", "\n", "\n", " except Exception as e:\n", "\n", " yield (\n", "\n", " \"\\n\\n\".join(\n", " results\n", " )\n", "\n", " + \"\\n\\n\"\n", "\n", " + \"========================================\\n\"\n", " + \"🧺 最终综合洗涤方案\\n\"\n", " + \"========================================\\n\\n\"\n", "\n", " + f\"❌ 综合分析失败：\"\n", " + f\"{type(e).__name__}: {e}\"\n", "\n", " )\n", "\n", "\n", "# ============================================================\n", "# 23. Main\n", "# ============================================================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " for output in stream_video_analysis(\n", "\n", " VIDEO_FILE,\n", "\n", " segment_sec=SEGMENT_SEC,\n", "\n", " gate_threshold=GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "\n", " simulate_realtime=SIMULATE_REALTIME,\n", "\n", " ):\n", "\n", " # 清屏\n", " print(\n", " \"\\033[H\\033[J\",\n", " end=\"\"\n", " )\n", "\n", " print(\n", " \"=== Mage-VL 第七版：\"\n", " \"洗衣机实时衣物分析 ===\\n\"\n", " )\n", "\n", " print(output)\n", "\n", " print(\n", " \"\\n\"\n", " + \"-\" * 60\n", " )" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "db96ccb479cc44c1a81948debffdbfd3", "85ddd6e4f41b4eefba1b6c999c8d6cf0", "1a64b38f45604a7495f63f08295d2894", "98bc43224aca44dfbad57948e040ffaf", "3319a7a3afdb401d8641b798a15b47a1", "d810da2540f94287a78b809f536eade2", "5256488a1c2f424689a0f3be6499562f", "fc9343dbdfe746bca5c11da450bce908", "3844a0d4b7c548c28d66f0e13b2db89e", "e9749446a0f54362bea8875b154b8b64", "110ba45ac8cc46069a760b03e71c4ef3" ] }, "executionInfo": { "elapsed": 143058, "status": "ok", "timestamp": 1787125503653, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "GCnAMlRnx0bV", "outputId": "4968f352-6d51-4fa7-af64-793d9beec429" }, "outputs": [], "source": [ "##=============第六版，紧凑格式\n", "import os, gc, time, subprocess, importlib, warnings\n", "from pathlib import Path\n", "\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\",\n", " \"expandable_segments:True,max_split_size_mb:128\")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\", \"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\", \"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "import torch\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "\n", "# ===================== 配置 =====================\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "VIDEO_FILE = \"/content/test.mp4\"\n", "\n", "SEGMENT_SEC = 4.0\n", "MAX_SEGMENTS = None # 测试可改成 8\n", "GATE_THRESHOLD = 0.50\n", "MAX_NEW_TOKENS = 64\n", "CODEC_MAX_PIXELS = 64_000\n", "CODEC_TARGET_CANVAS = 24\n", "SIMULATE_REALTIME = True\n", "\n", "PROMPT = \"\"\"\n", "请用中文分析这段视频。\n", "描述视频中准备洗涤的衣服类型，材质。\n", "重点判断这些衣服适合采用什么程序洗涤。\n", "联系前后片段中出现的衣服，推荐当前混合洗涤用什么程序。\n", "尽量简洁，适合实时视频分析。\n", "\"\"\".strip()\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA\")\n", "\n", "DEVICE = \"cuda\"\n", "DTYPE = torch.float16\n", "\n", "print(f\"[GPU] {torch.cuda.get_device_name(0)}\")\n", "print(f\"[CUDA] {torch.version.cuda}\")\n", "print(\"[init] 加载 Processor...\")\n", "\n", "processor = AutoProcessor.from_pretrained(\n", " MODEL_ID, trust_remote_code=True\n", ")\n", "\n", "print(\"[init] 加载 Mage-VL...\")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=DTYPE,\n", " attn_implementation=\"sdpa\",\n", ").to(DEVICE).eval()\n", "\n", "print(\"[init] 模型加载完成\")\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "codec_mod = importlib.import_module(\n", " _REMOTE_PKG + \".codec_video_processing_mage_vl\"\n", ")\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "try:\n", " model.model._load_streammind_gate()\n", " print(\"[gate] Cognition Gate 加载成功\")\n", "except Exception as e:\n", " print(f\"[gate] 加载失败: {e}\")\n", "\n", "\n", "# ===================== 工具函数 =====================\n", "\n", "def duration(path):\n", " try:\n", " r = subprocess.run(\n", " [\"ffprobe\", \"-v\", \"error\",\n", " \"-show_entries\", \"format=duration\",\n", " \"-of\", \"default=nw=1:nk=1\", str(path)],\n", " capture_output=True, text=True, check=True, timeout=60\n", " )\n", " return float(r.stdout.strip())\n", " except Exception:\n", " return 0.0\n", "\n", "\n", "def codec_config():\n", " override = {\n", " \"engine\": \"hevc\",\n", " \"target_canvas\": CODEC_TARGET_CANVAS,\n", " \"patch\": 16,\n", " }\n", " kwargs = dict(processor._codec_config_defaults)\n", " dcvc = dict(kwargs.get(\"dcvc\") or {})\n", " kwargs.update(override)\n", " if dcvc:\n", " kwargs[\"dcvc\"] = dcvc\n", " kwargs[\"max_pixels\"] = CODEC_MAX_PIXELS\n", " return CodecConfig(**kwargs), override\n", "\n", "\n", "def make_prompt():\n", " messages = [{\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"video\"},\n", " {\"type\": \"text\", \"text\": PROMPT},\n", " ],\n", " }]\n", " return processor.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", "\n", "\n", "def extract_clip(src, start, length, out):\n", " tmp = out.with_name(\".tmp_\" + out.name)\n", "\n", " if tmp.exists():\n", " tmp.unlink()\n", "\n", " subprocess.run([\n", " \"ffmpeg\", \"-y\", \"-loglevel\", \"error\",\n", " \"-ss\", f\"{start:.3f}\",\n", " \"-i\", str(src),\n", " \"-t\", f\"{length:.3f}\",\n", " \"-c:v\", \"libx264\",\n", " \"-preset\", \"ultrafast\",\n", " \"-crf\", \"23\",\n", " \"-an\",\n", " \"-pix_fmt\", \"yuv420p\",\n", " str(tmp)\n", " ], check=True, capture_output=True, timeout=180)\n", "\n", " tmp.replace(out)\n", " return out\n", "\n", "\n", "def to_cuda(inputs):\n", " result = {}\n", " for k, v in inputs.items():\n", " if hasattr(v, \"to\"):\n", " v = v.to(DEVICE)\n", " if k == \"pixel_values\":\n", " v = v.to(DTYPE)\n", " result[k] = v\n", " return result\n", "\n", "\n", "def cleanup(*objs):\n", " for x in objs:\n", " try:\n", " del x\n", " except Exception:\n", " pass\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", "\n", "def gpu_mem():\n", " a = torch.cuda.memory_allocated() / 1024**3\n", " r = torch.cuda.memory_reserved() / 1024**3\n", " return f\"GPU: {a:.2f}GB / reserved {r:.2f}GB\"\n", "\n", "\n", "# ===================== Gate =====================\n", "\n", "@torch.inference_mode()\n", "def run_gate(inputs):\n", " feat = {}\n", "\n", " for k in (\"pixel_values\", \"image_grid_thw\", \"patch_positions\"):\n", " if k in inputs:\n", " v = inputs[k].to(DEVICE)\n", " if k == \"pixel_values\":\n", " v = v.to(DTYPE)\n", " feat[k] = v\n", "\n", " logits = None\n", "\n", " try:\n", " logits = model.model.streammind_gate_forward_segments(\n", " [feat]\n", " )[0]\n", "\n", " length = int(inputs[\"image_grid_thw\"][:, 0].sum())\n", " prob = torch.softmax(\n", " logits[length - 1].float(), dim=-1\n", " )[1].item()\n", "\n", " return float(prob)\n", "\n", " finally:\n", " cleanup(feat, logits)\n", "\n", "\n", "# ===================== LLM =====================\n", "\n", "@torch.inference_mode()\n", "def generate(inputs):\n", " out = model.generate(\n", " **inputs,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", " do_sample=False,\n", " use_cache=True,\n", " )\n", "\n", " n = inputs[\"input_ids\"].shape[1]\n", " text = processor.tokenizer.decode(\n", " out[0, n:],\n", " skip_special_tokens=True\n", " ).strip()\n", "\n", " del out\n", " return text\n", "\n", "\n", "# ===================== 单段分析 =====================\n", "\n", "def analyze_clip(clip, prompt, codec_override):\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " inputs = processor(\n", " text=[prompt],\n", " videos=[str(clip)],\n", " video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS,\n", " codec_config=codec_override,\n", " return_tensors=\"pt\",\n", " padding=False,\n", " )\n", "\n", " prob = run_gate(inputs)\n", " return inputs, prob\n", "\n", "\n", "# ===================== 真正流式分析 =====================\n", "\n", "def stream_video_analysis(\n", " video_path,\n", " segment_sec=SEGMENT_SEC,\n", " gate_threshold=GATE_THRESHOLD,\n", " max_segments=MAX_SEGMENTS,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", " simulate_realtime=SIMULATE_REALTIME,\n", "):\n", " path = Path(video_path)\n", "\n", " if not path.exists():\n", " raise FileNotFoundError(video_path)\n", "\n", " total = duration(path)\n", " if total <= 0:\n", " raise RuntimeError(\"无法获取视频时长\")\n", "\n", " clip_dir = Path(os.environ[\"STREAMMIND_CLIP_CACHE\"])\n", " clip_dir.mkdir(parents=True, exist_ok=True)\n", "\n", " prompt = make_prompt()\n", " _, codec_override = codec_config()\n", "\n", " start = 0.0\n", " index = 0\n", " results = []\n", "\n", " yield (\n", " f\"开始实时分析 | 时长={total:.1f}s | \"\n", " f\"窗口={segment_sec:.1f}s | \"\n", " f\"Gate={gate_threshold:.2f}\"\n", " )\n", "\n", " while start < total - 0.05:\n", "\n", " if max_segments is not None and index >= max_segments:\n", " break\n", "\n", " end = min(total, start + segment_sec)\n", " length = end - start\n", " index += 1\n", "\n", " t0 = time.time()\n", "\n", " clip = clip_dir / (\n", " f\"{path.stem}_{index:06d}_\"\n", " f\"{start:.2f}_{end:.2f}.mp4\"\n", " )\n", "\n", " inputs = None\n", " cuda_inputs = None\n", "\n", " try:\n", " # 1. 当前 4 秒精确切片\n", " extract_clip(path, start, length, clip)\n", "\n", " # 2. Codec + Gate\n", " inputs, prob = analyze_clip(\n", " clip, prompt, codec_override\n", " )\n", "\n", " head = (\n", " f\"**{start:.1f}–{end:.1f}s** \"\n", " f\"Gate=`{prob:.2f}`\"\n", " )\n", "\n", " # 3. Gate 不通过\n", " if prob < gate_threshold:\n", "\n", " elapsed = time.time() - t0\n", " speed = length / elapsed\n", "\n", " line = (\n", " f\"{head} → 🔇 跳过 \"\n", " f\"({elapsed:.2f}s / {speed:.2f}×)\"\n", " )\n", "\n", " # 4. Gate 通过，进入 Mage-VL\n", " else:\n", "\n", " yield (\n", " \"\\n\\n\".join(results)\n", " + (\"\\n\\n\" if results else \"\")\n", " + f\"{head} → 🧠 生成中...\"\n", " )\n", "\n", " gt = time.time()\n", "\n", " cuda_inputs = to_cuda(inputs)\n", "\n", " said = generate(cuda_inputs)\n", "\n", " gen_time = time.time() - gt\n", " elapsed = time.time() - t0\n", " speed = length / elapsed\n", "\n", " line = (\n", " f\"{head} → 🗣️ {said}\\n\"\n", " f\" Generate={gen_time:.2f}s | \"\n", " f\"Total={elapsed:.2f}s | \"\n", " f\"Realtime={speed:.2f}×\"\n", " )\n", "\n", " results.append(line)\n", "\n", " yield \"\\n\\n\".join(results)\n", "\n", " # 5. 模拟真实播放速度\n", " elapsed = time.time() - t0\n", "\n", " if simulate_realtime and elapsed < length:\n", " time.sleep(length - elapsed)\n", "\n", " except Exception as e:\n", "\n", " results.append(\n", " f\"**{start:.1f}–{end:.1f}s** → \"\n", " f\"❌ {type(e).