{ "cells": [ { "cell_type": "code", "execution_count": 1, "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": [ "115" ] }, "execution_count": 1, "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", "torchvision\n", "opencv-python-headless<4.12\n", "codec-video-prep\n", "\"\"\",\n", "encoding=\"utf-8\"\n", ")" ] }, { "cell_type": "code", "execution_count": 2, "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" ] }, { "name": "stdout", "output_type": "stream", "text": [ "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 | 11.06 MiB/s, done.\n", "Resolving deltas: 100% (8/8), 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": [ "--2026-09-11 01:39:26-- 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 0%[ ] 0 --.-KB/s " ] }, { "name": "stdout", "output_type": "stream", "text": [ "test02.mp4 100%[===================>] 28.87M 33.2MB/s in 0.9s \n", "\n", "2026-09-11 01:39:27 (33.2 MB/s) - ‘test02.mp4’ saved [30270635/30270635]\n", "\n", "--2026-09-11 01:39:27-- 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 25.4MB/s in 0.5s \n", "\n", "2026-09-11 01:39:27 (25.4 MB/s) - ‘test03.mp4’ saved [13549203/13549203]\n", "\n", "--2026-09-11 01:39:27-- 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 43.0MB/s in 0.6s \n", "\n", "2026-09-11 01:39:28 (43.0 MB/s) - ‘test04.mp4’ saved [27143071/27143071]\n", "\n", "--2026-09-11 01:39:28-- 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 48.5MB/s in 0.7s \n", "\n", "2026-09-11 01:39:29 (48.5 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", "Hit:6 http://archive.ubuntu.com/ubuntu jammy InRelease \n", "Get:7 https://r2u.stat.illinois.edu/ubuntu jammy InRelease [6,555 B] \n", "Get:8 http://archive.ubuntu.com/ubuntu jammy-updates InRelease [128 kB] \n", "Get:9 https://cloud.r-project.org/bin/linux/ubuntu jammy-cran40/ Packages [113 kB]\n", "Get:10 https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2204/x86_64 Packages [2,993 kB]\n", "Get:11 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy InRelease [18.1 kB]\n", "Get:12 http://archive.ubuntu.com/ubuntu jammy-backports InRelease [127 kB] \n", "Hit:13 https://ppa.launchpadcontent.net/graphics-drivers/ppa/ubuntu jammy InRelease\n", "Get:14 https://r2u.stat.illinois.edu/ubuntu jammy/main amd64 Packages [3,198 kB]\n", "Hit:15 https://ppa.launchpadcontent.net/ubuntugis/ppa/ubuntu jammy InRelease\n", "Get:16 http://security.ubuntu.com/ubuntu jammy-security/universe amd64 Packages [1,317 kB]\n", "Get:17 https://r2u.stat.illinois.edu/ubuntu jammy/main all Packages [10.9 MB] \n", "Get:18 http://archive.ubuntu.com/ubuntu jammy-updates/multiverse amd64 Packages [93.2 kB]\n", "Get:19 http://archive.ubuntu.com/ubuntu jammy-updates/main amd64 Packages [4,623 kB]\n", "Get:20 https://ppa.launchpadcontent.net/deadsnakes/ppa/ubuntu jammy/main amd64 Packages [44.3 kB]\n", "Get:21 http://security.ubuntu.com/ubuntu jammy-security/main amd64 Packages [4,289 kB]\n", "Get:22 http://security.ubuntu.com/ubuntu jammy-security/multiverse amd64 Packages [84.6 kB]\n", "Get:23 http://security.ubuntu.com/ubuntu jammy-security/restricted amd64 Packages [7,717 kB]\n", "Get:24 http://archive.ubuntu.com/ubuntu jammy-updates/universe amd64 Packages [1,620 kB]\n", "Get:25 http://archive.ubuntu.com/ubuntu jammy-updates/restricted amd64 Packages [8,007 kB]\n", "Get:26 