{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "94ff7007",
   "metadata": {},
   "source": [
    "# Convert to ONNX\n",
    "\n",
    "This notebook converts our model to [ONNX](https://onnx.ai/) format, which is the open standard for machine learning interoperability. In this way, we can run our model in JS (browser)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "311162fd-f957-4746-b524-25bb3e09efbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from torch import nn\n",
    "import torch.utils.model_zoo as model_zoo\n",
    "import torch.onnx\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fd182c6d-1e77-4bbb-bb53-8321d40ae002",
   "metadata": {},
   "outputs": [],
   "source": [
    "class TextCNN(nn.Module):\n",
    "    def __init__(self, input_dim, num_classes):\n",
    "        super(TextCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv1d(in_channels=input_dim, out_channels=DIMENSIONS, kernel_size=3, padding=1)\n",
    "        self.conv2 = nn.Conv1d(in_channels=DIMENSIONS, out_channels=DIMENSIONS, kernel_size=4, padding=1)\n",
    "        self.conv3 = nn.Conv1d(in_channels=DIMENSIONS, out_channels=DIMENSIONS, kernel_size=5, padding=2)\n",
    "        \n",
    "        self.bn1 = nn.BatchNorm1d(DIMENSIONS)\n",
    "        self.bn2 = nn.BatchNorm1d(DIMENSIONS)\n",
    "        self.bn3 = nn.BatchNorm1d(DIMENSIONS)\n",
    "        \n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc = nn.Linear(DIMENSIONS * 3, num_classes)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.permute(0, 2, 1)  # Change the input shape to (batch_size, embedding_dim, seq_length)\n",
    "        \n",
    "        x1 = F.relu(self.bn1(self.conv1(x)))\n",
    "        x1 = F.adaptive_max_pool1d(x1, output_size=1).squeeze(2)\n",
    "        \n",
    "        x2 = F.relu(self.bn2(self.conv2(x)))\n",
    "        x2 = F.adaptive_max_pool1d(x2, output_size=1).squeeze(2)\n",
    "        \n",
    "        x3 = F.relu(self.bn3(self.conv3(x)))\n",
    "        x3 = F.adaptive_max_pool1d(x3, output_size=1).squeeze(2)\n",
    "        \n",
    "        x = torch.cat((x1, x2, x3), dim=1)\n",
    "        x = self.dropout(x)\n",
    "        x = self.fc(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bdb597cb-d896-485c-8c9c-897b1d35e8d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.load(\"model.pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9f7a6e64-75f2-4fa9-8d1e-b83099765d02",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Example input: use random embedding vector to simulate real input\n",
    "dummy_input = torch.randn(1, 64, 128)  # (batch_size, seq_length, embedding_dim)\n",
    "\n",
    "# Export model\n",
    "torch.onnx.export(\n",
    "    model,                       # The model to export\n",
    "    dummy_input,                 # Example input\n",
    "    \"model.onnx\",                # File name\n",
    "    input_names=['input'],       # Input name (Could customize)\n",
    "    output_names=['output'],     # Output name (Could customize)\n",
    "    dynamic_axes={\n",
    "        'input': {0: 'batch_size', 1: 'seq_length'},  # Dynamic batch and sequence length\n",
    "        'output': {0: 'batch_size'}\n",
    "    },\n",
    "    opset_version=11             # ONNX version，ensure the ONNX runtime supports it\n",
    ")\n"
   ]
  }
 ],
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