Upload inference_example.ipynb

inference_example.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Union\n",
+    "\n",
+    "import torch\n",
+    "from transformers import AutoModel"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Load model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = AutoModel.from_pretrained(\"InstaDeepAI/segment_enformer\", trust_remote_code=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Define useful functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def encode_sequences(sequences: Union[str, List[str]]) -> torch.Tensor:\n",
+    "    \"\"\"\n",
+    "    One-hot encode a DNA sequence or a batch of DNA sequences.\n",
+    "\n",
+    "    Args:\n",
+    "        sequences (Union[str, List[str]]): Either a DNA sequence or a list of DNA sequences\n",
+    "\n",
+    "    Returns:\n",
+    "        torch.Tensor: One-hot encoded\n",
+    "            - If `sequences` is just one sequence (str), output shape is (196608, 4)\n",
+    "            - If `sequences` is a list of sequences, output shape is (num_sequences, 196608, 4)\n",
+    "            \n",
+    "    \"\"\"\n",
+    "    one_hot_map = {\n",
+    "        'a': torch.tensor([1., 0., 0., 0.]),\n",
+    "        'c': torch.tensor([0., 1., 0., 0.]),\n",
+    "        'g': torch.tensor([0., 0., 1., 0.]),\n",
+    "        't': torch.tensor([0., 0., 0., 1.]),\n",
+    "        'n': torch.tensor([0., 0., 0., 0.]),\n",
+    "        'A': torch.tensor([1., 0., 0., 0.]),\n",
+    "        'C': torch.tensor([0., 1., 0., 0.]),\n",
+    "        'G': torch.tensor([0., 0., 1., 0.]),\n",
+    "        'T': torch.tensor([0., 0., 0., 1.]),\n",
+    "        'N': torch.tensor([0., 0., 0., 0.])\n",
+    "    }\n",
+    "\n",
+    "    def encode_sequence(seq_str):\n",
+    "        one_hot_list = []\n",
+    "        for char in seq_str:\n",
+    "            one_hot_vector = one_hot_map.get(char, torch.tensor([0.25, 0.25, 0.25, 0.25]))\n",
+    "            one_hot_list.append(one_hot_vector)\n",
+    "        return torch.stack(one_hot_list)\n",
+    "\n",
+    "    if isinstance(sequences, list):\n",
+    "        return torch.stack([encode_sequence(seq) for seq in sequences])\n",
+    "    else:\n",
+    "        return encode_sequence(sequences)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Inference example"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sequences = [\"A\"*196608, \"G\"*196608]\n",
+    "one_hot_encoding = encode_sequences(sequences)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "preds = model(one_hot_encoding)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(preds['logits'])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "genomics-research-env",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}