http://archive.ubuntu.com/ubuntu jammy-backports/universe amd64 Packages [35.6 kB]\n", "Get:27 http://archive.ubuntu.com/ubuntu jammy-backports/main amd64 Packages [82.8 kB]\n", "Fetched 45.5 MB in 4s (10.5 MB/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 212 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", "Collecting numpy<2 (from -r /content/requirements.txt (line 5))\n", " Downloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (61 kB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.0/61.0 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25hRequirement already satisfied: torchvision in /usr/local/lib/python3.12/dist-packages (from -r /content/requirements.txt (line 6)) (0.26.0+cu128)\n", "Collecting opencv-python-headless<4.12 (from -r /content/requirements.txt (line 7))\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 8))\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 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/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", "Requirement already satisfied: click<9.0.0,>=8.4.2 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.5.0->transformers==5.7.0->-r /content/requirements.txt (line 1)) (8.4.2)\n", "Requirement already satisfied: hf-xet<2.0.0,>=1.5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.5.0->transformers==5.7.0->-r /content/requirements.txt (line 1)) (1.5.1)\n", "Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface-hub<2.0,>=1.5.0->transformers==5.7.0->-r /content/requirements.txt (line 1)) (0.28.1)\n", "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer->transformers==5.7.0->-r /content/requirements.txt (line 1)) (1.5.4)\n", "Requirement already satisfied: rich>=13.8.0 in /usr/local/lib/python3.12/dist-packages (from typer->transformers==5.7.0->-r /content/requirements.txt (line 1)) (13.9.4)\n", "Requirement already satisfied: annotated-doc>=0.0.2 in 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in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers==5.7.0->-r /content/requirements.txt (line 1)) (3.18)\n", "Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface-hub<2.0,>=1.5.0->transformers==5.7.0->-r /content/requirements.txt (line 1)) (0.16.0)\n", "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer->transformers==5.7.0->-r /content/requirements.txt (line 1)) (4.2.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer->transformers==5.7.0->-r /content/requirements.txt (line 1)) (2.20.0)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (1.3.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.0.0->accelerate->-r /content/requirements.txt (line 2)) (3.0.3)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=13.8.0->typer->transformers==5.7.0->-r /content/requirements.txt (line 1)) (0.1.2)\n", "Downloading transformers-5.7.0-py3-none-any.whl (10.5 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.5/10.5 MB\u001b[0m \u001b[31m104.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m:01\u001b[0m\n", "\u001b[?25hDownloading numpy-1.26.4-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.0 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m18.0/18.0 MB\u001b[0m \u001b[31m109.