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greenhouse/yolov7/tools/reparameterization.ipynb
apoorva 9d13daf490 Add 'yolov7/' from commit '2fdc7f14395f6532ad05fb3e6970150a6a83d290' 
git-subtree-dir: yolov7
git-subtree-mainline: 19a8fee75d3bfbbb870049d5cb18c45036bd0283
git-subtree-split: 2fdc7f14395f6532ad05fb3e6970150a6a83d290
2023-02-21 21:35:10 +05:30

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{
"cells": [
{
"cell_type": "markdown",
"id": "d7cbe5ee",
"metadata": {},
"source": [
"# Reparameterization"
]
},
{
"cell_type": "markdown",
"id": "9725e211",
"metadata": {},
"source": [
"\n",
"### What is Reparameterization ?\n",
"Reparameterization is used to reduce trainable BoF modules into deploy model for fast inference. For example merge BN to conv, merge YOLOR to conv, ..etc\n",
"However, before reparameterization, the model has more parameters and computation cost.reparameterized model (cfg/deploy) used for deployment purpose\n",
"\n",
"\n",
"\n",
"### Steps required for model conversion.\n",
"1.train custom model & you will get your own weight i.e custom_weight.pt / use (pretrained weight which is available i.e yolov7_traing.pt)\n",
"\n",
"2.Converting this weight using Reparameterization method.\n",
"\n",
"3.Trained model (cfg/training) and reparameterized model (cfg/deploy) will get same prediction results.\n",
"However, before reparameterization, the model has more parameters and computation cost.\n",
"\n",
"4.Convert reparameterized weight into onnx & tensorrt\n",
"For faster inference & deployment purpose."
]
},
{
"cell_type": "markdown",
"id": "13393b70",
"metadata": {},
"source": [
"## YOLOv7 reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf53becf",
"metadata": {},
"outputs": [],
"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_training.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7.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.105.m.0.weight'].data[i, :, :, :] *= state_dict['model.105.im.0.implicit'].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.105.m.1.weight'].data[i, :, :, :] *= state_dict['model.105.im.1.implicit'].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.105.m.2.weight'].data[i, :, :, :] *= state_dict['model.105.im.2.implicit'].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.105.m.0.bias'].data += state_dict['model.105.m.0.weight'].mul(state_dict['model.105.ia.0.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.105.m.1.bias'].data += state_dict['model.105.m.1.weight'].mul(state_dict['model.105.ia.1.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.105.m.2.bias'].data += state_dict['model.105.m.2.weight'].mul(state_dict['model.105.ia.2.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.105.m.0.bias'].data *= state_dict['model.105.im.0.implicit'].data.squeeze()\n",
"model.state_dict()['model.105.m.1.bias'].data *= state_dict['model.105.im.1.implicit'].data.squeeze()\n",
"model.state_dict()['model.105.m.2.bias'].data *= state_dict['model.105.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.pt')\n"
]
},
{
"cell_type": "markdown",
"id": "5b396a53",
"metadata": {},
"source": [
"## YOLOv7x reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9d54d17f",
"metadata": {},
"outputs": [],
"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/yolov7x_trainig.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7x.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7x.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.121.m.0.weight'].data[i, :, :, :] *= state_dict['model.121.im.0.implicit'].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.121.m.1.weight'].data[i, :, :, :] *= state_dict['model.121.im.1.implicit'].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.121.m.2.weight'].data[i, :, :, :] *= state_dict['model.121.im.2.implicit'].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.121.m.0.bias'].data += state_dict['model.121.m.0.weight'].mul(state_dict['model.121.ia.0.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.121.m.1.bias'].data += state_dict['model.121.m.1.weight'].mul(state_dict['model.121.ia.1.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.121.m.2.bias'].data += state_dict['model.121.m.2.weight'].mul(state_dict['model.121.ia.2.implicit']).sum(1).squeeze()\n",
"model.state_dict()['model.121.m.0.bias'].data *= state_dict['model.121.im.0.implicit'].data.squeeze()\n",
"model.state_dict()['model.121.m.1.bias'].data *= state_dict['model.121.im.1.implicit'].data.squeeze()\n",
"model.state_dict()['model.121.m.2.bias'].data *= state_dict['model.121.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/yolov7x.pt')\n"
]
},
{
"cell_type": "markdown",
"id": "11a9108e",
"metadata": {},
"source": [
"## YOLOv7-W6 reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d032c629",
"metadata": {},
"outputs": [],
"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-w6_trainig.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7-w6.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7-w6.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",
"idx = 118\n",
"idx2 = 122\n",
"\n",
"# copy weights of lead head\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data -= model.state_dict()['model.{}.m.0.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data -= model.state_dict()['model.{}.m.1.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data -= model.state_dict()['model.{}.m.2.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data -= model.state_dict()['model.{}.m.3.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data -= model.state_dict()['model.{}.m.0.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data -= model.state_dict()['model.{}.m.1.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data -= model.state_dict()['model.{}.m.2.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data -= model.state_dict()['model.{}.m.3.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.bias'.format(idx2)].data\n",
"\n",
"# reparametrized YOLOR\n",
"for i in range((model.nc+5)*anchors):\n",
" model.state_dict()['model.{}.m.0.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.0.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.1.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.1.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.2.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.2.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.3.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.3.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].mul(state_dict['model.{}.ia.0.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].mul(state_dict['model.{}.ia.1.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].mul(state_dict['model.{}.ia.2.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].mul(state_dict['model.{}.ia.3.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data *= state_dict['model.{}.im.0.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data *= state_dict['model.{}.im.1.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data *= state_dict['model.{}.im.2.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data *= state_dict['model.{}.im.3.implicit'.format(idx2)].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-w6.pt')\n"
]
},
{
"cell_type": "markdown",
"id": "5f093d43",
"metadata": {},
"source": [
"## YOLOv7-E6 reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "aa2b2142",
"metadata": {},
"outputs": [],
"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-e6.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7-e6.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7-e6.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",
"idx = 140\n",
"idx2 = 144\n",
"\n",
