tudov,
created on Sunday, 26 May 2024, 14:39:44 (1716734384),
received on Sunday, 26 May 2024, 14:41:22 (1716734482)
Author identity: tudov <tudovvlad7777@gmail.com>
2da921cc60540ababc3f1432e7f6f6502aaa4cd6
main.ipynb
@@ -19,170 +19,234 @@
},
{
"cell_type": "code",
"execution_count": 2,
"id": "20c8a661575d1c6f",
"metadata": {
"ExecuteTime": {
"start_time": "2024-05-25T09:03:54.172573Z"
}
},
"source": [
"%pip install torchvision\n",
"%pip install torch torchvision\n",
"%pip install --upgrade ultralytics\n",
"%pip install pillow_heif\n",
"%pip install plotly\n",
"%pip install Pillow\n",
"%pip install tqdm\n",
"%pip install ruamel.yaml"
],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: torchvision in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (0.18.0)\r\n",
"Requirement already satisfied: numpy in /home/vlad/.local/lib/python3.10/site-packages (from torchvision) (1.23.5)\r\n",
"Requirement already satisfied: torch==2.3.0 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torchvision) (2.3.0)\r\n",
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /home/vlad/.local/lib/python3.10/site-packages (from torchvision) (9.5.0)\r\n",
"Requirement already satisfied: filelock in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (3.12.2)\r\n",
"Requirement already satisfied: typing-extensions>=4.8.0 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (4.11.0)\r\n",
"Requirement already satisfied: sympy in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (1.12)\r\n",
"Requirement already satisfied: networkx in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (3.1)\r\n",
"Requirement already satisfied: jinja2 in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (3.1.2)\r\n",
"Requirement already satisfied: fsspec in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (2024.5.0)\r\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (12.1.105)\r\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (12.1.105)\r\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (12.1.105)\r\n",
"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (8.9.2.26)\r\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/vlad/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (12.1.3.1)\r\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (11.0.2.54)\r\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (10.3.2.106)\r\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (11.4.5.107)\r\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (12.1.0.106)\r\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (2.20.5)\r\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (12.1.105)\r\n",
"Requirement already satisfied: triton==2.3.0 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (2.3.0)\r\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from nvidia-cusolver-cu12==11.4.5.107->torch==2.3.0->torchvision) (12.4.127)\r\n",
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"\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
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"\u001b[?25hCollecting nvidia-cuda-nvrtc-cu12==12.1.105\n",
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"\u001b[?25hRequirement already satisfied: jinja2 in /home/tudov/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (3.1.4)\n",
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" Downloading fsspec-2024.5.0-py3-none-any.whl (316 kB)\n",
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"\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26\n",
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"\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105\n",
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"\u001b[?25hCollecting triton==2.3.0\n",
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"\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105\n",
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"\u001b[?25hRequirement already satisfied: typing-extensions>=4.8.0 in /home/tudov/.local/lib/python3.10/site-packages (from torch==2.3.0->torchvision) (4.12.0)\n",
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"Installing collected packages: triton, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, networkx, fsspec, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, torchvision\n",
