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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "a7dd2ed2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Defaulting to user installation because normal site-packages is not writeable\n",
"Collecting timm\n",
" Downloading timm-1.0.15-py3-none-any.whl.metadata (52 kB)\n",
"Requirement already satisfied: torch in /home/ubuntu/.local/lib/python3.12/site-packages (from timm) (2.6.0)\n",
"Requirement already satisfied: torchvision in /home/ubuntu/.local/lib/python3.12/site-packages (from timm) (0.21.0)\n",
"Requirement already satisfied: pyyaml in /opt/anaconda3/lib/python3.12/site-packages (from timm) (6.0.1)\n",
"Requirement already satisfied: huggingface_hub in /home/ubuntu/.local/lib/python3.12/site-packages (from timm) (0.29.3)\n",
"Requirement already satisfied: safetensors in /home/ubuntu/.local/lib/python3.12/site-packages (from timm) (0.5.3)\n",
"Requirement already satisfied: filelock in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub->timm) (3.13.1)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub->timm) (2024.6.1)\n",
"Requirement already satisfied: packaging>=20.9 in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub->timm) (24.1)\n",
"Requirement already satisfied: requests in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub->timm) (2.32.3)\n",
"Requirement already satisfied: tqdm>=4.42.1 in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub->timm) (4.66.5)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/anaconda3/lib/python3.12/site-packages (from huggingface_hub->timm) (4.11.0)\n",
"Requirement already satisfied: networkx in /opt/anaconda3/lib/python3.12/site-packages (from torch->timm) (3.3)\n",
"Requirement already satisfied: jinja2 in /opt/anaconda3/lib/python3.12/site-packages (from torch->timm) (3.1.4)\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.4.127 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.4.127 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.4.127)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.4.127 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.4.127)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==9.1.0.70 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (9.1.0.70)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.4.5.8 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.4.5.8)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.2.1.3 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (11.2.1.3)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.5.147 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (10.3.5.147)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.6.1.9 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (11.6.1.9)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.3.1.170 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.3.1.170)\n",
"Requirement already satisfied: nvidia-cusparselt-cu12==0.6.2 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (0.6.2)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.21.5 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (2.21.5)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.4.127 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.4.127)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12==12.4.127 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (12.4.127)\n",
"Requirement already satisfied: triton==3.2.0 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (3.2.0)\n",
"Requirement already satisfied: setuptools in /opt/anaconda3/lib/python3.12/site-packages (from torch->timm) (75.1.0)\n",
"Requirement already satisfied: sympy==1.13.1 in /home/ubuntu/.local/lib/python3.12/site-packages (from torch->timm) (1.13.1)\n",
"Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/anaconda3/lib/python3.12/site-packages (from sympy==1.13.1->torch->timm) (1.3.0)\n",
"Requirement already satisfied: numpy in /opt/anaconda3/lib/python3.12/site-packages (from torchvision->timm) (1.26.4)\n",
"Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in /opt/anaconda3/lib/python3.12/site-packages (from torchvision->timm) (10.4.0)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/lib/python3.12/site-packages (from jinja2->torch->timm) (2.1.3)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /opt/anaconda3/lib/python3.12/site-packages (from requests->huggingface_hub->timm) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /opt/anaconda3/lib/python3.12/site-packages (from requests->huggingface_hub->timm) (3.7)\n",
"Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/anaconda3/lib/python3.12/site-packages (from requests->huggingface_hub->timm) (2.2.3)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/anaconda3/lib/python3.12/site-packages (from requests->huggingface_hub->timm) (2024.8.30)\n",
"Downloading timm-1.0.15-py3-none-any.whl (2.4 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.4/2.4 MB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
"\u001b[?25hInstalling collected packages: timm\n",
"Successfully installed timm-1.0.15\n"
]
}
],
"source": [
"!pip install timm"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "ff0cd74c",
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "e878415c59ba4f97a1fac91e71010469",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"config.json: 0%| | 0.00/742 [00:00<?, ?B/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "356e15c8e1434fbe9d52f6ec94d6e875",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"ename": "RuntimeError",
