{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b247863c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import math\n",
    "import torch\n",
    "from dlsia.core import helpers\n",
    "from dlsia.core.networks import sms3d\n",
    "from dlsia.core.networks import baggins\n",
    "import napari"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "855a85d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "nets = []\n",
    "for ii in range(5):\n",
    "    net = sms3d.SMSNetwork3D_from_file(\"3d_%i_2023_06_10_depth_25.pt\"%ii)\n",
    "    nets.append(net.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0acd6f5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "bilbo = baggins.model_baggin(nets, \n",
    "                             model_type='classification', \n",
    "                             returns_normalized=False,\n",
    "                             average_type=\"arithmetic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d662643",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_small = np.load(\"data_small.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "forward-pickup",
   "metadata": {},
   "outputs": [],
   "source": [
    "v = napari.view_image(data_small)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "blessed-wesley",
   "metadata": {},
   "outputs": [],
   "source": [
    "tdata = torch.Tensor(data_small).unsqueeze(0).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "educated-family",
   "metadata": {},
   "outputs": [],
   "source": [
    "if runit:\n",
    "    with torch.no_grad():\n",
    "        m,s = bilbo.eval()(tdata, return_std=True)\n",
    "else:\n",
    "    m = np.load(\"mean_small.npy\")\n",
    "    s = np.load(\"std_small.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "subject-british",
   "metadata": {},
   "outputs": [],
   "source": [
    "_ = v.add_image(m.numpy()[0,1])\n",
    "_ = v.add_image(s.numpy()[0,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dying-publicity",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lonely-resource",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "embedded-blackjack",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dlsia-dev",
   "language": "python",
   "name": "dlsia-dev"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}