"\u001b[33m WARNING: The scripts convert-caffe2-to-onnx, convert-onnx-to-caffe2 and torchrun are installed in '/home/tudov/.local/bin' which is not on PATH.\n",
" Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n",
"\u001b[0mSuccessfully installed fsspec-2024.5.0 networkx-3.3 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 torch-2.3.0 torchvision-0.18.0 triton-2.3.0\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: torch in /home/tudov/.local/lib/python3.10/site-packages (2.3.0)\n",
"Requirement already satisfied: torchvision in /home/tudov/.local/lib/python3.10/site-packages (0.18.0)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /home/tudov/.local/lib/python3.10/site-packages (from torch) (11.4.5.107)\n",
"Requirement already satisfied: triton==2.3.0 in /home/tudov/.local/lib/python3.10/site-packages (from torch) (2.3.0)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /home/tudov/.local/lib/python3.10/site-packages (from torch) (12.1.3.1)\n",
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"\u001b[33m WARNING: The script cpuinfo is installed in '/home/tudov/.local/bin' which is not on PATH.\n",
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"\u001b[0mSuccessfully installed numpy-1.26.4 opencv-python-4.9.0.80 pandas-2.2.2 py-cpuinfo-9.0.0 seaborn-0.13.2 thop-0.1.1.post2209072238 tqdm-4.66.4 tzdata-2024.1 ultralytics-8.2.22\n",
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"\u001b[?25hInstalling collected packages: pillow, pillow_heif\n",
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"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
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"\u001b[?25hCollecting tenacity>=6.2.0\n",
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"Requirement already satisfied: packaging in /home/tudov/.local/lib/python3.10/site-packages (from plotly) (24.0)\n",
"Installing collected packages: tenacity, plotly\n",
"Successfully installed plotly-5.22.0 tenacity-8.3.0\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Requirement already satisfied: ultralytics in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (8.2.18)\r\n",
"Collecting ultralytics\r\n",
" Obtaining dependency information for ultralytics from https://files.pythonhosted.org/packages/ee/6d/2ed4b01f55e29fffdf0d855d4af2c42d23f9db9ad085cde328c9b8266e25/ultralytics-8.2.21-py3-none-any.whl.metadata\r\n",
" Downloading ultralytics-8.2.21-py3-none-any.whl.metadata (40 kB)\r\n",
"\u001B[2K \u001B[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m40.7/40.7 kB\u001B[0m \u001B[31m1.4 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m\r\n",
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"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from torch>=1.8.0->ultralytics) (12.1.105)\r\n",
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"Downloading ultralytics-8.2.21-py3-none-any.whl (777 kB)\r\n",
"\u001B[2K \u001B[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001B[0m \u001B[32m777.9/777.9 kB\u001B[0m \u001B[31m5.5 MB/s\u001B[0m eta \u001B[36m0:00:00\u001B[0m[36m0:00:01\u001B[0m[36m0:00:01\u001B[0m:01\u001B[0m\r\n",
"\u001B[?25hInstalling collected packages: ultralytics\r\n",
" Attempting uninstall: ultralytics\r\n",
" Found existing installation: ultralytics 8.2.18\r\n",
" Uninstalling ultralytics-8.2.18:\r\n",
" Successfully uninstalled ultralytics-8.2.18\r\n",
"Successfully installed ultralytics-8.2.21\r\n",
"\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: Pillow in /home/tudov/.local/lib/python3.10/site-packages (10.3.0)\n",
"Note: you may need to restart the kernel to use updated packages.\n",
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"Requirement already satisfied: pillow>=9.5.0 in /home/vlad/.local/lib/python3.10/site-packages (from pillow_heif) (9.5.0)\r\n",
"^C\r\n",
"\u001B[31mERROR: Operation cancelled by user\u001B[0m\u001B[31m\r\n",
"\u001B[0m\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m A new release of pip is available: \u001B[0m\u001B[31;49m23.2.1\u001B[0m\u001B[39;49m -> \u001B[0m\u001B[32;49m24.0\u001B[0m\r\n",
"\u001B[1m[\u001B[0m\u001B[34;49mnotice\u001B[0m\u001B[1;39;49m]\u001B[0m\u001B[39;49m To update, run: \u001B[0m\u001B[32;49mpip install --upgrade pip\u001B[0m\r\n",
"Defaulting to user installation because normal site-packages is not writeable\n",
"Requirement already satisfied: tqdm in /home/tudov/.local/lib/python3.10/site-packages (4.66.4)\n",