"evalue": "Error(s) in loading state_dict for VisionTransformer:\n\tsize mismatch for blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.2.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.3.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.4.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.5.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.6.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.7.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.8.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.9.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.10.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.11.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.12.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.13.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.14.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.15.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.16.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.17.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.18.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.19.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.20.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.21.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.22.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.23.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.24.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.25.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.26.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.27.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.28.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.29.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.30.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.31.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[2], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtimm\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m model \u001b[38;5;241m=\u001b[39m timm\u001b[38;5;241m.\u001b[39mcreate_model(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhf_hub:paige-ai/Virchow2\u001b[39m\u001b[38;5;124m\"\u001b[39m, pretrained\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/timm/models/_factory.py:126\u001b[0m, in \u001b[0;36mcreate_model\u001b[0;34m(model_name, pretrained, pretrained_cfg, pretrained_cfg_overlay, checkpoint_path, cache_dir, scriptable, exportable, no_jit, **kwargs)\u001b[0m\n\u001b[1;32m 124\u001b[0m create_fn \u001b[38;5;241m=\u001b[39m model_entrypoint(model_name)\n\u001b[1;32m 125\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m set_layer_config(scriptable\u001b[38;5;241m=\u001b[39mscriptable, exportable\u001b[38;5;241m=\u001b[39mexportable, no_jit\u001b[38;5;241m=\u001b[39mno_jit):\n\u001b[0;32m--> 126\u001b[0m model \u001b[38;5;241m=\u001b[39m create_fn(\n\u001b[1;32m 127\u001b[0m pretrained\u001b[38;5;241m=\u001b[39mpretrained,\n\u001b[1;32m 128\u001b[0m pretrained_cfg\u001b[38;5;241m=\u001b[39mpretrained_cfg,\n\u001b[1;32m 129\u001b[0m pretrained_cfg_overlay\u001b[38;5;241m=\u001b[39mpretrained_cfg_overlay,\n\u001b[1;32m 130\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m 131\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 132\u001b[0m )\n\u001b[1;32m 134\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m checkpoint_path:\n\u001b[1;32m 135\u001b[0m load_checkpoint(model, checkpoint_path)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/timm/models/vision_transformer.py:2583\u001b[0m, in \u001b[0;36mvit_huge_patch14_224\u001b[0;34m(pretrained, **kwargs)\u001b[0m\n\u001b[1;32m 2580\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" ViT-Huge model (ViT-H/14) from original paper (https://arxiv.org/abs/2010.11929).\u001b[39;00m\n\u001b[1;32m 2581\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2582\u001b[0m model_args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(patch_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m, embed_dim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1280\u001b[39m, depth\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m32\u001b[39m, num_heads\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16\u001b[39m)\n\u001b[0;32m-> 2583\u001b[0m model \u001b[38;5;241m=\u001b[39m _create_vision_transformer(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvit_huge_patch14_224\u001b[39m\u001b[38;5;124m'\u001b[39m, pretrained\u001b[38;5;241m=\u001b[39mpretrained, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mdict\u001b[39m(model_args, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs))\n\u001b[1;32m 2584\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/timm/models/vision_transformer.py:2406\u001b[0m, in \u001b[0;36m_create_vision_transformer\u001b[0;34m(variant, pretrained, **kwargs)\u001b[0m\n\u001b[1;32m 2403\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msiglip\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;129;01min\u001b[39;00m variant \u001b[38;5;129;01mand\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mglobal_pool\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmap\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m 2404\u001b[0m strict \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m-> 2406\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m build_model_with_cfg(\n\u001b[1;32m 2407\u001b[0m VisionTransformer,\n\u001b[1;32m 2408\u001b[0m variant,\n\u001b[1;32m 2409\u001b[0m pretrained,\n\u001b[1;32m 2410\u001b[0m pretrained_filter_fn\u001b[38;5;241m=\u001b[39m_filter_fn,\n\u001b[1;32m 2411\u001b[0m pretrained_strict\u001b[38;5;241m=\u001b[39mstrict,\n\u001b[1;32m 2412\u001b[0m feature_cfg\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mdict\u001b[39m(out_indices\u001b[38;5;241m=\u001b[39mout_indices, feature_cls\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgetter\u001b[39m\u001b[38;5;124m'\u001b[39m),\n\u001b[1;32m 2413\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 2414\u001b[0m )\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/timm/models/_builder.py:436\u001b[0m, in \u001b[0;36mbuild_model_with_cfg\u001b[0;34m(model_cls, variant, pretrained, pretrained_cfg, pretrained_cfg_overlay, model_cfg, feature_cfg, pretrained_strict, pretrained_filter_fn, cache_dir, kwargs_filter, **kwargs)\u001b[0m\n\u001b[1;32m 