__name__}: {e}\"\n", " )\n", "\n", " yield \"\\n\\n\".join(results)\n", "\n", " finally:\n", "\n", " cleanup(cuda_inputs, inputs)\n", "\n", " if clip.exists():\n", " try:\n", " clip.unlink()\n", " except Exception:\n", " pass\n", "\n", " start = end\n", "\n", " print(\n", " f\"[segment {index}] \"\n", " f\"{start:.1f}/{total:.1f}s | \"\n", " f\"{gpu_mem()}\",\n", " flush=True\n", " )\n", "\n", " yield (\n", " \"\\n\\n\".join(results)\n", " + f\"\\n\\n✅ 完成，共 {index} 个片段\"\n", " )\n", "\n", "\n", "# ===================== Main =====================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " for output in stream_video_analysis(\n", " VIDEO_FILE,\n", " segment_sec=4.0,\n", " gate_threshold=0.50,\n", " max_segments=MAX_SEGMENTS,\n", " max_new_tokens=64,\n", " simulate_realtime=True,\n", " ):\n", " print(\"\\033[H\\033[J\", end=\"\")\n", " print(\"=== Mage-VL 实时流式分析 ===\\n\")\n", " print(output)\n", " print(\"\\n\" + \"-\" * 60)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": 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"00084e5557ac48b2960f1e675d6caf31", "52e1f32c7f244b54af8789acd02f03aa", "e1aa5b5be3cc4c0ca4d17f56e1a9a3fa", "d07ec898c235460795ce05db60068d1c", "b2789462ac0d4983a312d7feab456f45", "b77c6e55649c495ca73db4d291cb57b4", "f6cd0990745d4403b97f235cda98129a", "3998fbec24df4ebc95351e4f71f3d88f", "98bd39f607d44063ac84368b476e1d2d", "b05d0dff578d4363a82f8c503aec10c6", "96150427de0e4b1dbb8a6a24d0d2e4cc", "5e7b4207d419403293436dc9e0a5ba4b", "ec78cac2d07a4df2984f7515c252c8f8", "51adfe10aa5849ae8607536157a80c26", "7605feeeaf484969b8dc8aacd3e775a4", "473745732612410d80f51914b9846793", "6b9647878e394f1dbe2edf887f4fb74e", "011ebf22b0774420a05543478f1c56ac", "bd28fa7e015f4aac85da290232a55393", "1ba8e859274448d39f0e0422f411e812", "e1a412478969493982fa98572123e39e", "f0e4630b50b3496a963af138e122e828", "c7c194dfa23240a18b8d6a9d3c10a033", "8597ca6a1780404fa3234370f49f3a83", "cb42691d4a1046c28977786552e0c7f8", "237027c7db404890885ba9b539d1dcc1", "a48be8bc8bab4b0794aa8f8f4ffa0ad5", "4b8b8108d3db4f97a7f880ed1d8ad765", "5ceeb4854d744883be20bcde470b2292", "2893c648ed934848b94170c6f6b4147a", "5ffcda3617de41fdb7027f92f3249b2b", "2476e2ae2b984e4abde7f4bc56e5f465", "266b112828e94f43b6a338fa3a40ca0a", "36506f1cb24742ab96f586a0fccb87f1", "8d7ea6ac2d9e4c73854b24348e78f82a", "250fbe3a29374df19abfcd78422e011a", "b9fb23fa20464a27b37063b67380e7cf", "64e3c043defd4e97bc60e89a5d34a2a2", "aabfb488ad1d40588bee2dfcb9a36010", "d7552d10857f470fb23aa0315c8ed7a8", "eaa37a93f8e94dbea634e739ec2b45be", "70b9b52f2f0d49708b7fb0fd14b50297", "54bd1f02c1d54066a31f34e4f0389634", "887ced5ff2844aefb669f90bab2eec16", "dda842975d0d4314b41711b4cdddcdb9", "4a1ce71b5f45421483826a888a6a1e6f", "84d592b837e24bc292105020bc0fb454", "850083683652479db99e315c8a5551db" ] }, "executionInfo": { "elapsed": 777059, "status": "ok", "timestamp": 1787124549789, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "s3UM_L2v3Rgs", "outputId": "3a4cac45-411c-44c4-83cc-857e491f22ce" }, "outputs": [], "source": [ "##第7版，带滑动窗口和连贯上下文的测试\n", "\n", "import os, gc, time, subprocess, importlib, warnings\n", "from pathlib import Path\n", "\n", "os.environ.setdefault(\n", " \"PYTORCH_CUDA_ALLOC_CONF\",\n", " \"expandable_segments:True,max_split_size_mb:128\"\n", ")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\", \"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\", \"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "import torch\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "\n", "\n", "# ===================== 配置 =====================\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "VIDEO_FILE = \"/content/soccer-broadcast.mp4\"\n", "\n", "SEGMENT_SEC = 4.0\n", "\n", "# Gate 使用：\n", "# 前4秒 + 当前4秒 = 最多8秒视觉上下文\n", "CONTEXT_SEC = 4.0\n", "\n", "# LLM 保留最近几条文字事件\n", "HISTORY_SIZE = 3\n", "\n", "MAX_SEGMENTS = None # 测试可改成 8\n", "GATE_THRESHOLD = 0.50\n", "MAX_NEW_TOKENS = 64\n", "\n", "CODEC_MAX_PIXELS = 64_000\n", "CODEC_TARGET_CANVAS = 24\n", "\n", "SIMULATE_REALTIME = True\n", "\n", "\n", "# ===================== Prompt =====================\n", "\n", "GATE_PROMPT = \"\"\"\n", "分析当前视频内容。\n", "请判断视频末尾的当前时间窗口是否包含值得进一步分析的内容。\n", "\"\"\".strip()\n", "\n", "\n", "def make_prompt(start, end, history):\n", " history_text = \"\\n\".join(\n", " f\"- {x}\" for x in history[-HISTORY_SIZE:]\n", " )\n", "\n", " if not history_text:\n", " history_text = \"暂无历史事件。\"\n", "\n", " return f\"\"\"\n", "请用中文分析此视频。\n", "\n", "当前重点时间：\n", "{start:.1f}–{end:.1f} 秒\n", "\n", "视频包含：\n", "- 前面的视觉内容：用于理解动作上下文\n", "- 当前时间窗口：重点分析当前发生的事情\n", "\n", "最近已经观察到的事件：\n", "{history_text}\n", "\n", "请重点描述当前时间窗口发生了什么，并结合前面的视觉内容判断动作是否连续。\n", "\n", "重点关注：\n", "视频中人物变化，和重要事件。\n", "\n", "不要猜测无法从视频确认的信息。\n", "尽量简洁，适合实时视频分析。\n", "\"\"\".strip()\n", "\n", "\n", "# ===================== 模型 =====================\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\"未检测到 CUDA\")\n", "\n", "DEVICE = \"cuda\"\n", "DTYPE = torch.float16\n", "\n", "print(f\"[GPU] {torch.cuda.get_device_name(0)}\")\n", "print(f\"[CUDA] {torch.version.cuda}\")\n", "\n", "print(\"[init] 加载 Processor...\")\n", "\n", "processor = AutoProcessor.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True\n", ")\n", "\n", "print(\"[init] 加载 Mage-VL...\")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=DTYPE,\n", " attn_implementation=\"sdpa\",\n", ").to(DEVICE).eval()\n", "\n", "print(\"[init] 模型加载完成\")\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "\n", "codec_mod = importlib.import_module(\n", " _REMOTE_PKG + \".codec_video_processing_mage_vl\"\n", ")\n", "\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "try:\n", " model.model._load_streammind_gate()\n", " print(\"[gate] Cognition Gate 加载成功\")\n", "except Exception as e:\n", " print(f\"[gate] 加载失败: {e}\")\n", "\n", "\n", "# ===================== 工具 =====================\n", "\n", "def get_duration(path):\n", " try:\n", " r = subprocess.run(\n", " [\n", " \"ffprobe\", \"-v\", \"error\",\n", " \"-show_entries\", \"format=duration\",\n", " \"-of\", \"default=nw=1:nk=1\",\n", " str(path)\n", " ],\n", " capture_output=True,\n", " text=True,\n", " check=True,\n", " timeout=60\n", " )\n", " return float(r.stdout.strip())\n", " except Exception:\n", " return 0.0\n", "\n", "\n", "def codec_config():\n", " override = {\n", " \"engine\": \"hevc\",\n", " \"target_canvas\": CODEC_TARGET_CANVAS,\n", " \"patch\": 16,\n", " }\n", "\n", " kwargs = dict(processor._codec_config_defaults)\n", " dcvc = dict(kwargs.get(\"dcvc\") or {})\n", "\n", " kwargs.update(override)\n", "\n", " if dcvc:\n", " kwargs[\"dcvc\"] = dcvc\n", "\n", " kwargs[\"max_pixels\"] = CODEC_MAX_PIXELS\n", "\n", " return CodecConfig(**kwargs), override\n", "\n", "\n", "def make_chat_prompt(text):\n", " messages = [{\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\"type\": \"video\"},\n", " {\"type\": \"text\", \"text\": text},\n", " ],\n", " }]\n", "\n", " return processor.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", "\n", "\n", "def extract_clip(src, start, length, out):\n", " tmp = out.with_name(\".tmp_\" + out.name)\n", "\n", " if tmp.exists():\n", " tmp.unlink()\n", "\n", " subprocess.run(\n", " [\n", " \"ffmpeg\", \"-y\", \"-loglevel\", \"error\",\n", " \"-ss\", f\"{max(0, start):.3f}\",\n", " \"-i\", str(src),\n", " \"-t\", f\"{length:.3f}\",\n", " \"-c:v\", \"libx264\",\n", " \"-preset\", \"ultrafast\",\n", " \"-crf\", \"23\",\n", " \"-an\",\n", " \"-pix_fmt\", \"yuv420p\",\n", " str(tmp)\n", " ],\n", " check=True,\n", " capture_output=True,\n", " timeout=180\n", " )\n", "\n", " tmp.replace(out)\n", " return out\n", "\n", "\n", "def to_cuda(inputs):\n", " result = {}\n", "\n", " for k, v in inputs.items():\n", " if not hasattr(v, \"to\"):\n", " continue\n", "\n", " v = v.to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", " v = v.to(DTYPE)\n", "\n", " result[k] = v\n", "\n", " return result\n", "\n", "\n", "def cleanup(*objs):\n", " for obj in objs:\n", " try:\n", " del obj\n", " except Exception:\n", " pass\n", "\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", "\n", "def gpu_mem():\n", " a = torch.cuda.memory_allocated() / 1024**3\n", " r = torch.cuda.memory_reserved() / 1024**3\n", " return f\"{a:.2f}GB / reserved {r:.2f}GB\"\n", "\n", "\n", "# ===================== Gate =====================\n", "\n", "@torch.inference_mode()\n", "def run_gate(inputs):\n", " feat = {}\n", "\n", " for k in (\n", " \"pixel_values\",\n", " \"image_grid_thw\",\n", " \"patch_positions\",\n", " ):\n", " if k not in inputs:\n", " continue\n", "\n", " v = inputs[k].to(DEVICE)\n", "\n", " if k == \"pixel_values\":\n", " v = v.to(DTYPE)\n", "\n", " feat[k] = v\n", "\n", " logits = None\n", "\n", " try:\n", " logits = model.model.streammind_gate_forward_segments(\n", " [feat]\n", " )[0]\n", "\n", " # 这里取视觉序列最后位置。\n", " #\n", " # 因为输入是：\n", " # 前4秒 + 当前4秒\n", " #\n", " # 所以最后位置对应当前4秒末尾，\n", " # Gate 可以利用前面的视觉内容作为上下文。\n", "\n", " length = int(\n", " inputs[\"image_grid_thw\"][:, 0].sum()\n", " )\n", "\n", " prob = torch.softmax(\n", " logits[length - 1].float(),\n", " dim=-1\n", " )[1].item()\n", "\n", " return float(prob)\n", "\n", " finally:\n", " cleanup(feat, logits)\n", "\n", "\n", "# ===================== Codec =====================\n", "\n", "def encode_video(\n", " clip,\n", " prompt,\n", " codec_override\n", "):\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", "\n", " return processor(\n", " text=[prompt],\n", " videos=[str(clip)],\n", " video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS,\n", " codec_config=codec_override,\n", " return_tensors=\"pt\",\n", " padding=False,\n", " )\n", "\n", "\n", "# ===================== Mage-VL =====================\n", "\n", "@torch.inference_mode()\n", "def generate(inputs, max_new_tokens):\n", " out = model.generate(\n", " **inputs,\n", " max_new_tokens=max_new_tokens,\n", " do_sample=False,\n", " use_cache=True,\n", " )\n", "\n", " n = inputs[\"input_ids\"].shape[1]\n", "\n", " text = processor.tokenizer.decode(\n", " out[0, n:],\n", " skip_special_tokens=True\n", " ).strip()\n", "\n", " del out\n", "\n", " return text\n", "\n", "\n", "# ===================== 主循环 =====================\n", "\n", "def stream_video_analysis(\n", " video_path,\n", " segment_sec=SEGMENT_SEC,\n", " context_sec=CONTEXT_SEC,\n", " gate_threshold=GATE_THRESHOLD,\n", " max_segments=MAX_SEGMENTS,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", " simulate_realtime=SIMULATE_REALTIME,\n", "):\n", " path = Path(video_path)\n", "\n", " if not path.exists():\n", " raise FileNotFoundError(video_path)\n", "\n", " total = get_duration(path)\n", "\n", " if total <= 0:\n", " raise RuntimeError(\"无法获取视频时长\")\n", "\n", " clip_dir = Path(\n", " os.environ[\"STREAMMIND_CLIP_CACHE\"]\n", " )\n", " clip_dir.mkdir(\n", " parents=True,\n", " exist_ok=True\n", " )\n", "\n", " _, codec_override = codec_config()\n", "\n", " start = 0.0\n", " index = 0\n", "\n", " # 最近几次文字事件\n", " history = []\n", "\n", " # 所有输出\n", " results = []\n", "\n", " yield (\n", " f\"开始实时流式分析\\n\"\n", " f\"视频={total:.1f}s | \"\n", " f\"当前窗口={segment_sec:.1f}s | \"\n", " f\"视觉上下文={context_sec:.1f}s | \"\n", " f\"历史={HISTORY_SIZE}条\"\n", " )\n", "\n", " while start < total - 0.05:\n", "\n", " if (\n", " max_segments is not None\n", " and index >= max_segments\n", " ):\n", " break\n", "\n", " # 当前时间窗口\n", " end = min(\n", " total,\n", " start + segment_sec\n", " )\n", "\n", " current_length = end - start\n", "\n", " # --------------------------------------------------\n", " # 视觉上下文：\n", " #\n", " # 第1段：\n", " # 0-4\n", " #\n", " # 第2段：\n", " # 0-8\n", " #\n", " # 第3段：\n", " # 4-12\n", " #\n", " # 第4段：\n", " # 8-16\n", " # --------------------------------------------------\n", "\n", " context_start = max(\n", " 0.0,\n", " start - context_sec\n", " )\n", "\n", " context_end = end\n", "\n", " visual_length = (\n", " context_end - context_start\n", " )\n", "\n", " index += 1\n", "\n", " t0 = time.time()\n", "\n", " clip = clip_dir / (\n", " f\"{path.stem}_\"\n", " f\"{index:06d}_\"\n", " f\"{context_start:.2f}_\"\n", " f\"{context_end:.2f}.mp4\"\n", " )\n", "\n", " inputs = None\n", " cuda_inputs = None\n", "\n", " try:\n", "\n", " # ==============================================\n", " # 1. 