mirror of https://github.com/WongKinYiu/yolov7.git
Kin-Yiu, Wong
GitHub
parent
f5d50fa8c0
commit
261b1cfdb2
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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "d7cbe5ee",
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"metadata": {},
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"source": [
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"# Reparameterization"
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]
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},
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{
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"cell_type": "markdown",
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"id": "13393b70",
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"metadata": {},
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"source": [
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"## YOLOv7 reparameterization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bf53becf",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import\n",
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"from copy import deepcopy\n",
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"from models.yolo import Model\n",
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"import torch\n",
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"from utils.torch_utils import select_device, is_parallel\n",
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"\n",
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"device = select_device('0', batch_size=1)\n",
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"# model trained by cfg/training/*.yaml\n",
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"ckpt = torch.load('cfg/training/yolov7.pt', map_location=device)\n",
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"# reparameterized model in cfg/deploy/*.yaml\n",
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"model = Model('cfg/deploy/yolov7.yaml', ch=3, nc=80).to(device)\n",
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"\n",
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"# copy intersect weights\n",
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"state_dict = ckpt['model'].float().state_dict()\n",
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"exclude = []\n",
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"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",
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"model.load_state_dict(intersect_state_dict, strict=False)\n",
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"model.names = ckpt['model'].names\n",
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"model.nc = ckpt['model'].nc\n",
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"\n",
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"# reparametrized YOLOR\n",
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"for i in range(255):\n",
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" model.state_dict()['model.105.m.0.weight'].data[i, :, :, :] *= state_dict['model.105.im.0.implicit'].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.105.m.1.weight'].data[i, :, :, :] *= state_dict['model.105.im.1.implicit'].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.105.m.2.weight'].data[i, :, :, :] *= state_dict['model.105.im.2.implicit'].data[:, i, : :].squeeze()\n",
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"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",
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"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",
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"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",
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"model.state_dict()['model.105.m.0.bias'].data *= state_dict['model.105.im.0.implicit'].data.squeeze()\n",
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"model.state_dict()['model.105.m.1.bias'].data *= state_dict['model.105.im.1.implicit'].data.squeeze()\n",
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"model.state_dict()['model.105.m.2.bias'].data *= state_dict['model.105.im.2.implicit'].data.squeeze()\n",
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"\n",
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"# model to be saved\n",
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"ckpt = {'model': deepcopy(model.module if is_parallel(model) else model).half(),\n",
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" 'optimizer': None,\n",
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" 'training_results': None,\n",
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" 'epoch': -1}\n",
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"\n",
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"# save reparameterized model\n",
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"torch.save(ckpt, 'cfg/deploy/yolov7.pt')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5b396a53",
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"metadata": {},
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"source": [
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"## YOLOv7x reparameterization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9d54d17f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import\n",
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"from copy import deepcopy\n",
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"from models.yolo import Model\n",
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"import torch\n",
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"from utils.torch_utils import select_device, is_parallel\n",
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"\n",
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"device = select_device('0', batch_size=1)\n",
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"# model trained by cfg/training/*.yaml\n",
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"ckpt = torch.load('cfg/training/yolov7x.pt', map_location=device)\n",
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"# reparameterized model in cfg/deploy/*.yaml\n",
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"model = Model('cfg/deploy/yolov7x.yaml', ch=3, nc=80).to(device)\n",
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"\n",
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"# copy intersect weights\n",
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"state_dict = ckpt['model'].float().state_dict()\n",
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"exclude = []\n",
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"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",
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"model.load_state_dict(intersect_state_dict, strict=False)\n",
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"model.names = ckpt['model'].names\n",
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"model.nc = ckpt['model'].nc\n",
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"\n",
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"# reparametrized YOLOR\n",
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"for i in range(255):\n",