434\u001b[0m num_classes_pretrained \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m features \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(model, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_classes\u001b[39m\u001b[38;5;124m'\u001b[39m, kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnum_classes\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m1000\u001b[39m))\n\u001b[1;32m 435\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pretrained:\n\u001b[0;32m--> 436\u001b[0m load_pretrained(\n\u001b[1;32m 437\u001b[0m model,\n\u001b[1;32m 438\u001b[0m pretrained_cfg\u001b[38;5;241m=\u001b[39mpretrained_cfg,\n\u001b[1;32m 439\u001b[0m num_classes\u001b[38;5;241m=\u001b[39mnum_classes_pretrained,\n\u001b[1;32m 440\u001b[0m in_chans\u001b[38;5;241m=\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124min_chans\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m3\u001b[39m),\n\u001b[1;32m 441\u001b[0m filter_fn\u001b[38;5;241m=\u001b[39mpretrained_filter_fn,\n\u001b[1;32m 442\u001b[0m strict\u001b[38;5;241m=\u001b[39mpretrained_strict,\n\u001b[1;32m 443\u001b[0m cache_dir\u001b[38;5;241m=\u001b[39mcache_dir,\n\u001b[1;32m 444\u001b[0m )\n\u001b[1;32m 446\u001b[0m \u001b[38;5;66;03m# Wrap the model in a feature extraction module if enabled\u001b[39;00m\n\u001b[1;32m 447\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m features:\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/timm/models/_builder.py:260\u001b[0m, in \u001b[0;36mload_pretrained\u001b[0;34m(model, pretrained_cfg, num_classes, in_chans, filter_fn, strict, cache_dir)\u001b[0m\n\u001b[1;32m 257\u001b[0m classifier_bias \u001b[38;5;241m=\u001b[39m state_dict[classifier_name \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.bias\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m 258\u001b[0m state_dict[classifier_name \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m.bias\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m classifier_bias[label_offset:]\n\u001b[0;32m--> 260\u001b[0m load_result \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mload_state_dict(state_dict, strict\u001b[38;5;241m=\u001b[39mstrict)\n\u001b[1;32m 261\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m load_result\u001b[38;5;241m.\u001b[39mmissing_keys:\n\u001b[1;32m 262\u001b[0m _logger\u001b[38;5;241m.\u001b[39minfo(\n\u001b[1;32m 263\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing keys (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(load_result\u001b[38;5;241m.\u001b[39mmissing_keys)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) discovered while loading pretrained weights.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 264\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m This is expected if model is being adapted.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/torch/nn/modules/module.py:2581\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m 2573\u001b[0m error_msgs\u001b[38;5;241m.\u001b[39minsert(\n\u001b[1;32m 2574\u001b[0m \u001b[38;5;241m0\u001b[39m,\n\u001b[1;32m 2575\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMissing key(s) in state_dict: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2576\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m missing_keys)\n\u001b[1;32m 2577\u001b[0m ),\n\u001b[1;32m 2578\u001b[0m )\n\u001b[1;32m 2580\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2581\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m 2582\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 2583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)\n\u001b[1;32m 2584\u001b[0m )\n\u001b[1;32m 2585\u001b[0m )\n\u001b[1;32m 2586\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",
"\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for VisionTransformer:\n\tsize mismatch for blocks.0.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.1.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.2.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.3.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.4.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.5.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.6.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.7.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.8.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.9.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.10.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.11.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.12.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.13.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.14.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.15.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.16.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.17.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.18.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.19.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.20.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.21.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.22.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.23.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.24.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.25.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.26.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.27.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.28.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.29.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.30.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832]).\n\tsize mismatch for blocks.31.mlp.fc2.weight: copying a param with shape torch.Size([1280, 3416]) from checkpoint, the shape in current model is torch.Size([1280, 6832])."