当前滑动视觉窗口\n", " # ==============================================\n", "\n", " extract_clip(\n", " path,\n", " context_start,\n", " visual_length,\n", " clip\n", " )\n", "\n", " # ==============================================\n", " # 2. Gate\n", " #\n", " # Gate 看到：\n", " #\n", " # 前4秒 + 当前4秒\n", " # ==============================================\n", "\n", " gate_prompt = make_chat_prompt(\n", " GATE_PROMPT\n", " )\n", "\n", " inputs = encode_video(\n", " clip,\n", " gate_prompt,\n", " codec_override\n", " )\n", "\n", " prob = run_gate(inputs)\n", "\n", " head = (\n", " f\"**{start:.1f}–{end:.1f}s** \"\n", " f\"Gate=`{prob:.2f}` \"\n", " f\"[context={context_start:.1f}–{end:.1f}s]\"\n", " )\n", "\n", " # ==============================================\n", " # 3. Gate 不通过\n", " # ==============================================\n", "\n", " if prob < gate_threshold:\n", "\n", " elapsed = time.time() - t0\n", " speed = current_length / elapsed\n", "\n", " line = (\n", " f\"{head} → 🔇 跳过 \"\n", " f\"({elapsed:.2f}s / \"\n", " f\"{speed:.2f}×)\"\n", " )\n", "\n", " results.append(line)\n", "\n", " yield \"\\n\\n\".join(results)\n", "\n", " # ==============================================\n", " # 4. Gate 通过\n", " # ==============================================\n", "\n", " else:\n", "\n", " yield (\n", " \"\\n\\n\".join(results)\n", " + (\n", " \"\\n\\n\"\n", " if results else \"\"\n", " )\n", " + f\"{head} → 🧠 生成中...\"\n", " )\n", "\n", " # ------------------------------------------\n", " # LLM 使用：\n", " #\n", " # 视觉：\n", " # 前4秒 + 当前4秒\n", " #\n", " # 文本：\n", " # 最近3个事件\n", " # ------------------------------------------\n", "\n", " llm_prompt = make_prompt(\n", " start,\n", " end,\n", " history\n", " )\n", "\n", " # 重新生成带历史 prompt 的输入\n", " #\n", " # 这里重新 processor 是为了让：\n", " # Gate prompt 和 LLM prompt 分开。\n", " #\n", " # 视频 Codec 本身仍然是同一个滑动窗口。\n", " # ------------------------------------------\n", "\n", " cleanup(inputs)\n", " inputs = None\n", "\n", " inputs = encode_video(\n", " clip,\n", " make_chat_prompt(llm_prompt),\n", " codec_override\n", " )\n", "\n", " gt = time.time()\n", "\n", " cuda_inputs = to_cuda(\n", " inputs\n", " )\n", "\n", " said = generate(\n", " cuda_inputs,\n", " max_new_tokens\n", " )\n", "\n", " gen_time = time.time() - gt\n", " elapsed = time.time() - t0\n", " speed = current_length / elapsed\n", "\n", " # ------------------------------------------\n", " # 保存历史事件\n", " # ------------------------------------------\n", "\n", " history.append(\n", " f\"{start:.1f}–{end:.1f}s：{said}\"\n", " )\n", "\n", " if len(history) > HISTORY_SIZE:\n", " history.pop(0)\n", "\n", " line = (\n", " f\"{head} → 🗣️ {said}\\n\"\n", " f\" Generate={gen_time:.2f}s | \"\n", " f\"Total={elapsed:.2f}s | \"\n", " f\"Realtime={speed:.2f}×\"\n", " )\n", "\n", " results.append(line)\n", "\n", " yield \"\\n\\n\".join(results)\n", "\n", " # ==============================================\n", " # 5. 模拟实时\n", " # ==============================================\n", "\n", " elapsed = time.time() - t0\n", "\n", " if (\n", " simulate_realtime\n", " and elapsed < current_length\n", " ):\n", " time.sleep(\n", " current_length - elapsed\n", " )\n", "\n", " except Exception as e:\n", "\n", " results.append(\n", " f\"**{start:.1f}–{end:.1f}s** \"\n", " f\"→ ❌ \"\n", " f\"{type(e).__name__}: {e}\"\n", " )\n", "\n", " yield \"\\n\\n\".join(results)\n", "\n", " finally:\n", "\n", " cleanup(\n", " cuda_inputs,\n", " inputs\n", " )\n", "\n", " if clip.exists():\n", " try:\n", " clip.unlink()\n", " except Exception:\n", " pass\n", "\n", " start = end\n", "\n", " print(\n", " f\"[segment {index}] \"\n", " f\"{start:.1f}/{total:.1f}s | \"\n", " f\"GPU {gpu_mem()}\",\n", " flush=True\n", " )\n", "\n", " yield (\n", " \"\\n\\n\".join(results)\n", " + f\"\\n\\n✅ 完成，共 {index} 个片段\"\n", " )\n", "\n", "\n", "# ===================== Main =====================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " for output in stream_video_analysis(\n", " VIDEO_FILE,\n", " segment_sec=4.0,\n", " context_sec=4.0,\n", " gate_threshold=0.50,\n", " max_segments=MAX_SEGMENTS,\n", " max_new_tokens=64,\n", " simulate_realtime=True,\n", " ):\n", " print(\"\\033[H\\033[J\", end=\"\")\n", " print(\"=== Mage-VL 第六版：上下文实时分析 ===\\n\")\n", " print(output)\n", " print(\"\\n\" + \"-\" * 70)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "e0eae377758a4608bd2e897171fc9f27", "f995cfd2f753423f8b69973e347f1faa", "a059288bf12f4de18563826a745db9b6", "8069b50dcaab4b89918dfc0775492ab1", "94f941a89d314064b7620bede4ce7388", "315bd2061ae34a8490e3e0e6e7141c7e", "2dfbfb7748184eb2bb1a033acf5071d2", "18a36bbfcfa348ca9bd6ace54fc007bf", "8f6a2818bd7344d7893e88273a9c22db", "f00ea1a7cded4bb4b470bdb11546abb5", "2916e5b293ab4f73820a642d5f91f46b", "de4a68f06bbc4e8cb5dece4a23a2fe4b", "f20dc0e1df0940259039847093d8e43b", "48a20de78bcc4cc6a43e585605abf5ee", "8a96af57f4b142a982b31b9f241ffed3", "7eb204f78a5d45209a2c39d07d6901d1", "29dba98a852f4cf090701c3d05b718d1", "006d57bd68c2421b909e822fe76f3dc6", "4b0db516167b457fb3fc35eb0fface55", 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"a7b9b33d4bc74ea19bcbf444948883c9", "5e65915be34a4717aa59b0c09e0fc29a" ] }, "executionInfo": { "elapsed": 328265, "status": "ok", "timestamp": 1787049765844, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "B5SpYhYpb2sJ", "outputId": "dc8878b3-2b77-4711-e22e-b33c9ff0aec0" }, "outputs": [], "source": [ "# ============================================================\n", "# Mage-VL 第五版\n", "# T4 16GB / 足球视频 / 4秒一段 / 真正逐段实时流式分析\n", "#\n", "# 核心流程：\n", "#\n", "# 视频\n", "# ↓\n", "# 当前 4 秒\n", "# ↓\n", "# 精确切片（重新编码）\n", "# ↓\n", "# Codec\n", "# ↓\n", "# Gate\n", "# ↓\n", "# ┌───────────────┐\n", "# │ p < threshold │ → 跳过\n", "# │ p >= threshold│ → Mage-VL\n", "# └───────────────┘\n", "# ↓\n", "# 输出\n", "# ↓\n", "# 清理 GPU\n", "# ↓\n", "# 下一 4 秒\n", "#\n", "# 不会提前 Codec 全部视频\n", "# ============================================================\n", "\n", "import os\n", "\n", "# ------------------------------------------------------------\n", "# 0. 环境变量\n", "# ------------------------------------------------------------\n", "\n", "os.environ.setdefault(\n", " \"PYTORCH_CUDA_ALLOC_CONF\",\n", " \"expandable_segments:True,max_split_size_mb:128\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"ONLINE_CODEC_CACHE_DIR\",\n", " \"/tmp/mage_vl_codec_cache\"\n", ")\n", "\n", "os.environ.setdefault(\n", " \"STREAMMIND_CLIP_CACHE\",\n", " \"/tmp/mage_vl_clips\"\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"ONLINE_CODEC_CACHE_DIR\"],\n", " exist_ok=True\n", ")\n", "\n", "os.makedirs(\n", " os.environ[\"STREAMMIND_CLIP_CACHE\"],\n", " exist_ok=True\n", ")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 1. Python imports\n", "# ------------------------------------------------------------\n", "\n", "import gc\n", "import time\n", "import warnings\n", "import subprocess\n", "import importlib\n", "from pathlib import Path\n", "\n", "import torch\n", "\n", "from transformers import (\n", " AutoModelForCausalLM,\n", " AutoProcessor,\n", ")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 2. 基本配置\n", "# ------------------------------------------------------------\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "\n", "VIDEO_FILE = \"/content/soccer-broadcast.mp4\"\n", "\n", "# 每次分析多少秒\n", "SEGMENT_SEC = 4.0\n", "\n", "# 最多分析多少段\n", "#\n", "# None = 整个视频\n", "#\n", "# 如果只是测试，可以设置成 8\n", "MAX_SEGMENTS = None\n", "\n", "# Gate 阈值\n", "GATE_THRESHOLD = 0.50\n", "\n", "# 最大生成 token\n", "MAX_NEW_TOKENS = 64\n", "\n", "# Codec 最大像素\n", "#\n", "# T4 16GB 推荐先保持 64000\n", "#\n", "# 如果显存仍然紧张：\n", "# 48000\n", "# 32000\n", "#\n", "CODEC_MAX_PIXELS = 64_000\n", "\n", "# Codec target canvas\n", "CODEC_TARGET_CANVAS = 24\n", "\n", "# 是否模拟真实时间\n", "#\n", "# True：\n", "# 如果处理速度 > 1x，则等待\n", "#\n", "# False：\n", "# 处理完马上进入下一段\n", "SIMULATE_REALTIME = True\n", "\n", "# 为了避免频繁 empty_cache\n", "# 可以每 N 个 segment 强制一次\n", "EMPTY_CACHE_EVERY = 1\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 3. Prompt\n", "# ------------------------------------------------------------\n", "\n", "STREAM_PROMPT = \"\"\"\n", "请用中文分析这段足球视频。\n", "\n", "重点描述：\n", "\n", "1. 视频中有哪些人物或球员\n", "2. 球员正在做什么\n", "3. 当前比赛发生了什么\n", "4. 是否有射门、传球、带球、抢断、犯规、角球、任意球、进球等动作\n", "5. 如果可以判断，请说明球员的大致位置和动作\n", "6. 判断当前是什么体育项目\n", "\n", "只描述视频中能够观察到的内容，不要猜测无法确认的信息。\n", "尽量简洁，适合实时视频分析。\n", "\"\"\".strip()\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 4. CUDA / dtype\n", "# ------------------------------------------------------------\n", "\n", "if not torch.cuda.is_available():\n", " raise RuntimeError(\n", " \"没有检测到 CUDA。请确认当前环境已经启用 NVIDIA GPU。\"\n", " )\n", "\n", "DEVICE = \"cuda\"\n", "\n", "GPU_NAME = torch.cuda.get_device_name(0)\n", "\n", "print(\"=\" * 70)\n", "print(\"Mage-VL 第五版\")\n", "print(\"=\" * 70)\n", "print(f\"[GPU] {GPU_NAME}\")\n", "print(f\"[CUDA] {torch.version.cuda}\")\n", "print(f\"[PyTorch] {torch.__version__}\")\n", "print(\"=\" * 70)\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 5. T4 使用 FP16\n", "# ------------------------------------------------------------\n", "#\n", "# T4 是 Turing 架构。\n", "#\n", "# 相比 BF16：\n", "# T4 上 FP16 通常更加合适。\n", "#\n", "# ------------------------------------------------------------\n", "\n", "MODEL_DTYPE = torch.float16\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 6. 加载 Processor\n", "# ------------------------------------------------------------\n", "\n", "print()\n", "print(\"[init] 正在加载 Processor...\")\n", "\n", "processor = AutoProcessor.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", ")\n", "\n", "print(\"[init] Processor 加载完成\")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 7. 加载模型\n", "# ------------------------------------------------------------\n", "\n", "print(\"[init] 正在加载 Mage-VL...