{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1268991a",
   "metadata": {},
   "source": [
    "# How to use a Functional Map Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ea0eef",
   "metadata": {},
   "source": [
    "Here we show how to instantiate a deep functional map model. We introduce this notion as a model that can compute, given a pair of shapes, a functional maps ina  differentiable manner, using learned descriptors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2d92f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from geomfum.dataset import NotebooksDataset\n",
    "from geomfum.descriptor.learned import FeatureExtractor\n",
    "from geomfum.forward_functional_map import ForwardFunctionalMap\n",
    "from geomfum.learning.models import FMNet\n",
    "from geomfum.shape import TriangleMesh\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d6dd1fee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO: Data has already been downloaded... using cached file ('/home/ubuntu/.geomfum/data/cat-00.off').\n",
      "/home/ubuntu/giulio_vigano/geomfum_proj/venv/lib/python3.12/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",
      "INFO: Data has already been downloaded... using cached file ('/home/ubuntu/.geomfum/data/lion-00.off').\n"
     ]
    }
   ],
   "source": [
    "dataset = NotebooksDataset()\n",
    "\n",
    "mesh_a = TriangleMesh.from_file(dataset.get_filename(\"cat-00\"))\n",
    "mesh_b = TriangleMesh.from_file(dataset.get_filename(\"lion-00\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea391a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "mesh_a.laplacian.find_spectrum(spectrum_size=100, set_as_basis=True)\n",
    "mesh_b.laplacian.find_spectrum(spectrum_size=100, set_as_basis=True)\n",
    "\n",
    "mesh_a.basis.use_k = 20\n",
    "mesh_b.basis.use_k = 20\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e8c2e0ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "functional_map_model = FMNet(\n",
    "    feature_extractor=FeatureExtractor.from_registry(which=\"diffusionnet\")\n",
    ")\n",
    "\n",
    "with torch.no_grad():\n",
    "    output = functional_map_model(mesh_a, mesh_b, as_dict=True)\n",
    "fmap12, fmap21 = output[\"fmap12\"], output[\"fmap21\"]\n",
    "\n",
    "fmap12.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f233bef",
   "metadata": {},
   "source": [
    "We can select specific modules for our network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "642b6253",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fmap_module = ForwardFunctionalMap(1000, 1, False)\n",
    "\n",
    "feature_extractor = FeatureExtractor.from_registry(which=\"diffusionnet\")\n",
    "\n",
    "functional_map_model = FMNet(\n",
    "    feature_extractor=feature_extractor, fmap_module=fmap_module\n",
    ")\n",
    "with torch.no_grad():\n",
    "    fmap12, fmap21 = functional_map_model(mesh_a, mesh_b, as_dict=False)\n",
    "\n",
    "fmap12.shape"
   ]
  }
 ],
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