"Note: you may need to restart the kernel to use updated packages.\n",
"Requirement already satisfied: plotly in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (5.22.0)\r\n",
"Requirement already satisfied: tenacity>=6.2.0 in /home/vlad/miniconda3/envs/tf/lib/python3.10/site-packages (from plotly) (8.3.0)\r\n",
"Requirement already satisfied: packaging in /home/vlad/.local/lib/python3.10/site-packages (from plotly) (23.1)\r\n"
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting ruamel.yaml\n",
" Downloading ruamel.yaml-0.18.6-py3-none-any.whl (117 kB)\n",
"\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m117.8/117.8 KB\u001b[0m \u001b[31m773.9 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hCollecting ruamel.yaml.clib>=0.2.7\n",
" Downloading ruamel.yaml.clib-0.2.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.manylinux_2_24_x86_64.whl (526 kB)\n",
"\u001b[2K \u001b[38;2;114;156;31m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m526.7/526.7 KB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hInstalling collected packages: ruamel.yaml.clib, ruamel.yaml\n",
"Successfully installed ruamel.yaml-0.18.6 ruamel.yaml.clib-0.2.8\n",
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"execution_count": null
"source": [
"%pip install torchvision\n",
"%pip install torch torchvision\n",
"%pip install --upgrade ultralytics\n",
"%pip install pillow_heif\n",
"%pip install plotly\n",
"%pip install Pillow\n",
"%pip install tqdm\n",
"%pip install ruamel.yaml"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "45105e3c6e3463b4",
"metadata": {
"ExecuteTime": {
@@ -190,6 +254,7 @@
"start_time": "2024-05-25T09:04:16.444461Z"
}
},
"outputs": [],
"source": [
"import os # for file operations\n",
"import json # for loading the annotations file\n",
@@ -201,9 +266,7 @@
"from pillow_heif import register_heif_opener\n",
"\n",
"os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\""
],
"outputs": [],
"execution_count": 2
]
},
{
"cell_type": "markdown",
@@ -215,6 +278,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"id": "3af63db86b518835",
"metadata": {
"ExecuteTime": {
@@ -222,9 +286,8 @@
"start_time": "2024-05-25T09:04:18.245523Z"
}
},
"outputs": [],
"source": [
"# Correctly set CWD\n",
"os.chdir(\"/home/vlad/Desktop/litewaste\")\n",
"DATA_PATH = \"./data/\" # must contain multiple subdirectories - one for each class\n",
"ANNOTATIONS_PATH = \"./via-annotations.json\" # relative to the notebook\n",
"YOLO_CONFIG_PATH = \"yolo.yaml\"\n",
@@ -234,9 +297,7 @@
"CLASS_NAMES = sorted(list(set([os.path.basename(f).split(\",\")[0] for f in os.scandir(DATA_PATH) if f.is_dir()]))) # automatically generated\n",
"ALL_CLASS_NAMES = sorted(list(set([os.path.basename(f) for f in os.scandir(DATA_PATH) if f.is_dir()])))\n",
"CLASS_COUNT = len(CLASS_NAMES)"
],
"outputs": [],
"execution_count": 3
]
},
{
"cell_type": "markdown",
@@ -248,6 +309,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"id": "bfdbbe283d63cffc",
"metadata": {
"ExecuteTime": {
@@ -255,6 +317,24 @@
"start_time": "2024-05-25T09:29:24.848381Z"
}
},
"outputs": [
{
"data": {
"text/html": [
"<iframe\n",
" scrolling=\"no\"\n",
" width=\"2420px\"\n",
" height=\"1220\"\n",
" src=\"iframe_figures/figure_6.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
"></iframe>\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import collections\n",
@@ -324,26 +404,7 @@
")\n",
"\n",
"fig.show()\n"
],
"outputs": [
{
"data": {
"text/html": [
"<iframe\n",
" scrolling=\"no\"\n",
" width=\"2420px\"\n",
" height=\"1220\"\n",
" src=\"iframe_figures/figure_6.html\"\n",
" frameborder=\"0\"\n",
" allowfullscreen\n",
"></iframe>\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"execution_count": 6
]
},
{
"cell_type": "markdown",
@@ -366,8 +427,10 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "cdc4e80fe5566372",
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"import os\n",
@@ -552,9 +615,7 @@
"\n",
"OUTPUT_PATH = \"yolo_data/\"\n",
"class_mapping, annotated_images = save_photos(ANNOTATIONS_PATH, DATA_PATH, OUTPUT_PATH)"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@@ -566,18 +627,20 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "563b4ed069096a96",
"metadata": {},
"outputs": [],
"source": [