\")\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=MODEL_DTYPE,\n", " attn_implementation=\"sdpa\",\n", ")\n", "\n", "model = model.to(DEVICE).eval()\n", "\n", "print(\"[init] Mage-VL 加载完成\")\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 8. 找到远程 codec 模块\n", "# ------------------------------------------------------------\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "\n", "codec_mod = importlib.import_module(\n", " _REMOTE_PKG + \".codec_video_processing_mage_vl\"\n", ")\n", "\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 9. 加载 Cognition Gate\n", "# ------------------------------------------------------------\n", "\n", "try:\n", "\n", " model.model._load_streammind_gate()\n", "\n", " print(\"[gate] 认知门控加载成功\")\n", "\n", "except Exception as exc:\n", "\n", " print(\n", " f\"[gate] 认知门控加载失败: {type(exc).__name__}: {exc}\"\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 10. 显存信息\n", "# ------------------------------------------------------------\n", "\n", "def print_gpu_memory(prefix=\"GPU\"):\n", "\n", " if not torch.cuda.is_available():\n", " return\n", "\n", " allocated = torch.cuda.memory_allocated() / 1024**3\n", "\n", " reserved = torch.cuda.memory_reserved() / 1024**3\n", "\n", " total = torch.cuda.get_device_properties(0).total_memory\n", " total = total / 1024**3\n", "\n", " free = total - allocated\n", "\n", " print(\n", " f\"[{prefix}] \"\n", " f\"allocated={allocated:.2f}GB \"\n", " f\"reserved={reserved:.2f}GB \"\n", " f\"free≈{free:.2f}GB \"\n", " f\"total={total:.2f}GB\",\n", " flush=True,\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 11. FFprobe 获取视频时长\n", "# ------------------------------------------------------------\n", "\n", "def _ffprobe_duration(video_path):\n", "\n", " try:\n", "\n", " result = subprocess.run(\n", " [\n", " \"ffprobe\",\n", " \"-v\",\n", " \"error\",\n", " \"-show_entries\",\n", " \"format=duration\",\n", " \"-of\",\n", " \"default=nw=1:nk=1\",\n", " str(video_path),\n", " ],\n", " check=True,\n", " capture_output=True,\n", " timeout=60,\n", " text=True,\n", " )\n", "\n", " value = result.stdout.strip()\n", "\n", " if not value:\n", " return 0.0\n", "\n", " return float(value)\n", "\n", " except Exception as exc:\n", "\n", " print(\n", " f\"[ffprobe] 获取视频时长失败: {exc}\",\n", " flush=True,\n", " )\n", "\n", " return 0.0\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 12. Codec 配置\n", "# ------------------------------------------------------------\n", "\n", "def _codec_cfg(\n", " target_canvas=CODEC_TARGET_CANVAS,\n", " max_pixels=CODEC_MAX_PIXELS,\n", "):\n", "\n", " override = {\n", " \"engine\": \"hevc\",\n", " \"target_canvas\": int(target_canvas),\n", " \"patch\": 16,\n", " }\n", "\n", " kwargs = dict(\n", " processor._codec_config_defaults\n", " )\n", "\n", " merged_dcvc = dict(\n", " kwargs.get(\"dcvc\") or {}\n", " )\n", "\n", " merged_dcvc.update(\n", " override.get(\"dcvc\") or {}\n", " )\n", "\n", " kwargs.update(override)\n", "\n", " if merged_dcvc:\n", " kwargs[\"dcvc\"] = merged_dcvc\n", "\n", " kwargs[\"max_pixels\"] = int(max_pixels)\n", "\n", " return CodecConfig(**kwargs), override\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 13. Prompt\n", "# ------------------------------------------------------------\n", "\n", "def _prompt(kind, question):\n", "\n", " messages = [\n", " {\n", " \"role\": \"user\",\n", " \"content\": [\n", " {\n", " \"type\": kind\n", " },\n", " {\n", " \"type\": \"text\",\n", " \"text\": question,\n", " },\n", " ],\n", " }\n", " ]\n", "\n", " return processor.apply_chat_template(\n", " messages,\n", " tokenize=False,\n", " add_generation_prompt=True,\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 14. CPU → GPU\n", "# ------------------------------------------------------------\n", "\n", "def _to_cuda(inputs):\n", "\n", " output = {}\n", "\n", " for key, value in inputs.items():\n", "\n", " if not hasattr(value, \"to\"):\n", " continue\n", "\n", " value = value.to(DEVICE)\n", "\n", " if key == \"pixel_values\":\n", "\n", " value = value.to(MODEL_DTYPE)\n", "\n", " output[key] = value\n", "\n", " return output\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 15. 精确切片\n", "# ------------------------------------------------------------\n", "#\n", "# 重要：\n", "#\n", "# 原来的：\n", "#\n", "# -c copy\n", "#\n", "# 是直接复制码流。\n", "#\n", "# 这样 -ss 可能落在关键帧之外，\n", "# 导致实际得到的视频开始时间不够精确。\n", "#\n", "# 这里改成重新编码。\n", "#\n", "# 优点：\n", "# 时间窗口更加准确\n", "#\n", "# 缺点：\n", "# CPU 会多一点开销。\n", "#\n", "# 对“模拟实时分析”而言，\n", "# 这个方案更稳。\n", "#\n", "# ------------------------------------------------------------\n", "\n", "def _extract_subclip(\n", " src,\n", " start,\n", " duration,\n", " out_path,\n", "):\n", "\n", " if duration <= 0:\n", " raise ValueError(\n", " f\"duration 必须 > 0，当前={duration}\"\n", " )\n", "\n", " tmp = out_path.with_name(\n", " f\".tmp_{out_path.name}\"\n", " )\n", "\n", " if tmp.exists():\n", " try:\n", " tmp.unlink()\n", " except Exception:\n", " pass\n", "\n", " command = [\n", " \"ffmpeg\",\n", " \"-y\",\n", "\n", " \"-loglevel\",\n", " \"error\",\n", "\n", " # 精确时间定位\n", " \"-ss\",\n", " f\"{max(0.0, start):.3f}\",\n", "\n", " \"-i\",\n", " str(src),\n", "\n", " \"-t\",\n", " f\"{duration:.3f}\",\n", "\n", " # 视频重新编码\n", " \"-c:v\",\n", " \"libx264\",\n", "\n", " # 很快的 CPU 编码\n", " \"-preset\",\n", " \"ultrafast\",\n", "\n", " # 保证足够好的视觉质量\n", " \"-crf\",\n", " \"23\",\n", "\n", " # 避免音频处理\n", " \"-an\",\n", "\n", " # 独立可解码\n", " \"-pix_fmt\",\n", " \"yuv420p\",\n", "\n", " str(tmp),\n", " ]\n", "\n", " subprocess.run(\n", " command,\n", " check=True,\n", " capture_output=True,\n", " timeout=180,\n", " )\n", "\n", " if not tmp.exists() or tmp.stat().st_size <= 0:\n", "\n", " raise RuntimeError(\n", " f\"FFmpeg 没有生成有效文件: {tmp}\"\n", " )\n", "\n", " if out_path.exists():\n", "\n", " try:\n", " out_path.unlink()\n", " except Exception:\n", " pass\n", "\n", " tmp.replace(out_path)\n", "\n", " return out_path\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 16. 清理显存\n", "# ------------------------------------------------------------\n", "\n", "def _cleanup_cuda(*objects):\n", "\n", " for obj in objects:\n", "\n", " try:\n", " del obj\n", " except Exception:\n", " pass\n", "\n", " gc.collect()\n", "\n", " if torch.cuda.is_available():\n", "\n", " torch.cuda.empty_cache()\n", "\n", " try:\n", " torch.cuda.ipc_collect()\n", " except Exception:\n", " pass\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 17. Gate\n", "# ------------------------------------------------------------\n", "\n", "@torch.inference_mode()\n", "def _run_gate(cpu_inputs):\n", "\n", " \"\"\"\n", " 只把当前 segment 的 Gate 所需视觉特征送入 GPU。\n", "\n", " 不保存多个 segment。\n", " \"\"\"\n", "\n", " cuda_visual_features = {}\n", "\n", " for key in (\n", " \"pixel_values\",\n", " \"image_grid_thw\",\n", " \"patch_positions\",\n", " ):\n", "\n", " if key not in cpu_inputs:\n", " continue\n", "\n", " value = cpu_inputs[key].to(DEVICE)\n", "\n", " if key == \"pixel_values\":\n", "\n", " value = value.to(MODEL_DTYPE)\n", "\n", " cuda_visual_features[key] = value\n", "\n", " try:\n", "\n", " logits = (\n", " model.model.streammind_gate_forward_segments(\n", " [cuda_visual_features]\n", " )[0]\n", " )\n", "\n", " # 当前只有一个 segment\n", " #\n", " # image_grid_thw 第一维是 segment/frame 相关结构，\n", " # 与你第四版保持同样的计算方式。\n", "\n", " length = int(\n", " cpu_inputs[\"image_grid_thw\"][:, 0].sum()\n", " )\n", "\n", " boundary = length - 1\n", "\n", " probability = torch.softmax(\n", " logits[boundary].float(),\n", " dim=-1,\n", " )[1].item()\n", "\n", " return float(probability)\n", "\n", " finally:\n", "\n", " _cleanup_cuda(\n", " cuda_visual_features,\n", " locals().get(\"logits\"),\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 18. Mage-VL 生成\n", "# ------------------------------------------------------------\n", "\n", "@torch.inference_mode()\n", "def _generate(\n", " inputs,\n", " max_new_tokens=MAX_NEW_TOKENS,\n", "):\n", "\n", " output = model.generate(\n", " **inputs,\n", "\n", " max_new_tokens=int(\n", " max_new_tokens\n", " ),\n", "\n", " do_sample=False,\n", "\n", " use_cache=True,\n", " )\n", "\n", " prompt_length = inputs[\n", " \"input_ids\"\n", " ].shape[1]\n", "\n", " generated = output[\n", " 0,\n", " prompt_length:\n", " ]\n", "\n", " text = processor.tokenizer.decode(\n", " generated,\n", " skip_special_tokens=True,\n", " ).strip()\n", "\n", " del output\n", " del generated\n", "\n", " return text\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 19. 计算实时倍率\n", "# ------------------------------------------------------------\n", "\n", "def _realtime_speed(\n", " video_duration,\n", " processing_time,\n", "):\n", "\n", " if processing_time <= 0:\n", " return 0.0\n", "\n", " return (\n", " video_duration /\n", " processing_time\n", " )\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 20. 单 segment 分析\n", "# ------------------------------------------------------------\n", "\n", "def _analyze_segment(\n", " clip,\n", " text,\n", " codec_override,\n", "):\n", "\n", " # --------------------------------------------------------\n", " # Codec\n", " # --------------------------------------------------------\n", "\n", " with warnings.catch_warnings():\n", "\n", " warnings.simplefilter(\"ignore\")\n", "\n", " inputs = processor(\n", " text=[text],\n", "\n", " videos=[\n", " str(clip)\n", " ],\n", "\n", " video_backend=\"codec\",\n", "\n", " max_pixels=CODEC_MAX_PIXELS,\n", "\n", " codec_config=codec_override,\n", "\n", " return_tensors=\"pt\",\n", "\n", " padding=False,\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Gate\n", " # --------------------------------------------------------\n", "\n", " probability = _run_gate(\n", " inputs\n", " )\n", "\n", " return inputs, probability\n", "\n", "\n", "# ------------------------------------------------------------\n", "# 21. 真正流式分析\n", "# ------------------------------------------------------------\n", "\n", "def stream_video_analysis(\n", " video_path: str,\n", "\n", " segment_sec: float = SEGMENT_SEC,\n", "\n", " gate_threshold: float = GATE_THRESHOLD,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens: int = MAX_NEW_TOKENS,\n", "\n", " simulate_realtime: bool = SIMULATE_REALTIME,\n", "):\n", "\n", " path = Path(video_path)\n", "\n", " if not path.exists():\n", "\n", " raise FileNotFoundError(\n", " f\"找不到视频文件: {video_path}\"\n", " )\n", "\n", " # --------------------------------------------------------\n", " # 获取时长\n", " # --------------------------------------------------------\n", "\n", " duration = _ffprobe_duration(path)\n", "\n", " if duration <= 0:\n", "\n", " raise RuntimeError(\n", " \"无法获得视频时长。