"# copy weights of lead head\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data -= model.state_dict()['model.{}.m.0.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data -= model.state_dict()['model.{}.m.1.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data -= model.state_dict()['model.{}.m.2.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data -= model.state_dict()['model.{}.m.3.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data -= model.state_dict()['model.{}.m.0.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data -= model.state_dict()['model.{}.m.1.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data -= model.state_dict()['model.{}.m.2.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data -= model.state_dict()['model.{}.m.3.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.bias'.format(idx2)].data\n",
"\n",
"# reparametrized YOLOR\n",
"for i in range((model.nc+5)*anchors):\n",
" model.state_dict()['model.{}.m.0.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.0.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.1.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.1.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.2.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.2.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.3.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.3.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].mul(state_dict['model.{}.ia.0.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].mul(state_dict['model.{}.ia.1.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].mul(state_dict['model.{}.ia.2.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].mul(state_dict['model.{}.ia.3.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data *= state_dict['model.{}.im.0.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data *= state_dict['model.{}.im.1.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data *= state_dict['model.{}.im.2.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data *= state_dict['model.{}.im.3.implicit'.format(idx2)].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-e6.pt')\n"
]
},
{
"cell_type": "markdown",
"id": "a3bccf89",
"metadata": {},
"source": [
"## YOLOv7-D6 reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e5216b70",
"metadata": {},
"outputs": [],
"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-d6_trainig.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7-d6.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7-d6.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",
"idx = 162\n",
"idx2 = 166\n",
"\n",
"# copy weights of lead head\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data -= model.state_dict()['model.{}.m.0.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data -= model.state_dict()['model.{}.m.1.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data -= model.state_dict()['model.{}.m.2.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data -= model.state_dict()['model.{}.m.3.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data -= model.state_dict()['model.{}.m.0.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data -= model.state_dict()['model.{}.m.1.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data -= model.state_dict()['model.{}.m.2.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data -= model.state_dict()['model.{}.m.3.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.bias'.format(idx2)].data\n",
"\n",
"# reparametrized YOLOR\n",
"for i in range((model.nc+5)*anchors):\n",
" model.state_dict()['model.{}.m.0.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.0.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.1.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.1.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.2.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.2.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.3.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.3.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].mul(state_dict['model.{}.ia.0.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].mul(state_dict['model.{}.ia.1.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].mul(state_dict['model.{}.ia.2.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].mul(state_dict['model.{}.ia.3.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data *= state_dict['model.{}.im.0.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data *= state_dict['model.{}.im.1.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data *= state_dict['model.{}.im.2.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data *= state_dict['model.{}.im.3.implicit'.format(idx2)].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-d6.pt')\n"
]
},
{
"cell_type": "markdown",
"id": "334c273b",
"metadata": {},
"source": [
"## YOLOv7-E6E reparameterization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "635fd8d2",
"metadata": {},
"outputs": [],
"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-e6e_trainig.pt', map_location=device)\n",
"# reparameterized model in cfg/deploy/*.yaml\n",
"model = Model('cfg/deploy/yolov7-e6e.yaml', ch=3, nc=80).to(device)\n",
"\n",
"with open('cfg/deploy/yolov7-e6e.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",
"idx = 261\n",
"idx2 = 265\n",
"\n",
"# copy weights of lead head\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data -= model.state_dict()['model.{}.m.0.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data -= model.state_dict()['model.{}.m.1.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data -= model.state_dict()['model.{}.m.2.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data -= model.state_dict()['model.{}.m.3.weight'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.weight'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.weight'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.weight'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.weight'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data -= model.state_dict()['model.{}.m.0.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data -= model.state_dict()['model.{}.m.1.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data -= model.state_dict()['model.{}.m.2.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data -= model.state_dict()['model.{}.m.3.bias'.format(idx)].data\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.bias'.format(idx2)].data\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.bias'.format(idx2)].data\n",
"\n",
"# reparametrized YOLOR\n",
"for i in range((model.nc+5)*anchors):\n",
" model.state_dict()['model.{}.m.0.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.0.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.1.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.1.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.2.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.2.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
" model.state_dict()['model.{}.m.3.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.3.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].mul(state_dict['model.{}.ia.0.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].mul(state_dict['model.{}.ia.1.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].mul(state_dict['model.{}.ia.2.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].mul(state_dict['model.{}.ia.3.implicit'.format(idx2)]).sum(1).squeeze()\n",
"model.state_dict()['model.{}.m.0.bias'.format(idx)].data *= state_dict['model.{}.im.0.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.1.bias'.format(idx)].data *= state_dict['model.{}.im.1.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.2.bias'.format(idx)].data *= state_dict['model.{}.im.2.implicit'.format(idx2)].data.squeeze()\n",
"model.state_dict()['model.{}.m.3.bias'.format(idx)].data *= state_dict['model.{}.im.3.implicit'.format(idx2)].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-e6e.pt')\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "63a62625",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.7"
},
"vscode": {
"interpreter": {
"hash": "73080970ff6fd25f9fcdf9c6f9e85b950a97864bb936ee53fb633f473cbfae4b"
}
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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