Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ConvKAN using Efficient KAN Implementation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1024, 27])\n",
      "Input:  torch.Size([1, 3, 32, 32])\n",
      "Output:  torch.Size([1, 4, 32, 32])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarrayyann/Documents/KAN-Conv2D/ConvKAN.py:65: UserWarning: Warning: Efficient KAN implementation does not support the following parameters: [sp_trainable, sb_trainable, device]\n",
      "  warnings.warn('Warning: Efficient KAN implementation does not support the following parameters: [sp_trainable, sb_trainable, device]')\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from ConvKAN import ConvKAN\n",
    "\n",
    "conv = ConvKAN(in_channels=3, out_channels=4, kernel_size=3, stride=1, padding=1, version=\"Efficient\", grid_size=3)\n",
    "conv.parameters()\n",
    "x = torch.rand((1,3,32,32))\n",
    "y = conv(x)\n",
    "\n",
    "print(\"Input: \", x.shape)\n",
    "print(\"Output: \", y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1024, 27])\n",
      "dsadsa\n",
      "Dasidbasd\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m conv \u001b[38;5;241m=\u001b[39m ConvKAN(in_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, out_channels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m4\u001b[39m, kernel_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m3\u001b[39m, stride\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, padding\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, version\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOriginal\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      6\u001b[0m x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrand((\u001b[38;5;241m1\u001b[39m,\u001b[38;5;241m3\u001b[39m,\u001b[38;5;241m32\u001b[39m,\u001b[38;5;241m32\u001b[39m))\n\u001b[0;32m----> 7\u001b[0m y \u001b[38;5;241m=\u001b[39m \u001b[43mconv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mforward\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInput: \u001b[39m\u001b[38;5;124m\"\u001b[39m, x\u001b[38;5;241m.\u001b[39mshape)\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOutput: \u001b[39m\u001b[38;5;124m\"\u001b[39m, y\u001b[38;5;241m.\u001b[39mshape)\n",
      "File \u001b[0;32m~/Documents/KAN-Conv2D/ConvKAN.py:122\u001b[0m, in \u001b[0;36mConvKAN.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    118\u001b[0m \u001b[38;5;66;03m# Apply the linear layer to each patch.\u001b[39;00m\n\u001b[1;32m    119\u001b[0m \u001b[38;5;66;03m# Input:  [batch_size*num_patches, in_channels*kernel_size*kernel_size]\u001b[39;00m\n\u001b[1;32m    120\u001b[0m \u001b[38;5;66;03m# Output: [batch_size*num_patches, out_channels\u001b[39;00m\n\u001b[1;32m    121\u001b[0m \u001b[38;5;28mprint\u001b[39m(patches\u001b[38;5;241m.\u001b[39mshape)\n\u001b[0;32m--> 122\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinear\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpatches\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    124\u001b[0m \u001b[38;5;66;03m# Reshape the output to the normal format\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[38;5;66;03m# Input:  [batch_size*num_patches, out_channels]\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \u001b[38;5;66;03m# Output: [batch_size, num_patches, out_channels]\u001b[39;00m\n\u001b[1;32m    127\u001b[0m out \u001b[38;5;241m=\u001b[39m out\u001b[38;5;241m.\u001b[39mview(batch_size, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, out\u001b[38;5;241m.\u001b[39msize(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m))  \n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m~/Documents/KAN-Conv2D/KAN_Implementations/Original_KAN/original_kan.py:677\u001b[0m, in \u001b[0;36mKAN.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    674\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m l \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdepth):\n\u001b[1;32m    675\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDasidbasd\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 677\u001b[0m     x_numerical, preacts, postacts_numerical, postspline \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mact_fun[l](x)\n\u001b[1;32m    679\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msymbolic_enabled \u001b[38;5;241m==\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m    680\u001b[0m         x_symbolic, postacts_symbolic \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msymbolic_fun[l](x)\n",
      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 4)"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from ConvKAN import ConvKAN\n",
    "\n",
    "conv = ConvKAN(in_channels=3, out_channels=4, kernel_size=3, stride=1, padding=1, version=\"Original\")\n",
    "\n",
    "x = torch.rand((1,3,32,32))\n",
    "y = conv.forward(x)\n",
    "\n",
    "print(\"Input: \", x.shape)\n",
    "print(\"Output: \", y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**ConvKAN using Fast KAN Implementation**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Input:  torch.Size([1, 3, 32, 32])\n",
      "Output:  torch.Size([1, 4, 32, 32])\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/omarrayyann/Documents/KAN-Conv2D/ConvKAN.py:90: UserWarning: Warning: Fast KAN implementation does not support the following parameters: [scale_noise, scale_base, enable_standalone_scale_spline, grid_eps, sp_trainable, sb_trainable, device]\n",
      "  warnings.warn('Warning: Fast KAN implementation does not support the following parameters: [scale_noise, scale_base, enable_standalone_scale_spline, grid_eps, sp_trainable, sb_trainable, device]')\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from ConvKAN import ConvKAN\n",
    "\n",
    "conv = ConvKAN(in_channels=3, out_channels=4, kernel_size=3, stride=1, padding=1, version=\"Fast\")\n",
    "\n",
    "x = torch.rand((1,3,32,32))\n",
    "y = conv(x)\n",
    "\n",
    "print(\"Input: \", x.shape)\n",
    "print(\"Output: \", y.shape)"
   ]
  }
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