\"\n", " )\n", "\n", " # --------------------------------------------------------\n", " # 临时目录\n", " # --------------------------------------------------------\n", "\n", " clip_dir = Path(\n", " os.environ[\"STREAMMIND_CLIP_CACHE\"]\n", " )\n", "\n", " clip_dir.mkdir(\n", " parents=True,\n", " exist_ok=True,\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Prompt\n", " # --------------------------------------------------------\n", "\n", " text = _prompt(\n", " \"video\",\n", " STREAM_PROMPT,\n", " )\n", "\n", " # --------------------------------------------------------\n", " # Codec 配置\n", " # --------------------------------------------------------\n", "\n", " _, codec_override = _codec_cfg(\n", " CODEC_TARGET_CANVAS\n", " )\n", "\n", " # --------------------------------------------------------\n", " # 状态\n", " # --------------------------------------------------------\n", "\n", " start = 0.0\n", "\n", " index = 0\n", "\n", " total_processed = 0.0\n", "\n", " all_lines = []\n", "\n", " yield (\n", " f\"开始实时流式分析\\n\"\n", " f\"视频总时长: {duration:.1f}s\\n\"\n", " f\"分析窗口: {segment_sec:.1f}s\\n\"\n", " f\"Gate threshold: {gate_threshold:.2f}\\n\"\n", " f\"模拟实时: {simulate_realtime}\"\n", " )\n", "\n", " # ========================================================\n", " # 核心循环\n", " # ========================================================\n", "\n", " while start < duration - 0.05:\n", "\n", " if (\n", " max_segments is not None\n", " and index >= max_segments\n", " ):\n", " break\n", "\n", " # ----------------------------------------------------\n", " # 当前 segment\n", " # ----------------------------------------------------\n", "\n", " end = min(\n", " duration,\n", " start + segment_sec,\n", " )\n", "\n", " current_duration = end - start\n", "\n", " index += 1\n", "\n", " segment_begin_time = time.time()\n", "\n", " clip = clip_dir / (\n", " f\"{path.stem}\"\n", " f\"__segment_{index:06d}\"\n", " f\"__{start:.3f}_{end:.3f}.mp4\"\n", " )\n", "\n", " # ----------------------------------------------------\n", " # 状态输出\n", " # ----------------------------------------------------\n", "\n", " yield (\n", " \"\\n\".join(all_lines)\n", " + (\n", " \"\\n\\n\"\n", " if all_lines\n", " else \"\"\n", " )\n", " + f\"⏱️ [{index}] \"\n", " f\"正在分析 \"\n", " f\"{start:.1f}–{end:.1f}s ...\"\n", " )\n", "\n", " inputs = None\n", " cuda_inputs = None\n", "\n", " try:\n", "\n", " # =================================================\n", " # STEP 1\n", " # 精确切片\n", " # =================================================\n", "\n", " extract_begin = time.time()\n", "\n", " _extract_subclip(\n", " path,\n", " start,\n", " current_duration,\n", " clip,\n", " )\n", "\n", " extract_time = (\n", " time.time()\n", " - extract_begin\n", " )\n", "\n", " # =================================================\n", " # STEP 2\n", " # Codec + Gate\n", " # =================================================\n", "\n", " codec_begin = time.time()\n", "\n", " inputs, probability = (\n", " _analyze_segment(\n", " clip,\n", " text,\n", " codec_override,\n", " )\n", " )\n", "\n", " codec_gate_time = (\n", " time.time()\n", " - codec_begin\n", " )\n", "\n", " # =================================================\n", " # Gate 结果\n", " # =================================================\n", "\n", " head = (\n", " f\"**t = {start:.1f}–{end:.1f}s** \"\n", " f\"· Gate = `{probability:.2f}`\"\n", " )\n", "\n", " # -------------------------------------------------\n", " # Gate 不通过\n", " # -------------------------------------------------\n", "\n", " if probability < float(\n", " gate_threshold\n", " ):\n", "\n", " processing_time = (\n", " time.time()\n", " - segment_begin_time\n", " )\n", "\n", " speed = _realtime_speed(\n", " current_duration,\n", " processing_time,\n", " )\n", "\n", " line = (\n", " f\"{head} → 🔇 \"\n", " f\"*跳过生成* \"\n", " f\"(processing={processing_time:.2f}s, \"\n", " f\"realtime={speed:.2f}×)\"\n", " )\n", "\n", " all_lines.append(line)\n", "\n", " yield (\n", " \"\\n\\n\".join(all_lines)\n", " )\n", "\n", " # -------------------------------------------------\n", " # Gate 通过\n", " # -------------------------------------------------\n", "\n", " else:\n", "\n", " yield (\n", " \"\\n\\n\".join(all_lines)\n", " + (\n", " \"\\n\\n\"\n", " if all_lines\n", " else \"\"\n", " )\n", " + (\n", " f\"{head} → 🧠 \"\n", " f\"*正在生成...*\"\n", " )\n", " )\n", "\n", " # =============================================\n", " # STEP 3\n", " # 当前 segment 才进入 LLM\n", " # =============================================\n", "\n", " generate_begin = time.time()\n", "\n", " cuda_inputs = _to_cuda(\n", " inputs\n", " )\n", "\n", " said = _generate(\n", " cuda_inputs,\n", " max_new_tokens,\n", " )\n", "\n", " generate_time = (\n", " time.time()\n", " - generate_begin\n", " )\n", "\n", " processing_time = (\n", " time.time()\n", " - segment_begin_time\n", " )\n", "\n", " speed = _realtime_speed(\n", " current_duration,\n", " processing_time,\n", " )\n", "\n", " line = (\n", " f\"{head} → 🗣️ {said}\\n\"\n", " f\" Codec/Gate=\"\n", " f\"{codec_gate_time:.2f}s\"\n", " f\" · Generate=\"\n", " f\"{generate_time:.2f}s\"\n", " f\" · Total=\"\n", " f\"{processing_time:.2f}s\"\n", " f\" · realtime=\"\n", " f\"{speed:.2f}×\"\n", " )\n", "\n", " all_lines.append(line)\n", "\n", " yield (\n", " \"\\n\\n\".join(all_lines)\n", " )\n", "\n", " # -------------------------------------------------\n", " # 统计\n", " # -------------------------------------------------\n", "\n", " processing_time = (\n", " time.time()\n", " - segment_begin_time\n", " )\n", "\n", " total_processed += (\n", " processing_time\n", " )\n", "\n", " # -------------------------------------------------\n", " # 模拟实时\n", " #\n", " # 如果：\n", " #\n", " # 4 秒视频\n", " # 实际只花 2 秒\n", " #\n", " # 等 2 秒再进入下一段。\n", " #\n", " # 如果实际用了 6 秒：\n", " #\n", " # 不等待。\n", " #\n", " # 这样不会越来越落后。\n", " # -------------------------------------------------\n", "\n", " if simulate_realtime:\n", "\n", " remaining = (\n", " current_duration\n", " - processing_time\n", " )\n", "\n", " if remaining > 0:\n", "\n", " yield (\n", " \"\\n\\n\".join(all_lines)\n", " + \"\\n\\n\"\n", " + (\n", " f\"⏳ 当前处理速度 \"\n", " f\"{speed:.2f}×，\"\n", " f\"等待 {remaining:.2f}s \"\n", " f\"模拟实时...\"\n", " )\n", " )\n", "\n", " time.sleep(\n", " remaining\n", " )\n", "\n", " except Exception as exc:\n", "\n", " processing_time = (\n", " time.time()\n", " - segment_begin_time\n", " )\n", "\n", " line = (\n", " f\"**t = {start:.1f}–{end:.1f}s** \"\n", " f\"→ ❌ \"\n", " f\"{type(exc).__name__}: \"\n", " f\"{exc}\"\n", " )\n", "\n", " all_lines.append(line)\n", "\n", " yield (\n", " \"\\n\\n\".join(all_lines)\n", " )\n", "\n", " finally:\n", "\n", " # =================================================\n", " # STEP 4\n", " # 清理当前 segment\n", " # =================================================\n", "\n", " _cleanup_cuda(\n", " cuda_inputs,\n", " inputs,\n", " )\n", "\n", " cuda_inputs = None\n", " inputs = None\n", "\n", " # ------------------------------------------------\n", " # 删除临时视频\n", " # ------------------------------------------------\n", "\n", " try:\n", "\n", " if clip.exists():\n", " clip.unlink()\n", "\n", " except Exception:\n", " pass\n", "\n", " # ------------------------------------------------\n", " # 定期清理 GPU\n", " # ------------------------------------------------\n", "\n", " if (\n", " index % EMPTY_CACHE_EVERY\n", " == 0\n", " ):\n", "\n", " gc.collect()\n", "\n", " torch.cuda.empty_cache()\n", "\n", " # ------------------------------------------------\n", " # 下一段\n", " # ------------------------------------------------\n", "\n", " start = end\n", "\n", " # ------------------------------------------------\n", " # 显存监控\n", " # ------------------------------------------------\n", "\n", " print_gpu_memory(\n", " prefix=f\"segment {index}\"\n", " )\n", "\n", " # ========================================================\n", " # 完成\n", " # ========================================================\n", "\n", " yield (\n", " \"\\n\\n\".join(all_lines)\n", " + \"\\n\\n\"\n", " + \"=\" * 60\n", " + \"\\n\"\n", " + \"✅ 实时流式分析完成\"\n", " + \"\\n\"\n", " + f\"共处理 {index} 个 segment\"\n", " + \"\\n\"\n", " + f\"视频时长 {min(start, duration):.1f}s\"\n", " + \"\\n\"\n", " + f\"累计处理时间 {total_processed:.1f}s\"\n", " + \"\\n\"\n", " + \"=\" * 60\n", " )\n", "\n", "\n", "# ============================================================\n", "# 22. Main\n", "# ============================================================\n", "\n", "if __name__ == \"__main__\":\n", "\n", " print()\n", " print(\"=\" * 70)\n", " print(\"开始测试 Mage-VL 实时流式视频分析\")\n", " print(\"=\" * 70)\n", " print()\n", "\n", " for chunk_output in stream_video_analysis(\n", "\n", " video_path=VIDEO_FILE,\n", "\n", " segment_sec=4.0,\n", "\n", " gate_threshold=0.50,\n", "\n", " max_segments=MAX_SEGMENTS,\n", "\n", " max_new_tokens=64,\n", "\n", " simulate_realtime=True,\n", " ):\n", "\n", " # 清屏\n", " print(\n", " \"\\033[H\\033[J\",\n", " end=\"\"\n", " )\n", "\n", " print(\n", " \"=== Mage-VL 实时流式分析 ===\"\n", " )\n", "\n", " print()\n", "\n", " print(chunk_output)\n", "\n", " print()\n", " print(\"-\" * 70)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "sdSQEXsDcrne" }, "outputs": [], "source": [ "#######################################################" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { 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"718f1a6bbd9e4e568fde6dcf926cd086", "07b7c26020b04e7e9d694d44dcf76025", "e282bfd35ef24222b826ae23aa10adf9", "dff4e8141534449fad8c1a7c72906dd2", "9904eb68fe474dfe95b790826dc57946", "2eec76d0830140df9528771d97ae612a", "f57a5a7c27b94efa921512dca99c1cbe", "ec4d4a8c4abc42c1abbb11cdf8e1a41a", "820689a727d043a3a70653f18c996db8", "5229e5d8aaed4770a9aa997a87d8bdf2", "c0d06966194b481a8f0aab934701c3ab", "62abe9cea93f4f1496ca02831c2ac12f", "8c493fd5447342189ff7a2f4145bbefe", "fa0eb41711cc4809b80666ea84366ae6", "ebe048ae56a44244a124137b8198d7cb", "22675cc379294aeb9786b221324a7f79", "84d05efda65c433fba6a6af5a666c4c5", "5794027e2b7c4646a5ec0f85b8293fec", "d936fb6d95864c38835fd16d81a6c8f5", "196f171bc46b4f51a569087e9c25b9a5", "aae5784708e744eca474384b969e075f", "148c0c37ea204749ae257068dc4771c0", "38d087157c5149ad8f52269969c4cad6", "d5431a98664946389455cc6232e640a0", "177aaa5645b74f0aa8c18bab8f2f71ea", "93f0b1ad7bfb465d96e8e593d7545da5", "067703de7d4448b697786925a1c19418", "3fc39c61322041f5bc362b6f1d59eaa5", "f57386f0b9c94adc80920881a013b8ac", "2d878bdc81174adeaaf613a84b5780fc", "a13d649443b9475bae8d2971b9676cc2", "7f163066575f4e3cb2c9e03cff570504", "ae27aea08b2c489ea680804fdc9b4617", "be0617fef5e04a2a92661c31529b0e47", "7a2a255af3794830a9bf70069c0966fd", "70a1e9273fde4d23a0910de8f63d44bc", "bcd84a56c0aa4d3f853c38a4c4576c84", "803e71a38b63478e8706a96ca0a06cf5", "349fc26e4180499480978aadbf1907dc", "67412e3bbfe04de3b1d9ee171f26a262", "adc94720f2c146fbba1ef8831639cb04", "c969758fb2234ca697c8be453f35d767", "a0be6b1de64a431894cad8ef9f54292a", "ca3d70f6d68148b395634ce6bd3b530f", "0a14d32566a842019b0e55987e36c92e", "9493ea4e6804472385a3304123027843", "cac0a3c59e8c4fa696a2249af9bdeea1", "1fba63f3878c459eb7ec7eb8f0aae14f", "9fc67233b83f4f58a9c873c2f4d3502a", "9f77894b138d44ef9ea6b4fc9f28b0dd" ] }, "executionInfo": { "elapsed": 393487, "status": "ok", "timestamp": 1787042294944, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "aKuU-Vno1IsF", "outputId": "ce5ad785-de12-4596-cbd5-811d8c7b950b" }, "outputs": [], "source": [ "###第四版，优化可在T416G跑起来，模拟实时分析\n", "import os\n", "import gc\n", "import time\n", "import warnings\n", "import subprocess\n", "import importlib\n", "from pathlib import Path\n", "\n", "import torch\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "\n", "# ==========================================\n", "# 1. 