"print(f\"{annotated_images} images processed.\")"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "73a63f9dc63594a1",
"metadata": {},
"outputs": [],
"source": [
"from ruamel.yaml import YAML\n",
"print(len(class_mapping), class_mapping)\n",
@@ -588,9 +651,7 @@
" data = yaml.load(file)\n",
" data[\"nc\"] = len(class_mapping)\n",
" data[\"names\"] = class_mapping"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@@ -602,27 +663,27 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "65fa97a1ae1c78f2",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"model = YOLO(\"yolov8n.pt\")\n",
"\n",
"results = model.train(data=YOLO_CONFIG_PATH, epochs=72, verbose=True, imgsz=640)"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "138880b6a127671a",
"metadata": {},
"outputs": [],
"source": [
"torch.save(model.state_dict(), \"model.pt\")"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@@ -634,13 +695,16 @@
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "78e0a2a59ce4f120",
"metadata": {
"ExecuteTime": {
"end_time": "2024-05-25T09:04:30.920763Z",
"start_time": "2024-05-25T09:04:30.915195Z"
}
},
"cell_type": "code",
"outputs": [],
"source": [
"# Get the class mapping\n",
"from ruamel.yaml import YAML\n",
@@ -650,10 +714,7 @@
"with open(\"yolo.yaml\", \"r\") as file:\n",
" data = yaml.load(file)\n",
" class_mapping = data[\"names\"]"
],
"id": "78e0a2a59ce4f120",
"outputs": [],
"execution_count": 4
]
},
{
"cell_type": "markdown",
@@ -665,6 +726,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cf66bcb81e1b29f",
"metadata": {
"ExecuteTime": {
@@ -672,12 +734,31 @@
"start_time": "2024-05-25T09:04:33.280298Z"
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WARNING ⚠️ 'source' is missing. Using 'source=/home/tudov/.local/lib/python3.10/site-packages/ultralytics/assets'.\n"
]
},
{
"ename": "AttributeError",
"evalue": "'NoneType' object has no attribute '__array_interface__'",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 48\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(box\u001b[38;5;241m.\u001b[39mcls[\u001b[38;5;241m0\u001b[39m])\n\u001b[1;32m 47\u001b[0m \u001b[38;5;66;03m# Object label\u001b[39;00m\n\u001b[0;32m---> 48\u001b[0m img_pil \u001b[38;5;241m=\u001b[39m \u001b[43mImage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfromarray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 49\u001b[0m draw \u001b[38;5;241m=\u001b[39m ImageDraw\u001b[38;5;241m.\u001b[39mDraw(img_pil)\n\u001b[1;32m 50\u001b[0m draw\u001b[38;5;241m.\u001b[39mtext((x1, y1),\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m[\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfidence\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m%] \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mclass_mapping[\u001b[38;5;28mcls\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 52\u001b[0m font\u001b[38;5;241m=\u001b[39mfont,\n\u001b[1;32m 53\u001b[0m fill\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m255\u001b[39m, \u001b[38;5;241m255\u001b[39m),\n\u001b[1;32m 54\u001b[0m stroke_width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[1;32m 55\u001b[0m stroke_fill\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m0\u001b[39m))\n",
"File \u001b[0;32m~/.local/lib/python3.10/site-packages/PIL/Image.py:3119\u001b[0m, in \u001b[0;36mfromarray\u001b[0;34m(obj, mode)\u001b[0m\n\u001b[1;32m 3072\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfromarray\u001b[39m(obj, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m 3073\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 3074\u001b[0m \u001b[38;5;124;03m Creates an image memory from an object exporting the array interface\u001b[39;00m\n\u001b[1;32m 3075\u001b[0m \u001b[38;5;124;03m (using the buffer protocol)::\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3117\u001b[0m \u001b[38;5;124;03m .. versionadded:: 1.1.6\u001b[39;00m\n\u001b[1;32m 3118\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 3119\u001b[0m arr \u001b[38;5;241m=\u001b[39m \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m__array_interface__\u001b[49m\n\u001b[1;32m 3120\u001b[0m shape \u001b[38;5;241m=\u001b[39m arr[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 3121\u001b[0m ndim \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(shape)\n",
"\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute '__array_interface__'"
]
}
],
"source": [
