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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "4beac401",
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"from models.yolo import Model"
]
},
{
"cell_type": "markdown",
"id": "d1a8399f",
"metadata": {},
"source": [
"## Convert YOLOv9-S"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c7a40f10",
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cpu\")\n",
"cfg = \"./models/detect/gelan-s.yaml\"\n",
"model = Model(cfg, ch=3, nc=80, anchors=3)\n",
"#model = model.half()\n",
"model = model.to(device)\n",
"_ = model.eval()\n",
"ckpt = torch.load('./yolov9-s.pt', map_location='cpu')\n",
"model.names = ckpt['model'].names\n",
"model.nc = ckpt['model'].nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b046bb2",
"metadata": {},
"outputs": [],
"source": [
"idx = 0\n",
"for k, v in model.state_dict().items():\n",
" if \"model.{}.\".format(idx) in k:\n",
" if idx < 22:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" else:\n",
" while True:\n",
" idx += 1\n",
" if \"model.{}.\".format(idx) in k:\n",
" break\n",
" if idx < 22:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
"_ = model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "07eb0cde",
"metadata": {},
"outputs": [],
"source": [
"m_ckpt = {'model': model.half(),\n",
" 'optimizer': None,\n",
" 'best_fitness': None,\n",
" 'ema': None,\n",
" 'updates': None,\n",
" 'opt': None,\n",
" 'git': None,\n",
" 'date': None,\n",
" 'epoch': -1}\n",
"torch.save(m_ckpt, \"./yolov9-s-converted.pt\")"
]
},
{
"cell_type": "markdown",
"id": "ba87d10f",
"metadata": {},
"source": [
"## Convert YOLOv9-M"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cc41b027",
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cpu\")\n",
"cfg = \"./models/detect/gelan-m.yaml\"\n",
"model = Model(cfg, ch=3, nc=80, anchors=3)\n",
"#model = model.half()\n",
"model = model.to(device)\n",
"_ = model.eval()\n",
"ckpt = torch.load('./yolov9-m.pt', map_location='cpu')\n",
"model.names = ckpt['model'].names\n",
"model.nc = ckpt['model'].nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cf7c3978",
"metadata": {},
"outputs": [],
"source": [
"idx = 0\n",
"for k, v in model.state_dict().items():\n",
" if \"model.{}.\".format(idx) in k:\n",
" if idx < 22:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx+1))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" else:\n",
" while True:\n",
" idx += 1\n",
" if \"model.{}.\".format(idx) in k:\n",
" break\n",
" if idx < 22:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx+1))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
"_ = model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "00a92a45",
"metadata": {},
"outputs": [],
"source": [
"m_ckpt = {'model': model.half(),\n",
" 'optimizer': None,\n",
" 'best_fitness': None,\n",
" 'ema': None,\n",
" 'updates': None,\n",
" 'opt': None,\n",
" 'git': None,\n",
" 'date': None,\n",
" 'epoch': -1}\n",
"torch.save(m_ckpt, \"./yolov9-m-converted.pt\")"
]
},
{
"cell_type": "markdown",
"id": "8680f822",
"metadata": {},
"source": [
"## Convert YOLOv9-C"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "59f0198d",
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cpu\")\n",
"cfg = \"./models/detect/gelan-c.yaml\"\n",
"model = Model(cfg, ch=3, nc=80, anchors=3)\n",
"#model = model.half()\n",
"model = model.to(device)\n",
"_ = model.eval()\n",
"ckpt = torch.load('./yolov9-c.pt', map_location='cpu')\n",
"model.names = ckpt['model'].names\n",
"model.nc = ckpt['model'].nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2de7e1be",
"metadata": {},
"outputs": [],
"source": [
"idx = 0\n",
"for k, v in model.state_dict().items():\n",
" if \"model.{}.\".format(idx) in k:\n",
" if idx < 22:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx+1))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" else:\n",
" while True:\n",
" idx += 1\n",
" if \"model.{}.\".format(idx) in k:\n",
" break\n",
" if idx < 22:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx+1))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+16))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
"_ = model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "960796e3",
"metadata": {},
"outputs": [],
"source": [
"m_ckpt = {'model': model.half(),\n",
" 'optimizer': None,\n",
" 'best_fitness': None,\n",
" 'ema': None,\n",
" 'updates': None,\n",
" 'opt': None,\n",
" 'git': None,\n",
" 'date': None,\n",
" 'epoch': -1}\n",
"torch.save(m_ckpt, \"./yolov9-c-converted.pt\")"
]
},
{
"cell_type": "markdown",
"id": "47c6e6ae",
"metadata": {},
"source": [
"## Convert YOLOv9-E"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "801a1b7c",
"metadata": {},
"outputs": [],
"source": [
"device = torch.device(\"cpu\")\n",
"cfg = \"./models/detect/gelan-e.yaml\"\n",
"model = Model(cfg, ch=3, nc=80, anchors=3)\n",
"#model = model.half()\n",
"model = model.to(device)\n",
"_ = model.eval()\n",
"ckpt = torch.load('./yolov9-e.pt', map_location='cpu')\n",
"model.names = ckpt['model'].names\n",
"model.nc = ckpt['model'].nc"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a2ef4fe6",
"metadata": {},
"outputs": [],
"source": [
"idx = 0\n",
"for k, v in model.state_dict().items():\n",
" if \"model.{}.\".format(idx) in k:\n",
" if idx < 29:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif idx < 42:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" else:\n",
" while True:\n",
" idx += 1\n",
" if \"model.{}.\".format(idx) in k:\n",
" break\n",
" if idx < 29:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif idx < 42:\n",
" kr = k.replace(\"model.{}.\".format(idx), \"model.{}.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv2.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv2.\".format(idx), \"model.{}.cv4.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.cv3.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.cv3.\".format(idx), \"model.{}.cv5.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
" elif \"model.{}.dfl.\".format(idx) in k:\n",
" kr = k.replace(\"model.{}.dfl.\".format(idx), \"model.{}.dfl2.\".format(idx+7))\n",
" model.state_dict()[k] -= model.state_dict()[k]\n",
" model.state_dict()[k] += ckpt['model'].state_dict()[kr]\n",
" print(k, \"perfectly matched!!\")\n",
"_ = model.eval()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "27bc1869",
"metadata": {},
"outputs": [],
"source": [
"m_ckpt = {'model': model.half(),\n",
" 'optimizer': None,\n",
" 'best_fitness': None,\n",
" 'ema': None,\n",
" 'updates': None,\n",
" 'opt': None,\n",
" 'git': None,\n",
" 'date': None,\n",
" 'epoch': -1}\n",
"torch.save(m_ckpt, \"./yolov9-e-converted.pt\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|