环境变量与 CUDA 分配策略优化\n", "# ==========================================\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\", \"expandable_segments:True,max_split_size_mb:128\")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\", \"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\", \"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "CODEC_MAX_PIXELS = 64_000 # 优化1：稍微降低分辨率上限，显著减少 Token 显存占用\n", "# STREAM_PROMPT = \"Please describe the video content in detail based on the provided information.\"\n", "STREAM_PROMPT = \"用中文描述视频中的人物，分析他们在干什么，分析是什么体育项目.\"\n", "\n", "print(\"[init] 加载模型中...\")\n", "processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=torch.bfloat16,\n", " attn_implementation=\"sdpa\",\n", ").to(\"cuda\").eval()\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "codec_mod = importlib.import_module(_REMOTE_PKG + \".codec_video_processing_mage_vl\")\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "# 加载 Cognition Gate\n", "try:\n", " model.model._load_streammind_gate()\n", " print(\"[gate] 认知门控加载成功\")\n", "except Exception as exc:\n", " print(f\"[gate] 加载失败: {exc}\")\n", "\n", "# ==========================================\n", "# 2. 辅助工具函数\n", "# ==========================================\n", "def _ffprobe_duration(video_path):\n", " try:\n", " out = subprocess.run(\n", " [\"ffprobe\", \"-v\", \"error\", \"-show_entries\", \"format=duration\",\n", " \"-of\", \"default=nw=1:nk=1\", str(video_path)],\n", " check=True, capture_output=True, timeout=60, text=True,\n", " )\n", " return float(out.stdout.strip())\n", " except Exception:\n", " return 0.0\n", "\n", "def _codec_cfg(target_canvas=12, max_pixels=CODEC_MAX_PIXELS): # 优化2：target_canvas 从 32 降到 24\n", " override = {\"engine\": \"hevc\", \"target_canvas\": int(target_canvas), \"patch\": 16}\n", " kwargs = dict(processor._codec_config_defaults)\n", " merged_dcvc = dict(kwargs.get(\"dcvc\") or {})\n", " merged_dcvc.update(override.get(\"dcvc\") or {})\n", " kwargs.update(override)\n", " if merged_dcvc:\n", " kwargs[\"dcvc\"] = merged_dcvc\n", " kwargs[\"max_pixels\"] = int(max_pixels)\n", " return CodecConfig(**kwargs), override\n", "\n", "def _prompt(kind, question):\n", " messages = [{\"role\": \"user\", \"content\": [{\"type\": kind}, {\"type\": \"text\", \"text\": question}]}]\n", " return processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "\n", "def _to_cuda(inputs):\n", " out = {}\n", " for key, value in inputs.items():\n", " if not hasattr(value, \"to\"):\n", " continue\n", " out[key] = value.to(\"cuda\")\n", " if key == \"pixel_values\":\n", " out[key] = out[key].to(model.dtype)\n", " return out\n", "\n", "def _extract_subclip(src, start, duration, out_path):\n", " if out_path.exists() and out_path.stat().st_size > 0:\n", " return out_path\n", " tmp = out_path.with_name(f\".tmp_{out_path.name}\")\n", " subprocess.run(\n", " [\"ffmpeg\", \"-y\", \"-loglevel\", \"error\", \"-ss\", f\"{max(0.0, start):.3f}\",\n", " \"-t\", f\"{duration:.3f}\", \"-i\", str(src), \"-c\", \"copy\",\n", " \"-avoid_negative_ts\", \"make_zero\", str(tmp)],\n", " check=True, capture_output=True, timeout=180,\n", " )\n", " tmp.replace(out_path)\n", " return out_path\n", "\n", "@torch.inference_mode()\n", "def _generate(inputs, max_new_tokens):\n", " # 清理缓存以保证生成拥有最大连续显存块\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " out = model.generate(\n", " **inputs,\n", " max_new_tokens=int(max_new_tokens),\n", " do_sample=False,\n", " use_cache=True\n", " )\n", " new = out[0, inputs[\"input_ids\"].shape[1]:]\n", " res = processor.tokenizer.decode(new, skip_special_tokens=True).strip()\n", "\n", " # 手动释放临时缓存\n", " del out, new\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", " return res\n", "\n", "# ==========================================\n", "# 3. 优化后的流式分析函数\n", "# ==========================================\n", "def stream_video_analysis(\n", " video_path: str,\n", " segment_sec: float = 4.0,\n", " gate_threshold: float = 0.5,\n", " max_segments: int = 8,\n", " max_new_tokens: int = 64 # 优化3：适度缩减 token 生成长度，流式推理推荐 48-64\n", "):\n", " path = Path(video_path)\n", " if not path.exists():\n", " raise FileNotFoundError(f\"找不到视频文件: {video_path}\")\n", "\n", " duration = _ffprobe_duration(path)\n", " clip_dir = Path(\"/tmp/mage_vl_clips\")\n", " clip_dir.mkdir(parents=True, exist_ok=True)\n", " stem = path.stem\n", " text = _prompt(\"video\", STREAM_PROMPT)\n", " cfg, override = _codec_cfg(24)\n", "\n", " raw_cpu_segments, spans = [], []\n", " start = 0.0\n", " index = 0\n", "\n", " yield f\"开始切片视频 (总时长: {duration:.1f}s)...\"\n", "\n", " # 1. 切片与预处理（保留在 CPU，避免占用 GPU 显存）\n", " while start < duration - 0.2 and index < max_segments:\n", " end = min(duration, start + segment_sec)\n", " clip = clip_dir / f\"{stem}__t{int(start * 100):08d}__d{int((end - start) * 100):08d}.mp4\"\n", "\n", " yield f\"正在进行第 {index + 1} 个片段的 Codec 分析 (t={start:.1f}–{end:.1f}s)...\"\n", " try:\n", " _extract_subclip(path, start, end - start, clip)\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " inputs = processor(\n", " text=[text], videos=[str(clip)], video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS, codec_config=override,\n", " return_tensors=\"pt\", padding=False,\n", " )\n", " except Exception as exc:\n", " print(f\"[stream] segment {start:.1f}-{end:.1f} unusable: {exc}\", flush=True)\n", " start, index = end, index + 1\n", " continue\n", "\n", " raw_cpu_segments.append(inputs)\n", " spans.append((start, end))\n", " start, index = end, index + 1\n", "\n", " if not raw_cpu_segments:\n", " yield \"未检测到可用片段。\"\n", " return\n", "\n", " # 2. 评估阶段：仅将 Gate 关注的轻量级特征输入 GPU 计算分数\n", " yield f\"正在使用认知门控 (System 1) 评估 {len(raw_cpu_segments)} 个片段...\"\n", "\n", " cuda_visual_features = []\n", " for seg in raw_cpu_segments:\n", " feat = {}\n", " for k in (\"pixel_values\", \"image_grid_thw\", \"patch_positions\"):\n", " if k in seg:\n", " feat[k] = seg[k].to(\"cuda\")\n", " if k == \"pixel_values\":\n", " feat[k] = feat[k].to(model.dtype)\n", " cuda_visual_features.append(feat)\n", "\n", " with torch.inference_mode():\n", " logits = model.model.streammind_gate_forward_segments(cuda_visual_features)[0]\n", "\n", " lengths = [int(seg[\"image_grid_thw\"][:, 0].sum()) for seg in cuda_visual_features]\n", " boundaries = torch.tensor(lengths, device=logits.device).cumsum(0) - 1\n", " probs = torch.softmax(logits[boundaries].float(), dim=-1)[:, 1].tolist()\n", "\n", " # 释放 gate 特征占用的显存\n", " del cuda_visual_features, logits, boundaries\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " # 3. 按片段迭代生成并按需按时传输至 GPU\n", " lines = []\n", " for (span, cpu_inputs, prob) in zip(spans, raw_cpu_segments, probs):\n", " head = f\"**t = {span[0]:.1f}–{span[1]:.1f}s** · p(respond) = `{prob:.2f}`\"\n", "\n", " if prob < float(gate_threshold):\n", " lines.append(f\"{head} → 🔇 *silence*\")\n", " yield \"\\n\\n\".join(lines)\n", " continue\n", "\n", " lines.append(f\"{head} → 🗣️ *generating…*\")\n", " yield \"\\n\\n\".join(lines)\n", "\n", " try:\n", " # 仅在真正需要生成时，将当前片段输入移至 GPU\n", " cuda_inputs = _to_cuda(cpu_inputs)\n", " said = _generate(cuda_inputs, max_new_tokens)\n", " del cuda_inputs\n", " except Exception as exc:\n", " said = f\"(generation failed: {type(exc).__name__}: {exc})\"\n", "\n", " lines[-1] = f\"{head} → 🗣️ {said}\"\n", " yield \"\\n\\n\".join(lines)\n", "\n", " yield \"\\n\\n\".join(lines) + f\"\\n\\n> 处理完成。\"\n", "\n", "# ==========================================\n", "# 4. 执行测试\n", "# ==========================================\n", "if __name__ == \"__main__\":\n", " VIDEO_FILE = \"/content/soccer-broadcast.mp4\"\n", "\n", " for chunk_output in stream_video_analysis(\n", " video_path=VIDEO_FILE,\n", " segment_sec=4.0,\n", " gate_threshold=0.5,\n", " max_segments=8,\n", " max_new_tokens=64\n", " ):\n", " print(\"\\033[H\\033[J\")\n", " print(\"=== 实时流式分析状态 ===\")\n", " print(chunk_output)\n", " print(\"-\" * 40)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 91012, "status": "ok", "timestamp": 1787043762550, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "JLyb_6PnGmbD", "outputId": "38ca9fb1-8b5c-4ba1-ec19-32b05a814b0f" }, "outputs": [], "source": [ "if __name__ == \"__main__\":\n", " VIDEO_FILE = \"/content/soccer-broadcast.mp4\"\n", "\n", " for chunk_output in stream_video_analysis(\n", " video_path=VIDEO_FILE,\n", " segment_sec=4.0,\n", " gate_threshold=0.5,\n", " max_segments=8,\n", " max_new_tokens=128\n", " ):\n", " print(\"\\033[H\\033[J\")\n", " print(\"=== 实时流式分析状态 ===\")\n", " print(chunk_output)\n", " print(\"-\" * 40)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "gipuzki1RxiP" }, "outputs": [], "source": [ "#其他失败版本" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 425 }, "executionInfo": { "elapsed": 60110, "status": "error", "timestamp": 1787029766309, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "0fW39NBNiz1k", "outputId": "ce72aac4-4ca4-452e-851e-838daa919ec0" }, "outputs": [], "source": [ "import os\n", "import time\n", "import uuid\n", "import warnings\n", "import subprocess\n", "from pathlib import Path\n", "\n", "import torch\n", "import torch.nn.functional as F\n", "from PIL import Image\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "import importlib\n", "\n", "# 1. 环境变量配置与缓存路径\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\", \"expandable_segments:True\")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\", \"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\", \"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "CODEC_MAX_PIXELS = 150_000\n", "\n", "print(\"[1/4] 正在加载 Mage-VL 模型与 Processor...\")\n", "processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=torch.bfloat16,\n", " attn_implementation=\"sdpa\",\n", ").to(\"cuda\").eval()\n", "\n", "# 加载远程代码模块\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "codec_mod = importlib.import_module(_REMOTE_PKG + \".codec_video_processing_mage_vl\")\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "# 2. 