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", "\u001b[?25hDownloading opencv_python_headless-4.11.0.86-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (50.0 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.0/50.0 MB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m00:01\u001b[0m\n", "\u001b[?25hDownloading codec_video_prep-0.2.5-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl (29.8 MB)\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.8/29.8 MB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n", "\u001b[?25hInstalling collected packages: numpy, opencv-python-headless, codec-video-prep, transformers\n", " Attempting uninstall: numpy\n", " Found existing installation: numpy 2.0.2\n", " Uninstalling numpy-2.0.2:\n", " Successfully uninstalled numpy-2.0.2\n", " Attempting uninstall: opencv-python-headless\n", " Found existing installation: opencv-python-headless 5.0.0.93\n", " Uninstalling opencv-python-headless-5.0.0.93:\n", " Successfully uninstalled opencv-python-headless-5.0.0.93\n", " Attempting uninstall: transformers\n", " Found existing installation: transformers 5.13.1\n", " Uninstalling transformers-5.13.1:\n", " Successfully uninstalled transformers-5.13.1\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "cupy-cuda12x 14.0.1 requires numpy<2.6,>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "jax 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "jaxlib 0.7.2 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "opencv-contrib-python 4.13.0.92 requires numpy>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "opencv-python 5.0.0.93 requires numpy>=2; python_version >= \"3.9\", but you have numpy 1.26.4 which is incompatible.\n", "pytensor 2.38.3 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "rasterio 1.5.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", "shap 0.52.0 requires numpy>=2, but you have numpy 1.26.4 which is incompatible.\n", "tifffile 2026.4.11 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "tobler 0.14.0 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\n", "xarray-einstats 0.10.0 requires numpy>=2.0, but you have numpy 1.26.4 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed codec-video-prep-0.2.5 numpy-1.26.4 opencv-python-headless-4.11.0.86 transformers-5.7.0\n" ] }, { "data": { "application/vnd.colab-display-data+json": { "id": "4d53f1dc8a6940d2aa2803bbecf37f7e", "pip_warning": { "packages": [ "numpy" ] } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "!pip install -r /content/requirements.txt" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import login\n", "\n", "login(token=\"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\") " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "============================================================\n", "正在加载模型（只加载一次）...\n", "============================================================\n", "[GPU] Tesla T4\n", "[CUDA] 12.8\n", "[BF16] True\n", "[MODEL DTYPE] torch.bfloat16\n", "\n", "[init] 加载 Processor...