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" model.state_dict()['model.121.m.0.weight'].data[i, :, :, :] *= state_dict['model.121.im.0.implicit'].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.121.m.1.weight'].data[i, :, :, :] *= state_dict['model.121.im.1.implicit'].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.121.m.2.weight'].data[i, :, :, :] *= state_dict['model.121.im.2.implicit'].data[:, i, : :].squeeze()\n",
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"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",
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"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",
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"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",
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"model.state_dict()['model.121.m.0.bias'].data *= state_dict['model.121.im.0.implicit'].data.squeeze()\n",
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"model.state_dict()['model.121.m.1.bias'].data *= state_dict['model.121.im.1.implicit'].data.squeeze()\n",
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"model.state_dict()['model.121.m.2.bias'].data *= state_dict['model.121.im.2.implicit'].data.squeeze()\n",
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"\n",
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"# model to be saved\n",
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"ckpt = {'model': deepcopy(model.module if is_parallel(model) else model).half(),\n",
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" 'optimizer': None,\n",
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" 'training_results': None,\n",
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" 'epoch': -1}\n",
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"\n",
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"# save reparameterized model\n",
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"torch.save(ckpt, 'cfg/deploy/yolov7x.pt')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "11a9108e",
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"metadata": {},
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"source": [
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"## YOLOv7-W6 reparameterization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d032c629",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import\n",
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"from copy import deepcopy\n",
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"from models.yolo import Model\n",
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"import torch\n",
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"from utils.torch_utils import select_device, is_parallel\n",
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"\n",
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"device = select_device('0', batch_size=1)\n",
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"# model trained by cfg/training/*.yaml\n",
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"ckpt = torch.load('cfg/training/yolov7-w6.pt', map_location=device)\n",
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"# reparameterized model in cfg/deploy/*.yaml\n",
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"model = Model('cfg/deploy/yolov7-w6.yaml', ch=3, nc=80).to(device)\n",
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"\n",
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"# copy intersect weights\n",
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"state_dict = ckpt['model'].float().state_dict()\n",
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"exclude = []\n",
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"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",
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"model.load_state_dict(intersect_state_dict, strict=False)\n",
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"model.names = ckpt['model'].names\n",
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"model.nc = ckpt['model'].nc\n",
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"\n",
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"idx = 118\n",
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"idx2 = 122\n",
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"\n",
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"# copy weights of lead head\n",
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"model.state_dict()['model.{}.m.0.weight'.format(idx)].data -= model.state_dict()['model.{}.m.0.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.1.weight'.format(idx)].data -= model.state_dict()['model.{}.m.1.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.2.weight'.format(idx)].data -= model.state_dict()['model.{}.m.2.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.3.weight'.format(idx)].data -= model.state_dict()['model.{}.m.3.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.0.weight'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.1.weight'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.2.weight'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.3.weight'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.0.bias'.format(idx)].data -= model.state_dict()['model.{}.m.0.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.1.bias'.format(idx)].data -= model.state_dict()['model.{}.m.1.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.2.bias'.format(idx)].data -= model.state_dict()['model.{}.m.2.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.3.bias'.format(idx)].data -= model.state_dict()['model.{}.m.3.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.bias'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.bias'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.bias'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.bias'.format(idx2)].data\n",
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"\n",
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"# reparametrized YOLOR\n",
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"for i in range(255):\n",
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" model.state_dict()['model.{}.m.0.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.0.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.{}.m.1.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.1.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.{}.m.2.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.2.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.{}.m.3.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.3.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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"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",
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"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",
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"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",
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"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",
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"model.state_dict()['model.{}.m.0.bias'.format(idx)].data *= state_dict['model.{}.im.0.implicit'.format(idx2)].data.squeeze()\n",
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"model.state_dict()['model.{}.m.1.bias'.format(idx)].data *= state_dict['model.{}.im.1.implicit'.format(idx2)].data.squeeze()\n",
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"model.state_dict()['model.{}.m.2.bias'.format(idx)].data *= state_dict['model.{}.im.2.implicit'.format(idx2)].data.squeeze()\n",