加载 StreamMind 认知门控 (System 1)\n", "print(\"[2/4] 正在加载 StreamMind 认知门控...\")\n", "try:\n", " _t0 = time.perf_counter()\n", " model.model._load_streammind_gate()\n", " print(f\"-> 认知门控加载成功 (耗时: {time.perf_counter() - _t0:.1f}s)\")\n", "except Exception as exc:\n", " print(f\"-> 认知门控加载失败: {exc}\")\n", "\n", "# 3. 辅助函数定义\n", "def _ffprobe_duration(video_path):\n", " try:\n", " out = subprocess.run(\n", " [\"ffprobe\", \"-v\", \"error\", \"-show_entries\", \"format=duration\",\n", " \"-of\", \"default=nw=1:nk=1\", str(video_path)],\n", " check=True, capture_output=True, timeout=60, text=True,\n", " )\n", " return float(out.stdout.strip())\n", " except Exception:\n", " return 0.0\n", "\n", "def _codec_cfg(target_canvas, max_pixels=CODEC_MAX_PIXELS):\n", " override = {\"engine\": \"hevc\", \"target_canvas\": int(target_canvas), \"patch\": 16}\n", " kwargs = dict(processor._codec_config_defaults)\n", " merged_dcvc = dict(kwargs.get(\"dcvc\") or {})\n", " merged_dcvc.update(override.get(\"dcvc\") or {})\n", " kwargs.update(override)\n", " if merged_dcvc:\n", " kwargs[\"dcvc\"] = merged_dcvc\n", " kwargs[\"max_pixels\"] = int(max_pixels)\n", " return CodecConfig(**kwargs), override\n", "\n", "def _prompt(kind, question):\n", " messages = [{\"role\": \"user\", \"content\": [{\"type\": kind}, {\"type\": \"text\", \"text\": question}]}]\n", " return processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "\n", "def _to_cuda(inputs):\n", " out = {}\n", " for key, value in inputs.items():\n", " if not hasattr(value, \"to\"):\n", " continue\n", " out[key] = value.to(\"cuda\")\n", " if key == \"pixel_values\":\n", " out[key] = out[key].to(model.dtype)\n", " return out\n", "\n", "def _extract_subclip(src, start, duration, out_path):\n", " if out_path.exists() and out_path.stat().st_size > 0:\n", " return out_path\n", " tmp = out_path.with_name(f\".tmp_{out_path.name}\")\n", " subprocess.run(\n", " [\"ffmpeg\", \"-y\", \"-loglevel\", \"error\", \"-ss\", f\"{max(0.0, start):.3f}\",\n", " \"-t\", f\"{duration:.3f}\", \"-i\", str(src), \"-c\", \"copy\",\n", " \"-avoid_negative_ts\", \"make_zero\", str(tmp)],\n", " check=True, capture_output=True, timeout=180,\n", " )\n", " tmp.replace(out_path)\n", " return out_path\n", "\n", "@torch.inference_mode()\n", "def _generate(inputs, max_new_tokens):\n", " out = model.generate(**inputs, max_new_tokens=int(max_new_tokens), do_sample=False)\n", " new = out[0, inputs[\"input_ids\"].shape[1]:]\n", " return processor.tokenizer.decode(new, skip_special_tokens=True).strip()\n", "\n", "# 4. 核心流式推理逻辑函数\n", "def run_streaming_colab(\n", " video_path: str,\n", " prompt_text: str = \"Please describe the video content in detail based on the provided information.\",\n", " segment_sec: float = 8.0,\n", " gate_threshold: float = 0.5,\n", " max_segments: int = 4,\n", " max_new_tokens: int = 96\n", "):\n", " path = Path(video_path)\n", " if not path.exists():\n", " raise FileNotFoundError(f\"未找到指定的视频文件: {video_path}\")\n", "\n", " duration = _ffprobe_duration(path)\n", " print(f\"\\n[3/4] 开始对视频进行切片分析 (总时长: {duration:.1f}s)...\")\n", "\n", " clip_dir = Path(\"/tmp/mage_vl_clips\")\n", " clip_dir.mkdir(parents=True, exist_ok=True)\n", " stem = path.stem\n", " text = _prompt(\"video\", prompt_text)\n", " cfg, override = _codec_cfg(32)\n", "\n", " segments, spans = [], []\n", " start = 0.0\n", " index = 0\n", "\n", " while start < duration - 0.2 and index < max_segments:\n", " end = min(duration, start + segment_sec)\n", " clip = clip_dir / f\"{stem}__t{int(start * 100):08d}__d{int((end - start) * 100):08d}.mp4\"\n", " print(f\" -> 正在预处理片段 {index + 1} (时间窗口: {start:.1f}s – {end:.1f}s)...\")\n", "\n", " try:\n", " _extract_subclip(path, start, end - start, clip)\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " inputs = processor(\n", " text=[text], videos=[str(clip)], video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS, codec_config=override,\n", " return_tensors=\"pt\", padding=False,\n", " )\n", " except Exception as exc:\n", " print(f\" [警告] 片段 {start:.1f}-{end:.1f}s 解析失败: {exc}\")\n", " start, index = end, index + 1\n", " continue\n", "\n", " segments.append(_to_cuda(inputs))\n", " spans.append((start, end))\n", " start, index = end, index + 1\n", "\n", " if not segments:\n", " print(\"未提取到有效视频片段，请检查视频文件格式。\")\n", " return\n", "\n", " print(f\"\\n[4/4] 认知门控 (System 1) 打分并生成响应...\")\n", " print(\"=\" * 60)\n", "\n", " visual = [{k: seg[k] for k in (\"pixel_values\", \"image_grid_thw\", \"patch_positions\") if k in seg} for seg in segments]\n", "\n", " with torch.inference_mode():\n", " logits = model.model.streammind_gate_forward_segments(visual)[0]\n", "\n", " lengths = [int(seg[\"image_grid_thw\"][:, 0].sum()) for seg in visual]\n", " boundaries = torch.tensor(lengths, device=logits.device).cumsum(0) - 1\n", " probs = torch.softmax(logits[boundaries].float(), dim=-1)[:, 1].tolist()\n", "\n", " for (span, inputs, prob) in zip(spans, segments, probs):\n", " time_info = f\"[t = {span[0]:.1f}s – {span[1]:.1f}s] p(respond) = {prob:.2f}\"\n", " if prob < float(gate_threshold):\n", " print(f\"{time_info} -> 🔇 [静音 / 忽略片段]\")\n", " else:\n", " print(f\"{time_info} -> 🗣️ [触发生成中...]\")\n", " try:\n", " said = _generate(inputs, max_new_tokens)\n", " print(f\" 回答: {said}\")\n", " except Exception as exc:\n", " print(f\" (生成失败: {exc})\")\n", " print(\"-\" * 60)\n", "\n", "print(\"环境配置完成，准备运行！\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 98445, "status": "ok", "timestamp": 1787021159292, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "xSvGKLnbnf-T", "outputId": "ae85221f-de77-45fa-e576-eb22484c1d25" }, "outputs": [], "source": [ "VIDEO_PATH=\"/content/soccer-broadcast.mp4\"\n", "CUSTOM_PROMPT = \"请详细描述该时间段内视频中发生的关键事件或动作。中文\" #\n", "# 运行流式处理\n", "run_streaming_colab(\n", " video_path=VIDEO_PATH,\n", " prompt_text=CUSTOM_PROMPT,\n", " segment_sec=5.0, # 每个切片的长度（秒）\n", " gate_threshold=0.4, # 认知门控触发阈值 (0.0~1.0)，低于该值的片段将被忽略\n", " max_segments=6, # 最多处理前几个片段\n", " max_new_tokens=128 # 每次生成的最大 Token 数量\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 225, "referenced_widgets": [ "b8194b9d2fe342d59462722471657189", "5755db896752417bbbb2fe7d1fb9d06d", "c0435f50cbc741ce8da143801248f0ed", "73624886ed6f4c5a8b5f55664e3183ec", "0f97c6c0210c4a44a7e04f0e6ef98792", "c16ac0ef9b4b44d995f54cd8c5add522", "7548cf43e07f42d6908d898975299376", "2b7b9275e29d4a1aa020db15b03bc3ef", "ea9ab087767e45d69a8d933c3c5c8a8f", "aff65ddbc0964655a92c93e36aec3331", "d77b17ab8d50405bb5fed352eb9dc222" ] }, "executionInfo": { "elapsed": 166097, "status": "ok", "timestamp": 1787021583370, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "wizlh-L1s_u2", "outputId": "62f9a206-7252-4dde-9fe9-b52e61300d7f" }, "outputs": [], "source": [ "###===========测试2，优化，实时\n", "import os\n", "import gc\n", "import time\n", "import math\n", "import warnings\n", "import subprocess\n", "from pathlib import Path\n", "\n", "import cv2\n", "import torch\n", "from PIL import Image\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "\n", "# ==========================================\n", "# 1. 显存与环境变量配置 (针对 Colab T4 16G 优化)\n", "# ==========================================\n", "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"\n", "os.environ[\"ONLINE_CODEC_CACHE_DIR\"] = \"/tmp/mage_vl_codec_cache\"\n", "os.environ[\"STREAMMIND_CLIP_CACHE\"] = \"/tmp/mage_vl_clips\"\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "CODEC_MAX_PIXELS = 100_000 # 降低最大像素数以节省显存\n", "\n", "print(\"[1/3] 正在加载 Mage-VL 模型与 Processor (FP16 模式)...\")\n", "processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=torch.float16, # T4 GPU 建议使用 float16\n", " device_map=\"cuda\",\n", " attn_implementation=\"sdpa\",\n", ").eval()\n", "\n", "# 加载 StreamMind 认知门控 (System 1)\n", "print(\"[2/3] 加载 StreamMind 认知门控...\")\n", "try:\n", " model.model._load_streammind_gate()\n", " print(\"-> 认知门控加载成功！\")\n", "except Exception as exc:\n", " print(f\"-> 认知门控加载警告: {exc}\")\n", "\n", "\n", "def _prompt(kind, question):\n", " messages = [{\"role\": \"user\", \"content\": [{\"type\": kind}, {\"type\": \"text\", \"text\": question}]}]\n", " return processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "\n", "\n", "def _to_cuda(inputs):\n", " out = {}\n", " for key, value in inputs.items():\n", " if not hasattr(value, \"to\"):\n", " continue\n", " out[key] = value.to(\"cuda\")\n", " if key == \"pixel_values\":\n", " out[key] = out[key].to(model.dtype)\n", " return out\n", "\n", "\n", "# ==========================================\n", "# 2. 真正的 Proactive Streaming 逻辑\n", "# ==========================================\n", "def run_realtime_proactive_streaming(\n", " video_source: str,\n", " prompt_text: str = \"请详细描述该时间段内视频中发生的关键事件或动作。中文\",\n", " chunk_sec: float = 3.0, # 增量步进长度 (秒)\n", " window_sec: float = 6.0, # 观察上下文窗口 (秒)\n", " gate_threshold: float = 0.45, # 认知门控触发阈值\n", " max_new_tokens: int = 96\n", "):\n", " \"\"\"\n", " 模拟真实实时流 (Real-time Stream Engine)：\n", " 无需预知视频总长，按照固定时间步步进，维护增量缓冲帧并实时做出主动响应决策。\n", " \"\"\"\n", " cap = cv2.VideoCapture(video_source)\n", " if not cap.isOpened():\n", " raise FileNotFoundError(f\"无法打开视频源: {video_source}\")\n", "\n", " fps = cap.get(cv2.CAP_PROP_FPS)\n", " if fps <= 0:\n", " fps = 25.0\n", "\n", " chunk_frames = int(chunk_sec * fps)\n", " window_frames = int(window_sec * fps)\n", "\n", " frame_buffer = []\n", " current_timestamp = 0.0\n", " text_prompt = _prompt(\"video\", prompt_text)\n", "\n", " tmp_clip_path = Path(\"/tmp/mage_vl_clips/stream_chunk.mp4\")\n", "\n", " print(\"\\n[3/3] 🚀 启动 Proactive Streaming 实时推理引擎...\")\n", " print(\"=\" * 65)\n", "\n", " step_idx = 0\n", " while True:\n", " # 1. 模拟实时抓取新入帧 (Ingest chunk)\n", " new_frames = []\n", " for _ in range(chunk_frames):\n", " ret, frame = cap.read()\n", " if not ret:\n", " break\n", " new_frames.append(frame)\n", "\n", " if not new_frames:\n", " print(\"\\n[流结束] 未接收到更多数据帧。\")\n", " break\n", "\n", " frame_buffer.extend(new_frames)\n", " current_timestamp += len(new_frames) / fps\n", "\n", " # 保持滑动窗口大小 (Keep sliding window)\n", " if len(frame_buffer) > window_frames:\n", " frame_buffer = frame_buffer[-window_frames:]\n", "\n", " # 2. 将当前滑动窗口写为临时片段供 Codec 读取\n", " h, w, _ = frame_buffer[0].shape\n", " fourcc = cv2.VideoWriter_fourcc(*'mp4v')\n", " out = cv2.VideoWriter(str(tmp_clip_path), fourcc, fps, (w, h))\n", " for f in frame_buffer:\n", " out.write(f)\n", " out.release()\n", "\n", " # 3. 