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Jeremy Janella
2026-05-09 23:21:13 -04:00
commit 75faa5c410
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sparsity/elasticnet.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5373faae-c4aa-4f05-bbd7-4b7728215aea",
"metadata": {},
"outputs": [
{
"ename": "RuntimeError",
"evalue": "operator torchvision::nms does not exist",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[1]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m 3\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mnn\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mfunctional\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mF\u001b[39;00m\n\u001b[32m 4\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorch\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mutils\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mdata\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m DataLoader\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchvision\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m datasets, transforms\n\u001b[32m 6\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnumpy\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mnp\u001b[39;00m\n\u001b[32m 7\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mmatplotlib\u001b[39;00m\u001b[34;01m.\u001b[39;00m\u001b[34;01mpyplot\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mas\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mplt\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torchvision/__init__.py:10\u001b[39m\n\u001b[32m 7\u001b[39m \u001b[38;5;66;03m# Don't re-order these, we need to load the _C extension (done when importing\u001b[39;00m\n\u001b[32m 8\u001b[39m \u001b[38;5;66;03m# .extensions) before entering _meta_registrations.\u001b[39;00m\n\u001b[32m 9\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mextension\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _HAS_OPS \u001b[38;5;66;03m# usort:skip\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01mtorchvision\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m _meta_registrations, datasets, io, models, ops, transforms, utils \u001b[38;5;66;03m# usort:skip\u001b[39;00m\n\u001b[32m 12\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m 13\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34;01m.\u001b[39;00m\u001b[34;01mversion\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mimport\u001b[39;00m __version__ \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torchvision/_meta_registrations.py:163\u001b[39m\n\u001b[32m 153\u001b[39m torch._check(\n\u001b[32m 154\u001b[39m grad.dtype == rois.dtype,\n\u001b[32m 155\u001b[39m \u001b[38;5;28;01mlambda\u001b[39;00m: (\n\u001b[32m (...)\u001b[39m\u001b[32m 158\u001b[39m ),\n\u001b[32m 159\u001b[39m )\n\u001b[32m 160\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m grad.new_empty((batch_size, channels, height, width))\n\u001b[32m--> \u001b[39m\u001b[32m163\u001b[39m \u001b[38;5;129;43m@torch\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mlibrary\u001b[49m\u001b[43m.\u001b[49m\u001b[43mregister_fake\u001b[49m\u001b[43m(\u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mtorchvision::nms\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 164\u001b[39m \u001b[38;5;28;43;01mdef\u001b[39;49;00m\u001b[38;5;250;43m \u001b[39;49m\u001b[34;43mmeta_nms\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdets\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mscores\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miou_threshold\u001b[49m\u001b[43m)\u001b[49m\u001b[43m:\u001b[49m\n\u001b[32m 165\u001b[39m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mboxes should be a 2d tensor, got \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdim\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43mD\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m 166\u001b[39m \u001b[43m \u001b[49m\u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_check\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[43m==\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[33;43mf\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mboxes should have 4 elements in dimension 1, got \u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mdets\u001b[49m\u001b[43m.\u001b[49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torch/library.py:1073\u001b[39m, in \u001b[36mregister_fake.<locals>.register\u001b[39m\u001b[34m(func)\u001b[39m\n\u001b[32m 1071\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 1072\u001b[39m use_lib = lib\n\u001b[32m-> \u001b[39m\u001b[32m1073\u001b[39m \u001b[43muse_lib\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_register_fake\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 1074\u001b[39m \u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_stacklevel\u001b[49m\u001b[43m=\u001b[49m\u001b[43mstacklevel\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_override\u001b[49m\u001b[43m=\u001b[49m\u001b[43mallow_override\u001b[49m\n\u001b[32m 1075\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 1076\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m func\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torch/library.py:203\u001b[39m, in \u001b[36mLibrary._register_fake\u001b[39m\u001b[34m(self, op_name, fn, _stacklevel, allow_override)\u001b[39m\n\u001b[32m 200\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m 201\u001b[39m func_to_register = fn\n\u001b[32m--> \u001b[39m\u001b[32m203\u001b[39m handle = \u001b[43mentry\u001b[49m\u001b[43m.\u001b[49m\u001b[43mfake_impl\u001b[49m\u001b[43m.\u001b[49m\u001b[43mregister\u001b[49m\u001b[43m(\u001b[49m\n\u001b[32m 204\u001b[39m \u001b[43m \u001b[49m\u001b[43mfunc_to_register\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlib\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mallow_override\u001b[49m\u001b[43m=\u001b[49m\u001b[43mallow_override\u001b[49m\n\u001b[32m 205\u001b[39m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 206\u001b[39m \u001b[38;5;28mself\u001b[39m._registration_handles.append(handle)\n",