\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "ea0d8e9332a043c893d7d2317ba419cc", "version_major": 2, "version_minor": 0 }, "text/plain": [ "preprocessor_config.json: 0%| | 0.00/1.81k [00:00 max_change:\n", " print(f\"[warn] 单段变化过大 ({delta})，裁剪到 ±{max_change}\")\n", " delta = max(-max_change, min(max_change, delta))\n", " current_count = previous_count + delta\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(data.get(\"occupancy\", previous_occupancy))))\n", " except:\n", " occupancy = previous_occupancy\n", " occupancy = max(0, min(100, occupancy))\n", "\n", " try:\n", " confidence = float(data.get(\"confidence\", 0.0))\n", " except:\n", " confidence = 0.0\n", " confidence = max(0.0, min(1.0, confidence))\n", "\n", " print(f\"[debug] 模型绝对数={data.get('current_count')} → 最终 count={current_count}, delta={delta}\")\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", "@torch.inference_mode()\n", "def final_summary(history, final_state, final_max_tokens=128):\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(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(**inputs, max_new_tokens=final_max_tokens, do_sample=False, use_cache=True)\n", " n = inputs[\"input_ids\"].shape[1]\n", " return processor.tokenizer.decode(out[0, n:], skip_special_tokens=True).strip()\n", " finally:\n", " cleanup(inputs)\n", " try: del out\n", " except: pass\n", "\n", "def run_analysis(\n", " video_path=None,\n", " prompt_template=None,\n", " segment_sec=None,\n", " max_segments=None,\n", " gate_threshold=None,\n", " max_new_tokens=None,\n", " simulate_realtime=None,\n", " show_raw=None,\n", " max_change_per_segment=None,\n", " always_run_llm=None\n", "):\n", " video_path = video_path or DEFAULT_VIDEO\n", " prompt_template = prompt_template or PROMPT_TEMPLATE\n", " segment_sec = segment_sec if segment_sec is not None else DEFAULT_SEGMENT_SEC\n", " max_segments = max_segments if max_segments is not None else DEFAULT_MAX_SEGMENTS\n", " gate_threshold = gate_threshold if gate_threshold is not None else DEFAULT_GATE_THRESHOLD\n", " max_new_tokens = max_new_tokens if max_new_tokens is not None else DEFAULT_MAX_NEW_TOKENS\n", " simulate_realtime = simulate_realtime if simulate_realtime is not None else DEFAULT_SIMULATE_REALTIME\n", " show_raw = show_raw if show_raw is not None else DEFAULT_SHOW_RAW\n", " max_change = max_change_per_segment if max_change_per_segment is not None else DEFAULT_MAX_CHANGE_PER_SEGMENT\n", " always_run_llm = always_run_llm if always_run_llm is not None else DEFAULT_ALWAYS_RUN_LLM\n", "\n", " path = Path(video_path)\n", " if not path.exists():\n", " raise FileNotFoundError(f\"视频不存在: {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", " previous_count = 0\n", " previous_colors = []\n", " previous_occupancy = 0\n", " history = []\n", " results = []\n", " start = 0.0\n", " index = 0\n", " _, codec_override = get_codec_config(DEFAULT_CODEC_MAX_PIXELS, DEFAULT_CODEC_TARGET_CANVAS)\n", "\n", " print(f\"\\n开始分析 | 视频={path.name} | 时长={total:.1f}s | 窗口={segment_sec}s | 绝对计数模式\")\n", " print(\"-\" * 60)\n", "\n", " while start < total - 0.05:\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", "\n", " if length < 0.35:\n", " print(f\"**{start:.1f}–{end:.1f}s** → ⚠️ 片段过短 ({length:.2f}s)，跳过\")\n", " start = end\n", " continue\n", "\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(prompt_template, previous_count, previous_colors, previous_occupancy)\n", " extract_clip(path, start, length, clip)\n", " inputs, prob = analyze_clip(clip, prompt, codec_override, DEFAULT_CODEC_MAX_PIXELS)\n", "\n", " head = f\"**{start:.1f}–{end:.1f}s** Gate=`{prob:.2f}`\"\n", "\n", " if not always_run_llm and prob < gate_threshold:\n", " line = f\"{head} → 🔇 无明显变化\"\n", " results.append(line)\n", " print(line)\n", " continue\n", "\n", " print(f\"{head} → 🧠 识别中...