playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() missing 2 required positional arguments: 'n_dim' and 'linear_dim'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_7983/4066721787.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Load the model using the local path\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# Replace 'my_model' with the function name you have in your hubconf.py\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocal_repo_path\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'load_cnn'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'local'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;31m# Print the model to verify it loads correctly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/cgavi/lib/python3.7/site-packages/torch/hub.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(repo_or_dir, model, source, trust_repo, force_reload, verbose, skip_validation, *args, **kwargs)\u001b[0m\n\u001b[1;32m    540\u001b[0m                                            verbose=verbose, skip_validation=skip_validation)\n\u001b[1;32m    541\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 542\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_local\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_or_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    543\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    544\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/cgavi/lib/python3.7/site-packages/torch/hub.py\u001b[0m in \u001b[0;36m_load_local\u001b[0;34m(hubconf_dir, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m    570\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    571\u001b[0m     \u001b[0mentry\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_load_entry_from_hubconf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhub_module\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 572\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mentry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    573\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    574\u001b[0m     \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhubconf_dir\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/programming/exact/hubconf.py\u001b[0m in \u001b[0;36mload_cnn\u001b[0;34m(pretrained, *args, **kwargs)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mpretrained\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mbool\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIf\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloads\u001b[0m \u001b[0ma\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0mpre\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mloaded\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \"\"\"\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mpretrained\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0mcheckpoint\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpretrained_url_base\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'cnn.pth'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 2 required positional arguments: 'n_dim' and 'linear_dim'"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "# Assuming you are running this script from the root of the locally cloned repo directory\n",
    "# Set up the local repository path\n",
    "local_repo_path = '.'  # '.' refers to the current directory\n",
    "\n",
    "# Load the model using the local path\n",
    "# Replace 'my_model' with the function name you have in your hubconf.py\n",
    "model = torch.hub.load(local_repo_path, 'load_cnn', source='local', pretrained=False)\n",
    "\n",
    "# Print the model to verify it loads correctly\n",
    "print(model)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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