]
}
],
"source": [
"import timm\n",
"\n",
"model = timm.create_model(\"hf_hub:paige-ai/Virchow2\", pretrained=True)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "2999e249",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2025-04-18 16:09:01.177109: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:467] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1744967341.201028 2970384 cuda_dnn.cc:8579] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1744967341.209888 2970384 cuda_blas.cc:1407] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"W0000 00:00:1744967341.235808 2970384 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1744967341.235834 2970384 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1744967341.235838 2970384 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"W0000 00:00:1744967341.235840 2970384 computation_placer.cc:177] computation placer already registered. Please check linkage and avoid linking the same target more than once.\n",
"2025-04-18 16:09:01.243585: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"ename": "TypeError",
"evalue": "'NoneType' object cannot be interpreted as an integer",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[3], line 4\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m AutoModel, AutoImageProcessor\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# Load the model directly from Hugging Face\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m model \u001b[38;5;241m=\u001b[39m AutoModel\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpaige-ai/Virchow2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 5\u001b[0m processor \u001b[38;5;241m=\u001b[39m AutoImageProcessor\u001b[38;5;241m.\u001b[39mfrom_pretrained(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpaige-ai/Virchow2\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/transformers/models/auto/auto_factory.py:531\u001b[0m, in \u001b[0;36m_BaseAutoModelClass.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 528\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantization_config\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 529\u001b[0m _ \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mquantization_config\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 531\u001b[0m config, kwargs \u001b[38;5;241m=\u001b[39m AutoConfig\u001b[38;5;241m.\u001b[39mfrom_pretrained(\n\u001b[1;32m 532\u001b[0m pretrained_model_name_or_path,\n\u001b[1;32m 533\u001b[0m return_unused_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m 534\u001b[0m trust_remote_code\u001b[38;5;241m=\u001b[39mtrust_remote_code,\n\u001b[1;32m 535\u001b[0m code_revision\u001b[38;5;241m=\u001b[39mcode_revision,\n\u001b[1;32m 536\u001b[0m _commit_hash\u001b[38;5;241m=\u001b[39mcommit_hash,\n\u001b[1;32m 537\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mhub_kwargs,\n\u001b[1;32m 538\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 539\u001b[0m )\n\u001b[1;32m 541\u001b[0m \u001b[38;5;66;03m# if torch_dtype=auto was passed here, ensure to pass it on\u001b[39;00m\n\u001b[1;32m 542\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs_orig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_dtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mauto\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/transformers/models/auto/configuration_auto.py:1125\u001b[0m, in \u001b[0;36mAutoConfig.from_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, **kwargs)\u001b[0m\n\u001b[1;32m 1114\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m 1115\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1116\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe checkpoint you are trying to load has model type `\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel_type\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m` \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1117\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbut Transformers does not recognize this architecture. This could be because of an \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1123\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`pip install git+https://github.com/huggingface/transformers.git`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1124\u001b[0m )\n\u001b[0;32m-> 1125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m config_class\u001b[38;5;241m.\u001b[39mfrom_dict(config_dict, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39munused_kwargs)\n\u001b[1;32m 1126\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 1127\u001b[0m \u001b[38;5;66;03m# Fallback: use pattern matching on the string.\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;66;03m# We go from longer names to shorter names to catch roberta before bert (for instance)\u001b[39;00m\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m pattern \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msorted\u001b[39m(CONFIG_MAPPING\u001b[38;5;241m.