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{
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"import clip.clip as clip\n",
"import os\n",
"import torch\n",
"from collections import OrderedDict"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"path = 'your_model_path/clip_visual_encoder'"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"model, _ = clip.load(\"ViT-B/16\", device='cpu')\n",
"new_state_dict = OrderedDict()\n",
"for k, v in model.state_dict().items():\n",
" if 'visual.' in k:\n",
" new_state_dict[k[7:]] = v\n",
"torch.save(new_state_dict, os.path.join(path, 'vit_b16.pth'))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"model, _ = clip.load(\"ViT-L/14\", device='cpu')\n",
"new_state_dict = OrderedDict()\n",
"for k, v in model.state_dict().items():\n",
" if 'visual.' in k:\n",
" new_state_dict[k[7:]] = v\n",
"torch.save(new_state_dict, os.path.join(path, 'vit_l14.pth'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"model, _ = clip.load(\"ViT-L/14@336px\", device='cpu')\n",
"new_state_dict = OrderedDict()\n",
"for k, v in model.state_dict().items():\n",
" if 'visual.' in k:\n",
" new_state_dict[k[7:]] = v\n",
"torch.save(new_state_dict, os.path.join(path, 'vit_l14_336.pth'))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.7.13 ('torch1.9')",
"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": "3.7.13"
},
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "c30e0be9d1dabfc31a056b9daab5ce1d15284c0e9e5af7f56f8931344ec84c24"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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