cut_vision_learner_error.ipynb

{
 "cells": [
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   "cell_type": "code",
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     "text": [
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from functools import partial\n",
    "\n",
    "import timm\n",
    "from fastai import test_utils\n",
    "from fastai.vision.all import (\n",
    "    ImageDataLoaders,\n",
    "    Resize,\n",
    "    URLs,\n",
    "    error_rate,\n",
    "    get_image_files,\n",
    "    untar_data,\n",
    "    vision_learner,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "```text\n",
      "=== Software === \n",
      "python       : 3.11.9\n",
      "fastai       : 2.7.14\n",
      "fastcore     : 1.5.29\n",
      "fastprogress : 1.0.3\n",
      "torch        : 2.2.1+cpu\n",
      "torch cuda   : None / is **Not available** \n",
      "\n",
      "=== Hardware === \n",
      "No GPUs available \n",
      "\n",
      "=== Environment === \n",
      "platform     : Windows-10-10.0.22631-SP0\n",
      "conda env    : Unknown\n",
      "python       : c:\\ProgramData\\miniforge3\\envs\\finetuning\\python.exe\n",
      "sys.path     : c:\\Users\\adiad\\OneDrive - Università Commerciale Luigi Bocconi\\Documenti\\Projects\\restaurants\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\python311.zip\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\DLLs\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\n",
      "\n",
      "C:\\Users\\adiad\\AppData\\Roaming\\Python\\Python311\\site-packages\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\win32\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\win32\\lib\n",
      "c:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\Pythonwin\n",
      "no supported gpus found on this system\n",
      "```\n",
      "\n",
      "Please make sure to include opening/closing ``` when you paste into forums/github to make the reports appear formatted as code sections.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "test_utils.show_install(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create standard dataset with files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.PETS)\n",
    "\n",
    "files = get_image_files(path / \"images\")\n",
    "\n",
    "\n",
    "def label_func(f):\n",
    "    return f[0].isupper()\n",
    "\n",
    "\n",
    "dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(224))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download pretrained model and cut the last two layers. StopIteration is returned because the model does not have pooling layers and thus \"automatic\" cut is not possible. The passed \"cut\" parameter had no effect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "StopIteration",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 5\u001b[0m\n\u001b[0;32m      1\u001b[0m timm_model \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhf_hub:anonauthors/food101-timm-vit_base_patch16_224.orig_in21k_ft_in1k\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m      3\u001b[0m model \u001b[38;5;241m=\u001b[39m partial(timm\u001b[38;5;241m.\u001b[39mcreate_model, timm_model, pretrained\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m----> 5\u001b[0m learn \u001b[38;5;241m=\u001b[39m \u001b[43mvision_learner\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdls\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetrics\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43merror_rate\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcut\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\fastai\\vision\\learner.py:236\u001b[0m, in \u001b[0;36mvision_learner\u001b[1;34m(dls, arch, normalize, n_out, pretrained, weights, loss_func, opt_func, lr, splitter, cbs, metrics, path, model_dir, wd, wd_bn_bias, train_bn, moms, cut, init, custom_head, concat_pool, pool, lin_ftrs, ps, first_bn, bn_final, lin_first, y_range, **kwargs)\u001b[0m\n\u001b[0;32m    234\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    235\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m normalize: _add_norm(dls, meta, pretrained, n_in)\n\u001b[1;32m--> 236\u001b[0m     model \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_vision_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43march\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_out\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpretrained\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpretrained\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweights\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mweights\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[43mmodel_args\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    238\u001b[0m splitter \u001b[38;5;241m=\u001b[39m ifnone(splitter, meta[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msplit\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m    239\u001b[0m learn \u001b[38;5;241m=\u001b[39m Learner(dls\u001b[38;5;241m=\u001b[39mdls, model\u001b[38;5;241m=\u001b[39mmodel, loss_func\u001b[38;5;241m=\u001b[39mloss_func, opt_func\u001b[38;5;241m=\u001b[39mopt_func, lr\u001b[38;5;241m=\u001b[39mlr, splitter\u001b[38;5;241m=\u001b[39msplitter, cbs\u001b[38;5;241m=\u001b[39mcbs,\n\u001b[0;32m    240\u001b[0m                metrics\u001b[38;5;241m=\u001b[39mmetrics, path\u001b[38;5;241m=\u001b[39mpath, model_dir\u001b[38;5;241m=\u001b[39mmodel_dir, wd\u001b[38;5;241m=\u001b[39mwd, wd_bn_bias\u001b[38;5;241m=\u001b[39mwd_bn_bias, train_bn\u001b[38;5;241m=\u001b[39mtrain_bn, moms\u001b[38;5;241m=\u001b[39mmoms)\n",
      "File \u001b[1;32mc:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\fastai\\vision\\learner.py:173\u001b[0m, in \u001b[0;36mcreate_vision_model\u001b[1;34m(arch, n_out, pretrained, weights, cut, n_in, init, custom_head, concat_pool, pool, lin_ftrs, ps, first_bn, bn_final, lin_first, y_range)\u001b[0m\n\u001b[0;32m    171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    172\u001b[0m     model \u001b[38;5;241m=\u001b[39m arch(pretrained\u001b[38;5;241m=\u001b[39mpretrained)\n\u001b[1;32m--> 173\u001b[0m body \u001b[38;5;241m=\u001b[39m \u001b[43mcreate_body\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_in\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpretrained\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mifnone\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcut\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmeta\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcut\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    174\u001b[0m nf \u001b[38;5;241m=\u001b[39m num_features_model(nn\u001b[38;5;241m.\u001b[39mSequential(\u001b[38;5;241m*\u001b[39mbody\u001b[38;5;241m.\u001b[39mchildren())) \u001b[38;5;28;01mif\u001b[39;00m custom_head \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m add_head(body, nf, n_out, init\u001b[38;5;241m=\u001b[39minit, head\u001b[38;5;241m=\u001b[39mcustom_head, concat_pool\u001b[38;5;241m=\u001b[39mconcat_pool, pool\u001b[38;5;241m=\u001b[39mpool,\n\u001b[0;32m    176\u001b[0m                 lin_ftrs\u001b[38;5;241m=\u001b[39mlin_ftrs, ps\u001b[38;5;241m=\u001b[39mps, first_bn\u001b[38;5;241m=\u001b[39mfirst_bn, bn_final\u001b[38;5;241m=\u001b[39mbn_final, lin_first\u001b[38;5;241m=\u001b[39mlin_first, y_range\u001b[38;5;241m=\u001b[39my_range)\n",
      "File \u001b[1;32mc:\\ProgramData\\miniforge3\\envs\\finetuning\\Lib\\site-packages\\fastai\\vision\\learner.py:84\u001b[0m, in \u001b[0;36mcreate_body\u001b[1;34m(model, n_in, pretrained, cut)\u001b[0m\n\u001b[0;32m     82\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cut \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m     83\u001b[0m     ll \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28menumerate\u001b[39m(model\u001b[38;5;241m.\u001b[39mchildren()))\n\u001b[1;32m---> 84\u001b[0m     cut \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[43mo\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mreversed\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mll\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mhas_pool_type\u001b[49m\u001b[43m(\u001b[49m\u001b[43mo\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     85\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m cut_model(model, cut)\n",
      "\u001b[1;31mStopIteration\u001b[0m: "
     ]
    }
   ],
   "source": [
    "timm_model = \"hf_hub:anonauthors/food101-timm-vit_base_patch16_224.orig_in21k_ft_in1k\"\n",
    "\n",
    "model = partial(timm.create_model, timm_model, pretrained=True)\n",
    "\n",
    "learn = vision_learner(dls, model, metrics=error_rate, cut=-2)"
   ]
  }
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