"from ultralytics import YOLO\n",
"import os\n",
"\n",
"os.chdir(\"/home/vlad/Desktop/litewaste\")\n",
"\n",
"model = YOLO(\"runs/detect/train23/weights/best.pt\")\n",
"\n",
"import cv2 as cv\n",
@@ -696,7 +777,7 @@
"cap.set(4, 640)\n",
"\n",
"\n",
"font = ImageFont.truetype(\"/home/vlad/.local/share/fonts/Roboto-Medium.ttf\", 20)\n",
"font = ImageFont.truetype(\"/usr/share/fonts/truetype/dejavu/DejaVuSansMono.ttf\", 20)\n",
"\n",
"while True:\n",
" success, img = cap.read()\n",
@@ -706,18 +787,18 @@
" boxes = r.boxes\n",
"\n",
" for box in boxes:\n",
" confidence = round(float(box.conf[0] * 100))\n",
"\n",
" # Skip unsure detections\n",
" if confidence < 0:\n",
" continue\n",
" \n",
" # Get coordinates and draw the bounding box\n",
" x1, y1, x2, y2 = box.xyxy[0]\n",
" x1, y1, x2, y2 = int(x1), int(y1), int(x2), int(y2)\n",
" \n",
" cv.rectangle(img, (x1, y1), (x2, y2), (255, 255, 255), 2)\n",
" cv.rectangle(img, (x1-1, y1-1), (x2+1, y2+1), (136, 150, 0), 2)\n",
"\n",
" confidence = round(float(box.conf[0] * 100))\n",
"\n",
" # Skip unsure detections\n",
" if confidence < 50:\n",
" continue\n",
" \n",
" cls = int(box.cls[0])\n",
"\n",
@@ -740,35 +821,7 @@
"# Stop the webcam\n",
"cap.release()\n",
"cv.destroyAllWindows()"
],
"outputs": [
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
"\u001B[0;31mKeyboardInterrupt\u001B[0m Traceback (most recent call last)",
"Cell \u001B[0;32mIn[5], line 30\u001B[0m\n\u001B[1;32m 27\u001B[0m success, img \u001B[38;5;241m=\u001B[39m cap\u001B[38;5;241m.\u001B[39mread()\n\u001B[1;32m 28\u001B[0m results \u001B[38;5;241m=\u001B[39m model(img, stream\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m, verbose\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m)\n\u001B[0;32m---> 30\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m r \u001B[38;5;129;01min\u001B[39;00m results:\n\u001B[1;32m 31\u001B[0m boxes \u001B[38;5;241m=\u001B[39m r\u001B[38;5;241m.\u001B[39mboxes\n\u001B[1;32m 33\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m box \u001B[38;5;129;01min\u001B[39;00m boxes:\n\u001B[1;32m 34\u001B[0m \u001B[38;5;66;03m# Get coordinates and draw the bounding box\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/utils/_contextlib.py:35\u001B[0m, in \u001B[0;36m_wrap_generator.<locals>.generator_context\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m 32\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 33\u001B[0m \u001B[38;5;66;03m# Issuing `None` to a generator fires it up\u001B[39;00m\n\u001B[1;32m 34\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m ctx_factory():\n\u001B[0;32m---> 35\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[43mgen\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msend\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;28;43;01mNone\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[1;32m 37\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m 38\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 39\u001B[0m \u001B[38;5;66;03m# Forward the response to our caller and get its next request\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/engine/predictor.py:248\u001B[0m, in \u001B[0;36mBasePredictor.stream_inference\u001B[0;34m(self, source, model, *args, **kwargs)\u001B[0m\n\u001B[1;32m 246\u001B[0m \u001B[38;5;66;03m# Inference\u001B[39;00m\n\u001B[1;32m 247\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m profilers[\u001B[38;5;241m1\u001B[39m]:\n\u001B[0;32m--> 248\u001B[0m preds \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43minference\u001B[49m\u001B[43m(\u001B[49m\u001B[43mim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 249\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs\u001B[38;5;241m.\u001B[39membed:\n\u001B[1;32m 250\u001B[0m \u001B[38;5;28;01myield from\u001B[39;00m [preds] \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(preds, torch\u001B[38;5;241m.\u001B[39mTensor) \u001B[38;5;28;01melse\u001B[39;00m preds \u001B[38;5;66;03m# yield embedding tensors\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/engine/predictor.py:142\u001B[0m, in \u001B[0;36mBasePredictor.inference\u001B[0;34m(self, im, *args, **kwargs)\u001B[0m\n\u001B[1;32m 136\u001B[0m \u001B[38;5;250m\u001B[39m\u001B[38;5;124;03m\"\"\"Runs inference on a given image using the specified model and arguments.