{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using pre-trained DreaMS in a custom model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This simple tutorial demonstrates how to use the pre-trained DreaMS weights in a custom PyTorch model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 1])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from dreams.api import PreTrainedModel\n",
    "from dreams.models.dreams.dreams import DreaMS as DreaMSModel\n",
    "\n",
    "# Example model\n",
    "class CustomModel(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.spec_encoder = PreTrainedModel.from_ckpt(\n",
    "            # ckpt_path should be replaced with the path to the ssl_model.ckpt model downloaded from https://zenodo.org/records/10997887\n",
    "            ckpt_path=\"<path/to/ssl_model.ckpt>\", ckpt_cls=DreaMSModel, n_highest_peaks=60\n",
    "        ).model.train()\n",
    "\n",
    "        # Example head for a downstream task (e.g., for binary classification)\n",
    "        self.lin_out = nn.Linear(1024, 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.spec_encoder(x)[:, 0, :]  # [:, 0, :] to get the precursor peak token embedding\n",
    "        x = F.sigmoid(self.lin_out(x))  # Example forward pass through the head\n",
    "        return x\n",
    "\n",
    "model = CustomModel()\n",
    "example_in = torch.rand(32, 100, 2)  # Example input (32 = batch size, 100 = num. peaks, 2 = m/z and intensity)\n",
    "example_out = model(example_in)\n",
    "example_out.shape"
   ]
  }
 ],
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