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"model.state_dict()['model.{}.m.3.bias'.format(idx)].data *= state_dict['model.{}.im.3.implicit'.format(idx2)].data.squeeze()\n",
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"\n",
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"# model to be saved\n",
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"ckpt = {'model': deepcopy(model.module if is_parallel(model) else model).half(),\n",
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" 'optimizer': None,\n",
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" 'training_results': None,\n",
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" 'epoch': -1}\n",
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"\n",
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"# save reparameterized model\n",
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"torch.save(ckpt, 'cfg/deploy/yolov7-w6.pt')\n"
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]
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},
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{
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"cell_type": "markdown",
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"id": "5f093d43",
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"metadata": {},
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"source": [
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"## YOLOv7-E6 reparameterization"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "aa2b2142",
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"metadata": {},
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"outputs": [],
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"source": [
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"# import\n",
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"from copy import deepcopy\n",
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"from models.yolo import Model\n",
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"import torch\n",
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"from utils.torch_utils import select_device, is_parallel\n",
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"\n",
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"device = select_device('0', batch_size=1)\n",
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"# model trained by cfg/training/*.yaml\n",
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"ckpt = torch.load('cfg/training/yolov7-e6.pt', map_location=device)\n",
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"# reparameterized model in cfg/deploy/*.yaml\n",
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"model = Model('cfg/deploy/yolov7-e6.yaml', ch=3, nc=80).to(device)\n",
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"\n",
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"# copy intersect weights\n",
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"state_dict = ckpt['model'].float().state_dict()\n",
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"exclude = []\n",
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"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",
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"model.load_state_dict(intersect_state_dict, strict=False)\n",
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"model.names = ckpt['model'].names\n",
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"model.nc = ckpt['model'].nc\n",
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"\n",
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"idx = 140\n",
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"idx2 = 144\n",
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"\n",
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"# copy weights of lead head\n",
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"model.state_dict()['model.{}.m.0.weight'.format(idx)].data -= model.state_dict()['model.{}.m.0.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.1.weight'.format(idx)].data -= model.state_dict()['model.{}.m.1.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.2.weight'.format(idx)].data -= model.state_dict()['model.{}.m.2.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.3.weight'.format(idx)].data -= model.state_dict()['model.{}.m.3.weight'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.0.weight'.format(idx)].data += state_dict['model.{}.m.0.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.1.weight'.format(idx)].data += state_dict['model.{}.m.1.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.2.weight'.format(idx)].data += state_dict['model.{}.m.2.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.3.weight'.format(idx)].data += state_dict['model.{}.m.3.weight'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.0.bias'.format(idx)].data -= model.state_dict()['model.{}.m.0.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.1.bias'.format(idx)].data -= model.state_dict()['model.{}.m.1.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.2.bias'.format(idx)].data -= model.state_dict()['model.{}.m.2.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.3.bias'.format(idx)].data -= model.state_dict()['model.{}.m.3.bias'.format(idx)].data\n",
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"model.state_dict()['model.{}.m.0.bias'.format(idx)].data += state_dict['model.{}.m.0.bias'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.1.bias'.format(idx)].data += state_dict['model.{}.m.1.bias'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.2.bias'.format(idx)].data += state_dict['model.{}.m.2.bias'.format(idx2)].data\n",
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"model.state_dict()['model.{}.m.3.bias'.format(idx)].data += state_dict['model.{}.m.3.bias'.format(idx2)].data\n",
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"\n",
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"# reparametrized YOLOR\n",
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"for i in range(255):\n",
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" model.state_dict()['model.{}.m.0.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.0.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.{}.m.1.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.1.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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" model.state_dict()['model.{}.m.2.weight'.format(idx)].data[i, :, :, :] *= state_dict['model.{}.im.2.implicit'.format(idx2)].data[:, i, : :].squeeze()\n",
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" 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",
|
||||
"\n",
|
||||
"device = select_device('0', batch_size=1)\n",
|
||||
"# model trained by cfg/training/*.yaml\n",
|
||||
"ckpt = torch.load('cfg/training/yolov7-d6.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",
|
||||
"# 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(255):\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",
|
||||
"\n",
|
||||
"device = select_device('0', batch_size=1)\n",
|
||||
"# model trained by cfg/training/*.yaml\n",
|
||||
"ckpt = torch.load('cfg/training/yolov7-e6e.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",
|
||||
"# 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(255):\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": "3.8.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
Loading…
Reference in New Issue