"\u001b[36mFile \u001b[39m\u001b[32m~/.conda/envs/ai/lib/python3.11/site-packages/torch/_library/fake_impl.py:50\u001b[39m, in \u001b[36mFakeImplHolder.register\u001b[39m\u001b[34m(self, func, source, lib, allow_override)\u001b[39m\n\u001b[32m 44\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.kernel \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[32m 45\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 46\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mregister_fake(...): the operator \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.qualname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 47\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33malready has an fake impl registered at \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 48\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.kernel.source\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 49\u001b[39m )\n\u001b[32m---> \u001b[39m\u001b[32m50\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_C\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_dispatch_has_kernel_for_dispatch_key\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mqualname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mMeta\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m:\n\u001b[32m 51\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[32m 52\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mregister_fake(...): the operator \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m.qualname\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 53\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33malready has an DispatchKey::Meta implementation via a \u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m (...)\u001b[39m\u001b[32m 56\u001b[39m \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mregister_fake.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 57\u001b[39m )\n\u001b[32m 59\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m torch._C._dispatch_has_kernel_for_dispatch_key(\n\u001b[32m 60\u001b[39m \u001b[38;5;28mself\u001b[39m.qualname, \u001b[33m\"\u001b[39m\u001b[33mCompositeImplicitAutograd\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m 61\u001b[39m ):\n",
"\u001b[31mRuntimeError\u001b[39m: operator torchvision::nms does not exist"
]
}
],
"source": [
"# no torch no example womp womp\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"from torch.utils.data import DataLoader\n",
"from torchvision import datasets, transforms\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.patches as patches\n",
"from pathlib import Path\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"\n",
"# Import utilities from HTB Evasion Library\n",
"from htb_ai_library.utils import (\n",
" set_reproducibility,\n",
" save_model,\n",
" load_model,\n",
" HTB_GREEN,\n",
" NODE_BLACK,\n",
" HACKER_GREY,\n",
" WHITE,\n",
" AZURE,\n",
" NUGGET_YELLOW,\n",
" MALWARE_RED,\n",
" VIVID_PURPLE,\n",
" AQUAMARINE,\n",
")\n",
"from htb_ai_library.data import get_mnist_loaders\n",
"from htb_ai_library.models import MNISTClassifierWithDropout\n",
"from htb_ai_library.training import train_model, evaluate_accuracy\n",
"from htb_ai_library.visualization import use_htb_style\n",
"\n",
"# Apply HTB theme globally to all plots\n",
"use_htb_style()\n",
"\n",
"# Set reproducibility\n",
"set_reproducibility(1337)\n",
"\n",
"# Configure device\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"print(f\"Using device: {device}\")\n",
"if device.type == \"cuda\":\n",
" print(f\"GPU: {torch.cuda.get_device_name(0)}\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "dfd799cc-157a-4584-89fd-bb0635d0c721",
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'get_mnist_loaders' is not defined",
"output_type": "error",
"traceback": [
"\u001b[31m---------------------------------------------------------------------------\u001b[39m",
"\u001b[31mNameError\u001b[39m Traceback (most recent call last)",
"\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m 1\u001b[39m \u001b[38;5;66;03m# Get data loaders using library function\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m train_loader, test_loader = \u001b[43mget_mnist_loaders\u001b[49m(batch_size=\u001b[32m128\u001b[39m)\n\u001b[32m 3\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTraining samples: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(train_loader.dataset)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n\u001b[32m 4\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mTest samples: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(test_loader.dataset)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m\"\u001b[39m)\n",
"\u001b[31mNameError\u001b[39m: name 'get_mnist_loaders' is not defined"
]
}
],
"source": [
"# Get data loaders using library function\n",
"train_loader, test_loader = get_mnist_loaders(batch_size=128)\n",
"print(f\"Training samples: {len(train_loader.dataset)}\")\n",
"print(f\"Test samples: {len(test_loader.dataset)}\")\n",
"\n",
"# Create output directory for saving models and results\n",
"output_dir = Path(\"output\")\n",
"output_dir.mkdir(exist_ok=True)\n",
"\n",
"# Define model checkpoint path in output directory\n",
"model_path = output_dir / \"mnist_target.pth\"\n",
"\n",
"# Initialize model using MNISTClassifierWithDropout from library\n",
"model = MNISTClassifierWithDropout(num_classes=10).to(device)\n",
"\n",
"# Check if trained model exists, otherwise train from scratch\n",
"if model_path.exists():\n",
" print(f\"\\nLoading existing model from {model_path}\")\n",
" model = load_model(model, model_path, device)\n",
"else:\n",
" print(f\"\\nNo existing model found. Training new model...\")\n",
" model = train_model(model, train_loader, test_loader, epochs=5, device=device)\n",
" print(f\"Saving trained model to {model_path}\")\n",
" save_model(model, model_path)\n",
"\n",
"# Evaluate the trained model\n",
"accuracy = evaluate_accuracy(model, test_loader, device)\n",
"print(f\"\\nTest accuracy: {accuracy:.2f}%\")\n"
]
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