\"\"\"\u001B[39;00m\n\u001B[1;32m 137\u001B[0m visualize \u001B[38;5;241m=\u001B[39m (\n\u001B[1;32m 138\u001B[0m increment_path(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave_dir \u001B[38;5;241m/\u001B[39m Path(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mbatch[\u001B[38;5;241m0\u001B[39m][\u001B[38;5;241m0\u001B[39m])\u001B[38;5;241m.\u001B[39mstem, mkdir\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[1;32m 139\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39margs\u001B[38;5;241m.\u001B[39mvisualize \u001B[38;5;129;01mand\u001B[39;00m (\u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msource_type\u001B[38;5;241m.\u001B[39mtensor)\n\u001B[1;32m 140\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[1;32m 141\u001B[0m )\n\u001B[0;32m--> 142\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maugment\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43maugment\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvisualize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvisualize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43membed\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43margs\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43membed\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[1;32m 1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 1532\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[1;32m 1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[1;32m 1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[1;32m 1539\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[1;32m 1540\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[0;32m-> 1541\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 1544\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/autobackend.py:453\u001B[0m, in \u001B[0;36mAutoBackend.forward\u001B[0;34m(self, im, augment, visualize, embed)\u001B[0m\n\u001B[1;32m 451\u001B[0m \u001B[38;5;66;03m# PyTorch\u001B[39;00m\n\u001B[1;32m 452\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpt \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnn_module:\n\u001B[0;32m--> 453\u001B[0m y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmodel\u001B[49m\u001B[43m(\u001B[49m\u001B[43mim\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43maugment\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43maugment\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvisualize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mvisualize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43membed\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43membed\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 455\u001B[0m \u001B[38;5;66;03m# TorchScript\u001B[39;00m\n\u001B[1;32m 456\u001B[0m \u001B[38;5;28;01melif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mjit:\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[1;32m 1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 1532\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[1;32m 1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[1;32m 1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[1;32m 1539\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[1;32m 1540\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[0;32m-> 1541\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 1544\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/tasks.py:89\u001B[0m, in \u001B[0;36mBaseModel.forward\u001B[0;34m(self, x, *args, **kwargs)\u001B[0m\n\u001B[1;32m 87\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(x, \u001B[38;5;28mdict\u001B[39m): \u001B[38;5;66;03m# for cases of training and validating while training.\u001B[39;00m\n\u001B[1;32m 88\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mloss(x, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m---> 89\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mpredict\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/tasks.py:107\u001B[0m, in \u001B[0;36mBaseModel.predict\u001B[0;34m(self, x, profile, visualize, augment, embed)\u001B[0m\n\u001B[1;32m 105\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m augment:\n\u001B[1;32m 106\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_predict_augment(x)\n\u001B[0;32m--> 