\u001b[39mkeys(), key\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mlen\u001b[39m, reverse\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/transformers/models/timm_wrapper/configuration_timm_wrapper.py:106\u001b[0m, in \u001b[0;36mTimmWrapperConfig.from_dict\u001b[0;34m(cls, config_dict, **kwargs)\u001b[0m\n\u001b[1;32m 103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained_cfg\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_classes\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01min\u001b[39;00m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained_cfg\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n\u001b[1;32m 104\u001b[0m config_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpretrained_cfg\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_classes\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39mfrom_dict(config_dict, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/transformers/configuration_utils.py:765\u001b[0m, in \u001b[0;36mPretrainedConfig.from_dict\u001b[0;34m(cls, config_dict, **kwargs)\u001b[0m\n\u001b[1;32m 763\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(current_attr, PretrainedConfig) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mdict\u001b[39m):\n\u001b[1;32m 764\u001b[0m value \u001b[38;5;241m=\u001b[39m current_attr\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mvalue)\n\u001b[0;32m--> 765\u001b[0m \u001b[38;5;28msetattr\u001b[39m(config, key, value)\n\u001b[1;32m 766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtorch_dtype\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 767\u001b[0m to_remove\u001b[38;5;241m.\u001b[39mappend(key)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/transformers/configuration_utils.py:209\u001b[0m, in \u001b[0;36mPretrainedConfig.__setattr__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m 207\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattribute_map\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m 208\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__getattribute__\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mattribute_map\u001b[39m\u001b[38;5;124m\"\u001b[39m)[key]\n\u001b[0;32m--> 209\u001b[0m \u001b[38;5;28msuper\u001b[39m()\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__setattr__\u001b[39m(key, value)\n",
"File \u001b[0;32m~/.local/lib/python3.12/site-packages/transformers/configuration_utils.py:355\u001b[0m, in \u001b[0;36mPretrainedConfig.num_labels\u001b[0;34m(self, num_labels)\u001b[0m\n\u001b[1;32m 352\u001b[0m \u001b[38;5;129m@num_labels\u001b[39m\u001b[38;5;241m.\u001b[39msetter\n\u001b[1;32m 353\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnum_labels\u001b[39m(\u001b[38;5;28mself\u001b[39m, num_labels: \u001b[38;5;28mint\u001b[39m):\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mid2label\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid2label \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid2label) \u001b[38;5;241m!=\u001b[39m num_labels:\n\u001b[0;32m--> 355\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid2label \u001b[38;5;241m=\u001b[39m {i: \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLABEL_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(num_labels)}\n\u001b[1;32m 356\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlabel2id \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid2label\u001b[38;5;241m.\u001b[39mvalues(), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mid2label\u001b[38;5;241m.\u001b[39mkeys()))\n",
"\u001b[0;31mTypeError\u001b[0m: 'NoneType' object cannot be interpreted as an integer"
]
}
],
"source": [
"from transformers import AutoModel, AutoImageProcessor\n",
"\n",
"# Load the model directly from Hugging Face\n",
"model = AutoModel.from_pretrained(\"paige-ai/Virchow2\")\n",
"processor = AutoImageProcessor.from_pretrained(\"paige-ai/Virchow2\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "173d0ea6",
"metadata": {},
"outputs": [],
"source": [
"import timm\n",
"import torch\n",
"from timm.data import resolve_data_config\n",
"from timm.data.transforms_factory import create_transform\n",
"from timm.layers import SwiGLUPacked\n",
"from PIL import Image\n",
"\n",
"# need to specify MLP layer and activation function for proper init\n",
"model = timm.create_model(\"hf-hub:paige-ai/Virchow2\", pretrained=True, mlp_layer=SwiGLUPacked, act_layer=torch.nn.SiLU)\n",
"model = model.eval()\n",
"\n",
"transforms = create_transform(**resolve_data_config(model.pretrained_cfg, model=model))\n",
"\n",
"image = Image.open(r\"/home/ubuntu/vnet/TaoST/Brand.jpg\")\n",
"image = transforms(image).unsqueeze(0) # size: 1 x 3 x 224 x 224\n",
"\n",
"output = model(image) # size: 1 x 261 x 1280\n",
"\n",
"class_token = output[:, 0] # size: 1 x 1280\n",
"patch_tokens = output[:, 5:] # size: 1 x 256 x 1280, tokens 1-4 are register tokens so we ignore those\n",
"\n",
"# concatenate class token and average pool of patch tokens\n",
"embedding = torch.cat([class_token, patch_tokens.mean(1)], dim=-1) # size: 1 x 2560"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"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": "3.12.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|