\")\n", " gt = time.time()\n", "\n", " cuda_inputs = to_cuda(inputs)\n", " said = generate(cuda_inputs, max_new_tokens=max_new_tokens)\n", " gen_time = time.time() - gt\n", "\n", " if show_raw:\n", " print(f\"[raw LLM] {said}\")\n", "\n", " raw_data = parse_json(said)\n", " state = normalize_state(raw_data, previous_count, previous_colors, previous_occupancy, max_change)\n", "\n", " previous_count = state[\"current_count\"]\n", " previous_colors = state[\"colors\"]\n", " previous_occupancy = state[\"occupancy\"]\n", "\n", " change = state[\"delta\"]\n", " if change > 0:\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 | Total={elapsed:.2f}s | {speed:.2f}×\"\n", " )\n", " results.append(line)\n", " history.append(f\"{start:.1f}-{end:.1f}s：{state_to_text(state)}\")\n", " print(line)\n", "\n", " if simulate_realtime and elapsed < length:\n", " time.sleep(length - elapsed)\n", "\n", " except Exception as e:\n", " line = f\"**{start:.1f}–{end:.1f}s** → ❌ {type(e).__name__}: {e}\"\n", " results.append(line)\n", " print(line)\n", "\n", " finally:\n", " cleanup(cuda_inputs, inputs, said)\n", " if clip.exists():\n", " try: clip.unlink()\n", " except: pass\n", " start = end\n", " print(f\"[segment {index}] {start:.1f}/{total:.1f}s | {gpu_mem()}\", flush=True)\n", " try: torch.cuda.reset_peak_memory_stats()\n", " except: pass\n", "\n", " final_state = {\n", " \"current_count\": previous_count,\n", " \"colors\": previous_colors,\n", " \"occupancy\": previous_occupancy\n", " }\n", "\n", " colors_text = \"、\".join(previous_colors) if previous_colors else \"未知\"\n", " if previous_count == 0:\n", " occupancy_state = \"空\"\n", " elif previous_occupancy <= 20:\n", " occupancy_state = \"少量\"\n", " elif previous_occupancy <= 50:\n", " occupancy_state = \"中等\"\n", " elif previous_occupancy <= 80:\n", " occupancy_state = \"较多\"\n", " else:\n", " occupancy_state = \"接近装满\"\n", "\n", " print(\"\\n\" + \"=\" * 60)\n", " print(\"🧺 最终衣物状态\")\n", " print(\"=\" * 60)\n", " print(f\"【最终数量】 {previous_count} 件\")\n", " print(f\"【最终颜色】 {colors_text}\")\n", " print(f\"【空间占用】 {previous_occupancy}%\")\n", " print(f\"【状态】 {occupancy_state}\")\n", " print(\"=\" * 60)\n", "\n", " try:\n", " final = final_summary(history, final_state)\n", " print(\"\\n🧠 最终模型校验：\")\n", " print(final)\n", " except Exception as e:\n", " print(f\"\\n❌ 最终模型校验失败: {type(e).__name__}: {e}\")\n", "\n", " print(f\"\\n✅ 完成，共分析 {index} 个片段\")\n", " return {\n", " \"final_count\": previous_count,\n", " \"final_colors\": previous_colors,\n", " \"final_occupancy\": previous_occupancy,\n", " \"history\": history,\n", " \"results\": results\n", " }" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "开始分析 | 视频=test02.mp4 | 时长=14.1s | 窗口=1.5s | 绝对计数模式\n", "------------------------------------------------------------\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**0.0–1.5s** Gate=`0.17` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 1,\n", " \"delta\": 1,\n", " \"colors\": [\"浅灰色\"],\n", " \"occupancy\": 50,\n", " \"confidence\": 0.9\n", "}\n", "[debug] 模型绝对数=1 → 最终 count=1, delta=1\n", "**0.0–1.5s** Gate=`0.17`\n", " 🟢 放入 1 件 | 当前数量=1件\n", " 颜色=灰色 | 占用=50%\n", " 置信度=0.90 | Generate=16.17s | Total=22.06s | 0.07×\n", "[segment 1] 1.5/14.1s | GPU=9.85GB / reserved=9.87GB / peak=12.08GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**1.5–3.0s** Gate=`0.49` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 1,\n", " \"delta\": 0,\n", " \"colors\": [\"灰色\"],\n", " \"occupancy\": 50,\n", " \"confidence\": 0.99\n", "}\n", "[debug] 模型绝对数=1 → 最终 count=1, delta=0\n", "**1.5–3.0s** Gate=`0.49`\n", " ⚪ 数量无变化 | 当前数量=1件\n", " 颜色=灰色 | 占用=50%\n", " 置信度=0.99 | Generate=12.41s | Total=17.48s | 0.09×\n", "[segment 2] 3.0/14.1s | GPU=9.85GB / reserved=9.87GB / peak=11.26GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**3.0–4.5s** Gate=`0.12` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 2,\n", " \"delta\": 1,\n", " \"colors\": [\"黑色\", \"白色\"],\n", " \"occupancy\": 75,\n", " \"confidence\": 0.95\n", "}\n", "[debug] 模型绝对数=2 → 最终 count=2, delta=1\n", "**3.0–4.5s** Gate=`0.12`\n", " 🟢 放入 1 件 | 当前数量=2件\n", " 颜色=黑色、白色 | 占用=75%\n", " 置信度=0.95 | Generate=16.58s | Total=22.44s | 0.07×\n", "[segment 3] 4.5/14.1s | GPU=9.85GB / reserved=9.87GB / peak=12.07GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**4.5–6.0s** Gate=`0.36` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 3,\n", " \"delta\": 1,\n", " \"colors\": [\"黑色\", \"白色\", \"棕色\"],\n", " \"occupancy\": 80,\n", " \"confidence\": 0.92\n", "}\n", "[debug] 模型绝对数=3 → 最终 count=3, delta=1\n", "**4.5–6.0s** Gate=`0.36`\n", " 🟢 放入 1 件 | 当前数量=3件\n", " 颜色=黑色、白色、棕色 | 占用=80%\n", " 置信度=0.92 | Generate=12.51s | Total=17.70s | 0.08×\n", "[segment 4] 6.0/14.1s | GPU=9.85GB / reserved=9.87GB / peak=11.25GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**6.0–7.5s** Gate=`0.17` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 3,\n", " \"delta\": 0,\n", " \"colors\": [\"黑色\", \"白色\", \"棕色\"],\n", " \"occupancy\": 80,\n", " \"confidence\": 0.98\n", "}\n", "[debug] 模型绝对数=3 → 最终 count=3, delta=0\n", "**6.0–7.5s** Gate=`0.17`\n", " ⚪ 数量无变化 | 当前数量=3件\n", " 颜色=黑色、白色、棕色 | 占用=80%\n", " 置信度=0.98 | Generate=12.66s | Total=18.51s | 0.08×\n", "[segment 5] 7.5/14.1s | GPU=9.85GB / reserved=9.87GB / peak=11.26GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**7.5–9.0s** Gate=`0.26` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 3,\n", " \"delta\": 0,\n", " \"colors\": [\"黑色\", \"白色\", \"棕色\"],\n", " \"occupancy\": 80,\n", " \"confidence\": 0.98\n", "}\n", "[debug] 模型绝对数=3 → 最终 count=3, delta=0\n", "**7.5–9.0s** Gate=`0.26`\n", " ⚪ 数量无变化 | 当前数量=3件\n", " 颜色=黑色、白色、棕色 | 占用=80%\n", " 置信度=0.98 | Generate=12.74s | Total=17.84s | 0.08×\n", "[segment 6] 9.0/14.1s | GPU=9.85GB / reserved=9.87GB / peak=11.26GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**9.0–10.5s** Gate=`0.30` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 3,\n", " \"delta\": 0,\n", " \"colors\": [\"黑色\", \"白色\", \"棕色\"],\n", " \"occupancy\": 80,\n", " \"confidence\": 0.98\n", "}\n", "[debug] 模型绝对数=3 → 最终 count=3, delta=0\n", "**9.0–10.5s** Gate=`0.30`\n", " ⚪ 数量无变化 | 当前数量=3件\n", " 颜色=黑色、白色、棕色 | 占用=80%\n", " 置信度=0.98 | Generate=16.91s | Total=23.39s | 0.06×\n", "[segment 7] 10.5/14.1s | GPU=9.85GB / reserved=9.87GB / peak=12.10GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**10.5–12.0s** Gate=`0.47` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 4,\n", " \"delta\": 1,\n", " \"colors\": [\"黑色\", \"白色\", \"灰色\", \"棕色\"],\n", " \"occupancy\": 85,\n", " \"confidence\": 0.98\n", "}\n", "[debug] 模型绝对数=4 → 最终 