预处理当前窗口\n", " try:\n", " inputs = processor(\n", " text=[text_prompt],\n", " videos=[str(tmp_clip_path)],\n", " video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS,\n", " return_tensors=\"pt\",\n", " padding=False,\n", " )\n", " cuda_inputs = _to_cuda(inputs)\n", " except Exception as e:\n", " print(f\"[Warn] 帧处理跳过: {e}\")\n", " continue\n", "\n", " # 4. System 1 认知门控即时评估 (StreamMind Gate Evaluation)\n", " visual = [{k: cuda_inputs[k] for k in (\"pixel_values\", \"image_grid_thw\", \"patch_positions\") if k in cuda_inputs}]\n", "\n", " with torch.inference_mode():\n", " logits = model.model.streammind_gate_forward_segments(visual)[0]\n", " length = int(visual[0][\"image_grid_thw\"][:, 0].sum())\n", " prob = torch.softmax(logits[length - 1].float(), dim=-1)[1].item()\n", "\n", " window_start = max(0.0, current_timestamp - (len(frame_buffer) / fps))\n", " time_info = f\"[Stream Time: {window_start:05.1f}s – {current_timestamp:05.1f}s] Gate Score = {prob:.2f}\"\n", "\n", " # 5. 主动响应判断 (Proactive Trigger)\n", " if prob < gate_threshold:\n", " print(f\"{time_info} --> 🔇 [保持静音]\")\n", " else:\n", " print(f\"{time_info} --> 🗣️ [主动触发输出!]\")\n", " with torch.inference_mode():\n", " out_ids = model.generate(\n", " **cuda_inputs,\n", " max_new_tokens=max_new_tokens,\n", " do_sample=False\n", " )\n", " new_tokens = out_ids[0, cuda_inputs[\"input_ids\"].shape[1]:]\n", " response = processor.tokenizer.decode(new_tokens, skip_special_tokens=True).strip()\n", " print(f\" └─ 🤖 AI: {response}\")\n", "\n", " print(\"-\" * 65)\n", "\n", " # 6. 显存清理，防止 OOM\n", " del cuda_inputs, visual, inputs\n", " gc.collect()\n", " torch.cuda.empty_cache()\n", "\n", " step_idx += 1\n", "\n", " cap.release()\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "executionInfo": { "elapsed": 16885, "status": "ok", "timestamp": 1787021607296, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "Fak9w860xhXe", "outputId": "0fbd16c4-d105-497f-8741-c9ec9129d2b7" }, "outputs": [], "source": [ "# ==========================================\n", "# 3. 运行配置\n", "# ==========================================\n", "VIDEO_PATH = \"/content/soccer-broadcast.mp4\"\n", "CUSTOM_PROMPT = \"请详细描述该时间段内视频中发生的关键事件或动作。中文\"\n", "\n", "run_realtime_proactive_streaming(\n", " video_source=VIDEO_PATH,\n", " prompt_text=CUSTOM_PROMPT,\n", " chunk_sec=3.0, # 每 3 秒增量推流一次\n", " window_sec=6.0, # 每次评估最近 6 秒的内容\n", " gate_threshold=0.45, # 门控感知的触发敏感度\n", " max_new_tokens=96\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "d92eb1a4e45e481e9e4d716ab67f880b", "1e8f92e83b7a4f2699596da1ca6653f0", "44d5d7685a724a20bdae9d1009b246e5", "6b17b58599f1464b9a57a77df0e2c9f2", "093662a9efec443b81cbc1f3a97c402c", "4297c56862a84c1596311c63262a6772", "895b646c2ac14177b2e1387d93983ef8", "b39c93f4a1334943a8adf55306bc43b3", "15e895e8b8284998bb4dd4a6548f57a5", "512bd3fa9ea84a9292ea008f48ea74f7", "7463e054e35e404c9db2fe94078dd72c" ] }, "executionInfo": { "elapsed": 135720, "status": "ok", "timestamp": 1787022076010, "user": { "displayName": "ming zhao", "userId": "11679614087766135097" }, "user_tz": -480 }, "id": "VNdBeZ_Jzmw2", "outputId": "b1d56cfb-c449-4ae6-e57a-189da6c79d8d" }, "outputs": [], "source": [ "###优化第三版\n", "import os\n", "import time\n", "import warnings\n", "import subprocess\n", "import importlib\n", "from pathlib import Path\n", "\n", "import torch\n", "from transformers import AutoModelForCausalLM, AutoProcessor\n", "\n", "# ==========================================\n", "# 1. 环境变量与模型初始化\n", "# ==========================================\n", "os.environ.setdefault(\"PYTORCH_CUDA_ALLOC_CONF\", \"expandable_segments:True\")\n", "os.environ.setdefault(\"ONLINE_CODEC_CACHE_DIR\", \"/tmp/mage_vl_codec_cache\")\n", "os.environ.setdefault(\"STREAMMIND_CLIP_CACHE\", \"/tmp/mage_vl_clips\")\n", "os.makedirs(os.environ[\"ONLINE_CODEC_CACHE_DIR\"], exist_ok=True)\n", "os.makedirs(os.environ[\"STREAMMIND_CLIP_CACHE\"], exist_ok=True)\n", "\n", "MODEL_ID = \"microsoft/Mage-VL\"\n", "CODEC_MAX_PIXELS = 150_000\n", "STREAM_PROMPT = \"Please describe the video content in detail based on the provided information.\"\n", "\n", "print(\"[init] 加载模型中...\")\n", "processor = AutoProcessor.from_pretrained(MODEL_ID, trust_remote_code=True)\n", "model = AutoModelForCausalLM.from_pretrained(\n", " MODEL_ID,\n", " trust_remote_code=True,\n", " torch_dtype=torch.bfloat16,\n", " attn_implementation=\"sdpa\",\n", ").to(\"cuda\").eval()\n", "\n", "_REMOTE_PKG = type(processor).__module__.rsplit(\".\", 1)[0]\n", "codec_mod = importlib.import_module(_REMOTE_PKG + \".codec_video_processing_mage_vl\")\n", "CodecConfig = codec_mod.CodecConfig\n", "\n", "# 加载 Cognition Gate\n", "try:\n", " model.model._load_streammind_gate()\n", " print(\"[gate] 认知门控加载成功\")\n", "except Exception as exc:\n", " GATE_ERROR = f\"{type(exc).__name__}: {exc}\"\n", " print(f\"[gate] 加载失败: {GATE_ERROR}\")\n", "\n", "# ==========================================\n", "# 2. 核心辅助函数（修复 Codec 崩溃的关键）\n", "# ==========================================\n", "def _ffprobe_duration(video_path):\n", " try:\n", " out = subprocess.run(\n", " [\"ffprobe\", \"-v\", \"error\", \"-show_entries\", \"format=duration\",\n", " \"-of\", \"default=nw=1:nk=1\", str(video_path)],\n", " check=True, capture_output=True, timeout=60, text=True,\n", " )\n", " return float(out.stdout.strip())\n", " except Exception:\n", " return 0.0\n", "\n", "def _codec_cfg(target_canvas=32, max_pixels=CODEC_MAX_PIXELS):\n", " override = {\"engine\": \"hevc\", \"target_canvas\": int(target_canvas), \"patch\": 16}\n", " kwargs = dict(processor._codec_config_defaults)\n", " merged_dcvc = dict(kwargs.get(\"dcvc\") or {})\n", " merged_dcvc.update(override.get(\"dcvc\") or {})\n", " kwargs.update(override)\n", " if merged_dcvc:\n", " kwargs[\"dcvc\"] = merged_dcvc\n", " kwargs[\"max_pixels\"] = int(max_pixels)\n", " return CodecConfig(**kwargs), override\n", "\n", "def _prompt(kind, question):\n", " messages = [{\"role\": \"user\", \"content\": [{\"type\": kind}, {\"type\": \"text\", \"text\": question}]}]\n", " return processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", "\n", "def _to_cuda(inputs):\n", " out = {}\n", " for key, value in inputs.items():\n", " if not hasattr(value, \"to\"):\n", " continue\n", " out[key] = value.to(\"cuda\")\n", " if key == \"pixel_values\":\n", " out[key] = out[key].to(model.dtype)\n", " return out\n", "\n", "def _extract_subclip(src, start, duration, out_path):\n", " \"\"\"使用 ffmpeg 严格流式裁剪，保留原始比特流容器属性，防止 codec 预处理报错\"\"\"\n", " if out_path.exists() and out_path.stat().st_size > 0:\n", " return out_path\n", " tmp = out_path.with_name(f\".tmp_{out_path.name}\")\n", " subprocess.run(\n", " [\"ffmpeg\", \"-y\", \"-loglevel\", \"error\", \"-ss\", f\"{max(0.0, start):.3f}\",\n", " \"-t\", f\"{duration:.3f}\", \"-i\", str(src), \"-c\", \"copy\",\n", " \"-avoid_negative_ts\", \"make_zero\", str(tmp)],\n", " check=True, capture_output=True, timeout=180,\n", " )\n", " tmp.replace(out_path)\n", " return out_path\n", "\n", "@torch.inference_mode()\n", "def _generate(inputs, max_new_tokens):\n", " out = model.generate(**inputs, max_new_tokens=int(max_new_tokens), do_sample=False)\n", " new = out[0, inputs[\"input_ids\"].shape[1]:]\n", " return processor.tokenizer.decode(new, skip_special_tokens=True).strip()\n", "\n", "# ==========================================\n", "# 3. 纯 Python 原生流式生成器函数\n", "# ==========================================\n", "def stream_video_analysis(\n", " video_path: str,\n", " segment_sec: float = 8.0,\n", " gate_threshold: float = 0.5,\n", " max_segments: int = 4,\n", " max_new_tokens: int = 96\n", "):\n", " \"\"\"\n", " 完全复原 Hugging Face Space 中 run_streaming 的核心逻辑，\n", " 使用标准 Python `yield` 实现流式事件输出，不依赖 Gradio。\n", " \"\"\"\n", " path = Path(video_path)\n", " if not path.exists():\n", " raise FileNotFoundError(f\"找不到视频文件: {video_path}\")\n", "\n", " duration = _ffprobe_duration(path)\n", " clip_dir = Path(\"/tmp/mage_vl_clips\")\n", " clip_dir.mkdir(parents=True, exist_ok=True)\n", " stem = path.stem\n", " text = _prompt(\"video\", STREAM_PROMPT)\n", " cfg, override = _codec_cfg(32)\n", "\n", " segments, spans = [], []\n", " start = 0.0\n", " index = 0\n", "\n", " yield f\"开始切片视频 (总时长: {duration:.1f}s)...\"\n", "\n", " # 1. 循环切割片段并执行 codec 特征提取\n", " while start < duration - 0.2 and index < max_segments:\n", " end = min(duration, start + segment_sec)\n", " clip = clip_dir / f\"{stem}__t{int(start * 100):08d}__d{int((end - start) * 100):08d}.mp4\"\n", "\n", " yield f\"正在进行第 {index + 1} 个片段的 Codec 分析 (t={start:.1f}–{end:.1f}s)...\"\n", " try:\n", " _extract_subclip(path, start, end - start, clip)\n", " with warnings.catch_warnings():\n", " warnings.simplefilter(\"ignore\")\n", " inputs = processor(\n", " text=[text], videos=[str(clip)], video_backend=\"codec\",\n", " max_pixels=CODEC_MAX_PIXELS, codec_config=override,\n", " return_tensors=\"pt\", padding=False,\n", " )\n", " except Exception as exc:\n", " print(f\"[stream] segment {start:.1f}-{end:.1f} unusable: {exc}\", flush=True)\n", " start, index = end, index + 1\n", " continue\n", "\n", " segments.append(_to_cuda(inputs))\n", " spans.append((start, end))\n", " start, index = end, index + 1\n", "\n", " if not segments:\n", " yield \"未检测到可用片段，请尝试更长的视频或调整切片长度。\"\n", " return\n", "\n", " # 2. 认知门控统一打分\n", " yield f\"正在使用认知门控 (System 1) 评估 {len(segments)} 个片段...\"\n", " visual = [{k: seg[k] for k in (\"pixel_values\", \"image_grid_thw\", \"patch_positions\") if k in seg} for seg in segments]\n", "\n", " with torch.inference_mode():\n", " logits = model.model.streammind_gate_forward_segments(visual)[0]\n", "\n", " lengths = [int(seg[\"image_grid_thw\"][:, 0].sum()) for seg in visual]\n", " boundaries = torch.tensor(lengths, device=logits.device).cumsum(0) - 1\n", " probs = torch.softmax(logits[boundaries].float(), dim=-1)[:, 1].tolist()\n", "\n", " # 3. 按片段迭代输出结果（模拟实时流式更新）\n", " lines = []\n", " for (span, inputs, prob) in zip(spans, segments, probs):\n", " head = f\"**t = {span[0]:.1f}–{span[1]:.1f}s** · p(respond) = `{prob:.2f}`\"\n", "\n", " if prob < float(gate_threshold):\n", " lines.append(f\"{head} → 🔇 *silence*\")\n", " yield \"\\n\\n\".join(lines)\n", " continue\n", "\n", " lines.append(f\"{head} → 🗣️ *generating…*\")\n", " yield \"\\n\\n\".join(lines)\n", "\n", " try:\n", " said = _generate(inputs, max_new_tokens)\n", " except Exception as exc:\n", " said = f\"(generation failed: {type(exc).__name__}: {exc})\"\n", "\n", " lines[-1] = f\"{head} → 🗣️ {said}\"\n", " yield \"\\n\\n\".join(lines)\n", "\n", " yield \"\\n\\n\".join(lines) + f\"\\n\\n> 处理完成。\"\n", "\n", "\n", "# ==========================================\n", "# 4. 运行示例（原生 Python 消费生成器）\n", "# ==========================================\n", "if __name__ == \"__main__\":\n", " VIDEO_FILE = \"/content/soccer-broadcast.mp4\" # 替换为你的测试视频路径\n", "\n", " # 像消费流式数据一样迭代生成器\n", " for chunk_output in stream_video_analysis(\n", " video_path=VIDEO_FILE,\n", " segment_sec=8.0,\n", " gate_threshold=0.5,\n", " max_segments=4,\n", " 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