107\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_predict_once\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mprofile\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mvisualize\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43membed\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/tasks.py:128\u001B[0m, in \u001B[0;36mBaseModel._predict_once\u001B[0;34m(self, x, profile, visualize, embed)\u001B[0m\n\u001B[1;32m 126\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m profile:\n\u001B[1;32m 127\u001B[0m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_profile_one_layer(m, x, dt)\n\u001B[0;32m--> 128\u001B[0m x \u001B[38;5;241m=\u001B[39m \u001B[43mm\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m \u001B[38;5;66;03m# run\u001B[39;00m\n\u001B[1;32m 129\u001B[0m y\u001B[38;5;241m.\u001B[39mappend(x \u001B[38;5;28;01mif\u001B[39;00m m\u001B[38;5;241m.\u001B[39mi \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msave \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m) \u001B[38;5;66;03m# save output\u001B[39;00m\n\u001B[1;32m 130\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m visualize:\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1532\u001B[0m, in \u001B[0;36mModule._wrapped_call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1530\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_compiled_call_impl(\u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs) \u001B[38;5;66;03m# type: ignore[misc]\u001B[39;00m\n\u001B[1;32m 1531\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m-> 1532\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_call_impl\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/torch/nn/modules/module.py:1541\u001B[0m, in \u001B[0;36mModule._call_impl\u001B[0;34m(self, *args, **kwargs)\u001B[0m\n\u001B[1;32m 1536\u001B[0m \u001B[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001B[39;00m\n\u001B[1;32m 1537\u001B[0m \u001B[38;5;66;03m# this function, and just call forward.\u001B[39;00m\n\u001B[1;32m 1538\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m (\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_forward_pre_hooks\n\u001B[1;32m 1539\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_backward_pre_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_backward_hooks\n\u001B[1;32m 1540\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m _global_forward_hooks \u001B[38;5;129;01mor\u001B[39;00m _global_forward_pre_hooks):\n\u001B[0;32m-> 1541\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mforward_call\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[1;32m 1543\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m 1544\u001B[0m result \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m\n",
"File \u001B[0;32m~/miniconda3/envs/tf/lib/python3.10/site-packages/ultralytics/nn/modules/block.py:231\u001B[0m, in \u001B[0;36mC2f.forward\u001B[0;34m(self, x)\u001B[0m\n\u001B[1;32m 229\u001B[0m y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlist\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcv1(x)\u001B[38;5;241m.\u001B[39mchunk(\u001B[38;5;241m2\u001B[39m, \u001B[38;5;241m1\u001B[39m))\n\u001B[1;32m 230\u001B[0m y\u001B[38;5;241m.\u001B[39mextend(m(y[\u001B[38;5;241m-\u001B[39m\u001B[38;5;241m1\u001B[39m]) \u001B[38;5;28;01mfor\u001B[39;00m m \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mm)\n\u001B[0;32m--> 231\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcv2(\u001B[43mtorch\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mcat\u001B[49m\u001B[43m(\u001B[49m\u001B[43my\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m)\n",
"\u001B[0;31mKeyboardInterrupt\u001B[0m: "
]
}
],
"execution_count": 5
]
},
{
"cell_type": "markdown",
@@ -780,14 +833,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "5c3a7e358cf3efb5",
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"import os\n",
"\n",
"os.chdir(\"/home/vlad/Desktop/litewaste\")\n",
"\n",
"model = YOLO(\"runs/detect/train23/weights/last.pt\")\n",
"\n",
"import cv2 as cv\n",
@@ -847,17 +900,15 @@
" #cv.imshow(\"Photo\", img)\n",
" #cv.waitKey(27) # escape\n",
" #cv.destroyAllWindows()"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9a640c08f07c223f",
"metadata": {},
"source": [],
"outputs": [],
"execution_count": null
"source": []
}
],
"metadata": {
@@ -876,7 +927,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.0"
"version": "3.10.12"
}
},
"nbformat": 4,