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Add reparameterization for yolov7-tiny #2016

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Add reparameterization for yolov7-tiny #2016

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58 changes: 56 additions & 2 deletions tools/reparameterization.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -500,13 +500,67 @@
"torch.save(ckpt, 'cfg/deploy/yolov7-e6e.pt')\n"
]
},
{
"cell_type": "markdown",
"id": "1a1cbfa1",
"metadata": {},
"source": [
"## YOLOv7-tiny reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a62625",
"id": "aebfde10",
"metadata": {},
"outputs": [],
"source": []
"source": [
"# import\n",
"from copy import deepcopy\n",
"from models.yolo import Model\n",
"import torch\n",
"from utils.torch_utils import select_device, is_parallel\n",
"import yaml\n",
"\n",
"device = select_device('0', batch_size=1)\n",
"# model trained by cfg/training/*.yaml\n",
"ckpt = torch.load('cfg/training/yolov7-tiny_training.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7-tiny.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7-tiny.yaml') as f:\n",
" yml = yaml.load(f, Loader=yaml.SafeLoader)\n",
"anchors = len(yml['anchors'][0]) // 2\n",
"\n",
"# copy intersect weights\n",
"state_dict = ckpt['model'].float().state_dict()\n",
"exclude = []\n",
"intersect_state_dict = {k: v for k, v in state_dict.items() if k in model.state_dict() and not any(x in k for x in exclude) and v.shape == model.state_dict()[k].shape}\n",
"model.load_state_dict(intersect_state_dict, strict=False)\n",
"model.names = ckpt['model'].names\n",
"model.nc = ckpt['model'].nc\n",
"\n",
"# reparametrized YOLOR\n",
"for i in range((model.nc+5)*anchors):\n",
" model.state_dict()['model.77.m.0.weight'].data[i, :, :, :] *= state_dict['model.77.im.0.implicit'].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.77.m.1.weight'].data[i, :, :, :] *= state_dict['model.77.im.1.implicit'].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.77.m.2.weight'].data[i, :, :, :] *= state_dict['model.77.im.2.implicit'].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.77.m.0.bias'].data += state_dict['model.77.m.0.weight'].mul(state_dict['model.77.ia.0.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.77.m.1.bias'].data += state_dict['model.77.m.1.weight'].mul(state_dict['model.77.ia.1.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.77.m.2.bias'].data += state_dict['model.77.m.2.weight'].mul(state_dict['model.77.ia.2.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.77.m.0.bias'].data *= state_dict['model.77.im.0.implicit'].data.squeeze()\n",
"model.state_dict()['model.77.m.1.bias'].data *= state_dict['model.77.im.1.implicit'].data.squeeze()\n",
"model.state_dict()['model.77.m.2.bias'].data *= state_dict['model.77.im.2.implicit'].data.squeeze()\n",
"\n",
"# model to be saved\n",
"ckpt = {'model': deepcopy(model.module if is_parallel(model) else model).half(),\n",
" 'optimizer': None,\n",
" 'training_results': None,\n",
" 'epoch': -1}\n",
"\n",
"# save reparameterized model\n",
"torch.save(ckpt, 'cfg/deploy/yolov7-tiny.pt')"
]
}
],
"metadata": {
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