count=4, delta=1\n", "**10.5–12.0s** Gate=`0.47`\n", " 🟢 放入 1 件 | 当前数量=4件\n", " 颜色=黑色、白色、灰色、棕色 | 占用=85%\n", " 置信度=0.98 | Generate=17.07s | Total=23.17s | 0.06×\n", "[segment 8] 12.0/14.1s | GPU=9.85GB / reserved=9.87GB / peak=12.08GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**12.0–13.5s** Gate=`0.04` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 3,\n", " \"delta\": -1,\n", " \"colors\": [\"黑色\", \"灰色\"],\n", " \"occupancy\": 70,\n", " \"confidence\": 0.92\n", "}\n", "[debug] 模型绝对数=3 → 最终 count=3, delta=-1\n", "**12.0–13.5s** Gate=`0.04`\n", " 🔴 取出 1 件 | 当前数量=3件\n", " 颜色=黑色、灰色 | 占用=70%\n", " 置信度=0.92 | Generate=16.48s | Total=22.61s | 0.07×\n", "[segment 9] 13.5/14.1s | GPU=9.85GB / reserved=9.87GB / peak=12.07GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "**13.5–14.1s** Gate=`0.03` → 🧠 识别中...\n", "[raw LLM] {\n", " \"current_count\": 3,\n", " \"delta\": 0,\n", " \"colors\": [\"黑色\", \"灰色\"],\n", " \"occupancy\": 70,\n", " \"confidence\": 0.9\n", "}\n", "[debug] 模型绝对数=3 → 最终 count=3, delta=0\n", "**13.5–14.1s** Gate=`0.03`\n", " ⚪ 数量无变化 | 当前数量=3件\n", " 颜色=黑色、灰色 | 占用=70%\n", " 置信度=0.90 | Generate=7.88s | Total=10.73s | 0.06×\n", "[segment 10] 14.1/14.1s | GPU=9.84GB / reserved=9.87GB / peak=10.39GB\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "[transformers] Setting `pad_token_id` to `eos_token_id`:151645 for open-end generation.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "🧺 最终衣物状态\n", "============================================================\n", "【最终数量】 3 件\n", "【最终颜色】 黑色、灰色\n", "【空间占用】 70%\n", "【状态】 较多\n", "============================================================\n", "\n", "🧠 最终模型校验：\n", "最终状态：中等\n", "\n", "【最终数量】\n", "3 件\n", "\n", "【最终颜色】\n", "黑色, 灰色\n", "\n", "【空间占用】\n", "70%\n", "\n", "【状态】\n", "中等\n", "\n", "✅ 完成，共分析 10 个片段\n" ] } ], "source": [ "# ============================================================\n", "# 代码块 3：参数 + Prompt（绝对计数优化版）\n", "# ============================================================\n", "\n", "DEFAULT_VIDEO = \"/content/test02.mp4\"\n", "DEFAULT_SEGMENT_SEC = 1.5 # ★ 缩短到1.5秒，捕捉短暂动作\n", "DEFAULT_MAX_SEGMENTS = None\n", "DEFAULT_GATE_THRESHOLD = 0.35\n", "DEFAULT_MAX_NEW_TOKENS = 180\n", "DEFAULT_FINAL_MAX_NEW_TOKENS = 128\n", "DEFAULT_CODEC_MAX_PIXELS = 160000 # ★ 再提高分辨率\n", "DEFAULT_CODEC_TARGET_CANVAS = 20\n", "DEFAULT_SIMULATE_REALTIME = False\n", "DEFAULT_MAX_CHANGE_PER_SEGMENT = 6\n", "DEFAULT_ALWAYS_RUN_LLM = True\n", "DEFAULT_SHOW_RAW = True\n", "\n", "PROMPT_TEMPLATE = \"\"\"\n", "你是一个高精度的洗衣机滚筒衣物检测专家。\n", "\n", "【任务】\n", "仔细观察当前视频片段，完成以下判断：\n", "1. 当前滚筒内**总共能看到几件独立的衣物**？（即使折叠、有花纹、部分遮挡也要分别计数）\n", "2. 这些衣物的主要颜色是什么？\n", "3. 衣物大约占滚筒可见空间的百分比？\n", "4. 与上一时刻相比，是增加了还是减少了？\n", "\n", "【特别注意】\n", "- 手伸进滚筒拿衣服、把衣服往外拉，都算“取出”。\n", "- 把衣服推进滚筒、松手放下，都算“放入”。\n", "- 一件有花纹或格纹的衣服只算1件，不要把花纹当成多件。\n", "- 白色印花T恤、灰色长袖、棕色衣物、黑白格纹衬衫、浅灰花纹布都是不同的独立衣物。\n", "- 优先报告你看到的绝对数量（current_count），而不是只报告变化。\n", "\n", "【上一时刻状态】\n", "数量：{previous_count}\n", "颜色：{previous_colors}\n", "占用：{previous_occupancy}%\n", "\n", "【输出要求（只输出纯JSON）】\n", "{{\n", " \"current_count\": 当前滚筒内可见的衣物总件数（整数）,\n", " \"delta\": 当前数量减去上一时刻数量,\n", " \"colors\": [\"颜色1\", \"颜色2\", ...],\n", " \"occupancy\": 0-100的整数,\n", " \"confidence\": 0到1的小数\n", "}}\n", "\"\"\".strip()\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", 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